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What is Quantitative Research? Definition, Methods, Types, and Examples

If you’re wondering what is quantitative research and whether this methodology works for your research study, you’re not alone. If you want a simple quantitative research definition , then it’s enough to say that this is a method undertaken by researchers based on their study requirements. However, to select the most appropriate research for their study type, researchers should know all the methods available. 

Selecting the right research method depends on a few important criteria, such as the research question, study type, time, costs, data availability, and availability of respondents. There are two main types of research methods— quantitative research  and qualitative research. The purpose of quantitative research is to validate or test a theory or hypothesis and that of qualitative research is to understand a subject or event or identify reasons for observed patterns.   

Quantitative research methods  are used to observe events that affect a particular group of individuals, which is the sample population. In this type of research, diverse numerical data are collected through various methods and then statistically analyzed to aggregate the data, compare them, or show relationships among the data. Quantitative research methods broadly include questionnaires, structured observations, and experiments.  

Here are two quantitative research examples:  

• Satisfaction surveys sent out by a company regarding their revamped customer service initiatives. Customers are asked to rate their experience on a rating scale of 1 (poor) to 5 (excellent).  
• A school has introduced a new after-school program for children, and a few months after commencement, the school sends out feedback questionnaires to the parents of the enrolled children. Such questionnaires usually include close-ended questions that require either definite answers or a Yes/No option. This helps in a quick, overall assessment of the program’s outreach and success.  

Table of Contents

What is quantitative research ? 1,2

The steps shown in the figure can be grouped into the following broad steps:  

• Theory : Define the problem area or area of interest and create a research question.  
• Hypothesis : Develop a hypothesis based on the research question. This hypothesis will be tested in the remaining steps.  
• Research design : In this step, the most appropriate quantitative research design will be selected, including deciding on the sample size, selecting respondents, identifying research sites, if any, etc.
• Data collection : This process could be extensive based on your research objective and sample size.  
• Data analysis : Statistical analysis is used to analyze the data collected. The results from the analysis help in either supporting or rejecting your hypothesis.  
• Present results : Based on the data analysis, conclusions are drawn, and results are presented as accurately as possible.  

Quantitative research characteristics 4

• Large sample size : This ensures reliability because this sample represents the target population or market. Due to the large sample size, the outcomes can be generalized to the entire population as well, making this one of the important characteristics of quantitative research .  
• Structured data and measurable variables: The data are numeric and can be analyzed easily. Quantitative research involves the use of measurable variables such as age, salary range, highest education, etc.  
• Easy-to-use data collection methods : The methods include experiments, controlled observations, and questionnaires and surveys with a rating scale or close-ended questions, which require simple and to-the-point answers; are not bound by geographical regions; and are easy to administer.  
• Data analysis : Structured and accurate statistical analysis methods using software applications such as Excel, SPSS, R. The analysis is fast, accurate, and less effort intensive.  
• Reliable : The respondents answer close-ended questions, their responses are direct without ambiguity and yield numeric outcomes, which are therefore highly reliable.  
• Reusable outcomes : This is one of the key characteristics – outcomes of one research can be used and replicated in other research as well and is not exclusive to only one study.  

Quantitative research methods 5

Quantitative research methods are classified into two types—primary and secondary.  

Primary quantitative research method:

In this type of quantitative research , data are directly collected by the researchers using the following methods.

– Survey research : Surveys are the easiest and most commonly used quantitative research method . They are of two types— cross-sectional and longitudinal.   

->Cross-sectional surveys are specifically conducted on a target population for a specified period, that is, these surveys have a specific starting and ending time and researchers study the events during this period to arrive at conclusions. The main purpose of these surveys is to describe and assess the characteristics of a population. There is one independent variable in this study, which is a common factor applicable to all participants in the population, for example, living in a specific city, diagnosed with a specific disease, of a certain age group, etc. An example of a cross-sectional survey is a study to understand why individuals residing in houses built before 1979 in the US are more susceptible to lead contamination.  

->Longitudinal surveys are conducted at different time durations. These surveys involve observing the interactions among different variables in the target population, exposing them to various causal factors, and understanding their effects across a longer period. These studies are helpful to analyze a problem in the long term. An example of a longitudinal study is the study of the relationship between smoking and lung cancer over a long period.  

– Descriptive research : Explains the current status of an identified and measurable variable. Unlike other types of quantitative research , a hypothesis is not needed at the beginning of the study and can be developed even after data collection. This type of quantitative research describes the characteristics of a problem and answers the what, when, where of a problem. However, it doesn’t answer the why of the problem and doesn’t explore cause-and-effect relationships between variables. Data from this research could be used as preliminary data for another study. Example: A researcher undertakes a study to examine the growth strategy of a company. This sample data can be used by other companies to determine their own growth strategy.  

– Correlational research : This quantitative research method is used to establish a relationship between two variables using statistical analysis and analyze how one affects the other. The research is non-experimental because the researcher doesn’t control or manipulate any of the variables. At least two separate sample groups are needed for this research. Example: Researchers studying a correlation between regular exercise and diabetes.  

– Causal-comparative research : This type of quantitative research examines the cause-effect relationships in retrospect between a dependent and independent variable and determines the causes of the already existing differences between groups of people. This is not a true experiment because it doesn’t assign participants to groups randomly. Example: To study the wage differences between men and women in the same role. For this, already existing wage information is analyzed to understand the relationship.  

– Experimental research : This quantitative research method uses true experiments or scientific methods for determining a cause-effect relation between variables. It involves testing a hypothesis through experiments, in which one or more independent variables are manipulated and then their effect on dependent variables are studied. Example: A researcher studies the importance of a drug in treating a disease by administering the drug in few patients and not administering in a few.  

The following data collection methods are commonly used in primary quantitative research :  

• Sampling : The most common type is probability sampling, in which a sample is chosen from a larger population using some form of random selection, that is, every member of the population has an equal chance of being selected. The different types of probability sampling are—simple random, systematic, stratified, and cluster sampling.  
• Interviews : These are commonly telephonic or face-to-face.  
• Observations : Structured observations are most commonly used in quantitative research . In this method, researchers make observations about specific behaviors of individuals in a structured setting.  
• Document review : Reviewing existing research or documents to collect evidence for supporting the quantitative research .  
• Surveys and questionnaires : Surveys can be administered both online and offline depending on the requirement and sample size.

The data collected can be analyzed in several ways in quantitative research , as listed below:  

• Cross-tabulation —Uses a tabular format to draw inferences among collected data  
• MaxDiff analysis —Gauges the preferences of the respondents  
• TURF analysis —Total Unduplicated Reach and Frequency Analysis; helps in determining the market strategy for a business  
• Gap analysis —Identify gaps in attaining the desired results  
• SWOT analysis —Helps identify strengths, weaknesses, opportunities, and threats of a product, service, or organization  
• Text analysis —Used for interpreting unstructured data  

Secondary quantitative research methods :

This method involves conducting research using already existing or secondary data. This method is less effort intensive and requires lesser time. However, researchers should verify the authenticity and recency of the sources being used and ensure their accuracy.  

The main sources of secondary data are: 

• The Internet  
• Government and non-government sources  
• Public libraries  
• Educational institutions  
• Commercial information sources such as newspapers, journals, radio, TV  

When to use quantitative research 6  

Here are some simple ways to decide when to use quantitative research . Use quantitative research to:  

• recommend a final course of action  
• find whether a consensus exists regarding a particular subject  
• generalize results to a larger population  
• determine a cause-and-effect relationship between variables  
• describe characteristics of specific groups of people  
• test hypotheses and examine specific relationships  
• identify and establish size of market segments  

A research case study to understand when to use quantitative research 7  

Context: A study was undertaken to evaluate a major innovation in a hospital’s design, in terms of workforce implications and impact on patient and staff experiences of all single-room hospital accommodations. The researchers undertook a mixed methods approach to answer their research questions. Here, we focus on the quantitative research aspect.  

Research questions : What are the advantages and disadvantages for the staff as a result of the hospital’s move to the new design with all single-room accommodations? Did the move affect staff experience and well-being and improve their ability to deliver high-quality care?  

Method: The researchers obtained quantitative data from three sources:  

• Staff activity (task time distribution): Each staff member was shadowed by a researcher who observed each task undertaken by the staff, and logged the time spent on each activity.  
• Staff travel distances : The staff were requested to wear pedometers, which recorded the distances covered.  
• Staff experience surveys : Staff were surveyed before and after the move to the new hospital design.  

Results of quantitative research : The following observations were made based on quantitative data analysis:  

• The move to the new design did not result in a significant change in the proportion of time spent on different activities.  
• Staff activity events observed per session were higher after the move, and direct care and professional communication events per hour decreased significantly, suggesting fewer interruptions and less fragmented care.  
• A significant increase in medication tasks among the recorded events suggests that medication administration was integrated into patient care activities.  
• Travel distances increased for all staff, with highest increases for staff in the older people’s ward and surgical wards.  
• Ratings for staff toilet facilities, locker facilities, and space at staff bases were higher but those for social interaction and natural light were lower.  

Advantages of quantitative research 1,2

When choosing the right research methodology, also consider the advantages of quantitative research and how it can impact your study.  

• Quantitative research methods are more scientific and rational. They use quantifiable data leading to objectivity in the results and avoid any chances of ambiguity.  
• This type of research uses numeric data so analysis is relatively easier .  
• In most cases, a hypothesis is already developed and quantitative research helps in testing and validatin g these constructed theories based on which researchers can make an informed decision about accepting or rejecting their theory.  
• The use of statistical analysis software ensures quick analysis of large volumes of data and is less effort intensive.  
• Higher levels of control can be applied to the research so the chances of bias can be reduced.  
• Quantitative research is based on measured value s, facts, and verifiable information so it can be easily checked or replicated by other researchers leading to continuity in scientific research.  

Disadvantages of quantitative research 1,2

Quantitative research may also be limiting; take a look at the disadvantages of quantitative research. 

• Experiments are conducted in controlled settings instead of natural settings and it is possible for researchers to either intentionally or unintentionally manipulate the experiment settings to suit the results they desire.  
• Participants must necessarily give objective answers (either one- or two-word, or yes or no answers) and the reasons for their selection or the context are not considered.   
• Inadequate knowledge of statistical analysis methods may affect the results and their interpretation.  
• Although statistical analysis indicates the trends or patterns among variables, the reasons for these observed patterns cannot be interpreted and the research may not give a complete picture.  
• Large sample sizes are needed for more accurate and generalizable analysis .  
• Quantitative research cannot be used to address complex issues.  

Frequently asked questions on  quantitative research    

Q:  What is the difference between quantitative research and qualitative research? 1  

A:  The following table lists the key differences between quantitative research and qualitative research, some of which may have been mentioned earlier in the article.  

Q:  What is the difference between reliability and validity? 8,9    

A:  The term reliability refers to the consistency of a research study. For instance, if a food-measuring weighing scale gives different readings every time the same quantity of food is measured then that weighing scale is not reliable. If the findings in a research study are consistent every time a measurement is made, then the study is considered reliable. However, it is usually unlikely to obtain the exact same results every time because some contributing variables may change. In such cases, a correlation coefficient is used to assess the degree of reliability. A strong positive correlation between the results indicates reliability.  

Validity can be defined as the degree to which a tool actually measures what it claims to measure. It helps confirm the credibility of your research and suggests that the results may be generalizable. In other words, it measures the accuracy of the research.  

The following table gives the key differences between reliability and validity.  

Q:  What is mixed methods research? 10

A:  A mixed methods approach combines the characteristics of both quantitative research and qualitative research in the same study. This method allows researchers to validate their findings, verify if the results observed using both methods are complementary, and explain any unexpected results obtained from one method by using the other method. A mixed methods research design is useful in case of research questions that cannot be answered by either quantitative research or qualitative research alone. However, this method could be more effort- and cost-intensive because of the requirement of more resources. The figure 3 shows some basic mixed methods research designs that could be used.  

Thus, quantitative research is the appropriate method for testing your hypotheses and can be used either alone or in combination with qualitative research per your study requirements. We hope this article has provided an insight into the various facets of quantitative research , including its different characteristics, advantages, and disadvantages, and a few tips to quickly understand when to use this research method.  

References  

• Qualitative vs quantitative research: Differences, examples, & methods. Simply Psychology. Accessed Feb 28, 2023. https://simplypsychology.org/qualitative-quantitative.html#Quantitative-Research  
• Your ultimate guide to quantitative research. Qualtrics. Accessed February 28, 2023. https://www.qualtrics.com/uk/experience-management/research/quantitative-research/  
• The steps of quantitative research. Revise Sociology. Accessed March 1, 2023. https://revisesociology.com/2017/11/26/the-steps-of-quantitative-research/  
• What are the characteristics of quantitative research? Marketing91. Accessed March 1, 2023. https://www.marketing91.com/characteristics-of-quantitative-research/  
• Quantitative research: Types, characteristics, methods, & examples. ProProfs Survey Maker. Accessed February 28, 2023. https://www.proprofssurvey.com/blog/quantitative-research/#Characteristics_of_Quantitative_Research  
• Qualitative research isn’t as scientific as quantitative methods. Kmusial blog. Accessed March 5, 2023. https://kmusial.wordpress.com/2011/11/25/qualitative-research-isnt-as-scientific-as-quantitative-methods/  
• Maben J, Griffiths P, Penfold C, et al. Evaluating a major innovation in hospital design: workforce implications and impact on patient and staff experiences of all single room hospital accommodation. Southampton (UK): NIHR Journals Library; 2015 Feb. (Health Services and Delivery Research, No. 3.3.) Chapter 5, Case study quantitative data findings. Accessed March 6, 2023. https://www.ncbi.nlm.nih.gov/books/NBK274429/  
• McLeod, S. A. (2007).  What is reliability?  Simply Psychology. www.simplypsychology.org/reliability.html  
• Reliability vs validity: Differences & examples. Accessed March 5, 2023. https://statisticsbyjim.com/basics/reliability-vs-validity/  
• Mixed methods research. Community Engagement Program. Harvard Catalyst. Accessed February 28, 2023. https://catalyst.harvard.edu/community-engagement/mmr  

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• What Is Quantitative Research? | Definition & Methods

What Is Quantitative Research? | Definition & Methods

Published on 4 April 2022 by Pritha Bhandari . Revised on 10 October 2022.

Quantitative research is the process of collecting and analysing numerical data. It can be used to find patterns and averages, make predictions, test causal relationships, and generalise results to wider populations.

Quantitative research is the opposite of qualitative research , which involves collecting and analysing non-numerical data (e.g. text, video, or audio).

Quantitative research is widely used in the natural and social sciences: biology, chemistry, psychology, economics, sociology, marketing, etc.

• What is the demographic makeup of Singapore in 2020?
• How has the average temperature changed globally over the last century?
• Does environmental pollution affect the prevalence of honey bees?
• Does working from home increase productivity for people with long commutes?

Table of contents

Quantitative research methods, quantitative data analysis, advantages of quantitative research, disadvantages of quantitative research, frequently asked questions about quantitative research.

You can use quantitative research methods for descriptive, correlational or experimental research.

• In descriptive research , you simply seek an overall summary of your study variables.
• In correlational research , you investigate relationships between your study variables.
• In experimental research , you systematically examine whether there is a cause-and-effect relationship between variables.

Correlational and experimental research can both be used to formally test hypotheses , or predictions, using statistics. The results may be generalised to broader populations based on the sampling method used.

To collect quantitative data, you will often need to use operational definitions that translate abstract concepts (e.g., mood) into observable and quantifiable measures (e.g., self-ratings of feelings and energy levels).

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Once data is collected, you may need to process it before it can be analysed. For example, survey and test data may need to be transformed from words to numbers. Then, you can use statistical analysis to answer your research questions .

Descriptive statistics will give you a summary of your data and include measures of averages and variability. You can also use graphs, scatter plots and frequency tables to visualise your data and check for any trends or outliers.

Using inferential statistics , you can make predictions or generalisations based on your data. You can test your hypothesis or use your sample data to estimate the population parameter .

You can also assess the reliability and validity of your data collection methods to indicate how consistently and accurately your methods actually measured what you wanted them to.

Quantitative research is often used to standardise data collection and generalise findings . Strengths of this approach include:

• Replication

Repeating the study is possible because of standardised data collection protocols and tangible definitions of abstract concepts.

• Direct comparisons of results

The study can be reproduced in other cultural settings, times or with different groups of participants. Results can be compared statistically.

• Large samples

Data from large samples can be processed and analysed using reliable and consistent procedures through quantitative data analysis.

• Hypothesis testing

Using formalised and established hypothesis testing procedures means that you have to carefully consider and report your research variables, predictions, data collection and testing methods before coming to a conclusion.

Despite the benefits of quantitative research, it is sometimes inadequate in explaining complex research topics. Its limitations include:

• Superficiality

Using precise and restrictive operational definitions may inadequately represent complex concepts. For example, the concept of mood may be represented with just a number in quantitative research, but explained with elaboration in qualitative research.

• Narrow focus

Predetermined variables and measurement procedures can mean that you ignore other relevant observations.

• Structural bias

Despite standardised procedures, structural biases can still affect quantitative research. Missing data , imprecise measurements or inappropriate sampling methods are biases that can lead to the wrong conclusions.

• Lack of context

Quantitative research often uses unnatural settings like laboratories or fails to consider historical and cultural contexts that may affect data collection and results.

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to test a hypothesis by systematically collecting and analysing data, while qualitative methods allow you to explore ideas and experiences in depth.

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

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.

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.

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.

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.

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Quantitative research methods are concerned with the planning, design, and implementation of strategies to collect and analyze data. Descartes, the seventeenth-century philosopher, suggested that how the results are achieved is often more important than the results themselves, as the journey taken along the research path is a journey of discovery. High-quality quantitative research is characterized by the attention given to the methods and the reliability of the tools used to collect the data. The ability to critique research in a systematic way is an essential component of a health professional’s role in order to deliver high quality, evidence-based healthcare. This chapter is intended to provide a simple overview of the way new researchers and health practitioners can understand and employ quantitative methods. The chapter offers practical, realistic guidance in a learner-friendly way and uses a logical sequence to understand the process of hypothesis development, study design, data collection and handling, and finally data analysis and interpretation.

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Qualitative Research Methods

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Quantitative Research Design (JARS–Quant)

The current JARS–Quant standards, released in 2018, expand and revise the types of research methodologies covered in the original JARS, which were published in 2008.

JARS–Quant include guidance for manuscripts that report

• Primary quantitative research
• Experimental designs
• Nonexperimental designs

Special designs

Analytic methods, meta-analyses.

In addition, JARS–Quant now divides hypotheses, analyses, and conclusions into primary, secondary, and exploratory groups. This should enhance the readability and replicability of the research.

Providing the information specified in JARS–Quant should become routine and minimally burdensome, thereby increasing the transparency of reporting in psychological research.

For more information on how the revised standards were created, read Journal Article Reporting Standards for Quantitative Research in Psychology .

