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323 Experimental Research Titles

Experimental research is a study that follows a specific research design. Its main components are dependent and independent variables, hypotheses, research questions , and objectives. The examination can be qualitative or quantitative.

One of the critical aspects of experimental research is that it should be completed in a controlled scientific environment. For that, the researcher follows these steps:

• creating a plan;
• collecting the required data;
• applying statistical methods to analyze it;
• deciding whether to accept or reject hypotheses.

The purpose of experimental research is to determine the dependency between variables and estimate how they correlate.

Our team has collected experimental research titles to help you start this task. Besides, the article contains essential writing tips. With them, you will overcome all the challenges you may face while working on a scientific paper.

🔝 Top 18 Experimental Research Ideas

• 🚌 Titles for High School Students
• 🎓 Topics for College Students
• 🔬 Topics for STEM Students

🧫 Simple Experimental Research Topics

🧬 more experimental research titles, 🥼 how to conduct experimental research, 🔗 references.

• How effectively does AI fall technology decrease patient falls in hospitals?
• How does peer observation affect adolescents’ decision-making?
• Does nutrition affect students’ academic performance?
• Nano-Optics and Benefits: Possible Experiments.
• Comparing two machine learning models for detecting fake news.
• The effect of postabortion psychological intervention on women’s mental well-being.
• Quality Management Effects on Organizations’ Performance.
• How do genes affect wound healing?
• The investigation of photosynthesis by experimenting on spinach leaves.
• Impact of Culture on International Business.
• Studying the framing effect in cognitive psychology using experimental design.
• The impact of ads on American consumers’ attitudes towards eco-friendly beauty products.
• Long-Term Trends in Business and Their Impact.
• Nitrogen or potash: Which fertilizer is more effective for plant growth?
• Divorce and Its Impact on Children.
• Effects of a project-based learning program on low-income students’ performance.
• How do team-building activities affect a company’s general performance?
• How Fast Are We According to the Age? The Experiment.

🚌 Experimental Research Titles for High School Students

• An experiment proving the need for carbon dioxide for photosynthesis.
• Combustion of phosphorus in the air and oxygen.
• Autoignition of white phosphorus in the air.
• The pH change during water electrolysis.
• Reactions of protozoa to the action of various stimuli.
• The absorption of substances and the formation of digestive vacuoles in protozoa.
• The effect of water temperature on the rate of reproduction of protozoa.
• Technology and Nursing: The Impact of Technology .
• The impact of daphnia on water filtration.
• Finding out the importance of fins in the movement of fish.
• Protein denaturation using different temperatures.
• Plasmolysis and deplasmolysis in onion skin cells.
• The catalytic activity of enzymes in living tissues.
• An experiment on the distribution of temperature thresholds for wool pigment formation in ermine rabbits.
• An experiment with acetabularia demonstrating the leading role of the cell nucleus in heredity.
• Obtaining CO2 from copper hydroxide carbonate.
• Getting carbon dioxide from vitamin pills.
• Production of CO2 from limescale and acetic acid.
• Getting carbon dioxide from the sparkling water.
• Organizational Effectiveness Impact on Individual Performance.
• The reaction of carbon dioxide with alkali.
• Change in pH when carbon dioxide is dissolved.
• Combustion of magnesium in carbon dioxide.
• Dissolving ammonia in water.
• Changes in pH when ammonia is dissolved.
• The reaction of ammonia and hydrogen chloride.
• Preparation and analysis of chlorine.
• The mixture of chlorine and iodine.
• Photochemical reaction: Chlorine + hydrogen.
• Impact of Divorce on the Emotional Well-being of Children.
• The process of burning sodium in chlorine.
• Combustion of antimony in chlorine.
• The combination of sodium and water.
• The experiment of mixing lithium and water.
• The reaction of copper and nitric acid.
• The deposition of copper on iron.
• The peculiarities of mercury deposition on copper.
• Flame staining with sodium salt.
• Coloring flames with potassium salt.
• Approaches to Media: Audiences and Effects.
• Staining the flame with a lithium salt.
• How to color the flame with strontium salt?
• The effect of concentration on the reaction rate.
• Dependence of the reaction rate on the initial substance.
• Effects of temperature on chemical equilibrium.
• Chromium (III) oxide as a catalyst.
• Impact of agitation on dissolution rate.
• Temperature dependence of salt solubility.
• A universal indicator color scale.
• Determination of the pH of consumer products.
• The pH of salt solutions in hydrolysis.
• The reaction of base oxide with water.
• Employee Loyalty and Career Development Effects.
• The reaction of sodium and ethanol.
• Sulfuric acid reaction and carbohydrates.
• Combustion of boric acid ethyl ester.
• How Social Class Impacts Health Disparities.
• Accumulation of wind energy in the form of hydrogen.
• Storage of solar energy in the form of hydrogen.
• The reaction of rubidium and water.
• An experiment on creating a volcano.
• Oxidizing properties of manganese (VII) oxide.
• Production and combustion of silane.
• Combustion in potassium chlorate.
• Lack Sleep Effects on Teenagers.
• An experiment proving the need for light for photosynthesis.
• Study of the properties of normal, burnt, and decalcified bones.
• Determination of breath holding time before and after exercise.
• Conducting a family therapy roleplay.
• Roleplay: A therapist and a client.
• Developing a program for family health awareness.
• Seminar on improving mental health.
• Creating a schedule for daily exercising.
• Negative Impact of Technology on Children’s Growth .
• The effect of color on the heat caused by light absorption.
• Experiment with constructing an X-Ray device. 
• The conversion of potential energy into kinetic one.
• The measurement of water surface tension. 
• The construction of a gravity model.
• Relationship Between Health and Wealth .

🎓 Experimental Research Topics for College Students

• The effects of different sleeping patterns on cognitive function.
• What impact does technology have on students’ social interactions?
• Various diet plans: Effects on physical health.
• How does media consumption affect mental health?
• Psychology. Stanford Prison Experiment.
• The impact of online learning platforms on educational outcomes.
• Are exercise and stress levels in students related?
• Social media use and academic performance.
• Differentiation of study techniques: Exam performance comparison.
• The impact of different teaching styles on learning outcomes.
• Do in-person and online student support services foster student retention?
• The Influence of Non-Work-Related Factors on the Work Commitment.
• Comparing academic performance in online learning and traditional classroom.
• Social media and its impact on student engagement.
• Student mental health impact on academic performance.
• Work Environment Impact on Employee Performance.
• How do extracurricular student activities affect personal and social development?
• How is student debt related to decision-making?
• The relationship between college student sleep deprivation and academic performance.
• What types of financial aid can influence college student retention?
• Do college student internships affect employment opportunities?
• Organizational Culture and Performance Relationship.
• Comparing the impact of traditional vs. experiential learning.
• The cognitive load and academic performance: What is the relation?
• Time management strategies and college student success.
• Cyber-Bullying Versus Traditional Bullying and Its Psychological Effects.
• Can a new learning environment engage students?
• How can mindfulness affect academic performance?
• The impact of a teacher’s racial bias on cultural prejudice levels in the classroom.
• Effects of student-led group activities on learning outcomes.
• What types of testing have the most impact on student learning?
• Student motivation: The main types and their specifics.
• Does class size impact student academic performance?
• Voter behavior in the EU: A case study of political ads.
• The role of social media in political discourse in the US.
• The Effects of Physical Attractiveness on Persuasion .
• The effects of different political parties on voter attitudes.
• How can survey experiments be used to manipulate public opinion?
• Political debates and voter engagement: Are there any connections?
• Does encouraging voters by mail decrease voter turnout?
• How do social media messages affect citizens’ political mobilization?
• How do political ads affect voters’ attitudes?
• The Effects of School Feeding Program on Preschool Children.
• Does the provision of financial incentives increase full-time employment rates in welfare recipients?
• Does giving vouchers to low-income families increase their economic mobility?
• Why people don’t migrate to higher-developed countries: A lab experiment.
• Innovation Influences on Business Environment.
• Does better customer service at the Department of Revenue increase citizens’ income tax compliance?
• Covid-19 as a natural experiment on the effects of remote learning.
• Does providing customers with information on food’s health benefits affect their buying decisions?
• Gender discrimination in hiring: An experiment.
• Schizophrenia: Mental Status Evaluation and Experiment.
• Using experimental methods to study preschoolers’ language acquisition.
• Do multilingual people have better working memory than monolingual ones?
• Language immersion programs and their effects on student learning.
• The study of the effects of age on language acquisition.
• Organizational Behaviour Influence on Innovation Processes.
• The influence of language immersion programs on student motivation.
• The investigation of language learning software effects on academic performance.
• The study of the effectiveness of bilingual education programs in schools.
• Impact of Workplace Factors on Nurses Job Satisfaction and Retention.

🔬 Experimental Research Topics for STEM Students

• Estimation of ionizing radiation influence on the organism.
• Evaluation of the thalamus contribution to the generation of pain sensations.
• The impact of neurotransmitters in the formation of tactile sensitivity.
• Research of the integrative function of the brain.
• An analysis of current resources for bioinformatics research.
• An assessment of hemodynamic state in hypertension.
• How Parental Styles Influence Children With ADHD?
• Approbation of biological systems in technology.
• Contribution of biophysics to brain concepts.
• Music Therapy and Its Effects on Elderly People .
• Current approaches to robotics and mechanical engineering.
• Dependence of neuronal development rate on nutrient conditions.
• Determination of the role of neural circuits in multisensory integration.
• High Blood Pressure and Heart Attack Relationship.
• Impact of the factors on the biochemical processes in the organism.
• Investigating predictors of cellular apoptosis.
• Investigation of the antibiotic susceptibility trends.
• Nursing Leadership Styles Influence on Performance and Work .
• Lipid metabolism disorders as predictors of atherosclerosis.
• Modern methods for assessing the functional state of blood vessels.
• Current methods of blood plasma proteins research.
• Nanotechnology opportunities in heart surgeries.
• New understanding of neurogenesis.
• Optimization methods in systemic evolutionary doctrine.
• Steroid Effects on the Body.
• Robotic systems performance in large industries.
• Structural bases of organization of biopolymers in medicine.
• Studying the potential of discrete mathematics in nanotechnology.
• The effects of the structure of biological membranes in DNA replication disorders.
• The outcomes of neurodegenerative diseases.
• Comparison of Vitamin C Levels in Different Vegetables and the Effects of Processing (Fresh, Frozen and Canned) .
• The potential consequences of anticoagulant use at inappropriate dosages.
• The power of mathematical calculation to assess health prognoses.
• The rationality of nuclear fusion in modern conditions.
• Pregnancy and Ultrasound Effects on Fetus.
• The role of applied mathematics in space research.
• The study of membrane polarization levels in cardiac disorders.
• The significance of the applicability of number theory in cosmology.
• The use of abstract relational biology in science.
• Nurse-Client Relationship and Mental Health Knowledge.
• The effects of social media on self-esteem and mental health.
• The impact of stress on memory and concentration.
• Behavioural Reaction to Organizational Change.
• Gender differences in emotional intelligence and expression.
• The influence of music on mood and behavior.
• The impact of sleep deprivation on attention and cognitive functioning.
• The effect of parental attachment on child development and self-esteem.
• The role of technology use in adolescent socialization and identity formation.
• The effects of mindfulness training on anxiety and stress management.
• How Video Games Influence Aggressiveness?
• The impact of workplace stress on job performance and productivity.
• The effect of nutrition on cognitive functioning and brain health.
• The influence of exercise on mental health and well-being.
• The impact of color on memory and learning.
• Smoking: Its Cause and Effects.
• The role of social support in psychological well-being and coping strategies.
• The influence of culture on attitudes toward mental illness and stigma.
• The relationship between self-regulation and academic achievement and success.
• The impact of depression on intimate relationships and communication.
• Work Environment, Absenteeism, Performance and Productivity: Relationship.
• The effects of test anxiety on student performance and confidence.
• The association of social media uses with self-esteem and self-image.
• The role of social comparison in self-concept formation and self-evaluation.
• Impact of Technological Innovations on the Organization Performance.
• The impact of forgiveness on psychological well-being and stress management.
• The influence of social media on body image and eating habits.
• The effect of exercise on cognitive performance and brain health.
• The role of attachment style in romantic relationships and intimacy.
• The influence of parenting style on adolescent outcomes and self-esteem.
• The effect of music on stress management and relaxation.
• Motivation and Its Effects on the Workplace.
• The impact of gender roles on mental health and self-esteem.
• The effects of sleep deprivation on emotional regulation and mental health
• The relationship between sleep quality and stress levels and mood.
• The effect of family dynamics on mental health and coping strategies.
• The Impact of Human Resource Management Strategies.
• The influence of religion on coping strategies and resilience.
• The impact of social support on depression and stress.
• The role of nature exposure on psychological well-being and relaxation.
• The influence of social media on self-esteem and self-image.
• Paramedics and Effects of Shift Work .
• The effects of technology use on attention span and concentration.
• The impact of self-esteem on academic performance and motivation.
• The association of childhood trauma with mental health and resilience.
• The role of emotional intelligence in interpersonal relationships and communication.
• Abusive Supervision and Its Effects on Employees.
• The influence of music on achievement motivation and concentration.
• The impact of social media on interpersonal communication and self-expression.
• The effect of media use on body image and self-esteem.
• Behavioral Effects Associated With Marijuana.
• The relationship between self-efficacy and academic performance outcomes.
• The influence of social comparison on self-concept formation.
• The impact of loneliness on mental health and well-being.
• The effects of humor on stress reduction and management.
• The Impact of Organizational Change on Business.
• The role of mindfulness in coping with anxiety and stress.
• The influence of technology use on social interactions and relationships.
• The effect of goal-setting on achievement and motivation.
• Successful Leadership’s Influences on Productivity.
• The impact of self-acceptance on psychological well-being and resilience.
• The relationship between attachment style and mental health outcomes.
• The effects of stress on cognitive performance and attention.
• The impact of social media on decision-making processes.
• Jazz Music and Race Relationship.
• The role of parental support on academic performance and engagement.
• The influence of sleep quality on emotional regulation and self-control.
• The effect of exercise on memory retention and learning.
• Diversity Effects on the Workplace.
• The impact of media uses on self-esteem and body image.
• The association of bullying with mental health symptoms.
• The role of nature in stress reduction and management.
• Fast Food Restaurants’ Impact on People’s Health.
• The influence of gratitude on psychological well-being and life satisfaction.
• The effect of humor on interpersonal communication and relationships.
• The impact of self-regulation on achievement and success.
• The relationship between sleep deprivation and cognitive functioning impairment.
• Impact of Branding on Consumer Purchasing Behaviour.
• How do oral contraceptives affect the nervous system?
• How does yoga affect muscle relaxation that leads to sleep?
• How Does Packaging Influence Buyer Decision Making.
• Does parent-child interaction influence the development of white matter?
• How do stress and anxiety affect the capacity for creativity?
• Health Effects of Steroid Use Among Athletes.
• How does bilingualism influence academic achievement and performance?
• How does bilingualism prevent cognitive deterioration?
• How Does Child Neglect Might Affect a Child’s Self-Esteem in Adulthood?
• How does parent-child interaction influence a child’s understanding of COVID-19?
• How does sex education help to curb teenage pregnancy?
• Facebook Inc.’s Unethical Experiment with Users.
• How does the availability of contraception influence teenage pregnancy levels?
• What will happen if the Great Depression happened today?
• How does the Christmas marketing affect its traditions?
• What is the role of Black culture in music?
• Possible Side Effects of Morphine Use.
• How can the US government structure be changed?
• How is the US Constitution adapted to modern times?
• The Tuskegee Syphilis Experiment.
• How can elections be changed for better representation?
• What are the factors that predict levels of stress in children?
• Why do American and international students adapt differently to college?
• How does racial identity influence psychological well-being?
• Reasoning on the Topic of Crime – Ofshe’s Experiment.
• How does racial identity influence cognitive processes?
• How does racial discrimination affect the brain?
• How does racial discrimination cause mental distress?
• Cultural Influences on Business Ethics.
• What factors influence physical health in school children?
• How do race and ethnicity affect psychosocial adaptation?
• How does gender influence levels of impulsivity in alcohol addicts?
• How does sexual conservatism relate to emotional guilt?
• Impacts of Information Systems on Policies and Student Learning.
• How do social norms induce the brain’s guilt response?
• Which societal factors lead to increased levels of teenage pregnancy?
• What are the impacts of teenage pregnancy on society?
• How does teenage pregnancy affect social mobility?
• “Fat Talk” by Ambwani: Experimental Study .
• How are teenage pregnancy and birth mortality rates connected?
• What is the gender difference in teenage pregnancy consequences?
• Impact of Core Competence and Sustainability of Business.
• What is the correlation between teenage pregnancy and child development?
• What is the correlation between teenage pregnancy and miscarriage?
• The Impact of Culture on Dementia Healthcare .
• How can teenage pregnancy levels be reduced?
• Human Resource Impacts on Organizational Performance.

If you plan to conduct experimental research, you should know a particular set of rules. By following them, you will ensure that your findings are accurate and that the paper structure is appropriate. See the essential steps of experimental research below:

• Come up with a hypothesis. Decide what assumptions you will test in your study. Keep in mind that they should be applying scientific methods. So, make sure you will be able to perform proper analysis to test your ideas.
• Think of the context & theory. Gather the information that is already available on your topic and examine it. You should have solid theoretical ground before performing an actual experiment. Besides, consider what space in existing research your study can fill. Examine everything done in the field – you can do it quicker with our summarizer .
• Plan your study. Create a detailed plan for your research and follow it. It will help you structure your experiment, keep track of the progress, and keep up with the deadlines. Don’t forget to decide on the possible ways of data collection.
• Conduct an experiment. Once you’ve set up everything, start the actual investigation. Collect the required data and organize it logically. Finally, perform the chosen scientific manipulation to test your hypotheses. Remember to clearly understand your objectives and distinguish your dependent and independent variables to conduct the study.
• Examine your finding. This step involves the in-depth analysis of your data. Investigate your results and decide whether you accept or reject your hypotheses. Be attentive in this part: you will deal with numbers and figures here.
• Write about what you’ve found. Wrap up your experiential research by explaining your results. Consider the practical implication of your study. Did you contribute anything of value to the field? Will your study be helpful for future research? Make sure that you not only present pure findings but also explain them.

Thank you for reading our article. We hope our experimental research topics for college students were helpful. Plus, we have a handy tool for you. Our online sentence rephraser will help you make your writing sophisticated.

• Guide Designing and Conducting Experimental and Quasi-Experimental Research – Writing@CSU, Colorado State University
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• Understanding Nursing Research: Experimental Design — Mary and Jeff Bell Library
• Experimentation — Yale University
• Experiment Basics — Research Methods in Psychology
• Causal or Experimental Research Designs — Queensborough Community College
• Fundamental Experimental Research in Machine Learning — Oregon State University

146 Experiment Research Topics

Welcome to our collection of experimental research topics! Experiments are the cornerstone of empirical research, allowing scholars to test hypotheses and expand knowledge. With our experimental research questions ideas, you can uncover the diverse realms of empirical studies, from the natural sciences to social sciences and beyond.

🧪 7 Best Experimental Research Questions Ideas

🏆 best experimental research topics, 💡 simple experimental essay titles, 👍 catchy experimental research questions ideas, ❓ more experimental research questions ideas, 🎓 interesting experimental research topics.

