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An introduction to different types of study design

Posted on 6th April 2021 by Hadi Abbas

Study designs are the set of methods and procedures used to collect and analyze data in a study.

Broadly speaking, there are 2 types of study designs: descriptive studies and analytical studies.

Descriptive studies

• Describes specific characteristics in a population of interest
• The most common forms are case reports and case series
• In a case report, we discuss our experience with the patient’s symptoms, signs, diagnosis, and treatment
• In a case series, several patients with similar experiences are grouped.

Analytical Studies

Analytical studies are of 2 types: observational and experimental.

Observational studies are studies that we conduct without any intervention or experiment. In those studies, we purely observe the outcomes.  On the other hand, in experimental studies, we conduct experiments and interventions.

Observational studies

Observational studies include many subtypes. Below, I will discuss the most common designs.

Cross-sectional study:

• This design is transverse where we take a specific sample at a specific time without any follow-up
• It allows us to calculate the frequency of disease ( p revalence ) or the frequency of a risk factor
• This design is easy to conduct
• For example – if we want to know the prevalence of migraine in a population, we can conduct a cross-sectional study whereby we take a sample from the population and calculate the number of patients with migraine headaches.

Cohort study:

• We conduct this study by comparing two samples from the population: one sample with a risk factor while the other lacks this risk factor
• It shows us the risk of developing the disease in individuals with the risk factor compared to those without the risk factor ( RR = relative risk )
• Prospective : we follow the individuals in the future to know who will develop the disease
• Retrospective : we look to the past to know who developed the disease (e.g. using medical records)
• This design is the strongest among the observational studies
• For example – to find out the relative risk of developing chronic obstructive pulmonary disease (COPD) among smokers, we take a sample including smokers and non-smokers. Then, we calculate the number of individuals with COPD among both.

Case-Control Study:

• We conduct this study by comparing 2 groups: one group with the disease (cases) and another group without the disease (controls)
• This design is always retrospective
•  We aim to find out the odds of having a risk factor or an exposure if an individual has a specific disease (Odds ratio)
•  Relatively easy to conduct
• For example – we want to study the odds of being a smoker among hypertensive patients compared to normotensive ones. To do so, we choose a group of patients diagnosed with hypertension and another group that serves as the control (normal blood pressure). Then we study their smoking history to find out if there is a correlation.

Experimental Studies

• Also known as interventional studies
• Can involve animals and humans
• Pre-clinical trials involve animals
• Clinical trials are experimental studies involving humans
• In clinical trials, we study the effect of an intervention compared to another intervention or placebo. As an example, I have listed the four phases of a drug trial:

I:  We aim to assess the safety of the drug ( is it safe ? )

II: We aim to assess the efficacy of the drug ( does it work ? )

III: We want to know if this drug is better than the old treatment ( is it better ? )

IV: We follow-up to detect long-term side effects ( can it stay in the market ? )

• In randomized controlled trials, one group of participants receives the control, while the other receives the tested drug/intervention. Those studies are the best way to evaluate the efficacy of a treatment.

Finally, the figure below will help you with your understanding of different types of study designs.

References (pdf)

You may also be interested in the following blogs for further reading:

An introduction to randomized controlled trials

Case-control and cohort studies: a brief overview

Cohort studies: prospective and retrospective designs

Prevalence vs Incidence: what is the difference?

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No Comments on An introduction to different types of study design

you are amazing one!! if I get you I’m working with you! I’m student from Ethiopian higher education. health sciences student

Very informative and easy understandable

You are my kind of doctor. Do not lose sight of your objective.

Wow very erll explained and easy to understand

I’m Khamisu Habibu community health officer student from Abubakar Tafawa Balewa university teaching hospital Bauchi, Nigeria, I really appreciate your write up and you have make it clear for the learner. thank you

well understood,thank you so much

Well understood…thanks

Simply explained. Thank You.

Thanks a lot for this nice informative article which help me to understand different study designs that I felt difficult before

That’s lovely to hear, Mona, thank you for letting the author know how useful this was. If there are any other particular topics you think would be useful to you, and are not already on the website, please do let us know.

it is very informative and useful.

thank you statistician

Fabulous to hear, thank you John.