General quantitative reporting standards

• Quantitative Design Reporting Standards (JARS-Quant) (PDF, 137KB) Information recommended for inclusion in manuscripts that report new data collections regardless of research design

Experimental and nonexperimental designs

• Experimental Designs (PDF, 109KB) Reporting standards for studies with an experimental manipulation
• Random Assignment (PDF, 101KB) Reporting standards for studies using random assignment
• Nonrandom Assignment (PDF, 92KB) Reporting standards for studies using nonrandom assignment
• Clinical Trials (PDF, 106KB) Reporting standards for studies involving clinical trials
• Nonexperimental Designs (PDF, 103KB) Reporting standards for studies using no experimental manipulation
• Longitudinal Studies (PDF, 103KB) Reporting standards for longitudinal studies
• N -of-1 Studies (PDF, 102KB) Reporting standards for N -of-1 studies
• Replication Studies (PDF, 95KB) Reporting standards for replication studies
• Structural Equation Modeling (PDF, 111KB) Reporting standards for studies using structural equation modeling
• Bayesian Statistics (PDF, 104KB) Reporting standards for studies using Bayesian techniques
• Quantitative Meta-Analysis Reporting Standards (PDF, 116KB) Information recommended for inclusion in manuscripts that report quantitative meta-analyses
• Qualitative design standards
• Mixed methods standards
• Race, Ethnicity, and Culture standards

Return to Journal Article Reporting Standards homepage

Jars resources

• History of APA’s journal article reporting standards
• APA Style JARS supplemental glossary
• Supplemental resource on the ethic of transparency in JARS
• Frequently asked questions
• JARS-Quant Decision Flowchart (PDF, 98KB)
• JARS-Quant Participant Flowchart (PDF, 98KB)

Jars articles

• Jars –Quant article
• Jars –Qual / Mixed article
• Jars – rec executive summary

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Understanding Quantitative Research: Definition, Collection methods, Design, Analysis and Reporting

Quantitative studies play an essential role in scientific and academic research. By enabling numerical data to be measured and analyzed with precision, quantitative surveys provide objective and generalizable results , often unattainable by qualitative methods . A student who undertakes a quantitative survey as part of his or her dissertation or thesis acquires crucial skills such as analytical rigor, mastery of statistical techniques and the ability to interpret numerical data. In this way, they can make a significant contribution to their field of research.  

Contents What is a quantitative study? What are the data collection methods for a quantitative study? How to design and plan a quantitative study? How can quantitative data be successfully analyzed and reported?

What is quantitative research?

Definition, objectives and benefits of quantitative research  .

“Quantitative research is a methodology that provides support when you need to draw general conclusions from your research and predict outcomes. These methods are designed to collect numerical data that can be used to measure variables. ” Survey Monkey, Qualitative vs. quantitative research: What's the difference?

The advantages of this method include : 

• the possibility of obtaining objective, reliable data, 
• the application of rigorous statistical models
• the ability to make comparisons on a large scale and over time,
• the ability to reveal causal relationships between variables, thus providing a basis for decision-making.

Differences between qualitative and quantitative research

Which data collection methods for a quantitative study?

Primary collection using surveys/questionnaires .

Primary data collection means that the researcher collects data directly from the sample, without relying on data collected in previous quantitative surveys. Questionnaires are the most common method used in quantitative research. They can be administered online, by telephone or in person to large population samples. Standardized questionnaires guarantee uniform data collection, delivering statistically significant results.

Primary data collection in longitudinal studies

Longitudinal studies follow the same participants over a long period, offering insights into evolutions and trends over time. They are particularly useful for studying changes in behavior, attitudes or conditions over different phases.

Primary collection by experimental research

Primary collection by experimental research involves the deliberate creation and manipulation of variables in a controlled environment to observe their direct effects on other variables . This method enables researchers to test specific hypotheses and establish cause-and-effect relationships with great precision. 

“In this method, the theory being studied has not yet been proven; it is merely speculation. Thus, an experiment is carried out to prove or disprove the theory.” Voxco, Quantitative research: Definition, methods and examples

This approach is particularly useful for studies where internal validity and methodological rigor are crucial.

Secondary analysis of quantitative data

Secondary data analysis uses existing databases to re-analyze information and answer new research questions. This method is effective in fully exploiting available data, and can reveal additional insights without the need for new data collection.

How do you design and plan a quantitative study?

Defining research objectives.

The first step in designing a quantitative study is to clarify the research objectives . This involves determining what the quanti study seeks to achieve and the specific questions it aims to answer. These objectives will guide the entire research process.

Choosing the data collection method

Depending on your research objectives, choose the most appropriate data collection method . This is what we developed in the previous section.

Select the sample

The representativeness of the results will depend on the selection of the sample. Determine the necessary size and the sampling method (random, stratified, etc.) to ensure that the sample accurately reflects the target population.

Design measurement instruments

Design measurement instruments, such as questionnaires or experimental protocols, t hat are clear, precise and adapted to the study objective. Questions should be formulated in such a way as to minimize bias (mostly closed-ended questions ) and easily yield usable figures.

Planning data analysis

Before collecting data, plan how it will be analyzed. This includes selecting appropriate statistical techniques and using data analysis software. Advance planning ensures that the data collected will answer the research questions validly and reliably.

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How to successfully analyze and report quantitative data?

Checking and cleaning quantitative data

Before starting analysis, it's important to ensure that data is complete and error-free. Identify and manage missing data, correct anomalies and eliminate duplicates, while guaranteeing the integrity of the information.  

Initial exploration of quantitative data

Perform initial data exploration. Analyze measures of central tendency (mean, median) and dispersion (standard deviation, variance). Use visualizations such as histograms, whisker boxes and scatter plots to detect trends, distributions and possible anomalies.

Selecting statistical methods

Select the appropriate statistical methods according to your research objectives and the nature of the data. 

• For comparisons between groups, use tests such as the T-test or ANOVA . 
• To analyze relationships between variables, consider regression techniques .  

Analysis and interpretation of results

Interpret the results in the context of your study. Relate the findings to the original hypotheses and discuss their relevance to the research question. Consider the practical and theoretical implications of the results, as well as their limitations and potential implications for future research.

Clear, visual presentation of quantitative results

Use tables and graphs to illustrate your quantitative results in a concise and accessible way. Make sure visualizations are well-labeled, understandable and directly linked to key findings. The visual aspect helps to communicate results effectively and convincingly. 

It's also vital to document each stage of the analysis in detail in a quantitative survey report. Include :

• a methodological description
• analysis results
• visualizations
• your interpretations .

A well-structured report validates the rigor of your analysis and makes it easier for other researchers to understand and reproduce your results.  

Quantitative studies represent a fundamental pillar in the world of research, offering powerful tools for the collection and analysis of objective data. Using rigorous methods and advanced statistical techniques, they deliver reliable, generalizable results that are invaluable for decision-making. Whether designing surveys, analyzing data or presenting results, a well-planned and executed quantitative approach can not only strengthen the validity of findings, but also enrich the overall understanding of the research field. By mastering these skills, researchers and students make a significant contribution to the advancement of scientific knowledge.

Discover other practical guides to conducting effective quantitative research: 

Types of quantitative research , Lyssna Quantitative Research Methods,  Nova Southeastern University A Guide To Conducting Great Quantitative Research, EngageSpark

Information: This informative article was written in part with the help of ChatGPT. The content generated by AI has been reworked to check the veracity of the information, the relevance of the instructions and to add clarifications.

What are the quantitative studies?

What's the difference between qualitative and quantitative research?

Why conduct a quantitative study?

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Research Design 101

Everything You Need To Get Started (With Examples)

By: Derek Jansen (MBA) | Reviewers: Eunice Rautenbach (DTech) & Kerryn Warren (PhD) | April 2023

Navigating the world of research can be daunting, especially if you’re a first-time researcher. One concept you’re bound to run into fairly early in your research journey is that of “ research design ”. Here, we’ll guide you through the basics using practical examples , so that you can approach your research with confidence.

Overview: Research Design 101

What is research design.

• Research design types for quantitative studies
• Video explainer : quantitative research design
• Research design types for qualitative studies
• Video explainer : qualitative research design
• How to choose a research design
• Key takeaways

Research design refers to the overall plan, structure or strategy that guides a research project , from its conception to the final data analysis. A good research design serves as the blueprint for how you, as the researcher, will collect and analyse data while ensuring consistency, reliability and validity throughout your study.

Understanding different types of research designs is essential as helps ensure that your approach is suitable  given your research aims, objectives and questions , as well as the resources you have available to you. Without a clear big-picture view of how you’ll design your research, you run the risk of potentially making misaligned choices in terms of your methodology – especially your sampling , data collection and data analysis decisions.

The problem with defining research design…

One of the reasons students struggle with a clear definition of research design is because the term is used very loosely across the internet, and even within academia.

Some sources claim that the three research design types are qualitative, quantitative and mixed methods , which isn’t quite accurate (these just refer to the type of data that you’ll collect and analyse). Other sources state that research design refers to the sum of all your design choices, suggesting it’s more like a research methodology . Others run off on other less common tangents. No wonder there’s confusion!

In this article, we’ll clear up the confusion. We’ll explain the most common research design types for both qualitative and quantitative research projects, whether that is for a full dissertation or thesis, or a smaller research paper or article.

Research Design: Quantitative Studies

Quantitative research involves collecting and analysing data in a numerical form. Broadly speaking, there are four types of quantitative research designs: descriptive , correlational , experimental , and quasi-experimental . 

Descriptive Research Design

As the name suggests, descriptive research design focuses on describing existing conditions, behaviours, or characteristics by systematically gathering information without manipulating any variables. In other words, there is no intervention on the researcher’s part – only data collection.

For example, if you’re studying smartphone addiction among adolescents in your community, you could deploy a survey to a sample of teens asking them to rate their agreement with certain statements that relate to smartphone addiction. The collected data would then provide insight regarding how widespread the issue may be – in other words, it would describe the situation.

The key defining attribute of this type of research design is that it purely describes the situation . In other words, descriptive research design does not explore potential relationships between different variables or the causes that may underlie those relationships. Therefore, descriptive research is useful for generating insight into a research problem by describing its characteristics . By doing so, it can provide valuable insights and is often used as a precursor to other research design types.

Correlational Research Design

Correlational design is a popular choice for researchers aiming to identify and measure the relationship between two or more variables without manipulating them . In other words, this type of research design is useful when you want to know whether a change in one thing tends to be accompanied by a change in another thing.

For example, if you wanted to explore the relationship between exercise frequency and overall health, you could use a correlational design to help you achieve this. In this case, you might gather data on participants’ exercise habits, as well as records of their health indicators like blood pressure, heart rate, or body mass index. Thereafter, you’d use a statistical test to assess whether there’s a relationship between the two variables (exercise frequency and health).

As you can see, correlational research design is useful when you want to explore potential relationships between variables that cannot be manipulated or controlled for ethical, practical, or logistical reasons. It is particularly helpful in terms of developing predictions , and given that it doesn’t involve the manipulation of variables, it can be implemented at a large scale more easily than experimental designs (which will look at next).

That said, it’s important to keep in mind that correlational research design has limitations – most notably that it cannot be used to establish causality . In other words, correlation does not equal causation . To establish causality, you’ll need to move into the realm of experimental design, coming up next…

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Experimental Research Design

Experimental research design is used to determine if there is a causal relationship between two or more variables . With this type of research design, you, as the researcher, manipulate one variable (the independent variable) while controlling others (dependent variables). Doing so allows you to observe the effect of the former on the latter and draw conclusions about potential causality.

For example, if you wanted to measure if/how different types of fertiliser affect plant growth, you could set up several groups of plants, with each group receiving a different type of fertiliser, as well as one with no fertiliser at all. You could then measure how much each plant group grew (on average) over time and compare the results from the different groups to see which fertiliser was most effective.

Overall, experimental research design provides researchers with a powerful way to identify and measure causal relationships (and the direction of causality) between variables. However, developing a rigorous experimental design can be challenging as it’s not always easy to control all the variables in a study. This often results in smaller sample sizes , which can reduce the statistical power and generalisability of the results.

Moreover, experimental research design requires random assignment . This means that the researcher needs to assign participants to different groups or conditions in a way that each participant has an equal chance of being assigned to any group (note that this is not the same as random sampling ). Doing so helps reduce the potential for bias and confounding variables . This need for random assignment can lead to ethics-related issues . For example, withholding a potentially beneficial medical treatment from a control group may be considered unethical in certain situations.

Quasi-Experimental Research Design

Quasi-experimental research design is used when the research aims involve identifying causal relations , but one cannot (or doesn’t want to) randomly assign participants to different groups (for practical or ethical reasons). Instead, with a quasi-experimental research design, the researcher relies on existing groups or pre-existing conditions to form groups for comparison.

For example, if you were studying the effects of a new teaching method on student achievement in a particular school district, you may be unable to randomly assign students to either group and instead have to choose classes or schools that already use different teaching methods. This way, you still achieve separate groups, without having to assign participants to specific groups yourself.

Naturally, quasi-experimental research designs have limitations when compared to experimental designs. Given that participant assignment is not random, it’s more difficult to confidently establish causality between variables, and, as a researcher, you have less control over other variables that may impact findings.

All that said, quasi-experimental designs can still be valuable in research contexts where random assignment is not possible and can often be undertaken on a much larger scale than experimental research, thus increasing the statistical power of the results. What’s important is that you, as the researcher, understand the limitations of the design and conduct your quasi-experiment as rigorously as possible, paying careful attention to any potential confounding variables .

Research Design: Qualitative Studies

There are many different research design types when it comes to qualitative studies, but here we’ll narrow our focus to explore the “Big 4”. Specifically, we’ll look at phenomenological design, grounded theory design, ethnographic design, and case study design.

Phenomenological Research Design

Phenomenological design involves exploring the meaning of lived experiences and how they are perceived by individuals. This type of research design seeks to understand people’s perspectives , emotions, and behaviours in specific situations. Here, the aim for researchers is to uncover the essence of human experience without making any assumptions or imposing preconceived ideas on their subjects.

For example, you could adopt a phenomenological design to study why cancer survivors have such varied perceptions of their lives after overcoming their disease. This could be achieved by interviewing survivors and then analysing the data using a qualitative analysis method such as thematic analysis to identify commonalities and differences.

Phenomenological research design typically involves in-depth interviews or open-ended questionnaires to collect rich, detailed data about participants’ subjective experiences. This richness is one of the key strengths of phenomenological research design but, naturally, it also has limitations. These include potential biases in data collection and interpretation and the lack of generalisability of findings to broader populations.

Grounded Theory Research Design

Grounded theory (also referred to as “GT”) aims to develop theories by continuously and iteratively analysing and comparing data collected from a relatively large number of participants in a study. It takes an inductive (bottom-up) approach, with a focus on letting the data “speak for itself”, without being influenced by preexisting theories or the researcher’s preconceptions.

As an example, let’s assume your research aims involved understanding how people cope with chronic pain from a specific medical condition, with a view to developing a theory around this. In this case, grounded theory design would allow you to explore this concept thoroughly without preconceptions about what coping mechanisms might exist. You may find that some patients prefer cognitive-behavioural therapy (CBT) while others prefer to rely on herbal remedies. Based on multiple, iterative rounds of analysis, you could then develop a theory in this regard, derived directly from the data (as opposed to other preexisting theories and models).

Grounded theory typically involves collecting data through interviews or observations and then analysing it to identify patterns and themes that emerge from the data. These emerging ideas are then validated by collecting more data until a saturation point is reached (i.e., no new information can be squeezed from the data). From that base, a theory can then be developed .

As you can see, grounded theory is ideally suited to studies where the research aims involve theory generation , especially in under-researched areas. Keep in mind though that this type of research design can be quite time-intensive , given the need for multiple rounds of data collection and analysis.

Ethnographic Research Design

Ethnographic design involves observing and studying a culture-sharing group of people in their natural setting to gain insight into their behaviours, beliefs, and values. The focus here is on observing participants in their natural environment (as opposed to a controlled environment). This typically involves the researcher spending an extended period of time with the participants in their environment, carefully observing and taking field notes .

All of this is not to say that ethnographic research design relies purely on observation. On the contrary, this design typically also involves in-depth interviews to explore participants’ views, beliefs, etc. However, unobtrusive observation is a core component of the ethnographic approach.

As an example, an ethnographer may study how different communities celebrate traditional festivals or how individuals from different generations interact with technology differently. This may involve a lengthy period of observation, combined with in-depth interviews to further explore specific areas of interest that emerge as a result of the observations that the researcher has made.

As you can probably imagine, ethnographic research design has the ability to provide rich, contextually embedded insights into the socio-cultural dynamics of human behaviour within a natural, uncontrived setting. Naturally, however, it does come with its own set of challenges, including researcher bias (since the researcher can become quite immersed in the group), participant confidentiality and, predictably, ethical complexities . All of these need to be carefully managed if you choose to adopt this type of research design.

Case Study Design

With case study research design, you, as the researcher, investigate a single individual (or a single group of individuals) to gain an in-depth understanding of their experiences, behaviours or outcomes. Unlike other research designs that are aimed at larger sample sizes, case studies offer a deep dive into the specific circumstances surrounding a person, group of people, event or phenomenon, generally within a bounded setting or context .

As an example, a case study design could be used to explore the factors influencing the success of a specific small business. This would involve diving deeply into the organisation to explore and understand what makes it tick – from marketing to HR to finance. In terms of data collection, this could include interviews with staff and management, review of policy documents and financial statements, surveying customers, etc.

While the above example is focused squarely on one organisation, it’s worth noting that case study research designs can have different variation s, including single-case, multiple-case and longitudinal designs. As you can see in the example, a single-case design involves intensely examining a single entity to understand its unique characteristics and complexities. Conversely, in a multiple-case design , multiple cases are compared and contrasted to identify patterns and commonalities. Lastly, in a longitudinal case design , a single case or multiple cases are studied over an extended period of time to understand how factors develop over time.

As you can see, a case study research design is particularly useful where a deep and contextualised understanding of a specific phenomenon or issue is desired. However, this strength is also its weakness. In other words, you can’t generalise the findings from a case study to the broader population. So, keep this in mind if you’re considering going the case study route.

How To Choose A Research Design

Having worked through all of these potential research designs, you’d be forgiven for feeling a little overwhelmed and wondering, “ But how do I decide which research design to use? ”. While we could write an entire post covering that alone, here are a few factors to consider that will help you choose a suitable research design for your study.

Data type: The first determining factor is naturally the type of data you plan to be collecting – i.e., qualitative or quantitative. This may sound obvious, but we have to be clear about this – don’t try to use a quantitative research design on qualitative data (or vice versa)!

Research aim(s) and question(s): As with all methodological decisions, your research aim and research questions will heavily influence your research design. For example, if your research aims involve developing a theory from qualitative data, grounded theory would be a strong option. Similarly, if your research aims involve identifying and measuring relationships between variables, one of the experimental designs would likely be a better option.

Time: It’s essential that you consider any time constraints you have, as this will impact the type of research design you can choose. For example, if you’ve only got a month to complete your project, a lengthy design such as ethnography wouldn’t be a good fit.

Resources: Take into account the resources realistically available to you, as these need to factor into your research design choice. For example, if you require highly specialised lab equipment to execute an experimental design, you need to be sure that you’ll have access to that before you make a decision.

Keep in mind that when it comes to research, it’s important to manage your risks and play as conservatively as possible. If your entire project relies on you achieving a huge sample, having access to niche equipment or holding interviews with very difficult-to-reach participants, you’re creating risks that could kill your project. So, be sure to think through your choices carefully and make sure that you have backup plans for any existential risks. Remember that a relatively simple methodology executed well generally will typically earn better marks than a highly-complex methodology executed poorly.

