quasi experiment disadvantages

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Experiments and quasi-experiments.

This page includes an explanation of the types, key components, validity, ethics, and advantages and disadvantages of experimental design.

An experiment is a study in which the researcher manipulates the level of some independent variable and then measures the outcome. Experiments are powerful techniques for evaluating cause-and-effect relationships. Many researchers consider experiments the "gold standard" against which all other research designs should be judged. Experiments are conducted both in the laboratory and in real life situations.

Types of Experimental Design

There are two basic types of research design:

• True experiments
• Quasi-experiments

The purpose of both is to examine the cause of certain phenomena.

True experiments, in which all the important factors that might affect the phenomena of interest are completely controlled, are the preferred design. Often, however, it is not possible or practical to control all the key factors, so it becomes necessary to implement a quasi-experimental research design.

Similarities between true and quasi-experiments:

• Study participants are subjected to some type of treatment or condition
• Some outcome of interest is measured
• The researchers test whether differences in this outcome are related to the treatment

Differences between true experiments and quasi-experiments:

• In a true experiment, participants are randomly assigned to either the treatment or the control group, whereas they are not assigned randomly in a quasi-experiment
• In a quasi-experiment, the control and treatment groups differ not only in terms of the experimental treatment they receive, but also in other, often unknown or unknowable, ways. Thus, the researcher must try to statistically control for as many of these differences as possible
• Because control is lacking in quasi-experiments, there may be several "rival hypotheses" competing with the experimental manipulation as explanations for observed results

Key Components of Experimental Research Design

The manipulation of predictor variables.

In an experiment, the researcher manipulates the factor that is hypothesized to affect the outcome of interest. The factor that is being manipulated is typically referred to as the treatment or intervention. The researcher may manipulate whether research subjects receive a treatment (e.g., antidepressant medicine: yes or no) and the level of treatment (e.g., 50 mg, 75 mg, 100 mg, and 125 mg).

Suppose, for example, a group of researchers was interested in the causes of maternal employment. They might hypothesize that the provision of government-subsidized child care would promote such employment. They could then design an experiment in which some subjects would be provided the option of government-funded child care subsidies and others would not. The researchers might also manipulate the value of the child care subsidies in order to determine if higher subsidy values might result in different levels of maternal employment.

Random Assignment

• Study participants are randomly assigned to different treatment groups
• All participants have the same chance of being in a given condition
• Participants are assigned to either the group that receives the treatment, known as the "experimental group" or "treatment group," or to the group which does not receive the treatment, referred to as the "control group"
• Random assignment neutralizes factors other than the independent and dependent variables, making it possible to directly infer cause and effect

Random Sampling

Traditionally, experimental researchers have used convenience sampling to select study participants. However, as research methods have become more rigorous, and the problems with generalizing from a convenience sample to the larger population have become more apparent, experimental researchers are increasingly turning to random sampling. In experimental policy research studies, participants are often randomly selected from program administrative databases and randomly assigned to the control or treatment groups.

Validity of Results

The two types of validity of experiments are internal and external. It is often difficult to achieve both in social science research experiments.

Internal Validity

• When an experiment is internally valid, we are certain that the independent variable (e.g., child care subsidies) caused the outcome of the study (e.g., maternal employment)
• When subjects are randomly assigned to treatment or control groups, we can assume that the independent variable caused the observed outcomes because the two groups should not have differed from one another at the start of the experiment
• For example, take the child care subsidy example above. Since research subjects were randomly assigned to the treatment (child care subsidies available) and control (no child care subsidies available) groups, the two groups should not have differed at the outset of the study. If, after the intervention, mothers in the treatment group were more likely to be working, we can assume that the availability of child care subsidies promoted maternal employment

One potential threat to internal validity in experiments occurs when participants either drop out of the study or refuse to participate in the study. If particular types of individuals drop out or refuse to participate more often than individuals with other characteristics, this is called differential attrition. For example, suppose an experiment was conducted to assess the effects of a new reading curriculum. If the new curriculum was so tough that many of the slowest readers dropped out of school, the school with the new curriculum would experience an increase in the average reading scores. The reason they experienced an increase in reading scores, however, is because the worst readers left the school, not because the new curriculum improved students' reading skills.

External Validity

• External validity is also of particular concern in social science experiments
• It can be very difficult to generalize experimental results to groups that were not included in the study
• Studies that randomly select participants from the most diverse and representative populations are more likely to have external validity
• The use of random sampling techniques makes it easier to generalize the results of studies to other groups

For example, a research study shows that a new curriculum improved reading comprehension of third-grade children in Iowa. To assess the study's external validity, you would ask whether this new curriculum would also be effective with third graders in New York or with children in other elementary grades.

Glossary terms related to validity:

• internal validity
• external validity
• differential attrition

It is particularly important in experimental research to follow ethical guidelines. Protecting the health and safety of research subjects is imperative. In order to assure subject safety, all researchers should have their project reviewed by the Institutional Review Boards (IRBS). The  National Institutes of Health  supplies strict guidelines for project approval. Many of these guidelines are based on the  Belmont Report  (pdf).

The basic ethical principles:

• Respect for persons  -- requires that research subjects are not coerced into participating in a study and requires the protection of research subjects who have diminished autonomy
• Beneficence  -- requires that experiments do not harm research subjects, and that researchers minimize the risks for subjects while maximizing the benefits for them
• Justice  -- requires that all forms of differential treatment among research subjects be justified

Advantages and Disadvantages of Experimental Design

The environment in which the research takes place can often be carefully controlled. Consequently, it is easier to estimate the true effect of the variable of interest on the outcome of interest.

Disadvantages

It is often difficult to assure the external validity of the experiment, due to the frequently nonrandom selection processes and the artificial nature of the experimental context.

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Quasi Experimental Design Overview & Examples

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What is a Quasi Experimental Design?

A quasi experimental design is a method for identifying causal relationships that does not randomly assign participants to the experimental groups. Instead, researchers use a non-random process. For example, they might use an eligibility cutoff score or preexisting groups to determine who receives the treatment.

Quasi-experimental research is a design that closely resembles experimental research but is different. The term “quasi” means “resembling,” so you can think of it as a cousin to actual experiments. In these studies, researchers can manipulate an independent variable — that is, they change one factor to see what effect it has. However, unlike true experimental research, participants are not randomly assigned to different groups.

Learn more about Experimental Designs: Definition & Types .

When to Use Quasi-Experimental Design

Researchers typically use a quasi-experimental design because they can’t randomize due to practical or ethical concerns. For example:

• Practical Constraints : A school interested in testing a new teaching method can only implement it in preexisting classes and cannot randomly assign students.
• Ethical Concerns : A medical study might not be able to randomly assign participants to a treatment group for an experimental medication when they are already taking a proven drug.

Quasi-experimental designs also come in handy when researchers want to study the effects of naturally occurring events, like policy changes or environmental shifts, where they can’t control who is exposed to the treatment.

Quasi-experimental designs occupy a unique position in the spectrum of research methodologies, sitting between observational studies and true experiments. This middle ground offers a blend of both worlds, addressing some limitations of purely observational studies while navigating the constraints often accompanying true experiments.

A significant advantage of quasi-experimental research over purely observational studies and correlational research is that it addresses the issue of directionality, determining which variable is the cause and which is the effect. In quasi-experiments, an intervention typically occurs during the investigation, and the researchers record outcomes before and after it, increasing the confidence that it causes the observed changes.

However, it’s crucial to recognize its limitations as well. Controlling confounding variables is a larger concern for a quasi-experimental design than a true experiment because it lacks random assignment.

In sum, quasi-experimental designs offer a valuable research approach when random assignment is not feasible, providing a more structured and controlled framework than observational studies while acknowledging and attempting to address potential confounders.

Types of Quasi-Experimental Designs and Examples

Quasi-experimental studies use various methods, depending on the scenario.

Natural Experiments

This design uses naturally occurring events or changes to create the treatment and control groups. Researchers compare outcomes between those whom the event affected and those it did not affect. Analysts use statistical controls to account for confounders that the researchers must also measure.

Natural experiments are related to observational studies, but they allow for a clearer causality inference because the external event or policy change provides both a form of quasi-random group assignment and a definite start date for the intervention.

For example, in a natural experiment utilizing a quasi-experimental design, researchers study the impact of a significant economic policy change on small business growth. The policy is implemented in one state but not in neighboring states. This scenario creates an unplanned experimental setup, where the state with the new policy serves as the treatment group, and the neighboring states act as the control group.

Researchers are primarily interested in small business growth rates but need to record various confounders that can impact growth rates. Hence, they record state economic indicators, investment levels, and employment figures. By recording these metrics across the states, they can include them in the model as covariates and control them statistically. This method allows researchers to estimate differences in small business growth due to the policy itself, separate from the various confounders.

Nonequivalent Groups Design

This method involves matching existing groups that are similar but not identical. Researchers attempt to find groups that are as equivalent as possible, particularly for factors likely to affect the outcome.

For instance, researchers use a nonequivalent groups quasi-experimental design to evaluate the effectiveness of a new teaching method in improving students’ mathematics performance. A school district considering the teaching method is planning the study. Students are already divided into schools, preventing random assignment.

The researchers matched two schools with similar demographics, baseline academic performance, and resources. The school using the traditional methodology is the control, while the other uses the new approach. Researchers are evaluating differences in educational outcomes between the two methods.

They perform a pretest to identify differences between the schools that might affect the outcome and include them as covariates to control for confounding. They also record outcomes before and after the intervention to have a larger context for the changes they observe.

Regression Discontinuity

This process assigns subjects to a treatment or control group based on a predetermined cutoff point (e.g., a test score). The analysis primarily focuses on participants near the cutoff point, as they are likely similar except for the treatment received. By comparing participants just above and below the cutoff, the design controls for confounders that vary smoothly around the cutoff.

For example, in a regression discontinuity quasi-experimental design focusing on a new medical treatment for depression, researchers use depression scores as the cutoff point. Individuals with depression scores just above a certain threshold are assigned to receive the latest treatment, while those just below the threshold do not receive it. This method creates two closely matched groups: one that barely qualifies for treatment and one that barely misses out.

By comparing the mental health outcomes of these two groups over time, researchers can assess the effectiveness of the new treatment. The assumption is that the only significant difference between the groups is whether they received the treatment, thereby isolating its impact on depression outcomes.

Controlling Confounders in a Quasi-Experimental Design

Accounting for confounding variables is a challenging but essential task for a quasi-experimental design.

In a true experiment, the random assignment process equalizes confounders across the groups to nullify their overall effect. It’s the gold standard because it works on all confounders, known and unknown.

Unfortunately, the lack of random assignment can allow differences between the groups to exist before the intervention. These confounding factors might ultimately explain the results rather than the intervention.

Consequently, researchers must use other methods to equalize the groups roughly using matching and cutoff values or statistically adjust for preexisting differences they measure to reduce the impact of confounders.

A key strength of quasi-experiments is their frequent use of “pre-post testing.” This approach involves conducting initial tests before collecting data to check for preexisting differences between groups that could impact the study’s outcome. By identifying these variables early on and including them as covariates, researchers can more effectively control potential confounders in their statistical analysis.

Additionally, researchers frequently track outcomes before and after the intervention to better understand the context for changes they observe.

Statisticians consider these methods to be less effective than randomization. Hence, quasi-experiments fall somewhere in the middle when it comes to internal validity , or how well the study can identify causal relationships versus mere correlation . They’re more conclusive than correlational studies but not as solid as true experiments.

In conclusion, quasi-experimental designs offer researchers a versatile and practical approach when random assignment is not feasible. This methodology bridges the gap between controlled experiments and observational studies, providing a valuable tool for investigating cause-and-effect relationships in real-world settings. Researchers can address ethical and logistical constraints by understanding and leveraging the different types of quasi-experimental designs while still obtaining insightful and meaningful results.

Cook, T. D., & Campbell, D. T. (1979).  Quasi-experimentation: Design & analysis issues in field settings . Boston, MA: Houghton Mifflin

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Quasi-Experiment: Understand What It Is, Types & Examples

Discover the concept of quasi-experiment, its various types, real-world examples, and how QuestionPro aids in conducting these studies.

Quasi-experimental research designs have gained significant recognition in the scientific community due to their unique ability to study cause-and-effect relationships in real-world settings. Unlike true experiments, quasi-experiment lack random assignment of participants to groups, making them more practical and ethical in certain situations. In this article, we will delve into the concept, applications, and advantages of quasi-experiments, shedding light on their relevance and significance in the scientific realm.

What Is A Quasi-Experiment Research Design?

Quasi-experimental research designs are research methodologies that resemble true experiments but lack the randomized assignment of participants to groups. In a true experiment, researchers randomly assign participants to either an experimental group or a control group, allowing for a comparison of the effects of an independent variable on the dependent variable. However, in quasi-experiments, this random assignment is often not possible or ethically permissible, leading to the adoption of alternative strategies.

Types Of Quasi-Experimental Designs

There are several types of quasi-experiment designs to study causal relationships in specific contexts. Some common types include:

Non-Equivalent Groups Design

This design involves selecting pre-existing groups that differ in some key characteristics and comparing their responses to the independent variable. Although the researcher does not randomly assign the groups, they can still examine the effects of the independent variable.

Regression Discontinuity

This design utilizes a cutoff point or threshold to determine which participants receive the treatment or intervention. It assumes that participants on either side of the cutoff are similar in all other aspects, except for their exposure to the independent variable.

Interrupted Time Series Design

This design involves measuring the dependent variable multiple times before and after the introduction of an intervention or treatment. By comparing the trends in the dependent variable, researchers can infer the impact of the intervention.

Natural Experiments

Natural experiments take advantage of naturally occurring events or circumstances that mimic the random assignment found in true experiments. Participants are exposed to different conditions in situations identified by researchers without any manipulation from them.

Application of the Quasi-Experiment Design

Quasi-experimental research designs find applications in various fields, ranging from education to public health and beyond. One significant advantage of quasi-experiments is their feasibility in real-world settings where randomization is not always possible or ethical.

Ethical Reasons

Ethical concerns often arise in research when randomizing participants to different groups could potentially deny individuals access to beneficial treatments or interventions. In such cases, quasi-experimental designs provide an ethical alternative, allowing researchers to study the impact of interventions without depriving anyone of potential benefits.

Examples Of Quasi-Experimental Design

Let’s explore a few examples of quasi-experimental designs to understand their application in different contexts.

Design Of Non-Equivalent Groups

Determining the effectiveness of math apps in supplementing math classes.

Imagine a study aiming to determine the effectiveness of math apps in supplementing traditional math classes in a school. Randomly assigning students to different groups might be impractical or disrupt the existing classroom structure. Instead, researchers can select two comparable classes, one receiving the math app intervention and the other continuing with traditional teaching methods. By comparing the performance of the two groups, researchers can draw conclusions about the app’s effectiveness.

To conduct a quasi-experiment study like the one mentioned above, researchers can utilize QuestionPro , an advanced research platform that offers comprehensive survey and data analysis tools. With QuestionPro, researchers can design surveys to collect data, analyze results, and gain valuable insights for their quasi-experimental research.

How QuestionPro Helps In Quasi-Experimental Research?

QuestionPro’s powerful features, such as random assignment of participants, survey branching, and data visualization, enable researchers to efficiently conduct and analyze quasi-experimental studies. The platform provides a user-friendly interface and robust reporting capabilities, empowering researchers to gather data, explore relationships, and draw meaningful conclusions.

In some cases, researchers can leverage natural experiments to examine causal relationships. 

Determining The Effectiveness Of Teaching Modern Leadership Techniques In Start-Up Businesses

Consider a study evaluating the effectiveness of teaching modern leadership techniques in start-up businesses. Instead of artificially assigning businesses to different groups, researchers can observe those that naturally adopt modern leadership techniques and compare their outcomes to those of businesses that have not implemented such practices.

Advantages and Disadvantages Of The Quasi-Experimental Design

Quasi-experimental designs offer several advantages over true experiments, making them valuable tools in research:

• Scope of the research : Quasi-experiments allow researchers to study cause-and-effect relationships in real-world settings, providing valuable insights into complex phenomena that may be challenging to replicate in a controlled laboratory environment.
• Regression Discontinuity : Researchers can utilize regression discontinuity to evaluate the effects of interventions or treatments when random assignment is not feasible. This design leverages existing data and naturally occurring thresholds to draw causal inferences.

Disadvantage

Lack of random assignment : Quasi-experimental designs lack the random assignment of participants, which introduces the possibility of confounding variables affecting the results. Researchers must carefully consider potential alternative explanations for observed effects.

What Are The Different Quasi-Experimental Study Designs?

Quasi-experimental designs encompass various approaches, including nonequivalent group designs, interrupted time series designs, and natural experiments. Each design offers unique advantages and limitations, providing researchers with versatile tools to explore causal relationships in different contexts.

Example Of The Natural Experiment Approach

Researchers interested in studying the impact of a public health campaign aimed at reducing smoking rates may take advantage of a natural experiment. By comparing smoking rates in a region that has implemented the campaign to a similar region that has not, researchers can examine the effectiveness of the intervention.

Differences Between Quasi-Experiments And True Experiments

Quasi-experiments and true experiments differ primarily in their ability to randomly assign participants to groups. While true experiments provide a higher level of control, quasi-experiments offer practical and ethical alternatives in situations where randomization is not feasible or desirable.

Example Comparing A True Experiment And Quasi-Experiment

In a true experiment investigating the effects of a new medication on a specific condition, researchers would randomly assign participants to either the experimental group, which receives the medication, or the control group, which receives a placebo. In a quasi-experiment, researchers might instead compare patients who voluntarily choose to take the medication to those who do not, examining the differences in outcomes between the two groups.

Quasi-Experiment: A Quick Wrap-Up

Quasi-experimental research designs play a vital role in scientific inquiry by allowing researchers to investigate cause-and-effect relationships in real-world settings. These designs offer practical and ethical alternatives to true experiments, making them valuable tools in various fields of study. With their versatility and applicability, quasi-experimental designs continue to contribute to our understanding of complex phenomena.

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Quasi-Experimental Design Disadvantages

Quasi-experimental research designs lack full randomization and control, making them different from accurate  experimental designs.  While quasi-experimental designs have their benefits, they also come with several disadvantages that researchers should consider:

• Lack of Randomization:  Quasi-experimental designs do not involve randomly assigning participants to treatment and control groups. This introduces the potential for selection biases, as the groups may differ in ways that could affect the outcomes  peer review  being studied. The lack of randomization limits the ability to make causal inferences with certainty.
• Internal Validity Concerns:  Quasi-experimental designs are susceptible to threats to internal validity, such as history, maturation, selection bias, and regression to the mean. Without random assignment, it becomes challenging to attribute the observed effects solely to the treatment or intervention being studied.
• Limited Control over Extraneous Variables:  A manuscript review of experimental designs often lacks control over extraneous variables that can influence the outcomes. This makes it difficult to isolate the effects of the independent variable and increases the risk of confounding factors affecting the results.
• Ethical Constraints:  Quasi-experimental designs may face ethical constraints in terms of assigning participants to different groups or manipulating variables. Researchers may be limited in their ability to implement specific interventions or treatments due to ethical concerns, which can impact the validity of manuscript examples and the generalizability of the findings.
• Generalizability Issues:  The findings from quasi-experimental designs may have limited generalizability beyond the specific context and sample used in the  case study . The lack of randomization and control over extraneous variables can make applying the findings to broader populations or settings challenging.
• Limited Causal Inferences:  Establishing causal relationships can be difficult due to the inherent limitations of quasi-experimental designs. While quasi-experiments can provide valuable insights and suggest associations, they often fall short of providing strong evidence for causal claims.

Researchers should carefully consider these disadvantages when deciding to use a quasi-experimental design and take appropriate measures to mitigate potential biases and threats to validity. Supplementing  quasi-experimental designs  with other research methods, such as pre-and post-test measures or comparison groups, can help strengthen the validity of the findings.