• Bean Seed Germination Experiment Results
• Static and Kinetic Friction: A Lab Experiment
• Water Quality and Contamination Experiment Report
• Physical Health Indicator: Pulse Rate Experiment
• Archimedes’ Principle Experiment: Determining Gravity of Objects
• Motor Speed and Input Characteristics Experiment
• Experiment: Flame Test and Chemical Fingerprinting
• “Stanford Prison Experiment Ethics” by Philip Zimbardo The primary purpose of Zimbardo’s work was to explore how quickly individuals would identify with corrections officers and prisoner roles during the prison simulation.
• Kant’s Ethical Philosophy and Milgram’s Experiments The problem for Kant’s ethical philosophy is whether moral principles are applicable to nonhumans, such as Galacticans.
• Fiji Water Quality: Biology Lab Experiment Since Fiji water is among the popular brands in the US, it is essential to evaluate whether it is clean, that is, safe for human consumption.
• Human Transport Systems: The Pulse Rate Experiment The report provides an analysis of the pulse rate experiment aimed at determining the pulse rates before and after a five-minute exercise conducted by the researcher.
• John Watson and the “Little Albert” Experiment John Watson is considered to be the founder of behaviorism, a psychological theory that focuses on visible behavior while diminishing the notion of consciousness.
• Chemical Experiment on Enzyme Amylase This paper presents an experiment that was conducted to determine the activity of amylase on starch at various pH levels.
• Putnam’s “Twin Earth” Thought-Experiment Throughout the history of analytic philosophy, the problem of meaning has been and remains one of its central themes.
• Scientific Report Draft on Osmosis Egg Experiment Understanding how an egg reacts when placed in solutions of different concentrations enables one to understand the role of osmosis in the human body.
• Metal and Non-metal Redox Reactions Experiment The following experiment aimed to investigate Redox reaction and hence determine which elements were reactive; metal v. metal redox reactions, and non-metal v. non-metal reactions.
• Helicopter Experiment Assessment This report of a paper helicopter experiment involved designating a paper helicopter in varied designs and then dropping it severally while recording the flight time.
• Conducting a Titration Experiment Titration studies are conducted to quantify the amount of an unidentified element in the sample using a methodological approach.
• Unethical Research Experiments Violation of ethical principles can be traced in two analyzed cases; only in Landis’s experiment harm and killing were real in relation to animals.
• Should Animals Be Used for Scientific Experiments? Unfortunately, at the moment, the use of animals in science and medicine cannot be excluded entirely. However, it is possible to conduct experiments using mathematical models.
• Hawthorne Experiments – Elton Mayo With Roethlisberger and Dickson The Hawthorne theories have brought about a positive change in the behavior and attitude of the managers as well as the workers.
• Air Pressure Experiment Methods and Results The plastic mesh fabric was placed over the mouth of the Mason jar, and the metal screw band of the latter was fastened firmly over the plastic mesh sheet.
• P. Zimbardo’s Stanford Experiment A psychological experiment is an event conducted to acquire new scientific knowledge about psychology through the researcher’s deliberate intervention in the life of the examinee.
• Extraneous Variables in Experiments There are some variables in experiments besides the independent variables that usually cause a variation or a change to the dependent variables.
• Why People Obey Authority: Milgram Experiment and Real-World Situation Human beings would obey authority depending on the overall rewards, potential personal gains, and the consequences of failing to do so.
• Ideal Gas Expansion Law: Experiment The purpose of the experiment was to understand the differences between different types of ideal gas expansions, paying attention to the amount of work done.
• Scientific Experiments on Animals from Ethical Perspectives This paper discusses using animals in scientific experiments from the consequentialist, Kantian deontological and Donna Yarri’s Christian character-based perspectives.
• The Stanford Prison Experiment Analysis Abuse between guards and prisoners is an imminent factor attributed to the differential margin on duties and responsibilities.
• Stanford Prison Experiment: Behind the Mask Stanford Prison Experiment organized by Stanford researcher Philip Zimbardo led to a strong public response and still discussed today.
• “Tuskegee Syphilis Experiment – The Deadly Deception”: Unethical Scientific Experiment “Tuskegee Syphilis Experiment – The Deadly Deception” reviews an unethical scientific experiment on humans that was conducted by White physicians on African-Americans.
• The Power of Conformity: Asch’s Experiments The article examines a series of experiments by Asch that helped him identify the factors influencing social conformity.
• Social Experiment: Informal Norms of Gender Issues The social experiment presents a contradiction between the socially-accepted norms and the understanding of equality between men and women.
• Social Experiment: Wrong Outfit in a Wedding Event The attendees of the wedding event displayed disappointment, discomfort, and open resentment towards the dressing style.
• Acoustics Experiment in Brunel’s Thames Tunnel In this project, tunnels that exist below London streets for a variety of communications, civil defense, and military purposes will be used as the objects of the experiment.
• The Marshmallow Experiment Articles The two works, “Don’t Eat the Marshmallow” by Joachim de Posada and “Why Rich Kids Are So Good at the Marshmallow Test” by Anindya Kundu, both focus on the marshmallow experiment.
• Osmosis Experiment With Parsnip Through Differing Concentration of Sucrose
• Identifying the Benefits of Home Ownership: A Swedish Experiment
• Experiment for Cancer Risk Factors
• Hydrochloric Acid Into Tubes of Water and Sodium Thiosulphate Experiment
• General Information about Monkey Drug Trials Experiment
• Reaction Rates Experiment Hydrochloric Acid
• Hydrochloric Acid and Marble Chips Experiment
• Physical Disability and Labor Market Discrimination: Evidence From a Field Experiment
• Canadian Advanced Nanosatellite Experiment Biology
• Dr. Heidegger’s Experiment: Reality or Illusion
• Experiment and Multi-Grid Modeling of Evacuation From a Classroom
• High-Performance Liquid Chromatography Experiment
• Social Capital and Contributions in a Public-Goods Experiment
• Illusory Gains From Chile’s Targeted School Voucher Experiment
• Short Selling and Earnings Management: A Controlled Experiment
• Theft and Rural Poverty: Results of a Natural Experiment
• Lab Experiment: The Effectiveness of Different Antibiotics on Bacteria
• Brucellosis and Its Treatment: Experiment With Doxycycline
• The Link Between Stanford Prison Experiment and Milgram Study
• Four Fundamental Results From the Mice Experiment
• Psychology: Milgram Obedience Experiment Milgram’s experiment may be the last psychological experiment that has had a significant impact on psychology and public opinion.
• Isopods and Their Use in Experiments Isopod is a large family belonging to the crayfish order. The fact that isopods are good to use in various experiments is related to their habitat.
• Ideal Experiment Design: Independent and Dependent Variables This work describes the ideal experiment, that is designed to verify the causal relationship between independent and dependent variables.
• Ethical Analysis of the Tuskegee Syphilis Experiments The Tuskegee Syphilis Study failed to take into account several critical ethical considerations. This essay examines some of the ethical problems linked to the investigation.
• The Use of Animals in Psychological Experiments The method of experimentation is of great significance for multiple fields of psychology, especially for the behaviorist branch.
• Metrology Experiment with Measurement Tools The experiment concerned testing the efficacy of the measurement tools such as the Vernier caliper, a depth gauge, a micrometer, and a gauge in an uncertainty analysis.
• The Stanford Prison Experiment The Stanford prison experiment is an example of how outside social situations influence changes in thought and behavior among humans.
• Situation, Institutional Norms, and Roles: The Stanford Experiment of Zimbardo Philip Zimbardo’s Stanford Experiment brought him critical acclaim. At the same time, it accorded him a certain level of notoriety; because of the methodologies he utilized to conduct the experiment.
• Pasture Experiment: Fertiliser Treatments Response This work is an experiment that defines the role of fertilizers in pasture production and to establish the appropriate use of pasture sampling to assess pasture mass.
• The Importance of Safety in Chemical Experiments Chemical experiments can teach students a lot and show new unknown properties of substances. To protect oneself and others, it is crucial to adhere to rules.
• The Stanford Prison Experiment’s Historical Record The Stanford Prison Experiment is a seminal investigation into the dynamics of peer pressure in human psychology.
• Socioeconomic Status and Sentencing Severity Experiment There are two types of validity threats: external and internal. External validity refers to the degree to which the study can be applied to situations outside the research context.
• Psychology: Zimbardo Prison Experiment Despite all the horrors that contradict ethics, Zimbardo’s research contributed to the formation of social psychology. It was unethical to conduct this experiment.
• Post-Covid Adaptation Laboratory Experiment The goal of the laboratory experiment that this paper will outline is to test the hypothesis about the needs of senior citizens in the post-pandemic era.
• Predicting the Replicability of Social Science Lab Experiments The quality of work is the most significant factor for any academic organization. A research process for any scientific project requires careful evaluation of information sources.
• Moral Dilemma and Thought Experiments The aim of this essay is to set up a thought experiment in which a moral dilemma must be resolved. A person is invited to make a choice as a result of which people should suffer.
• Experiments in High-Frequency Trading High-frequency trading (HFT) is becoming increasingly popular with private businesses and traders. HFT allows traders to make transactions within fractions of seconds.
• The Ethical Issues in 1940’s U.S. Experiments With Syphilis in Guatemala The Guatemala tests have been viewed as a dark side of the U.S. clinical examination: in the 1940s, they purposely uncovered over 5,000 individuals with syphilis and gonorrhea.
• Sociological Experiment: The Salience of Social Norms Based on the sociological experiment described in the paper, the author demonstrated the salience of social norms that exist in our culture.
• Thought Experiment: The Morality of Human Actions A thought experiment aimed at assessing the morality of human actions motivated by divine punishment or reward raises the question of morality and religion correlation.
• Inductor-Capacitor-Resistor Circuit Experiment The article presents the experiment that will demonstrate the relationship between an inductor, voltmeter, and resistor in an inductor-capacitor-resistor (LCR) circuit.
• Ethical Implications of the Early Studies in Psychology: Milgram’s Experiment Milgram’s experiment on obedience content and results are valuable for understanding the ethical issues that may occur in social and behavioral research.
• Blue-Eyed vs. Brown-Eyed Experiment Elliot exposed the learners to discrimination, in which blue-eyed children were initially preferred and given more privileges in the classroom than brown-eyed students.
• Experiment: Science Meets Real Life The experiment involves the sequential study of the dog’s behavior and its reaction to a change in some factors, such as food and bowl.
• Experiment on Effect of Energy Drinks on Athletic Performance Experimental research is a study that a researcher sets up to evaluate a given situation, such as a drug or treatment intervention.
• Miles Davis and Steve Reich: Geniuses of Experiments and Creativity Although Miles Davis’ and Steve Reich’s music belongs to different genres, they are connected in their constant search for a new sound by experimenting and improvising.
• Smoking: An Idea for a Statistical Experiment The hypothesis is that people who smoke cigarettes daily tend to earn more than others: this is a personal observation that requires careful experimental testing.
• The Stanford Jail Experiment Critiques One of the most important critiques leveled at the Stanford Jail Experiment is the length of time it took Zimbardo to call a halt to the experiment.
• Super Size Me and Jogn Cisna Experiments In comparison to Super Size Me, the experiment of John Cisna immediately stands out with a positive attitude towards fast food.
• The Milgram Experiment: Ethical Issues The Milgram experiment is a controversial study on the subject of obedience to authority figures. The participants were asked to deliver electric shocks to other people.
• Can Nonrandomized Experiments Yield Accurate Answers?
• What Kind of Experiments Are Done on Animals?
• Is It Good to Use Animals for Experiments?
• What Are the Types of Experiments?
• Is There Any Healthy Way to Experiment With Drugs?
• What Are the Top Experiments of All Time?
• Are Breaching Experiments Ethical?
• What Does It Mean to Experiment With a Drug?
• Why Do We Use Factorial Experiments?
• How Does Temperature Affect the Rate of Reaction Experiment?
• What Are the Easiest Experiments to Do?
• How Can Rushing Harm the Data and the Experiment Overall?
• What Are the Steps to a Science Experiment?
• How Do Errors Affect the Experiment?
• What Is the Purpose of the Wax Experiment and What Conclusion Does Descartes Reach on Its Basis?
• Can an Experiment Be Invalid but Reliable?
• What Is the Most Influential Experiment in Psychology?
• Why Are Fruit Flies Used for Experiments?
• How Can You Improve the Accuracy of an Experiment?
• What Was Galileo’s Famous Cannonball Drop Experiment?
• What Can Knowledge Be Gained From Conducting a Breaching Experiment?
• How Do You Identify the Independent and Dependent Variables in an Experiment?
• What Was Griffith’s Experiment and Why Was It Important?
• What Is the Difference Between Contingent Valuation and Choice Experiment?
• What Is the Choice Experiment Valuation Method?
• Health and Medicine: Experiments and Discussions In the first experiment, researchers tested the subjectivity of polygraph examiners’ assessments. The specialist was given a specific name before the test began to do it.
• An Experiment in DNA Cloning and Sequencing The aim of this experiment is to clone a fragment of DNA that includes the Green Fluorescent Protein (GFP) gene into the vector pTTQ18, which is an expression vector.
• Lab Experiment on Animals’ Taste or Smell Senses The hypothesis of the study is that taste perception and detection of different sugars by insects were similar to that of humans.
• Triacylglycerols: Definition and Extraction Experiment The sequence of the triacylglycerols matches the published data for linseed as a source to extract triacylglycerol compounds.
• An Enzyme Linked Immunosorbent Assay Experiment In our society presently, immunoassay techniques used in data analyses have assumed a place of high significance, particularly as it applies to pure/applied research.
• Anaerobic Threshold: An Experiment Anaerobic Threshold refers to the minimum level below which no increase in blood lactose can occur. At levels above AT, supplementing aerobic production needs aerobic energy.
• Comparative Effectiveness of Various Surfactants: Experiment Surfactants refer to chemical substances that lessen the surface tension in water. This experiment aimed at establishing the comparative effectiveness of various surfactants.
• A Hypothesis and an Experiment: A Case Study On the control experiment, there would be a seed grown at normal aeration, and wind conditions. All should have a viable bean seed planted centrally on watered soil preferably.
• An Experiment on Data Mining Extend This experiment aims to utilize knowledge and principles of data mining in depicting the investigation of emergent data in biology- particularly on the development of ELISAs.
• Bolted & Welded Connections and Tension Experiment Exploring and comparing the expected and actual failure modes of both bottled and welded connections in tension are the primary purposes of the paper.
• Lab Experiment on Photovoltaics The experiment was done specifically to ascertain how various connected units could be coordinated to give a more reliable and controllable functioning.
• Mind Control: Ethics of the Experiment The topics of mind control and free will has always been seen as a morally grey area in terms of its research potential.
• A Personal Behavior Modification Experiment Using Operant Conditioning This research paper points out the positive outcomes of swearing: it can relieve stress and help one cope with emotional work.
• Jane Elliott’s Experiment on Discrimination The teacher Jane Elliott from Iowa decided to conduct an experiment demonstrating to her students what discrimination is and what it feels like.
• The Tuskegee Syphilis Experiment When the Tuskegee Syphilis Experiment was begun, over 75 years ago, no such principles were officially in place.
• The Critical Characteristics of an Experiment The main aim of this assignment is to evaluate the thought control experiment by famous psychologist Ellen Langer and determine whether it is a qualitative experiment.
• Milgram Experiment: The Question of Ethics This essay will discuss the Milgram experiment and also argue that it was ethical as medical research standards were met, and no undue harm to the participants was caused.
• Boston’s Experiment: Harvard Business Review’s Lessons In Harvard Business Review’s Lessons from Boston’s Experiment with The One Fund, Mitchell discusses his experience with fund distribution to the victims of the Boston bombing.
• The Stanford Prison Experiment Review The video presents an experiment held in 1971. In general, a viewer can observe that people are subjected to behavior and opinion change when affected by others.
• The Way to Come To Terms With Yourself: Social Distancing Experiment In this work, the author describes the course and results of an experiment on social distance: refusal to use gadgets, any communication, and going out.
• Experiment: Bacteria vs Antibiotics The experiment aimed was to test the reaction of bacteria towards some antibiotics and determine the effectiveness of those antibiotics in treating some diseases.
• Ethics: Experiments on Animals Industrial and biomedical research is often painful and most of the test ends up killing the animals. Experiments such as these often incur the wrath of the animal rights movement.
• Impact of the Stanford Prison Experiment Have on Psychology This essay will begin with a brief description of Zimbardo’s Stanford Prison Experiment then it will move to explore two main issues that arose from the said experiment.
• Medical Pharmacology: The Langendorff Experiment The Langendorff experiment aimed at using an ex vivo isolated rat heart preparation to demonstrate the pharmacological effects of two unknown drugs.
• Studying Organisations: The Hawthorne Experiments The Hawthorn experiments marked a new direction in research of motivation and productivity. More than half a century has passed, and productivity remains a concern of management.
• Chemistry of Cooking. Saffron Rice Experiment This research project outlines an experiment that aims to determine the temperature at which Saffron rice turns yellow.
• Evaluation of the Stanford Prison Experiment’ Role The Stanford Prison Experiment is a study that was conducted on August 20, 1971 by a group of researchers headed by the psychology professor Philip Zimbardo.
• Heat Transfer Rates in a Hot Jet: Experiment The experiment is aimed at determining the heat transfer rates in a hot jet. The reasons for the hot jet to have different heat rates in different areas will be determined.
• Inattentive Blindness in Psychological Experiment The features of the human consciousness not to notice quite obvious changes are natural and innate. Such blindness can be caused by several factors.
• Virtue Ethics in Stanford and Milgram’s Experiments This paper investigates the notion of virtue ethics, discussing two major studies, the Stanford prison experiment, and Milgram’s obedience studies.
• An Observable Experiment: Control Over the Variables An observable experiment is defined as the experiment in which the independent variables cannot possibly be controlled by the person or person setting the test.
• Tuskegee Syphilis Experiment: Ethical Controversy Tuskegee case set the background for the reconsideration of healthcare ethics, which means that the ethical value of the given case deserves reconsideration.
• Gender Stereotyping Experiment: The Level of Gender Stereotyping in Society The present study measures the effects of stereotyping women. It examines the first impression formed by subjects based on the information about a fictitious man or a woman.
• Psychological Studies and Experiments: Code of Conduct The following paper is based on past psychological studies i.e. Stanly Milgram’s ‘Obedience Experiment’, Philip Zimbardo’s ‘Stanford Prison Experiment, and Jane Elliott’s ‘Class Divided’.
• Using Animals in Medical Experiments This paper explores how the principles of the character-based ethical approach can be applied to the discussion of using animals in the medical research and experiments.
• The Stanford Experiment by Philip Zimbardo Philip Zimbardo’s Stanford Experiment shows that situational power and norms dictate the behavior of the individual more than the core beliefs that made up his personal identity.

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These essay examples and topics on Experiment were carefully selected by the StudyCorgi editorial team. They meet our highest standards in terms of grammar, punctuation, style, and fact accuracy. Please ensure you properly reference the materials if you’re using them to write your assignment.

This essay topic collection was updated on June 22, 2024 .

200+ Research Title Ideas To Explore In 2024

Choosing a compelling research title is a critical step in the research process, as it serves as the gateway to capturing the attention of readers and potential collaborators. A well-crafted research title not only encapsulates the essence of your study but also entices readers to delve deeper into your work. 

In this blog post, we will explore the significance of research title ideas, the characteristics of an effective title, strategies for generating compelling titles, examples of successful titles, common pitfalls to avoid, the importance of iterative refinement, and ethical considerations in title creation.

Characteristics of a Good Research Title

Table of Contents

Clarity and Precision

A good research title should communicate the core idea of your study clearly and precisely. Avoid vague or overly complex language that might confuse readers.

Relevance to the Research Topic

Ensure that your title accurately reflects the content and focus of your research. It should provide a clear indication of what readers can expect from your study.

Conciseness and Avoidance of Ambiguity

Keep your title concise and to the point. Avoid unnecessary words or phrases that may add ambiguity. Aim for clarity and directness to make your title more impactful.

Use of Keywords

Incorporating relevant keywords in your title can enhance its visibility and accessibility. Consider the terms that researchers in your field are likely to search for and integrate them into your title.

Reflecting the Research Methodology or Approach

If your research employs a specific methodology or approach, consider incorporating that information into your title. This helps set expectations for readers and indicates the uniqueness of your study.

What are the Strategies for Generating Research Title Ideas?

• Brainstorming
• Individual Brainstorming: Set aside time to generate title ideas on your own. Consider different angles, perspectives, and aspects of your research.
• Group Brainstorming: Collaborate with peers or mentors to gather diverse perspectives and insights. Group brainstorming can lead to innovative and multidimensional title ideas.
• Keyword Analysis
• Identifying Key Terms and Concepts: Break down your research into key terms and concepts. These will form the foundation of your title.
• Exploring Synonyms and Related Terms: Expand your search by exploring synonyms and related terms. This can help you discover alternative ways to express your research focus.
• Literature Review
• Examining Existing Titles in the Field: Review titles of relevant studies in your field to identify common patterns and effective strategies.
• Analyzing Successful Titles for Inspiration: Analyze successful research titles to understand what makes them stand out. Look for elements that resonate with your own research.
• Consultation with Peers and Mentors
• Seek feedback from peers and mentors during the title creation process. External perspectives can offer valuable insights and help refine your ideas.
• Use of Online Tools and Title Generators
• Explore online tools and title generators designed to aid in the generation of creative and relevant research titles. While these tools can be helpful, exercise discretion and ensure the generated titles align with the essence of your research.

200+ Research Title Ideas: Category-Wise

Technology and computer science.

• “Cybersecurity Measures in the Age of Quantum Computing”
• “Machine Learning Applications for Predictive Maintenance”
• “The Impact of Augmented Reality on Learning Outcomes”
• “Blockchain Technology: Enhancing Supply Chain Transparency”
• “Human-Computer Interaction in Virtual Reality Environments”

Environmental Science and Sustainability

• “Evaluating the Efficacy of Green Infrastructure in Urban Areas”
• “Climate Change Resilience Strategies for Coastal Communities”
• “Biodiversity Conservation in Tropical Rainforests”
• “Renewable Energy Adoption in Developing Economies”
• “Assessing the Environmental Impact of Plastic Alternatives”

Health and Medicine

• “Precision Medicine Approaches in Cancer Treatment”
• “Mental Health Interventions for Youth in Urban Settings”
• “Telemedicine: Bridging Gaps in Rural Healthcare Access”
• “The Role of Gut Microbiota in Metabolic Disorders”
• “Ethical Considerations in Genetic Editing Technologies”

Social Sciences and Psychology

• “Social Media Influence on Body Image Perception”
• “Impact of Cultural Diversity on Team Performance”
• “Psychological Resilience in the Face of Global Crises”
• “Parental Involvement and Academic Achievement in Adolescents”
• “Exploring the Dynamics of Online Communities and Identity”

Business and Economics

• “Sustainable Business Practices and Consumer Behavior”
• “The Role of Big Data in Financial Decision-Making”
• “Entrepreneurship and Innovation in Emerging Markets”
• “Corporate Social Responsibility and Brand Loyalty”
• “Economic Implications of Remote Work Adoption”

Education and Pedagogy

• “Inclusive Education Models for Diverse Learning Needs”
• “Gamification in STEM Education: A Comparative Analysis”
• “Online Learning Effectiveness in Higher Education”
• “Teacher Training for Integrating Technology in Classrooms”
• “Assessment Strategies for Measuring Critical Thinking Skills”

Psychology and Behavior

• “The Influence of Social Media on Adolescent Well-being”
• “Cognitive Biases in Decision-Making: A Cross-Cultural Study”
• “The Role of Empathy in Conflict Resolution”
• “Positive Psychology Interventions for Workplace Satisfaction”
• “Exploring the Relationship Between Sleep Patterns and Mental Health”

Biology and Genetics

• “Genetic Markers for Predisposition to Neurodegenerative Diseases”
• “CRISPR-Cas9 Technology: Ethical Implications and Future Prospects”
• “Evolutionary Adaptations in Response to Environmental Changes”
• “Understanding the Microbiome’s Impact on Immune System Function”
• “Epigenetic Modifications and Their Role in Disease Development”

Urban Planning and Architecture

• “Smart Cities: Balancing Technological Innovation and Privacy”
• “Revitalizing Urban Spaces: Community Engagement in Design”
• “Sustainable Architecture: Integrating Nature into Urban Designs”
• “Transit-Oriented Development and Its Impact on City Dynamics”
• “Assessing the Cultural Significance of Urban Landscapes”

Linguistics and Communication

• “The Influence of Language on Cross-Cultural Communication”
• “Language Development in Multilingual Environments”
• “The Impact of Nonverbal Communication on Interpersonal Relationships”
• “Digital Communication and the Evolution of Language”
• “Language Processing in Bilingual Individuals: A Neuroscientific Approach”

Political Science and International Relations

• “The Role of Social Media in Political Mobilization”
• “Global Governance in the Era of Transnational Challenges”
• “Human Rights and the Ethics of Intervention in International Affairs”
• “Political Polarization: Causes and Consequences”
• “Climate Change Diplomacy: Assessing International Agreements”

Physics and Astronomy

• “Dark Matter: Unraveling the Mysteries of the Universe”
• “Quantum Entanglement and Its Potential Applications”
• “The Search for Exoplanets in Habitable Zones”
• “Astrophysical Phenomena: Exploring Black Holes and Neutron Stars”
• “Advancements in Quantum Computing Algorithms”

Education Technology (EdTech)

• “Adaptive Learning Platforms: Personalizing Education for Every Student”
•  “The Impact of Virtual Reality Simulations on STEM Education”
• “E-Learning Accessibility for Students with Disabilities”
• “Gamified Learning: Enhancing Student Engagement and Retention”
• “Digital Literacy Education: Navigating the Information Age”

Sociology and Anthropology

• “Cultural Shifts in Modern Society: An Anthropological Exploration”
• “Social Movements in the Digital Age: Activism and Connectivity”
• “Gender Roles and Equality: A Cross-Cultural Perspective”
•  “Urbanization and Its Effects on Traditional Societal Structures”
• “Cultural Appropriation: Understanding Boundaries and Respect”

Materials Science and Engineering

• “Nanostructured Materials: Innovations in Manufacturing and Applications”
•  “Biodegradable Polymers: Towards Sustainable Packaging Solutions”
• “Materials for Energy Storage: Advancements and Challenges”
• “Smart Materials in Healthcare: From Diagnosis to Treatment”
• “Robust Coatings for Extreme Environments: Applications in Aerospace”

History and Archaeology

• “Digital Reconstruction of Historical Sites: Preserving the Past”
• “Trade Routes in Ancient Civilizations: A Comparative Study”
• “Archaeogenetics: Unraveling Human Migrations Through DNA Analysis”
• “Historical Linguistics: Tracing Language Evolution Over Millennia”
• “The Archaeology of Conflict: Studying War through Artifacts”

Marketing and Consumer Behavior

• “Influencer Marketing: Impact on Consumer Trust and Purchasing Decisions”
• “The Role of Brand Storytelling in Consumer Engagement”
• “E-commerce Personalization Strategies: Balancing Customization and Privacy”
• “Cross-Cultural Marketing: Adapting Campaigns for Global Audiences”
• “Consumer Perceptions of Sustainable Products: A Market Analysis”