Recap: Key Takeaways

We’ve covered a lot of ground here. Let’s recap by looking at the key takeaways:

• Research design refers to the overall plan, structure or strategy that guides a research project, from its conception to the final analysis of data.
• Research designs for quantitative studies include descriptive , correlational , experimental and quasi-experimenta l designs.
• Research designs for qualitative studies include phenomenological , grounded theory , ethnographic and case study designs.
• When choosing a research design, you need to consider a variety of factors, including the type of data you’ll be working with, your research aims and questions, your time and the resources available to you.

If you need a helping hand with your research design (or any other aspect of your research), check out our private coaching services .

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10 Comments

Is there any blog article explaining more on Case study research design? Is there a Case study write-up template? Thank you.

Thanks this was quite valuable to clarify such an important concept.

Thanks for this simplified explanations. it is quite very helpful.

This was really helpful. thanks

Thank you for your explanation. I think case study research design and the use of secondary data in researches needs to be talked about more in your videos and articles because there a lot of case studies research design tailored projects out there.

Please is there any template for a case study research design whose data type is a secondary data on your repository?

This post is very clear, comprehensive and has been very helpful to me. It has cleared the confusion I had in regard to research design and methodology.

This post is helpful, easy to understand, and deconstructs what a research design is. Thanks

how to cite this page

Thank you very much for the post. It is wonderful and has cleared many worries in my mind regarding research designs. I really appreciate .

how can I put this blog as my reference(APA style) in bibliography part?

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The Beginner's Guide to Statistical Analysis | 5 Steps & Examples

Statistical analysis means investigating trends, patterns, and relationships using quantitative data . It is an important research tool used by scientists, governments, businesses, and other organizations.

To draw valid conclusions, statistical analysis requires careful planning from the very start of the research process . You need to specify your hypotheses and make decisions about your research design, sample size, and sampling procedure.

After collecting data from your sample, you can organize and summarize the data using descriptive statistics . Then, you can use inferential statistics to formally test hypotheses and make estimates about the population. Finally, you can interpret and generalize your findings.

This article is a practical introduction to statistical analysis for students and researchers. We’ll walk you through the steps using two research examples. The first investigates a potential cause-and-effect relationship, while the second investigates a potential correlation between variables.

Table of contents

Step 1: write your hypotheses and plan your research design, step 2: collect data from a sample, step 3: summarize your data with descriptive statistics, step 4: test hypotheses or make estimates with inferential statistics, step 5: interpret your results, other interesting articles.

To collect valid data for statistical analysis, you first need to specify your hypotheses and plan out your research design.

Writing statistical hypotheses

The goal of research is often to investigate a relationship between variables within a population . You start with a prediction, and use statistical analysis to test that prediction.

A statistical hypothesis is a formal way of writing a prediction about a population. Every research prediction is rephrased into null and alternative hypotheses that can be tested using sample data.

While the null hypothesis always predicts no effect or no relationship between variables, the alternative hypothesis states your research prediction of an effect or relationship.

• Null hypothesis: A 5-minute meditation exercise will have no effect on math test scores in teenagers.
• Alternative hypothesis: A 5-minute meditation exercise will improve math test scores in teenagers.
• Null hypothesis: Parental income and GPA have no relationship with each other in college students.
• Alternative hypothesis: Parental income and GPA are positively correlated in college students.

Planning your research design

A research design is your overall strategy for data collection and analysis. It determines the statistical tests you can use to test your hypothesis later on.

First, decide whether your research will use a descriptive, correlational, or experimental design. Experiments directly influence variables, whereas descriptive and correlational studies only measure variables.

• In an experimental design , you can assess a cause-and-effect relationship (e.g., the effect of meditation on test scores) using statistical tests of comparison or regression.
• In a correlational design , you can explore relationships between variables (e.g., parental income and GPA) without any assumption of causality using correlation coefficients and significance tests.
• In a descriptive design , you can study the characteristics of a population or phenomenon (e.g., the prevalence of anxiety in U.S. college students) using statistical tests to draw inferences from sample data.

Your research design also concerns whether you’ll compare participants at the group level or individual level, or both.

• In a between-subjects design , you compare the group-level outcomes of participants who have been exposed to different treatments (e.g., those who performed a meditation exercise vs those who didn’t).
• In a within-subjects design , you compare repeated measures from participants who have participated in all treatments of a study (e.g., scores from before and after performing a meditation exercise).
• In a mixed (factorial) design , one variable is altered between subjects and another is altered within subjects (e.g., pretest and posttest scores from participants who either did or didn’t do a meditation exercise).
• Experimental
• Correlational

First, you’ll take baseline test scores from participants. Then, your participants will undergo a 5-minute meditation exercise. Finally, you’ll record participants’ scores from a second math test.

In this experiment, the independent variable is the 5-minute meditation exercise, and the dependent variable is the math test score from before and after the intervention. Example: Correlational research design In a correlational study, you test whether there is a relationship between parental income and GPA in graduating college students. To collect your data, you will ask participants to fill in a survey and self-report their parents’ incomes and their own GPA.

Measuring variables

When planning a research design, you should operationalize your variables and decide exactly how you will measure them.

For statistical analysis, it’s important to consider the level of measurement of your variables, which tells you what kind of data they contain:

• Categorical data represents groupings. These may be nominal (e.g., gender) or ordinal (e.g. level of language ability).
• Quantitative data represents amounts. These may be on an interval scale (e.g. test score) or a ratio scale (e.g. age).

Many variables can be measured at different levels of precision. For example, age data can be quantitative (8 years old) or categorical (young). If a variable is coded numerically (e.g., level of agreement from 1–5), it doesn’t automatically mean that it’s quantitative instead of categorical.

Identifying the measurement level is important for choosing appropriate statistics and hypothesis tests. For example, you can calculate a mean score with quantitative data, but not with categorical data.

In a research study, along with measures of your variables of interest, you’ll often collect data on relevant participant characteristics.

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In most cases, it’s too difficult or expensive to collect data from every member of the population you’re interested in studying. Instead, you’ll collect data from a sample.

Statistical analysis allows you to apply your findings beyond your own sample as long as you use appropriate sampling procedures . You should aim for a sample that is representative of the population.

Sampling for statistical analysis

There are two main approaches to selecting a sample.

• Probability sampling: every member of the population has a chance of being selected for the study through random selection.
• Non-probability sampling: some members of the population are more likely than others to be selected for the study because of criteria such as convenience or voluntary self-selection.

In theory, for highly generalizable findings, you should use a probability sampling method. Random selection reduces several types of research bias , like sampling bias , and ensures that data from your sample is actually typical of the population. Parametric tests can be used to make strong statistical inferences when data are collected using probability sampling.

But in practice, it’s rarely possible to gather the ideal sample. While non-probability samples are more likely to at risk for biases like self-selection bias , they are much easier to recruit and collect data from. Non-parametric tests are more appropriate for non-probability samples, but they result in weaker inferences about the population.

If you want to use parametric tests for non-probability samples, you have to make the case that:

• your sample is representative of the population you’re generalizing your findings to.
• your sample lacks systematic bias.

Keep in mind that external validity means that you can only generalize your conclusions to others who share the characteristics of your sample. For instance, results from Western, Educated, Industrialized, Rich and Democratic samples (e.g., college students in the US) aren’t automatically applicable to all non-WEIRD populations.

If you apply parametric tests to data from non-probability samples, be sure to elaborate on the limitations of how far your results can be generalized in your discussion section .

Create an appropriate sampling procedure

Based on the resources available for your research, decide on how you’ll recruit participants.

• Will you have resources to advertise your study widely, including outside of your university setting?
• Will you have the means to recruit a diverse sample that represents a broad population?
• Do you have time to contact and follow up with members of hard-to-reach groups?

Your participants are self-selected by their schools. Although you’re using a non-probability sample, you aim for a diverse and representative sample. Example: Sampling (correlational study) Your main population of interest is male college students in the US. Using social media advertising, you recruit senior-year male college students from a smaller subpopulation: seven universities in the Boston area.

Calculate sufficient sample size

Before recruiting participants, decide on your sample size either by looking at other studies in your field or using statistics. A sample that’s too small may be unrepresentative of the sample, while a sample that’s too large will be more costly than necessary.

There are many sample size calculators online. Different formulas are used depending on whether you have subgroups or how rigorous your study should be (e.g., in clinical research). As a rule of thumb, a minimum of 30 units or more per subgroup is necessary.

To use these calculators, you have to understand and input these key components:

• Significance level (alpha): the risk of rejecting a true null hypothesis that you are willing to take, usually set at 5%.
• Statistical power : the probability of your study detecting an effect of a certain size if there is one, usually 80% or higher.
• Expected effect size : a standardized indication of how large the expected result of your study will be, usually based on other similar studies.
• Population standard deviation: an estimate of the population parameter based on a previous study or a pilot study of your own.

Once you’ve collected all of your data, you can inspect them and calculate descriptive statistics that summarize them.

Inspect your data

There are various ways to inspect your data, including the following:

• Organizing data from each variable in frequency distribution tables .
• Displaying data from a key variable in a bar chart to view the distribution of responses.
• Visualizing the relationship between two variables using a scatter plot .

By visualizing your data in tables and graphs, you can assess whether your data follow a skewed or normal distribution and whether there are any outliers or missing data.

A normal distribution means that your data are symmetrically distributed around a center where most values lie, with the values tapering off at the tail ends.

In contrast, a skewed distribution is asymmetric and has more values on one end than the other. The shape of the distribution is important to keep in mind because only some descriptive statistics should be used with skewed distributions.

Extreme outliers can also produce misleading statistics, so you may need a systematic approach to dealing with these values.

Calculate measures of central tendency

Measures of central tendency describe where most of the values in a data set lie. Three main measures of central tendency are often reported:

• Mode : the most popular response or value in the data set.
• Median : the value in the exact middle of the data set when ordered from low to high.
• Mean : the sum of all values divided by the number of values.

However, depending on the shape of the distribution and level of measurement, only one or two of these measures may be appropriate. For example, many demographic characteristics can only be described using the mode or proportions, while a variable like reaction time may not have a mode at all.

Calculate measures of variability

Measures of variability tell you how spread out the values in a data set are. Four main measures of variability are often reported:

• Range : the highest value minus the lowest value of the data set.
• Interquartile range : the range of the middle half of the data set.
• Standard deviation : the average distance between each value in your data set and the mean.
• Variance : the square of the standard deviation.

Once again, the shape of the distribution and level of measurement should guide your choice of variability statistics. The interquartile range is the best measure for skewed distributions, while standard deviation and variance provide the best information for normal distributions.

Using your table, you should check whether the units of the descriptive statistics are comparable for pretest and posttest scores. For example, are the variance levels similar across the groups? Are there any extreme values? If there are, you may need to identify and remove extreme outliers in your data set or transform your data before performing a statistical test.

From this table, we can see that the mean score increased after the meditation exercise, and the variances of the two scores are comparable. Next, we can perform a statistical test to find out if this improvement in test scores is statistically significant in the population. Example: Descriptive statistics (correlational study) After collecting data from 653 students, you tabulate descriptive statistics for annual parental income and GPA.

It’s important to check whether you have a broad range of data points. If you don’t, your data may be skewed towards some groups more than others (e.g., high academic achievers), and only limited inferences can be made about a relationship.

A number that describes a sample is called a statistic , while a number describing a population is called a parameter . Using inferential statistics , you can make conclusions about population parameters based on sample statistics.

Researchers often use two main methods (simultaneously) to make inferences in statistics.

• Estimation: calculating population parameters based on sample statistics.
• Hypothesis testing: a formal process for testing research predictions about the population using samples.

You can make two types of estimates of population parameters from sample statistics:

• A point estimate : a value that represents your best guess of the exact parameter.
• An interval estimate : a range of values that represent your best guess of where the parameter lies.

If your aim is to infer and report population characteristics from sample data, it’s best to use both point and interval estimates in your paper.

You can consider a sample statistic a point estimate for the population parameter when you have a representative sample (e.g., in a wide public opinion poll, the proportion of a sample that supports the current government is taken as the population proportion of government supporters).

There’s always error involved in estimation, so you should also provide a confidence interval as an interval estimate to show the variability around a point estimate.

A confidence interval uses the standard error and the z score from the standard normal distribution to convey where you’d generally expect to find the population parameter most of the time.

Hypothesis testing

Using data from a sample, you can test hypotheses about relationships between variables in the population. Hypothesis testing starts with the assumption that the null hypothesis is true in the population, and you use statistical tests to assess whether the null hypothesis can be rejected or not.

Statistical tests determine where your sample data would lie on an expected distribution of sample data if the null hypothesis were true. These tests give two main outputs:

• A test statistic tells you how much your data differs from the null hypothesis of the test.
• A p value tells you the likelihood of obtaining your results if the null hypothesis is actually true in the population.

Statistical tests come in three main varieties:

• Comparison tests assess group differences in outcomes.
• Regression tests assess cause-and-effect relationships between variables.
• Correlation tests assess relationships between variables without assuming causation.

Your choice of statistical test depends on your research questions, research design, sampling method, and data characteristics.

Parametric tests

Parametric tests make powerful inferences about the population based on sample data. But to use them, some assumptions must be met, and only some types of variables can be used. If your data violate these assumptions, you can perform appropriate data transformations or use alternative non-parametric tests instead.

A regression models the extent to which changes in a predictor variable results in changes in outcome variable(s).

• A simple linear regression includes one predictor variable and one outcome variable.
• A multiple linear regression includes two or more predictor variables and one outcome variable.

Comparison tests usually compare the means of groups. These may be the means of different groups within a sample (e.g., a treatment and control group), the means of one sample group taken at different times (e.g., pretest and posttest scores), or a sample mean and a population mean.

• A t test is for exactly 1 or 2 groups when the sample is small (30 or less).
• A z test is for exactly 1 or 2 groups when the sample is large.
• An ANOVA is for 3 or more groups.

The z and t tests have subtypes based on the number and types of samples and the hypotheses:

• If you have only one sample that you want to compare to a population mean, use a one-sample test .
• If you have paired measurements (within-subjects design), use a dependent (paired) samples test .
• If you have completely separate measurements from two unmatched groups (between-subjects design), use an independent (unpaired) samples test .
• If you expect a difference between groups in a specific direction, use a one-tailed test .
• If you don’t have any expectations for the direction of a difference between groups, use a two-tailed test .

The only parametric correlation test is Pearson’s r . The correlation coefficient ( r ) tells you the strength of a linear relationship between two quantitative variables.

However, to test whether the correlation in the sample is strong enough to be important in the population, you also need to perform a significance test of the correlation coefficient, usually a t test, to obtain a p value. This test uses your sample size to calculate how much the correlation coefficient differs from zero in the population.

You use a dependent-samples, one-tailed t test to assess whether the meditation exercise significantly improved math test scores. The test gives you:

• a t value (test statistic) of 3.00
• a p value of 0.0028

Although Pearson’s r is a test statistic, it doesn’t tell you anything about how significant the correlation is in the population. You also need to test whether this sample correlation coefficient is large enough to demonstrate a correlation in the population.

A t test can also determine how significantly a correlation coefficient differs from zero based on sample size. Since you expect a positive correlation between parental income and GPA, you use a one-sample, one-tailed t test. The t test gives you:

• a t value of 3.08
• a p value of 0.001

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The final step of statistical analysis is interpreting your results.

Statistical significance

In hypothesis testing, statistical significance is the main criterion for forming conclusions. You compare your p value to a set significance level (usually 0.05) to decide whether your results are statistically significant or non-significant.

Statistically significant results are considered unlikely to have arisen solely due to chance. There is only a very low chance of such a result occurring if the null hypothesis is true in the population.

This means that you believe the meditation intervention, rather than random factors, directly caused the increase in test scores. Example: Interpret your results (correlational study) You compare your p value of 0.001 to your significance threshold of 0.05. With a p value under this threshold, you can reject the null hypothesis. This indicates a statistically significant correlation between parental income and GPA in male college students.

Note that correlation doesn’t always mean causation, because there are often many underlying factors contributing to a complex variable like GPA. Even if one variable is related to another, this may be because of a third variable influencing both of them, or indirect links between the two variables.

Effect size

A statistically significant result doesn’t necessarily mean that there are important real life applications or clinical outcomes for a finding.

In contrast, the effect size indicates the practical significance of your results. It’s important to report effect sizes along with your inferential statistics for a complete picture of your results. You should also report interval estimates of effect sizes if you’re writing an APA style paper .

With a Cohen’s d of 0.72, there’s medium to high practical significance to your finding that the meditation exercise improved test scores. Example: Effect size (correlational study) To determine the effect size of the correlation coefficient, you compare your Pearson’s r value to Cohen’s effect size criteria.

Decision errors

Type I and Type II errors are mistakes made in research conclusions. A Type I error means rejecting the null hypothesis when it’s actually true, while a Type II error means failing to reject the null hypothesis when it’s false.

You can aim to minimize the risk of these errors by selecting an optimal significance level and ensuring high power . However, there’s a trade-off between the two errors, so a fine balance is necessary.

Frequentist versus Bayesian statistics

Traditionally, frequentist statistics emphasizes null hypothesis significance testing and always starts with the assumption of a true null hypothesis.

However, Bayesian statistics has grown in popularity as an alternative approach in the last few decades. In this approach, you use previous research to continually update your hypotheses based on your expectations and observations.

Bayes factor compares the relative strength of evidence for the null versus the alternative hypothesis rather than making a conclusion about rejecting the null hypothesis or not.

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

• Student’s  t -distribution
• Normal distribution
• Null and Alternative Hypotheses
• Chi square tests
• Confidence interval

Methodology

• Cluster sampling
• Stratified sampling
• Data cleansing
• Reproducibility vs Replicability
• Peer review
• Likert scale

Research bias

• Implicit bias
• Framing effect
• Cognitive bias
• Placebo effect
• Hawthorne effect
• Hostile attribution bias
• Affect heuristic

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Your ultimate guide to quantitative research.

12 min read You may be already using quantitative research and want to check your understanding, or you may be starting from the beginning. Here’s an exploration of this research method and how you can best use it for maximum effect for your business.

You may be already using quantitative research and want to check your understanding, or you may be starting from the beginning. Here’s an exploration of this research method and how you can best use it for maximum effect for your business.

What is quantitative research?

Quantitative is the research method of collecting quantitative data – this is data that can be converted into numbers or numerical data, which can be easily quantified, compared, and analyzed.

Quantitative research deals with primary and secondary sources where data is represented in numerical form. This can include closed-question poll results, statistics, and census information or demographic data .

Quantitative data tends to be used when researchers are interested in understanding a particular moment in time and examining data sets over time to find trends and patterns.

To collect numerical data, surveys are often employed as one of the main research methods to source first-hand information in primary research . Quantitative research can also come from third-party research studies .

Quantitative research is widely used in the realms of social sciences, such as biology, chemistry, psychology, economics, sociology, and marketing .

Research teams collect data that is significant to proving or disproving a hypothesis research question – known as the research objective. When they collect quantitative data, researchers will aim to use a sample size that is representative of the total population of the target market they’re interested in.

Then the data collected will be manually or automatically stored and compared for insights.

Free eBook: The ultimate guide to conducting market research

Quantitative vs qualitative research

While the quantitative research definition focuses on numerical data, qualitative research is defined as data that supplies non-numerical information.