Thanks for this information

Thanks so much for this information….I have clearly known the types of study design Thanks

That’s so good to hear, Mirembe, thank you for letting the author know.

Very helpful article!! U have simplified everything for easy understanding

I’m a health science major currently taking statistics for health care workers…this is a challenging class…thanks for the simified feedback.

That’s good to hear this has helped you. Hopefully you will find some of the other blogs useful too. If you see any topics that are missing from the website, please do let us know!

Hello. I liked your presentation, the fact that you ranked them clearly is very helpful to understand for people like me who is a novelist researcher. However, I was expecting to read much more about the Experimental studies. So please direct me if you already have or will one day. Thank you

Dear Ay. My sincere apologies for not responding to your comment sooner. You may find it useful to filter the blogs by the topic of ‘Study design and research methods’ – here is a link to that filter: https://s4be.cochrane.org/blog/topic/study-design/ This will cover more detail about experimental studies. Or have a look on our library page for further resources there – you’ll find that on the ‘Resources’ drop down from the home page.

However, if there are specific things you feel you would like to learn about experimental studies, that are missing from the website, it would be great if you could let me know too. Thank you, and best of luck. Emma

Great job Mr Hadi. I advise you to prepare and study for the Australian Medical Board Exams as soon as you finish your undergrad study in Lebanon. Good luck and hope we can meet sometime in the future. Regards ;)

You have give a good explaination of what am looking for. However, references am not sure of where to get them from.

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Study designs: Part 1 – An overview and classification

Priya ranganathan, rakesh aggarwal.

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There are several types of research study designs, each with its inherent strengths and flaws. The study design used to answer a particular research question depends on the nature of the question and the availability of resources. In this article, which is the first part of a series on “study designs,” we provide an overview of research study designs and their classification. The subsequent articles will focus on individual designs.

Keywords: Epidemiologic methods, research design, research methodology

INTRODUCTION

Research study design is a framework, or the set of methods and procedures used to collect and analyze data on variables specified in a particular research problem.

Research study designs are of many types, each with its advantages and limitations. The type of study design used to answer a particular research question is determined by the nature of question, the goal of research, and the availability of resources. Since the design of a study can affect the validity of its results, it is important to understand the different types of study designs and their strengths and limitations.

There are some terms that are used frequently while classifying study designs which are described in the following sections.

A variable represents a measurable attribute that varies across study units, for example, individual participants in a study, or at times even when measured in an individual person over time. Some examples of variables include age, sex, weight, height, health status, alive/dead, diseased/healthy, annual income, smoking yes/no, and treated/untreated.

Exposure (or intervention) and outcome variables

A large proportion of research studies assess the relationship between two variables. Here, the question is whether one variable is associated with or responsible for change in the value of the other variable. Exposure (or intervention) refers to the risk factor whose effect is being studied. It is also referred to as the independent or the predictor variable. The outcome (or predicted or dependent) variable develops as a consequence of the exposure (or intervention). Typically, the term “exposure” is used when the “causative” variable is naturally determined (as in observational studies – examples include age, sex, smoking, and educational status), and the term “intervention” is preferred where the researcher assigns some or all participants to receive a particular treatment for the purpose of the study (experimental studies – e.g., administration of a drug). If a drug had been started in some individuals but not in the others, before the study started, this counts as exposure, and not as intervention – since the drug was not started specifically for the study.

Observational versus interventional (or experimental) studies

Observational studies are those where the researcher is documenting a naturally occurring relationship between the exposure and the outcome that he/she is studying. The researcher does not do any active intervention in any individual, and the exposure has already been decided naturally or by some other factor. For example, looking at the incidence of lung cancer in smokers versus nonsmokers, or comparing the antenatal dietary habits of mothers with normal and low-birth babies. In these studies, the investigator did not play any role in determining the smoking or dietary habit in individuals.