Neuroscience and Cognitive Science

• “Neuroplasticity and Cognitive Rehabilitation: Implications for Therapy”
• “The Neuroscience of Decision-Making: Insights from Brain Imaging”
• “Cognitive Aging: Understanding Memory Decline and Cognitive Resilience”
• “The Role of Neurotransmitters in Emotional Regulation”
• “Neuroethical Considerations in Brain-Computer Interface Technologies”

Public Health and Epidemiology

• “Epidemiological Trends in Infectious Diseases: Lessons from Global Outbreaks”
• “Public Health Interventions for Reducing Non-Communicable Diseases”
• “Health Disparities Among Marginalized Communities: Addressing the Gaps”
• “The Impact of Climate Change on Vector-Borne Diseases”
• “Community-Based Approaches to Promoting Health Equity”

Robotics and Automation

• “Human-Robot Collaboration in Manufacturing: Enhancing Productivity and Safety”
• “Autonomous Vehicles: Navigating the Path to Mainstream Adoption”
• “Soft Robotics: Engineering Flexibility for Real-World Applications”
• “Ethical Considerations in the Development of AI-powered Robotics”
• “Bio-Inspired Robotics: Learning from Nature to Enhance Machine Intelligence”

Literature and Literary Criticism

• “Postcolonial Narratives: Deconstructing Power Structures in Literature”
• “Digital Storytelling Platforms: Changing the Landscape of Narrative Arts”
• “Literature and Cultural Identity: Exploring Representations in Global Contexts”
• “Eco-Critical Perspectives in Contemporary Literature”
• “Feminist Literary Criticism: Reinterpreting Classic Texts Through a New Lens”

Chemistry and Chemical Engineering

• “Green Chemistry: Sustainable Approaches to Chemical Synthesis”
• “Nanomaterials for Drug Delivery: Innovations in Biomedical Applications”
• “Chemical Process Optimization: Towards Energy-Efficient Production”
• “The Chemistry of Taste: Molecular Insights into Food Flavors”
•  “Catalytic Converters: Advancements in Pollution Control Technologies”

Cultural Studies and Media

• “Media Representations of Social Movements: Framing and Impact”
• “Pop Culture and Identity: Exploring Trends in a Globalized World”
• “The Influence of Social Media on Political Discourse”
• “Reality Television and Perceptions of Reality: A Cultural Analysis”
• “Media Literacy Education: Navigating the Digital Information Age”

Astronomy and Astrophysics

• “Gravitational Waves: Probing the Cosmos for New Discoveries”
• “The Life Cycle of Stars: From Birth to Supernova”
•  “Astrobiology: Searching for Extraterrestrial Life in the Universe”
• “Dark Energy and the Accelerating Expansion of the Universe”
• “Cosmic Microwave Background: Insights into the Early Universe”

Social Work and Community Development

• “Community-Based Mental Health Interventions: A Social Work Perspective”
• “Youth Empowerment Programs: Fostering Resilience in Vulnerable Communities”
• “Social Justice Advocacy in Contemporary Social Work Practice”
• “Intersectionality in Social Work: Addressing the Complex Needs of Individuals”
• “The Role of Technology in Enhancing Social Services Delivery”

Artificial Intelligence and Ethics

• “Ethical Considerations in AI Decision-Making: Balancing Autonomy and Accountability”
• “Bias and Fairness in Machine Learning Algorithms: A Critical Examination”
•  “Explainable AI: Bridging the Gap Between Complexity and Transparency”
• “The Social Implications of AI-Generated Content: Challenges and Opportunities”
• “AI and Personal Privacy: Navigating the Ethical Dimensions of Data Usage”

Linguistics and Computational Linguistics

• “Natural Language Processing: Advancements in Understanding Human Communication”
• “Multilingualism in the Digital Age: Challenges and Opportunities”
•  “Cognitive Linguistics: Exploring the Relationship Between Language and Thought”
• “Speech Recognition Technologies: Applications in Everyday Life”
• “Syntax and Semantics: Unraveling the Structure of Language”

Geology and Earth Sciences

• “Geological Hazards Assessment in Urban Planning: A Case Study”
• “Paleoclimatology: Reconstructing Past Climate Patterns for Future Predictions”
• “Geomorphological Processes in Coastal Landscapes: Implications for Conservation”
• “Volcanic Activity Monitoring: Early Warning Systems and Mitigation Strategies”
• “The Impact of Human Activities on Soil Erosion: An Ecological Perspective”

Political Economy and Global Governance

• “Global Trade Agreements: Assessing Economic Impacts and Equity”
• “Political Economy of Energy Transition: Policies and Socioeconomic Effects”
• “The Role of International Organizations in Global Governance”
• “Financial Inclusion and Economic Development: A Comparative Analysis”
•  “The Political Economy of Pandemics: Governance and Crisis Response”

Food Science and Nutrition

• “Nutrigenomics: Personalized Nutrition for Optimal Health”
• “Functional Foods: Exploring Health Benefits Beyond Basic Nutrition”
• “Sustainable Food Production: Innovations in Agriculture and Aquaculture”
•  “Dietary Patterns and Mental Health: A Comprehensive Review”
• “Food Allergies and Sensitivities: Mechanisms and Management Strategies”

Sociology and Technology

• “Digital Inequalities: Examining Access and Usage Patterns Across Demographics”
• “The Impact of Social Media on Social Capital and Community Building”
• “Technological Surveillance and Privacy Concerns: A Sociological Analysis”
• “Virtual Communities: An Exploration of Identity Formation in Online Spaces”
• “The Social Dynamics of Online Activism: Mobilization and Participation”

Materials Science and Nanotechnology

• “Nanomaterials for Biomedical Imaging: Enhancing Diagnostic Precision”
• “Self-Healing Materials: Advances in Sustainable and Resilient Infrastructure”
• “Smart Textiles: Integrating Nanotechnology for Enhanced Functionality”
• “Multifunctional Nanoparticles in Drug Delivery: Targeted Therapies and Beyond”
• “Nanocomposites for Energy Storage: Engineering Efficient Capacitors”

Communication and Media Studies

• “Media Convergence: The Evolution of Content Delivery in the Digital Age”
• “The Impact of Social Media Influencers on Consumer Behavior”
• “Crisis Communication in a Hyperconnected World: Lessons from Global Events”
• “Media Framing of Environmental Issues: A Comparative Analysis”
• “Digital Detox: Understanding Media Consumption Patterns and Well-being”

Developmental Psychology

• “Early Childhood Attachment and Its Long-Term Impact on Adult Relationships”
• “Cognitive Development in Adolescence: Challenges and Opportunities”
• “Parenting Styles and Academic Achievement: A Cross-Cultural Perspective”
• “Identity Formation in Emerging Adulthood: The Role of Social Influences”
• “Interventions for Promoting Resilience in At-Risk Youth Populations”

Aerospace Engineering

• “Advancements in Aerodynamics: Redefining Flight Efficiency”
• “Space Debris Management: Mitigating Risks in Earth’s Orbit”
• “Aerodynamic Design Optimization for Supersonic Flight”
• “Hypersonic Propulsion Technologies: Pushing the Boundaries of Speed”
• “Materials for Space Exploration: Engineering Solutions for Harsh Environments”

Political Psychology

• “Political Polarization and Public Opinion: Exploring Cognitive Biases”
• “Leadership Styles and Public Perception: A Psychological Analysis”
• “Nationalism and Identity: Psychological Factors Shaping Political Beliefs”
• “The Influence of Emotional Appeals in Political Communication”
• “Crisis Leadership: The Psychological Dynamics of Decision-Making in Times of Uncertainty”

Marine Biology and Conservation

• “Coral Reef Restoration: Strategies for Biodiversity Conservation”
• “Ocean Plastic Pollution: Assessing Impacts on Marine Ecosystems”
• “Marine Mammal Communication: Insights from Bioacoustics”
• “Sustainable Fisheries Management: Balancing Ecological and Economic Concerns”
• “The Role of Mangrove Ecosystems in Coastal Resilience”

Artificial Intelligence and Creativity

• “Generative AI in Creative Industries: Challenges and Innovations”
• “AI-Enhanced Creativity Tools: Empowering Artists and Designers”
• “Machine Learning for Music Composition: Bridging Art and Technology”
• “Creative AI in Film and Entertainment: Transforming Storytelling”
• “Ethical Considerations in AI-Generated Art and Content”

Cultural Anthropology

• “Cultural Relativism in Anthropological Research: Opportunities and Challenges”
• “Rituals and Symbolism: Unraveling Cultural Practices Across Societies”
• “Migration and Cultural Identity: An Ethnographic Exploration”
• “Material Culture Studies: Understanding Societies through Objects”
• “Indigenous Knowledge Systems: Preserving and Promoting Cultural Heritage”

Quantum Computing and Information Science

• “Quantum Information Processing: Algorithms and Applications”
• “Quantum Cryptography: Securing Communication in the Quantum Era”
•  “Quantum Machine Learning: Enhancing AI through Quantum Computing”
• “Quantum Computing in Finance: Opportunities and Challenges”
• “Quantum Internet: Building the Next Generation of Information Networks”

Public Policy and Urban Planning

• “Smart Cities and Inclusive Urban Development: A Policy Perspective”
• “Public-Private Partnerships in Infrastructure Development: Lessons Learned”
• “The Impact of Transportation Policies on Urban Mobility Patterns”
• “Housing Affordability: Policy Approaches to Addressing Urban Challenges”
• “Data-Driven Decision-Making in Urban Governance: Opportunities and Risks”

Gerontology and Aging Studies

• “Healthy Aging Interventions: Promoting Quality of Life in Older Adults”
• “Social Isolation and Mental Health in Aging Populations: Interventions and Support”
• “Technology Adoption Among Older Adults: Bridging the Digital Divide”
• “End-of-Life Decision-Making: Ethical Considerations and Legal Frameworks”
• “Cognitive Resilience in Aging: Strategies for Maintaining Mental Sharpness”

Examples of Effective Research Titles

Illustrative Examples from Various Disciplines

Here are examples of effective research titles from different disciplines:

• “Unlocking the Mysteries of Neural Plasticity: A Multidisciplinary Approach”
• “Sustainable Urban Development: Integrating Environmental and Social Perspectives”
• “Quantum Computing: Navigating the Path to Practical Applications”

Analysis of What Makes Each Title Effective

• Clear indication of the research focus.
• Inclusion of key terms relevant to the field.
• Incorporation of a multidisciplinary or integrated approach where applicable.

Common Pitfalls to Avoid in Research Title Creation

A. Vagueness and Ambiguity

Vague or ambiguous titles can deter readers from engaging with your research. Ensure your title is straightforward and leaves no room for misinterpretation.

B. Overuse of Jargon

While technical terms are essential, excessive jargon can alienate readers who may not be familiar with the specific terminology. Strike a balance between precision and accessibility.

C. Lack of Alignment with Research Objectives

Your title should align seamlessly with the objectives and findings of your research. Avoid creating titles that misrepresent the core contributions of your study.

D. Lengthy and Complicated Titles

Lengthy titles can be overwhelming and may not effectively convey the essence of your research. Aim for brevity while maintaining clarity and informativeness.

E. Lack of Creativity and Engagement

A bland title may not capture the interest of potential readers. Inject creativity where appropriate and strive to create a title that sparks curiosity.

Ethical Considerations in Research Title Creation

• Avoiding Sensationalism and Misleading Titles

Ensure that your title accurately represents the content of your research. Avoid sensationalism or misleading language that may compromise the integrity of your work.

• Ensuring Accuracy and Integrity in Representing Research Content

Your title should uphold the principles of accuracy and integrity. Any claims or implications in the title should be supported by the actual findings of your research.

Crafting a captivating research title is a nuanced process that requires careful consideration of various factors. From clarity and relevance to creativity and ethical considerations, each element plays a crucial role in the success of your title. 

By following the outlined strategies and avoiding common pitfalls for research title ideas, researchers can enhance the visibility and impact of their work, contributing to the broader scholarly conversation. Remember, your research title is the first impression readers have of your work, so make it count.

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211+ Best Experimental Research Topics for Students [2024]

Experimental research serves as a cornerstone in scientific inquiry, allowing researchers to test hypotheses through controlled experiments. 

For students, engaging in experimental research not only fosters a deeper understanding of theoretical concepts but also cultivates critical thinking and problem-solving skills essential for academic success. 

By exploring experimental research topics, students gain hands-on experience, honing their analytical abilities while gaining practical insights into their chosen fields of study. 

In this blog, we will delve into a myriad of experimental research topics for students across various disciplines, providing inspiration and guidance for conducting meaningful experiments and advancing academic endeavors.

What is Experimental Research?

Table of Contents

Experimental research is a systematic approach to scientific inquiry where researchers manipulate one or more variables to observe the effect on another variable, known as the dependent variable, while controlling other factors. 

This method aims to establish cause-and-effect relationships between variables, providing empirical evidence to support or refute hypotheses. Through controlled experiments conducted in laboratory or field settings, researchers can investigate phenomena, test theories, and draw conclusions about the underlying mechanisms governing natural phenomena. 

Experimental research plays a crucial role in advancing knowledge across various disciplines, from psychology and medicine to physics and engineering, by providing empirical evidence to support theoretical claims.

Importance of Experimental Research Topics for Students

Experimental research topics for students are crucial for several reasons:

Hands-on Learning

Experimental research topics offer students practical experience in applying theoretical knowledge to real-world scenarios, enhancing their understanding of complex concepts.

Critical Thinking Skills

Engaging in experimental research cultivates critical thinking skills as students design experiments, analyze data, and draw conclusions, fostering a deeper understanding of scientific methodologies.

Problem-Solving Abilities

By tackling experimental challenges, students develop problem-solving abilities essential for navigating academic and professional environments.

Personalized Learning

Students can explore topics aligned with their interests and passions, fostering a sense of ownership and motivation in their academic pursuits.

Preparation for Future Endeavors

Experimental research equips students with essential skills and experiences valuable for future academic pursuits, research endeavors, and professional careers.

List of Experimental Research Topics for Students

Here’s a list of experimental research topics for students across various fields can explore:

• The effects of mindfulness meditation on stress reduction.
• Investigating the impact of social media usage on self-esteem.
• Examining the relationship between sleep quality and academic performance.
• The influence of music on cognitive function and memory.
• Exploring the bystander effect in emergency situations.
• Investigating the effects of color on mood and productivity.
• The relationship between exercise and mental health outcomes.
• Examining the efficacy of cognitive-behavioral therapy in anxiety management.
• Investigating the effects of peer pressure on decision-making.
• The impact of parental involvement on children’s academic achievement.
• Exploring the psychology of addiction and its treatment.
• Investigating the role of genetics in personality traits.
• Examining the effects of early childhood trauma on adult mental health.
• The influence of cultural factors on perception and behavior.
• Investigating the placebo effect and its implications for medical treatment.
• Investigating the effects of different diets on gut microbiota composition.
• Examining the impact of environmental pollutants on amphibian populations.
• Investigating the efficacy of natural remedies in treating common ailments.
• Exploring the genetics of aging and longevity.
• The effects of climate change on plant phenology and growth patterns.
• Investigating the role of gut-brain axis in mental health disorders.
• Examining the effects of exercise on cardiovascular health.
• Exploring the mechanisms of antibiotic resistance in bacteria.
• Investigating the ecological impacts of invasive species.
• Examining the effects of light pollution on nocturnal animals.
• Exploring the genetics of rare diseases and potential treatments.
• Investigating the biodiversity of coral reef ecosystems.
• Examining the effects of different pollutants on aquatic organisms.
• Exploring the role of epigenetics in gene expression.
• Investigating the evolutionary origins of human behavior.
• Investigating the properties of superconductors at different temperatures.
• Exploring the behavior of quantum particles in entangled states.
• Investigating the relationship between temperature and electrical conductivity in metals.
• Examining the principles of wave-particle duality in quantum mechanics.
• Exploring the physics of renewable energy sources such as solar and wind power.
• Investigating the properties of materials under extreme pressure conditions.
• Examining the behavior of fluids in microgravity environments.
• Exploring the principles of chaos theory and deterministic systems.
• Investigating the physics of sound and its applications in acoustics.
• Examining the behavior of particles in accelerators and colliders.
• Exploring the properties of electromagnetic waves and their applications.
• Investigating the phenomenon of gravitational waves and their detection.
• Examining the principles of thermodynamics and heat transfer.
• Exploring the physics of nanomaterials and their applications.
• Investigating the principles of quantum computing and its potential applications.
• Investigating the properties of different catalysts in chemical reactions.
• Exploring the principles of green chemistry and sustainable synthesis methods.
• Investigating the kinetics of enzyme-catalyzed reactions.
• Examining the behavior of nanoparticles in solution.
• Exploring the chemistry of medicinal plants and natural remedies.
• Investigating the effects of pH on chemical reactions.
• Examining the properties of polymers and their applications.
• Exploring the chemistry of atmospheric pollutants and their effects on the environment.
• Investigating the principles of electrochemistry and battery technology.
• Examining the synthesis and properties of novel materials for electronic devices.
• Exploring the chemistry of food additives and preservatives.
• Investigating the mechanisms of drug metabolism in the human body.
• Examining the properties of supercritical fluids and their applications.
• Exploring the chemistry of fermentation and its industrial applications.
• Investigating the synthesis and properties of nanomaterials for biomedical applications.

Computer Science

• Investigating the effectiveness of machine learning algorithms in predicting stock prices.
• Exploring the security vulnerabilities of blockchain technology.
• Investigating the impact of virtual reality on learning outcomes.
• Examining the effectiveness of different programming languages in software development.
• Exploring the potential of quantum computing in solving complex problems.
• Investigating the impact of social media algorithms on user behavior.
• Examining the privacy implications of data mining techniques.
• Exploring the principles of artificial intelligence and its ethical considerations.
• Investigating the effectiveness of cybersecurity measures in protecting against cyber threats.
• Examining the potential of augmented reality in enhancing user experiences.
• Exploring the applications of natural language processing in text analysis.
• Investigating the impact of mobile technology on daily life.
• Examining the effectiveness of different encryption techniques in securing data.
• Exploring the principles of distributed computing and its applications.
• Investigating the potential of autonomous vehicles in improving transportation systems.

Environmental Science

• Investigating the impact of deforestation on biodiversity loss.
• Exploring the effects of climate change on ocean acidification.
• Investigating the efficacy of renewable energy technologies in reducing greenhouse gas emissions.
• Examining the effects of pollution on air quality and public health.
• Exploring the restoration of degraded ecosystems and their ecological benefits.
• Investigating the relationship between urbanization and heat island effects.
• Examining the effects of plastic pollution on marine ecosystems.
• Exploring the principles of sustainable agriculture and food production.
• Investigating the impacts of invasive species on native biodiversity.
• Examining the effectiveness of conservation strategies in protecting endangered species.
• Exploring the effects of water pollution on aquatic ecosystems and human health.
• Investigating the potential of carbon sequestration techniques in mitigating climate change.
• Examining the impacts of land use changes on ecosystem services.
• Exploring the principles of ecological modeling and their applications in conservation.
• Investigating the effects of habitat fragmentation on wildlife populations.
• Investigating the effects of social media on interpersonal relationships.
• Exploring the impact of income inequality on social mobility.
• Investigating the factors influencing voting behavior in democratic societies.
• Examining the effects of globalization on cultural diversity.
• Exploring the dynamics of family structures and their impact on child development.
• Investigating the correlation between socioeconomic status and access to education.
• Examining the effects of mass media on shaping public opinion.
• Exploring the relationship between gender equality and economic development.
• Investigating the impact of immigration on social cohesion.
• Examining the role of religion in shaping societal norms and values.
• Exploring the dynamics of social movements and their impact on policy change.
• Investigating the effects of racial discrimination on mental health outcomes.
• Examining the relationship between crime rates and socioeconomic factors.
• Exploring the influence of cultural norms on gender roles and identity.
• Investigating the impact of technology on social interactions and community cohesion.
• Investigating the effectiveness of flipped classrooms in improving student learning outcomes.
• Exploring the impact of inclusive education on students with disabilities.
• Investigating the effects of parental involvement on student achievement.
• Examining the role of teacher-student relationships in academic success.
• Exploring the efficacy of project-based learning in fostering critical thinking skills.
• Investigating the impact of standardized testing on student stress levels.
• Examining the effectiveness of online learning platforms in distance education.
• Exploring the benefits of early childhood education on long-term academic success.
• Investigating the effects of classroom environment on student motivation.
• Examining the impact of socioeconomic factors on educational attainment.
• Exploring the role of technology in personalized learning and adaptive instruction.
• Investigating the effectiveness of bilingual education programs in language acquisition.
• Examining the impact of school nutrition programs on student health and academic performance.
• Exploring the benefits of arts education on cognitive development and creativity.
• Investigating the relationship between school climate and student behavior.
• Investigating the impact of minimum wage laws on employment levels.
• Exploring the effects of globalization on income inequality.
• Investigating the relationship between economic growth and environmental sustainability.
• Examining the effects of government subsidies on agricultural markets.
• Exploring the impact of foreign direct investment on economic development.
• Investigating the effects of trade tariffs on international trade flows.
• Examining the relationship between inflation and interest rates.
• Exploring the impact of unemployment on mental health and well-being.
• Investigating the effectiveness of fiscal policy in mitigating economic recessions.
• Examining the role of entrepreneurship in economic growth and innovation.
• Exploring the effects of income taxation on labor supply and consumer behavior.
• Investigating the relationship between education levels and earning potential.
• Examining the impacts of economic sanctions on target countries.
• Exploring the principles of behavioral economics and decision-making.
• Investigating the role of central banks in monetary policy and economic stability.

Political Science

• Investigating the factors influencing voter turnout in elections.
• Exploring the effects of political polarization on democratic institutions.
• Investigating the impact of media framing on public opinion.
• Examining the role of political parties in shaping policy agendas.
• Exploring the dynamics of international diplomacy and conflict resolution.
• Investigating the effects of electoral systems on political representation.
• Examining the relationship between political ideology and policy preferences.
• Exploring the impact of campaign finance regulations on electoral outcomes.
• Investigating the effects of gerrymandering on political representation.
• Examining the role of interest groups in the policy-making process.
• Exploring the impact of political propaganda on public perceptions.
• Investigating the effects of term limits on political accountability.
• Examining the role of social movements in driving political change.
• Exploring the dynamics of political leadership and decision-making.
• Investigating the impact of globalization on national sovereignty.

Health Sciences

• Investigating the effects of lifestyle factors on cardiovascular health.
• Exploring the efficacy of alternative medicine approaches in pain management.
• Investigating the relationship between diet and mental health outcomes.
• Examining the effects of stress on immune system function.
• Exploring the efficacy of vaccination programs in preventing infectious diseases.
• Investigating the impact of healthcare disparities on health outcomes.
• Examining the effects of air pollution on respiratory health.
• Exploring the relationship between sleep quality and cognitive function.
• Investigating the efficacy of telemedicine in delivering healthcare services.
• Examining the effects of aging on musculoskeletal health.
• Exploring the relationship between gut microbiota and metabolic disorders.
• Investigating the impact of exercise on mental health and well-being.
• Examining the effects of environmental toxins on reproductive health.
• Exploring the efficacy of mindfulness-based interventions in stress management.
• Investigating the relationship between social support and health outcomes.