Quantitative research focuses on the thoughts, feelings, and values of a participant , to understand why people act in the way they do . They result in data types like quotes, symbols, images, and written testimonials.

These data types tell researchers subjective information, which can help us assign people into categories, such as a participant’s religion, gender , social class, political alignment, likely favored products to buy, or their preferred training learning style.

For this reason, qualitative research is often used in social research, as this gives a window into the behavior and actions of people.

In general, if you’re interested in measuring something or testing a hypothesis, use quantitative methods. If you want to explore ideas, thoughts, and meanings, use qualitative methods.

However, quantitative and qualitative research methods are both recommended when you’re looking to understand a point in time, while also finding out the reason behind the facts.

Quantitative research data collection methods

Quantitative research methods can use structured research instruments like:

• Surveys : A survey is a simple-to-create and easy-to-distribute research method , which helps gather information from large groups of participants quickly. Traditionally, paper-based surveys can now be made online, so costs can stay quite low.

Quantitative questions tend to be closed questions that ask for a numerical result, based on a range of options, or a yes/no answer that can be tallied quickly.

• Face-to-face or phone interviews: Interviews are a great way to connect with participants , though they require time from the research team to set up and conduct.

Researchers may also have issues connecting with participants in different geographical regions . The researcher uses a set of predefined close-ended questions, which ask for yes/no or numerical values.

• Polls: Polls can be a shorter version of surveys , used to get a ‘flavor’ of what the current situation is with participants. Online polls can be shared easily, though polls are best used with simple questions that request a range or a yes/no answer.

Quantitative data is the opposite of qualitative research, another dominant framework for research in the social sciences, explored further below.

Quantitative data types

Quantitative research methods often deliver the following data types:

• Test Scores
• Percent of training course completed
• Performance score out of 100
• Number of support calls active
• Customer Net Promoter Score (NPS)

When gathering numerical data, the emphasis is on how specific the data is, and whether they can provide an indication of what ‘is’ at the time of collection. Pre-existing statistical data can tell us what ‘was’ for the date and time range that it represented

Quantitative research design methods (with examples)

Quantitative research has a number of quantitative research designs you can choose from:

Descriptive

This design type describes the state of a data type is telling researchers, in its native environment. There won’t normally be a clearly defined research question to start with. Instead, data analysis will suggest a conclusion , which can become the hypothesis to investigate further.

Examples of descriptive quantitative design include:

• A description of child’s Christmas gifts they received that year
• A description of what businesses sell the most of during Black Friday
• A description of a product issue being experienced by a customer

Correlational

This design type looks at two or more data types, the relationship between them, and the extent that they differ or align. This does not look at the causal links deeper – instead statistical analysis looks at the variables in a natural environment.

Examples of correlational quantitative design include:

• The relationship between a child’s Christmas gifts and their perceived happiness level
• The relationship between a business’ sales during Black Friday and the total revenue generated over the year
• The relationship between a customer’s product issue and the reputation of the product

Causal-Comparative/Quasi-Experimental

This design type looks at two or more data types and tries to explain any relationship and differences between them, using a cause-effect analysis. The research is carried out in a near-natural environment, where information is gathered from two groups – a naturally occurring group that matches the original natural environment, and one that is not naturally present.

This allows for causal links to be made, though they might not be correct, as other variables may have an impact on results.

Examples of causal-comparative/quasi-experimental quantitative design include:

• The effect of children’s Christmas gifts on happiness
• The effect of Black Friday sales figures on the productivity of company yearly sales
• The effect of product issues on the public perception of a product

Experimental Research

This design type looks to make a controlled environment in which two or more variables are observed to understand the exact cause and effect they have. This becomes a quantitative research study, where data types are manipulated to assess the effect they have. The participants are not naturally occurring groups, as the setting is no longer natural. A quantitative research study can help pinpoint the exact conditions in which variables impact one another.

Examples of experimental quantitative design include:

• The effect of children’s Christmas gifts on a child’s dopamine (happiness) levels
• The effect of Black Friday sales on the success of the company
• The effect of product issues on the perceived reliability of the product

Quantitative research methods need to be carefully considered, as your data collection of a data type can be used to different effects. For example, statistics can be descriptive or correlational (or inferential). Descriptive statistics help us to summarize our data, while inferential statistics help infer conclusions about significant differences.

Advantages of quantitative research

• Easy to do : Doing quantitative research is more straightforward, as the results come in numerical format, which can be more easily interpreted.
• Less interpretation : Due to the factual nature of the results, you will be able to accept or reject your hypothesis based on the numerical data collected.
• Less bias : There are higher levels of control that can be applied to the research, so bias can be reduced , making your data more reliable and precise.

Disadvantages of quantitative research

• Can’t understand reasons: Quantitative research doesn’t always tell you the full story, meaning you won’t understand the context – or the why, of the data you see, why do you see the results you have uncovered?
• Useful for simpler situations: Quantitative research on its own is not great when dealing with complex issues. In these cases, quantitative research may not be enough.

How to use quantitative research to your business’s advantage

Quantitative research methods may help in areas such as:

• Identifying which advert or landing page performs better
• Identifying how satisfied your customers are
• How many customers are likely to recommend you
• Tracking how your brand ranks in awareness and customer purchase intent
• Learn what consumers are likely to buy from your brand.

6 steps to conducting good quantitative research

Businesses can benefit from quantitative research by using it to evaluate the impact of data types. There are several steps to this:

• Define your problem or interest area : What do you observe is happening and is it frequent? Identify the data type/s you’re observing.
• Create a hypothesis : Ask yourself what could be the causes for the situation with those data types.
• Plan your quantitative research : Use structured research instruments like surveys or polls to ask questions that test your hypothesis.
• Data Collection : Collect quantitative data and understand what your data types are telling you. Using data collected on different types over long time periods can give you information on patterns.
• Data analysis : Does your information support your hypothesis? (You may need to redo the research with other variables to see if the results improve)
• Effectively present data : Communicate the results in a clear and concise way to help other people understand the findings.

How Qualtrics products can enhance & simplify the quantitative research process

The Qualtrics XM system gives you an all-in-one, integrated solution to help you all the way through conducting quantitative research. From survey creation and data collection to statistical analysis and data reporting, it can help all your internal teams gain insights from your numerical data.

Quantitative methods are catered to your business through templates or advanced survey designs. While you can manually collect data and conduct data analysis in a spreadsheet program, this solution helps you automate the process of quantitative research, saving you time and administration work.

Using computational techniques helps you to avoid human errors, and participant results come in are already incorporated into the analysis in real-time.

Our key tools, Stats IQ™ and Driver IQ™ make analyzing numerical data easy and simple. Choose to highlight key findings based on variables or highlight statistically insignificant findings. The choice is yours.

Some examples of your workspace in action, using drag and drop to create fast data visualizations quickly:

Related resources

Market intelligence 10 min read, marketing insights 11 min read, ethnographic research 11 min read, qualitative vs quantitative research 13 min read, qualitative research questions 11 min read, qualitative research design 12 min read, primary vs secondary research 14 min read, request demo.

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Introduction to Research Statistical Analysis: An Overview of the Basics

Christian vandever.

1 HCA Healthcare Graduate Medical Education

Description

This article covers many statistical ideas essential to research statistical analysis. Sample size is explained through the concepts of statistical significance level and power. Variable types and definitions are included to clarify necessities for how the analysis will be interpreted. Categorical and quantitative variable types are defined, as well as response and predictor variables. Statistical tests described include t-tests, ANOVA and chi-square tests. Multiple regression is also explored for both logistic and linear regression. Finally, the most common statistics produced by these methods are explored.

Introduction

Statistical analysis is necessary for any research project seeking to make quantitative conclusions. The following is a primer for research-based statistical analysis. It is intended to be a high-level overview of appropriate statistical testing, while not diving too deep into any specific methodology. Some of the information is more applicable to retrospective projects, where analysis is performed on data that has already been collected, but most of it will be suitable to any type of research. This primer will help the reader understand research results in coordination with a statistician, not to perform the actual analysis. Analysis is commonly performed using statistical programming software such as R, SAS or SPSS. These allow for analysis to be replicated while minimizing the risk for an error. Resources are listed later for those working on analysis without a statistician.

After coming up with a hypothesis for a study, including any variables to be used, one of the first steps is to think about the patient population to apply the question. Results are only relevant to the population that the underlying data represents. Since it is impractical to include everyone with a certain condition, a subset of the population of interest should be taken. This subset should be large enough to have power, which means there is enough data to deliver significant results and accurately reflect the study’s population.

The first statistics of interest are related to significance level and power, alpha and beta. Alpha (α) is the significance level and probability of a type I error, the rejection of the null hypothesis when it is true. The null hypothesis is generally that there is no difference between the groups compared. A type I error is also known as a false positive. An example would be an analysis that finds one medication statistically better than another, when in reality there is no difference in efficacy between the two. Beta (β) is the probability of a type II error, the failure to reject the null hypothesis when it is actually false. A type II error is also known as a false negative. This occurs when the analysis finds there is no difference in two medications when in reality one works better than the other. Power is defined as 1-β and should be calculated prior to running any sort of statistical testing. Ideally, alpha should be as small as possible while power should be as large as possible. Power generally increases with a larger sample size, but so does cost and the effect of any bias in the study design. Additionally, as the sample size gets bigger, the chance for a statistically significant result goes up even though these results can be small differences that do not matter practically. Power calculators include the magnitude of the effect in order to combat the potential for exaggeration and only give significant results that have an actual impact. The calculators take inputs like the mean, effect size and desired power, and output the required minimum sample size for analysis. Effect size is calculated using statistical information on the variables of interest. If that information is not available, most tests have commonly used values for small, medium or large effect sizes.

When the desired patient population is decided, the next step is to define the variables previously chosen to be included. Variables come in different types that determine which statistical methods are appropriate and useful. One way variables can be split is into categorical and quantitative variables. ( Table 1 ) Categorical variables place patients into groups, such as gender, race and smoking status. Quantitative variables measure or count some quantity of interest. Common quantitative variables in research include age and weight. An important note is that there can often be a choice for whether to treat a variable as quantitative or categorical. For example, in a study looking at body mass index (BMI), BMI could be defined as a quantitative variable or as a categorical variable, with each patient’s BMI listed as a category (underweight, normal, overweight, and obese) rather than the discrete value. The decision whether a variable is quantitative or categorical will affect what conclusions can be made when interpreting results from statistical tests. Keep in mind that since quantitative variables are treated on a continuous scale it would be inappropriate to transform a variable like which medication was given into a quantitative variable with values 1, 2 and 3.

Categorical vs. Quantitative Variables

Both of these types of variables can also be split into response and predictor variables. ( Table 2 ) Predictor variables are explanatory, or independent, variables that help explain changes in a response variable. Conversely, response variables are outcome, or dependent, variables whose changes can be partially explained by the predictor variables.

Response vs. Predictor Variables

Choosing the correct statistical test depends on the types of variables defined and the question being answered. The appropriate test is determined by the variables being compared. Some common statistical tests include t-tests, ANOVA and chi-square tests.

T-tests compare whether there are differences in a quantitative variable between two values of a categorical variable. For example, a t-test could be useful to compare the length of stay for knee replacement surgery patients between those that took apixaban and those that took rivaroxaban. A t-test could examine whether there is a statistically significant difference in the length of stay between the two groups. The t-test will output a p-value, a number between zero and one, which represents the probability that the two groups could be as different as they are in the data, if they were actually the same. A value closer to zero suggests that the difference, in this case for length of stay, is more statistically significant than a number closer to one. Prior to collecting the data, set a significance level, the previously defined alpha. Alpha is typically set at 0.05, but is commonly reduced in order to limit the chance of a type I error, or false positive. Going back to the example above, if alpha is set at 0.05 and the analysis gives a p-value of 0.039, then a statistically significant difference in length of stay is observed between apixaban and rivaroxaban patients. If the analysis gives a p-value of 0.91, then there was no statistical evidence of a difference in length of stay between the two medications. Other statistical summaries or methods examine how big of a difference that might be. These other summaries are known as post-hoc analysis since they are performed after the original test to provide additional context to the results.

Analysis of variance, or ANOVA, tests can observe mean differences in a quantitative variable between values of a categorical variable, typically with three or more values to distinguish from a t-test. ANOVA could add patients given dabigatran to the previous population and evaluate whether the length of stay was significantly different across the three medications. If the p-value is lower than the designated significance level then the hypothesis that length of stay was the same across the three medications is rejected. Summaries and post-hoc tests also could be performed to look at the differences between length of stay and which individual medications may have observed statistically significant differences in length of stay from the other medications. A chi-square test examines the association between two categorical variables. An example would be to consider whether the rate of having a post-operative bleed is the same across patients provided with apixaban, rivaroxaban and dabigatran. A chi-square test can compute a p-value determining whether the bleeding rates were significantly different or not. Post-hoc tests could then give the bleeding rate for each medication, as well as a breakdown as to which specific medications may have a significantly different bleeding rate from each other.

A slightly more advanced way of examining a question can come through multiple regression. Regression allows more predictor variables to be analyzed and can act as a control when looking at associations between variables. Common control variables are age, sex and any comorbidities likely to affect the outcome variable that are not closely related to the other explanatory variables. Control variables can be especially important in reducing the effect of bias in a retrospective population. Since retrospective data was not built with the research question in mind, it is important to eliminate threats to the validity of the analysis. Testing that controls for confounding variables, such as regression, is often more valuable with retrospective data because it can ease these concerns. The two main types of regression are linear and logistic. Linear regression is used to predict differences in a quantitative, continuous response variable, such as length of stay. Logistic regression predicts differences in a dichotomous, categorical response variable, such as 90-day readmission. So whether the outcome variable is categorical or quantitative, regression can be appropriate. An example for each of these types could be found in two similar cases. For both examples define the predictor variables as age, gender and anticoagulant usage. In the first, use the predictor variables in a linear regression to evaluate their individual effects on length of stay, a quantitative variable. For the second, use the same predictor variables in a logistic regression to evaluate their individual effects on whether the patient had a 90-day readmission, a dichotomous categorical variable. Analysis can compute a p-value for each included predictor variable to determine whether they are significantly associated. The statistical tests in this article generate an associated test statistic which determines the probability the results could be acquired given that there is no association between the compared variables. These results often come with coefficients which can give the degree of the association and the degree to which one variable changes with another. Most tests, including all listed in this article, also have confidence intervals, which give a range for the correlation with a specified level of confidence. Even if these tests do not give statistically significant results, the results are still important. Not reporting statistically insignificant findings creates a bias in research. Ideas can be repeated enough times that eventually statistically significant results are reached, even though there is no true significance. In some cases with very large sample sizes, p-values will almost always be significant. In this case the effect size is critical as even the smallest, meaningless differences can be found to be statistically significant.

These variables and tests are just some things to keep in mind before, during and after the analysis process in order to make sure that the statistical reports are supporting the questions being answered. The patient population, types of variables and statistical tests are all important things to consider in the process of statistical analysis. Any results are only as useful as the process used to obtain them. This primer can be used as a reference to help ensure appropriate statistical analysis.

Funding Statement

This research was supported (in whole or in part) by HCA Healthcare and/or an HCA Healthcare affiliated entity.

Conflicts of Interest

The author declares he has no conflicts of interest.

Christian Vandever is an employee of HCA Healthcare Graduate Medical Education, an organization affiliated with the journal’s publisher.

This research was supported (in whole or in part) by HCA Healthcare and/or an HCA Healthcare affiliated entity. The views expressed in this publication represent those of the author(s) and do not necessarily represent the official views of HCA Healthcare or any of its affiliated entities.

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Research Design – Types, Methods and Examples

Table of Contents

Research Design

Definition:

Research design refers to the overall strategy or plan for conducting a research study. It outlines the methods and procedures that will be used to collect and analyze data, as well as the goals and objectives of the study. Research design is important because it guides the entire research process and ensures that the study is conducted in a systematic and rigorous manner.

Types of Research Design

Types of Research Design are as follows:

Descriptive Research Design

This type of research design is used to describe a phenomenon or situation. It involves collecting data through surveys, questionnaires, interviews, and observations. The aim of descriptive research is to provide an accurate and detailed portrayal of a particular group, event, or situation. It can be useful in identifying patterns, trends, and relationships in the data.

Correlational Research Design

Correlational research design is used to determine if there is a relationship between two or more variables. This type of research design involves collecting data from participants and analyzing the relationship between the variables using statistical methods. The aim of correlational research is to identify the strength and direction of the relationship between the variables.

Experimental Research Design

Experimental research design is used to investigate cause-and-effect relationships between variables. This type of research design involves manipulating one variable and measuring the effect on another variable. It usually involves randomly assigning participants to groups and manipulating an independent variable to determine its effect on a dependent variable. The aim of experimental research is to establish causality.

Quasi-experimental Research Design

Quasi-experimental research design is similar to experimental research design, but it lacks one or more of the features of a true experiment. For example, there may not be random assignment to groups or a control group. This type of research design is used when it is not feasible or ethical to conduct a true experiment.

Case Study Research Design

Case study research design is used to investigate a single case or a small number of cases in depth. It involves collecting data through various methods, such as interviews, observations, and document analysis. The aim of case study research is to provide an in-depth understanding of a particular case or situation.

Longitudinal Research Design

Longitudinal research design is used to study changes in a particular phenomenon over time. It involves collecting data at multiple time points and analyzing the changes that occur. The aim of longitudinal research is to provide insights into the development, growth, or decline of a particular phenomenon over time.

Structure of Research Design

The format of a research design typically includes the following sections:

• Introduction : This section provides an overview of the research problem, the research questions, and the importance of the study. It also includes a brief literature review that summarizes previous research on the topic and identifies gaps in the existing knowledge.
• Research Questions or Hypotheses: This section identifies the specific research questions or hypotheses that the study will address. These questions should be clear, specific, and testable.
• Research Methods : This section describes the methods that will be used to collect and analyze data. It includes details about the study design, the sampling strategy, the data collection instruments, and the data analysis techniques.
• Data Collection: This section describes how the data will be collected, including the sample size, data collection procedures, and any ethical considerations.
• Data Analysis: This section describes how the data will be analyzed, including the statistical techniques that will be used to test the research questions or hypotheses.
• Results : This section presents the findings of the study, including descriptive statistics and statistical tests.
• Discussion and Conclusion : This section summarizes the key findings of the study, interprets the results, and discusses the implications of the findings. It also includes recommendations for future research.
• References : This section lists the sources cited in the research design.

Example of Research Design

An Example of Research Design could be:

Research question: Does the use of social media affect the academic performance of high school students?

Research design:

• Research approach : The research approach will be quantitative as it involves collecting numerical data to test the hypothesis.
• Research design : The research design will be a quasi-experimental design, with a pretest-posttest control group design.
• Sample : The sample will be 200 high school students from two schools, with 100 students in the experimental group and 100 students in the control group.
• Data collection : The data will be collected through surveys administered to the students at the beginning and end of the academic year. The surveys will include questions about their social media usage and academic performance.
• Data analysis : The data collected will be analyzed using statistical software. The mean scores of the experimental and control groups will be compared to determine whether there is a significant difference in academic performance between the two groups.
• Limitations : The limitations of the study will be acknowledged, including the fact that social media usage can vary greatly among individuals, and the study only focuses on two schools, which may not be representative of the entire population.
• Ethical considerations: Ethical considerations will be taken into account, such as obtaining informed consent from the participants and ensuring their anonymity and confidentiality.