For an exposure to determine the outcome, it must precede the latter. Any variable that occurs simultaneously with or following the outcome cannot be causative, and hence is not considered as an “exposure.”

Observational studies can be either descriptive (nonanalytical) or analytical (inferential) – this is discussed later in this article.

Interventional studies are experiments where the researcher actively performs an intervention in some or all members of a group of participants. This intervention could take many forms – for example, administration of a drug or vaccine, performance of a diagnostic or therapeutic procedure, and introduction of an educational tool. For example, a study could randomly assign persons to receive aspirin or placebo for a specific duration and assess the effect on the risk of developing cerebrovascular events.

Descriptive versus analytical studies

Descriptive (or nonanalytical) studies, as the name suggests, merely try to describe the data on one or more characteristics of a group of individuals. These do not try to answer questions or establish relationships between variables. Examples of descriptive studies include case reports, case series, and cross-sectional surveys (please note that cross-sectional surveys may be analytical studies as well – this will be discussed in the next article in this series). Examples of descriptive studies include a survey of dietary habits among pregnant women or a case series of patients with an unusual reaction to a drug.

Analytical studies attempt to test a hypothesis and establish causal relationships between variables. In these studies, the researcher assesses the effect of an exposure (or intervention) on an outcome. As described earlier, analytical studies can be observational (if the exposure is naturally determined) or interventional (if the researcher actively administers the intervention).

Directionality of study designs

Based on the direction of inquiry, study designs may be classified as forward-direction or backward-direction. In forward-direction studies, the researcher starts with determining the exposure to a risk factor and then assesses whether the outcome occurs at a future time point. This design is known as a cohort study. For example, a researcher can follow a group of smokers and a group of nonsmokers to determine the incidence of lung cancer in each. In backward-direction studies, the researcher begins by determining whether the outcome is present (cases vs. noncases [also called controls]) and then traces the presence of prior exposure to a risk factor. These are known as case–control studies. For example, a researcher identifies a group of normal-weight babies and a group of low-birth weight babies and then asks the mothers about their dietary habits during the index pregnancy.

Prospective versus retrospective study designs

The terms “prospective” and “retrospective” refer to the timing of the research in relation to the development of the outcome. In retrospective studies, the outcome of interest has already occurred (or not occurred – e.g., in controls) in each individual by the time s/he is enrolled, and the data are collected either from records or by asking participants to recall exposures. There is no follow-up of participants. By contrast, in prospective studies, the outcome (and sometimes even the exposure or intervention) has not occurred when the study starts and participants are followed up over a period of time to determine the occurrence of outcomes. Typically, most cohort studies are prospective studies (though there may be retrospective cohorts), whereas case–control studies are retrospective studies. An interventional study has to be, by definition, a prospective study since the investigator determines the exposure for each study participant and then follows them to observe outcomes.

The terms “prospective” versus “retrospective” studies can be confusing. Let us think of an investigator who starts a case–control study. To him/her, the process of enrolling cases and controls over a period of several months appears prospective. Hence, the use of these terms is best avoided. Or, at the very least, one must be clear that the terms relate to work flow for each individual study participant, and not to the study as a whole.

Classification of study designs

Figure 1 depicts a simple classification of research study designs. The Centre for Evidence-based Medicine has put forward a useful three-point algorithm which can help determine the design of a research study from its methods section:[ 1 ]

Classification of research study designs

Does the study describe the characteristics of a sample or does it attempt to analyze (or draw inferences about) the relationship between two variables? – If no, then it is a descriptive study, and if yes, it is an analytical (inferential) study

If analytical, did the investigator determine the exposure? – If no, it is an observational study, and if yes, it is an experimental study

If observational, when was the outcome determined? – at the start of the study (case–control study), at the end of a period of follow-up (cohort study), or simultaneously (cross sectional).

In the next few pieces in the series, we will discuss various study designs in greater detail.

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• 1. Centre for Evidence-Based Medicine. Study Designs. 2016. [Last accessed on 2018 Sep 04]. Available from: https://www.cebm.net/2014/04/study-designs/
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Quantitative Research with Nonexperimental Designs

by Janet Salmons, PhD Manager, Sage Research Methods Community

What is the difference between experimental and non-experimental research designs?