Engineering

• Investigating the efficiency of renewable energy technologies in power generation.
• Exploring the potential of 3D printing in manufacturing and prototyping.
• Investigating the effects of material properties on structural integrity in engineering design.
• Examining the efficiency of water treatment technologies in wastewater management.
• Exploring the potential of nanotechnology in drug delivery systems.
• Investigating the impact of transportation infrastructure on urban development.
• Examining the effects of seismic retrofitting on building resilience in earthquake-prone areas.
• Exploring the principles of artificial intelligence in autonomous vehicle navigation.
• Investigating the efficacy of biodegradable materials in sustainable packaging.
• Examining the potential of robotics in healthcare applications.
• Exploring the effects of climate change on civil engineering infrastructure.
• Investigating the efficiency of smart grid technologies in electricity distribution.
• Examining the impact of renewable energy integration on power grid stability.
• Exploring the potential of biomimicry in engineering design.
• Investigating the principles of quantum computing in information technology.
• Investigating the effects of corporate social responsibility initiatives on brand reputation.
• Exploring the impact of organizational culture on employee satisfaction and productivity.
• Investigating the relationship between customer satisfaction and loyalty in service industries.
• Examining the effects of e-commerce on traditional retail markets.
• Exploring the impact of supply chain disruptions on business resilience.
• Investigating the effectiveness of marketing strategies in influencing consumer behavior.
• Examining the relationship between leadership styles and organizational performance.
• Exploring the effects of globalization on multinational corporations.
• Investigating the impact of technology adoption on business innovation.
• Examining the effects of workplace diversity on team performance and creativity.
• Exploring the relationship between financial incentives and employee motivation.
• Investigating the effects of mergers and acquisitions on corporate profitability.
• Examining the impact of digital transformation on business operations.
• Exploring the principles of risk management and its applications in business decision-making.
• Investigating the relationship between organizational structure and agility in fast-paced markets.

Literature and Language Studies

• Investigating the impact of translation on the reception of literary works in different cultures.
• Exploring the evolution of language through historical literature analysis .
• Investigating the portrayal of gender roles in contemporary literature.
• Examining the influence of literary movements on societal attitudes and values.
• Exploring the use of symbolism in literary works and its interpretation.
• Investigating the effects of bilingualism on cognitive development and language proficiency.
• Examining the relationship between language and identity in immigrant communities.
• Exploring the depiction of mental illness in literature and its impact on stigma.
• Investigating the role of literature in fostering empathy and understanding.
• Examining the influence of political ideology on literary censorship.
• Exploring the use of narrative techniques in autobiographical literature.
• Investigating the portrayal of cultural diversity in contemporary literature.
• Examining the relationship between language and power in political discourse.
• Exploring the representation of marginalized voices in literature.
• Investigating the effects of translation strategies on the fidelity of literary texts.
• Investigating the influence of digital media on storytelling techniques in contemporary literature.
• Exploring the portrayal of environmental themes and sustainability in literature across different cultural contexts.

These experimental research topics for students span various disciplines, offering students a wide range of avenues for exploration and inquiry in their academic pursuits.

Tips for Conducting Experimental Research Topics

Conducting experimental research can be a challenging but rewarding endeavor. Here are some tips to help students effectively plan and carry out their experiments:

• Clearly define your research question and objectives to guide your experimental design.
• Develop a detailed experimental protocol outlining procedures, variables, and controls.
• Ensure proper randomization and blinding techniques to minimize bias and ensure validity.
• Collect data meticulously, recording observations accurately and consistently.
• Analyze data rigorously using appropriate statistical methods to draw meaningful conclusions.
• Consider ethical considerations throughout the research process, obtaining necessary approvals and consent.
• Communicate findings effectively through clear and concise reporting in academic formats.
• Iterate and refine your experimental approach based on feedback and further analysis for continuous improvement.

Wrapping Up

Exploring experimental research topics for students is a valuable opportunity for intellectual growth and academic development. 

Through hands-on inquiry and investigation, students can deepen their understanding of theoretical concepts, hone critical thinking skills, and cultivate a passion for scientific exploration. 

Engaging in experimental research fosters creativity, resilience, and problem-solving abilities essential for success in both academic and professional realms. Moreover, the interdisciplinary nature of experimental research encourages students to bridge gaps between various fields, fostering a holistic approach to knowledge acquisition. 

By embracing experimentation, students not only contribute to the advancement of scientific knowledge but also empower themselves to become lifelong learners and innovative thinkers prepared to tackle the challenges of the future.

1. How do I narrow down my topic?

Start by brainstorming broad areas of interest and gradually narrow down your focus based on feasibility, resources, and academic relevance.

2. Can I change my topic midway through the research?

While it’s best to stick with your chosen topic, sometimes unforeseen circumstances may require adjustments. Consult with your supervisor or mentor before making any significant changes.

3. How long does it take to conduct experimental research?

The duration of experimental research varies depending on the complexity of the topic, availability of resources, and experimental design. It could range from a few weeks to several months or even years.

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How to Make a Research Paper Title with Examples

What is a research paper title and why does it matter?

A research paper title summarizes the aim and purpose of your research study. Making a title for your research is one of the most important decisions when writing an article to publish in journals. The research title is the first thing that journal editors and reviewers see when they look at your paper and the only piece of information that fellow researchers will see in a database or search engine query. Good titles that are concise and contain all the relevant terms have been shown to increase citation counts and Altmetric scores .

Therefore, when you title research work, make sure it captures all of the relevant aspects of your study, including the specific topic and problem being investigated. It also should present these elements in a way that is accessible and will captivate readers. Follow these steps to learn how to make a good research title for your work.

How to Make a Research Paper Title in 5 Steps

You might wonder how you are supposed to pick a title from all the content that your manuscript contains—how are you supposed to choose? What will make your research paper title come up in search engines and what will make the people in your field read it? 

In a nutshell, your research title should accurately capture what you have done, it should sound interesting to the people who work on the same or a similar topic, and it should contain the important title keywords that other researchers use when looking for literature in databases. To make the title writing process as simple as possible, we have broken it down into 5 simple steps.

Step 1: Answer some key questions about your research paper

What does your paper seek to answer and what does it accomplish? Try to answer these questions as briefly as possible. You can create these questions by going through each section of your paper and finding the MOST relevant information to make a research title.

Step 2: Identify research study keywords

Now that you have answers to your research questions, find the most important parts of these responses and make these your study keywords. Note that you should only choose the most important terms for your keywords–journals usually request anywhere from 3 to 8 keywords maximum.

Step 3: Research title writing: use these keywords

“We employed a case study of 60 liver transplant patients around the US aged 20-50 years to assess how waiting list volume affects the outcomes of liver transplantation in patients; results indicate a positive correlation between increased waiting list volume and negative prognosis after the transplant procedure.”

The sentence above is clearly much too long for a research paper title. This is why you will trim and polish your title in the next two steps.

Step 4: Create a working research paper title

To create a working title, remove elements that make it a complete “sentence” but keep everything that is important to what the study is about. Delete all unnecessary and redundant words that are not central to the study or that researchers would most likely not use in a database search.

“ We employed a case study of 60 liver transplant patients around the US aged 20-50 years to assess how the waiting list volume affects the outcome of liver transplantation in patients ; results indicate a positive correlation between increased waiting list volume and a negative prognosis after transplant procedure ”

Now shift some words around for proper syntax and rephrase it a bit to shorten the length and make it leaner and more natural. What you are left with is:

“A case study of 60 liver transplant patients around the US aged 20-50 years assessing the impact of waiting list volume on outcome of transplantation and showing a positive correlation between increased waiting list volume and a negative prognosis” (Word Count: 38)

This text is getting closer to what we want in a research title, which is just the most important information. But note that the word count for this working title is still 38 words, whereas the average length of published journal article titles is 16 words or fewer. Therefore, we should eliminate some words and phrases that are not essential to this title.

Step 5: Remove any nonessential words and phrases from your title

Because the number of patients studied and the exact outcome are not the most essential parts of this paper, remove these elements first:

 “A case study of 60 liver transplant patients around the US aged 20-50 years assessing the impact of waiting list volume on outcomes of transplantation and showing a positive correlation between increased waiting list volume and a negative prognosis” (Word Count: 19)

In addition, the methods used in a study are not usually the most searched-for keywords in databases and represent additional details that you may want to remove to make your title leaner. So what is left is:

“Assessing the impact of waiting list volume on outcome and prognosis in liver transplantation patients” (Word Count: 15)

In this final version of the title, one can immediately recognize the subject and what objectives the study aims to achieve. Note that the most important terms appear at the beginning and end of the title: “Assessing,” which is the main action of the study, is placed at the beginning; and “liver transplantation patients,” the specific subject of the study, is placed at the end.

This will aid significantly in your research paper title being found in search engines and database queries, which means that a lot more researchers will be able to locate your article once it is published. In fact, a 2014 review of more than 150,000 papers submitted to the UK’s Research Excellence Framework (REF) database found the style of a paper’s title impacted the number of citations it would typically receive. In most disciplines, articles with shorter, more concise titles yielded more citations.

Adding a Research Paper Subtitle

If your title might require a subtitle to provide more immediate details about your methodology or sample, you can do this by adding this information after a colon:

“ : a case study of US adult patients ages 20-25”

If we abide strictly by our word count rule this may not be necessary or recommended. But every journal has its own standard formatting and style guidelines for research paper titles, so it is a good idea to be aware of the specific journal author instructions , not just when you write the manuscript but also to decide how to create a good title for it.

Research Paper Title Examples

The title examples in the following table illustrate how a title can be interesting but incomplete, complete by uninteresting, complete and interesting but too informal in tone, or some other combination of these. A good research paper title should meet all the requirements in the four columns below.

Tips on Formulating a Good Research Paper Title

In addition to the steps given above, there are a few other important things you want to keep in mind when it comes to how to write a research paper title, regarding formatting, word count, and content:

• Write the title after you’ve written your paper and abstract
• Include all of the essential terms in your paper
• Keep it short and to the point (~16 words or fewer)
• Avoid unnecessary jargon and abbreviations
• Use keywords that capture the content of your paper
• Never include a period at the end—your title is NOT a sentence

Research Paper Writing Resources

We hope this article has been helpful in teaching you how to craft your research paper title. But you might still want to dig deeper into different journal title formats and categories that might be more suitable for specific article types or need help with writing a cover letter for your manuscript submission.

In addition to getting English proofreading services , including paper editing services , before submission to journals, be sure to visit our academic resources papers. Here you can find dozens of articles on manuscript writing, from drafting an outline to finding a target journal to submit to.

Experimental Research Design — 6 mistakes you should never make!

Since school days’ students perform scientific experiments that provide results that define and prove the laws and theorems in science. These experiments are laid on a strong foundation of experimental research designs.

An experimental research design helps researchers execute their research objectives with more clarity and transparency.

In this article, we will not only discuss the key aspects of experimental research designs but also the issues to avoid and problems to resolve while designing your research study.

Table of Contents

What Is Experimental Research Design?

Experimental research design is a framework of protocols and procedures created to conduct experimental research with a scientific approach using two sets of variables. Herein, the first set of variables acts as a constant, used to measure the differences of the second set. The best example of experimental research methods is quantitative research .

Experimental research helps a researcher gather the necessary data for making better research decisions and determining the facts of a research study.

When Can a Researcher Conduct Experimental Research?

A researcher can conduct experimental research in the following situations —

• When time is an important factor in establishing a relationship between the cause and effect.
• When there is an invariable or never-changing behavior between the cause and effect.
• Finally, when the researcher wishes to understand the importance of the cause and effect.

Importance of Experimental Research Design

To publish significant results, choosing a quality research design forms the foundation to build the research study. Moreover, effective research design helps establish quality decision-making procedures, structures the research to lead to easier data analysis, and addresses the main research question. Therefore, it is essential to cater undivided attention and time to create an experimental research design before beginning the practical experiment.

By creating a research design, a researcher is also giving oneself time to organize the research, set up relevant boundaries for the study, and increase the reliability of the results. Through all these efforts, one could also avoid inconclusive results. If any part of the research design is flawed, it will reflect on the quality of the results derived.

Types of Experimental Research Designs

Based on the methods used to collect data in experimental studies, the experimental research designs are of three primary types:

1. Pre-experimental Research Design

A research study could conduct pre-experimental research design when a group or many groups are under observation after implementing factors of cause and effect of the research. The pre-experimental design will help researchers understand whether further investigation is necessary for the groups under observation.

Pre-experimental research is of three types —

• One-shot Case Study Research Design
• One-group Pretest-posttest Research Design
• Static-group Comparison

2. True Experimental Research Design

A true experimental research design relies on statistical analysis to prove or disprove a researcher’s hypothesis. It is one of the most accurate forms of research because it provides specific scientific evidence. Furthermore, out of all the types of experimental designs, only a true experimental design can establish a cause-effect relationship within a group. However, in a true experiment, a researcher must satisfy these three factors —

• There is a control group that is not subjected to changes and an experimental group that will experience the changed variables
• A variable that can be manipulated by the researcher
• Random distribution of the variables

This type of experimental research is commonly observed in the physical sciences.

3. Quasi-experimental Research Design

The word “Quasi” means similarity. A quasi-experimental design is similar to a true experimental design. However, the difference between the two is the assignment of the control group. In this research design, an independent variable is manipulated, but the participants of a group are not randomly assigned. This type of research design is used in field settings where random assignment is either irrelevant or not required.

The classification of the research subjects, conditions, or groups determines the type of research design to be used.

Advantages of Experimental Research

Experimental research allows you to test your idea in a controlled environment before taking the research to clinical trials. Moreover, it provides the best method to test your theory because of the following advantages:

• Researchers have firm control over variables to obtain results.
• The subject does not impact the effectiveness of experimental research. Anyone can implement it for research purposes.
• The results are specific.
• Post results analysis, research findings from the same dataset can be repurposed for similar research ideas.
• Researchers can identify the cause and effect of the hypothesis and further analyze this relationship to determine in-depth ideas.
• Experimental research makes an ideal starting point. The collected data could be used as a foundation to build new research ideas for further studies.

6 Mistakes to Avoid While Designing Your Research

There is no order to this list, and any one of these issues can seriously compromise the quality of your research. You could refer to the list as a checklist of what to avoid while designing your research.

1. Invalid Theoretical Framework

Usually, researchers miss out on checking if their hypothesis is logical to be tested. If your research design does not have basic assumptions or postulates, then it is fundamentally flawed and you need to rework on your research framework.

2. Inadequate Literature Study

Without a comprehensive research literature review , it is difficult to identify and fill the knowledge and information gaps. Furthermore, you need to clearly state how your research will contribute to the research field, either by adding value to the pertinent literature or challenging previous findings and assumptions.

3. Insufficient or Incorrect Statistical Analysis

Statistical results are one of the most trusted scientific evidence. The ultimate goal of a research experiment is to gain valid and sustainable evidence. Therefore, incorrect statistical analysis could affect the quality of any quantitative research.

4. Undefined Research Problem

This is one of the most basic aspects of research design. The research problem statement must be clear and to do that, you must set the framework for the development of research questions that address the core problems.

5. Research Limitations

Every study has some type of limitations . You should anticipate and incorporate those limitations into your conclusion, as well as the basic research design. Include a statement in your manuscript about any perceived limitations, and how you considered them while designing your experiment and drawing the conclusion.

6. Ethical Implications

The most important yet less talked about topic is the ethical issue. Your research design must include ways to minimize any risk for your participants and also address the research problem or question at hand. If you cannot manage the ethical norms along with your research study, your research objectives and validity could be questioned.

Experimental Research Design Example

In an experimental design, a researcher gathers plant samples and then randomly assigns half the samples to photosynthesize in sunlight and the other half to be kept in a dark box without sunlight, while controlling all the other variables (nutrients, water, soil, etc.)

By comparing their outcomes in biochemical tests, the researcher can confirm that the changes in the plants were due to the sunlight and not the other variables.

Experimental research is often the final form of a study conducted in the research process which is considered to provide conclusive and specific results. But it is not meant for every research. It involves a lot of resources, time, and money and is not easy to conduct, unless a foundation of research is built. Yet it is widely used in research institutes and commercial industries, for its most conclusive results in the scientific approach.

Have you worked on research designs? How was your experience creating an experimental design? What difficulties did you face? Do write to us or comment below and share your insights on experimental research designs!

Frequently Asked Questions

Randomization is important in an experimental research because it ensures unbiased results of the experiment. It also measures the cause-effect relationship on a particular group of interest.

Experimental research design lay the foundation of a research and structures the research to establish quality decision making process.

There are 3 types of experimental research designs. These are pre-experimental research design, true experimental research design, and quasi experimental research design.

The difference between an experimental and a quasi-experimental design are: 1. The assignment of the control group in quasi experimental research is non-random, unlike true experimental design, which is randomly assigned. 2. Experimental research group always has a control group; on the other hand, it may not be always present in quasi experimental research.

Experimental research establishes a cause-effect relationship by testing a theory or hypothesis using experimental groups or control variables. In contrast, descriptive research describes a study or a topic by defining the variables under it and answering the questions related to the same.

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• Guide to Experimental Design | Overview, Steps, & Examples

Guide to Experimental Design | Overview, 5 steps & Examples

Published on December 3, 2019 by Rebecca Bevans . Revised on June 21, 2023.

Experiments are used to study causal relationships . You manipulate one or more independent variables and measure their effect on one or more dependent variables.

Experimental design create a set of procedures to systematically test a hypothesis . A good experimental design requires a strong understanding of the system you are studying.

There are five key steps in designing an experiment:

• Consider your variables and how they are related
• Write a specific, testable hypothesis
• Design experimental treatments to manipulate your independent variable
• Assign subjects to groups, either between-subjects or within-subjects
• Plan how you will measure your dependent variable

For valid conclusions, you also need to select a representative sample and control any  extraneous variables that might influence your results. If random assignment of participants to control and treatment groups is impossible, unethical, or highly difficult, consider an observational study instead. This minimizes several types of research bias, particularly sampling bias , survivorship bias , and attrition bias as time passes.

Table of contents

Step 1: define your variables, step 2: write your hypothesis, step 3: design your experimental treatments, step 4: assign your subjects to treatment groups, step 5: measure your dependent variable, other interesting articles, frequently asked questions about experiments.

You should begin with a specific research question . We will work with two research question examples, one from health sciences and one from ecology:

To translate your research question into an experimental hypothesis, you need to define the main variables and make predictions about how they are related.

Start by simply listing the independent and dependent variables .

Then you need to think about possible extraneous and confounding variables and consider how you might control  them in your experiment.

Finally, you can put these variables together into a diagram. Use arrows to show the possible relationships between variables and include signs to show the expected direction of the relationships.

Here we predict that increasing temperature will increase soil respiration and decrease soil moisture, while decreasing soil moisture will lead to decreased soil respiration.

Prevent plagiarism. Run a free check.

Now that you have a strong conceptual understanding of the system you are studying, you should be able to write a specific, testable hypothesis that addresses your research question.

The next steps will describe how to design a controlled experiment . In a controlled experiment, you must be able to:

• Systematically and precisely manipulate the independent variable(s).
• Precisely measure the dependent variable(s).
• Control any potential confounding variables.

If your study system doesn’t match these criteria, there are other types of research you can use to answer your research question.

How you manipulate the independent variable can affect the experiment’s external validity – that is, the extent to which the results can be generalized and applied to the broader world.

First, you may need to decide how widely to vary your independent variable.

• just slightly above the natural range for your study region.
• over a wider range of temperatures to mimic future warming.
• over an extreme range that is beyond any possible natural variation.

Second, you may need to choose how finely to vary your independent variable. Sometimes this choice is made for you by your experimental system, but often you will need to decide, and this will affect how much you can infer from your results.

• a categorical variable : either as binary (yes/no) or as levels of a factor (no phone use, low phone use, high phone use).
• a continuous variable (minutes of phone use measured every night).

How you apply your experimental treatments to your test subjects is crucial for obtaining valid and reliable results.

First, you need to consider the study size : how many individuals will be included in the experiment? In general, the more subjects you include, the greater your experiment’s statistical power , which determines how much confidence you can have in your results.

Then you need to randomly assign your subjects to treatment groups . Each group receives a different level of the treatment (e.g. no phone use, low phone use, high phone use).

You should also include a control group , which receives no treatment. The control group tells us what would have happened to your test subjects without any experimental intervention.

When assigning your subjects to groups, there are two main choices you need to make:

• A completely randomized design vs a randomized block design .
• A between-subjects design vs a within-subjects design .

Randomization

An experiment can be completely randomized or randomized within blocks (aka strata):

• In a completely randomized design , every subject is assigned to a treatment group at random.
• In a randomized block design (aka stratified random design), subjects are first grouped according to a characteristic they share, and then randomly assigned to treatments within those groups.

Sometimes randomization isn’t practical or ethical , so researchers create partially-random or even non-random designs. An experimental design where treatments aren’t randomly assigned is called a quasi-experimental design .

Between-subjects vs. within-subjects

In a between-subjects design (also known as an independent measures design or classic ANOVA design), individuals receive only one of the possible levels of an experimental treatment.

In medical or social research, you might also use matched pairs within your between-subjects design to make sure that each treatment group contains the same variety of test subjects in the same proportions.

In a within-subjects design (also known as a repeated measures design), every individual receives each of the experimental treatments consecutively, and their responses to each treatment are measured.

Within-subjects or repeated measures can also refer to an experimental design where an effect emerges over time, and individual responses are measured over time in order to measure this effect as it emerges.

Counterbalancing (randomizing or reversing the order of treatments among subjects) is often used in within-subjects designs to ensure that the order of treatment application doesn’t influence the results of the experiment.

Finally, you need to decide how you’ll collect data on your dependent variable outcomes. You should aim for reliable and valid measurements that minimize research bias or error.

Some variables, like temperature, can be objectively measured with scientific instruments. Others may need to be operationalized to turn them into measurable observations.

• Ask participants to record what time they go to sleep and get up each day.
• Ask participants to wear a sleep tracker.

How precisely you measure your dependent variable also affects the kinds of statistical analysis you can use on your data.

Experiments are always context-dependent, and a good experimental design will take into account all of the unique considerations of your study system to produce information that is both valid and relevant to your research question.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Student’s  t -distribution
• Normal distribution
• Null and Alternative Hypotheses
• Chi square tests
• Confidence interval
• Cluster sampling
• Stratified sampling
• Data cleansing
• Reproducibility vs Replicability
• Peer review
• Likert scale

Research bias

• Implicit bias
• Framing effect
• Cognitive bias
• Placebo effect
• Hawthorne effect
• Hindsight bias
• Affect heuristic

Experimental design means planning a set of procedures to investigate a relationship between variables . To design a controlled experiment, you need:

• A testable hypothesis
• At least one independent variable that can be precisely manipulated
• At least one dependent variable that can be precisely measured

When designing the experiment, you decide:

• How you will manipulate the variable(s)
• How you will control for any potential confounding variables
• How many subjects or samples will be included in the study
• How subjects will be assigned to treatment levels

Experimental design is essential to the internal and external validity of your experiment.

The key difference between observational studies and experimental designs is that a well-done observational study does not influence the responses of participants, while experiments do have some sort of treatment condition applied to at least some participants by random assignment .