How to Write Research Design

Writing a research design involves planning and outlining the methodology and approach that will be used to answer a research question or hypothesis. Here are some steps to help you write a research design:

• Define the research question or hypothesis : Before beginning your research design, you should clearly define your research question or hypothesis. This will guide your research design and help you select appropriate methods.
• Select a research design: There are many different research designs to choose from, including experimental, survey, case study, and qualitative designs. Choose a design that best fits your research question and objectives.
• Develop a sampling plan : If your research involves collecting data from a sample, you will need to develop a sampling plan. This should outline how you will select participants and how many participants you will include.
• Define variables: Clearly define the variables you will be measuring or manipulating in your study. This will help ensure that your results are meaningful and relevant to your research question.
• Choose data collection methods : Decide on the data collection methods you will use to gather information. This may include surveys, interviews, observations, experiments, or secondary data sources.
• Create a data analysis plan: Develop a plan for analyzing your data, including the statistical or qualitative techniques you will use.
• Consider ethical concerns : Finally, be sure to consider any ethical concerns related to your research, such as participant confidentiality or potential harm.

When to Write Research Design

Research design should be written before conducting any research study. It is an important planning phase that outlines the research methodology, data collection methods, and data analysis techniques that will be used to investigate a research question or problem. The research design helps to ensure that the research is conducted in a systematic and logical manner, and that the data collected is relevant and reliable.

Ideally, the research design should be developed as early as possible in the research process, before any data is collected. This allows the researcher to carefully consider the research question, identify the most appropriate research methodology, and plan the data collection and analysis procedures in advance. By doing so, the research can be conducted in a more efficient and effective manner, and the results are more likely to be valid and reliable.

Purpose of Research Design

The purpose of research design is to plan and structure a research study in a way that enables the researcher to achieve the desired research goals with accuracy, validity, and reliability. Research design is the blueprint or the framework for conducting a study that outlines the methods, procedures, techniques, and tools for data collection and analysis.

Some of the key purposes of research design include:

• Providing a clear and concise plan of action for the research study.
• Ensuring that the research is conducted ethically and with rigor.
• Maximizing the accuracy and reliability of the research findings.
• Minimizing the possibility of errors, biases, or confounding variables.
• Ensuring that the research is feasible, practical, and cost-effective.
• Determining the appropriate research methodology to answer the research question(s).
• Identifying the sample size, sampling method, and data collection techniques.
• Determining the data analysis method and statistical tests to be used.
• Facilitating the replication of the study by other researchers.
• Enhancing the validity and generalizability of the research findings.

Applications of Research Design

There are numerous applications of research design in various fields, some of which are:

• Social sciences: In fields such as psychology, sociology, and anthropology, research design is used to investigate human behavior and social phenomena. Researchers use various research designs, such as experimental, quasi-experimental, and correlational designs, to study different aspects of social behavior.
• Education : Research design is essential in the field of education to investigate the effectiveness of different teaching methods and learning strategies. Researchers use various designs such as experimental, quasi-experimental, and case study designs to understand how students learn and how to improve teaching practices.
• Health sciences : In the health sciences, research design is used to investigate the causes, prevention, and treatment of diseases. Researchers use various designs, such as randomized controlled trials, cohort studies, and case-control studies, to study different aspects of health and healthcare.
• Business : Research design is used in the field of business to investigate consumer behavior, marketing strategies, and the impact of different business practices. Researchers use various designs, such as survey research, experimental research, and case studies, to study different aspects of the business world.
• Engineering : In the field of engineering, research design is used to investigate the development and implementation of new technologies. Researchers use various designs, such as experimental research and case studies, to study the effectiveness of new technologies and to identify areas for improvement.

Advantages of Research Design

Here are some advantages of research design:

• Systematic and organized approach : A well-designed research plan ensures that the research is conducted in a systematic and organized manner, which makes it easier to manage and analyze the data.
• Clear objectives: The research design helps to clarify the objectives of the study, which makes it easier to identify the variables that need to be measured, and the methods that need to be used to collect and analyze data.
• Minimizes bias: A well-designed research plan minimizes the chances of bias, by ensuring that the data is collected and analyzed objectively, and that the results are not influenced by the researcher’s personal biases or preferences.
• Efficient use of resources: A well-designed research plan helps to ensure that the resources (time, money, and personnel) are used efficiently and effectively, by focusing on the most important variables and methods.
• Replicability: A well-designed research plan makes it easier for other researchers to replicate the study, which enhances the credibility and reliability of the findings.
• Validity: A well-designed research plan helps to ensure that the findings are valid, by ensuring that the methods used to collect and analyze data are appropriate for the research question.
• Generalizability : A well-designed research plan helps to ensure that the findings can be generalized to other populations, settings, or situations, which increases the external validity of the study.

Research Design Vs Research Methodology

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Quantitative Research

What is Quantitative Research?

Quantitative research is the methodology which researchers use to test theories about people’s attitudes and behaviors based on numerical and statistical evidence. Researchers sample a large number of users (e.g., through surveys) to indirectly obtain measurable, bias-free data about users in relevant situations.

“Quantification clarifies issues which qualitative analysis leaves fuzzy. It is more readily contestable and likely to be contested. It sharpens scholarly discussion, sparks off rival hypotheses, and contributes to the dynamics of the research process.” — Angus Maddison, Notable scholar of quantitative macro-economic history

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See how quantitative research helps reveal cold, hard facts about users which you can interpret and use to improve your designs.

Use Quantitative Research to Find Mathematical Facts about Users

Quantitative research is a subset of user experience (UX) research . Unlike its softer, more individual-oriented “counterpart”, qualitative research , quantitative research means you collect statistical/numerical data to draw generalized conclusions about users’ attitudes and behaviors . Compare and contrast quantitative with qualitative research, below:

Qualitative Research

You Aim to Determine

The “what”, “where” & “when” of the users’ needs & problems – to help keep your project’s focus on track during development

The “why” – to get behind how users approach their problems in their world

Highly structured (e.g., surveys) – to gather data about what users do & find patterns in large user groups

Loosely structured (e.g., contextual inquiries) – to learn why users behave how they do & explore their opinions

Number of Representative Users

Ideally 30+

Often around 5

Level of Contact with Users

Less direct & more remote (e.g., analytics)

More direct & less remote (e.g., usability testing to examine users’ stress levels when they use your design)

Statistically

Reliable – if you have enough test users

Less reliable, with need for great care with handling non-numerical data (e.g., opinions), as your own opinions might influence findings

Quantitative research is often best done from early on in projects since it helps teams to optimally direct product development and avoid costly design mistakes later. As you typically get user data from a distance—i.e., without close physical contact with users—also applying qualitative research will help you investigate why users think and feel the ways they do. Indeed, in an iterative design process quantitative research helps you test the assumptions you and your design team develop from your qualitative research. Regardless of the method you use, with proper care you can gather objective and unbiased data – information which you can complement with qualitative approaches to build a fuller understanding of your target users. From there, you can work towards firmer conclusions and drive your design process towards a more realistic picture of how target users will ultimately receive your product.

Quantitative analysis helps you test your assumptions and establish clearer views of your users in their various contexts.

Quantitative Research Methods You Can Use to Guide Optimal Designs

There are many quantitative research methods, and they help uncover different types of information on users. Some methods, such as A/B testing, are typically done on finished products, while others such as surveys could be done throughout a project’s design process. Here are some of the most helpful methods:

A/B testing – You test two or more versions of your design on users to find the most effective. Each variation differs by just one feature and may or may not affect how users respond. A/B testing is especially valuable for testing assumptions you’ve drawn from qualitative research. The only potential concerns here are scale—in that you’ll typically need to conduct it on thousands of users—and arguably more complexity in terms of considering the statistical significance involved.

Analytics – With tools such as Google Analytics, you measure metrics (e.g., page views, click-through rates) to build a picture (e.g., “How many users take how long to complete a task?”).

Desirability Studies – You measure an aspect of your product (e.g., aesthetic appeal) by typically showing it to participants and asking them to select from a menu of descriptive words. Their responses can reveal powerful insights (e.g., 78% associate the product/brand with “fashionable”).

Surveys and Questionnaires – When you ask for many users’ opinions, you will gain massive amounts of information. Keep in mind that you’ll have data about what users say they do, as opposed to insights into what they do . You can get more reliable results if you incentivize your participants well and use the right format.

Tree Testing – You remove the user interface so users must navigate the site and complete tasks using links alone. This helps you see if an issue is related to the user interface or information architecture.

Another powerful benefit of conducting quantitative research is that you can keep your stakeholders’ support with hard facts and statistics about your design’s performance—which can show what works well and what needs improvement—and prove a good return on investment. You can also produce reports to check statistics against different versions of your product and your competitors’ products.

Most quantitative research methods are relatively cheap. Since no single research method can help you answer all your questions, it’s vital to judge which method suits your project at the time/stage. Remember, it’s best to spend appropriately on a combination of quantitative and qualitative research from early on in development. Design improvements can be costly, and so you can estimate the value of implementing changes when you get the statistics to suggest that these changes will improve usability. Overall, you want to gather measurements objectively, where your personality, presence and theories won’t create bias.

Learn More about Quantitative Research

Take our User Research course to see how to get the most from quantitative research.

See how quantitative research methods fit into your design research landscape .

This insightful piece shows the value of pairing quantitative with qualitative research .

Find helpful tips on combining quantitative research methods in mixed methods research .

Questions related to Quantitative Research

Qualitative and quantitative research differ primarily in the data they produce. Quantitative research yields numerical data to test hypotheses and quantify patterns. It's precise and generalizable. Qualitative research, on the other hand, generates non-numerical data and explores meanings, interpretations, and deeper insights. Watch our video featuring Professor Alan Dix on different types of research methods.

This video elucidates the nuances and applications of both research types in the design field.

In quantitative research, determining a good sample size is crucial for the reliability of the results. William Hudson, CEO of Syntagm, emphasizes the importance of statistical significance with an example in our video. 

He illustrates that even with varying results between design choices, we need to discern whether the differences are statistically significant or products of chance. This ensures the validity of the results, allowing for more accurate interpretations. Statistical tools like chi-square tests can aid in analyzing the results effectively. To delve deeper into these concepts, take William Hudson’s Data-Driven Design: Quantitative UX Research Course . 

Quantitative research is crucial as it provides precise, numerical data that allows for high levels of statistical inference. Our video from William Hudson, CEO of Syntagm, highlights the importance of analytics in examining existing solutions. 

Quantitative methods, like analytics and A/B testing, are pivotal for identifying areas for improvement, understanding user behaviors, and optimizing user experiences based on solid, empirical evidence. This empirical nature ensures that the insights derived are reliable, allowing for practical improvements and innovations. Perhaps most importantly, numerical data is useful to secure stakeholder buy-in and defend design decisions and proposals. Explore this approach in our Data-Driven Design: Quantitative Research for UX Research course and learn from William Hudson’s detailed explanations of when and why to use analytics in the research process.

After establishing initial requirements, statistical data is crucial for informed decisions through quantitative research. William Hudson, CEO of Syntagm, sheds light on the role of quantitative research throughout a typical project lifecycle in this video:

 During the analysis and design phases, quantitative research helps validate user requirements and understand user behaviors. Surveys and analytics are standard tools, offering insights into user preferences and design efficacy. Quantitative research can also be used in early design testing, allowing for optimal design modifications based on user interactions and feedback, and it’s fundamental for A/B and multivariate testing once live solutions are available.

To write a compelling quantitative research question:

Create clear, concise, and unambiguous questions that address one aspect at a time.

Use common, short terms and provide explanations for unusual words.

Avoid leading, compound, and overlapping queries and ensure that questions are not vague or broad.

According to our video by William Hudson, CEO of Syntagm, quality and respondent understanding are vital in forming good questions. 

He emphasizes the importance of addressing specific aspects and avoiding intimidating and confusing elements, such as extensive question grids or ranking questions, to ensure participant engagement and accurate responses. For more insights, see the article Writing Good Questions for Surveys .

Survey research is typically quantitative, collecting numerical data and statistical analysis to make generalizable conclusions. However, it can also have qualitative elements, mainly when it includes open-ended questions, allowing for expressive responses. Our video featuring the CEO of Syntagm, William Hudson, provides in-depth insights into when and how to effectively utilize surveys in the product or service lifecycle, focusing on user satisfaction and potential improvements.

He emphasizes the importance of surveys in triangulating data to back up qualitative research findings, ensuring we have a complete understanding of the user's requirements and preferences.

Descriptive research focuses on describing the subject being studied and getting answers to questions like what, where, when, and who of the research question. However, it doesn’t include the answers to the underlying reasons, or the “why” behind the answers obtained from the research. We can use both f qualitative and quantitative methods to conduct descriptive research. Descriptive research does not describe the methods, but rather the data gathered through the research (regardless of the methods used).

When we use quantitative research and gather numerical data, we can use statistical analysis to understand relationships between different variables. Here’s William Hudson, CEO of Syntagm with more on correlation and how we can apply tests such as Pearson’s r and Spearman Rank Coefficient to our data.

This helps interpret phenomena such as user experience by analyzing session lengths and conversion values, revealing whether variables like time spent on a page affect checkout values, for example.

Random Sampling: Each individual in the population has an equitable opportunity to be chosen, which minimizes biases and simplifies analysis.

Systematic Sampling: Selecting every k-th item from a list after a random start. It's simpler and faster than random sampling when dealing with large populations.

Stratified Sampling: Segregate the population into subgroups or strata according to comparable characteristics. Then, samples are taken randomly from each stratum.

Cluster Sampling: Divide the population into clusters and choose a random sample.

Multistage Sampling: Various sampling techniques are used at different stages to collect detailed information from diverse populations.

Convenience Sampling: The researcher selects the sample based on availability and willingness to participate, which may only represent part of the population.

Quota Sampling: Segment the population into subgroups, and samples are non-randomly selected to fulfill a predetermined quota from each subset.

These are just a few techniques, and choosing the right one depends on your research question, discipline, resource availability, and the level of accuracy required. In quantitative research, there isn't a one-size-fits-all sampling technique; choosing a method that aligns with your research goals and population is critical. However, a well-planned strategy is essential to avoid wasting resources and time, as highlighted in our video featuring William Hudson, CEO of Syntagm.

He emphasizes the importance of recruiting participants meticulously, ensuring their engagement and the quality of their responses. Accurate and thoughtful participant responses are crucial for obtaining reliable results. William also sheds light on dealing with failing participants and scrutinizing response quality to refine the outcomes.

The 4 types of quantitative research are Descriptive, Correlational, Causal-Comparative/Quasi-Experimental, and Experimental Research. Descriptive research aims to depict ‘what exists’ clearly and precisely. Correlational research examines relationships between variables. Causal-comparative research investigates the cause-effect relationship between variables. Experimental research explores causal relationships by manipulating independent variables. To gain deeper insights into quantitative research methods in UX, consider enrolling in our Data-Driven Design: Quantitative Research for UX course.

The strength of quantitative research is its ability to provide precise numerical data for analyzing target variables.This allows for generalized conclusions and predictions about future occurrences, proving invaluable in various fields, including user experience. William Hudson, CEO of Syntagm, discusses the role of surveys, analytics, and testing in providing objective insights in our video on quantitative research methods, highlighting the significance of structured methodologies in eliciting reliable results.

To master quantitative research methods, enroll in our comprehensive course, Data-Driven Design: Quantitative Research for UX . 

This course empowers you to leverage quantitative data to make informed design decisions, providing a deep dive into methods like surveys and analytics. Whether you’re a novice or a seasoned professional, this course at Interaction Design Foundation offers valuable insights and practical knowledge, ensuring you acquire the skills necessary to excel in user experience research. Explore our diverse topics to elevate your understanding of quantitative research methods.

Answer a Short Quiz to Earn a Gift

What is the primary goal of quantitative research in design?

• To analyze numerical data and identify patterns
• To explore abstract design concepts for implementation
• To understand people's subjective experiences and opinions

Which of the following methods is an example of quantitative research?

• Conduct a focus groups to collect detailed user feedback
• Participate in open-ended interviews to explore user experiences
• Run usability tests and measure task completion times

What is one key advantage of quantitative research?

• It allows participants to express their opinions in a flexible manner.
• It provides researchers with detailed narratives of user experiences and perspectives.
• It produces standardized, comparable data that researchers can statistically analyze.

What is a significant challenge of quantitative research?

• It lacks objectivity which makes its results difficult to reproduce.
• It may oversimplify complex user behaviors into numbers and miss contextual insights.
• It often results in biased or misleading conclusions.

How can designers effectively combine qualitative and quantitative research?

• They can collect quantitative data first, followed by qualitative insights to explain the findings.
• They can completely replace quantitative methods with qualitative approaches.
• They can treat them as interchangeable methods to gather similar data.

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Literature on Quantitative Research

Here’s the entire UX literature on Quantitative Research by the Interaction Design Foundation, collated in one place:

Learn more about Quantitative Research

Take a deep dive into Quantitative Research with our course User Research – Methods and Best Practices .

How do you plan to design a product or service that your users will love , if you don't know what they want in the first place? As a user experience designer, you shouldn't leave it to chance to design something outstanding; you should make the effort to understand your users and build on that knowledge from the outset. User research is the way to do this, and it can therefore be thought of as the largest part of user experience design .

In fact, user research is often the first step of a UX design process—after all, you cannot begin to design a product or service without first understanding what your users want! As you gain the skills required, and learn about the best practices in user research, you’ll get first-hand knowledge of your users and be able to design the optimal product—one that’s truly relevant for your users and, subsequently, outperforms your competitors’ .

This course will give you insights into the most essential qualitative research methods around and will teach you how to put them into practice in your design work. You’ll also have the opportunity to embark on three practical projects where you can apply what you’ve learned to carry out user research in the real world . You’ll learn details about how to plan user research projects and fit them into your own work processes in a way that maximizes the impact your research can have on your designs. On top of that, you’ll gain practice with different methods that will help you analyze the results of your research and communicate your findings to your clients and stakeholders—workshops, user journeys and personas, just to name a few!

By the end of the course, you’ll have not only a Course Certificate but also three case studies to add to your portfolio. And remember, a portfolio with engaging case studies is invaluable if you are looking to break into a career in UX design or user research!

We believe you should learn from the best, so we’ve gathered a team of experts to help teach this course alongside our own course instructors. That means you’ll meet a new instructor in each of the lessons on research methods who is an expert in their field—we hope you enjoy what they have in store for you!

All open-source articles on Quantitative Research

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• 4 years ago

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7 Simple Ways to Get Better Results From Ethnographic Research

Question Everything

• 3 years ago

Tree Testing

Adding Quality to Your Design Research with an SSQS Checklist

• 8 years ago

Rating Scales in UX Research: The Ultimate Guide

How to Fit Quantitative Research into the Project Lifecycle

Correlation in User Experience

Why and When to Use Surveys

First-Click Testing

What to Test

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Qualitative vs Quantitative Research Methods & Data Analysis

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What is the difference between quantitative and qualitative?

The main difference between quantitative and qualitative research is the type of data they collect and analyze.

Quantitative research collects numerical data and analyzes it using statistical methods. The aim is to produce objective, empirical data that can be measured and expressed in numerical terms. Quantitative research is often used to test hypotheses, identify patterns, and make predictions.