There are two types of quantitative research designs: experimental and nonexperimental. This introduction draws from The SAGE Encyclopedia of Educational Research, Measurement, and Evaluation . Leung and Shek (2018) explain:

Experimental research design utilizes the principle of manipulation of the independent variables and examines its cause-and-effect relationship on the dependent variables by controlling the effects of other variables. Usually, the experimenter assigns two or more groups with similar characteristics. Different interventions will be given to the groups. In case there are differences in the outcomes among the groups, the experimenter can conclude that the differences result from the interventions that the experimenter performed. [Learn more about experimental design in an earlier Sage Research Methods Community post, Quantitative Research with Experimental Designs . ] Nonexperimental research designs examine social phenomena without direct manipulation of the conditions that subjects experience. Subjects are not randomly assigned to different groups. As such, evidence that supports the cause-and-effect relationships is largely limited.

There are two main types of nonexperimental research designs: comparative design and correlational design.

In comparative research, the researcher examines the differences between two or more groups on the phenomenon that is being studied. For example, studying gender difference in learning mathematics is a comparative research.

The correlational design is a study of relationships between two or more constructs. A positive correlation means that high values of a variable are associated with high values of another variable. For instance, academic performance of students is positively related to their self-esteem. On the contrary, a negative correlation means that high values of a variable are associated with low values of the other variable. For example, teacher–student conflicts are negatively related to the students’ sense of belonging to the school.

See how researchers use non-experimental research design in this multidisciplinary collection of open-access articles.

Open Access Comparative Studies

Humprecht, E., Hellmueller, L., & Lischka, J. A. (2020). Hostile Emotions in News Comments: A Cross-National Analysis of Facebook Discussions .  Social Media + Society .  https://doi.org/10.1177/2056305120912481

Abstract. Recent work demonstrates that hostile emotions can contribute to a strong polarization of political discussion on social media. However, little is known regarding the extent to which media organizations and media systems trigger hostile emotions. We content-analyzed comments on Facebook pages from six news organizations ( N  = 1,800) based in the United States and Germany. Our results indicate that German news organizations’ Facebook comments are more balanced, containing lower levels of hostile emotions. Such emotions are particularly prevalent in the polarized information environment of the United States—in both news posts and comments. Moreover, alternative right-wing media outlets in both countries provoke significantly higher levels of hostile emotions, thus limiting deliberative discussions. Our results demonstrate that the application of technology—such as the use of comment sections—has different implications depending on cultural and social contexts.

Huang, J., Kumar, S., & Hu, C. (2020). Does Culture Matter? A Comparative Study on the Motivations for Online Identity Reconstruction Between China and Malaysia .  SAGE Open .  https://doi.org/10.1177/2158244020929311

Abstract. On social network platforms, people may reconstruct an identity due to various reasons, such as vanity, disinhibition, bridging social capital, and privacy concerns. This study aims to identify cultural differences in the motivations for online identity reconstruction between China and Malaysia. Data were collected from China and Malaysia using an online survey. A total of 815 respondents (418 Chinese and 397 Malaysians) participated in this study. Differences were found not only between Chinese and Malaysian participants but also among participants from different ethnic groups (e.g., the Malaysian-Malays and the Malaysian-Chinese). This study adds knowledge to the research concerning online identity reconstruction by taking into account national culture. It also extends the cross-cultural research concerning social network platforms and sheds light on the specific differences between Chinese and Malaysian participants. The findings of this study can help service providers to deploy specific strategies to better serve social network platform users from different countries.