A confounding variable , also called a confounder or confounding factor, is a third variable in a study examining a potential cause-and-effect relationship.

A confounding variable is related to both the supposed cause and the supposed effect of the study. It can be difficult to separate the true effect of the independent variable from the effect of the confounding variable.

In your research design , it’s important to identify potential confounding variables and plan how you will reduce their impact.

In a between-subjects design , every participant experiences only one condition, and researchers assess group differences between participants in various conditions.

In a within-subjects design , each participant experiences all conditions, and researchers test the same participants repeatedly for differences between conditions.

The word “between” means that you’re comparing different conditions between groups, while the word “within” means you’re comparing different conditions within the same group.

An experimental group, also known as a treatment group, receives the treatment whose effect researchers wish to study, whereas a control group does not. They should be identical in all other ways.

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10 Experimental research

Experimental research—often considered to be the ‘gold standard’ in research designs—is one of the most rigorous of all research designs. In this design, one or more independent variables are manipulated by the researcher (as treatments), subjects are randomly assigned to different treatment levels (random assignment), and the results of the treatments on outcomes (dependent variables) are observed. The unique strength of experimental research is its internal validity (causality) due to its ability to link cause and effect through treatment manipulation, while controlling for the spurious effect of extraneous variable.

Experimental research is best suited for explanatory research—rather than for descriptive or exploratory research—where the goal of the study is to examine cause-effect relationships. It also works well for research that involves a relatively limited and well-defined set of independent variables that can either be manipulated or controlled. Experimental research can be conducted in laboratory or field settings. Laboratory experiments , conducted in laboratory (artificial) settings, tend to be high in internal validity, but this comes at the cost of low external validity (generalisability), because the artificial (laboratory) setting in which the study is conducted may not reflect the real world. Field experiments are conducted in field settings such as in a real organisation, and are high in both internal and external validity. But such experiments are relatively rare, because of the difficulties associated with manipulating treatments and controlling for extraneous effects in a field setting.

Experimental research can be grouped into two broad categories: true experimental designs and quasi-experimental designs. Both designs require treatment manipulation, but while true experiments also require random assignment, quasi-experiments do not. Sometimes, we also refer to non-experimental research, which is not really a research design, but an all-inclusive term that includes all types of research that do not employ treatment manipulation or random assignment, such as survey research, observational research, and correlational studies.

Basic concepts

Treatment and control groups. In experimental research, some subjects are administered one or more experimental stimulus called a treatment (the treatment group ) while other subjects are not given such a stimulus (the control group ). The treatment may be considered successful if subjects in the treatment group rate more favourably on outcome variables than control group subjects. Multiple levels of experimental stimulus may be administered, in which case, there may be more than one treatment group. For example, in order to test the effects of a new drug intended to treat a certain medical condition like dementia, if a sample of dementia patients is randomly divided into three groups, with the first group receiving a high dosage of the drug, the second group receiving a low dosage, and the third group receiving a placebo such as a sugar pill (control group), then the first two groups are experimental groups and the third group is a control group. After administering the drug for a period of time, if the condition of the experimental group subjects improved significantly more than the control group subjects, we can say that the drug is effective. We can also compare the conditions of the high and low dosage experimental groups to determine if the high dose is more effective than the low dose.

Treatment manipulation. Treatments are the unique feature of experimental research that sets this design apart from all other research methods. Treatment manipulation helps control for the ‘cause’ in cause-effect relationships. Naturally, the validity of experimental research depends on how well the treatment was manipulated. Treatment manipulation must be checked using pretests and pilot tests prior to the experimental study. Any measurements conducted before the treatment is administered are called pretest measures , while those conducted after the treatment are posttest measures .

Random selection and assignment. Random selection is the process of randomly drawing a sample from a population or a sampling frame. This approach is typically employed in survey research, and ensures that each unit in the population has a positive chance of being selected into the sample. Random assignment, however, is a process of randomly assigning subjects to experimental or control groups. This is a standard practice in true experimental research to ensure that treatment groups are similar (equivalent) to each other and to the control group prior to treatment administration. Random selection is related to sampling, and is therefore more closely related to the external validity (generalisability) of findings. However, random assignment is related to design, and is therefore most related to internal validity. It is possible to have both random selection and random assignment in well-designed experimental research, but quasi-experimental research involves neither random selection nor random assignment.

Threats to internal validity. Although experimental designs are considered more rigorous than other research methods in terms of the internal validity of their inferences (by virtue of their ability to control causes through treatment manipulation), they are not immune to internal validity threats. Some of these threats to internal validity are described below, within the context of a study of the impact of a special remedial math tutoring program for improving the math abilities of high school students.

History threat is the possibility that the observed effects (dependent variables) are caused by extraneous or historical events rather than by the experimental treatment. For instance, students’ post-remedial math score improvement may have been caused by their preparation for a math exam at their school, rather than the remedial math program.

Maturation threat refers to the possibility that observed effects are caused by natural maturation of subjects (e.g., a general improvement in their intellectual ability to understand complex concepts) rather than the experimental treatment.

Testing threat is a threat in pre-post designs where subjects’ posttest responses are conditioned by their pretest responses. For instance, if students remember their answers from the pretest evaluation, they may tend to repeat them in the posttest exam.

Not conducting a pretest can help avoid this threat.

Instrumentation threat , which also occurs in pre-post designs, refers to the possibility that the difference between pretest and posttest scores is not due to the remedial math program, but due to changes in the administered test, such as the posttest having a higher or lower degree of difficulty than the pretest.

Mortality threat refers to the possibility that subjects may be dropping out of the study at differential rates between the treatment and control groups due to a systematic reason, such that the dropouts were mostly students who scored low on the pretest. If the low-performing students drop out, the results of the posttest will be artificially inflated by the preponderance of high-performing students.

Regression threat —also called a regression to the mean—refers to the statistical tendency of a group’s overall performance to regress toward the mean during a posttest rather than in the anticipated direction. For instance, if subjects scored high on a pretest, they will have a tendency to score lower on the posttest (closer to the mean) because their high scores (away from the mean) during the pretest were possibly a statistical aberration. This problem tends to be more prevalent in non-random samples and when the two measures are imperfectly correlated.

Two-group experimental designs

Pretest-posttest control group design . In this design, subjects are randomly assigned to treatment and control groups, subjected to an initial (pretest) measurement of the dependent variables of interest, the treatment group is administered a treatment (representing the independent variable of interest), and the dependent variables measured again (posttest). The notation of this design is shown in Figure 10.1.

Statistical analysis of this design involves a simple analysis of variance (ANOVA) between the treatment and control groups. The pretest-posttest design handles several threats to internal validity, such as maturation, testing, and regression, since these threats can be expected to influence both treatment and control groups in a similar (random) manner. The selection threat is controlled via random assignment. However, additional threats to internal validity may exist. For instance, mortality can be a problem if there are differential dropout rates between the two groups, and the pretest measurement may bias the posttest measurement—especially if the pretest introduces unusual topics or content.

Posttest -only control group design . This design is a simpler version of the pretest-posttest design where pretest measurements are omitted. The design notation is shown in Figure 10.2.

The treatment effect is measured simply as the difference in the posttest scores between the two groups:

The appropriate statistical analysis of this design is also a two-group analysis of variance (ANOVA). The simplicity of this design makes it more attractive than the pretest-posttest design in terms of internal validity. This design controls for maturation, testing, regression, selection, and pretest-posttest interaction, though the mortality threat may continue to exist.

Because the pretest measure is not a measurement of the dependent variable, but rather a covariate, the treatment effect is measured as the difference in the posttest scores between the treatment and control groups as:

Due to the presence of covariates, the right statistical analysis of this design is a two-group analysis of covariance (ANCOVA). This design has all the advantages of posttest-only design, but with internal validity due to the controlling of covariates. Covariance designs can also be extended to pretest-posttest control group design.

Factorial designs

Two-group designs are inadequate if your research requires manipulation of two or more independent variables (treatments). In such cases, you would need four or higher-group designs. Such designs, quite popular in experimental research, are commonly called factorial designs. Each independent variable in this design is called a factor , and each subdivision of a factor is called a level . Factorial designs enable the researcher to examine not only the individual effect of each treatment on the dependent variables (called main effects), but also their joint effect (called interaction effects).

In a factorial design, a main effect is said to exist if the dependent variable shows a significant difference between multiple levels of one factor, at all levels of other factors. No change in the dependent variable across factor levels is the null case (baseline), from which main effects are evaluated. In the above example, you may see a main effect of instructional type, instructional time, or both on learning outcomes. An interaction effect exists when the effect of differences in one factor depends upon the level of a second factor. In our example, if the effect of instructional type on learning outcomes is greater for three hours/week of instructional time than for one and a half hours/week, then we can say that there is an interaction effect between instructional type and instructional time on learning outcomes. Note that the presence of interaction effects dominate and make main effects irrelevant, and it is not meaningful to interpret main effects if interaction effects are significant.

Hybrid experimental designs

Hybrid designs are those that are formed by combining features of more established designs. Three such hybrid designs are randomised bocks design, Solomon four-group design, and switched replications design.

Randomised block design. This is a variation of the posttest-only or pretest-posttest control group design where the subject population can be grouped into relatively homogeneous subgroups (called blocks ) within which the experiment is replicated. For instance, if you want to replicate the same posttest-only design among university students and full-time working professionals (two homogeneous blocks), subjects in both blocks are randomly split between the treatment group (receiving the same treatment) and the control group (see Figure 10.5). The purpose of this design is to reduce the ‘noise’ or variance in data that may be attributable to differences between the blocks so that the actual effect of interest can be detected more accurately.

Solomon four-group design . In this design, the sample is divided into two treatment groups and two control groups. One treatment group and one control group receive the pretest, and the other two groups do not. This design represents a combination of posttest-only and pretest-posttest control group design, and is intended to test for the potential biasing effect of pretest measurement on posttest measures that tends to occur in pretest-posttest designs, but not in posttest-only designs. The design notation is shown in Figure 10.6.

Switched replication design . This is a two-group design implemented in two phases with three waves of measurement. The treatment group in the first phase serves as the control group in the second phase, and the control group in the first phase becomes the treatment group in the second phase, as illustrated in Figure 10.7. In other words, the original design is repeated or replicated temporally with treatment/control roles switched between the two groups. By the end of the study, all participants will have received the treatment either during the first or the second phase. This design is most feasible in organisational contexts where organisational programs (e.g., employee training) are implemented in a phased manner or are repeated at regular intervals.

Quasi-experimental designs

Quasi-experimental designs are almost identical to true experimental designs, but lacking one key ingredient: random assignment. For instance, one entire class section or one organisation is used as the treatment group, while another section of the same class or a different organisation in the same industry is used as the control group. This lack of random assignment potentially results in groups that are non-equivalent, such as one group possessing greater mastery of certain content than the other group, say by virtue of having a better teacher in a previous semester, which introduces the possibility of selection bias . Quasi-experimental designs are therefore inferior to true experimental designs in interval validity due to the presence of a variety of selection related threats such as selection-maturation threat (the treatment and control groups maturing at different rates), selection-history threat (the treatment and control groups being differentially impacted by extraneous or historical events), selection-regression threat (the treatment and control groups regressing toward the mean between pretest and posttest at different rates), selection-instrumentation threat (the treatment and control groups responding differently to the measurement), selection-testing (the treatment and control groups responding differently to the pretest), and selection-mortality (the treatment and control groups demonstrating differential dropout rates). Given these selection threats, it is generally preferable to avoid quasi-experimental designs to the greatest extent possible.

In addition, there are quite a few unique non-equivalent designs without corresponding true experimental design cousins. Some of the more useful of these designs are discussed next.

Regression discontinuity (RD) design . This is a non-equivalent pretest-posttest design where subjects are assigned to the treatment or control group based on a cut-off score on a preprogram measure. For instance, patients who are severely ill may be assigned to a treatment group to test the efficacy of a new drug or treatment protocol and those who are mildly ill are assigned to the control group. In another example, students who are lagging behind on standardised test scores may be selected for a remedial curriculum program intended to improve their performance, while those who score high on such tests are not selected from the remedial program.

Because of the use of a cut-off score, it is possible that the observed results may be a function of the cut-off score rather than the treatment, which introduces a new threat to internal validity. However, using the cut-off score also ensures that limited or costly resources are distributed to people who need them the most, rather than randomly across a population, while simultaneously allowing a quasi-experimental treatment. The control group scores in the RD design do not serve as a benchmark for comparing treatment group scores, given the systematic non-equivalence between the two groups. Rather, if there is no discontinuity between pretest and posttest scores in the control group, but such a discontinuity persists in the treatment group, then this discontinuity is viewed as evidence of the treatment effect.

Proxy pretest design . This design, shown in Figure 10.11, looks very similar to the standard NEGD (pretest-posttest) design, with one critical difference: the pretest score is collected after the treatment is administered. A typical application of this design is when a researcher is brought in to test the efficacy of a program (e.g., an educational program) after the program has already started and pretest data is not available. Under such circumstances, the best option for the researcher is often to use a different prerecorded measure, such as students’ grade point average before the start of the program, as a proxy for pretest data. A variation of the proxy pretest design is to use subjects’ posttest recollection of pretest data, which may be subject to recall bias, but nevertheless may provide a measure of perceived gain or change in the dependent variable.

Separate pretest-posttest samples design . This design is useful if it is not possible to collect pretest and posttest data from the same subjects for some reason. As shown in Figure 10.12, there are four groups in this design, but two groups come from a single non-equivalent group, while the other two groups come from a different non-equivalent group. For instance, say you want to test customer satisfaction with a new online service that is implemented in one city but not in another. In this case, customers in the first city serve as the treatment group and those in the second city constitute the control group. If it is not possible to obtain pretest and posttest measures from the same customers, you can measure customer satisfaction at one point in time, implement the new service program, and measure customer satisfaction (with a different set of customers) after the program is implemented. Customer satisfaction is also measured in the control group at the same times as in the treatment group, but without the new program implementation. The design is not particularly strong, because you cannot examine the changes in any specific customer’s satisfaction score before and after the implementation, but you can only examine average customer satisfaction scores. Despite the lower internal validity, this design may still be a useful way of collecting quasi-experimental data when pretest and posttest data is not available from the same subjects.

An interesting variation of the NEDV design is a pattern-matching NEDV design , which employs multiple outcome variables and a theory that explains how much each variable will be affected by the treatment. The researcher can then examine if the theoretical prediction is matched in actual observations. This pattern-matching technique—based on the degree of correspondence between theoretical and observed patterns—is a powerful way of alleviating internal validity concerns in the original NEDV design.

Perils of experimental research

Experimental research is one of the most difficult of research designs, and should not be taken lightly. This type of research is often best with a multitude of methodological problems. First, though experimental research requires theories for framing hypotheses for testing, much of current experimental research is atheoretical. Without theories, the hypotheses being tested tend to be ad hoc, possibly illogical, and meaningless. Second, many of the measurement instruments used in experimental research are not tested for reliability and validity, and are incomparable across studies. Consequently, results generated using such instruments are also incomparable. Third, often experimental research uses inappropriate research designs, such as irrelevant dependent variables, no interaction effects, no experimental controls, and non-equivalent stimulus across treatment groups. Findings from such studies tend to lack internal validity and are highly suspect. Fourth, the treatments (tasks) used in experimental research may be diverse, incomparable, and inconsistent across studies, and sometimes inappropriate for the subject population. For instance, undergraduate student subjects are often asked to pretend that they are marketing managers and asked to perform a complex budget allocation task in which they have no experience or expertise. The use of such inappropriate tasks, introduces new threats to internal validity (i.e., subject’s performance may be an artefact of the content or difficulty of the task setting), generates findings that are non-interpretable and meaningless, and makes integration of findings across studies impossible.

The design of proper experimental treatments is a very important task in experimental design, because the treatment is the raison d’etre of the experimental method, and must never be rushed or neglected. To design an adequate and appropriate task, researchers should use prevalidated tasks if available, conduct treatment manipulation checks to check for the adequacy of such tasks (by debriefing subjects after performing the assigned task), conduct pilot tests (repeatedly, if necessary), and if in doubt, use tasks that are simple and familiar for the respondent sample rather than tasks that are complex or unfamiliar.

In summary, this chapter introduced key concepts in the experimental design research method and introduced a variety of true experimental and quasi-experimental designs. Although these designs vary widely in internal validity, designs with less internal validity should not be overlooked and may sometimes be useful under specific circumstances and empirical contingencies.

Social Science Research: Principles, Methods and Practices (Revised edition) Copyright © 2019 by Anol Bhattacherjee is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Experimental Research: What it is + Types of designs

Any research conducted under scientifically acceptable conditions uses experimental methods. The success of experimental studies hinges on researchers confirming the change of a variable is based solely on the manipulation of the constant variable. The research should establish a notable cause and effect.

What is Experimental Research?

Experimental research is a study conducted with a scientific approach using two sets of variables. The first set acts as a constant, which you use to measure the differences of the second set. Quantitative research methods , for example, are experimental.

If you don’t have enough data to support your decisions, you must first determine the facts. This research gathers the data necessary to help you make better decisions.

You can conduct experimental research in the following situations:

• Time is a vital factor in establishing a relationship between cause and effect.
• Invariable behavior between cause and effect.
• You wish to understand the importance of cause and effect.

Experimental Research Design Types

The classic experimental design definition is: “The methods used to collect data in experimental studies.”

There are three primary types of experimental design:

• Pre-experimental research design
• True experimental research design
• Quasi-experimental research design

The way you classify research subjects based on conditions or groups determines the type of research design  you should use.

0 1. Pre-Experimental Design

A group, or various groups, are kept under observation after implementing cause and effect factors. You’ll conduct this research to understand whether further investigation is necessary for these particular groups.

You can break down pre-experimental research further into three types:

• One-shot Case Study Research Design
• One-group Pretest-posttest Research Design
• Static-group Comparison

0 2. True Experimental Design

It relies on statistical analysis to prove or disprove a hypothesis, making it the most accurate form of research. Of the types of experimental design, only true design can establish a cause-effect relationship within a group. In a true experiment, three factors need to be satisfied:

• There is a Control Group, which won’t be subject to changes, and an Experimental Group, which will experience the changed variables.
• A variable that can be manipulated by the researcher
• Random distribution

This experimental research method commonly occurs in the physical sciences.

0 3. Quasi-Experimental Design

The word “Quasi” indicates similarity. A quasi-experimental design is similar to an experimental one, but it is not the same. The difference between the two is the assignment of a control group. In this research, an independent variable is manipulated, but the participants of a group are not randomly assigned. Quasi-research is used in field settings where random assignment is either irrelevant or not required.

Importance of Experimental Design

Experimental research is a powerful tool for understanding cause-and-effect relationships. It allows us to manipulate variables and observe the effects, which is crucial for understanding how different factors influence the outcome of a study.

But the importance of experimental research goes beyond that. It’s a critical method for many scientific and academic studies. It allows us to test theories, develop new products, and make groundbreaking discoveries.

For example, this research is essential for developing new drugs and medical treatments. Researchers can understand how a new drug works by manipulating dosage and administration variables and identifying potential side effects.

Similarly, experimental research is used in the field of psychology to test theories and understand human behavior. By manipulating variables such as stimuli, researchers can gain insights into how the brain works and identify new treatment options for mental health disorders.

It is also widely used in the field of education. It allows educators to test new teaching methods and identify what works best. By manipulating variables such as class size, teaching style, and curriculum, researchers can understand how students learn and identify new ways to improve educational outcomes.

In addition, experimental research is a powerful tool for businesses and organizations. By manipulating variables such as marketing strategies, product design, and customer service, companies can understand what works best and identify new opportunities for growth.

Advantages of Experimental Research

When talking about this research, we can think of human life. Babies do their own rudimentary experiments (such as putting objects in their mouths) to learn about the world around them, while older children and teens do experiments at school to learn more about science.

Ancient scientists used this research to prove that their hypotheses were correct. For example, Galileo Galilei and Antoine Lavoisier conducted various experiments to discover key concepts in physics and chemistry. The same is true of modern experts, who use this scientific method to see if new drugs are effective, discover treatments for diseases, and create new electronic devices (among others).

It’s vital to test new ideas or theories. Why put time, effort, and funding into something that may not work?

This research allows you to test your idea in a controlled environment before marketing. It also provides the best method to test your theory thanks to the following advantages:

• Researchers have a stronger hold over variables to obtain desired results.
• The subject or industry does not impact the effectiveness of experimental research. Any industry can implement it for research purposes.
• The results are specific.
• After analyzing the results, you can apply your findings to similar ideas or situations.
• You can identify the cause and effect of a hypothesis. Researchers can further analyze this relationship to determine more in-depth ideas.
• Experimental research makes an ideal starting point. The data you collect is a foundation for building more ideas and conducting more action research .

Whether you want to know how the public will react to a new product or if a certain food increases the chance of disease, experimental research is the best place to start. Begin your research by finding subjects using  QuestionPro Audience  and other tools today.

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• Experimental Research Designs: Types, Examples & Methods

Experimental research is the most familiar type of research design for individuals in the physical sciences and a host of other fields. This is mainly because experimental research is a classical scientific experiment, similar to those performed in high school science classes.

Imagine taking 2 samples of the same plant and exposing one of them to sunlight, while the other is kept away from sunlight. Let the plant exposed to sunlight be called sample A, while the latter is called sample B.

If after the duration of the research, we find out that sample A grows and sample B dies, even though they are both regularly wetted and given the same treatment. Therefore, we can conclude that sunlight will aid growth in all similar plants.

What is Experimental Research?

Experimental research is a scientific approach to research, where one or more independent variables are manipulated and applied to one or more dependent variables to measure their effect on the latter. The effect of the independent variables on the dependent variables is usually observed and recorded over some time, to aid researchers in drawing a reasonable conclusion regarding the relationship between these 2 variable types.

The experimental research method is widely used in physical and social sciences, psychology, and education. It is based on the comparison between two or more groups with a straightforward logic, which may, however, be difficult to execute.

Mostly related to a laboratory test procedure, experimental research designs involve collecting quantitative data and performing statistical analysis on them during research. Therefore, making it an example of quantitative research method .

What are The Types of Experimental Research Design?

The types of experimental research design are determined by the way the researcher assigns subjects to different conditions and groups. They are of 3 types, namely; pre-experimental, quasi-experimental, and true experimental research.

Pre-experimental Research Design

In pre-experimental research design, either a group or various dependent groups are observed for the effect of the application of an independent variable which is presumed to cause change. It is the simplest form of experimental research design and is treated with no control group.

Although very practical, experimental research is lacking in several areas of the true-experimental criteria. The pre-experimental research design is further divided into three types

• One-shot Case Study Research Design

In this type of experimental study, only one dependent group or variable is considered. The study is carried out after some treatment which was presumed to cause change, making it a posttest study.

• One-group Pretest-posttest Research Design: 

This research design combines both posttest and pretest study by carrying out a test on a single group before the treatment is administered and after the treatment is administered. With the former being administered at the beginning of treatment and later at the end.

• Static-group Comparison: 

In a static-group comparison study, 2 or more groups are placed under observation, where only one of the groups is subjected to some treatment while the other groups are held static. All the groups are post-tested, and the observed differences between the groups are assumed to be a result of the treatment.