Conclusion:

In conclusion, while quasi-experimental designs offer certain advantages in terms of their feasibility and applicability in real-world settings, they also come with notable disadvantages. The lack of randomization and limited control over extraneous variables pose significant challenges to establishing causal relationships and ensuring internal validity. Researchers must exercise caution when interpreting the findings derived from quasi-experimental designs. Ultimately, quasi-experimental designs can provide valuable insights into complex phenomena when true experimental designs are not feasible or ethical. However,  Purbica  researchers should carefully weigh the advantages and disadvantages, consider the specific research question and context, and employ appropriate measures to minimize biases and enhance the validity of their findings.

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Experimental and Quasi-Experimental Designs for Research on Learning

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• Norbert M. Seel 2  

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Research designs

Research on learning applies various designs which refer to plans that outline how information is to be gathered for testing a hypothesis or theoretical assumption. Research designs are the heart of quantitative research. They include systematic observations, measures, treatments, their random assignment to groups, and time. Accordingly, research designs include identifying the data gathering method(s), the instruments to be used or created for assessment, how the instruments will be administered, and how the information will be organized and analyzed in accordance with the subject to be investigated. Among the various designs to consider in the area of research on learning are

Experimental designs

Quasi-experimental designs

Nonexperimental designs

Each design offers its particular advantages and disadvantages concerning validity, reliability, and feasibility. Although all experiments share common features, their applications vary in accordance with...

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Seel, N.M. (2012). Experimental and Quasi-Experimental Designs for Research on Learning. In: Seel, N.M. (eds) Encyclopedia of the Sciences of Learning. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-1428-6_716

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An Introduction to Quasi-Experimental Design

3-minute read

• 9th January 2022

If you’re a researcher or student in the sciences, you’ll probably come across the term “quasi-experimental design” at some point. But what exactly does it mean?

In this post, we’ll guide you through the different forms of quasi-experimental design and how it compares to true experiments.

What is Quasi-Experimental Design?

Quasi-experimental design (QED) is a research design method that’s useful when regular experimental conditions are impractical or unethical.

Both quasi-experimental designs and true experiments show a cause-and-effect relationship between a dependent and independent variable . Participants in a true experiment are randomly assigned to different treatment groups. The quasi-experimental design, on the other hand, assigns groups based on criteria instead of randomly.

Quasi-Experimental Design Vs. True Experimental Design

The main difference between a quasi-experimental and true experimental design is that in the former, groups aren’t randomly assigned. There are also some other key differences between these research methods.

True experimental design involves:

●     Having control as a researcher over the design of the treatment or program that participants receive (i.e., the independent variable)

●     Control variables as a necessary component

In contrast, a quasi-experimental design involves:

●     The researcher studying groups after they’ve received a treatment or program

●     Control variables as a common element but they aren’t necessary for the experiment to work

Examples of Experimental Design

Perhaps the easiest way to understand quasi-experimental design is to look at how it might be used in practice.

Let’s say you hypothesize that having access to free art lessons will improve the mental health of children from low-income families.

In a true experiment, you’d randomly assign participants to two groups: one that receives free art lessons and another that doesn’t.

However, it’s ethically questionable to deny one group of children access to something that might benefit them.

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Instead, you might decide to compare the data from a community that’s already offered free art classes to these children with that of a community that’s not yet done so.

This second example would be a quasi-experimental design.

Advantages and Disadvantages of Quasi-Experimental Design

Quasi-experimental design has some advantages and disadvantages you’ll need to consider when designing your research.

On the plus side, quasi-experimental design:

●     Has a higher external validity than true experimental design, as it usually involves real-world scenarios

●     Allows you to control for unexpected, confounding variables, resulting in a higher internal validity than other non-experimental methods of research

●     Enables the study of cause-and-effect relationships without the ethical issue of denying a treatment to those who may benefit from it

●     Does not require access to large-scale funding and other practical concerns, as the treatment has already been issued by others

The disadvantages of quasi-experimental design, however, include:

●     Lower internal validity than found in true experiments, as it’s more difficult to account for all confounding variables without using random assignment

●     The necessary data required for research potentially being inaccurate, outdated, or difficult to access

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Quasi-Experimental Design: Definition, Types, Examples

Appinio Research · 19.12.2023 · 36min read

Ever wondered how researchers uncover cause-and-effect relationships in the real world, where controlled experiments are often elusive? Quasi-experimental design holds the key. In this guide, we'll unravel the intricacies of quasi-experimental design, shedding light on its definition, purpose, and applications across various domains. Whether you're a student, a professional, or simply curious about the methods behind meaningful research, join us as we delve into the world of quasi-experimental design, making complex concepts sound simple and embarking on a journey of knowledge and discovery.

What is Quasi-Experimental Design?

Quasi-experimental design is a research methodology used to study the effects of independent variables on dependent variables when full experimental control is not possible or ethical. It falls between controlled experiments, where variables are tightly controlled, and purely observational studies, where researchers have little control over variables. Quasi-experimental design mimics some aspects of experimental research but lacks randomization.

The primary purpose of quasi-experimental design is to investigate cause-and-effect relationships between variables in real-world settings. Researchers use this approach to answer research questions, test hypotheses, and explore the impact of interventions or treatments when they cannot employ traditional experimental methods. Quasi-experimental studies aim to maximize internal validity and make meaningful inferences while acknowledging practical constraints and ethical considerations.

Quasi-Experimental vs. Experimental Design

It's essential to understand the distinctions between Quasi-Experimental and Experimental Design to appreciate the unique characteristics of each approach:

• Randomization:  In Experimental Design, random assignment of participants to groups is a defining feature. Quasi-experimental design, on the other hand, lacks randomization due to practical constraints or ethical considerations.
• Control Groups :  Experimental Design typically includes control groups that are subjected to no treatment or a placebo. The quasi-experimental design may have comparison groups but lacks the same level of control.
• Manipulation of IV:  Experimental Design involves the intentional manipulation of the independent variable. Quasi-experimental design often deals with naturally occurring independent variables.
• Causal Inference:  Experimental Design allows for stronger causal inferences due to randomization and control. Quasi-experimental design permits causal inferences but with some limitations.

When to Use Quasi-Experimental Design?

A quasi-experimental design is particularly valuable in several situations:

• Ethical Constraints:  When manipulating the independent variable is ethically unacceptable or impractical, quasi-experimental design offers an alternative to studying naturally occurring variables.
• Real-World Settings:  When researchers want to study phenomena in real-world contexts, quasi-experimental design allows them to do so without artificial laboratory settings.
• Limited Resources:  In cases where resources are limited and conducting a controlled experiment is cost-prohibitive, quasi-experimental design can provide valuable insights.
• Policy and Program Evaluation:  Quasi-experimental design is commonly used in evaluating the effectiveness of policies, interventions, or programs that cannot be randomly assigned to participants.

Importance of Quasi-Experimental Design in Research

Quasi-experimental design plays a vital role in research for several reasons:

• Addressing Real-World Complexities:  It allows researchers to tackle complex real-world issues where controlled experiments are not feasible. This bridges the gap between controlled experiments and purely observational studies.
• Ethical Research:  It provides an honest approach when manipulating variables or assigning treatments could harm participants or violate ethical standards.
• Policy and Practice Implications:  Quasi-experimental studies generate findings with direct applications in policy-making and practical solutions in fields such as education, healthcare, and social sciences.
• Enhanced External Validity:  Findings from Quasi-Experimental research often have high external validity, making them more applicable to broader populations and contexts.

By embracing the challenges and opportunities of quasi-experimental design, researchers can contribute valuable insights to their respective fields and drive positive changes in the real world.

Key Concepts in Quasi-Experimental Design

In quasi-experimental design, it's essential to grasp the fundamental concepts underpinning this research methodology. Let's explore these key concepts in detail.

Independent Variable

The independent variable (IV) is the factor you aim to study or manipulate in your research. Unlike controlled experiments, where you can directly manipulate the IV, quasi-experimental design often deals with naturally occurring variables. For example, if you're investigating the impact of a new teaching method on student performance, the teaching method is your independent variable.

Dependent Variable

The dependent variable (DV) is the outcome or response you measure to assess the effects of changes in the independent variable. Continuing with the teaching method example, the dependent variable would be the students' academic performance, typically measured using test scores, grades, or other relevant metrics.

Control Groups vs. Comparison Groups

While quasi-experimental design lacks the luxury of randomly assigning participants to control and experimental groups, you can still establish comparison groups to make meaningful inferences. Control groups consist of individuals who do not receive the treatment, while comparison groups are exposed to different levels or variations of the treatment. These groups help researchers gauge the effect of the independent variable.

Pre-Test and Post-Test Measures

In quasi-experimental design, it's common practice to collect data both before and after implementing the independent variable. The initial data (pre-test) serves as a baseline, allowing you to measure changes over time (post-test). This approach helps assess the impact of the independent variable more accurately. For instance, if you're studying the effectiveness of a new drug, you'd measure patients' health before administering the drug (pre-test) and afterward (post-test).

Threats to Internal Validity

Internal validity is crucial for establishing a cause-and-effect relationship between the independent and dependent variables. However, in a quasi-experimental design, several threats can compromise internal validity. These threats include:

• Selection Bias :  When non-randomized groups differ systematically in ways that affect the study's outcome.
• History Effects:  External events or changes over time that influence the results.
• Maturation Effects:  Natural changes or developments that occur within participants during the study.
• Regression to the Mean:  The tendency for extreme scores on a variable to move closer to the mean upon retesting.
• Attrition and Mortality:  The loss of participants over time, potentially skewing the results.
• Testing Effects:  The mere act of testing or assessing participants can impact their subsequent performance.

Understanding these threats is essential for designing and conducting Quasi-Experimental studies that yield valid and reliable results.

Randomization and Non-Randomization

In traditional experimental designs, randomization is a powerful tool for ensuring that groups are equivalent at the outset of a study. However, quasi-experimental design often involves non-randomization due to the nature of the research. This means that participants are not randomly assigned to treatment and control groups. Instead, researchers must employ various techniques to minimize biases and ensure that the groups are as similar as possible.

For example, if you are conducting a study on the effects of a new teaching method in a real classroom setting, you cannot randomly assign students to the treatment and control groups. Instead, you might use statistical methods to match students based on relevant characteristics such as prior academic performance or socioeconomic status. This matching process helps control for potential confounding variables, increasing the validity of your study.

Types of Quasi-Experimental Designs

In quasi-experimental design, researchers employ various approaches to investigate causal relationships and study the effects of independent variables when complete experimental control is challenging. Let's explore these types of quasi-experimental designs.

One-Group Posttest-Only Design

The One-Group Posttest-Only Design is one of the simplest forms of quasi-experimental design. In this design, a single group is exposed to the independent variable, and data is collected only after the intervention has taken place. Unlike controlled experiments, there is no comparison group. This design is useful when researchers cannot administer a pre-test or when it is logistically difficult to do so.

Example : Suppose you want to assess the effectiveness of a new time management seminar. You offer the seminar to a group of employees and measure their productivity levels immediately afterward to determine if there's an observable impact.

One-Group Pretest-Posttest Design

Similar to the One-Group Posttest-Only Design, this approach includes a pre-test measure in addition to the post-test. Researchers collect data both before and after the intervention. By comparing the pre-test and post-test results within the same group, you can gain a better understanding of the changes that occur due to the independent variable.

Example : If you're studying the impact of a stress management program on participants' stress levels, you would measure their stress levels before the program (pre-test) and after completing the program (post-test) to assess any changes.

Non-Equivalent Groups Design

The Non-Equivalent Groups Design involves multiple groups, but they are not randomly assigned. Instead, researchers must carefully match or control for relevant variables to minimize biases. This design is particularly useful when random assignment is not possible or ethical.

Example : Imagine you're examining the effectiveness of two teaching methods in two different schools. You can't randomly assign students to the schools, but you can carefully match them based on factors like age, prior academic performance, and socioeconomic status to create equivalent groups.

Time Series Design

Time Series Design is an approach where data is collected at multiple time points before and after the intervention. This design allows researchers to analyze trends and patterns over time, providing valuable insights into the sustained effects of the independent variable.

Example : If you're studying the impact of a new marketing campaign on product sales, you would collect sales data at regular intervals (e.g., monthly) before and after the campaign's launch to observe any long-term trends.

Regression Discontinuity Design

Regression Discontinuity Design is employed when participants are assigned to different groups based on a specific cutoff score or threshold. This design is often used in educational and policy research to assess the effects of interventions near a cutoff point.

Example : Suppose you're evaluating the impact of a scholarship program on students' academic performance. Students who score just above or below a certain GPA threshold are assigned differently to the program. This design helps assess the program's effectiveness at the cutoff point.

Propensity Score Matching

Propensity Score Matching is a technique used to create comparable treatment and control groups in non-randomized studies. Researchers calculate propensity scores based on participants' characteristics and match individuals in the treatment group to those in the control group with similar scores.

Example : If you're studying the effects of a new medication on patient outcomes, you would use propensity scores to match patients who received the medication with those who did not but have similar health profiles.

Interrupted Time Series Design

The Interrupted Time Series Design involves collecting data at multiple time points before and after the introduction of an intervention. However, in this design, the intervention occurs at a specific point in time, allowing researchers to assess its immediate impact.

Example : Let's say you're analyzing the effects of a new traffic management system on traffic accidents. You collect accident data before and after the system's implementation to observe any abrupt changes right after its introduction.

Each of these quasi-experimental designs offers unique advantages and is best suited to specific research questions and scenarios. Choosing the right design is crucial for conducting robust and informative studies.

Advantages and Disadvantages of Quasi-Experimental Design

Quasi-experimental design offers a valuable research approach, but like any methodology, it comes with its own set of advantages and disadvantages. Let's explore these in detail.

Quasi-Experimental Design Advantages

Quasi-experimental design presents several advantages that make it a valuable tool in research:

• Real-World Applicability:  Quasi-experimental studies often take place in real-world settings, making the findings more applicable to practical situations. Researchers can examine the effects of interventions or variables in the context where they naturally occur.
• Ethical Considerations:  In situations where manipulating the independent variable in a controlled experiment would be unethical, quasi-experimental design provides an ethical alternative. For example, it would be unethical to assign participants to smoke for a study on the health effects of smoking, but you can study naturally occurring groups of smokers and non-smokers.
• Cost-Efficiency:  Conducting Quasi-Experimental research is often more cost-effective than conducting controlled experiments. The absence of controlled environments and extensive manipulations can save both time and resources.

These advantages make quasi-experimental design an attractive choice for researchers facing practical or ethical constraints in their studies.

Quasi-Experimental Design Disadvantages

However, quasi-experimental design also comes with its share of challenges and disadvantages:

• Limited Control:  Unlike controlled experiments, where researchers have full control over variables, quasi-experimental design lacks the same level of control. This limited control can result in confounding variables that make it difficult to establish causality.
• Threats to Internal Validity:  Various threats to internal validity, such as selection bias, history effects, and maturation effects, can compromise the accuracy of causal inferences. Researchers must carefully address these threats to ensure the validity of their findings.
• Causality Inference Challenges:  Establishing causality can be challenging in quasi-experimental design due to the absence of randomization and control. While you can make strong arguments for causality, it may not be as conclusive as in controlled experiments.
• Potential Confounding Variables:  In a quasi-experimental design, it's often challenging to control for all possible confounding variables that may affect the dependent variable. This can lead to uncertainty in attributing changes solely to the independent variable.

Despite these disadvantages, quasi-experimental design remains a valuable research tool when used judiciously and with a keen awareness of its limitations. Researchers should carefully consider their research questions and the practical constraints they face before choosing this approach.

How to Conduct a Quasi-Experimental Study?

Conducting a Quasi-Experimental study requires careful planning and execution to ensure the validity of your research. Let's dive into the essential steps you need to follow when conducting such a study.

1. Define Research Questions and Objectives

The first step in any research endeavor is clearly defining your research questions and objectives. This involves identifying the independent variable (IV) and the dependent variable (DV) you want to study. What is the specific relationship you want to explore, and what do you aim to achieve with your research?

• Specify Your Research Questions :  Start by formulating precise research questions that your study aims to answer. These questions should be clear, focused, and relevant to your field of study.
• Identify the Independent Variable:  Define the variable you intend to manipulate or study in your research. Understand its significance in your study's context.
• Determine the Dependent Variable:  Identify the outcome or response variable that will be affected by changes in the independent variable.
• Establish Hypotheses (If Applicable):  If you have specific hypotheses about the relationship between the IV and DV, state them clearly. Hypotheses provide a framework for testing your research questions.

2. Select the Appropriate Quasi-Experimental Design

Choosing the right quasi-experimental design is crucial for achieving your research objectives. Select a design that aligns with your research questions and the available data. Consider factors such as the feasibility of implementing the design and the ethical considerations involved.

• Evaluate Your Research Goals:  Assess your research questions and objectives to determine which type of quasi-experimental design is most suitable. Each design has its strengths and limitations, so choose one that aligns with your goals.
• Consider Ethical Constraints:  Take into account any ethical concerns related to your research. Depending on your study's context, some designs may be more ethically sound than others.
• Assess Data Availability:  Ensure you have access to the necessary data for your chosen design. Some designs may require extensive historical data, while others may rely on data collected during the study.

3. Identify and Recruit Participants

Selecting the right participants is a critical aspect of Quasi-Experimental research. The participants should represent the population you want to make inferences about, and you must address ethical considerations, including informed consent.

• Define Your Target Population:  Determine the population that your study aims to generalize to. Your sample should be representative of this population.
• Recruitment Process:  Develop a plan for recruiting participants. Depending on your design, you may need to reach out to specific groups or institutions.
• Informed Consent:  Ensure that you obtain informed consent from participants. Clearly explain the nature of the study, potential risks, and their rights as participants.

4. Collect Data

Data collection is a crucial step in Quasi-Experimental research. You must adhere to a consistent and systematic process to gather relevant information before and after the intervention or treatment.

• Pre-Test Measures:  If applicable, collect data before introducing the independent variable. Ensure that the pre-test measures are standardized and reliable.
• Post-Test Measures:  After the intervention, collect post-test data using the same measures as the pre-test. This allows you to assess changes over time.
• Maintain Data Consistency:  Ensure that data collection procedures are consistent across all participants and time points to minimize biases.

5. Analyze Data

Once you've collected your data, it's time to analyze it using appropriate statistical techniques . The choice of analysis depends on your research questions and the type of data you've gathered.

• Statistical Analysis :  Use statistical software to analyze your data. Common techniques include t-tests, analysis of variance (ANOVA), regression analysis, and more, depending on the design and variables.
• Control for Confounding Variables:  Be aware of potential confounding variables and include them in your analysis as covariates to ensure accurate results.

6. Interpret Results

With the analysis complete, you can interpret the results to draw meaningful conclusions about the relationship between the independent and dependent variables.

• Examine Effect Sizes:  Assess the magnitude of the observed effects to determine their practical significance.
• Consider Significance Levels:  Determine whether the observed results are statistically significant . Understand the p-values and their implications.
• Compare Findings to Hypotheses:  Evaluate whether your findings support or reject your hypotheses and research questions.

7. Draw Conclusions

Based on your analysis and interpretation of the results, draw conclusions about the research questions and objectives you set out to address.

• Causal Inferences:  Discuss the extent to which your study allows for causal inferences. Be transparent about the limitations and potential alternative explanations for your findings.
• Implications and Applications:  Consider the practical implications of your research. How do your findings contribute to existing knowledge, and how can they be applied in real-world contexts?
• Future Research:  Identify areas for future research and potential improvements in study design. Highlight any limitations or constraints that may have affected your study's outcomes.

By following these steps meticulously, you can conduct a rigorous and informative Quasi-Experimental study that advances knowledge in your field of research.

Quasi-Experimental Design Examples

Quasi-experimental design finds applications in a wide range of research domains, including business-related and market research scenarios. Below, we delve into some detailed examples of how this research methodology is employed in practice:

Example 1: Assessing the Impact of a New Marketing Strategy

Suppose a company wants to evaluate the effectiveness of a new marketing strategy aimed at boosting sales. Conducting a controlled experiment may not be feasible due to the company's existing customer base and the challenge of randomly assigning customers to different marketing approaches. In this scenario, a quasi-experimental design can be employed.