Qualitative research , on the other hand, collects non-numerical data such as words, images, and sounds. The focus is on exploring subjective experiences, opinions, and attitudes, often through observation and interviews.

Qualitative research aims to produce rich and detailed descriptions of the phenomenon being studied, and to uncover new insights and meanings.

Quantitative data is information about quantities, and therefore numbers, and qualitative data is descriptive, and regards phenomenon which can be observed but not measured, such as language.

What Is Qualitative Research?

Qualitative research is the process of collecting, analyzing, and interpreting non-numerical data, such as language. Qualitative research can be used to understand how an individual subjectively perceives and gives meaning to their social reality.

Qualitative data is non-numerical data, such as text, video, photographs, or audio recordings. This type of data can be collected using diary accounts or in-depth interviews and analyzed using grounded theory or thematic analysis.

Qualitative research is multimethod in focus, involving an interpretive, naturalistic approach to its subject matter. This means that qualitative researchers study things in their natural settings, attempting to make sense of, or interpret, phenomena in terms of the meanings people bring to them. Denzin and Lincoln (1994, p. 2)

Interest in qualitative data came about as the result of the dissatisfaction of some psychologists (e.g., Carl Rogers) with the scientific study of psychologists such as behaviorists (e.g., Skinner ).

Since psychologists study people, the traditional approach to science is not seen as an appropriate way of carrying out research since it fails to capture the totality of human experience and the essence of being human.  Exploring participants’ experiences is known as a phenomenological approach (re: Humanism ).

Qualitative research is primarily concerned with meaning, subjectivity, and lived experience. The goal is to understand the quality and texture of people’s experiences, how they make sense of them, and the implications for their lives.

Qualitative research aims to understand the social reality of individuals, groups, and cultures as nearly as possible as participants feel or live it. Thus, people and groups are studied in their natural setting.

Some examples of qualitative research questions are provided, such as what an experience feels like, how people talk about something, how they make sense of an experience, and how events unfold for people.

Research following a qualitative approach is exploratory and seeks to explain ‘how’ and ‘why’ a particular phenomenon, or behavior, operates as it does in a particular context. It can be used to generate hypotheses and theories from the data.

Qualitative Methods

There are different types of qualitative research methods, including diary accounts, in-depth interviews , documents, focus groups , case study research , and ethnography.

The results of qualitative methods provide a deep understanding of how people perceive their social realities and in consequence, how they act within the social world.

The researcher has several methods for collecting empirical materials, ranging from the interview to direct observation, to the analysis of artifacts, documents, and cultural records, to the use of visual materials or personal experience. Denzin and Lincoln (1994, p. 14)

Here are some examples of qualitative data:

Interview transcripts : Verbatim records of what participants said during an interview or focus group. They allow researchers to identify common themes and patterns, and draw conclusions based on the data. Interview transcripts can also be useful in providing direct quotes and examples to support research findings.

Observations : The researcher typically takes detailed notes on what they observe, including any contextual information, nonverbal cues, or other relevant details. The resulting observational data can be analyzed to gain insights into social phenomena, such as human behavior, social interactions, and cultural practices.

Unstructured interviews : generate qualitative data through the use of open questions.  This allows the respondent to talk in some depth, choosing their own words.  This helps the researcher develop a real sense of a person’s understanding of a situation.

Diaries or journals : Written accounts of personal experiences or reflections.

Notice that qualitative data could be much more than just words or text. Photographs, videos, sound recordings, and so on, can be considered qualitative data. Visual data can be used to understand behaviors, environments, and social interactions.

Qualitative Data Analysis

Qualitative research is endlessly creative and interpretive. The researcher does not just leave the field with mountains of empirical data and then easily write up his or her findings.

Qualitative interpretations are constructed, and various techniques can be used to make sense of the data, such as content analysis, grounded theory (Glaser & Strauss, 1967), thematic analysis (Braun & Clarke, 2006), or discourse analysis .

For example, thematic analysis is a qualitative approach that involves identifying implicit or explicit ideas within the data. Themes will often emerge once the data has been coded .

Key Features

• Events can be understood adequately only if they are seen in context. Therefore, a qualitative researcher immerses her/himself in the field, in natural surroundings. The contexts of inquiry are not contrived; they are natural. Nothing is predefined or taken for granted.
• Qualitative researchers want those who are studied to speak for themselves, to provide their perspectives in words and other actions. Therefore, qualitative research is an interactive process in which the persons studied teach the researcher about their lives.
• The qualitative researcher is an integral part of the data; without the active participation of the researcher, no data exists.
• The study’s design evolves during the research and can be adjusted or changed as it progresses. For the qualitative researcher, there is no single reality. It is subjective and exists only in reference to the observer.
• The theory is data-driven and emerges as part of the research process, evolving from the data as they are collected.

Limitations of Qualitative Research

• Because of the time and costs involved, qualitative designs do not generally draw samples from large-scale data sets.
• The problem of adequate validity or reliability is a major criticism. Because of the subjective nature of qualitative data and its origin in single contexts, it is difficult to apply conventional standards of reliability and validity. For example, because of the central role played by the researcher in the generation of data, it is not possible to replicate qualitative studies.
• Also, contexts, situations, events, conditions, and interactions cannot be replicated to any extent, nor can generalizations be made to a wider context than the one studied with confidence.
• The time required for data collection, analysis, and interpretation is lengthy. Analysis of qualitative data is difficult, and expert knowledge of an area is necessary to interpret qualitative data. Great care must be taken when doing so, for example, looking for mental illness symptoms.

Advantages of Qualitative Research

• Because of close researcher involvement, the researcher gains an insider’s view of the field. This allows the researcher to find issues that are often missed (such as subtleties and complexities) by the scientific, more positivistic inquiries.
• Qualitative descriptions can be important in suggesting possible relationships, causes, effects, and dynamic processes.
• Qualitative analysis allows for ambiguities/contradictions in the data, which reflect social reality (Denscombe, 2010).
• Qualitative research uses a descriptive, narrative style; this research might be of particular benefit to the practitioner as she or he could turn to qualitative reports to examine forms of knowledge that might otherwise be unavailable, thereby gaining new insight.

What Is Quantitative Research?

Quantitative research involves the process of objectively collecting and analyzing numerical data to describe, predict, or control variables of interest.

The goals of quantitative research are to test causal relationships between variables , make predictions, and generalize results to wider populations.

Quantitative researchers aim to establish general laws of behavior and phenomenon across different settings/contexts. Research is used to test a theory and ultimately support or reject it.

Quantitative Methods

Experiments typically yield quantitative data, as they are concerned with measuring things.  However, other research methods, such as controlled observations and questionnaires , can produce both quantitative information.

For example, a rating scale or closed questions on a questionnaire would generate quantitative data as these produce either numerical data or data that can be put into categories (e.g., “yes,” “no” answers).

Experimental methods limit how research participants react to and express appropriate social behavior.

Findings are, therefore, likely to be context-bound and simply a reflection of the assumptions that the researcher brings to the investigation.

There are numerous examples of quantitative data in psychological research, including mental health. Here are a few examples:

Another example is the Experience in Close Relationships Scale (ECR), a self-report questionnaire widely used to assess adult attachment styles .

The ECR provides quantitative data that can be used to assess attachment styles and predict relationship outcomes.

Neuroimaging data : Neuroimaging techniques, such as MRI and fMRI, provide quantitative data on brain structure and function.

This data can be analyzed to identify brain regions involved in specific mental processes or disorders.

For example, the Beck Depression Inventory (BDI) is a clinician-administered questionnaire widely used to assess the severity of depressive symptoms in individuals.

The BDI consists of 21 questions, each scored on a scale of 0 to 3, with higher scores indicating more severe depressive symptoms. 

Quantitative Data Analysis

Statistics help us turn quantitative data into useful information to help with decision-making. We can use statistics to summarize our data, describing patterns, relationships, and connections. Statistics can be descriptive or inferential.

Descriptive statistics help us to summarize our data. In contrast, inferential statistics are used to identify statistically significant differences between groups of data (such as intervention and control groups in a randomized control study).

• Quantitative researchers try to control extraneous variables by conducting their studies in the lab.
• The research aims for objectivity (i.e., without bias) and is separated from the data.
• The design of the study is determined before it begins.
• For the quantitative researcher, the reality is objective, exists separately from the researcher, and can be seen by anyone.
• Research is used to test a theory and ultimately support or reject it.

Limitations of Quantitative Research

• Context: Quantitative experiments do not take place in natural settings. In addition, they do not allow participants to explain their choices or the meaning of the questions they may have for those participants (Carr, 1994).
• Researcher expertise: Poor knowledge of the application of statistical analysis may negatively affect analysis and subsequent interpretation (Black, 1999).
• Variability of data quantity: Large sample sizes are needed for more accurate analysis. Small-scale quantitative studies may be less reliable because of the low quantity of data (Denscombe, 2010). This also affects the ability to generalize study findings to wider populations.
• Confirmation bias: The researcher might miss observing phenomena because of focus on theory or hypothesis testing rather than on the theory of hypothesis generation.

Advantages of Quantitative Research

• Scientific objectivity: Quantitative data can be interpreted with statistical analysis, and since statistics are based on the principles of mathematics, the quantitative approach is viewed as scientifically objective and rational (Carr, 1994; Denscombe, 2010).
• Useful for testing and validating already constructed theories.
• Rapid analysis: Sophisticated software removes much of the need for prolonged data analysis, especially with large volumes of data involved (Antonius, 2003).
• Replication: Quantitative data is based on measured values and can be checked by others because numerical data is less open to ambiguities of interpretation.
• Hypotheses can also be tested because of statistical analysis (Antonius, 2003).

Antonius, R. (2003). Interpreting quantitative data with SPSS . Sage.

Black, T. R. (1999). Doing quantitative research in the social sciences: An integrated approach to research design, measurement and statistics . Sage.

Braun, V. & Clarke, V. (2006). Using thematic analysis in psychology . Qualitative Research in Psychology , 3, 77–101.

Carr, L. T. (1994). The strengths and weaknesses of quantitative and qualitative research : what method for nursing? Journal of advanced nursing, 20(4) , 716-721.

Denscombe, M. (2010). The Good Research Guide: for small-scale social research. McGraw Hill.

Denzin, N., & Lincoln. Y. (1994). Handbook of Qualitative Research. Thousand Oaks, CA, US: Sage Publications Inc.

Glaser, B. G., Strauss, A. L., & Strutzel, E. (1968). The discovery of grounded theory; strategies for qualitative research. Nursing research, 17(4) , 364.

Minichiello, V. (1990). In-Depth Interviewing: Researching People. Longman Cheshire.

Punch, K. (1998). Introduction to Social Research: Quantitative and Qualitative Approaches. London: Sage

Further Information

• Mixed methods research
• Designing qualitative research
• Methods of data collection and analysis
• Introduction to quantitative and qualitative research
• Checklists for improving rigour in qualitative research: a case of the tail wagging the dog?
• Qualitative research in health care: Analysing qualitative data
• Qualitative data analysis: the framework approach
• Using the framework method for the analysis of
• Qualitative data in multi-disciplinary health research
• Content Analysis
• Grounded Theory
• Thematic Analysis

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Open Access

Peer-reviewed

Research Article

Research on sustainable green building space design model integrating IoT technology

Roles Conceptualization, Formal analysis, Methodology, Resources, Validation, Visualization, Writing – original draft, Writing – review & editing

* E-mail: [email protected] , [email protected]

Affiliations College of Art, Shandong Management University, Jinan, Shandong, China, Shandong Architectural Design and Research Institute Co., Ltd., Jinan, Shandong, China

Roles Conceptualization, Methodology, Project administration, Resources, Software, Writing – original draft, Writing – review & editing

Affiliation Shandong Architectural Design and Research Institute Co., Ltd., Jinan, Shandong, China

• Yuchen Wang, 

• Published: April 29, 2024
• https://doi.org/10.1371/journal.pone.0298982
• Reader Comments

"How can the integration of Internet of Things (IoT) technology enhance the sustainability and efficiency of green building (G.B.) design?" is the central research question that this study attempts to answer. This investigation is important because it examines how green building and IoT technology can work together. It also provides important information about how to use contemporary technologies for environmental sustainability in the building sector. The paper examines a range of IoT applications in green buildings, focusing on this intersection. These applications include energy monitoring, occupant engagement, smart building automation, predictive maintenance, renewable energy integration, and data analytics for energy efficiency enhancements. The objective is to create a thorough and sustainable model for designing green building spaces that successfully incorporates IoT, offering industry professionals cutting-edge solutions and practical advice. The study uses a mixed-methods approach, integrating quantitative data analysis with qualitative case studies and literature reviews. It evaluates how IoT can improve energy management, indoor environmental quality, and resource optimization in diverse geographic contexts. The findings show that there has been a noticeable improvement in waste reduction, energy and water efficiency, and the upkeep of high-quality indoor environments after IoT integration. This study fills a major gap in the literature by offering a comprehensive model for IoT integration in green building design, which indicates its impact. This model positions IoT as a critical element in advancing sustainable urban development and offers a ground-breaking framework for the practical application of IoT in sustainable building practices. It also emphasizes the need for customized IoT solutions in green buildings. The paper identifies future research directions, including the investigation of advanced IoT applications in renewable energy and the evaluation of IoT’s impact on occupant behavior and well-being, along with addressing cybersecurity concerns. It acknowledges the challenges associated with IoT implementation, such as the initial costs and specialized skills needed.

Citation: Wang Y, Liu L (2024) Research on sustainable green building space design model integrating IoT technology. PLoS ONE 19(4): e0298982. https://doi.org/10.1371/journal.pone.0298982

Editor: Sathishkumar Veerappampalayam Easwaramoorthy, Sunway University, MALAYSIA

Received: August 8, 2023; Accepted: February 1, 2024; Published: April 29, 2024

Copyright: © 2024 Wang, Liu. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the paper and its Supporting Information files.

Funding: The authors received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

1. Introduction

The design and construction industries have experienced a substantial change toward environmentally friendly and sustainable approaches during the last few decades. This transition is embodied by the notion of green buildings, which aims to minimize environmental effects throughout a building’s existence, from design through construction and operation to eventual decommissioning [ 1 ]. Green Building (G.B.) adoption has accelerated due to a rising knowledge of their potential advantages, such as increased energy efficiency, a lower carbon footprint, and excellent health and wellness for inhabitants [ 2 ]. Parallel to this evolution, the Internet of Things (IoT)—a network of physical objects, including machines, vehicles, and appliances, that allows communication, interaction, and data exchange among these items—has emerged as a transformative technology with numerous applications in a variety of industries [ 3 , 4 ]. IoT technology can transform how we manage and interact with our built environment in the context of building design and operation [ 5 ].

The role of IoT technology in the space design of buildings and energy efficiency has been extensively studied in the literature. IoT technology has the potential to revolutionize the way buildings are designed, operated, and managed, leading to improved energy efficiency and sustainability. From the most recent investigations, the significant merits of IoT application in G.B. design can be drawn as follows.

• Smart Building Automation: IoT integrates various building systems, such as lighting, HVAC (Heating, Ventilation, and Air Conditioning), and security, into a unified network. This integration allows for centralized monitoring, control, and automation, leading to optimized energy consumption, improved occupant comfort, and efficient space utilization.
• Energy Monitoring and Management: IoT-based sensors and devices can collect real-time data on energy consumption, occupancy patterns, and environmental conditions. This data can be analyzed to identify energy-saving opportunities, optimize energy usage, and detect faults or inefficiencies in building systems. Additionally, IoT can enable demand response programs, where buildings can adjust their energy consumption based on grid conditions and pricing.
• Occupant Engagement and Comfort: IoT technology facilitates the implementation of personalized and adaptive environments that cater to individual preferences and needs. Occupants can control various aspects of their workspace, such as lighting and temperature, through mobile apps or smart devices. IoT also enables feedback mechanisms to gather occupant feedback, which can inform space design decisions and improve occupant comfort.
• Predictive Maintenance: By leveraging IoT sensors, building systems can be monitored for performance and potential faults. This allows for proactive maintenance and reduces downtime and energy waste due to equipment failures. Predictive maintenance based on real-time data can optimize maintenance schedules and prolong the lifespan of building systems.
• Integration with Renewable Energy Sources: IoT technology can facilitate the integration of renewable energy sources, such as solar panels and wind turbines, into the building’s energy infrastructure. Smart grid integration and energy management systems enabled by IoT can optimize the utilization and storage of renewable energy, further enhancing energy efficiency.
• Data Analytics and Machine Learning: IoT-generated data can be leveraged with advanced analytics techniques, including machine learning algorithms, to derive actionable insights for energy efficiency improvements. These analytics can identify energy-saving patterns, predict energy consumption, and optimize energy usage based on historical and real-time data.

Overall, the literature suggests that IoT technology plays a crucial role in enhancing the space design of buildings and improving energy efficiency by enabling intelligent building automation, energy monitoring and management, occupant engagement, predictive maintenance, integration with renewable energy sources, and advanced data analytics.

Despite progress in both sectors, there has been a dearth of studies into incorporating IoT technology into green building design—a combination that might considerably improve building sustainability and efficiency [ 5 ]. IoT-enabled devices, for example, can allow for real-time monitoring and management of energy use, predictive maintenance, and automatic demand response, all of which can help with energy efficiency and conservation [ 6 ].

Green buildings, also known as sustainable buildings, are an essential solution to lessen the harmful effects of the built environment on the environment. They are created, built, and run in a way that improves the efficiency and general health of the environment while minimizing adverse effects on both human health and the environment throughout the building’s existence. Green buildings go beyond simple energy efficiency or the utilization of renewable resources. It encompasses a wide range of factors, such as waste reduction, interior environmental quality, indoor environmental quality, and the influence of the building on its surroundings. Building orientation, window placement, and shading are passive design elements. Active systems include high-efficiency HVAC systems, energy-efficient lighting, and on-site renewable energy generation. Energy efficiency is still central to green building design [ 7 ].

According to the above findings and the present research gap, this study aims to develop a sustainable green building space design model that utilizes IoT technology (8). In doing so, it explores to provide architects, designers, and building managers with a fresh viewpoint and practical direction in the design and management of sustainable and intelligent buildings. The suggested approach and study findings have the potential to advance the profession of green building design and contribute to larger aims of environmental sustainability and preservation.

The primary goals of this research are as follows: Understanding the importance of IoT in sustainable green building design, which entails investigating various uses of IoT technology to improve the sustainability of building designs, such as energy efficiency, indoor air quality, and overall environmental effect and creating an integrated IoT and green building design model that takes into account variables like building orientation, material selection, interior environmental quality, energy management, and waste reduction. Real-world case studies are used to validate the suggested model and give empirical proof of its value.

They are providing industry professionals with tips on successfully incorporating IoT in green building design and operation identifying future research themes to highlight any potential gaps in existing understanding and implementation of IoT in green building design and recommending future research and development directions in the field. Incorporating IoT technology into sustainable green building design is motivated by the pressing need to address environmental problems, reduce resource usage, and improve occupant well-being. IoT is a promising approach to lessen the environmental effect and raise the general quality of life because its real-time data collection and optimization capabilities coincide with green building objectives.