Kalogeropoulos, A., Negredo, S., Picone, I., & Nielsen, R. K. (2017). Who Shares and Comments on News?: A Cross-National Comparative Analysis of Online and Social Media Participation . Social Media + Society. https://doi.org/10.1177/2056305117735754

Abstract. In this article, we present a cross-national comparative analysis of which online news users in practice engage with the participatory potential for sharing and commenting on news afforded by interactive features in news websites and social media technologies across a strategic sample of six different countries. Based on data from the 2016 Reuters Institute Digital News Report, and controlling for a range of factors, we find that (1) people who use social media for news and a high number of different social media platforms are more likely to also engage more actively with news outside social media by commenting on news sites and sharing news via email, (2) political partisans on both sides are more likely to engage in sharing and commenting particularly on news stories in social media, and (3) people with high interest in hard news are more likely to comment on news on both news sites and social media and share stores via social media (and people with high interest in any kind of news [hard or soft] are more likely to share stories via email). Our analysis suggests that the online environment reinforces some long-standing inequalities in participation while countering other long-standing inequalities. The findings indicate a self-reinforcing positive spiral where the already motivated are more likely in practice to engage with the potential for participation offered by digital media, and a negative spiral where those who are less engaged participate less.

Phillips, M., & Smith, D. P. (2018). Comparative approaches to gentrification: Lessons from the rural .  Dialogues in Human Geography ,  8 (1), 3–25.  https://doi.org/10.1177/2043820617752009

Abstract. The epistemologies and politics of comparative research are prominently debated within urban studies, with ‘comparative urbanism’ emerging as a contemporary lexicon of urban studies. The study of urban gentrification has, after some delay, come to engage with these debates, which can be seen to pose a major challenge to the very concept of gentrification. To date, similar debates or developments have not unfolded within the study of rural gentrification. This article seeks to address some of the challenges posed to gentrification studies through an examination of strategies of comparison and how they might be employed within a comparative study of rural gentrification. Drawing on Tilly ( Big structures Large Processes Huge Comparisons . New York: Russell Sage), examples of four ‘strategies of comparison’ are identified within studies of urban and rural gentrification, before the paper explores how ‘geographies of the concept’ and ‘geographies of the phenomenon’ of rural gentrification in the United Kingdom, United States and France may be investigated using Latour’s ( Pandora’s Hope . London: Harvard University Press) notion of ‘circulatory sociologies of translation’. The aim of our comparative discussion is to open up dialogues on the challenges of comparative studies that employ conceptions of gentrification and also to promote reflections of the metrocentricity of recent discussions of comparative research.

Yaşar, H., & Sağsan, M. (2020). The Mediating Effect of Organizational Stress on Organizational Culture and Time Management: A Comparative Study With Two Universities. SAGE Open .  https://doi.org/10.1177/2158244020919507

Abstract. This research was designed to investigate whether organizational stress had an intermediary role in the effect of Hofstede cultural dimensions on time management. Near East University from Cyprus, which represents the individual culture, and Hakkari University from Turkey representing the collectivist culture were selected for the research analyses. In all, 638 administrative and academic members from both universities were interviewed face-to-face on a voluntary basis, and data were collected by the simple random sampling method. The research findings suggest that time should be managed after identifying the type of culture—individualistic or collectivist—to decrease the level of stress experienced by university staff. In other words, Hofstede’s cultural dimension has an impact on time management, and organizational stress has a partial mediation effect on this dimension. Although the variables in the study have been studied in the literature together with many different factors, Hofstede is significant in terms of determining the role of organizational stress in the effect of cultural dimensions on time management. The effectiveness of Hofstede’s cultural dimensions through organizational stress in time management allows business and project plans to be carried out in a way that manages individual, team or departmental performances taking into account the organizational stress elements. It is considered that this study will particularly be effective in medicine, project management, and independent auditing.

Open Access Correlational Studies

Adams, R. V., & Blair, E. (2019). Impact of Time Management Behaviors on Undergraduate Engineering Students’ Performance . SAGE Open. https://doi.org/10.1177/2158244018824506

Abstract. Effective time management is associated with greater academic performance and lower levels of anxiety in students; however many students find it hard to find a balance between their studies and their day-to-day lives. This article examines the self-reported time management behaviors of undergraduate engineering students using the Time Management Behavior Scale. Correlation analysis, regression analysis, and model reduction are used to attempt to determine which aspects of time management the students practiced, which time management behaviors were more strongly associated with higher grades within the program, and whether or not those students who self-identified with specific time management behaviors achieved better grades in the program. It was found that students’ perceived control of time was the factor that correlated significantly with cumulative grade point average. On average, it was found that time management behaviors were not significantly different across gender, age, entry qualification, and time already spent in the program.