• Independent Variable:  The new marketing strategy.
• Dependent Variable:  Sales revenue.
• Design:  The company could implement the new strategy for one group of customers while maintaining the existing strategy for another group. Both groups are selected based on similar demographics and purchase history , reducing selection bias. Pre-implementation data (sales records) can serve as the baseline, and post-implementation data can be collected to assess the strategy's impact.

Example 2: Evaluating the Effectiveness of Employee Training Programs

In the context of human resources and employee development, organizations often seek to evaluate the impact of training programs. A randomized controlled trial (RCT) with random assignment may not be practical or ethical, as some employees may need specific training more than others. Instead, a quasi-experimental design can be employed.

• Independent Variable:  Employee training programs.
• Dependent Variable:  Employee performance metrics, such as productivity or quality of work.
• Design:  The organization can offer training programs to employees who express interest or demonstrate specific needs, creating a self-selected treatment group. A comparable control group can consist of employees with similar job roles and qualifications who did not receive the training. Pre-training performance metrics can serve as the baseline, and post-training data can be collected to assess the impact of the training programs.

Example 3: Analyzing the Effects of a Tax Policy Change

In economics and public policy, researchers often examine the effects of tax policy changes on economic behavior. Conducting a controlled experiment in such cases is practically impossible. Therefore, a quasi-experimental design is commonly employed.

• Independent Variable:  Tax policy changes (e.g., tax rate adjustments).
• Dependent Variable:  Economic indicators, such as consumer spending or business investments.
• Design:  Researchers can analyze data from different regions or jurisdictions where tax policy changes have been implemented. One region could represent the treatment group (with tax policy changes), while a similar region with no tax policy changes serves as the control group. By comparing economic data before and after the policy change in both groups, researchers can assess the impact of the tax policy changes.

These examples illustrate how quasi-experimental design can be applied in various research contexts, providing valuable insights into the effects of independent variables in real-world scenarios where controlled experiments are not feasible or ethical. By carefully selecting comparison groups and controlling for potential biases, researchers can draw meaningful conclusions and inform decision-making processes.

How to Publish Quasi-Experimental Research?

Publishing your Quasi-Experimental research findings is a crucial step in contributing to the academic community's knowledge. We'll explore the essential aspects of reporting and publishing your Quasi-Experimental research effectively.

Structuring Your Research Paper

When preparing your research paper, it's essential to adhere to a well-structured format to ensure clarity and comprehensibility. Here are key elements to include:

Title and Abstract

• Title:  Craft a concise and informative title that reflects the essence of your study. It should capture the main research question or hypothesis.
• Abstract:  Summarize your research in a structured abstract, including the purpose, methods, results, and conclusions. Ensure it provides a clear overview of your study.

Introduction

• Background and Rationale:  Provide context for your study by discussing the research gap or problem your study addresses. Explain why your research is relevant and essential.
• Research Questions or Hypotheses:  Clearly state your research questions or hypotheses and their significance.

Literature Review

• Review of Related Work:  Discuss relevant literature that supports your research. Highlight studies with similar methodologies or findings and explain how your research fits within this context.
• Participants:  Describe your study's participants, including their characteristics and how you recruited them.
• Quasi-Experimental Design:  Explain your chosen design in detail, including the independent and dependent variables, procedures, and any control measures taken.
• Data Collection:  Detail the data collection methods , instruments used, and any pre-test or post-test measures.
• Data Analysis:  Describe the statistical techniques employed, including any control for confounding variables.
• Presentation of Findings:  Present your results clearly, using tables, graphs, and descriptive statistics where appropriate. Include p-values and effect sizes, if applicable.
• Interpretation of Results:  Discuss the implications of your findings and how they relate to your research questions or hypotheses.
• Interpretation and Implications:  Analyze your results in the context of existing literature and theories. Discuss the practical implications of your findings.
• Limitations:  Address the limitations of your study, including potential biases or threats to internal validity.
• Future Research:  Suggest areas for future research and how your study contributes to the field.

Ethical Considerations in Reporting

Ethical reporting is paramount in Quasi-Experimental research. Ensure that you adhere to ethical standards, including:

• Informed Consent:  Clearly state that informed consent was obtained from all participants, and describe the informed consent process.
• Protection of Participants:  Explain how you protected the rights and well-being of your participants throughout the study.
• Confidentiality:  Detail how you maintained privacy and anonymity, especially when presenting individual data.
• Disclosure of Conflicts of Interest:  Declare any potential conflicts of interest that could influence the interpretation of your findings.

Common Pitfalls to Avoid

When reporting your Quasi-Experimental research, watch out for common pitfalls that can diminish the quality and impact of your work:

• Overgeneralization:  Be cautious not to overgeneralize your findings. Clearly state the limits of your study and the populations to which your results can be applied.
• Misinterpretation of Causality:  Clearly articulate the limitations in inferring causality in Quasi-Experimental research. Avoid making strong causal claims unless supported by solid evidence.
• Ignoring Ethical Concerns:  Ethical considerations are paramount. Failing to report on informed consent, ethical oversight, and participant protection can undermine the credibility of your study.

Guidelines for Transparent Reporting

To enhance the transparency and reproducibility of your Quasi-Experimental research, consider adhering to established reporting guidelines, such as:

• CONSORT Statement:  If your study involves interventions or treatments, follow the CONSORT guidelines for transparent reporting of randomized controlled trials.
• STROBE Statement:  For observational studies, the STROBE statement provides guidance on reporting essential elements.
• PRISMA Statement:  If your research involves systematic reviews or meta-analyses, adhere to the PRISMA guidelines.
• Transparent Reporting of Evaluations with Non-Randomized Designs (TREND):  TREND guidelines offer specific recommendations for transparently reporting non-randomized designs, including Quasi-Experimental research.

By following these reporting guidelines and maintaining the highest ethical standards, you can contribute to the advancement of knowledge in your field and ensure the credibility and impact of your Quasi-Experimental research findings.

Quasi-Experimental Design Challenges

Conducting a Quasi-Experimental study can be fraught with challenges that may impact the validity and reliability of your findings. We'll take a look at some common challenges and provide strategies on how you can address them effectively.

Selection Bias

Challenge:  Selection bias occurs when non-randomized groups differ systematically in ways that affect the study's outcome. This bias can undermine the validity of your research, as it implies that the groups are not equivalent at the outset of the study.

Addressing Selection Bias:

• Matching:  Employ matching techniques to create comparable treatment and control groups. Match participants based on relevant characteristics, such as age, gender, or prior performance, to balance the groups.
• Statistical Controls:  Use statistical controls to account for differences between groups. Include covariates in your analysis to adjust for potential biases.
• Sensitivity Analysis:  Conduct sensitivity analyses to assess how vulnerable your results are to selection bias. Explore different scenarios to understand the impact of potential bias on your conclusions.

History Effects

Challenge:  History effects refer to external events or changes over time that influence the study's results. These external factors can confound your research by introducing variables you did not account for.

Addressing History Effects:

• Collect Historical Data:  Gather extensive historical data to understand trends and patterns that might affect your study. By having a comprehensive historical context, you can better identify and account for historical effects.
• Control Groups:  Include control groups whenever possible. By comparing the treatment group's results to those of a control group, you can account for external influences that affect both groups equally.
• Time Series Analysis:  If applicable, use time series analysis to detect and account for temporal trends. This method helps differentiate between the effects of the independent variable and external events.

Maturation Effects

Challenge:  Maturation effects occur when participants naturally change or develop throughout the study, independent of the intervention. These changes can confound your results, making it challenging to attribute observed effects solely to the independent variable.

Addressing Maturation Effects:

• Randomization:  If possible, use randomization to distribute maturation effects evenly across treatment and control groups. Random assignment minimizes the impact of maturation as a confounding variable.
• Matched Pairs:  If randomization is not feasible, employ matched pairs or statistical controls to ensure that both groups experience similar maturation effects.
• Shorter Time Frames:  Limit the duration of your study to reduce the likelihood of significant maturation effects. Shorter studies are less susceptible to long-term maturation.

Regression to the Mean

Challenge:  Regression to the mean is the tendency for extreme scores on a variable to move closer to the mean upon retesting. This can create the illusion of an intervention's effectiveness when, in reality, it's a natural statistical phenomenon.

Addressing Regression to the Mean:

• Use Control Groups:  Include control groups in your study to provide a baseline for comparison. This helps differentiate genuine intervention effects from regression to the mean.
• Multiple Data Points:  Collect numerous data points to identify patterns and trends. If extreme scores regress to the mean in subsequent measurements, it may be indicative of regression to the mean rather than a true intervention effect.
• Statistical Analysis:  Employ statistical techniques that account for regression to the mean when analyzing your data. Techniques like analysis of covariance (ANCOVA) can help control for baseline differences.

Attrition and Mortality

Challenge:  Attrition refers to the loss of participants over the course of your study, while mortality is the permanent loss of participants. High attrition rates can introduce biases and affect the representativeness of your sample.

Addressing Attrition and Mortality:

• Careful Participant Selection:  Select participants who are likely to remain engaged throughout the study. Consider factors that may lead to attrition, such as participant motivation and commitment.
• Incentives:  Provide incentives or compensation to participants to encourage their continued participation.
• Follow-Up Strategies:  Implement effective follow-up strategies to reduce attrition. Regular communication and reminders can help keep participants engaged.
• Sensitivity Analysis:  Conduct sensitivity analyses to assess the impact of attrition and mortality on your results. Compare the characteristics of participants who dropped out with those who completed the study.

Testing Effects

Challenge:  Testing effects occur when the mere act of testing or assessing participants affects their subsequent performance. This phenomenon can lead to changes in the dependent variable that are unrelated to the independent variable.

Addressing Testing Effects:

• Counterbalance Testing:  If possible, counterbalance the order of tests or assessments between treatment and control groups. This helps distribute the testing effects evenly across groups.
• Control Groups:  Include control groups subjected to the same testing or assessment procedures as the treatment group. By comparing the two groups, you can determine whether testing effects have influenced the results.
• Minimize Testing Frequency:  Limit the frequency of testing or assessments to reduce the likelihood of testing effects. Conducting fewer assessments can mitigate the impact of repeated testing on participants.

By proactively addressing these common challenges, you can enhance the validity and reliability of your Quasi-Experimental study, making your findings more robust and trustworthy.

Quasi-experimental design is a powerful tool that helps researchers investigate cause-and-effect relationships in real-world situations where strict control is not always possible. By understanding the key concepts, types of designs, and how to address challenges, you can conduct robust research and contribute valuable insights to your field. Remember, quasi-experimental design bridges the gap between controlled experiments and purely observational studies, making it an essential approach in various fields, from business and market research to public policy and beyond. So, whether you're a researcher, student, or decision-maker, the knowledge of quasi-experimental design empowers you to make informed choices and drive positive changes in the world.

How to Supercharge Quasi-Experimental Design with Real-Time Insights?

Introducing Appinio , the real-time market research platform that transforms the world of quasi-experimental design. Imagine having the power to conduct your own market research in minutes, obtaining actionable insights that fuel your data-driven decisions. Appinio takes care of the research and tech complexities, freeing you to focus on what truly matters for your business.

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• Cite this Page Eldredge J. Evidence Based Practice: A Decision-Making Guide for Health Information Professionals [Internet]. Albuquerque (NM): University of New Mexico Health Sciences Library and Informatics Center; 2024. Table 5. [Quasi-Experiments].
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Frequently asked questions

What is a quasi-experiment.

A quasi-experiment is a type of research design that attempts to establish a cause-and-effect relationship. The main difference between this and a true experiment is that the groups are not randomly assigned.

Frequently asked questions: Methodology

Quantitative observations involve measuring or counting something and expressing the result in numerical form, while qualitative observations involve describing something in non-numerical terms, such as its appearance, texture, or color.

To make quantitative observations , you need to use instruments that are capable of measuring the quantity you want to observe. For example, you might use a ruler to measure the length of an object or a thermometer to measure its temperature.

Scope of research is determined at the beginning of your research process , prior to the data collection stage. Sometimes called “scope of study,” your scope delineates what will and will not be covered in your project. It helps you focus your work and your time, ensuring that you’ll be able to achieve your goals and outcomes.

Defining a scope can be very useful in any research project, from a research proposal to a thesis or dissertation . A scope is needed for all types of research: quantitative , qualitative , and mixed methods .

To define your scope of research, consider the following:

• Budget constraints or any specifics of grant funding
• Your proposed timeline and duration
• Specifics about your population of study, your proposed sample size , and the research methodology you’ll pursue
• Any inclusion and exclusion criteria
• Any anticipated control , extraneous , or confounding variables that could bias your research if not accounted for properly.

Inclusion and exclusion criteria are predominantly used in non-probability sampling . In purposive sampling and snowball sampling , restrictions apply as to who can be included in the sample .

Inclusion and exclusion criteria are typically presented and discussed in the methodology section of your thesis or dissertation .

The purpose of theory-testing mode is to find evidence in order to disprove, refine, or support a theory. As such, generalisability is not the aim of theory-testing mode.

Due to this, the priority of researchers in theory-testing mode is to eliminate alternative causes for relationships between variables . In other words, they prioritise internal validity over external validity , including ecological validity .

Convergent validity shows how much a measure of one construct aligns with other measures of the same or related constructs .

On the other hand, concurrent validity is about how a measure matches up to some known criterion or gold standard, which can be another measure.

Although both types of validity are established by calculating the association or correlation between a test score and another variable , they represent distinct validation methods.

Validity tells you how accurately a method measures what it was designed to measure. There are 4 main types of validity :

• Construct validity : Does the test measure the construct it was designed to measure?
• Face validity : Does the test appear to be suitable for its objectives ?
• Content validity : Does the test cover all relevant parts of the construct it aims to measure.
• Criterion validity : Do the results accurately measure the concrete outcome they are designed to measure?

Criterion validity evaluates how well a test measures the outcome it was designed to measure. An outcome can be, for example, the onset of a disease.

Criterion validity consists of two subtypes depending on the time at which the two measures (the criterion and your test) are obtained:

• Concurrent validity is a validation strategy where the the scores of a test and the criterion are obtained at the same time
• Predictive validity is a validation strategy where the criterion variables are measured after the scores of the test

Attrition refers to participants leaving a study. It always happens to some extent – for example, in randomised control trials for medical research.

Differential attrition occurs when attrition or dropout rates differ systematically between the intervention and the control group . As a result, the characteristics of the participants who drop out differ from the characteristics of those who stay in the study. Because of this, study results may be biased .

Criterion validity and construct validity are both types of measurement validity . In other words, they both show you how accurately a method measures something.

While construct validity is the degree to which a test or other measurement method measures what it claims to measure, criterion validity is the degree to which a test can predictively (in the future) or concurrently (in the present) measure something.

Construct validity is often considered the overarching type of measurement validity . You need to have face validity , content validity , and criterion validity in order to achieve construct validity.

Convergent validity and discriminant validity are both subtypes of construct validity . Together, they help you evaluate whether a test measures the concept it was designed to measure.

• Convergent validity indicates whether a test that is designed to measure a particular construct correlates with other tests that assess the same or similar construct.
• Discriminant validity indicates whether two tests that should not be highly related to each other are indeed not related. This type of validity is also called divergent validity .

You need to assess both in order to demonstrate construct validity. Neither one alone is sufficient for establishing construct validity.

Face validity and content validity are similar in that they both evaluate how suitable the content of a test is. The difference is that face validity is subjective, and assesses content at surface level.

When a test has strong face validity, anyone would agree that the test’s questions appear to measure what they are intended to measure.

For example, looking at a 4th grade math test consisting of problems in which students have to add and multiply, most people would agree that it has strong face validity (i.e., it looks like a math test).

On the other hand, content validity evaluates how well a test represents all the aspects of a topic. Assessing content validity is more systematic and relies on expert evaluation. of each question, analysing whether each one covers the aspects that the test was designed to cover.

A 4th grade math test would have high content validity if it covered all the skills taught in that grade. Experts(in this case, math teachers), would have to evaluate the content validity by comparing the test to the learning objectives.

Content validity shows you how accurately a test or other measurement method taps  into the various aspects of the specific construct you are researching.

In other words, it helps you answer the question: “does the test measure all aspects of the construct I want to measure?” If it does, then the test has high content validity.

The higher the content validity, the more accurate the measurement of the construct.

If the test fails to include parts of the construct, or irrelevant parts are included, the validity of the instrument is threatened, which brings your results into question.

Construct validity refers to how well a test measures the concept (or construct) it was designed to measure. Assessing construct validity is especially important when you’re researching concepts that can’t be quantified and/or are intangible, like introversion. To ensure construct validity your test should be based on known indicators of introversion ( operationalisation ).

On the other hand, content validity assesses how well the test represents all aspects of the construct. If some aspects are missing or irrelevant parts are included, the test has low content validity.

• Discriminant validity indicates whether two tests that should not be highly related to each other are indeed not related

Construct validity has convergent and discriminant subtypes. They assist determine if a test measures the intended notion.

The reproducibility and replicability of a study can be ensured by writing a transparent, detailed method section and using clear, unambiguous language.

Reproducibility and replicability are related terms.

• A successful reproduction shows that the data analyses were conducted in a fair and honest manner.
• A successful replication shows that the reliability of the results is high.
• Reproducing research entails reanalysing the existing data in the same manner.
• Replicating (or repeating ) the research entails reconducting the entire analysis, including the collection of new data . 

Snowball sampling is a non-probability sampling method . Unlike probability sampling (which involves some form of random selection ), the initial individuals selected to be studied are the ones who recruit new participants.

Because not every member of the target population has an equal chance of being recruited into the sample, selection in snowball sampling is non-random.

Snowball sampling is a non-probability sampling method , where there is not an equal chance for every member of the population to be included in the sample .

This means that you cannot use inferential statistics and make generalisations – often the goal of quantitative research . As such, a snowball sample is not representative of the target population, and is usually a better fit for qualitative research .

Snowball sampling relies on the use of referrals. Here, the researcher recruits one or more initial participants, who then recruit the next ones. 

Participants share similar characteristics and/or know each other. Because of this, not every member of the population has an equal chance of being included in the sample, giving rise to sampling bias .

Snowball sampling is best used in the following cases:

• If there is no sampling frame available (e.g., people with a rare disease)
• If the population of interest is hard to access or locate (e.g., people experiencing homelessness)
• If the research focuses on a sensitive topic (e.g., extra-marital affairs)

Stratified sampling and quota sampling both involve dividing the population into subgroups and selecting units from each subgroup. The purpose in both cases is to select a representative sample and/or to allow comparisons between subgroups.

The main difference is that in stratified sampling, you draw a random sample from each subgroup ( probability sampling ). In quota sampling you select a predetermined number or proportion of units, in a non-random manner ( non-probability sampling ).

Random sampling or probability sampling is based on random selection. This means that each unit has an equal chance (i.e., equal probability) of being included in the sample.

On the other hand, convenience sampling involves stopping people at random, which means that not everyone has an equal chance of being selected depending on the place, time, or day you are collecting your data.

Convenience sampling and quota sampling are both non-probability sampling methods. They both use non-random criteria like availability, geographical proximity, or expert knowledge to recruit study participants.

However, in convenience sampling, you continue to sample units or cases until you reach the required sample size.

In quota sampling, you first need to divide your population of interest into subgroups (strata) and estimate their proportions (quota) in the population. Then you can start your data collection , using convenience sampling to recruit participants, until the proportions in each subgroup coincide with the estimated proportions in the population.

A sampling frame is a list of every member in the entire population . It is important that the sampling frame is as complete as possible, so that your sample accurately reflects your population.

Stratified and cluster sampling may look similar, but bear in mind that groups created in cluster sampling are heterogeneous , so the individual characteristics in the cluster vary. In contrast, groups created in stratified sampling are homogeneous , as units share characteristics.

Relatedly, in cluster sampling you randomly select entire groups and include all units of each group in your sample. However, in stratified sampling, you select some units of all groups and include them in your sample. In this way, both methods can ensure that your sample is representative of the target population .

When your population is large in size, geographically dispersed, or difficult to contact, it’s necessary to use a sampling method .