Quasi-experimental Research Design

  The word “quasi” means partial, half, or pseudo. Therefore, the quasi-experimental research bearing a resemblance to the true experimental research, but not the same.  In quasi-experiments, the participants are not randomly assigned, and as such, they are used in settings where randomization is difficult or impossible.

 This is very common in educational research, where administrators are unwilling to allow the random selection of students for experimental samples.

Some examples of quasi-experimental research design include; the time series, no equivalent control group design, and the counterbalanced design.

True Experimental Research Design

The true experimental research design relies on statistical analysis to approve or disprove a hypothesis. It is the most accurate type of experimental design and may be carried out with or without a pretest on at least 2 randomly assigned dependent subjects.

The true experimental research design must contain a control group, a variable that can be manipulated by the researcher, and the distribution must be random. The classification of true experimental design include:

• The posttest-only Control Group Design: In this design, subjects are randomly selected and assigned to the 2 groups (control and experimental), and only the experimental group is treated. After close observation, both groups are post-tested, and a conclusion is drawn from the difference between these groups.
• The pretest-posttest Control Group Design: For this control group design, subjects are randomly assigned to the 2 groups, both are presented, but only the experimental group is treated. After close observation, both groups are post-tested to measure the degree of change in each group.
• Solomon four-group Design: This is the combination of the pretest-only and the pretest-posttest control groups. In this case, the randomly selected subjects are placed into 4 groups.

The first two of these groups are tested using the posttest-only method, while the other two are tested using the pretest-posttest method.

Examples of Experimental Research

Experimental research examples are different, depending on the type of experimental research design that is being considered. The most basic example of experimental research is laboratory experiments, which may differ in nature depending on the subject of research.

Administering Exams After The End of Semester

During the semester, students in a class are lectured on particular courses and an exam is administered at the end of the semester. In this case, the students are the subjects or dependent variables while the lectures are the independent variables treated on the subjects.

Only one group of carefully selected subjects are considered in this research, making it a pre-experimental research design example. We will also notice that tests are only carried out at the end of the semester, and not at the beginning.

Further making it easy for us to conclude that it is a one-shot case study research. 

Employee Skill Evaluation

Before employing a job seeker, organizations conduct tests that are used to screen out less qualified candidates from the pool of qualified applicants. This way, organizations can determine an employee’s skill set at the point of employment.

In the course of employment, organizations also carry out employee training to improve employee productivity and generally grow the organization. Further evaluation is carried out at the end of each training to test the impact of the training on employee skills, and test for improvement.

Here, the subject is the employee, while the treatment is the training conducted. This is a pretest-posttest control group experimental research example.

Evaluation of Teaching Method

Let us consider an academic institution that wants to evaluate the teaching method of 2 teachers to determine which is best. Imagine a case whereby the students assigned to each teacher is carefully selected probably due to personal request by parents or due to stubbornness and smartness.

This is a no equivalent group design example because the samples are not equal. By evaluating the effectiveness of each teacher’s teaching method this way, we may conclude after a post-test has been carried out.

However, this may be influenced by factors like the natural sweetness of a student. For example, a very smart student will grab more easily than his or her peers irrespective of the method of teaching.

What are the Characteristics of Experimental Research?  

Experimental research contains dependent, independent and extraneous variables. The dependent variables are the variables being treated or manipulated and are sometimes called the subject of the research.

The independent variables are the experimental treatment being exerted on the dependent variables. Extraneous variables, on the other hand, are other factors affecting the experiment that may also contribute to the change.

The setting is where the experiment is carried out. Many experiments are carried out in the laboratory, where control can be exerted on the extraneous variables, thereby eliminating them. 

Other experiments are carried out in a less controllable setting. The choice of setting used in research depends on the nature of the experiment being carried out.

• Multivariable

Experimental research may include multiple independent variables, e.g. time, skills, test scores, etc.

Why Use Experimental Research Design?  

Experimental research design can be majorly used in physical sciences, social sciences, education, and psychology. It is used to make predictions and draw conclusions on a subject matter. 

Some uses of experimental research design are highlighted below.

• Medicine: Experimental research is used to provide the proper treatment for diseases. In most cases, rather than directly using patients as the research subject, researchers take a sample of the bacteria from the patient’s body and are treated with the developed antibacterial

The changes observed during this period are recorded and evaluated to determine its effectiveness. This process can be carried out using different experimental research methods.

• Education: Asides from science subjects like Chemistry and Physics which involves teaching students how to perform experimental research, it can also be used in improving the standard of an academic institution. This includes testing students’ knowledge on different topics, coming up with better teaching methods, and the implementation of other programs that will aid student learning.
• Human Behavior: Social scientists are the ones who mostly use experimental research to test human behaviour. For example, consider 2 people randomly chosen to be the subject of the social interaction research where one person is placed in a room without human interaction for 1 year.

The other person is placed in a room with a few other people, enjoying human interaction. There will be a difference in their behaviour at the end of the experiment.

• UI/UX: During the product development phase, one of the major aims of the product team is to create a great user experience with the product. Therefore, before launching the final product design, potential are brought in to interact with the product.

For example, when finding it difficult to choose how to position a button or feature on the app interface, a random sample of product testers are allowed to test the 2 samples and how the button positioning influences the user interaction is recorded.

What are the Disadvantages of Experimental Research?  

• It is highly prone to human error due to its dependency on variable control which may not be properly implemented. These errors could eliminate the validity of the experiment and the research being conducted.
• Exerting control of extraneous variables may create unrealistic situations. Eliminating real-life variables will result in inaccurate conclusions. This may also result in researchers controlling the variables to suit his or her personal preferences.
• It is a time-consuming process. So much time is spent on testing dependent variables and waiting for the effect of the manipulation of dependent variables to manifest.
• It is expensive. 
• It is very risky and may have ethical complications that cannot be ignored. This is common in medical research, where failed trials may lead to a patient’s death or a deteriorating health condition.
• Experimental research results are not descriptive.
• Response bias can also be supplied by the subject of the conversation.
• Human responses in experimental research can be difficult to measure. 

What are the Data Collection Methods in Experimental Research?  

Data collection methods in experimental research are the different ways in which data can be collected for experimental research. They are used in different cases, depending on the type of research being carried out.

1. Observational Study

This type of study is carried out over a long period. It measures and observes the variables of interest without changing existing conditions.

When researching the effect of social interaction on human behavior, the subjects who are placed in 2 different environments are observed throughout the research. No matter the kind of absurd behavior that is exhibited by the subject during this period, its condition will not be changed.

This may be a very risky thing to do in medical cases because it may lead to death or worse medical conditions.

2. Simulations

This procedure uses mathematical, physical, or computer models to replicate a real-life process or situation. It is frequently used when the actual situation is too expensive, dangerous, or impractical to replicate in real life.

This method is commonly used in engineering and operational research for learning purposes and sometimes as a tool to estimate possible outcomes of real research. Some common situation software are Simulink, MATLAB, and Simul8.

Not all kinds of experimental research can be carried out using simulation as a data collection tool . It is very impractical for a lot of laboratory-based research that involves chemical processes.

A survey is a tool used to gather relevant data about the characteristics of a population and is one of the most common data collection tools. A survey consists of a group of questions prepared by the researcher, to be answered by the research subject.

Surveys can be shared with the respondents both physically and electronically. When collecting data through surveys, the kind of data collected depends on the respondent, and researchers have limited control over it.

Formplus is the best tool for collecting experimental data using survey s. It has relevant features that will aid the data collection process and can also be used in other aspects of experimental research.

Differences between Experimental and Non-Experimental Research 

1. In experimental research, the researcher can control and manipulate the environment of the research, including the predictor variable which can be changed. On the other hand, non-experimental research cannot be controlled or manipulated by the researcher at will.

This is because it takes place in a real-life setting, where extraneous variables cannot be eliminated. Therefore, it is more difficult to conclude non-experimental studies, even though they are much more flexible and allow for a greater range of study fields.

2. The relationship between cause and effect cannot be established in non-experimental research, while it can be established in experimental research. This may be because many extraneous variables also influence the changes in the research subject, making it difficult to point at a particular variable as the cause of a particular change

3. Independent variables are not introduced, withdrawn, or manipulated in non-experimental designs, but the same may not be said about experimental research.

Conclusion  

Experimental research designs are often considered to be the standard in research designs. This is partly due to the common misconception that research is equivalent to scientific experiments—a component of experimental research design.

In this research design, one or more subjects or dependent variables are randomly assigned to different treatments (i.e. independent variables manipulated by the researcher) and the results are observed to conclude. One of the uniqueness of experimental research is in its ability to control the effect of extraneous variables.

Experimental research is suitable for research whose goal is to examine cause-effect relationships, e.g. explanatory research. It can be conducted in the laboratory or field settings, depending on the aim of the research that is being carried out. 

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10 Real-Life Experimental Research Examples

Chris Drew (PhD)

Dr. Chris Drew is the founder of the Helpful Professor. He holds a PhD in education and has published over 20 articles in scholarly journals. He is the former editor of the Journal of Learning Development in Higher Education. [Image Descriptor: Photo of Chris]

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Experimental research is research that involves using a scientific approach to examine research variables.

Below are some famous experimental research examples. Some of these studies were conducted quite a long time ago. Some were so controversial that they would never be attempted today. And some were so unethical that they would never be permitted again.

A few of these studies have also had very practical implications for modern society involving criminal investigations, the impact of television and the media, and the power of authority figures.

Examples of Experimental Research

1. pavlov’s dog: classical conditioning.

Dr. Ivan Pavlov was a physiologist studying animal digestive systems in the 1890s. In one study, he presented food to a dog and then collected its salivatory juices via a tube attached to the inside of the animal’s mouth.

As he was conducting his experiments, an annoying thing kept happening; every time his assistant would enter the lab with a bowl of food for the experiment, the dog would start to salivate at the sound of the assistant’s footsteps.

Although this disrupted his experimental procedures, eventually, it dawned on Pavlov that something else was to be learned from this problem.

Pavlov learned that animals could be conditioned into responding on a physiological level to various stimuli, such as food, or even the sound of the assistant bringing the food down the hall.

Hence, the creation of the theory of classical conditioning. One of the most influential theories in psychology still to this day.

2. Bobo Doll Experiment: Observational Learning

Dr. Albert Bandura conducted one of the most influential studies in psychology in the 1960s at Stanford University.

His intention was to demonstrate that cognitive processes play a fundamental role in learning. At the time, Behaviorism was the predominant theoretical perspective, which completely rejected all inferences to constructs not directly observable .

So, Bandura made two versions of a video. In version #1, an adult behaved aggressively with a Bobo doll by throwing it around the room and striking it with a wooden mallet. In version #2, the adult played gently with the doll by carrying it around to different parts of the room and pushing it gently.

After showing children one of the two versions, they were taken individually to a room that had a Bobo doll. Their behavior was observed and the results indicated that children that watched version #1 of the video were far more aggressive than those that watched version #2.

Not only did Bandura’s Bobo doll study form the basis of his social learning theory, it also helped start the long-lasting debate about the harmful effects of television on children.

Worth Checking Out: What’s the Difference between Experimental and Observational Studies?

3. The Asch Study: Conformity  

Dr. Solomon Asch was interested in conformity and the power of group pressure. His study was quite simple. Different groups of students were shown lines of varying lengths and asked, “which line is longest.”

However, out of each group, only one was an actual participant. All of the others in the group were working with Asch and instructed to say that one of the shorter lines was actually the longest.

Nearly every time, the real participant gave an answer that was clearly wrong, but the same as the rest of the group.

The study is one of the most famous in psychology because it demonstrated the power of social pressure so clearly.  

4. Car Crash Experiment: Leading Questions

In 1974, Dr. Elizabeth Loftus and her undergraduate student John Palmer designed a study to examine how fallible human judgement is under certain conditions.

They showed groups of research participants videos that depicted accidents between two cars. Later, the participants were asked to estimate the rate of speed of the cars.

Here’s the interesting part. All participants were asked the same question with the exception of a single word: “How fast were the two cars going when they ______into each other?” The word in the blank varied in its implied severity.

Participants’ estimates were completely affected by the word in the blank. When the word “smashed” was used, participants estimated the cars were going much faster than when the word “contacted” was used. 

This line of research has had a huge impact on law enforcement interrogation practices, line-up procedures, and the credibility of eyewitness testimony .

5. The 6 Universal Emotions

The research by Dr. Paul Ekman has been influential in the study of emotions. His early research revealed that all human beings, regardless of culture, experience the same 6 basic emotions: happiness, sadness, disgust, fear, surprise, and anger.

In the late 1960s, Ekman traveled to Papua New Guinea. He approached a tribe of people that were extremely isolated from modern culture. With the help of a guide, he would describe different situations to individual members and take a photo of their facial expressions.

The situations included: if a good friend had come; their child had just died; they were about to get into a fight; or had just stepped on a dead pig.

The facial expressions of this highly isolated tribe were nearly identical to those displayed by people in his studies in California.

6. The Little Albert Study: Development of Phobias  

Dr. John Watson and Dr. Rosalie Rayner sought to demonstrate how irrational fears were developed.

Their study involved showing a white rat to an infant. Initially, the child had no fear of the rat. However, the researchers then began to create a loud noise each time they showed the child the rat by striking a steel bar with a hammer.

Eventually, the child started to cry and feared the white rat. The child also developed a fear of other white, furry objects such as white rabbits and a Santa’s beard.

This study is famous because it demonstrated one way in which phobias are developed in humans, and also because it is now considered highly unethical for its mistreatment of children, lack of study debriefing , and intent to instil fear.  

7. A Class Divided: Discrimination

Perhaps one of the most famous psychological experiments of all time was not conducted by a psychologist. In 1968, third grade teacher Jane Elliott conducted one of the most famous studies on discrimination in history. It took place shortly after the assassination of Dr. Martin Luther King, Jr.

She divided her class into two groups: brown-eyed and blue-eyed students. On the first day of the experiment, she announced the blue-eyed group as superior. They received extra privileges and were told not to intermingle with the brown-eyed students.

They instantly became happier, more self-confident, and started performing better academically.

The next day, the roles were reversed. The brown-eyed students were announced as superior and given extra privileges. Their behavior changed almost immediately and exhibited the same patterns as the other group had the day before.

This study was a remarkable demonstration of the harmful effects of discrimination.

8. The Milgram Study: Obedience to Authority

Dr. Stanley Milgram conducted one of the most influential experiments on authority and obedience in 1961 at Yale University.

Participants were told they were helping study the effects of punishment on learning. Their job was to administer an electric shock to another participant each time they made an error on a test. The other participant was actually an actor in another room that only pretended to be shocked.

However, each time a mistake was made, the level of shock was supposed to increase, eventually reaching quite high voltage levels. When the real participants expressed reluctance to administer the next level of shock, the experimenter, who served as the authority figure in the room, pressured the participant to deliver the next level of shock.

The results of this study were truly astounding. A surprisingly high percentage of participants continued to deliver the shocks to the highest level possible despite the very strong objections by the “other participant.”

This study demonstrated the power of authority figures.

9. The Marshmallow Test: Delay of Gratification

The Marshmallow Test was designed by Dr. Walter Mischel to examine the role of delay of gratification and academic success.

Children ages 4-6 years old were seated at a table with one marshmallow placed in front of them. The experimenter explained that if they did not eat the marshmallow, they would receive a second one. They could then eat both.

The children that were able to delay gratification the longest were rated as significantly more competent later in life and earned higher SAT scores than children that could not withstand the temptation.  

The study has since been conceptually replicated by other researchers that have revealed additional factors involved in delay of gratification and academic achievement.

10. Stanford Prison Study: Deindividuation

Dr. Philip Zimbardo conducted one of the most famous psychological studies of all time in 1971. The purpose of the study was to investigate how the power structure in some situations can lead people to behave in ways highly uncharacteristic of their usual behavior.

College students were recruited to participate in the study. Some were randomly assigned to play the role of prison guard. The others were actually “arrested” by real police officers. They were blindfolded and taken to the basement of the university’s psychology building which had been converted to look like a prison.

Although the study was supposed to last 2 weeks, it had to be halted due to the abusive actions of the guards.

The study demonstrated that people will behave in ways they never thought possible when placed in certain roles and power structures. Although the Stanford Prison Study is so well-known for what it revealed about human nature, it is also famous because of the numerous violations of ethical principles.

The studies above are varied and focused on many different aspects of human behavior . However, each example of experimental research listed above has had a lasting impact on society. Some have had tremendous sway in how very practical matters are conducted, such as criminal investigations and legal proceedings.

Psychology is a field of study that is often not fully understood by the general public. When most people hear the term “psychology,” they think of a therapist that listens carefully to the revealing statements of a patient. The therapist then tries to help their patient learn to cope with many of life’s challenges. Nothing wrong with that.

In reality however, most psychologists are researchers. They spend most of their time designing and conducting experiments to enhance our understanding of the human condition.

Asch SE. (1956). Studies of independence and conformity: I. A minority of one against a unanimous majority . Psychological Monographs: General and Applied, 70 (9),1-70. https://doi.org/doi:10.1037/h0093718

Bandura A. (1965). Influence of models’ reinforcement contingencies on the acquisition of imitative responses. Journal of Personality and Social Psychology, 1 (6), 589-595. https://doi.org/doi:10.1037/h0022070

Beck, H. P., Levinson, S., & Irons, G. (2009). Finding little Albert: A journey to John B. Watson’s infant laboratory.  American Psychologist, 64(7),  605-614.

Ekman, P. & Friesen, W. V. (1971).  Constants Across Cultures in the Face and motion .  Journal of Personality and Social Psychology, 17(2) , 124-129.

Loftus, E. F., & Palmer, J. C. (1974). Reconstruction of automobile destruction: An example of

the interaction between language and memory. Journal of Verbal Learning and Verbal

Behavior, 13 (5), 585–589.

Milgram S (1965). Some Conditions of Obedience and Disobedience to Authority. Human Relations, 18(1), 57–76.

Mischel, W., & Ebbesen, E. B. (1970). Attention in delay of gratification . Journal of Personality and Social Psychology, 16 (2), 329-337.

Pavlov, I.P. (1927). Conditioned Reflexes . London: Oxford University Press.

Watson, J. & Rayner, R. (1920). Conditioned emotional reactions.  Journal of Experimental Psychology, 3 , 1-14. Zimbardo, P., Haney, C., Banks, W. C., & Jaffe, D. (1971). The Stanford Prison Experiment: A simulation study of the psychology of imprisonment . Stanford University, Stanford Digital Repository, Stanford.

• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd/ 101 Class Group Name Ideas (for School Students)
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FAQs About Experimental Research Papers (APA)

What is a research paper? 

A researcher uses a research paper to explain how they conducted a research study to answer a question or test a hypothesis. They explain why they conducted the study, the research question or hypothesis they tested, how they conducted the study, the results of their study, and the implications of these results. 

What is the purpose of an experimental research paper? 

A research paper is intended to inform others about advancement in a particular field of study. The researcher who wrote the paper identified a gap in the research in a field of study and used their research to help fill this gap. The researcher uses their paper to inform others about the knowledge that the results of their study contribute. 

What sections are included in an experimental research paper?

A typical research paper contains a Title Page, Abstract, Introduction, Methods, Results, Discussion, and References section. Some also contain a Table and Figures section and Appendix section. 

What citation style is used for experimental research papers? 

APA (American Psychological Association) style is most commonly used for research papers. 

Structure Of Experimental Research Papers (APA)

• Answers the question of “What is this paper about and who wrote it?”
• Located on the first page of the paper 
• The author’s note acknowledges any support that the authors received from others
• A student paper also includes the course number and name, instructor’s name, and assignment due date
• Contains a title that summarizes the purpose and content of the research study and engages the audience 
• No longer than 250 words
• Summarizes important background information, the research questions and/or hypothesis, methods, key findings, and implications of the findings
• Explains what the topic of the research is and why the topic is worth studying
• Summarizes and discusses prior research conducted on the topic 
• Identifies unresolved issues and gaps in past research that the current research will address
• Ends with an overview of the current research study, including how the independent and dependent variables, the research questions or hypotheses, and the objective of the research 
• Explains how the research study was conducted 
• Typically includes 3 sections: Participants, Materials, and Procedure
• Includes characteristics of the subjects, how the subjects were selected and recruited, how their anonymity was protected, and what feedback was provided to the participants
• Describes any equipment, surveys, tests, questionnaires, informed consent forms, and observational techniques 
• Describes the independent and dependent variables, the type of research design, and how the data was collected
• Explains what results were found in the research study 
• Describes the data that was collected and the results of statistical tests 
• Explains the significance of the results 
• Accepts or denies the hypotheses 
• Details the implications of these findings 
• Addresses the limitations of the study and areas for future research 
• Includes all sources that were mentioned in the research study 
• Adheres to APA citation styles
• Includes all tables and/or figures that were used in the research study 
• Each table and figure is placed on a separate page 
• Tables are included before figures
• Begins with a bolded, centered header such as “ Table 1 ”
• Appends all forms, surveys, tests, etc. that were used in the study 
• Only includes documents that were referenced in the Methods section 
• Each entry is placed on a separate page 
• Begins with a bolded, centered header such as “ Appendix A ”

Tips For Experimental Research Papers (APA)

• Initial interest will motivate you to complete your study 
• Your entire study will be centered around this question or statement 
• Use only verifiable sources that provide accurate information about your topic 
• You need to thoroughly understand the field of study your topic is on to help you recognize the gap your research will fill and the significance of your results
• This will help you identify what you should study and what the significance of your study will be 
• Create an outline before you begin writing to help organize your thoughts and direct you in your writing 
• This will prevent you from losing the source or forgetting to cite the source 
• Work on one section at a time, rather than trying to complete multiple sections at once
• This information can be easily referred to as your write your various sections 
• When conducting your research, working general to specific will help you narrow your topic and fully understand the field your topic is in 
• When writing your literature review, writing from general to specific will help the audience understand your overall topic and the narrow focus of your research 
• This will prevent you from losing sources you may need later 
• Incorporate correct APA formatting as you write, rather than changing the formatting at the end of the writing process 

Checklist For Experimental Research Papers (APA)

• If the paper is a student paper, it contains the title of the project, the author’s name(s), the instructor's name, course number and name, and assignment due date
• If the paper is a professional paper, it includes the title of the paper, the author’s name(s), the institutional affiliation, and the author note
• Begins on the first page of the paper
• The title is typed in upper and lowercase letters, four spaces below the top of the paper, and written in boldface 
• Other information is separated by a space from the title

Title (found on title page)

• Informs the audience about the purpose of the paper 
• Captures the attention of the audience 
• Accurately reflects the purpose and content of the research paper 

Abstract 

• Labeled as “ Abstract ”
• Begins on the second page 
• Provides a short, concise summary of the content of the research paper 
• Includes background information necessary to understand the topic 
• Background information demonstrates the purpose of the paper
• Contains the hypothesis and/or research questions addressed in the paper
• Has a brief description of the methods used 
• Details the key findings and significance of the results
• Illustrates the implications of the research study 
• Contains less than 250 words

Introduction 

• Starts on the third page 
• Includes the title of the paper in bold at the top of the page
• Contains a clear statement of the problem that the paper sets out to address 
• Places the research paper within the context of previous research on the topic 
• Explains the purpose of the research study and what you hope to find
• Describes the significance of the study 
• Details what new insights the research will contribute
• Concludes with a brief description of what information will be mentioned in the literature review