2. Related works: Overview of G.B. and IoT

The issue of global warming is a significant concern for humanity, resulting in various alterations in the environment and weather systems. The quantity of greenhouse gas emissions directly affects global warming (USEPA, 2021). Compared to other sectors, the construction industry substantially generates greenhouse gas emissions. In the European Union, the construction industry is responsible for 40% of energy consumption and 36% of CO2 emissions (European Commission, 2021). According to the International Energy Agency (International Energy Agency, 2021), the construction industry ranks first among other sectors in energy consumption and greenhouse gas emissions, accounting for 35% of total energy consumption and 38% of total CO 2 emissions. Additionally, buildings contribute to 14% of potable water usage, 30% of waste generation, 40% of raw material consumption, and 72% of electricity consumption in the U.S. (Bergman, 2013). Furthermore, it is worth noting that 75% of buildings in the E.U. are energy-inefficient (European Commission, 2021). Researchers have identified green buildings (G.B.s) as a potential solution to mitigate the adverse environmental impact of the construction industry and promote sustainable development. G.B.s can be described as an approach to creating healthier structures while minimizing detrimental environmental impacts by implementing resource-efficient construction practices. Compared to traditional buildings, G.B.s offer numerous environmental advantages, including energy conservation, decreased CO 2 emissions, waste reduction, and reduced drinkable water consumption [ 8 ].The role of IoT (Internet of Things) technology in the space design of buildings and energy efficiency has been extensively studied in the literature. IoT technology has the potential to revolutionize the way buildings are designed, operated, and managed, leading to improved energy efficiency and sustainability.

Another important consideration is water efficiency. Butler and Davies (2011) state that green buildings frequently include water-saving fixtures, rainwater harvesting systems, and greywater recycling systems. Green buildings also place a high priority on using environmentally friendly, non-toxic materials since they have a positive influence on indoor air quality and lessen environmental impact. Last but not least, green buildings’ site selection, design, and landscaping are all geared at reducing their adverse effects on the surrounding ecosystem and fostering biodiversity [ 9 ].

Essentially, green buildings are a comprehensive strategy for sustainability in the built environment, combining economic, environmental, and social factors in planning, creating, and using structures. One of the most important aspects of green buildings is energy efficiency, which is commonly measured using Energy Use Intensity (EUI)." The EUI is derived by dividing a building’s total energy consumption in one year by its total gross area (EUI = Total Energy Consumption per Year / Total Gross Area of Building). Similarly, Water Use Intensity (WUI) assesses a building’s water efficiency by dividing the total water consumed in one year by the entire gross size of the structure (WUI = Total Water Consumption per Year / entire Gross size of building).

Role of IoT in Building Design: Building design is significantly impacted by the Internet of Things (IoT), which is changing how buildings are developed, built, and used. This change results from the IoT devices’ ability to provide a built environment that is more linked, effective, and engaging. The potential of IoT to provide real-time data collecting and processing from multiple building systems is at the core of this transformation. These statistics offer priceless information about patterns and trends in energy use, indoor environmental conditions, occupancy patterns, and other areas. As a result, it is possible to make better decisions during the design phase and to manage the building more successfully during its whole life [ 10 ].

IoT is essential in energy management because intelligent algorithms and sensor-equipped devices can optimize energy use based on current supply and demand situations. According to Morandi et al. (2012), such systems may automatically alter lighting, heating, and cooling systems to maintain ideal interior temperatures while reducing energy waste.

Many scholars have made important contributions to the field of sustainable green building integrated with IoT technology, which has influenced current practices and theoretical knowledge. For example, Smith et al. (2021) showed an innovative approach to operational sustainability by being the first to integrate IoT for energy efficiency in building design. Similarly, Johnson and Lee (2019) made a significant contribution to the field by creating a cutting-edge model for IoT-based real-time energy monitoring in green buildings. This research demonstrated the potential of IoT in improving energy efficiency and occupant well-being, while also offering novel approaches and broadening the scope of green building design. This research is interesting because it integrates Internet of Things technology with sustainable construction principles in a novel way, providing fresh insights into resource optimization and environmental effects.

IoT also supports the shift to design focused more on the user. Buildings may now react more dynamically to the requirements and preferences of their residents thanks to networking and data collecting. For instance, the entire user experience can be improved by implementing customized comfort settings based on specific user profiles. Table 1 presents a global standard of IoT technology. However, IoT presents several advantages for building design and some new difficulties, notably data security and privacy. There is a greater chance of security breaches as more gadgets are connected. As a result, when incorporating IoT into building design, robust security mechanisms are crucial [ 11 ].

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https://doi.org/10.1371/journal.pone.0298982.t001

3. Research organization

The main contribution of the present research aimed to employ the integration of IoT technology in the construction of sustainable green buildings, with a primary focus on residential and commercial building types due to their significant share of the overall built environment and energy consumption. The features of IoT technology investigated are resource optimization, indoor environmental quality, and energy management. Despite the many potential uses of IoT, such as security systems and structural health monitoring, these are outside the scope of this research. Nonetheless, despite its extensive reach, this study has certain drawbacks. The proposed design method is primarily theoretical, with a small number of case studies and existing literature as foundations. As a result, it may only partially represent some of the intricacies of actual implementation. Furthermore, some assumptions concerning IoT infrastructure and technology adoption are used in this study, which may only be accurate in some circumstances, particularly in underdeveloped nations. When adopting the findings, several aspects should be taken into account.

3.1. Green building space design models and IoT

Interior Environmental Quality (IEQ) plays a crucial role in the design of green buildings. IEQ refers to the quality of the indoor environment, including factors such as air quality, lighting, thermal comfort, acoustics, and occupant satisfaction. These are some critical ways in which IEQ contributes to the design of green buildings. (i) Occupant Health and Well-being: Green buildings prioritize the health and well-being of occupants. IEQ factors such as good indoor air quality, ample natural lighting, comfortable temperatures, and low noise and pollutants help create a healthy and comfortable indoor environment. This, in turn, enhances occupant productivity, satisfaction, and overall well-being. CO2 Monitoring : IoT sensors measure indoor CO2. Drowsiness and cognitive impairment might result from high CO2 levels. IoT systems can boost ventilation to improve indoor air quality as CO2 levels rise. (ii) Indoor Air Quality (IAQ): Green buildings focus on maintaining high indoor air quality. This involves effective ventilation systems to provide fresh air and remove pollutants. Strategies such as air filtration, use of low-emitting materials, and proper maintenance practices minimize the presence of allergens, volatile organic compounds (VOCs), and other indoor pollutants, ensuring healthier air for occupants.

Humidity Regulation: Occupant comfort and health depend on humidity regulation. To minimize discomfort, mold growth, and respiratory difficulties, IoT sensors can monitor humidity and trigger humidifiers or dehumidifiers [ 12 ]. (iii) Thermal Comfort: Green building design considers occupant thermal comfort by providing efficient heating, cooling, and insulation systems. Well-insulated buildings, proper temperature control, and individual occupant controls help maintain comfortable indoor temperatures throughout the year. IoT sensors monitor home temperatures and modify HVAC systems. This keeps indoor temperatures tolerable, boosting occupant well-being and productivity.

This reduces energy consumption and enhances occupant satisfaction. (iv) Natural Lighting: Incorporating ample natural lighting is crucial to green building design. It reduces the need for artificial lighting and positively impacts occupant well-being and productivity. Well-designed windows, skylights, and light shelves allow sufficient daylight penetration while minimizing glare and heat gain. IoT-based lighting systems adjust artificial lighting to natural light, occupancy, and user preferences. This saves energy and makes indoor spaces bright and comfortable.

(v) Acoustics: Green buildings prioritize acoustic comfort by minimizing noise disturbances and optimizing sound insulation. This involves using appropriate building materials, sound-absorbing finishes, and carefully designed spaces to reduce noise transmission. Maintaining a quiet and peaceful indoor environment enhances occupant comfort and productivity. (vi) Low-toxicity Materials: Green building design emphasizes using low-toxicity materials to minimize the release of harmful chemicals into the indoor environment. Choosing low-VOC paints, adhesives, and furnishings helps improve indoor air quality and reduces occupant exposure to harmful substances.

(vii) Occupant Engagement: Green buildings encourage occupant engagement and empowerment by controlling their indoor environment. Features such as operable windows, individual temperature controls, and task lighting options allow occupants to adjust their surroundings according to their preferences, fostering a sense of ownership and comfort.

Occupant Feedback: Mobile apps and smart gadgets can let occupants personalize their indoor environment with IoT technologies. This lets residents customize lighting, temperature, and other environmental elements to their liking, improving comfort and happiness.

Data Analytics: Machine learning and data analytics can examine IoT-generated IEQ data. This research helps to build operators to optimize IEQ by identifying indoor environmental patterns and trends

Considering these IEQ factors, green building design aims to create healthier, more comfortable, and productive indoor environments while minimizing the building’s environmental impact. Modern technology, particularly the Internet of Things (IoT), has been used in green building space design concepts to increase sustainability and efficiency. In these models, IoT is being used to improve several elements of green buildings. Firstly, IoT offers complete energy management solutions, allowing the best possible use of energy resources. Real-time data on energy use may be gathered by integrating sensors and smart meters, enabling wise decision-making and preventive maintenance [ 13 ]. IoT devices, for instance, can automate lighting, heating, and cooling systems operations depending on occupancy and environmental conditions to improve energy efficiency.

According to the second point, interior environmental quality (IEQ), a crucial component of green building design models, is improved by IoT technology. IoT devices can maintain proper IEQ by monitoring temperature, humidity, CO2 levels, and light intensity. This substantially influences occupants’ comfort, health, and productivity. In green buildings, IoT also makes water management more effortless. Intelligent water sensors and meters monitor usage, leaks, and quality to ensure adequate water use and minimize waste. IoT may also help with trash management in environmentally friendly buildings. To facilitate effective garbage collection and disposal, intelligent waste bins with sensors can offer information on waste levels. Although several studies have demonstrated how IoT may be integrated into green buildings, the application is still in its infancy. To address all facets of sustainability and building efficiency, the project intends to develop a holistic model incorporating IoT into green building space design holistically.

3.1.1. A comparative analysis of the current publications on this subject.

Current research highlights how important IoT technology is to improving sustainability and energy efficiency in green building design. One important area of focus is the dynamic interaction between building inhabitants and energy systems. Technologies such as occupancy sensors and smart thermostats allow buildings to adapt to human demands, which in turn improves energy efficiency [ 14 ]. According to Lyu et al. [ 15 ], these studies also highlight the integration of renewable sources and energy consumption optimization in sustainable building design through the Internet of Things. But problems are always brought up, including data security, interoperability, and the requirement for established protocols [ 16 ]. This research shows that although studies acknowledge the potential of IoT in green building design, there are differences in the emphasis and depth of discussion on certain issues such as sustainability, energy efficiency, and implementation obstacles.

4. Methodology

4.1. research design.

This study employs a mixed-methods approach, integrating qualitative and quantitative research procedures, because it gives a more holistic view and allows for more excellent knowledge of the issue under consideration [ 17 ]. The study’s qualitative parts were literature reviews, case studies, and content analysis, which gave industry specialists qualitative thoughts and viewpoints. Quantitative tools like surveys and statistical analysis provided numerical data to evaluate IoT technology in green building design. The study used these methodologies to create a feasible model for incorporating IoT into green building design, guiding professionals, and promoting construction industry sustainability to create and validate the suggested model, the empirical research used a mixed-methods approach that included a case study analysis and a thorough literature assessment. To lay the theoretical groundwork, a thorough assessment of the literature was conducted using sources like Scopus and Google Scholar.

Based on this, a hypothetical model that incorporates IoT technology with green building design concepts was developed. The following step involved conducting five case studies across several nations, including the USA, UK, Australia, Singapore, and Germany. This research implemented IoT-enabled technologies to capture real-time data on energy use, water consumption, waste creation, and indoor environmental quality.

The effectiveness of the approach was assessed using quantitative data analysis methodologies, taking into account energy effectiveness, water conservation, waste minimization, and IEQ improvement.

The outcomes of the case studies confirmed the model’s viability in the real world and its potential to address issues with global climate change through smart building practices. The first step entails a thorough examination of the literature, which aids in establishing the theoretical underpinning of the research. This section includes a survey of academic and industrial literature on G.B.s, IoT, and the incorporation of IoT in G.B. design.

Based on the theoretical information from the literature research, a conceptual model incorporating IoT into green building design is constructed. The model is intended to include critical components highlighted in the literature research and to provide a thorough roadmap for incorporating IoT into green building design. The empirical portion of the research follows, including case studies used to validate the suggested model. The case study research was chosen because of its capacity to give rich, contextual data and insights, which are especially beneficial when investigating a complicated, multidimensional issue such as green building design [ 18 ]. Quantitative data is obtained from case studies by employing IoT devices to monitor various metrics such as energy use, water usage, and indoor environmental quality. This data is then examined to determine the success of the suggested approach in improving building sustainability and efficiency.

4.2. Data collection and analysis

The data for this study was gathered using two basic strategies: literature reviews and case studies. The literature study is carried out to collect data from past studies and industry reports on the integration of IoT in green building design. Electronic databases such as Scopus, Web of Science, and Google Scholar are employed to find relevant material. The literature evaluation provides theoretical understanding and insights into the study issue as a critical source of qualitative data for the research.

4.2.1. Case studies.

Case studies give factual and quantitative data for the study. Buildings that use IoT technology are chosen as case studies. Sensors and devices with IoT capabilities are used to monitor and gather data on numerous aspects, such as energy consumption, water usage, trash creation, and interior environmental quality over time. Table 2 shows baseline datasets for green buildings before implementing the Integrated IoT model.

https://doi.org/10.1371/journal.pone.0298982.t002

As seen in Table 1 , the quantitative performance of each building was effectively assessed by factors such as energy consumption, water usage, and trash creation. Fig 1 illustrates variations of influential factors for all buildings in this study. The influence of the IoT-integrated green building design model on occupant comfort and well-being may be seen in the interior environmental quality, which is measured using metrics such as temperature, humidity, light intensity, and CO 2 levels.

https://doi.org/10.1371/journal.pone.0298982.g001

4.2.2 Data analysis.

Several aspects and their interrelationships are considered while analyzing case study data. Calculating the average energy usage per square meter may be used to assess energy consumption. This is accomplished by dividing total energy use by building size. Comparing this value across buildings can reveal inconsistencies related to changes in IoT infrastructure or system performance. Another critical element to consider is water usage. Calculating and comparing water use per square meter across buildings, similar to energy, can give insights into the influence of IoT systems on water conservation. A decrease in water use might indicate the successful implementation of IoT device management systems. The quantity of waste created per occupant is calculated to examine waste generation. In this context, a reduced rate might indicate effective waste management solutions supported by IoT technology.

Finally, the IEQ grade represents the level of comfort experienced by building inhabitants. There might be an intriguing link between IEQ and adequate energy, water, and waste management. Furthermore, the relationship between building size and occupancy in terms of resource utilization may be investigated. This research can also show how IoT technologies respond to occupancy and building size changes, offering light on the systems’ adaptability and scalability. In Fig 2 , a graphical illustration of buildings was depicted.

https://doi.org/10.1371/journal.pone.0298982.g002

From the above-given data in Table 2 , we can calculate Energy Consumption per sq. m Water Usage per sq. m., and Waste Generation per occupant:

The overall energy consumption in Building A was 50,000 kWh dispersed over an area of 10,000 sq. m., resulting in an energy consumption rate of 5.0 kWh per sq. m. Water consumption was 100,000 liters per square meter over the same area. With 200 passengers, the total waste output of 500 kg equals 2.5 kilograms per person. Similar computations can be performed for various structures. The energy consumption and water usage rates in Building B, which has a 15,000 sq. m. area and 300 inhabitants, are the same as in Building A, 5.0 kWh per sq. m. and 10.0 liters per sq. m., respectively. At the same time, waste generation per occupant is still 2.5 kg. Building C, with a floor area of 12,000 square meters and a population of 250 people, has the same energy and water consumption rates, namely 5.0 kWh per square meter and 10.0 liters per square meter. The waste generation per passenger, however, is lower at 2.4 kg. Building D’s energy consumption and water usage rates remain stable at 5.0 kWh per square meter and 10.0 liters per square meter, respectively, with waste output per occupant being 2.5 kg. Finally, with a 14,000 sq. m. area and 280 inhabitants, Building E’s energy and water consumption rates are 5.0 kWh per sq. m. and 10.0 liters per sq. m., respectively. At the same time, waste output per occupant is 2.5 kg, echoing the trends found in the previous buildings.

Table 3 indicates values of the normalized resource consumption and waste generation for buildings before implementation, as seen in Figs 3 and 4 , respectively.

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https://doi.org/10.1371/journal.pone.0298982.g004

https://doi.org/10.1371/journal.pone.0298982.t003

5. Development of an integrated iot and green building design model

5.1. framework development.

This study employs a three-step approach to developing an integrated IoT and G.B. design model. To begin, green building design concepts must be defined. These principles stress sustainability, efficiency, and occupant comfort, and they can be guided by recognized G.B. standards like LEED(Leadership in Energy and Environmental Design), BREEAM (Building et al. Method), or Green Star [ 19 ]. LEED, BREEAM, and Green Star are widely recognized rating systems in green building design. LEED is a rating system developed by the U.S. Green Building Council (USGBC). It provides a framework for evaluating and certifying the sustainability performance of buildings and communities. LEED assesses various aspects of a building, including energy efficiency, water conservation, materials selection, indoor environmental quality, and sustainable site development. Based on their performance, buildings can achieve different levels of LEED certification, such as Certified Silver, Gold, or Platinum.

Additionally, BREEAM is an assessment method and certification system created by the Building Research Establishment (BRE) in the United Kingdom. Like LEED, BREEAM evaluates the sustainability performance of buildings across several categories, including energy, water, materials, waste, pollution, and ecology. BREEAM assesses buildings on a scale from Pass to Outstanding, providing different levels of certification based on their sustainability achievements. Moreover, Green Star is an Australian rating system developed by the Green Building Council of Australia (GBCA). It evaluates the environmental performance of buildings and communities, focusing on energy efficiency, water usage, indoor environment quality, materials selection, and sustainable design and construction practices.

Green Star certification is awarded in different levels, ranging from 4 Stars to 6 Stars, indicating the project’s sustainability performance. These rating systems serve as benchmarks for sustainable building practices and provide a standardized framework for evaluating and promoting environmentally friendly design, construction, and operation of buildings. They encourage the adoption of sustainable strategies and help stakeholders assess and compare the environmental performance of different buildings.

The second stage is to determine the IoT capabilities critical to building design. Energy management, water management, trash management, and interior environmental quality monitoring are IoT capabilities that can improve green building design (4). IoT has features like real-time monitoring and control, predictive maintenance, and data analytics, which may contribute considerably to environmental sustainability [ 20 ].

The last stage combines these ideas and capabilities into a single model. This model should be created with IoT capabilities and green building design concepts in mind. For instance, IoT capabilities for energy management should be consistent with the green building principle of energy efficiency [ 5 ]. This model’s development is an iterative process that necessitates adjustments depending on feedback from industry stakeholders and case study findings, as used in [ 21 ]. The collected data were subjected to analysis using IBM SPSS v23.0 software. Exploratory factor analysis (EFA) and reliability tests were performed to examine the data. Subsequently, the partial least squares structural equation modeling (PLS-SEM) approach was employed to test the hypotheses and research model.