This allows you to gather information from a smaller part of the population, i.e. the sample, and make accurate statements by using statistical analysis. A few sampling methods include simple random sampling , convenience sampling , and snowball sampling .

The two main types of social desirability bias are:

• Self-deceptive enhancement (self-deception): The tendency to see oneself in a favorable light without realizing it.
• Impression managemen t (other-deception): The tendency to inflate one’s abilities or achievement in order to make a good impression on other people.

Response bias refers to conditions or factors that take place during the process of responding to surveys, affecting the responses. One type of response bias is social desirability bias .

Demand characteristics are aspects of experiments that may give away the research objective to participants. Social desirability bias occurs when participants automatically try to respond in ways that make them seem likeable in a study, even if it means misrepresenting how they truly feel.

Participants may use demand characteristics to infer social norms or experimenter expectancies and act in socially desirable ways, so you should try to control for demand characteristics wherever possible.

A systematic review is secondary research because it uses existing research. You don’t collect new data yourself.

Ethical considerations in research are a set of principles that guide your research designs and practices. These principles include voluntary participation, informed consent, anonymity, confidentiality, potential for harm, and results communication.

Scientists and researchers must always adhere to a certain code of conduct when collecting data from others .

These considerations protect the rights of research participants, enhance research validity , and maintain scientific integrity.

Research ethics matter for scientific integrity, human rights and dignity, and collaboration between science and society. These principles make sure that participation in studies is voluntary, informed, and safe.

Research misconduct means making up or falsifying data, manipulating data analyses, or misrepresenting results in research reports. It’s a form of academic fraud.

These actions are committed intentionally and can have serious consequences; research misconduct is not a simple mistake or a point of disagreement but a serious ethical failure.

Anonymity means you don’t know who the participants are, while confidentiality means you know who they are but remove identifying information from your research report. Both are important ethical considerations .

You can only guarantee anonymity by not collecting any personally identifying information – for example, names, phone numbers, email addresses, IP addresses, physical characteristics, photos, or videos.

You can keep data confidential by using aggregate information in your research report, so that you only refer to groups of participants rather than individuals.

Peer review is a process of evaluating submissions to an academic journal. Utilising rigorous criteria, a panel of reviewers in the same subject area decide whether to accept each submission for publication.

For this reason, academic journals are often considered among the most credible sources you can use in a research project – provided that the journal itself is trustworthy and well regarded.

In general, the peer review process follows the following steps:

• First, the author submits the manuscript to the editor.
• Reject the manuscript and send it back to author, or
• Send it onward to the selected peer reviewer(s)
• Next, the peer review process occurs. The reviewer provides feedback, addressing any major or minor issues with the manuscript, and gives their advice regarding what edits should be made.
• Lastly, the edited manuscript is sent back to the author. They input the edits, and resubmit it to the editor for publication.

Peer review can stop obviously problematic, falsified, or otherwise untrustworthy research from being published. It also represents an excellent opportunity to get feedback from renowned experts in your field.

It acts as a first defence, helping you ensure your argument is clear and that there are no gaps, vague terms, or unanswered questions for readers who weren’t involved in the research process.

Peer-reviewed articles are considered a highly credible source due to this stringent process they go through before publication.

Many academic fields use peer review , largely to determine whether a manuscript is suitable for publication. Peer review enhances the credibility of the published manuscript.

However, peer review is also common in non-academic settings. The United Nations, the European Union, and many individual nations use peer review to evaluate grant applications. It is also widely used in medical and health-related fields as a teaching or quality-of-care measure.

Peer assessment is often used in the classroom as a pedagogical tool. Both receiving feedback and providing it are thought to enhance the learning process, helping students think critically and collaboratively.

• In a single-blind study , only the participants are blinded.
• In a double-blind study , both participants and experimenters are blinded.
• In a triple-blind study , the assignment is hidden not only from participants and experimenters, but also from the researchers analysing the data.

Blinding is important to reduce bias (e.g., observer bias , demand characteristics ) and ensure a study’s internal validity .

If participants know whether they are in a control or treatment group , they may adjust their behaviour in ways that affect the outcome that researchers are trying to measure. If the people administering the treatment are aware of group assignment, they may treat participants differently and thus directly or indirectly influence the final results.

Blinding means hiding who is assigned to the treatment group and who is assigned to the control group in an experiment .

Explanatory research is a research method used to investigate how or why something occurs when only a small amount of information is available pertaining to that topic. It can help you increase your understanding of a given topic.

Explanatory research is used to investigate how or why a phenomenon occurs. Therefore, this type of research is often one of the first stages in the research process , serving as a jumping-off point for future research.

Exploratory research is a methodology approach that explores research questions that have not previously been studied in depth. It is often used when the issue you’re studying is new, or the data collection process is challenging in some way.

Exploratory research is often used when the issue you’re studying is new or when the data collection process is challenging for some reason.

You can use exploratory research if you have a general idea or a specific question that you want to study but there is no preexisting knowledge or paradigm with which to study it.

To implement random assignment , assign a unique number to every member of your study’s sample .

Then, you can use a random number generator or a lottery method to randomly assign each number to a control or experimental group. You can also do so manually, by flipping a coin or rolling a die to randomly assign participants to groups.

Random selection, or random sampling , is a way of selecting members of a population for your study’s sample.

In contrast, random assignment is a way of sorting the sample into control and experimental groups.

Random sampling enhances the external validity or generalisability of your results, while random assignment improves the internal validity of your study.

Random assignment is used in experiments with a between-groups or independent measures design. In this research design, there’s usually a control group and one or more experimental groups. Random assignment helps ensure that the groups are comparable.

In general, you should always use random assignment in this type of experimental design when it is ethically possible and makes sense for your study topic.

Clean data are valid, accurate, complete, consistent, unique, and uniform. Dirty data include inconsistencies and errors.

Dirty data can come from any part of the research process, including poor research design , inappropriate measurement materials, or flawed data entry.

Data cleaning takes place between data collection and data analyses. But you can use some methods even before collecting data.

For clean data, you should start by designing measures that collect valid data. Data validation at the time of data entry or collection helps you minimize the amount of data cleaning you’ll need to do.

After data collection, you can use data standardisation and data transformation to clean your data. You’ll also deal with any missing values, outliers, and duplicate values.

Data cleaning involves spotting and resolving potential data inconsistencies or errors to improve your data quality. An error is any value (e.g., recorded weight) that doesn’t reflect the true value (e.g., actual weight) of something that’s being measured.

In this process, you review, analyse, detect, modify, or remove ‘dirty’ data to make your dataset ‘clean’. Data cleaning is also called data cleansing or data scrubbing.

Data cleaning is necessary for valid and appropriate analyses. Dirty data contain inconsistencies or errors , but cleaning your data helps you minimise or resolve these.

Without data cleaning, you could end up with a Type I or II error in your conclusion. These types of erroneous conclusions can be practically significant with important consequences, because they lead to misplaced investments or missed opportunities.

Observer bias occurs when a researcher’s expectations, opinions, or prejudices influence what they perceive or record in a study. It usually affects studies when observers are aware of the research aims or hypotheses. This type of research bias is also called detection bias or ascertainment bias .

The observer-expectancy effect occurs when researchers influence the results of their own study through interactions with participants.

Researchers’ own beliefs and expectations about the study results may unintentionally influence participants through demand characteristics .

You can use several tactics to minimise observer bias .

• Use masking (blinding) to hide the purpose of your study from all observers.
• Triangulate your data with different data collection methods or sources.
• Use multiple observers and ensure inter-rater reliability.
• Train your observers to make sure data is consistently recorded between them.
• Standardise your observation procedures to make sure they are structured and clear.

Naturalistic observation is a valuable tool because of its flexibility, external validity , and suitability for topics that can’t be studied in a lab setting.

The downsides of naturalistic observation include its lack of scientific control , ethical considerations , and potential for bias from observers and subjects.

Naturalistic observation is a qualitative research method where you record the behaviours of your research subjects in real-world settings. You avoid interfering or influencing anything in a naturalistic observation.

You can think of naturalistic observation as ‘people watching’ with a purpose.

Closed-ended, or restricted-choice, questions offer respondents a fixed set of choices to select from. These questions are easier to answer quickly.

Open-ended or long-form questions allow respondents to answer in their own words. Because there are no restrictions on their choices, respondents can answer in ways that researchers may not have otherwise considered.

You can organise the questions logically, with a clear progression from simple to complex, or randomly between respondents. A logical flow helps respondents process the questionnaire easier and quicker, but it may lead to bias. Randomisation can minimise the bias from order effects.

Questionnaires can be self-administered or researcher-administered.

Self-administered questionnaires can be delivered online or in paper-and-pen formats, in person or by post. All questions are standardised so that all respondents receive the same questions with identical wording.

Researcher-administered questionnaires are interviews that take place by phone, in person, or online between researchers and respondents. You can gain deeper insights by clarifying questions for respondents or asking follow-up questions.

In a controlled experiment , all extraneous variables are held constant so that they can’t influence the results. Controlled experiments require:

• A control group that receives a standard treatment, a fake treatment, or no treatment
• Random assignment of participants to ensure the groups are equivalent

Depending on your study topic, there are various other methods of controlling variables .

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.

A true experiment (aka a controlled experiment) always includes at least one control group that doesn’t receive the experimental treatment.

However, some experiments use a within-subjects design to test treatments without a control group. In these designs, you usually compare one group’s outcomes before and after a treatment (instead of comparing outcomes between different groups).

For strong internal validity , it’s usually best to include a control group if possible. Without a control group, it’s harder to be certain that the outcome was caused by the experimental treatment and not by other variables.

A questionnaire is a data collection tool or instrument, while a survey is an overarching research method that involves collecting and analysing data from people using questionnaires.

A Likert scale is a rating scale that quantitatively assesses opinions, attitudes, or behaviours. It is made up of four or more questions that measure a single attitude or trait when response scores are combined.

To use a Likert scale in a survey , you present participants with Likert-type questions or statements, and a continuum of items, usually with five or seven possible responses, to capture their degree of agreement.

Individual Likert-type questions are generally considered ordinal data , because the items have clear rank order, but don’t have an even distribution.

Overall Likert scale scores are sometimes treated as interval data. These scores are considered to have directionality and even spacing between them.

The type of data determines what statistical tests you should use to analyse your data.

A research hypothesis is your proposed answer to your research question. The research hypothesis usually includes an explanation (‘ x affects y because …’).

A statistical hypothesis, on the other hand, is a mathematical statement about a population parameter. Statistical hypotheses always come in pairs: the null and alternative hypotheses. In a well-designed study , the statistical hypotheses correspond logically to the research hypothesis.

A hypothesis states your predictions about what your research will find. It is a tentative answer to your research question that has not yet been tested. For some research projects, you might have to write several hypotheses that address different aspects of your research question.

A hypothesis is not just a guess. It should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations, and statistical analysis of data).

Cross-sectional studies are less expensive and time-consuming than many other types of study. They can provide useful insights into a population’s characteristics and identify correlations for further research.

Sometimes only cross-sectional data are available for analysis; other times your research question may only require a cross-sectional study to answer it.

Cross-sectional studies cannot establish a cause-and-effect relationship or analyse behaviour over a period of time. To investigate cause and effect, you need to do a longitudinal study or an experimental study .

Longitudinal studies and cross-sectional studies are two different types of research design . In a cross-sectional study you collect data from a population at a specific point in time; in a longitudinal study you repeatedly collect data from the same sample over an extended period of time.

Longitudinal studies are better to establish the correct sequence of events, identify changes over time, and provide insight into cause-and-effect relationships, but they also tend to be more expensive and time-consuming than other types of studies.

The 1970 British Cohort Study , which has collected data on the lives of 17,000 Brits since their births in 1970, is one well-known example of a longitudinal study .

Longitudinal studies can last anywhere from weeks to decades, although they tend to be at least a year long.

A correlation reflects the strength and/or direction of the association between two or more variables.

• A positive correlation means that both variables change in the same direction.
• A negative correlation means that the variables change in opposite directions.
• A zero correlation means there’s no relationship between the variables.

A correlational research design investigates relationships between two variables (or more) without the researcher controlling or manipulating any of them. It’s a non-experimental type of quantitative research .

A correlation coefficient is a single number that describes the strength and direction of the relationship between your variables.

Different types of correlation coefficients might be appropriate for your data based on their levels of measurement and distributions . The Pearson product-moment correlation coefficient (Pearson’s r ) is commonly used to assess a linear relationship between two quantitative variables.

Controlled experiments establish causality, whereas correlational studies only show associations between variables.

• In an experimental design , you manipulate an independent variable and measure its effect on a dependent variable. Other variables are controlled so they can’t impact the results.
• In a correlational design , you measure variables without manipulating any of them. You can test whether your variables change together, but you can’t be sure that one variable caused a change in another.

In general, correlational research is high in external validity while experimental research is high in internal validity .

The third variable and directionality problems are two main reasons why correlation isn’t causation .

The third variable problem means that a confounding variable affects both variables to make them seem causally related when they are not.

The directionality problem is when two variables correlate and might actually have a causal relationship, but it’s impossible to conclude which variable causes changes in the other.

As a rule of thumb, questions related to thoughts, beliefs, and feelings work well in focus groups . Take your time formulating strong questions, paying special attention to phrasing. Be careful to avoid leading questions , which can bias your responses.

Overall, your focus group questions should be:

• Open-ended and flexible
• Impossible to answer with ‘yes’ or ‘no’ (questions that start with ‘why’ or ‘how’ are often best)
• Unambiguous, getting straight to the point while still stimulating discussion
• Unbiased and neutral

Social desirability bias is the tendency for interview participants to give responses that will be viewed favourably by the interviewer or other participants. It occurs in all types of interviews and surveys , but is most common in semi-structured interviews , unstructured interviews , and focus groups .

Social desirability bias can be mitigated by ensuring participants feel at ease and comfortable sharing their views. Make sure to pay attention to your own body language and any physical or verbal cues, such as nodding or widening your eyes.

This type of bias in research can also occur in observations if the participants know they’re being observed. They might alter their behaviour accordingly.

A focus group is a research method that brings together a small group of people to answer questions in a moderated setting. The group is chosen due to predefined demographic traits, and the questions are designed to shed light on a topic of interest. It is one of four types of interviews .

The four most common types of interviews are:

• Structured interviews : The questions are predetermined in both topic and order.
• Semi-structured interviews : A few questions are predetermined, but other questions aren’t planned.
• Unstructured interviews : None of the questions are predetermined.
• Focus group interviews : The questions are presented to a group instead of one individual.

An unstructured interview is the most flexible type of interview, but it is not always the best fit for your research topic.

Unstructured interviews are best used when:

• You are an experienced interviewer and have a very strong background in your research topic, since it is challenging to ask spontaneous, colloquial questions
• Your research question is exploratory in nature. While you may have developed hypotheses, you are open to discovering new or shifting viewpoints through the interview process.
• You are seeking descriptive data, and are ready to ask questions that will deepen and contextualise your initial thoughts and hypotheses
• Your research depends on forming connections with your participants and making them feel comfortable revealing deeper emotions, lived experiences, or thoughts

A semi-structured interview is a blend of structured and unstructured types of interviews. Semi-structured interviews are best used when:

• You have prior interview experience. Spontaneous questions are deceptively challenging, and it’s easy to accidentally ask a leading question or make a participant uncomfortable.
• Your research question is exploratory in nature. Participant answers can guide future research questions and help you develop a more robust knowledge base for future research.

The interviewer effect is a type of bias that emerges when a characteristic of an interviewer (race, age, gender identity, etc.) influences the responses given by the interviewee.

There is a risk of an interviewer effect in all types of interviews , but it can be mitigated by writing really high-quality interview questions.

A structured interview is a data collection method that relies on asking questions in a set order to collect data on a topic. They are often quantitative in nature. Structured interviews are best used when:

• You already have a very clear understanding of your topic. Perhaps significant research has already been conducted, or you have done some prior research yourself, but you already possess a baseline for designing strong structured questions.
• You are constrained in terms of time or resources and need to analyse your data quickly and efficiently
• Your research question depends on strong parity between participants, with environmental conditions held constant

More flexible interview options include semi-structured interviews , unstructured interviews , and focus groups .

When conducting research, collecting original data has significant advantages:

• You can tailor data collection to your specific research aims (e.g., understanding the needs of your consumers or user testing your website).
• You can control and standardise the process for high reliability and validity (e.g., choosing appropriate measurements and sampling methods ).

However, there are also some drawbacks: data collection can be time-consuming, labour-intensive, and expensive. In some cases, it’s more efficient to use secondary data that has already been collected by someone else, but the data might be less reliable.

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organisations.

A mediator variable explains the process through which two variables are related, while a moderator variable affects the strength and direction of that relationship.

A confounder is a third variable that affects variables of interest and makes them seem related when they are not. In contrast, a mediator is the mechanism of a relationship between two variables: it explains the process by which they are related.

If something is a mediating variable :

• It’s caused by the independent variable
• It influences the dependent variable
• When it’s taken into account, the statistical correlation between the independent and dependent variables is higher than when it isn’t considered

Including mediators and moderators in your research helps you go beyond studying a simple relationship between two variables for a fuller picture of the real world. They are important to consider when studying complex correlational or causal relationships.

Mediators are part of the causal pathway of an effect, and they tell you how or why an effect takes place. Moderators usually help you judge the external validity of your study by identifying the limitations of when the relationship between variables holds.

You can think of independent and dependent variables in terms of cause and effect: an independent variable is the variable you think is the cause , while a dependent variable is the effect .

In an experiment, you manipulate the independent variable and measure the outcome in the dependent variable. For example, in an experiment about the effect of nutrients on crop growth:

• The  independent variable  is the amount of nutrients added to the crop field.
• The  dependent variable is the biomass of the crops at harvest time.

Defining your variables, and deciding how you will manipulate and measure them, is an important part of experimental design .

Discrete and continuous variables are two types of quantitative variables :

• Discrete variables represent counts (e.g., the number of objects in a collection).
• Continuous variables represent measurable amounts (e.g., water volume or weight).

Quantitative variables are any variables where the data represent amounts (e.g. height, weight, or age).

Categorical variables are any variables where the data represent groups. This includes rankings (e.g. finishing places in a race), classifications (e.g. brands of cereal), and binary outcomes (e.g. coin flips).

You need to know what type of variables you are working with to choose the right statistical test for your data and interpret your results .

Determining cause and effect is one of the most important parts of scientific research. It’s essential to know which is the cause – the independent variable – and which is the effect – the dependent variable.

You want to find out how blood sugar levels are affected by drinking diet cola and regular cola, so you conduct an experiment .

• The type of cola – diet or regular – is the independent variable .
• The level of blood sugar that you measure is the dependent variable – it changes depending on the type of cola.

No. The value of a dependent variable depends on an independent variable, so a variable cannot be both independent and dependent at the same time. It must be either the cause or the effect, not both.

Yes, but including more than one of either type requires multiple research questions .

For example, if you are interested in the effect of a diet on health, you can use multiple measures of health: blood sugar, blood pressure, weight, pulse, and many more. Each of these is its own dependent variable with its own research question.

You could also choose to look at the effect of exercise levels as well as diet, or even the additional effect of the two combined. Each of these is a separate independent variable .

To ensure the internal validity of an experiment , you should only change one independent variable at a time.

To ensure the internal validity of your research, you must consider the impact of confounding variables. If you fail to account for them, you might over- or underestimate the causal relationship between your independent and dependent variables , or even find a causal relationship where none exists.

A confounding variable is closely related to both the independent and dependent variables in a study. An independent variable represents the supposed cause , while the dependent variable is the supposed effect . A confounding variable is a third variable that influences both the independent and dependent variables.

Failing to account for confounding variables can cause you to wrongly estimate the relationship between your independent and dependent variables.

There are several methods you can use to decrease the impact of confounding variables on your research: restriction, matching, statistical control, and randomisation.

In restriction , you restrict your sample by only including certain subjects that have the same values of potential confounding variables.

In matching , you match each of the subjects in your treatment group with a counterpart in the comparison group. The matched subjects have the same values on any potential confounding variables, and only differ in the independent variable .