Literature Review

• Labeled as “ Literature Review”
• Presents a general description of the problem area 
• Defines any necessary terms 
• Discusses and summarizes prior research on the selected topic 
• Identifies any unresolved issues or gaps in research that the current research plans to address
• Concludes with a summary of the current research study, including the independent and dependent variables, the research questions or hypotheses, and the objective of the research  
• Labeled as “ Methods ”
• Efficiently explains how the research study was conducted 
• Appropriately divided into sections
• Describes the characteristics of the participants 
• Explains how the participants were selected 
• Details how the anonymity of the participants was protected 
• Notes what feedback the participants will be provided 
• Describes all materials and instruments that were used 
• Mentions how the procedure was conducted and data collected
• Notes the independent and dependent variables 
• Includes enough information that another researcher could duplicate the research 

Results 

• Labeled as “ Results ”
• Describes the data was collected
• Explains the results of statistical tests that were performed
• Omits any analysis or discussion of the implications of the study 

Discussion 

• Labeled as “ Discussion ”
• Describes the significance of the results 
• Relates the results to the research questions and/or hypotheses
• States whether the hypotheses should be rejected or accepted 
• Addresses limitations of the study, including potential bias, confounds, imprecision of measures, and limits to generalizability
• Explains how the study adds to the knowledge base and expands upon past research
• Labeled as “ References ”
• Correctly cites sources according to APA formatting 
• Orders sources alphabetically
• All sources included in the study are cited in the reference section 

Table and Figures (optional)

•  Each table and each figure is placed on a separate page 
• Tables and figures are included after the reference page
• Tables and figures are correctly labeled
• Each table and figure begins with a bolded, centered header such as “ Table 1 ,” “ Table 2 ,”

Appendix (optional) 

• Any forms, surveys, tests, etc. are placed in the Appendix
• All appendix entries are mentioned in the Methods section 
• Each appendix begins on a new page
• Each appendix begins with a bolded, centered header such as “ Appendix A, ” “ Appendix B ”

Additional Resources For Experimental Research Papers (APA)

• https://www.mcwritingcenterblog.org/single-post/how-to-conduct-research-using-the-library-s-resources
• https://www.mcwritingcenterblog.org/single-post/how-to-read-academic-articles
• https://researchguides.ben.edu/source-evaluation   
• https://researchguides.library.brocku.ca/external-analysis/evaluating-sources
• https://writing.wisc.edu/handbook/assignments/planresearchpaper/
• https://nmu.edu/writingcenter/tips-writing-research-paper
• https://writingcenter.gmu.edu/guides/how-to-write-a-research-question
• https://www.unr.edu/writing-speaking-center/student-resources/writing-speaking-resources/guide-to-writing-research-papers
• https://drive.google.com/drive/folders/1F4DFWf85zEH4aZvm10i8Ahm_3xnAekal?usp=sharing
• https://owl.purdue.edu/owl/research_and_citation/apa_style/apa_formatting_and_style_guide/general_format.html
• https://libguides.elmira.edu/research
• https://www.nhcc.edu/academics/library/doing-library-research/basic-steps-research-process
• https://libguides.wustl.edu/research
• << Previous: Science Papers
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• Last Updated: Sep 14, 2023 10:30 AM
• URL: https://mc.libguides.com/writingcenter

45+ Experimental Research Topics And Examples For School & College Students

Sourav Mahahjan

Whether it is school or college, identifying a good and quality research topic can take time for students. Experimental research, also known as methodological or analytical research, uses two or more variables and arguments for a particular scenario. In this type of argument, the influence of the independent variable on the dependent variable is considered when conducting an experimental exploration. To make a particular decision in empirical research, it is important to provide a large number of evidence. The evidence collected in practical research helps identify the consequences and reasons related to different quantities of the variables. Experimental research design is an important part of the academic cycle of any student, and often, the student needs help in preparing experimental research designs. Different types of experimental research are available for the students, such as pre-experimental research, accurate experimental research, and quasi-experimental research.

What are the different types of experimental research?

Different subjects and topics required different types of experimental research. Some commonly used experimental research are quasi-experimental research, true experiment research, and pre-experimental research.

What are the different elements of experimental research?

Any experimental research consists of three essential elements. The first element is the independent variable, which the researcher manipulates. The second variable is the dependent variable, which changes according to the first variable's manipulation. The third element is the controlled variable, which is kept constant to prevent any kind of impact on the effects created by the independent variable after the manipulation by the researcher.

What are the advantages and disadvantages of experimental research?

The use of experimental research by the researcher helps provide strong evidence regarding the different types of cause-and-impact relationships in different scenarios. The experimental research service allows the researcher to maintain control of various elements of the experimental environment. On the other hand, one of the significant disadvantages of experimental research is that it is a very time-consuming process, and sometimes, the results obtained may be disconnected from the ordinary world. 

Examples of experimental research titles:

Creating an experimental research design is very frustrating, and selecting the appropriate title becomes essential as it forms the basis of experimental research. Before choosing a topic, it becomes necessary for the students to find out literature providing disparity and research provision. This results in investing significant time and effort to search for an appropriate experimental research title. This makes the students lose patience and select the wrong research topic, impacting the overall quality of experimental research.  Examples of experimental research design are

Experimental research titles on natural science for school students:

• Impact of Light  on the Plant Growth
• Role of Different Salt Concentrations over the Freezing Point of Water
• Comparing Battery Life among Different Brands
• Analysis of  pH on Enzyme Activity
• Impact of Magnet Strength on a Paperclip over a long distance

Experimental research design on behavioural science for school students:

• Role of music in affecting Concentration
• Individual Study vs Group Study on Academic Performance
• Part of Reward Systems on Increasing Student Motivation
• Impact of Various Colors on Mood
• How Sleep Patterns Effect Academic Performance

Experimental Research title on Social Science for college students:

• Part of  Socioeconomic Status over the Mental Health
• How Media Representation influences the body image of an individual 
• Bilingual Education and their Role in Academic Success
• importance of Social Media during Political Campaigns
• How Gender Stereotypes Influence the Career Choices in the society

Experimental Research title on natural Science for college students:

• What is the role of Genetics in causing Obesity? 
• How Climate Change Affects the Marine Life
• Role of Pesticides in declining Bee Populations
• Increasing Pollution and Its Impact on Urban Wildlife
• What is the role of microplastics in the destruction of Freshwater Ecosystems

Experimental Research title on applied Science for college students:

• How Machine Learning Algorithms are helping in predicting Stock Prices? 
• How is data Encryption improving Data Security?
• How does Aerodynamics influence the vehicle Fuel Efficiency? 
• Bridge Stability and its dependency on the material properties.
• How do different Angles of solar panel impacts their efficiency?

Experimental research titles in health science for college students:

• How does Exercise help in managing Type 2 Diabetes? 
• Cognitive Performance under the influence of caffeine
• How do Plant-Based Diets improve our heart health?
• How do Different Forms of Physical therapy help speed the process of Knee Rehabilitation?
• Mindfulness Meditation and their Impact on Stress Reduction

Experimental titles on environmental studies for college students:

• How does deforestation affect the  Local Climate?
• What are the Different types of Oil Spill Cleanup methods, and how effective are they? 
• Does Organic Farming help in improving Crop Yield?
• What is the role of noise Pollution on the growth of  Urban Wildlife?
• Impacts of increasing E-Waste on Soil Quality

Experimental research topics for computer studies in colleges:

• What are the  different Sorting Algorithms
• Analysing the security efficiency of various types of  password Policies
• How User Experience depends on the user interface
• Artificial Intelligence  and Its Importance in Image Recognition
• Energy Efficiency analysis between different types of  computer processors

Experimental research topics for college students on economics:

• How do economic policies impact the Inflation growth in the economy?
• How does microfinance can help in reducing poverty in the society? 
• Globalisation and its Impact on Small Businesses
• Why do exchange rates are essential for the export market?
• Role of Large Scale Unemployment Rates in increasing crime Rates

Tips for selecting suitable experimental research title:

Establishing the appropriate research title is very helpful in completing a practical research assignment . Some of the recommendations for the students are 

• Interest:  The research tile should be based on the student's interest. This helps in improving the quality of the research.
• Relevance:  The selected title should be relevant to the subject of the student.  It should fulfil the objectives of the course. 
• Feasibility:  The selected topic should be practical and have adequate resources required for the study. 

Conclusion 

Experimental research is essential in conducting scientific inquiry during an academic study. Experimental research helps students use their knowledge to improve their problem-solving and critical-thinking abilities in their academic cycle.

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Experimental Research

Experimental research is commonly used in sciences such as sociology and psychology, physics, chemistry, biology and medicine etc.

This article is a part of the guide:

• Pretest-Posttest
• Third Variable
• Research Bias
• Independent Variable
• Between Subjects

Browse Full Outline

• 1 Experimental Research
• 2.1 Independent Variable
• 2.2 Dependent Variable
• 2.3 Controlled Variables
• 2.4 Third Variable
• 3.1 Control Group
• 3.2 Research Bias
• 3.3.1 Placebo Effect
• 3.3.2 Double Blind Method
• 4.1 Randomized Controlled Trials
• 4.2 Pretest-Posttest
• 4.3 Solomon Four Group
• 4.4 Between Subjects
• 4.5 Within Subject
• 4.6 Repeated Measures
• 4.7 Counterbalanced Measures
• 4.8 Matched Subjects

It is a collection of research designs which use manipulation and controlled testing to understand causal processes. Generally, one or more variables are manipulated to determine their effect on a dependent variable.

The experimental method is a systematic and scientific approach to research in which the researcher manipulates one or more variables, and controls and measures any change in other variables.

Experimental Research is often used where:

• There is time priority in a causal relationship ( cause precedes effect )
• There is consistency in a causal relationship (a cause will always lead to the same effect)
• The magnitude of the correlation is great.

(Reference: en.wikipedia.org)

The word experimental research has a range of definitions. In the strict sense, experimental research is what we call a true experiment .

This is an experiment where the researcher manipulates one variable, and control/randomizes the rest of the variables. It has a control group , the subjects have been randomly assigned between the groups, and the researcher only tests one effect at a time. It is also important to know what variable(s) you want to test and measure.

A very wide definition of experimental research, or a quasi experiment , is research where the scientist actively influences something to observe the consequences. Most experiments tend to fall in between the strict and the wide definition.

A rule of thumb is that physical sciences, such as physics, chemistry and geology tend to define experiments more narrowly than social sciences, such as sociology and psychology, which conduct experiments closer to the wider definition.

Aims of Experimental Research

Experiments are conducted to be able to predict phenomenons. Typically, an experiment is constructed to be able to explain some kind of causation . Experimental research is important to society - it helps us to improve our everyday lives.

Identifying the Research Problem

After deciding the topic of interest, the researcher tries to define the research problem . This helps the researcher to focus on a more narrow research area to be able to study it appropriately.  Defining the research problem helps you to formulate a  research hypothesis , which is tested against the  null hypothesis .

The research problem is often operationalizationed , to define how to measure the research problem. The results will depend on the exact measurements that the researcher chooses and may be operationalized differently in another study to test the main conclusions of the study.

An ad hoc analysis is a hypothesis invented after testing is done, to try to explain why the contrary evidence. A poor ad hoc analysis may be seen as the researcher's inability to accept that his/her hypothesis is wrong, while a great ad hoc analysis may lead to more testing and possibly a significant discovery.

Constructing the Experiment

There are various aspects to remember when constructing an experiment. Planning ahead ensures that the experiment is carried out properly and that the results reflect the real world, in the best possible way.

Sampling Groups to Study

Sampling groups correctly is especially important when we have more than one condition in the experiment. One sample group often serves as a control group , whilst others are tested under the experimental conditions.

Deciding the sample groups can be done in using many different sampling techniques. Population sampling may chosen by a number of methods, such as randomization , "quasi-randomization" and pairing.

Reducing sampling errors is vital for getting valid results from experiments. Researchers often adjust the sample size to minimize chances of random errors .

Here are some common sampling techniques :

• probability sampling
• non-probability sampling
• simple random sampling
• convenience sampling
• stratified sampling
• systematic sampling
• cluster sampling
• sequential sampling
• disproportional sampling
• judgmental sampling
• snowball sampling
• quota sampling

Creating the Design

The research design is chosen based on a range of factors. Important factors when choosing the design are feasibility, time, cost, ethics, measurement problems and what you would like to test. The design of the experiment is critical for the validity of the results.

Typical Designs and Features in Experimental Design

• Pretest-Posttest Design Check whether the groups are different before the manipulation starts and the effect of the manipulation. Pretests sometimes influence the effect.
• Control Group Control groups are designed to measure research bias and measurement effects, such as the Hawthorne Effect or the Placebo Effect . A control group is a group not receiving the same manipulation as the experimental group. Experiments frequently have 2 conditions, but rarely more than 3 conditions at the same time.
• Randomized Controlled Trials Randomized Sampling, comparison between an Experimental Group and a Control Group and strict control/randomization of all other variables
• Solomon Four-Group Design With two control groups and two experimental groups. Half the groups have a pretest and half do not have a pretest. This to test both the effect itself and the effect of the pretest.
• Between Subjects Design Grouping Participants to Different Conditions
• Within Subject Design Participants Take Part in the Different Conditions - See also: Repeated Measures Design
• Counterbalanced Measures Design Testing the effect of the order of treatments when no control group is available/ethical
• Matched Subjects Design Matching Participants to Create Similar Experimental- and Control-Groups
• Double-Blind Experiment Neither the researcher, nor the participants, know which is the control group. The results can be affected if the researcher or participants know this.
• Bayesian Probability Using bayesian probability to "interact" with participants is a more "advanced" experimental design. It can be used for settings were there are many variables which are hard to isolate. The researcher starts with a set of initial beliefs, and tries to adjust them to how participants have responded

Pilot Study

It may be wise to first conduct a pilot-study or two before you do the real experiment. This ensures that the experiment measures what it should, and that everything is set up right.

Minor errors, which could potentially destroy the experiment, are often found during this process. With a pilot study, you can get information about errors and problems, and improve the design, before putting a lot of effort into the real experiment.

If the experiments involve humans, a common strategy is to first have a pilot study with someone involved in the research, but not too closely, and then arrange a pilot with a person who resembles the subject(s) . Those two different pilots are likely to give the researcher good information about any problems in the experiment.

Conducting the Experiment

An experiment is typically carried out by manipulating a variable, called the independent variable , affecting the experimental group. The effect that the researcher is interested in, the dependent variable(s) , is measured.

Identifying and controlling non-experimental factors which the researcher does not want to influence the effects, is crucial to drawing a valid conclusion. This is often done by controlling variables , if possible, or randomizing variables to minimize effects that can be traced back to third variables . Researchers only want to measure the effect of the independent variable(s) when conducting an experiment , allowing them to conclude that this was the reason for the effect.

Analysis and Conclusions

In quantitative research , the amount of data measured can be enormous. Data not prepared to be analyzed is called "raw data". The raw data is often summarized as something called "output data", which typically consists of one line per subject (or item). A cell of the output data is, for example, an average of an effect in many trials for a subject. The output data is used for statistical analysis, e.g. significance tests, to see if there really is an effect.

The aim of an analysis is to draw a conclusion , together with other observations. The researcher might generalize the results to a wider phenomenon, if there is no indication of confounding variables "polluting" the results.

If the researcher suspects that the effect stems from a different variable than the independent variable, further investigation is needed to gauge the validity of the results. An experiment is often conducted because the scientist wants to know if the independent variable is having any effect upon the dependent variable. Variables correlating are not proof that there is causation .

Experiments are more often of quantitative nature than qualitative nature, although it happens.

Examples of Experiments

This website contains many examples of experiments. Some are not true experiments , but involve some kind of manipulation to investigate a phenomenon. Others fulfill most or all criteria of true experiments.

Here are some examples of scientific experiments:

Social Psychology

• Stanley Milgram Experiment - Will people obey orders, even if clearly dangerous?
• Asch Experiment - Will people conform to group behavior?
• Stanford Prison Experiment - How do people react to roles? Will you behave differently?
• Good Samaritan Experiment - Would You Help a Stranger? - Explaining Helping Behavior
• Law Of Segregation - The Mendel Pea Plant Experiment
• Transforming Principle - Griffith's Experiment about Genetics
• Ben Franklin Kite Experiment - Struck by Lightning
• J J Thomson Cathode Ray Experiment
• Psychology 101
• Flags and Countries
• Capitals and Countries

Oskar Blakstad (Jul 10, 2008). Experimental Research. Retrieved Jul 02, 2024 from Explorable.com: https://explorable.com/experimental-research

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Study/Experimental/Research Design: Much More Than Statistics

Kenneth l. knight.

Brigham Young University, Provo, UT

The purpose of study, experimental, or research design in scientific manuscripts has changed significantly over the years. It has evolved from an explanation of the design of the experiment (ie, data gathering or acquisition) to an explanation of the statistical analysis. This practice makes “Methods” sections hard to read and understand.

To clarify the difference between study design and statistical analysis, to show the advantages of a properly written study design on article comprehension, and to encourage authors to correctly describe study designs.

Description:

The role of study design is explored from the introduction of the concept by Fisher through modern-day scientists and the AMA Manual of Style . At one time, when experiments were simpler, the study design and statistical design were identical or very similar. With the complex research that is common today, which often includes manipulating variables to create new variables and the multiple (and different) analyses of a single data set, data collection is very different than statistical design. Thus, both a study design and a statistical design are necessary.

Advantages:

Scientific manuscripts will be much easier to read and comprehend. A proper experimental design serves as a road map to the study methods, helping readers to understand more clearly how the data were obtained and, therefore, assisting them in properly analyzing the results.

Study, experimental, or research design is the backbone of good research. It directs the experiment by orchestrating data collection, defines the statistical analysis of the resultant data, and guides the interpretation of the results. When properly described in the written report of the experiment, it serves as a road map to readers, 1 helping them negotiate the “Methods” section, and, thus, it improves the clarity of communication between authors and readers.

A growing trend is to equate study design with only the statistical analysis of the data. The design statement typically is placed at the end of the “Methods” section as a subsection called “Experimental Design” or as part of a subsection called “Data Analysis.” This placement, however, equates experimental design and statistical analysis, minimizing the effect of experimental design on the planning and reporting of an experiment. This linkage is inappropriate, because some of the elements of the study design that should be described at the beginning of the “Methods” section are instead placed in the “Statistical Analysis” section or, worse, are absent from the manuscript entirely.

Have you ever interrupted your reading of the “Methods” to sketch out the variables in the margins of the paper as you attempt to understand how they all fit together? Or have you jumped back and forth from the early paragraphs of the “Methods” section to the “Statistics” section to try to understand which variables were collected and when? These efforts would be unnecessary if a road map at the beginning of the “Methods” section outlined how the independent variables were related, which dependent variables were measured, and when they were measured. When they were measured is especially important if the variables used in the statistical analysis were a subset of the measured variables or were computed from measured variables (such as change scores).

The purpose of this Communications article is to clarify the purpose and placement of study design elements in an experimental manuscript. Adopting these ideas may improve your science and surely will enhance the communication of that science. These ideas will make experimental manuscripts easier to read and understand and, therefore, will allow them to become part of readers' clinical decision making.

WHAT IS A STUDY (OR EXPERIMENTAL OR RESEARCH) DESIGN?

The terms study design, experimental design, and research design are often thought to be synonymous and are sometimes used interchangeably in a single paper. Avoid doing so. Use the term that is preferred by the style manual of the journal for which you are writing. Study design is the preferred term in the AMA Manual of Style , 2 so I will use it here.

A study design is the architecture of an experimental study 3 and a description of how the study was conducted, 4 including all elements of how the data were obtained. 5 The study design should be the first subsection of the “Methods” section in an experimental manuscript (see the Table ). “Statistical Design” or, preferably, “Statistical Analysis” or “Data Analysis” should be the last subsection of the “Methods” section.

Table. Elements of a “Methods” Section

The “Study Design” subsection describes how the variables and participants interacted. It begins with a general statement of how the study was conducted (eg, crossover trials, parallel, or observational study). 2 The second element, which usually begins with the second sentence, details the number of independent variables or factors, the levels of each variable, and their names. A shorthand way of doing so is with a statement such as “A 2 × 4 × 8 factorial guided data collection.” This tells us that there were 3 independent variables (factors), with 2 levels of the first factor, 4 levels of the second factor, and 8 levels of the third factor. Following is a sentence that names the levels of each factor: for example, “The independent variables were sex (male or female), training program (eg, walking, running, weight lifting, or plyometrics), and time (2, 4, 6, 8, 10, 15, 20, or 30 weeks).” Such an approach clearly outlines for readers how the various procedures fit into the overall structure and, therefore, enhances their understanding of how the data were collected. Thus, the design statement is a road map of the methods.

The dependent (or measurement or outcome) variables are then named. Details of how they were measured are not given at this point in the manuscript but are explained later in the “Instruments” and “Procedures” subsections.

Next is a paragraph detailing who the participants were and how they were selected, placed into groups, and assigned to a particular treatment order, if the experiment was a repeated-measures design. And although not a part of the design per se, a statement about obtaining written informed consent from participants and institutional review board approval is usually included in this subsection.

The nuts and bolts of the “Methods” section follow, including such things as equipment, materials, protocols, etc. These are beyond the scope of this commentary, however, and so will not be discussed.

The last part of the “Methods” section and last part of the “Study Design” section is the “Data Analysis” subsection. It begins with an explanation of any data manipulation, such as how data were combined or how new variables (eg, ratios or differences between collected variables) were calculated. Next, readers are told of the statistical measures used to analyze the data, such as a mixed 2 × 4 × 8 analysis of variance (ANOVA) with 2 between-groups factors (sex and training program) and 1 within-groups factor (time of measurement). Researchers should state and reference the statistical package and procedure(s) within the package used to compute the statistics. (Various statistical packages perform analyses slightly differently, so it is important to know the package and specific procedure used.) This detail allows readers to judge the appropriateness of the statistical measures and the conclusions drawn from the data.

STATISTICAL DESIGN VERSUS STATISTICAL ANALYSIS

Avoid using the term statistical design . Statistical methods are only part of the overall design. The term gives too much emphasis to the statistics, which are important, but only one of many tools used in interpreting data and only part of the study design:

The most important issues in biostatistics are not expressed with statistical procedures. The issues are inherently scientific, rather than purely statistical, and relate to the architectural design of the research, not the numbers with which the data are cited and interpreted. 6

Stated another way, “The justification for the analysis lies not in the data collected but in the manner in which the data were collected.” 3 “Without the solid foundation of a good design, the edifice of statistical analysis is unsafe.” 7 (pp4–5)

The intertwining of study design and statistical analysis may have been caused (unintentionally) by R.A. Fisher, “… a genius who almost single-handedly created the foundations for modern statistical science.” 8 Most research did not involve statistics until Fisher invented the concepts and procedures of ANOVA (in 1921) 9 , 10 and experimental design (in 1935). 11 His books became standard references for scientists in many disciplines. As a result, many ANOVA books were titled Experimental Design (see, for example, Edwards 12 ), and ANOVA courses taught in psychology and education departments included the words experimental design in their course titles.