Cooper, B., & Glaesser, J. (2010). Contrasting Variable-Analytic and Case-Based Approaches to the Analysis of Survey Datasets: Exploring How Achievement Varies by Ability across Configurations of Social Class and Sex. Methodological Innovations Online, 5(1), 3–23. https://doi.org/10.4256/mio.2010.0007

Abstract. The context for this paper is the ongoing debate concerning the relative merits, for the analysis of quantitative data, of, on the one hand, variable-analytic correlational methods, and, on the other, the case-based set theoretic methods developed by Charles Ragin. While correlational approaches, based in linear algebra, typically use regression to establish the net effects of several “independent” variables on an outcome, the set theoretic approach analyses, more holistically, the conjunctions of factors sufficient and/or necessary for an outcome to occur. Here, in order to bring out key differences between the approaches, we focus our attention on the basic building blocks of the two approaches: respectively, the concept of linear correlation and the concept of a sufficient and/or necessary condition. We initially use invented data (for ability, educational achievement, and social class) to simulate what is at stake in this methodological debate and we then employ data taken from the British National Child Development Study to explore the structuring of the relationship between respondents' early measured ability and later educational achievement across various configurations of parental and grandparental class origin and sex. The substantive idea informing the analysis, derived from Boudon's work, is that, for respondents from higher class origins, ability will tend to be sufficient but not necessary for later educational achievement while, for lower class respondents, ability will tend to be necessary but not sufficient. We compare correlational analyses, controlling for class and gender, with fuzzy set analyses to show that set theoretic indices can better capture these varying relationships than correlational measures. In conclusion, we briefly consider how our demonstration of some of the advantages of the set theoretic approach for modelling empirical relationships might be related to the debate concerning the relation between observed regularities and causal mechanisms.

Fang, C., Gai, Q., He, C., & Shi, Q. (2020). The Experience of Poverty Reduction in Rural China. SAGE Open. https://doi.org/10.1177/2158244020982288

Abstract. Since 1978, China has greatly reduced the rural poverty rate. This article provides an overview of the experience of China’s poverty reduction. Using panel data from 1996 to 2013 to calculate farmers’ income dynamics, we found that the pace of poverty reduction was relatively slow from 1996 to 2002 and that the rate of reversion to poverty was high. Since 2003, the pace of poverty reduction has accelerated, whereas the rate of reversion has decreased. Using econometric ordinary least squares and probit models, we explore the factors that drive poverty reduction. We found correlational evidence that the main reasons for poverty reduction in China since 1996 have been the increase in income from household farms and migrant work. In addition, rural public insurance prevented farmers from falling into poverty.

Hayn-Leichsenring, G. U., Lehmann, T., & Redies, C. (2017). Subjective Ratings of Beauty and Aesthetics: Correlations With Statistical Image Properties in Western Oil Paintings . I-Perception. https://doi.org/10.1177/2041669517715474

Abstract. For centuries, oil paintings have been a major segment of the visual arts. The JenAesthetics data set consists of a large number of high-quality images of oil paintings of Western provenance from different art periods. With this database, we studied the relationship between objective image measures and subjective evaluations of the images, especially evaluations on  aesthetics  (defined as artistic value) and  beauty  (defined as individual liking). The objective measures represented low-level statistical image properties that have been associated with aesthetic value in previous research. Subjective rating scores on  aesthetics  and  beauty  correlated not only with each other but also with different combinations of the objective measures. Furthermore, we found that paintings from different art periods vary with regard to the objective measures, that is, they exhibit specific patterns of statistical image properties. In addition, clusters of participants preferred different combinations of these properties. In conclusion, the results of the present study provide evidence that statistical image properties vary between art periods and subject matters and, in addition, they correlate with the subjective evaluation of paintings by the participants.