In statistical control , you include potential confounders as variables in your regression .

In randomisation , you randomly assign the treatment (or independent variable) in your study to a sufficiently large number of subjects, which allows you to control for all potential confounding variables.

In scientific research, concepts are the abstract ideas or phenomena that are being studied (e.g., educational achievement). Variables are properties or characteristics of the concept (e.g., performance at school), while indicators are ways of measuring or quantifying variables (e.g., yearly grade reports).

The process of turning abstract concepts into measurable variables and indicators is called operationalisation .

In statistics, ordinal and nominal variables are both considered categorical variables .

Even though ordinal data can sometimes be numerical, not all mathematical operations can be performed on them.

A control variable is any variable that’s held constant in a research study. It’s not a variable of interest in the study, but it’s controlled because it could influence the outcomes.

Control variables help you establish a correlational or causal relationship between variables by enhancing internal validity .

If you don’t control relevant extraneous variables , they may influence the outcomes of your study, and you may not be able to demonstrate that your results are really an effect of your independent variable .

‘Controlling for a variable’ means measuring extraneous variables and accounting for them statistically to remove their effects on other variables.

Researchers often model control variable data along with independent and dependent variable data in regression analyses and ANCOVAs . That way, you can isolate the control variable’s effects from the relationship between the variables of interest.

An extraneous variable is any variable that you’re not investigating that can potentially affect the dependent variable of your research study.

A confounding variable is a type of extraneous variable that not only affects the dependent variable, but is also related to the independent variable.

There are 4 main types of extraneous variables :

• Demand characteristics : Environmental cues that encourage participants to conform to researchers’ expectations
• Experimenter effects : Unintentional actions by researchers that influence study outcomes
• Situational variables : Eenvironmental variables that alter participants’ behaviours
• Participant variables : Any characteristic or aspect of a participant’s background that could affect study results

The difference between explanatory and response variables is simple:

• An explanatory variable is the expected cause, and it explains the results.
• A response variable is the expected effect, and it responds to other variables.

The term ‘ explanatory variable ‘ is sometimes preferred over ‘ independent variable ‘ because, in real-world contexts, independent variables are often influenced by other variables. This means they aren’t totally independent.

Multiple independent variables may also be correlated with each other, so ‘explanatory variables’ is a more appropriate term.

On graphs, the explanatory variable is conventionally placed on the x -axis, while the response variable is placed on the y -axis.

• If you have quantitative variables , use a scatterplot or a line graph.
• If your response variable is categorical, use a scatterplot or a line graph.
• If your explanatory variable is categorical, use a bar graph.

A correlation is usually tested for two variables at a time, but you can test correlations between three or more variables.

An independent variable is the variable you manipulate, control, or vary in an experimental study to explore its effects. It’s called ‘independent’ because it’s not influenced by any other variables in the study.

Independent variables are also called:

• Explanatory variables (they explain an event or outcome)
• Predictor variables (they can be used to predict the value of a dependent variable)
• Right-hand-side variables (they appear on the right-hand side of a regression equation)

A dependent variable is what changes as a result of the independent variable manipulation in experiments . It’s what you’re interested in measuring, and it ‘depends’ on your independent variable.

In statistics, dependent variables are also called:

• Response variables (they respond to a change in another variable)
• Outcome variables (they represent the outcome you want to measure)
• Left-hand-side variables (they appear on the left-hand side of a regression equation)

Deductive reasoning is commonly used in scientific research, and it’s especially associated with quantitative research .

In research, you might have come across something called the hypothetico-deductive method . It’s the scientific method of testing hypotheses to check whether your predictions are substantiated by real-world data.

Deductive reasoning is a logical approach where you progress from general ideas to specific conclusions. It’s often contrasted with inductive reasoning , where you start with specific observations and form general conclusions.

Deductive reasoning is also called deductive logic.

Inductive reasoning is a method of drawing conclusions by going from the specific to the general. It’s usually contrasted with deductive reasoning, where you proceed from general information to specific conclusions.

Inductive reasoning is also called inductive logic or bottom-up reasoning.

In inductive research , you start by making observations or gathering data. Then, you take a broad scan of your data and search for patterns. Finally, you make general conclusions that you might incorporate into theories.

Inductive reasoning is a bottom-up approach, while deductive reasoning is top-down.

Inductive reasoning takes you from the specific to the general, while in deductive reasoning, you make inferences by going from general premises to specific conclusions.

There are many different types of inductive reasoning that people use formally or informally.

Here are a few common types:

• Inductive generalisation : You use observations about a sample to come to a conclusion about the population it came from.
• Statistical generalisation: You use specific numbers about samples to make statements about populations.
• Causal reasoning: You make cause-and-effect links between different things.
• Sign reasoning: You make a conclusion about a correlational relationship between different things.
• Analogical reasoning: You make a conclusion about something based on its similarities to something else.

It’s often best to ask a variety of people to review your measurements. You can ask experts, such as other researchers, or laypeople, such as potential participants, to judge the face validity of tests.

While experts have a deep understanding of research methods , the people you’re studying can provide you with valuable insights you may have missed otherwise.

Face validity is important because it’s a simple first step to measuring the overall validity of a test or technique. It’s a relatively intuitive, quick, and easy way to start checking whether a new measure seems useful at first glance.

Good face validity means that anyone who reviews your measure says that it seems to be measuring what it’s supposed to. With poor face validity, someone reviewing your measure may be left confused about what you’re measuring and why you’re using this method.

Face validity is about whether a test appears to measure what it’s supposed to measure. This type of validity is concerned with whether a measure seems relevant and appropriate for what it’s assessing only on the surface.

Statistical analyses are often applied to test validity with data from your measures. You test convergent validity and discriminant validity with correlations to see if results from your test are positively or negatively related to those of other established tests.

You can also use regression analyses to assess whether your measure is actually predictive of outcomes that you expect it to predict theoretically. A regression analysis that supports your expectations strengthens your claim of construct validity .

When designing or evaluating a measure, construct validity helps you ensure you’re actually measuring the construct you’re interested in. If you don’t have construct validity, you may inadvertently measure unrelated or distinct constructs and lose precision in your research.

Construct validity is often considered the overarching type of measurement validity ,  because it covers all of the other types. You need to have face validity , content validity, and criterion validity to achieve construct validity.

Construct validity is about how well a test measures the concept it was designed to evaluate. It’s one of four types of measurement validity , which includes construct validity, face validity , and criterion validity.

There are two subtypes of construct validity.

• Convergent validity : The extent to which your measure corresponds to measures of related constructs
• Discriminant validity: The extent to which your measure is unrelated or negatively related to measures of distinct constructs

Attrition bias can skew your sample so that your final sample differs significantly from your original sample. Your sample is biased because some groups from your population are underrepresented.

With a biased final sample, you may not be able to generalise your findings to the original population that you sampled from, so your external validity is compromised.

There are seven threats to external validity : selection bias , history, experimenter effect, Hawthorne effect , testing effect, aptitude-treatment, and situation effect.

The two types of external validity are population validity (whether you can generalise to other groups of people) and ecological validity (whether you can generalise to other situations and settings).

The external validity of a study is the extent to which you can generalise your findings to different groups of people, situations, and measures.

Attrition bias is a threat to internal validity . In experiments, differential rates of attrition between treatment and control groups can skew results.

This bias can affect the relationship between your independent and dependent variables . It can make variables appear to be correlated when they are not, or vice versa.

Internal validity is the extent to which you can be confident that a cause-and-effect relationship established in a study cannot be explained by other factors.

There are eight threats to internal validity : history, maturation, instrumentation, testing, selection bias , regression to the mean, social interaction, and attrition .

A sampling error is the difference between a population parameter and a sample statistic .

A statistic refers to measures about the sample , while a parameter refers to measures about the population .

Populations are used when a research question requires data from every member of the population. This is usually only feasible when the population is small and easily accessible.

Systematic sampling is a probability sampling method where researchers select members of the population at a regular interval – for example, by selecting every 15th person on a list of the population. If the population is in a random order, this can imitate the benefits of simple random sampling .

There are three key steps in systematic sampling :

• Define and list your population , ensuring that it is not ordered in a cyclical or periodic order.
• Decide on your sample size and calculate your interval, k , by dividing your population by your target sample size.
• Choose every k th member of the population as your sample.

Yes, you can create a stratified sample using multiple characteristics, but you must ensure that every participant in your study belongs to one and only one subgroup. In this case, you multiply the numbers of subgroups for each characteristic to get the total number of groups.

For example, if you were stratifying by location with three subgroups (urban, rural, or suburban) and marital status with five subgroups (single, divorced, widowed, married, or partnered), you would have 3 × 5 = 15 subgroups.

You should use stratified sampling when your sample can be divided into mutually exclusive and exhaustive subgroups that you believe will take on different mean values for the variable that you’re studying.

Using stratified sampling will allow you to obtain more precise (with lower variance ) statistical estimates of whatever you are trying to measure.

For example, say you want to investigate how income differs based on educational attainment, but you know that this relationship can vary based on race. Using stratified sampling, you can ensure you obtain a large enough sample from each racial group, allowing you to draw more precise conclusions.

In stratified sampling , researchers divide subjects into subgroups called strata based on characteristics that they share (e.g., race, gender, educational attainment).

Once divided, each subgroup is randomly sampled using another probability sampling method .

Multistage sampling can simplify data collection when you have large, geographically spread samples, and you can obtain a probability sample without a complete sampling frame.

But multistage sampling may not lead to a representative sample, and larger samples are needed for multistage samples to achieve the statistical properties of simple random samples .

In multistage sampling , you can use probability or non-probability sampling methods.

For a probability sample, you have to probability sampling at every stage. You can mix it up by using simple random sampling , systematic sampling , or stratified sampling to select units at different stages, depending on what is applicable and relevant to your study.

Cluster sampling is a probability sampling method in which you divide a population into clusters, such as districts or schools, and then randomly select some of these clusters as your sample.

The clusters should ideally each be mini-representations of the population as a whole.

There are three types of cluster sampling : single-stage, double-stage and multi-stage clustering. In all three types, you first divide the population into clusters, then randomly select clusters for use in your sample.

• In single-stage sampling , you collect data from every unit within the selected clusters.
• In double-stage sampling , you select a random sample of units from within the clusters.
• In multi-stage sampling , you repeat the procedure of randomly sampling elements from within the clusters until you have reached a manageable sample.

Cluster sampling is more time- and cost-efficient than other probability sampling methods , particularly when it comes to large samples spread across a wide geographical area.

However, it provides less statistical certainty than other methods, such as simple random sampling , because it is difficult to ensure that your clusters properly represent the population as a whole.

If properly implemented, simple random sampling is usually the best sampling method for ensuring both internal and external validity . However, it can sometimes be impractical and expensive to implement, depending on the size of the population to be studied,

If you have a list of every member of the population and the ability to reach whichever members are selected, you can use simple random sampling.

The American Community Survey  is an example of simple random sampling . In order to collect detailed data on the population of the US, the Census Bureau officials randomly select 3.5 million households per year and use a variety of methods to convince them to fill out the survey.

Simple random sampling is a type of probability sampling in which the researcher randomly selects a subset of participants from a population . Each member of the population has an equal chance of being selected. Data are then collected from as large a percentage as possible of this random subset.

Sampling bias occurs when some members of a population are systematically more likely to be selected in a sample than others.

In multistage sampling , or multistage cluster sampling, you draw a sample from a population using smaller and smaller groups at each stage.

This method is often used to collect data from a large, geographically spread group of people in national surveys, for example. You take advantage of hierarchical groupings (e.g., from county to city to neighbourhood) to create a sample that’s less expensive and time-consuming to collect data from.

In non-probability sampling , the sample is selected based on non-random criteria, and not every member of the population has a chance of being included.

Common non-probability sampling methods include convenience sampling , voluntary response sampling, purposive sampling , snowball sampling , and quota sampling .

Probability sampling means that every member of the target population has a known chance of being included in the sample.

Probability sampling methods include simple random sampling , systematic sampling , stratified sampling , and cluster sampling .

Samples are used to make inferences about populations . Samples are easier to collect data from because they are practical, cost-effective, convenient, and manageable.

While a between-subjects design has fewer threats to internal validity , it also requires more participants for high statistical power than a within-subjects design .

Advantages:

• Prevents carryover effects of learning and fatigue.
• Shorter study duration.

Disadvantages:

• Needs larger samples for high power.
• Uses more resources to recruit participants, administer sessions, cover costs, etc.
• Individual differences may be an alternative explanation for results.

In a factorial design, multiple independent variables are tested.

If you test two variables, each level of one independent variable is combined with each level of the other independent variable to create different conditions.

Yes. Between-subjects and within-subjects designs can be combined in a single study when you have two or more independent variables (a factorial design). In a mixed factorial design, one variable is altered between subjects and another is altered within subjects.

Within-subjects designs have many potential threats to internal validity , but they are also very statistically powerful .

• Only requires small samples
• Statistically powerful
• Removes the effects of individual differences on the outcomes
• Internal validity threats reduce the likelihood of establishing a direct relationship between variables
• Time-related effects, such as growth, can influence the outcomes
• Carryover effects mean that the specific order of different treatments affect the outcomes

Quasi-experimental design is most useful in situations where it would be unethical or impractical to run a true experiment .

Quasi-experiments have lower internal validity than true experiments, but they often have higher external validity  as they can use real-world interventions instead of artificial laboratory settings.

In experimental research, random assignment is a way of placing participants from your sample into different groups using randomisation. With this method, every member of the sample has a known or equal chance of being placed in a control group or an experimental group.

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.

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.

Triangulation can help:

• Reduce bias that comes from using a single method, theory, or investigator
• Enhance validity by approaching the same topic with different tools
• Establish credibility by giving you a complete picture of the research problem

But triangulation can also pose problems:

• It’s time-consuming and labour-intensive, often involving an interdisciplinary team.
• Your results may be inconsistent or even contradictory.

There are four main types of triangulation :

• Data triangulation : Using data from different times, spaces, and people
• Investigator triangulation : Involving multiple researchers in collecting or analysing data
• Theory triangulation : Using varying theoretical perspectives in your research
• Methodological triangulation : Using different methodologies to approach the same topic

Experimental designs are a set of procedures that you plan in order to examine the relationship between variables that interest you.

To design a successful experiment, first identify:

• A testable hypothesis
• One or more independent variables that you will manipulate
• One or more dependent variables that you will measure

When designing the experiment, first decide:

• How your variable(s) will be manipulated
• How you will control for any potential confounding or lurking variables
• How many subjects you will include
• How you will assign treatments to your subjects

Exploratory research explores the main aspects of a new or barely researched question.

Explanatory research explains the causes and effects of an already widely researched question.

The key difference between observational studies and experiments is that, done correctly, an observational study will never influence the responses or behaviours of participants. Experimental designs will have a treatment condition applied to at least a portion of participants.

An observational study could be a good fit for your research if your research question is based on things you observe. If you have ethical, logistical, or practical concerns that make an experimental design challenging, consider an observational study. Remember that in an observational study, it is critical that there be no interference or manipulation of the research subjects. Since it’s not an experiment, there are no control or treatment groups either.

These are four of the most common mixed methods designs :

• Convergent parallel: Quantitative and qualitative data are collected at the same time and analysed separately. After both analyses are complete, compare your results to draw overall conclusions. 
• Embedded: Quantitative and qualitative data are collected at the same time, but within a larger quantitative or qualitative design. One type of data is secondary to the other.
• Explanatory sequential: Quantitative data is collected and analysed first, followed by qualitative data. You can use this design if you think your qualitative data will explain and contextualise your quantitative findings.
• Exploratory sequential: Qualitative data is collected and analysed first, followed by quantitative data. You can use this design if you think the quantitative data will confirm or validate your qualitative findings.

Triangulation in research means using multiple datasets, methods, theories and/or investigators to address a research question. It’s a research strategy that can help you enhance the validity and credibility of your findings.

Triangulation is mainly used in qualitative research , but it’s also commonly applied in quantitative research . Mixed methods research always uses triangulation.

Operationalisation means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioural avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalise the variables that you want to measure.

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses , by calculating how likely it is that a pattern or relationship between variables could have arisen by chance.

There are five common approaches to qualitative research :

• Grounded theory involves collecting data in order to develop new theories.
• Ethnography involves immersing yourself in a group or organisation to understand its culture.
• Narrative research involves interpreting stories to understand how people make sense of their experiences and perceptions.
• Phenomenological research involves investigating phenomena through people’s lived experiences.
• Action research links theory and practice in several cycles to drive innovative changes.

There are various approaches to qualitative data analysis , but they all share five steps in common:

• Prepare and organise your data.
• Review and explore your data.
• Develop a data coding system.
• Assign codes to the data.
• Identify recurring themes.

The specifics of each step depend on the focus of the analysis. Some common approaches include textual analysis , thematic analysis , and discourse analysis .

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

Methodology refers to the overarching strategy and rationale of your research project . It involves studying the methods used in your field and the theories or principles behind them, in order to develop an approach that matches your objectives.

Methods are the specific tools and procedures you use to collect and analyse data (e.g. experiments, surveys , and statistical tests ).

In shorter scientific papers, where the aim is to report the findings of a specific study, you might simply describe what you did in a methods section .

In a longer or more complex research project, such as a thesis or dissertation , you will probably include a methodology section , where you explain your approach to answering the research questions and cite relevant sources to support your choice of methods.

The research methods you use depend on the type of data you need to answer your research question .

• If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts, and meanings, use qualitative methods .
• If you want to analyse a large amount of readily available data, use secondary data. If you want data specific to your purposes with control over how they are generated, collect primary data.
• If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

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Explaining Quasi-Experimental Design And Its Various Methods

• September 27, 2021

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 As you strive to uncover causal (cause-and-effect) relationships between variables, you may often encounter ethical or practical constraints while conducting controlled experiments. 

Quasi-experimental design steps in as a powerful alternative that helps you overcome these challenges and offer valuable insights. 

In this blog, we’ll look into its characteristics, examples, types, and how it differs from true-experimental research design. The purpose of this blog is to understand how this research methodology bridges the gap between a fully controlled experiment and a purely observational study.

What Is Quasi-Experimental Design?

A quasi-experimental design is pretty much different from an experimental design, except for the fact that they both manifest the cause-effect relationship between the independent and dependent variables . 

So, how is quasi-experimental design different? 

Well, unlike experimental design, quasi-experiments do not include random assignments of participants meaning, the participants are placed in the experimental groups based on some of the other criteria. Let us take a deeper look at how quasi-experimental design works.

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Experimental design has three characteristics:, 1. manipulation.

Manipulation simply means evaluating the effect of the independent variable on the dependent variable. 

Example: A chocolate and a crying child.

• Independent variable:  Type of chocolate. 
• Dependent variable: The child is crying for chocolate.

So manipulation means the effect of an independent variable, that is, chocolate, on the dependent variable, that is, the crying child. In short, you are using an outside source on the dependent variable. This proves that after getting the chocolate (independent variable), the child stops crying (dependent variable).

2. Randomization

Randomization means sudden selection without any plan. Example: A lottery system. The lottery numbers are announced at random so everyone who buys a lottery has an equal chance. Hence, it means you select a sample without any plan and everyone has an equal chance of getting into any one of the experimental groups.

This means using a control group in the experiment. In this group, researchers keep the independent variable constant. This control group is then compared to a treatment group, where the researchers have changed the independent variable. Well, for obvious reasons, researchers are more interested in the treatment group as it has a scope of change in the dependent variable. 

Example: You want to find out whether the workers work more efficiently if there is a pay raise. 

Here, you will put certain workers in the treatment group and some in the control group.

• Treatment group: You pay more to the workers
• Control group: You don’t pay any extra to the workers, and things remain the same. 

By comparing these two groups, you understand that the workers who got paid more worked more efficiently than the workers who didn’t. 

As for the quasi-experimental design, the manipulation characteristic of the true experiment remains the same. However randomization or control characteristics are present in contrast to each other or none at all. 

Hence, these experiments are conducted where random selection is difficult or even impossible. The quasi-experiment does not include random assignment, as the independent variable is manipulated before the measurement of the dependent variable.