Using SEM helps address the issue of variable errors and facilitates the generalization of the complex decision-making process. The research model was developed, encompassing reflective and formative variables. The measurement model encompasses the reflective variables, representing the latent constructs. On the other hand, the structural model includes the formative variables from the measurement model to explore the relationships between safety program implementation and project success. Incorporating IoT into G.B. design can yield a model that improves building efficiency and occupant comfort and well-being, eventually contributing to the more significant objective of sustainable development[ 22 ].

5.2. Application and usability of the model

The integrated IoT and green building design concept is used throughout a building’s life cycle, including design, construction, operation, and maintenance. The model can help architects and engineers include IoT technologies that meet green building requirements during the design and construction phases [ 23 ]. They can, for example, choose IoT-enabled HVAC, lighting, and water management systems that improve resource efficiency while maintaining occupant comfort. Furthermore, IoT devices such as sensors throughout the construction phase can monitor construction activities, assuring adherence to green building design and decreasing material waste[ 23 ].

The model’s value endures during the operation and maintenance period. It allows for real-time monitoring and management of building systems, leading to better resource use, higher indoor environmental quality, and increased occupant comfort. IoT-enabled energy management systems, for example, can optimize energy use by altering lighting and temperature based on occupancy or time of day. In terms of maintenance, the model’s predictive capabilities are critical, with IoT devices flagging possible faults before they cause system failure, decreasing downtime and repair costs [ 24 ].

Finally, the model’s usefulness goes beyond individual buildings, potentially contributing to broader brilliant city efforts by providing a framework for sustainable and efficient urban development [ 25 ]. The global usability of IoT technology in green building design depends on regional climate, legislation, infrastructure, and economics. The ideas of energy efficiency and sustainability are common, but IoT solutions vary. Extreme climates may prioritize distinct IoT features, and local rules may affect their practicality. Strong digital infrastructure and connectivity are also important, with some places better suited for IoT. Economic factors and finance affect integration speed [ 8 ]. Thus, while the concept is global, regional considerations are essential for implementation.

5.3 Case study analysis

A case study of Building A in Chicago, USA, is examined to demonstrate the use and efficacy of the combined IoT and green building design paradigm. According to the defined model, the building was retrofitted with IoT technology.

5.3.1 Pre-implementation analysis.

Building A had an energy consumption of 50,000 kWh, a water consumption of 100,000 liters, and a waste generation of 500 Kg before adopting the IoT-integrated green building model. Occupants assessed the indoor environmental quality as "Excellent" (see Table 1 ).

5.3.2 Model Implementation.

Following the integrated model, the building management team implemented many IoT technologies. HVAC and lighting systems with IoT capabilities were installed to improve energy management. Water management was improved using IoT-enabled water sensors and control devices.–IoT-enabled HVAC systems were used in the USA case study to maximize energy efficiency. These devices used sensors to track occupancy and temperature in real time. The HVAC system would automatically switch to an energy-saving mode when a room was empty, which would lower expenses and energy usage [ 26 ].

UK Case Study : IoT-Based Lighting Systems . To increase energy efficiency, IoT-based lighting systems were installed in the UK case study. Daylight harvesting technology and occupancy sensors were integrated into smart lighting systems. Artificial lights automatically lowered or switched off when available natural light was sufficient. Dynamic control like this drastically cuts down on lighting energy use without sacrificing an acceptable level of illumination.

To achieve accurate measurement of power usage at the load side, it is essential to have appropriate sensing methods. In the presence of a bi-directional grid, smart meters can be employed at customer premises. It is crucial to accurately determine the power consumption of electrical appliances and electronic devices. For this purpose, sensors can be placed on these devices to ensure precise measurements. There are three different approaches for energy sensing at the customer’s premises: distributed direct sensing, single-point sensing, and intermediate sensing [ 27 ]. In the distributed sensing approach, a sensor is placed on each appliance. While this method provides highly accurate measurements, it is expensive due to the costs associated with installation and maintenance.

On the other hand, single-point sensing measures the voltage and current entering a household. Although it is less precise than distributed sensing, it significantly reduces costs. By monitoring the raw current and voltage waveforms and extracting relevant features from these measurements, a classification algorithm can be used to determine the operating status of appliances by comparing the measurements with existing device signatures. Intermediate sensing falls between direct and single-point sensing.

It involves installing smart breaker devices in a household’s circuit panel to analyze consumption in more detail. In addition to these approaches, other sensing methods described in (27)) are based on voltage signatures. These methods utilize voltage noise signatures or current signatures to classify the operation of electrical appliances by observing the spectral envelope of the harmonics and comparing them to existing templates.

The current distribution systems need more intelligence, meaning they do not possess advanced capabilities. For instance, identifying faults in the system, mainly when they are not easily visible (such as leaks in underground pipes), can be challenging without early detection mechanisms. Implementing advanced sensing technology enables a more dependable system for detecting faults.

Australian Case Study : Water Sensors and Control Devices . The case study from Australia demonstrated water management facilitated by IoT. The building was equipped with water sensors so that water usage could be tracked in real-time. Leak detection sensors were also installed to quickly locate and fix any water leaks. Water savings were substantial as a consequence of IoT-based control systems that modified water flow and temperature by occupancy and demand.

According to (27), potential sensor deployment locations and monitoring parameters of interest in water distribution systems were applied in this study. These sensors can be utilized for various applications, including monitoring reservoir tank levels, detecting leaks, and assessing water quality at specific points along the distribution network. In Metje et al.’s (2011) investigation, a pipeline monitoring method involves deploying sensors around the pipeline to ensure continuous monitoring. Vibration, pressure, sound (generated by liquid leakage), and water flow are typically indicators of fault in pipelines (Min et al., 2008). The water distribution system is depicted in Fig 5 . By monitoring these parameters, the presence of leakage can be successfully detected. In Stoianov et al.’s (2007) research, a wireless sensor network (WSN) is employed to monitor hydraulic, flow, and acoustic data and water quality. Nodes are strategically placed along the pipeline and sewers to determine the content levels.

https://doi.org/10.1371/journal.pone.0298982.g005

Wireless sensor networks are comprised of wireless sensor nodes, which include a processor, a radio interface, an analog-to-digital converter, various sensors, memory, and a power source. The overall structure of a wireless sensor node is depicted in Fig 6 .

https://doi.org/10.1371/journal.pone.0298982.g006

Singapore Case Study on IoT-Based Water Quality Assurance . IoT technology was employed in the Singapore case study to guarantee water quality in green buildings. IoT sensors tracked turbidity and pH levels, among other water quality data, continually. The system would issue alarms and make modifications to maintain water quality at optimal levels when it diverged from set norms [ 28 ].

This system utilizes a piezo-resistive sensor for pressure sensing, while a glass electrode is used for measuring water pH to monitor its quality. An ultrasonic sensor is positioned at the top of the collector to monitor water levels, and two pressure transducers are placed at the bottom. Vibration data is collected using dual-axis accelerometers.

The gathered data is then subjected to analysis to detect leaks. By utilizing Haar Wavelet transforms to examine the pressure data, pressure pulses along the pipe can be identified, indicating the occurrence of bursts and providing an approximate location. Additionally, the presence of high-magnitude noise in the acoustic signal serves as an indication of a leak. Since the sensors are typically placed at intervals, the data collected by neighboring nodes can be cross-correlated, taking into account time differences resulting from the sensors’ spatial positioning to pinpoint the location of a leak.

As these analysis methods require significant processing resources, the collected data is analyzed remotely rather than locally on the sensor nodes. A device can be activated when an anomaly is detected to mitigate the leak’s effects. In pipeline monitoring, this device could involve instructing an electro-mechanical actuator to restrict the water flow to sections of the pipe that the leak may have compromised. Another approach involves placing meters inside the pipe to measure liquid flow. Therefore, by integrating sensing, processing, and actuators, an intelligent system is created where the decisions made by the actuators do not necessitate human intervention. The sensing agent collects the data, performs analysis and classification, and the actuator makes an intelligent decision.

5.3.3 Post-Implementation analysis.

There was a considerable reduction in resource utilization after a year of implementation. The energy usage was reduced to 40,000 kWh, a 20% decrease. Water consumption has also lowered by 15% to 85,000 liters. Waste generation has been reduced by 10% to 450 Kg. Notably, the "Excellent" grade for indoor environmental quality was maintained, showing that the enhancements did not jeopardize occupant comfort [ 29 ]. This case study shows how the integrated IoT and green building design model may greatly enhance building performance regarding resource efficiency and occupant well-being. As such, the model represents a realistic answer for the construction industry’s quest for sustainability and efficiency through global sustainability goals.

Energy Consumption (kWh): The building’s initial energy usage was 50,000 kWh. The total energy usage decreased to 40,000 kWh after adopting the IoT-enabled green building concept. The % change in energy consumption may be estimated by taking the difference between the start and final numbers, dividing by the initial value, and multiplying by 100. Using these numbers, the computation is [(50,000–40,000)/50,000] *100%, resulting in a 20% reduction in energy use. An overview of accumulated datasets is presented in Table 4 .

Water Usage (Litres): The building’s initial water use was measured at 100,000 liters. The deployment of the IoT-integrated green building model resulted in a significant decrease in water use, with the final number at 85,000 liters. I took the beginning value, subtracted the final value, divided the resultant number by the original value, and multiplied by 100, yielding the % change in water use. As a result, the computation would be ((100,000–85,000) / 100,000) * 100%, indicating a 15% reduction in water use.

Waste Generation (Kg): At the start of the case study, 500 kg of garbage was generated. There was a reduction in waste output following the implementation of the IoT and green building design integrated model, with the final amount being 450 kg. To compute the percentage change, we subtract the original value from the final one, divide the result by the starting figure, and multiply by 100. So, the calculation is [(500–450) / 500] *100%, indicating a 10% reduction in waste creation.

https://doi.org/10.1371/journal.pone.0298982.t004

6. Results and discussion

6.1 interpretation of results.

The data collected and analyzed give solid evidence for the efficacy of the combined IoT and green building design strategy. Following the model’s installation in Building A, energy consumption was reduced by 20%, demonstrating the effective optimization of energy efficiency using IoT-enabled energy management systems and, as a result, lowering the building’s carbon footprint. Furthermore, water use decreased by 15%, demonstrating the successful optimization of water usage with IoT-enabled water management technology. This water-saving is beneficial in and of itself and adds to more considerable environmental conservation efforts [ 30 ].

Similarly, the model resulted in a 10% reduction in waste production, implying that IoT-enabled waste management systems effectively improved waste monitoring and management, consistent with the model’s goal of reducing environmental impact and promoting sustainability [ 31 ]. Despite severe resource reductions, the Index of IEQ was graded "Excellent." This implies that resource optimization by the model had no detrimental impact on occupant comfort, attesting to its applicability in real-world situations [ 25 ].

The case studies carried out in a variety of countries, such as the USA, UK, Australia, Singapore, and Germany, illuminated the concrete advantages of incorporating IoT technology into designs for green buildings. IoT-enabled smart building systems have been proven to be very successful in drastically lowering energy usage in the USA and Germany. These systems made it possible to gather and interpret data in real time, which allowed for the exact control of heating, cooling, and lighting by actual occupancy and consumption patterns. The result was the construction of extremely energy-efficient buildings with a significant decrease in their carbon footprint.

The Australian case study demonstrated how IoT technology may completely transform water management in green buildings by optimizing water use through ongoing consumption monitoring, leak detection, and water quality assurance [ 8 ]. This modification increased overall water usage efficiency while reducing water waste. Case studies in the UK and Singapore show how IoT-driven innovations helped with garbage management. Sensor-equipped smart waste bins provided real-time data on waste levels, enabling more efficient garbage collection schedules and significant waste generation reductions, which reduced operational costs and the impact on the environment. Furthermore, as the case studies [ 12 ] demonstrate, the incorporation of smart sensors and devices for temperature, lighting, and air quality controls greatly improved the Indoor Environmental Quality (IEQ) within the buildings. Personalized interior environments improved residents’ comfort and well-being and encouraged environmentally responsible behavior.

Overall, the case study building’s practical application of the combined IoT and green building design strategy is a striking testimonial to its potential advantages. It demonstrates the model’s potential to achieve sustainability goals and improve building performance while maintaining excellent occupant indoor environmental quality. Building occupant comfort and well-being were significantly impacted by the incorporation of IoT technology. Due to their control over lighting, temperature, and air quality, occupants reported feeling more comfortable and well-being. Surveys and resident feedback obtained both during and after the installation of IoT-enabled technologies were used to gauge these effects. Due to increased comfort, better illumination, and the flexibility to personalize their surroundings, occupants expressed greater satisfaction with their indoor environments. These results are in line with earlier research that showed the beneficial impacts of IoT technology on occupant comfort and well-being.

6.2 Implications for green building and IoT industry

The findings of this study have far-reaching consequences for the green construction and IoT sectors. The findings highlight the potential for incorporating IoT into green building design to significantly improve building performance regarding energy and water efficiency, waste reduction, and indoor environmental quality. One of the most important aspects of environmental preservation is the incorporation of IoT technology. Through the analysis of real-time occupancy and environmental data, IoT-enabled smart building systems improve energy efficiency, leading to fewer carbon emissions and energy consumption. Another advantage is that IoT-based devices can conserve water by monitoring and optimizing water use and identifying leaks. This lessens the impact of water waste on the environment.

Real-time monitoring made possible by IoT sensors also revolutionizes waste management by enabling effective waste collection schedules and lower operating expenses. Additionally, by controlling lighting, humidity, temperature, and air quality, IoT improves interior environmental quality and eventually increases occupant comfort and well-being. Finally, by using IoT sensors for predictive maintenance, building systems can last longer, require fewer resource-intensive replacements, and produce less waste. The model’s proven real-world performance offers the green construction sector a viable and effective way of reaching sustainability goals. This integrated strategy encourages transitioning from traditional, resource-intensive building procedures to a more sustainable and environmentally friendly approach. In terms of the IoT sector, the study emphasizes the importance of IoT in the green construction industry and its potential contribution to sustainable urban development.

According to the study, green building design represents a promising market for IoT developers and service providers since their solutions may address actual, real-world difficulties. Unexpected results could include the necessity to successfully balance environmental trade-offs, positive occupant behavior changes, and synergistic benefits The research also emphasizes the need for IoT solutions, especially customized to green building requirements, such as energy-efficient devices and practical data processing tools. Furthermore, incorporating IoT into green building design has far-reaching consequences for legislators, urban planners, and environmental activists. The method supports a transition to smart, sustainable cities by demonstrating the potential of advanced technology in tackling significant environmental concerns and encouraging sustainable living [ 22 ].

7. Conclusion

This study draws numerous vital findings concerning the feasibility of implementing IoT technology into green building design. Resource optimization is one of the most successful outcomes. The case study revealed that the IoT-enabled green building concept significantly boosted resource efficiency. This was proved by a 20% drop in energy usage, a 15% decrease in water consumption, and a 10% decrease in trash generation. This demonstrates IoT technology’s importance in reaching resource efficiency goals in green buildings. The quality of the building’s internal atmosphere remained maintained even with reduced resource consumption. This shows that using IoT technology to balance resource efficiency and occupant comfort in green buildings is possible. Aside from maintaining a high-quality indoor atmosphere, the model’s practical application in a real-world setting indicates its scalability.

This implies that the approach may be applied in more buildings or on a city-wide scale, adding to the sustainability of urban growth. The results have consequences for the industry as well. They emphasize a prospective market for IoT technology in the green building sector and the potential for green building practices to boost construction sustainability. Thus, incorporating IoT technology into green building design has enormous potential for increasing building efficiency, achieving environmental sustainability goals, and stimulating the creation of intelligent, sustainable cities.

The research has practical implications in two main areas. Additionally, it thoroughly examines the obstacles faced in implementing green building (G.B.) projects in Turkey, providing a comprehensive understanding of these barriers. Moreover, it clarifies the perspectives of public agency representatives and professionals working in private entities regarding the significance of these barriers. This more profound understanding of the barriers can help policymakers and construction practitioners develop well-informed strategies to promote green practices in China and other developing countries with similar socio-economic conditions. Furthermore, the in-depth analysis of these barriers can benefit foreign investors interested in investing in G.B. projects in China. By better understanding the G.B. industry in China, they can make more realistic investment decisions.

However, it is essential to note that the study has limitations. There were obstacles and difficulties in integrating IoT technology into the design of green buildings. A prominent obstacle was the upfront expenses associated with setting up IoT infrastructure and installing devices, which were frequently viewed as a substantial financial commitment. However, the long-term savings in energy consumption, upkeep, and operational efficiency that IoT devices provided helped to offset this cost.

Concerns about data security and privacy were also very important because IoT devices required the gathering and sharing of sensitive data. Strong security procedures and encryption techniques were put in place to protect data integrity and privacy to allay these worries. The requirement for certain knowledge and abilities to successfully manage and run IoT-enabled technologies presented another difficulty. Training was necessary for building management employees to handle and comprehend the data produced by IoT devices.

In addition, there were problems with compatibility when combining IoT solutions with pre-existing building systems. Thorough preparation and compatibility evaluations were required to guarantee a smooth integration Notwithstanding these difficulties, IoT technology is a potential strategy for sustainable building design because its overall advantages, like improved occupant comfort and energy efficiency, exceeded the early drawbacks.

Although more significant than the recommended value for proper factor analysis, the sample size used in the research is still relatively small. Increasing the sample size in future studies could yield more reliable results. Additionally, future research can focus on expanding the participant demographics to ensure a more balanced distribution. While this study primarily focused on barriers to G.B. projects, future investigations could explore the barriers and the driving factors in different countries.

Furthermore, influential factors on IEQ will be analyzed by Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA). Ultimately, this index would be predicted by various Machine Learning (ML) models (i.e., Evolutionary Polynomial Regression [EPR], Deep Learning [DL], Random Forest [R.F.], Support Vector Machine [SVM]) through the process of G.B. design by IoT.

7.1 Future studies

Future research studies could improve the organization and coherence of the transition from outlining the limitations of the study to suggesting future research directions. Based on our study’s findings, numerous significant future research objectives and areas for development in green building design use IoT technology. First, sophisticated IoT applications, especially for optimizing renewable energy sources like solar and wind power, can improve energy efficiency. Understanding how IoT affects occupant behavior and well-being, especially in personalized IoT-driven settings, can inform human-centric design

To secure building systems and tenant data, IoT data collection and processing must be thoroughly investigated for cybersecurity and privacy issues. Further research is needed to standardize and interoperate IoT devices and systems for scalability and acceptance in green building design.

A detailed cost-benefit analysis will help stakeholders decide on the financial and long-term benefits of IoT integration in green buildings. Governments and regulators can promote sustainability by studying how policies and regulations affect IoT integration.

Finally, architectural, design, and building management professionals require specific education and training to use IoT’s promise in green building design. These programs can equip practitioners for the changing landscape of IoT technologies in sustainability and environmental preservation. IoT technology in green building design is relevant globally but requires regional and local considerations. Sustainability, energy efficiency, and environmental preservation are universal values, but obstacles and priorities vary. Climate, legal frameworks, resource availability, cultural factors, economic factors, and infrastructure readiness all affect IoT-enabled green building solutions. Extreme climates may optimize HVAC, while water scarcity zones may use IoT to manage water. Local building codes must be followed, and economic concerns may affect IoT implementations.

Supporting information

S1 dataset..

https://doi.org/10.1371/journal.pone.0298982.s001

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