Srinivasan P, Rentala S, Kumar P. Impulsivity and Aggression Among Male Delinquent Adolescents Residing in Observation Homes—A Descriptive Correlation Study from East India . Journal of Indian Association for Child and Adolescent Mental Health . 2022;18(4):327-336. doi: 10.1177/09731342231171305

Aggression and crime are connected and highly reported among juveniles in recent times as compared to adults, which ends up in delinquency. It is not just aggression that dominates but the associated impulsiveness also plays a vital role. This study was intended to assess impulsivity and aggression, and their relationship among male delinquent adolescents residing in observation homes. A quantitative research approach with the nonexperimental descriptive correlation design was adopted. One hundred and seventy-nine male delinquent adolescents residing in 2 observation homes in the state of Bihar, India, were selected by convenience sampling technique. The standardized Buss & Perry Aggression questionnaire, and Barratt Impulsiveness scale were used for collecting the data regarding impulsivity and aggression among male delinquent adolescents.

Yamak, O. U., & Eyupoglu, S. Z. (2021). Authentic Leadership and Service Innovative Behavior: Mediating Role of Proactive Personality . SAGE Open. https://doi.org/10.1177/2158244021989629

Abstract. The present study aims to examine the effect of authentic leadership (AL) on service innovative behavior (SIB) of employees as well as to identify whether proactive personality (PP) mediates this connection at an individual level. The quantitative cross-sectional study design was utilized to gather information from a study sample which consisted of 428 front-line employees (FLE) working at banks located in North Cyprus. Specifically, the study uses confirmatory factor analysis (CFA), correlation, structural equation modeling (SEM), and bootstrapping techniques to test the hypothesized relationships. The results reveal that both AL and PP have a significant positive effect on SIB; AL has a positive impact on PP of FLE, and PP plays a partial mediating role between AL and SIB of FLE. By relating the study findings, authenticity and proactivity in the banking sector in North Cyprus play a critical role in fostering the innovative behaviors of FLE. The study also discusses the practical and managerial implications, as well as the future scope.

Books about Quantitative Methods from Sage Publishing

Use the code COMMUNITY3 for a 20% discount when you order her book, valid worldwide until March 31, 2024.

The Research Experience: Planning, Conducting, and Reporting Research by Ann Sloan Devlin (2020). (See Sage Research Methods Community posts by Dr. Devlin.)

Understanding Quantitative Data in Educational Research by Nicoleta Gaciu (2021).

Essentials of Social Statistics for a Diverse Society by Anna Leon-Guerrero, Chava Frankfort-Nachmias, and Georgiann Davis.(2020). (See an author interview for more on this book.)

Understanding Correlation Matrices by Alexandria Hadd and Joseph Lee Rodgers (2021)

Know Your Variables: Little Quick Fix and other titles in the LQF series by John MacInnes

Research Methods for the Behavioral Sciences by Gregory J. Privitera (2022)

Reference Frey, B. (2018).  The SAGE encyclopedia of educational research, measurement, and evaluation  (Vols. 1-4). Thousand Oaks,, CA: SAGE Publications, Inc. doi: 10.4135/9781506326139

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Listen to Dr. Stephen Gorard discuss his no-nonsense approach to statistics.

Professor Julie Scott Jones discusses lessons learned from teaching quantitative research methods online.

 After 20 years of teaching research and quantitative methods to students in Political Science in the US, UK, and the EU, Dr. Loveless has developed a teaching method that has resulted in greater student success in statistics in each passing year

Mentor in Residence Stephen Gorard explains how to use population data.

Dr. Ann Sloan Devlin, author of The Research Experience, discusses first steps in data analysis for quantitative studies.

Want to use R for statistical analysis? These open-access resources might help!

Learn about R and find books about using this language and environment for statistical computing and graphics.

Learn about research with online Experiments from Dr. Giuseppe Veltri.

What is the difference between experimental and non-experimental research design? We look at how non-experimental design distinguishes itself from experimental design and how it can be applied in the research process with open-access examples.