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What are the types of quasi-experimental design?

Amongst all the various types of quasi-experimental design, let us first get to know two main types of quasi-experimental design:

• Non-equivalent group design (NEGD)
• Regression discontinuity design

1. Non-Equivalent Group Design (NEGD)

You can picture non-equivalent group designs as a mixture of both true experimental design as well as quasi-experimental design. The reason is, that it uses both their qualities. Like a true experiment, NEGD uses the pre-existing groups that we feel are similar, namely treatment and control groups. However it lacks the randomization characteristic of a quasi-experiment. 

While grouping, researchers see to it that they are not influenced by any third variables or confounding variables. Hence, the groups are as similar as possible. For example, when talking about political study, we might select groups that are more similar to each other. 

Let us understand it with an example:

Take the previous example where you studied whether the workers work more efficiently if there is a pay rise. 

You give a pre-test to the workers in one company while their pay is normal. Then you put them under the treatment group where they work and their pay is being increased. After the experiment, you take their post-test about their experience and attitude towards their work. 

Later, you give the same pre-test to the workers from a similar company and put them in a control group where their pay is not raised, and then conduct a post-test. 

Hence, the Non-equivalent design has a name to remind us that the groups are not equivalent and are not assigned on a random practice. 

2. Regression discontinuity design or RDD

Regression discontinuity design, or RDD, is a quasi-experimental design technique that computes the influence of a treatment or intervention. It does so by using a mechanism that assigns the treatment based on eligibility, known as a “cut-off”.

So the participants above the cut-off get to be in a treatment group and those below the cut-off doesn’t. Although the difference between these two groups is negligible. 

Let’s take a look at an example:

A school wants to grant a $50 scholarship to students, depending on an independent test taken to measure their intellect and household. 

Those who pass the test will get a scholarship. However, the students who are just below the cut-off and those just above it can be considered similar. We can say the differences in their scores occurred randomly. Hence you can keep on studying both groups to get a long-term outcome.

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What are the advantages of a quasi-experimental design?

The quasi-experiment design, also known as external validity, can be perfect for determining what is best for the population. Let’s look at some advantages of this research methodology type. 

• It gives the researchers power over the variables by being able to control them.
• The quasi-experiment method can be combined with other experimental methods too.
• It provides transferability to a greater extent.
• It is an intuitive process that is well-shaped by the researchers. 
• Involves real-world problems and solutions and not any artificial ones. 
• Offers better control over the third variable, known as the confounding variable, which influences the cause and effect. 

What are the disadvantages of a quasi-experimental design?

As a research design, it is bound to have some limitations, let’s look at some of the disadvantages you should consider when selecting the design for your research. 

• It serves less internal validity than true experiments.
• Due to no randomization, you cannot tell for sure that the confounding or third variable is eradicated. 
• It has scope for human errors.
• It can allow the researcher’s personal bias to get involved. 
• Human responses are difficult to measure; hence, there is a chance that the results will be produced artificially.
• Using old or backdated data can be incorrect and inadequate for the study.

Other Quasi-Experimental Designs

Apart from the above-mentioned types, there are other equally important quasi-experimental designs that have different applications depending on their characteristics and their respective design notations . 

Let’s take a look at all of them in detail:

1. The proxy Pre-Test Design

The proxy pre-test design works the same as a typical pre-test and post-test design. Except, the pre-test here is conducted AFTER the treatment is given. Got confused? How is it pre-test if it is conducted after? Well, the keyword here is “proxy”. These proxy variables tell where the groups would have been in the pre-test. 

You ask the group after their program about how they’d have answered the same questions before their treatment. Although, this technique is not very reliable as we cannot expect the participants to remember how they felt a long time ago, and we surely cannot tell if they are faking their answers. 

As this design is highly not recommended, you can use this under some unavoidable circumstances like the treatment has already begun and you couldn’t take the pre-test. 

In such cases, this approach will help rather than depending totally on the post-test.

You want to study the workers’ performance after the pay rise. But you were called to do the pre-test after the program had started. In that case, you will have to take the post-test and study a proxy variable, such as productivity from the time before the program and after the program

2. The Separate Pre-Post Samples Design

This technique also works on the pre-test and post-test designs. The difference is that the participants you used for the pre-test won’t be the same for the post-test. 

You want to study the client satisfaction of two similar companies. You take one for the treatment and the other for the control. Let’s say you conducted a pre-test in both companies at the same time and then begin your experiment. 

After a while, when the program is complete, you go to take a post-test. Now, the set of clients you take in for the test is going to be different than the pre-test ones, the reason being clients change after the course of the period. 

In this case, you cannot derive one-to-one results, but you can tell the average client satisfaction in both companies. 

3. The Double Pre-Test Design

The double pre-test design is a very robust quasi-experimental design designed to rule out the internal validity problem we had with the non-equivalent design. It has two pre-tests before the program. It is when the two groups are progressing at a different pace that you should change from pre-test 1 to pre-test 2. 

Due to the benefit of two pre-tests, you can determine the null case scenario. It assumes the difference between the scores in the pre-test and post-test is due to random chance, as it doesn’t allow one person to take the pre-test twice.

4. The Switching Replications Design

In the switching replications design, as the name suggests, the role of the group is switched. It follows the same treatment-control group pattern, except it has two phases.

Phase 1: Both the groups are pre-tested, then they undergo their respective program. Later they are post-tested.

Phase 2: In this phase, an original treatment group is now a control group and an original control group is now a treatment group.

The main benefit of inculcating this design is that it proves strong against internal validation as well as external validation. The reason is that two parallel implementations of the program allow all the participants to experience the program, making it ethically strong as well.

5.The Non-equivalent Dependent Variables (NEDV) Design

NEDV design, in its simplest form, is not the most reliable one and does not work wonders against internal validity either. But then, what is the use of NEDV? 

Well, sometimes the treatment group may be affected by some external factors. Hence, there are two pre and post-tests applied to the participants, one regarding the treatment itself and the other regarding that external variable. 

Wait, how about we take an example to understand this?

Let us say you started a program to test history teaching techniques. You design standards tests for history (treatment group) and show historical movies (external variable). Later in the post-tests, you find out that along with the history scores, students’ interest in historical movies has also increased, suggesting that showing historical movies has influenced students to study the subject.

6. The regression Point Displacement (RPD) Design

RPD design is used when measures for already existing groups are available and can be compared with those for treatment groups. The treatment group is the only group present, and both pre-test and post-tests are conducted. 

This method is widely beneficial for larger groups, communities, and companies. RPD works by comparing a single program unit with a larger comparison unit.

Consider a community-based COVID awareness program. It has been decided to start the initiative in a particular town or a vast district. The representatives forecast the active cases in that town and use the remaining towns as a comparison. Now rather than giving the average for the rest of the towns’ COVID cases, they show their count.

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All that studying but shouldn’t you know when to perfectly use quasi-experiments? Well, now as we are to the end of the matter, let us discuss when to use quasi-experiments and for what reasons. 

1. For ethical reasons

Remember when we discussed the “willingness” of obese people to participate in the experiment? That is when ethics start to matter. You cannot go on putting random participants under treatments as you do with true experiments. 

Especially when it directly affects the participants’ lives. One of the best examples is Oregon Health Study where health insurance is given to certain people while others were restricted from it. 

2. For practical reasons

True experiments, despite having higher internal validity, can be expensive. Also, it requires enough participants so that the true experiment can be justified. Unlike that, in a quasi-experiment, you can use the already gathered data. 

The data is collected and paid by some strong entity, say the government, and you use that to study your questions. 

Well, that concludes our guide. If you’re looking for extensive research tools, Voxco offers a complete market research tool kit that includes market research trends, a guide to online surveys, an agile market research guide, and five market research templates.  

Also read: Experimental Research .

Differences between quasi-experiments and true experiments

The above description is overwhelming? Don’t worry. Here is the straight difference between the quasi-experiments and true experiments so that you can understand how both vary from each other.

Example of true-experimental design:

While starting the true experiment, you assign some participants in the treatment group where they are fed only junk food. While the other half of the participants go to the control group , where they have their regular ongoing diet (standard course).

You decide to take obese people’s reports every day after their meals to note down their health and discomfort, if any.

However, participants who are assigned to the treatment group would not like to change their diet to complete junk food for personal reasons. In this case, you cannot conduct a true experiment against their will. This is when quasi-experiment comes in.

Example of quasi-experimental design:

While talking to the participants, you find out that some of the participants want to try the junk food effect while the others don’t want to experiment with their diet and choose to stick with a regular diet.

You can now assign already existing groups to the participants according to their choices. Study how the regular consumption of junk food affects the obese from that group. 

Here, you did not assign groups to the random participants and can be confident about the difference occurring due to the conducted experiment. 

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Quasi-experimental design has a unique approach that allows you to uncover causal relationship between variables when controlled experiments are not feasible or ethical. While it may not posses the level of control and randomization that you have when performing true-experiment; quasi-experimental research design enables you to make meaningful contribution by providing valuable insights to various fields.

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Quasi-Experiment Advantages & Disadvantages

By katherine bradley / in science & education.

Experimental research has been touted as one of the most rigorous research designs, due to a built-in safeguard for internal validity -- randomisation. A quasi-experimental design is very similar to an experimental research design, but lacks the key element of randomisation. Both designs feature an experimental group and a control group, but the manner of group selection differs. Therefore, the researcher ends up with non-equivalent groups. This design is referred to as a non-equivalent groups design (NEGD), the most common quasi-experimental design. Quasi-experimental designs offer some advantages and disadvantages.

Disadvantage One -- Potential for Non-Equivalent Groups

Using a sampling method other than random sampling increases the potential for constructing non-equivalent groups. Ideally, researchers endeavour to obtain experimental and control groups that are alike. This is most effectively achieved and most likely to occur through random selection. Quasi-experimental designs do not use random sampling in constructing experimental and control groups.

• Using a sampling method other than random sampling increases the potential for constructing non-equivalent groups.
• Quasi-experimental designs do not use random sampling in constructing experimental and control groups.

Disadvantage Two -- Potential for Low Internal Validity

Beginning research with non-equivalent groups presents a threat to internal validity. Internal validity refers to the degree to which a researcher can be sure that the treatment was responsible for the change in the experimental group. If the researcher does not start with equivalent groups, then the researcher cannot be sure that the treatment was the sole factor causing change. Other confounding factors may have contributed to the change. Therefore, not using random sampling methods to construct the experimental and control groups, increases the potential for low internal validity.

• Beginning research with non-equivalent groups presents a threat to internal validity.
• If the researcher does not start with equivalent groups, then the researcher cannot be sure that the treatment was the sole factor causing change.

Advantage One -- Logistically Easy to Conduct

Quasi-experimental designs are often used in social research. These designs are often used in education to test the effectiveness of a program (treatment). In a typical quasi-experimental design, two classes may be selected, a pretest given to both, and then the treatment given to the experimental group. A post test is conducted to determine if there was a change in the groups. In education, these groups often come predetermined, such as in a school or class. Therefore, the researcher is not required to group individuals, as they come pre-grouped.

• Quasi-experimental designs are often used in social research.
• In education, these groups often come predetermined, such as in a school or class.

Advantage Two -- Control Group Comparisons Possible

Quasi-experimental research offers the benefit of comparison between groups. The experimental group is exposed to the treatment and the control group receives no treatment. The performance of the two groups can then be compared, after data is collected to determine if there was a difference in performance of the groups, after treatment. This is a major advantage because it helps the researcher to make inferences about the possible existence of a cause and effect relationship of the treatment.

• Quasi-experimental research offers the benefit of comparison between groups.
• The performance of the two groups can then be compared, after data is collected to determine if there was a difference in performance of the groups, after treatment.

Quasi-Experiment Advantages & Disadvantages

Katherine bradley, 27 aug 2018.

Experimental research has been touted as one of the most rigorous research designs, due to a built-in safeguard for internal validity known as randomization. A quasi-experimental design is very similar to an experimental research design, but lacks the key element of randomization. Both designs feature an experimental group and a control group, but the manner of group selection differs. Therefore, the researcher ends up with non-equivalent groups. This design is referred to as a non-equivalent groups design, the most common quasi-experimental design. There are advantages and disadvantages of quasi-experimental designs.

Explore this article

• Limited Ability to Compare
• Weaknesses of Quasi-Experimental Design
• Logistically Easy to Manage
• Generalization Possible with Control Group

1 Limited Ability to Compare

Using a sampling method other than random sampling increases the potential for constructing non-equivalent groups. Ideally, researchers endeavor to obtain experimental and control groups that are alike. The best study design is most effectively achieved and most likely to occur through random selection. Quasi-experimental designs do not use random sampling in constructing experimental and control groups. Using non-uniform comparison groups can limit generalization of the findings because non-controlled variables may have influenced the results.

2 Weaknesses of Quasi-Experimental Design

Beginning research with non-equivalent groups presents a threat to internal validity and can be weaknesses of quasi-experimental design. Internal validity refers to the degree to which a researcher can be sure that the treatment was responsible for the change in the experimental group. If the researcher does not start with equivalent groups, then the researcher cannot be sure that the treatment was the sole factor causing change. Weaknesses of quasi-experimental design may contribute to the change. Therefore, not using random sampling methods to construct the experimental and control groups, increases the potential for low internal validity.

3 Logistically Easy to Manage

Quasi-experimental designs are commonly utilized in social research. These designs are also used in education to test the effectiveness of a program. In a typical quasi-experimental design, two classes may be selected, a pretest given to both and then the program or treatment that is given to the experimental group. A post test is conducted to determine if there was a change in the groups. In education, these groups often come pre-determined such as in a school or class. Therefore, the researcher is not required to group individuals as they come pre-grouped.

4 Generalization Possible with Control Group

Some quasi-experimental research designs offer the benefit of comparison between groups that can be statistically analyzed as quasi experiment strengths and weaknesses. For example, if an experimental group of elderly arthritis sufferers is given treatment and the control group receives no treatment, the findings could potentially reveal a statistically significant difference in pain relief or increased mobility among the treated group. This is a major advantage because it helps the researcher to make inferences about the possible existence of a cause and effect relationship of the treatment.

• 1 Research Connections: Experiments and Quasi-Experiments
• 2 Energypedia: Quasi-Experimental or Non-Experimental Designs

About the Author

Katherine Bradley began writing in 2006. Her education and leadership articles have been published on Education.com, Montessori Leadership Online and the Georgia Educational Researcher. Bradley completed a Ph.D. in educational leadership from Mercer University in 2009.

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Frequently asked questions

What is a quasi-experiment.

A quasi-experiment is a type of research design that attempts to establish a cause-and-effect relationship. The main difference with a true experiment is that the groups are not randomly assigned.

Frequently asked questions: Methodology

Attrition refers to participants leaving a study. It always happens to some extent—for example, in randomized controlled trials for medical research.

Differential attrition occurs when attrition or dropout rates differ systematically between the intervention and the control group . As a result, the characteristics of the participants who drop out differ from the characteristics of those who stay in the study. Because of this, study results may be biased .

Action research is conducted in order to solve a particular issue immediately, while case studies are often conducted over a longer period of time and focus more on observing and analyzing a particular ongoing phenomenon.

Action research is focused on solving a problem or informing individual and community-based knowledge in a way that impacts teaching, learning, and other related processes. It is less focused on contributing theoretical input, instead producing actionable input.

Action research is particularly popular with educators as a form of systematic inquiry because it prioritizes reflection and bridges the gap between theory and practice. Educators are able to simultaneously investigate an issue as they solve it, and the method is very iterative and flexible.

A cycle of inquiry is another name for action research . It is usually visualized in a spiral shape following a series of steps, such as “planning → acting → observing → reflecting.”

To make quantitative observations , you need to use instruments that are capable of measuring the quantity you want to observe. For example, you might use a ruler to measure the length of an object or a thermometer to measure its temperature.

Criterion validity and construct validity are both types of measurement validity . In other words, they both show you how accurately a method measures something.

While construct validity is the degree to which a test or other measurement method measures what it claims to measure, criterion validity is the degree to which a test can predictively (in the future) or concurrently (in the present) measure something.

Construct validity is often considered the overarching type of measurement validity . You need to have face validity , content validity , and criterion validity in order to achieve construct validity.

Convergent validity and discriminant validity are both subtypes of construct validity . Together, they help you evaluate whether a test measures the concept it was designed to measure.

• Convergent validity indicates whether a test that is designed to measure a particular construct correlates with other tests that assess the same or similar construct.
• Discriminant validity indicates whether two tests that should not be highly related to each other are indeed not related. This type of validity is also called divergent validity .

You need to assess both in order to demonstrate construct validity. Neither one alone is sufficient for establishing construct validity.

• Discriminant validity indicates whether two tests that should not be highly related to each other are indeed not related

Content validity shows you how accurately a test or other measurement method taps  into the various aspects of the specific construct you are researching.

In other words, it helps you answer the question: “does the test measure all aspects of the construct I want to measure?” If it does, then the test has high content validity.

The higher the content validity, the more accurate the measurement of the construct.

If the test fails to include parts of the construct, or irrelevant parts are included, the validity of the instrument is threatened, which brings your results into question.

Face validity and content validity are similar in that they both evaluate how suitable the content of a test is. The difference is that face validity is subjective, and assesses content at surface level.

When a test has strong face validity, anyone would agree that the test’s questions appear to measure what they are intended to measure.

For example, looking at a 4th grade math test consisting of problems in which students have to add and multiply, most people would agree that it has strong face validity (i.e., it looks like a math test).

On the other hand, content validity evaluates how well a test represents all the aspects of a topic. Assessing content validity is more systematic and relies on expert evaluation. of each question, analyzing whether each one covers the aspects that the test was designed to cover.

A 4th grade math test would have high content validity if it covered all the skills taught in that grade. Experts(in this case, math teachers), would have to evaluate the content validity by comparing the test to the learning objectives.

Snowball sampling is a non-probability sampling method . Unlike probability sampling (which involves some form of random selection ), the initial individuals selected to be studied are the ones who recruit new participants.

Because not every member of the target population has an equal chance of being recruited into the sample, selection in snowball sampling is non-random.

Snowball sampling is a non-probability sampling method , where there is not an equal chance for every member of the population to be included in the sample .

This means that you cannot use inferential statistics and make generalizations —often the goal of quantitative research . As such, a snowball sample is not representative of the target population and is usually a better fit for qualitative research .

Snowball sampling relies on the use of referrals. Here, the researcher recruits one or more initial participants, who then recruit the next ones.

Participants share similar characteristics and/or know each other. Because of this, not every member of the population has an equal chance of being included in the sample, giving rise to sampling bias .

Snowball sampling is best used in the following cases:

• If there is no sampling frame available (e.g., people with a rare disease)
• If the population of interest is hard to access or locate (e.g., people experiencing homelessness)
• If the research focuses on a sensitive topic (e.g., extramarital affairs)

The reproducibility and replicability of a study can be ensured by writing a transparent, detailed method section and using clear, unambiguous language.

Reproducibility and replicability are related terms.

• Reproducing research entails reanalyzing the existing data in the same manner.
• Replicating (or repeating ) the research entails reconducting the entire analysis, including the collection of new data . 
• A successful reproduction shows that the data analyses were conducted in a fair and honest manner.
• A successful replication shows that the reliability of the results is high.

Stratified sampling and quota sampling both involve dividing the population into subgroups and selecting units from each subgroup. The purpose in both cases is to select a representative sample and/or to allow comparisons between subgroups.

The main difference is that in stratified sampling, you draw a random sample from each subgroup ( probability sampling ). In quota sampling you select a predetermined number or proportion of units, in a non-random manner ( non-probability sampling ).

Purposive and convenience sampling are both sampling methods that are typically used in qualitative data collection.

A convenience sample is drawn from a source that is conveniently accessible to the researcher. Convenience sampling does not distinguish characteristics among the participants. On the other hand, purposive sampling focuses on selecting participants possessing characteristics associated with the research study.

The findings of studies based on either convenience or purposive sampling can only be generalized to the (sub)population from which the sample is drawn, and not to the entire population.