Before the widespread use of computers to analyze data, designs were much simpler, and often there was little difference between study design and statistical analysis. So combining the 2 elements did not cause serious problems. This is no longer true, however, for 3 reasons: (1) Research studies are becoming more complex, with multiple independent and dependent variables. The procedures sections of these complex studies can be difficult to understand if your only reference point is the statistical analysis and design. (2) Dependent variables are frequently measured at different times. (3) How the data were collected is often not directly correlated with the statistical design.

For example, assume the goal is to determine the strength gain in novice and experienced athletes as a result of 3 strength training programs. Rate of change in strength is not a measurable variable; rather, it is calculated from strength measurements taken at various time intervals during the training. So the study design would be a 2 × 2 × 3 factorial with independent variables of time (pretest or posttest), experience (novice or advanced), and training (isokinetic, isotonic, or isometric) and a dependent variable of strength. The statistical design , however, would be a 2 × 3 factorial with independent variables of experience (novice or advanced) and training (isokinetic, isotonic, or isometric) and a dependent variable of strength gain. Note that data were collected according to a 3-factor design but were analyzed according to a 2-factor design and that the dependent variables were different. So a single design statement, usually a statistical design statement, would not communicate which data were collected or how. Readers would be left to figure out on their own how the data were collected.

MULTIVARIATE RESEARCH AND THE NEED FOR STUDY DESIGNS

With the advent of electronic data gathering and computerized data handling and analysis, research projects have increased in complexity. Many projects involve multiple dependent variables measured at different times, and, therefore, multiple design statements may be needed for both data collection and statistical analysis. Consider, for example, a study of the effects of heat and cold on neural inhibition. The variables of H max and M max are measured 3 times each: before, immediately after, and 30 minutes after a 20-minute treatment with heat or cold. Muscle temperature might be measured each minute before, during, and after the treatment. Although the minute-by-minute data are important for graphing temperature fluctuations during the procedure, only 3 temperatures (time 0, time 20, and time 50) are used for statistical analysis. A single dependent variable H max :M max ratio is computed to illustrate neural inhibition. Again, a single statistical design statement would tell little about how the data were obtained. And in this example, separate design statements would be needed for temperature measurement and H max :M max measurements.

As stated earlier, drawing conclusions from the data depends more on how the data were measured than on how they were analyzed. 3 , 6 , 7 , 13 So a single study design statement (or multiple such statements) at the beginning of the “Methods” section acts as a road map to the study and, thus, increases scientists' and readers' comprehension of how the experiment was conducted (ie, how the data were collected). Appropriate study design statements also increase the accuracy of conclusions drawn from the study.

CONCLUSIONS

The goal of scientific writing, or any writing, for that matter, is to communicate information. Including 2 design statements or subsections in scientific papers—one to explain how the data were collected and another to explain how they were statistically analyzed—will improve the clarity of communication and bring praise from readers. To summarize:

• Purge from your thoughts and vocabulary the idea that experimental design and statistical design are synonymous.
• Study or experimental design plays a much broader role than simply defining and directing the statistical analysis of an experiment.
• A properly written study design serves as a road map to the “Methods” section of an experiment and, therefore, improves communication with the reader.
• Study design should include a description of the type of design used, each factor (and each level) involved in the experiment, and the time at which each measurement was made.
• Clarify when the variables involved in data collection and data analysis are different, such as when data analysis involves only a subset of a collected variable or a resultant variable from the mathematical manipulation of 2 or more collected variables.

Acknowledgments

Thanks to Thomas A. Cappaert, PhD, ATC, CSCS, CSE, for suggesting the link between R.A. Fisher and the melding of the concepts of research design and statistics.

Chapter 10 Experimental Research

Experimental research, often considered to be the “gold standard” in research designs, is one of the most rigorous of all research designs. In this design, one or more independent variables are manipulated by the researcher (as treatments), subjects are randomly assigned to different treatment levels (random assignment), and the results of the treatments on outcomes (dependent variables) are observed. The unique strength of experimental research is its internal validity (causality) due to its ability to link cause and effect through treatment manipulation, while controlling for the spurious effect of extraneous variable.

Experimental research is best suited for explanatory research (rather than for descriptive or exploratory research), where the goal of the study is to examine cause-effect relationships. It also works well for research that involves a relatively limited and well-defined set of independent variables that can either be manipulated or controlled. Experimental research can be conducted in laboratory or field settings. Laboratory experiments , conducted in laboratory (artificial) settings, tend to be high in internal validity, but this comes at the cost of low external validity (generalizability), because the artificial (laboratory) setting in which the study is conducted may not reflect the real world. Field experiments , conducted in field settings such as in a real organization, and high in both internal and external validity. But such experiments are relatively rare, because of the difficulties associated with manipulating treatments and controlling for extraneous effects in a field setting.

Experimental research can be grouped into two broad categories: true experimental designs and quasi-experimental designs. Both designs require treatment manipulation, but while true experiments also require random assignment, quasi-experiments do not. Sometimes, we also refer to non-experimental research, which is not really a research design, but an all-inclusive term that includes all types of research that do not employ treatment manipulation or random assignment, such as survey research, observational research, and correlational studies.

Basic Concepts

Treatment and control groups. In experimental research, some subjects are administered one or more experimental stimulus called a treatment (the treatment group ) while other subjects are not given such a stimulus (the control group ). The treatment may be considered successful if subjects in the treatment group rate more favorably on outcome variables than control group subjects. Multiple levels of experimental stimulus may be administered, in which case, there may be more than one treatment group. For example, in order to test the effects of a new drug intended to treat a certain medical condition like dementia, if a sample of dementia patients is randomly divided into three groups, with the first group receiving a high dosage of the drug, the second group receiving a low dosage, and the third group receives a placebo such as a sugar pill (control group), then the first two groups are experimental groups and the third group is a control group. After administering the drug for a period of time, if the condition of the experimental group subjects improved significantly more than the control group subjects, we can say that the drug is effective. We can also compare the conditions of the high and low dosage experimental groups to determine if the high dose is more effective than the low dose.

Treatment manipulation. Treatments are the unique feature of experimental research that sets this design apart from all other research methods. Treatment manipulation helps control for the “cause” in cause-effect relationships. Naturally, the validity of experimental research depends on how well the treatment was manipulated. Treatment manipulation must be checked using pretests and pilot tests prior to the experimental study. Any measurements conducted before the treatment is administered are called pretest measures , while those conducted after the treatment are posttest measures .

Random selection and assignment. Random selection is the process of randomly drawing a sample from a population or a sampling frame. This approach is typically employed in survey research, and assures that each unit in the population has a positive chance of being selected into the sample. Random assignment is however a process of randomly assigning subjects to experimental or control groups. This is a standard practice in true experimental research to ensure that treatment groups are similar (equivalent) to each other and to the control group, prior to treatment administration. Random selection is related to sampling, and is therefore, more closely related to the external validity (generalizability) of findings. However, random assignment is related to design, and is therefore most related to internal validity. It is possible to have both random selection and random assignment in well-designed experimental research, but quasi-experimental research involves neither random selection nor random assignment.

Threats to internal validity. Although experimental designs are considered more rigorous than other research methods in terms of the internal validity of their inferences (by virtue of their ability to control causes through treatment manipulation), they are not immune to internal validity threats. Some of these threats to internal validity are described below, within the context of a study of the impact of a special remedial math tutoring program for improving the math abilities of high school students.

• History threat is the possibility that the observed effects (dependent variables) are caused by extraneous or historical events rather than by the experimental treatment. For instance, students’ post-remedial math score improvement may have been caused by their preparation for a math exam at their school, rather than the remedial math program.
• Maturation threat refers to the possibility that observed effects are caused by natural maturation of subjects (e.g., a general improvement in their intellectual ability to understand complex concepts) rather than the experimental treatment.
• Testing threat is a threat in pre-post designs where subjects’ posttest responses are conditioned by their pretest responses. For instance, if students remember their answers from the pretest evaluation, they may tend to repeat them in the posttest exam. Not conducting a pretest can help avoid this threat.
• Instrumentation threat , which also occurs in pre-post designs, refers to the possibility that the difference between pretest and posttest scores is not due to the remedial math program, but due to changes in the administered test, such as the posttest having a higher or lower degree of difficulty than the pretest.
• Mortality threat refers to the possibility that subjects may be dropping out of the study at differential rates between the treatment and control groups due to a systematic reason, such that the dropouts were mostly students who scored low on the pretest. If the low-performing students drop out, the results of the posttest will be artificially inflated by the preponderance of high-performing students.
• Regression threat , also called a regression to the mean, refers to the statistical tendency of a group’s overall performance on a measure during a posttest to regress toward the mean of that measure rather than in the anticipated direction. For instance, if subjects scored high on a pretest, they will have a tendency to score lower on the posttest (closer to the mean) because their high scores (away from the mean) during the pretest was possibly a statistical aberration. This problem tends to be more prevalent in non-random samples and when the two measures are imperfectly correlated.

Two-Group Experimental Designs

The simplest true experimental designs are two group designs involving one treatment group and one control group, and are ideally suited for testing the effects of a single independent variable that can be manipulated as a treatment. The two basic two-group designs are the pretest-posttest control group design and the posttest-only control group design, while variations may include covariance designs. These designs are often depicted using a standardized design notation, where R represents random assignment of subjects to groups, X represents the treatment administered to the treatment group, and O represents pretest or posttest observations of the dependent variable (with different subscripts to distinguish between pretest and posttest observations of treatment and control groups).

Pretest-posttest control group design . In this design, subjects are randomly assigned to treatment and control groups, subjected to an initial (pretest) measurement of the dependent variables of interest, the treatment group is administered a treatment (representing the independent variable of interest), and the dependent variables measured again (posttest). The notation of this design is shown in Figure 10.1.

Figure 10.1. Pretest-posttest control group design

The effect E of the experimental treatment in the pretest posttest design is measured as the difference in the posttest and pretest scores between the treatment and control groups:

E = (O 2 – O 1 ) – (O 4 – O 3 )

Statistical analysis of this design involves a simple analysis of variance (ANOVA) between the treatment and control groups. The pretest posttest design handles several threats to internal validity, such as maturation, testing, and regression, since these threats can be expected to influence both treatment and control groups in a similar (random) manner. The selection threat is controlled via random assignment. However, additional threats to internal validity may exist. For instance, mortality can be a problem if there are differential dropout rates between the two groups, and the pretest measurement may bias the posttest measurement (especially if the pretest introduces unusual topics or content).

Posttest-only control group design . This design is a simpler version of the pretest-posttest design where pretest measurements are omitted. The design notation is shown in Figure 10.2.

Figure 10.2. Posttest only control group design.

The treatment effect is measured simply as the difference in the posttest scores between the two groups:

E = (O 1 – O 2 )

The appropriate statistical analysis of this design is also a two- group analysis of variance (ANOVA). The simplicity of this design makes it more attractive than the pretest-posttest design in terms of internal validity. This design controls for maturation, testing, regression, selection, and pretest-posttest interaction, though the mortality threat may continue to exist.

Covariance designs . Sometimes, measures of dependent variables may be influenced by extraneous variables called covariates . Covariates are those variables that are not of central interest to an experimental study, but should nevertheless be controlled in an experimental design in order to eliminate their potential effect on the dependent variable and therefore allow for a more accurate detection of the effects of the independent variables of interest. The experimental designs discussed earlier did not control for such covariates. A covariance design (also called a concomitant variable design) is a special type of pretest posttest control group design where the pretest measure is essentially a measurement of the covariates of interest rather than that of the dependent variables. The design notation is shown in Figure 10.3, where C represents the covariates:

Figure 10.3. Covariance design

Because the pretest measure is not a measurement of the dependent variable, but rather a covariate, the treatment effect is measured as the difference in the posttest scores between the treatment and control groups as:

Figure 10.4. 2 x 2 factorial design

Factorial designs can also be depicted using a design notation, such as that shown on the right panel of Figure 10.4. R represents random assignment of subjects to treatment groups, X represents the treatment groups themselves (the subscripts of X represents the level of each factor), and O represent observations of the dependent variable. Notice that the 2 x 2 factorial design will have four treatment groups, corresponding to the four combinations of the two levels of each factor. Correspondingly, the 2 x 3 design will have six treatment groups, and the 2 x 2 x 2 design will have eight treatment groups. As a rule of thumb, each cell in a factorial design should have a minimum sample size of 20 (this estimate is derived from Cohen’s power calculations based on medium effect sizes). So a 2 x 2 x 2 factorial design requires a minimum total sample size of 160 subjects, with at least 20 subjects in each cell. As you can see, the cost of data collection can increase substantially with more levels or factors in your factorial design. Sometimes, due to resource constraints, some cells in such factorial designs may not receive any treatment at all, which are called incomplete factorial designs . Such incomplete designs hurt our ability to draw inferences about the incomplete factors.

In a factorial design, a main effect is said to exist if the dependent variable shows a significant difference between multiple levels of one factor, at all levels of other factors. No change in the dependent variable across factor levels is the null case (baseline), from which main effects are evaluated. In the above example, you may see a main effect of instructional type, instructional time, or both on learning outcomes. An interaction effect exists when the effect of differences in one factor depends upon the level of a second factor. In our example, if the effect of instructional type on learning outcomes is greater for 3 hours/week of instructional time than for 1.5 hours/week, then we can say that there is an interaction effect between instructional type and instructional time on learning outcomes. Note that the presence of interaction effects dominate and make main effects irrelevant, and it is not meaningful to interpret main effects if interaction effects are significant.

Hybrid Experimental Designs

Hybrid designs are those that are formed by combining features of more established designs. Three such hybrid designs are randomized bocks design, Solomon four-group design, and switched replications design.

Randomized block design. This is a variation of the posttest-only or pretest-posttest control group design where the subject population can be grouped into relatively homogeneous subgroups (called blocks ) within which the experiment is replicated. For instance, if you want to replicate the same posttest-only design among university students and full -time working professionals (two homogeneous blocks), subjects in both blocks are randomly split between treatment group (receiving the same treatment) or control group (see Figure 10.5). The purpose of this design is to reduce the “noise” or variance in data that may be attributable to differences between the blocks so that the actual effect of interest can be detected more accurately.

Figure 10.5. Randomized blocks design.

Solomon four-group design . In this design, the sample is divided into two treatment groups and two control groups. One treatment group and one control group receive the pretest, and the other two groups do not. This design represents a combination of posttest-only and pretest-posttest control group design, and is intended to test for the potential biasing effect of pretest measurement on posttest measures that tends to occur in pretest-posttest designs but not in posttest only designs. The design notation is shown in Figure 10.6.

Figure 10.6. Solomon four-group design

Switched replication design . This is a two-group design implemented in two phases with three waves of measurement. The treatment group in the first phase serves as the control group in the second phase, and the control group in the first phase becomes the treatment group in the second phase, as illustrated in Figure 10.7. In other words, the original design is repeated or replicated temporally with treatment/control roles switched between the two groups. By the end of the study, all participants will have received the treatment either during the first or the second phase. This design is most feasible in organizational contexts where organizational programs (e.g., employee training) are implemented in a phased manner or are repeated at regular intervals.

Figure 10.7. Switched replication design.

Quasi-Experimental Designs

Quasi-experimental designs are almost identical to true experimental designs, but lacking one key ingredient: random assignment. For instance, one entire class section or one organization is used as the treatment group, while another section of the same class or a different organization in the same industry is used as the control group. This lack of random assignment potentially results in groups that are non-equivalent, such as one group possessing greater mastery of a certain content than the other group, say by virtue of having a better teacher in a previous semester, which introduces the possibility of selection bias . Quasi-experimental designs are therefore inferior to true experimental designs in interval validity due to the presence of a variety of selection related threats such as selection-maturation threat (the treatment and control groups maturing at different rates), selection-history threat (the treatment and control groups being differentially impact by extraneous or historical events), selection-regression threat (the treatment and control groups regressing toward the mean between pretest and posttest at different rates), selection-instrumentation threat (the treatment and control groups responding differently to the measurement), selection-testing (the treatment and control groups responding differently to the pretest), and selection-mortality (the treatment and control groups demonstrating differential dropout rates). Given these selection threats, it is generally preferable to avoid quasi-experimental designs to the greatest extent possible.

Many true experimental designs can be converted to quasi-experimental designs by omitting random assignment. For instance, the quasi-equivalent version of pretest-posttest control group design is called nonequivalent groups design (NEGD), as shown in Figure 10.8, with random assignment R replaced by non-equivalent (non-random) assignment N . Likewise, the quasi -experimental version of switched replication design is called non-equivalent switched replication design (see Figure 10.9).

Figure 10.8. NEGD design.

Figure 10.9. Non-equivalent switched replication design.

In addition, there are quite a few unique non -equivalent designs without corresponding true experimental design cousins. Some of the more useful of these designs are discussed next.

Regression-discontinuity (RD) design . This is a non-equivalent pretest-posttest design where subjects are assigned to treatment or control group based on a cutoff score on a preprogram measure. For instance, patients who are severely ill may be assigned to a treatment group to test the efficacy of a new drug or treatment protocol and those who are mildly ill are assigned to the control group. In another example, students who are lagging behind on standardized test scores may be selected for a remedial curriculum program intended to improve their performance, while those who score high on such tests are not selected from the remedial program. The design notation can be represented as follows, where C represents the cutoff score:

Figure 10.10. RD design.

Because of the use of a cutoff score, it is possible that the observed results may be a function of the cutoff score rather than the treatment, which introduces a new threat to internal validity. However, using the cutoff score also ensures that limited or costly resources are distributed to people who need them the most rather than randomly across a population, while simultaneously allowing a quasi-experimental treatment. The control group scores in the RD design does not serve as a benchmark for comparing treatment group scores, given the systematic non-equivalence between the two groups. Rather, if there is no discontinuity between pretest and posttest scores in the control group, but such a discontinuity persists in the treatment group, then this discontinuity is viewed as evidence of the treatment effect.

Proxy pretest design . This design, shown in Figure 10.11, looks very similar to the standard NEGD (pretest-posttest) design, with one critical difference: the pretest score is collected after the treatment is administered. A typical application of this design is when a researcher is brought in to test the efficacy of a program (e.g., an educational program) after the program has already started and pretest data is not available. Under such circumstances, the best option for the researcher is often to use a different prerecorded measure, such as students’ grade point average before the start of the program, as a proxy for pretest data. A variation of the proxy pretest design is to use subjects’ posttest recollection of pretest data, which may be subject to recall bias, but nevertheless may provide a measure of perceived gain or change in the dependent variable.

Figure 10.11. Proxy pretest design.

Separate pretest-posttest samples design . This design is useful if it is not possible to collect pretest and posttest data from the same subjects for some reason. As shown in Figure 10.12, there are four groups in this design, but two groups come from a single non-equivalent group, while the other two groups come from a different non-equivalent group. For instance, you want to test customer satisfaction with a new online service that is implemented in one city but not in another. In this case, customers in the first city serve as the treatment group and those in the second city constitute the control group. If it is not possible to obtain pretest and posttest measures from the same customers, you can measure customer satisfaction at one point in time, implement the new service program, and measure customer satisfaction (with a different set of customers) after the program is implemented. Customer satisfaction is also measured in the control group at the same times as in the treatment group, but without the new program implementation. The design is not particularly strong, because you cannot examine the changes in any specific customer’s satisfaction score before and after the implementation, but you can only examine average customer satisfaction scores. Despite the lower internal validity, this design may still be a useful way of collecting quasi-experimental data when pretest and posttest data are not available from the same subjects.

Figure 10.12. Separate pretest-posttest samples design.

Nonequivalent dependent variable (NEDV) design . This is a single-group pre-post quasi-experimental design with two outcome measures, where one measure is theoretically expected to be influenced by the treatment and the other measure is not. For instance, if you are designing a new calculus curriculum for high school students, this curriculum is likely to influence students’ posttest calculus scores but not algebra scores. However, the posttest algebra scores may still vary due to extraneous factors such as history or maturation. Hence, the pre-post algebra scores can be used as a control measure, while that of pre-post calculus can be treated as the treatment measure. The design notation, shown in Figure 10.13, indicates the single group by a single N , followed by pretest O 1 and posttest O 2 for calculus and algebra for the same group of students. This design is weak in internal validity, but its advantage lies in not having to use a separate control group.

An interesting variation of the NEDV design is a pattern matching NEDV design , which employs multiple outcome variables and a theory that explains how much each variable will be affected by the treatment. The researcher can then examine if the theoretical prediction is matched in actual observations. This pattern-matching technique, based on the degree of correspondence between theoretical and observed patterns is a powerful way of alleviating internal validity concerns in the original NEDV design.

Figure 10.13. NEDV design.

Perils of Experimental Research

Experimental research is one of the most difficult of research designs, and should not be taken lightly. This type of research is often best with a multitude of methodological problems. First, though experimental research requires theories for framing hypotheses for testing, much of current experimental research is atheoretical. Without theories, the hypotheses being tested tend to be ad hoc, possibly illogical, and meaningless. Second, many of the measurement instruments used in experimental research are not tested for reliability and validity, and are incomparable across studies. Consequently, results generated using such instruments are also incomparable. Third, many experimental research use inappropriate research designs, such as irrelevant dependent variables, no interaction effects, no experimental controls, and non-equivalent stimulus across treatment groups. Findings from such studies tend to lack internal validity and are highly suspect. Fourth, the treatments (tasks) used in experimental research may be diverse, incomparable, and inconsistent across studies and sometimes inappropriate for the subject population. For instance, undergraduate student subjects are often asked to pretend that they are marketing managers and asked to perform a complex budget allocation task in which they have no experience or expertise. The use of such inappropriate tasks, introduces new threats to internal validity (i.e., subject’s performance may be an artifact of the content or difficulty of the task setting), generates findings that are non-interpretable and meaningless, and makes integration of findings across studies impossible.

The design of proper experimental treatments is a very important task in experimental design, because the treatment is the raison d’etre of the experimental method, and must never be rushed or neglected. To design an adequate and appropriate task, researchers should use prevalidated tasks if available, conduct treatment manipulation checks to check for the adequacy of such tasks (by debriefing subjects after performing the assigned task), conduct pilot tests (repeatedly, if necessary), and if doubt, using tasks that are simpler and familiar for the respondent sample than tasks that are complex or unfamiliar.

In summary, this chapter introduced key concepts in the experimental design research method and introduced a variety of true experimental and quasi-experimental designs. Although these designs vary widely in internal validity, designs with less internal validity should not be overlooked and may sometimes be useful under specific circumstances and empirical contingencies.

• Social Science Research: Principles, Methods, and Practices. Authored by : Anol Bhattacherjee. Provided by : University of South Florida. Located at : http://scholarcommons.usf.edu/oa_textbooks/3/ . License : CC BY-NC-SA: Attribution-NonCommercial-ShareAlike

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Title: three-nucleon correlations in light nuclei yields ratios from ampt model for qcd critical point investigation.

Abstract: This research use the AMPT model in Au+Au collisions to study the influence of the three nucleons correlation $C_{n2p}$ on the light nuclei yield ratios. It is found that neglecting $C_{n2p}$ leads to an overestimated relative neutron density fluctuation extraction. Including $C_{n2p}$ will enhances the agreement with experimental results with higher yield ratios, yet it does not change the energy dependence of the yield ratio. Since there is no first-order phase transition or critical physics in the AMPT model, our work fails to reproduce the experimental energy-dependent peak around $\sqrt{s_\text{NN}} = $20-30 GeV. Our work might offer a baseline for investigating critical physics phenomena using the light nuclei production as a probe.

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