Download the free report from the project, “Fostering Data Literacy: Teaching with Quantitative Data in the Social Sciences.” This report explores two key challenges to teaching with data: helping students overcome anxieties about math and synchronizing the interconnected methodological, software, and analytic competencies.

In the day-to-day of political communication, politicians constantly decide how to amplify or constrain emotional expression, in service of signalling policy priorities or persuading colleagues and voters. We propose a new method for quantifying emotionality in politics using the transcribed text of politicians’ speeches. This new approach, described in more detail below, uses computational linguistics tools and can be validated against human judgments of emotionality.

Institutions — rules that govern behavior — are among the most important social artifacts of society. So it should come as a great shock that we still understand them so poorly. How are institutions designed? What makes institutions work? Is there a way to systematically compare the language of different institutions? One recent advance is bringing us closer to making these questions quantitatively approachable. The Institutional Grammar (IG) 2.0 is an analytical approach, drawn directly from classic work by Nobel Laureate Elinor Ostrom, that is providing the foundation for computational representations of institutions. IG 2.0 is a formalism for translating between human-language outputs — policies, rules, laws, decisions, and the like. It defines abstract structures precisely enough to be manipulable by computer. Recent work, supported by the National Science Foundation ( RCN: Coordinating and Advancing Analytical Approaches for Policy Design & GCR: Collaborative Research: Jumpstarting Successful Open-Source Software Projects With Evidence-Based Rules and Structures ), leveraging recent advances in natural language processing highlighted on this blog , is vastly accelerating the rate and quality of computational translations of written rules.

What is case study methodology? It is unique given one characteristic: case studies draw from more than one data source. In this post find definitions and a collection of multidisciplinary examples.

Learn about experimental research designs and read open-access studies.

In the field of artificial intelligence (AI), Transformers have revolutionized language analysis. Never before has a new technology universally improved the benchmarks of nearly all language processing tasks: e.g., general language understanding, question - answering , and Web search . The transformer method itself, which probabilistically models words in their context (i.e. “language modeling”), was introduced in 2017 and the first large-scale pre-trained general purpose transformer, BERT, was released open source from Google in 2018. Since then, BERT has been followed by a wave of new transformer models including GPT, RoBERTa, DistilBERT, XLNet, Transformer-XL, CamemBERT, XLM-RoBERTa, etc. The text package makes all of these language models and many more easily accessible to use for R-users; and includes functions optimized for human-level analyses tailored to social scientists.

This year’s lockdown challenged the absolute core of higher education and accelerated or rather imposed the adoption of digital tooling to fully replace the interactivity of the physical classroom. And while other industries might have suffered losses, the edtech space flourished, with funding for edtech almost doubling in the first half of 2020 vs 2019 . Even before the pandemic, lecturers were starting to feel overwhelmed by the amount of choice to support their teaching. More funding just meant more hype, more tools, and more tools working on similar or slightly improved solutions, making it even harder and more time-consuming to find and adapt them in a rush.

Below, we take a look at several tools and startups that are already supporting many of you in teaching quantitative research methods; and some cool new tools you could use to enhance your classroom.

As technology becomes more integral to everything we do, the time we spend in front of screens such as smartphones and computers continues to increase. The pervasiveness of screen time has raised concerns among researchers, policymakers, educators, and health care professionals about the effects of digital technology on well-being. Despite growing concerns about digital well-being, it has been a challenge for scientists to measure how we actually navigate the digital landscape through our screens. For example, it is well documented that self-reports of one’s media use are often inaccurate despite survey respondents’ best efforts. Just knowing screen time spent on individual applications does not fully capture a person’s usage of the digital device either. Some could spend an hour on YouTube watching people play video games whereas others might spend the same amount of time watching late night television talk shows to keep up to date with the news. Even though the screen time is the same for the same application, the intentions and values of consumption of certain types of content can be vastly different among users.

Pros and Cons of Online Survey Research

Research with black participants: scholars rethink methods and methodology.