Random sampling or probability sampling is based on random selection. This means that each unit has an equal chance (i.e., equal probability) of being included in the sample.

On the other hand, convenience sampling involves stopping people at random, which means that not everyone has an equal chance of being selected depending on the place, time, or day you are collecting your data.

Convenience sampling and quota sampling are both non-probability sampling methods. They both use non-random criteria like availability, geographical proximity, or expert knowledge to recruit study participants.

However, in convenience sampling, you continue to sample units or cases until you reach the required sample size.

In quota sampling, you first need to divide your population of interest into subgroups (strata) and estimate their proportions (quota) in the population. Then you can start your data collection, using convenience sampling to recruit participants, until the proportions in each subgroup coincide with the estimated proportions in the population.

A sampling frame is a list of every member in the entire population . It is important that the sampling frame is as complete as possible, so that your sample accurately reflects your population.

Stratified and cluster sampling may look similar, but bear in mind that groups created in cluster sampling are heterogeneous , so the individual characteristics in the cluster vary. In contrast, groups created in stratified sampling are homogeneous , as units share characteristics.

Relatedly, in cluster sampling you randomly select entire groups and include all units of each group in your sample. However, in stratified sampling, you select some units of all groups and include them in your sample. In this way, both methods can ensure that your sample is representative of the target population .

A systematic review is secondary research because it uses existing research. You don’t collect new data yourself.

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 .

An observational study is a great choice for you if your research question is based purely on observations. If there are ethical, logistical, or practical concerns that prevent you from conducting a traditional experiment , an observational study may be a good choice. In an observational study, there is no interference or manipulation of the research subjects, as well as no control or treatment groups .

It’s often best to ask a variety of people to review your measurements. You can ask experts, such as other researchers, or laypeople, such as potential participants, to judge the face validity of tests.

While experts have a deep understanding of research methods , the people you’re studying can provide you with valuable insights you may have missed otherwise.

Face validity is important because it’s a simple first step to measuring the overall validity of a test or technique. It’s a relatively intuitive, quick, and easy way to start checking whether a new measure seems useful at first glance.

Good face validity means that anyone who reviews your measure says that it seems to be measuring what it’s supposed to. With poor face validity, someone reviewing your measure may be left confused about what you’re measuring and why you’re using this method.

Face validity is about whether a test appears to measure what it’s supposed to measure. This type of validity is concerned with whether a measure seems relevant and appropriate for what it’s assessing only on the surface.

Statistical analyses are often applied to test validity with data from your measures. You test convergent validity and discriminant validity with correlations to see if results from your test are positively or negatively related to those of other established tests.

You can also use regression analyses to assess whether your measure is actually predictive of outcomes that you expect it to predict theoretically. A regression analysis that supports your expectations strengthens your claim of construct validity .

When designing or evaluating a measure, construct validity helps you ensure you’re actually measuring the construct you’re interested in. If you don’t have construct validity, you may inadvertently measure unrelated or distinct constructs and lose precision in your research.

Construct validity is often considered the overarching type of measurement validity ,  because it covers all of the other types. You need to have face validity , content validity , and criterion validity to achieve construct validity.

Construct validity is about how well a test measures the concept it was designed to evaluate. It’s one of four types of measurement validity , which includes construct validity, face validity , and criterion validity.

There are two subtypes of construct validity.

• Convergent validity : The extent to which your measure corresponds to measures of related constructs
• Discriminant validity : The extent to which your measure is unrelated or negatively related to measures of distinct constructs

Naturalistic observation is a valuable tool because of its flexibility, external validity , and suitability for topics that can’t be studied in a lab setting.

The downsides of naturalistic observation include its lack of scientific control , ethical considerations , and potential for bias from observers and subjects.

Naturalistic observation is a qualitative research method where you record the behaviors of your research subjects in real world settings. You avoid interfering or influencing anything in a naturalistic observation.

You can think of naturalistic observation as “people watching” with a purpose.

A dependent variable is what changes as a result of the independent variable manipulation in experiments . It’s what you’re interested in measuring, and it “depends” on your independent variable.

In statistics, dependent variables are also called:

• Response variables (they respond to a change in another variable)
• Outcome variables (they represent the outcome you want to measure)
• Left-hand-side variables (they appear on the left-hand side of a regression equation)

An independent variable is the variable you manipulate, control, or vary in an experimental study to explore its effects. It’s called “independent” because it’s not influenced by any other variables in the study.

Independent variables are also called:

• Explanatory variables (they explain an event or outcome)
• Predictor variables (they can be used to predict the value of a dependent variable)
• Right-hand-side variables (they appear on the right-hand side of a regression equation).

As a rule of thumb, questions related to thoughts, beliefs, and feelings work well in focus groups. Take your time formulating strong questions, paying special attention to phrasing. Be careful to avoid leading questions , which can bias your responses.

Overall, your focus group questions should be:

• Open-ended and flexible
• Impossible to answer with “yes” or “no” (questions that start with “why” or “how” are often best)
• Unambiguous, getting straight to the point while still stimulating discussion
• Unbiased and neutral

A structured interview is a data collection method that relies on asking questions in a set order to collect data on a topic. They are often quantitative in nature. Structured interviews are best used when: 

• You already have a very clear understanding of your topic. Perhaps significant research has already been conducted, or you have done some prior research yourself, but you already possess a baseline for designing strong structured questions.
• You are constrained in terms of time or resources and need to analyze your data quickly and efficiently.
• Your research question depends on strong parity between participants, with environmental conditions held constant.

More flexible interview options include semi-structured interviews , unstructured interviews , and focus groups .

Social desirability bias is the tendency for interview participants to give responses that will be viewed favorably by the interviewer or other participants. It occurs in all types of interviews and surveys , but is most common in semi-structured interviews , unstructured interviews , and focus groups .

Social desirability bias can be mitigated by ensuring participants feel at ease and comfortable sharing their views. Make sure to pay attention to your own body language and any physical or verbal cues, such as nodding or widening your eyes.

This type of bias can also occur in observations if the participants know they’re being observed. They might alter their behavior accordingly.

The interviewer effect is a type of bias that emerges when a characteristic of an interviewer (race, age, gender identity, etc.) influences the responses given by the interviewee.

There is a risk of an interviewer effect in all types of interviews , but it can be mitigated by writing really high-quality interview questions.

A semi-structured interview is a blend of structured and unstructured types of interviews. Semi-structured interviews are best used when:

• You have prior interview experience. Spontaneous questions are deceptively challenging, and it’s easy to accidentally ask a leading question or make a participant uncomfortable.
• Your research question is exploratory in nature. Participant answers can guide future research questions and help you develop a more robust knowledge base for future research.

An unstructured interview is the most flexible type of interview, but it is not always the best fit for your research topic.

Unstructured interviews are best used when:

• You are an experienced interviewer and have a very strong background in your research topic, since it is challenging to ask spontaneous, colloquial questions.
• Your research question is exploratory in nature. While you may have developed hypotheses, you are open to discovering new or shifting viewpoints through the interview process.
• You are seeking descriptive data, and are ready to ask questions that will deepen and contextualize your initial thoughts and hypotheses.
• Your research depends on forming connections with your participants and making them feel comfortable revealing deeper emotions, lived experiences, or thoughts.

The four most common types of interviews are:

• Structured interviews : The questions are predetermined in both topic and order. 
• Semi-structured interviews : A few questions are predetermined, but other questions aren’t planned.
• Unstructured interviews : None of the questions are predetermined.
• Focus group interviews : The questions are presented to a group instead of one individual.

Deductive reasoning is commonly used in scientific research, and it’s especially associated with quantitative research .

In research, you might have come across something called the hypothetico-deductive method . It’s the scientific method of testing hypotheses to check whether your predictions are substantiated by real-world data.

Deductive reasoning is a logical approach where you progress from general ideas to specific conclusions. It’s often contrasted with inductive reasoning , where you start with specific observations and form general conclusions.

Deductive reasoning is also called deductive logic.

There are many different types of inductive reasoning that people use formally or informally.

Here are a few common types:

• Inductive generalization : You use observations about a sample to come to a conclusion about the population it came from.
• Statistical generalization: You use specific numbers about samples to make statements about populations.
• Causal reasoning: You make cause-and-effect links between different things.
• Sign reasoning: You make a conclusion about a correlational relationship between different things.
• Analogical reasoning: You make a conclusion about something based on its similarities to something else.

Inductive reasoning is a bottom-up approach, while deductive reasoning is top-down.

Inductive reasoning takes you from the specific to the general, while in deductive reasoning, you make inferences by going from general premises to specific conclusions.

In inductive research , you start by making observations or gathering data. Then, you take a broad scan of your data and search for patterns. Finally, you make general conclusions that you might incorporate into theories.

Inductive reasoning is a method of drawing conclusions by going from the specific to the general. It’s usually contrasted with deductive reasoning, where you proceed from general information to specific conclusions.

Inductive reasoning is also called inductive logic or bottom-up reasoning.

A hypothesis states your predictions about what your research will find. It is a tentative answer to your research question that has not yet been tested. For some research projects, you might have to write several hypotheses that address different aspects of your research question.

A hypothesis is not just a guess — it should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations and statistical analysis of data).

Triangulation can help:

• Reduce research bias that comes from using a single method, theory, or investigator
• Enhance validity by approaching the same topic with different tools
• Establish credibility by giving you a complete picture of the research problem

But triangulation can also pose problems:

• It’s time-consuming and labor-intensive, often involving an interdisciplinary team.
• Your results may be inconsistent or even contradictory.

There are four main types of triangulation :

• Data triangulation : Using data from different times, spaces, and people
• Investigator triangulation : Involving multiple researchers in collecting or analyzing data
• Theory triangulation : Using varying theoretical perspectives in your research
• Methodological triangulation : Using different methodologies to approach the same topic

Many academic fields use peer review , largely to determine whether a manuscript is suitable for publication. Peer review enhances the credibility of the published manuscript.

However, peer review is also common in non-academic settings. The United Nations, the European Union, and many individual nations use peer review to evaluate grant applications. It is also widely used in medical and health-related fields as a teaching or quality-of-care measure. 

Peer assessment is often used in the classroom as a pedagogical tool. Both receiving feedback and providing it are thought to enhance the learning process, helping students think critically and collaboratively.

Peer review can stop obviously problematic, falsified, or otherwise untrustworthy research from being published. It also represents an excellent opportunity to get feedback from renowned experts in your field. It acts as a first defense, helping you ensure your argument is clear and that there are no gaps, vague terms, or unanswered questions for readers who weren’t involved in the research process.

Peer-reviewed articles are considered a highly credible source due to this stringent process they go through before publication.

In general, the peer review process follows the following steps: 

• First, the author submits the manuscript to the editor.
• Reject the manuscript and send it back to author, or 
• Send it onward to the selected peer reviewer(s) 
• Next, the peer review process occurs. The reviewer provides feedback, addressing any major or minor issues with the manuscript, and gives their advice regarding what edits should be made. 
• Lastly, the edited manuscript is sent back to the author. They input the edits, and resubmit it to the editor for publication.

Exploratory research is often used when the issue you’re studying is new or when the data collection process is challenging for some reason.

You can use exploratory research if you have a general idea or a specific question that you want to study but there is no preexisting knowledge or paradigm with which to study it.

Exploratory research is a methodology approach that explores research questions that have not previously been studied in depth. It is often used when the issue you’re studying is new, or the data collection process is challenging in some way.

Explanatory research is used to investigate how or why a phenomenon occurs. Therefore, this type of research is often one of the first stages in the research process , serving as a jumping-off point for future research.

Exploratory research aims to explore the main aspects of an under-researched problem, while explanatory research aims to explain the causes and consequences of a well-defined problem.

Explanatory research is a research method used to investigate how or why something occurs when only a small amount of information is available pertaining to that topic. It can help you increase your understanding of a given topic.

Clean data are valid, accurate, complete, consistent, unique, and uniform. Dirty data include inconsistencies and errors.

Dirty data can come from any part of the research process, including poor research design , inappropriate measurement materials, or flawed data entry.

Data cleaning takes place between data collection and data analyses. But you can use some methods even before collecting data.

For clean data, you should start by designing measures that collect valid data. Data validation at the time of data entry or collection helps you minimize the amount of data cleaning you’ll need to do.

After data collection, you can use data standardization and data transformation to clean your data. You’ll also deal with any missing values, outliers, and duplicate values.

Every dataset requires different techniques to clean dirty data , but you need to address these issues in a systematic way. You focus on finding and resolving data points that don’t agree or fit with the rest of your dataset.

These data might be missing values, outliers, duplicate values, incorrectly formatted, or irrelevant. You’ll start with screening and diagnosing your data. Then, you’ll often standardize and accept or remove data to make your dataset consistent and valid.

Data cleaning is necessary for valid and appropriate analyses. Dirty data contain inconsistencies or errors , but cleaning your data helps you minimize or resolve these.

Without data cleaning, you could end up with a Type I or II error in your conclusion. These types of erroneous conclusions can be practically significant with important consequences, because they lead to misplaced investments or missed opportunities.

Data cleaning involves spotting and resolving potential data inconsistencies or errors to improve your data quality. An error is any value (e.g., recorded weight) that doesn’t reflect the true value (e.g., actual weight) of something that’s being measured.

In this process, you review, analyze, detect, modify, or remove “dirty” data to make your dataset “clean.” Data cleaning is also called data cleansing or data scrubbing.

Research misconduct means making up or falsifying data, manipulating data analyses, or misrepresenting results in research reports. It’s a form of academic fraud.

These actions are committed intentionally and can have serious consequences; research misconduct is not a simple mistake or a point of disagreement but a serious ethical failure.

Anonymity means you don’t know who the participants are, while confidentiality means you know who they are but remove identifying information from your research report. Both are important ethical considerations .

You can only guarantee anonymity by not collecting any personally identifying information—for example, names, phone numbers, email addresses, IP addresses, physical characteristics, photos, or videos.

You can keep data confidential by using aggregate information in your research report, so that you only refer to groups of participants rather than individuals.

Research ethics matter for scientific integrity, human rights and dignity, and collaboration between science and society. These principles make sure that participation in studies is voluntary, informed, and safe.

Ethical considerations in research are a set of principles that guide your research designs and practices. These principles include voluntary participation, informed consent, anonymity, confidentiality, potential for harm, and results communication.

Scientists and researchers must always adhere to a certain code of conduct when collecting data from others .

These considerations protect the rights of research participants, enhance research validity , and maintain scientific integrity.

In multistage sampling , you can use probability or non-probability sampling methods .

For a probability sample, you have to conduct probability sampling at every stage.

You can mix it up by using simple random sampling , systematic sampling , or stratified sampling to select units at different stages, depending on what is applicable and relevant to your study.

Multistage sampling can simplify data collection when you have large, geographically spread samples, and you can obtain a probability sample without a complete sampling frame.

But multistage sampling may not lead to a representative sample, and larger samples are needed for multistage samples to achieve the statistical properties of simple random samples .

These are four of the most common mixed methods designs :

• Convergent parallel: Quantitative and qualitative data are collected at the same time and analyzed separately. After both analyses are complete, compare your results to draw overall conclusions. 
• Embedded: Quantitative and qualitative data are collected at the same time, but within a larger quantitative or qualitative design. One type of data is secondary to the other.
• Explanatory sequential: Quantitative data is collected and analyzed first, followed by qualitative data. You can use this design if you think your qualitative data will explain and contextualize your quantitative findings.
• Exploratory sequential: Qualitative data is collected and analyzed first, followed by quantitative data. You can use this design if you think the quantitative data will confirm or validate your qualitative findings.

Triangulation in research means using multiple datasets, methods, theories and/or investigators to address a research question. It’s a research strategy that can help you enhance the validity and credibility of your findings.

Triangulation is mainly used in qualitative research , but it’s also commonly applied in quantitative research . Mixed methods research always uses triangulation.

In multistage sampling , or multistage cluster sampling, you draw a sample from a population using smaller and smaller groups at each stage.

This method is often used to collect data from a large, geographically spread group of people in national surveys, for example. You take advantage of hierarchical groupings (e.g., from state to city to neighborhood) to create a sample that’s less expensive and time-consuming to collect data from.

No, the steepness or slope of the line isn’t related to the correlation coefficient value. The correlation coefficient only tells you how closely your data fit on a line, so two datasets with the same correlation coefficient can have very different slopes.

To find the slope of the line, you’ll need to perform a regression analysis .

Correlation coefficients always range between -1 and 1.

The sign of the coefficient tells you the direction of the relationship: a positive value means the variables change together in the same direction, while a negative value means they change together in opposite directions.

The absolute value of a number is equal to the number without its sign. The absolute value of a correlation coefficient tells you the magnitude of the correlation: the greater the absolute value, the stronger the correlation.

These are the assumptions your data must meet if you want to use Pearson’s r :

• Both variables are on an interval or ratio level of measurement
• Data from both variables follow normal distributions
• Your data have no outliers
• Your data is from a random or representative sample
• You expect a linear relationship between the two variables

Quantitative research designs can be divided into two main categories:

• Correlational and descriptive designs are used to investigate characteristics, averages, trends, and associations between variables.
• Experimental and quasi-experimental designs are used to test causal relationships .

Qualitative research designs tend to be more flexible. Common types of qualitative design include case study , ethnography , and grounded theory designs.

A well-planned research design helps ensure that your methods match your research aims, that you collect high-quality data, and that you use the right kind of analysis to answer your questions, utilizing credible sources . This allows you to draw valid , trustworthy conclusions.

The priorities of a research design can vary depending on the field, but you usually have to specify:

• Your research questions and/or hypotheses
• Your overall approach (e.g., qualitative or quantitative )
• The type of design you’re using (e.g., a survey , experiment , or case study )
• Your sampling methods or criteria for selecting subjects
• Your data collection methods (e.g., questionnaires , observations)
• Your data collection procedures (e.g., operationalization , timing and data management)
• Your data analysis methods (e.g., statistical tests  or thematic analysis )

A research design is a strategy for answering your   research question . It defines your overall approach and determines how you will collect and analyze data.

Questionnaires can be self-administered or researcher-administered.

Self-administered questionnaires can be delivered online or in paper-and-pen formats, in person or through mail. All questions are standardized so that all respondents receive the same questions with identical wording.

Researcher-administered questionnaires are interviews that take place by phone, in-person, or online between researchers and respondents. You can gain deeper insights by clarifying questions for respondents or asking follow-up questions.

You can organize the questions logically, with a clear progression from simple to complex, or randomly between respondents. A logical flow helps respondents process the questionnaire easier and quicker, but it may lead to bias. Randomization can minimize the bias from order effects.

Closed-ended, or restricted-choice, questions offer respondents a fixed set of choices to select from. These questions are easier to answer quickly.

Open-ended or long-form questions allow respondents to answer in their own words. Because there are no restrictions on their choices, respondents can answer in ways that researchers may not have otherwise considered.

A questionnaire is a data collection tool or instrument, while a survey is an overarching research method that involves collecting and analyzing data from people using questionnaires.

The third variable and directionality problems are two main reasons why correlation isn’t causation .

The third variable problem means that a confounding variable affects both variables to make them seem causally related when they are not.

The directionality problem is when two variables correlate and might actually have a causal relationship, but it’s impossible to conclude which variable causes changes in the other.

Correlation describes an association between variables : when one variable changes, so does the other. A correlation is a statistical indicator of the relationship between variables.

Causation means that changes in one variable brings about changes in the other (i.e., there is a cause-and-effect relationship between variables). The two variables are correlated with each other, and there’s also a causal link between them.

While causation and correlation can exist simultaneously, correlation does not imply causation. In other words, correlation is simply a relationship where A relates to B—but A doesn’t necessarily cause B to happen (or vice versa). Mistaking correlation for causation is a common error and can lead to false cause fallacy .

Controlled experiments establish causality, whereas correlational studies only show associations between variables.

• In an experimental design , you manipulate an independent variable and measure its effect on a dependent variable. Other variables are controlled so they can’t impact the results.
• In a correlational design , you measure variables without manipulating any of them. You can test whether your variables change together, but you can’t be sure that one variable caused a change in another.

In general, correlational research is high in external validity while experimental research is high in internal validity .

A correlation is usually tested for two variables at a time, but you can test correlations between three or more variables.

A correlation coefficient is a single number that describes the strength and direction of the relationship between your variables.

Different types of correlation coefficients might be appropriate for your data based on their levels of measurement and distributions . The Pearson product-moment correlation coefficient (Pearson’s r ) is commonly used to assess a linear relationship between two quantitative variables.

A correlational research design investigates relationships between two variables (or more) without the researcher controlling or manipulating any of them. It’s a non-experimental type of quantitative research .

A correlation reflects the strength and/or direction of the association between two or more variables.

• A positive correlation means that both variables change in the same direction.
• A negative correlation means that the variables change in opposite directions.
• A zero correlation means there’s no relationship between the variables.

Random error  is almost always present in scientific studies, even in highly controlled settings. While you can’t eradicate it completely, you can reduce random error by taking repeated measurements, using a large sample, and controlling extraneous variables .

You can avoid systematic error through careful design of your sampling , data collection , and analysis procedures. For example, use triangulation to measure your variables using multiple methods; regularly calibrate instruments or procedures; use random sampling and random assignment ; and apply masking (blinding) where possible.

Systematic error is generally a bigger problem in research.

With random error, multiple measurements will tend to cluster around the true value. When you’re collecting data from a large sample , the errors in different directions will cancel each other out.

Systematic errors are much more problematic because they can skew your data away from the true value. This can lead you to false conclusions ( Type I and II errors ) about the relationship between the variables you’re studying.

Random and systematic error are two types of measurement error.

Random error is a chance difference between the observed and true values of something (e.g., a researcher misreading a weighing scale records an incorrect measurement).

Systematic error is a consistent or proportional difference between the observed and true values of something (e.g., a miscalibrated scale consistently records weights as higher than they actually are).

On graphs, the explanatory variable is conventionally placed on the x-axis, while the response variable is placed on the y-axis.

• If you have quantitative variables , use a scatterplot or a line graph.
• If your response variable is categorical, use a scatterplot or a line graph.
• If your explanatory variable is categorical, use a bar graph.

The term “ explanatory variable ” is sometimes preferred over “ independent variable ” because, in real world contexts, independent variables are often influenced by other variables. This means they aren’t totally independent.

Multiple independent variables may also be correlated with each other, so “explanatory variables” is a more appropriate term.

The difference between explanatory and response variables is simple:

• An explanatory variable is the expected cause, and it explains the results.
• A response variable is the expected effect, and it responds to other variables.

In a controlled experiment , all extraneous variables are held constant so that they can’t influence the results. Controlled experiments require:

• A control group that receives a standard treatment, a fake treatment, or no treatment.
• Random assignment of participants to ensure the groups are equivalent.

Depending on your study topic, there are various other methods of controlling variables .

There are 4 main types of extraneous variables :

• Demand characteristics : environmental cues that encourage participants to conform to researchers’ expectations.
• Experimenter effects : unintentional actions by researchers that influence study outcomes.
• Situational variables : environmental variables that alter participants’ behaviors.
• Participant variables : any characteristic or aspect of a participant’s background that could affect study results.

An extraneous variable is any variable that you’re not investigating that can potentially affect the dependent variable of your research study.

A confounding variable is a type of extraneous variable that not only affects the dependent variable, but is also related to the independent variable.

In a factorial design, multiple independent variables are tested.

If you test two variables, each level of one independent variable is combined with each level of the other independent variable to create different conditions.

Within-subjects designs have many potential threats to internal validity , but they are also very statistically powerful .

Advantages:

• Only requires small samples
• Statistically powerful
• Removes the effects of individual differences on the outcomes

Disadvantages:

• Internal validity threats reduce the likelihood of establishing a direct relationship between variables
• Time-related effects, such as growth, can influence the outcomes
• Carryover effects mean that the specific order of different treatments affect the outcomes

While a between-subjects design has fewer threats to internal validity , it also requires more participants for high statistical power than a within-subjects design .

• Prevents carryover effects of learning and fatigue.
• Shorter study duration.
• Needs larger samples for high power.
• Uses more resources to recruit participants, administer sessions, cover costs, etc.
• Individual differences may be an alternative explanation for results.

Yes. Between-subjects and within-subjects designs can be combined in a single study when you have two or more independent variables (a factorial design). In a mixed factorial design, one variable is altered between subjects and another is altered within subjects.

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.

Random assignment is used in experiments with a between-groups or independent measures design. In this research design, there’s usually a control group and one or more experimental groups. Random assignment helps ensure that the groups are comparable.

In general, you should always use random assignment in this type of experimental design when it is ethically possible and makes sense for your study topic.

To implement random assignment , assign a unique number to every member of your study’s sample .

Then, you can use a random number generator or a lottery method to randomly assign each number to a control or experimental group. You can also do so manually, by flipping a coin or rolling a dice to randomly assign participants to groups.

Random selection, or random sampling , is a way of selecting members of a population for your study’s sample.

In contrast, random assignment is a way of sorting the sample into control and experimental groups.

Random sampling enhances the external validity or generalizability of your results, while random assignment improves the internal validity of your study.

In experimental research, random assignment is a way of placing participants from your sample into different groups using randomization. With this method, every member of the sample has a known or equal chance of being placed in a control group or an experimental group.

“Controlling for a variable” means measuring extraneous variables and accounting for them statistically to remove their effects on other variables.

Researchers often model control variable data along with independent and dependent variable data in regression analyses and ANCOVAs . That way, you can isolate the control variable’s effects from the relationship between the variables of interest.

Control variables help you establish a correlational or causal relationship between variables by enhancing internal validity .

If you don’t control relevant extraneous variables , they may influence the outcomes of your study, and you may not be able to demonstrate that your results are really an effect of your independent variable .

A control variable is any variable that’s held constant in a research study. It’s not a variable of interest in the study, but it’s controlled because it could influence the outcomes.

Including mediators and moderators in your research helps you go beyond studying a simple relationship between two variables for a fuller picture of the real world. They are important to consider when studying complex correlational or causal relationships.

Mediators are part of the causal pathway of an effect, and they tell you how or why an effect takes place. Moderators usually help you judge the external validity of your study by identifying the limitations of when the relationship between variables holds.

If something is a mediating variable :

• It’s caused by the independent variable .
• It influences the dependent variable
• When it’s taken into account, the statistical correlation between the independent and dependent variables is higher than when it isn’t considered.

A confounder is a third variable that affects variables of interest and makes them seem related when they are not. In contrast, a mediator is the mechanism of a relationship between two variables: it explains the process by which they are related.

A mediator variable explains the process through which two variables are related, while a moderator variable affects the strength and direction of that relationship.

There are three key steps in systematic sampling :

• Define and list your population , ensuring that it is not ordered in a cyclical or periodic order.
• Decide on your sample size and calculate your interval, k , by dividing your population by your target sample size.
• Choose every k th member of the population as your sample.

Systematic sampling is a probability sampling method where researchers select members of the population at a regular interval – for example, by selecting every 15th person on a list of the population. If the population is in a random order, this can imitate the benefits of simple random sampling .

Yes, you can create a stratified sample using multiple characteristics, but you must ensure that every participant in your study belongs to one and only one subgroup. In this case, you multiply the numbers of subgroups for each characteristic to get the total number of groups.

For example, if you were stratifying by location with three subgroups (urban, rural, or suburban) and marital status with five subgroups (single, divorced, widowed, married, or partnered), you would have 3 x 5 = 15 subgroups.

You should use stratified sampling when your sample can be divided into mutually exclusive and exhaustive subgroups that you believe will take on different mean values for the variable that you’re studying.

Using stratified sampling will allow you to obtain more precise (with lower variance ) statistical estimates of whatever you are trying to measure.

For example, say you want to investigate how income differs based on educational attainment, but you know that this relationship can vary based on race. Using stratified sampling, you can ensure you obtain a large enough sample from each racial group, allowing you to draw more precise conclusions.

In stratified sampling , researchers divide subjects into subgroups called strata based on characteristics that they share (e.g., race, gender, educational attainment).

Once divided, each subgroup is randomly sampled using another probability sampling method.

Cluster sampling is more time- and cost-efficient than other probability sampling methods , particularly when it comes to large samples spread across a wide geographical area.

However, it provides less statistical certainty than other methods, such as simple random sampling , because it is difficult to ensure that your clusters properly represent the population as a whole.

There are three types of cluster sampling : single-stage, double-stage and multi-stage clustering. In all three types, you first divide the population into clusters, then randomly select clusters for use in your sample.

• In single-stage sampling , you collect data from every unit within the selected clusters.
• In double-stage sampling , you select a random sample of units from within the clusters.
• In multi-stage sampling , you repeat the procedure of randomly sampling elements from within the clusters until you have reached a manageable sample.

Cluster sampling is a probability sampling method in which you divide a population into clusters, such as districts or schools, and then randomly select some of these clusters as your sample.

The clusters should ideally each be mini-representations of the population as a whole.

If properly implemented, simple random sampling is usually the best sampling method for ensuring both internal and external validity . However, it can sometimes be impractical and expensive to implement, depending on the size of the population to be studied,

If you have a list of every member of the population and the ability to reach whichever members are selected, you can use simple random sampling.

The American Community Survey  is an example of simple random sampling . In order to collect detailed data on the population of the US, the Census Bureau officials randomly select 3.5 million households per year and use a variety of methods to convince them to fill out the survey.

Simple random sampling is a type of probability sampling in which the researcher randomly selects a subset of participants from a population . Each member of the population has an equal chance of being selected. Data is then collected from as large a percentage as possible of this random subset.

Quasi-experimental design is most useful in situations where it would be unethical or impractical to run a true experiment .

Quasi-experiments have lower internal validity than true experiments, but they often have higher external validity  as they can use real-world interventions instead of artificial laboratory settings.

Blinding is important to reduce research bias (e.g., observer bias , demand characteristics ) and ensure a study’s internal validity .

If participants know whether they are in a control or treatment group , they may adjust their behavior in ways that affect the outcome that researchers are trying to measure. If the people administering the treatment are aware of group assignment, they may treat participants differently and thus directly or indirectly influence the final results.

• In a single-blind study , only the participants are blinded.
• In a double-blind study , both participants and experimenters are blinded.
• In a triple-blind study , the assignment is hidden not only from participants and experimenters, but also from the researchers analyzing the data.

Blinding means hiding who is assigned to the treatment group and who is assigned to the control group in an experiment .

A true experiment (a.k.a. a controlled experiment) always includes at least one control group that doesn’t receive the experimental treatment.

However, some experiments use a within-subjects design to test treatments without a control group. In these designs, you usually compare one group’s outcomes before and after a treatment (instead of comparing outcomes between different groups).

For strong internal validity , it’s usually best to include a control group if possible. Without a control group, it’s harder to be certain that the outcome was caused by the experimental treatment and not by other variables.

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.

Individual Likert-type questions are generally considered ordinal data , because the items have clear rank order, but don’t have an even distribution.

Overall Likert scale scores are sometimes treated as interval data. These scores are considered to have directionality and even spacing between them.

The type of data determines what statistical tests you should use to analyze your data.

A Likert scale is a rating scale that quantitatively assesses opinions, attitudes, or behaviors. It is made up of 4 or more questions that measure a single attitude or trait when response scores are combined.

To use a Likert scale in a survey , you present participants with Likert-type questions or statements, and a continuum of items, usually with 5 or 7 possible responses, to capture their degree of agreement.

In scientific research, concepts are the abstract ideas or phenomena that are being studied (e.g., educational achievement). Variables are properties or characteristics of the concept (e.g., performance at school), while indicators are ways of measuring or quantifying variables (e.g., yearly grade reports).

The process of turning abstract concepts into measurable variables and indicators is called operationalization .

There are various approaches to qualitative data analysis , but they all share five steps in common:

• Prepare and organize your data.
• Review and explore your data.
• Develop a data coding system.
• Assign codes to the data.
• Identify recurring themes.

The specifics of each step depend on the focus of the analysis. Some common approaches include textual analysis , thematic analysis , and discourse analysis .

There are five common approaches to qualitative research :

• Grounded theory involves collecting data in order to develop new theories.
• Ethnography involves immersing yourself in a group or organization to understand its culture.
• Narrative research involves interpreting stories to understand how people make sense of their experiences and perceptions.
• Phenomenological research involves investigating phenomena through people’s lived experiences.
• Action research links theory and practice in several cycles to drive innovative changes.

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses , by calculating how likely it is that a pattern or relationship between variables could have arisen by chance.

Operationalization means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioral avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalize the variables that you want to measure.

When conducting research, collecting original data has significant advantages:

• You can tailor data collection to your specific research aims (e.g. understanding the needs of your consumers or user testing your website)
• You can control and standardize the process for high reliability and validity (e.g. choosing appropriate measurements and sampling methods )

However, there are also some drawbacks: data collection can be time-consuming, labor-intensive and expensive. In some cases, it’s more efficient to use secondary data that has already been collected by someone else, but the data might be less reliable.

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organizations.

There are several methods you can use to decrease the impact of confounding variables on your research: restriction, matching, statistical control and randomization.

In restriction , you restrict your sample by only including certain subjects that have the same values of potential confounding variables.

In matching , you match each of the subjects in your treatment group with a counterpart in the comparison group. The matched subjects have the same values on any potential confounding variables, and only differ in the independent variable .

In statistical control , you include potential confounders as variables in your regression .

In randomization , you randomly assign the treatment (or independent variable) in your study to a sufficiently large number of subjects, which allows you to control for all potential confounding variables.

A confounding variable is closely related to both the independent and dependent variables in a study. An independent variable represents the supposed cause , while the dependent variable is the supposed effect . A confounding variable is a third variable that influences both the independent and dependent variables.

Failing to account for confounding variables can cause you to wrongly estimate the relationship between your independent and dependent variables.

To ensure the internal validity of your research, you must consider the impact of confounding variables. If you fail to account for them, you might over- or underestimate the causal relationship between your independent and dependent variables , or even find a causal relationship where none exists.

Yes, but including more than one of either type requires multiple research questions .

For example, if you are interested in the effect of a diet on health, you can use multiple measures of health: blood sugar, blood pressure, weight, pulse, and many more. Each of these is its own dependent variable with its own research question.

You could also choose to look at the effect of exercise levels as well as diet, or even the additional effect of the two combined. Each of these is a separate independent variable .

To ensure the internal validity of an experiment , you should only change one independent variable at a time.

No. The value of a dependent variable depends on an independent variable, so a variable cannot be both independent and dependent at the same time. It must be either the cause or the effect, not both!

You want to find out how blood sugar levels are affected by drinking diet soda and regular soda, so you conduct an experiment .

• The type of soda – diet or regular – is the independent variable .
• The level of blood sugar that you measure is the dependent variable – it changes depending on the type of soda.

Determining cause and effect is one of the most important parts of scientific research. It’s essential to know which is the cause – the independent variable – and which is the effect – the dependent variable.

In non-probability sampling , the sample is selected based on non-random criteria, and not every member of the population has a chance of being included.

Common non-probability sampling methods include convenience sampling , voluntary response sampling, purposive sampling , snowball sampling, and quota sampling .

Probability sampling means that every member of the target population has a known chance of being included in the sample.

Probability sampling methods include simple random sampling , systematic sampling , stratified sampling , and cluster sampling .

Using careful research design and sampling procedures can help you avoid sampling bias . Oversampling can be used to correct undercoverage bias .

Some common types of sampling bias include self-selection bias , nonresponse bias , undercoverage bias , survivorship bias , pre-screening or advertising bias, and healthy user bias.

Sampling bias is a threat to external validity – it limits the generalizability of your findings to a broader group of people.

A sampling error is the difference between a population parameter and a sample statistic .

A statistic refers to measures about the sample , while a parameter refers to measures about the population .

Populations are used when a research question requires data from every member of the population. This is usually only feasible when the population is small and easily accessible.

Samples are used to make inferences about populations . Samples are easier to collect data from because they are practical, cost-effective, convenient, and manageable.

There are seven threats to external validity : selection bias , history, experimenter effect, Hawthorne effect , testing effect, aptitude-treatment and situation effect.

The two types of external validity are population validity (whether you can generalize to other groups of people) and ecological validity (whether you can generalize to other situations and settings).

The external validity of a study is the extent to which you can generalize your findings to different groups of people, situations, and measures.

Cross-sectional studies cannot establish a cause-and-effect relationship or analyze behavior over a period of time. To investigate cause and effect, you need to do a longitudinal study or an experimental study .

Cross-sectional studies are less expensive and time-consuming than many other types of study. They can provide useful insights into a population’s characteristics and identify correlations for further research.

Sometimes only cross-sectional data is available for analysis; other times your research question may only require a cross-sectional study to answer it.

Longitudinal studies can last anywhere from weeks to decades, although they tend to be at least a year long.

The 1970 British Cohort Study , which has collected data on the lives of 17,000 Brits since their births in 1970, is one well-known example of a longitudinal study .

Longitudinal studies are better to establish the correct sequence of events, identify changes over time, and provide insight into cause-and-effect relationships, but they also tend to be more expensive and time-consuming than other types of studies.

Longitudinal studies and cross-sectional studies are two different types of research design . In a cross-sectional study you collect data from a population at a specific point in time; in a longitudinal study you repeatedly collect data from the same sample over an extended period of time.

There are eight threats to internal validity : history, maturation, instrumentation, testing, selection bias , regression to the mean, social interaction and attrition .

Internal validity is the extent to which you can be confident that a cause-and-effect relationship established in a study cannot be explained by other factors.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

The research methods you use depend on the type of data you need to answer your research question .

• If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts and meanings, use qualitative methods .
• If you want to analyze a large amount of readily-available data, use secondary data. If you want data specific to your purposes with control over how it is generated, collect primary data.
• If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

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.

Discrete and continuous variables are two types of quantitative variables :

• Discrete variables represent counts (e.g. the number of objects in a collection).
• Continuous variables represent measurable amounts (e.g. water volume or weight).

Quantitative variables are any variables where the data represent amounts (e.g. height, weight, or age).

Categorical variables are any variables where the data represent groups. This includes rankings (e.g. finishing places in a race), classifications (e.g. brands of cereal), and binary outcomes (e.g. coin flips).

You need to know what type of variables you are working with to choose the right statistical test for your data and interpret your results .

You can think of independent and dependent variables in terms of cause and effect: an independent variable is the variable you think is the cause , while a dependent variable is the effect .

In an experiment, you manipulate the independent variable and measure the outcome in the dependent variable. For example, in an experiment about the effect of nutrients on crop growth:

• The  independent variable  is the amount of nutrients added to the crop field.
• The  dependent variable is the biomass of the crops at harvest time.

Defining your variables, and deciding how you will manipulate and measure them, is an important part of experimental design .

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.

I nternal validity is the degree of confidence that the causal relationship you are testing is not influenced by other factors or variables .

External validity is the extent to which your results can be generalized to other contexts.

The validity of your experiment depends on your experimental design .

Reliability and validity are both about how well a method measures something:

• Reliability refers to the  consistency of a measure (whether the results can be reproduced under the same conditions).
• Validity   refers to the  accuracy of a measure (whether the results really do represent what they are supposed to measure).

If you are doing experimental research, you also have to consider the internal and external validity of your experiment.

A sample is a subset of individuals from a larger population . Sampling means selecting the group that you will actually collect data from in your research. For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

In statistics, sampling allows you to test a hypothesis about the characteristics of a population.

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

Methodology refers to the overarching strategy and rationale of your research project . It involves studying the methods used in your field and the theories or principles behind them, in order to develop an approach that matches your objectives.

Methods are the specific tools and procedures you use to collect and analyze data (for example, experiments, surveys , and statistical tests ).

In shorter scientific papers, where the aim is to report the findings of a specific study, you might simply describe what you did in a methods section .

In a longer or more complex research project, such as a thesis or dissertation , you will probably include a methodology section , where you explain your approach to answering the research questions and cite relevant sources to support your choice of methods.

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Quasi experiment

• Created by: Sarah18
• Created on: 21-10-13 21:51

• Experiments

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