This article begins by defining the term variable and the terms independent variable and dependent variable, providing examples of each. It then proceeds to describe and discuss synonyms for the terms independent variable and dependent variable, including treatment, intervention, predictor, and risk factor, and synonyms for dependent variable, such as response variables and outcomes. The article explains that the terms extraneous, nuisance, and confounding variables refer to any variable that can interfere with the ability to establish relationships between independent variables and dependent variables, and it describes ways to control for such confounds. It further explains that even though intervening, mediating, and moderating variables explicitly alter the relationship between independent variables and dependent variables, they help to explain the causal relationship between them. In addition, the article links terminology about variables with the concept of levels of measurement in research.

Mediation has to do with the transfer of causality from an independent variable to a dependent variable via a third variable called a “mediator.” Because the experimental method is the universally recognized gold standard for establishing causality, we propose that conducting two experiments, one manipulating the independent variable and another manipulating the hypothesized mediator, most rigorously tests mediation hypotheses. When there are several experiments in which the independent variable was manipulated and also several experiments in which the mediator was manipulated, synthesizing these two sets of experiments using meta-analysis yields the ultimate mediation evidence. If these experiments were conducted in the field, both internal validity and external validity would be maximized. An example of the synthesis of multi-experiment mediation tests is provided and its potential and limitations are discussed.

SummaryResearchers are often interested in the relationship between two variables, with no single data set containing both. A common strategy is to use proxies for the dependent variable that are common to two surveys to impute the dependent variable into the data set containing the independent variable. We show that commonly employed regression or matching‐based imputation procedures lead to inconsistent estimates. We offer a consistent and easily implemented two‐step estimator, “rescaled regression prediction.” We derive the correct asymptotic standard errors for this estimator and demonstrate its relationship to alternative approaches. We illustrate with empirical examples using data from the US Consumer Expenditure Survey (CE) and the Panel Study of Income Dynamics (PSID).

Strategy research often aims to empirically establish a causal relationship between an independent variable and a dependent variable such as firm performance. For many important strategy research questions, however, traditional empirical techniques are not sufficient to establish causal effects with high confidence. We propose that field experiments have potential to be used more widely in strategy research, leveraging methodological innovations from other disciplines to address persistent puzzles in the literature. We first review the advantages and disadvantages of using field experiments to answer questions in strategy. We define two types of experiments, “strategy field experiments” and “process field experiments,” and present an original example of each variety. The first study explores the liability of foreignness and the second study tests theories regarding corporate culture. Copyright © 2015 John Wiley & Sons, Ltd.

Instrumental variable (IV) analysis assumes that the instrument only affects the dependent variable via its relationship with the independent variable. Other possible causal routes from the IV to the dependent variable are exclusion-restriction violations and make the instrument invalid. Weather has been widely used as an instrumental variable in social science to predict many different variables. The use of weather to instrument different independent variables represents strong prima facie evidence of exclusion violations for all studies using weather as an IV. A review of 279 studies reveals 176 variables which have been linked to weather: all of which represent potential exclusion violations. I show that the magnitude of several of these violations is sufficient overturn many existing IV results. I conclude with practical steps to systematically review existing literature to identify possible exclusion violations when using IV designs. I demonstrate how sensitivity analysis can quantify the vulnerability of a particular IV estimate to exclusion restriction violations in the literature.

SummaryConducting thought experiments prior to conducting a pilot study followed by a field experiment or correlational study have proven invaluable to the author for selecting an issue worthy of study, generating testable hypotheses, ways of manipulating the independent variable, selecting/developing a reliable dependent variable, thinking through a doable procedure, and coming up with ways to persuade senior managers to approve the proposed studies/experiments. Nevertheless, arguments are presented as to why thought experiments are at best the beginning rather than the endpoint in the research process.

Determining independent variable relative importance is a highly useful practice in organizational science. Whereas techniques to determine independent variable importance are available for normally distributed and binary dependent variable models, such techniques have not been extended to multicategory dependent variables (MCDVs). The current work extends previous research on binary dependent variable relative importance analysis to provide a methodology for conducting relative importance analysis on MCDV models from a dominance analysis (DA) perspective. Moreover, the current work provides a set of comprehensive data analytic examples that demonstrate how and when to use MCDV models in a DA and the advantages general DA statistics offer in interpreting MCDV model results. Moreover, the current work outlines best practices for determining independent variable relative importance for MCDVs using replicable examples on data from the publicly available General Social Survey. The present work then contributes to the literature by using in-depth data analytic examples to outline best practices in conducting relative importance analysis for MCDV models and by highlighting unique information DA results provide about MCDV models.

Instrumental variable (IV) analysis assumes the instrument only affects the dependent variable via its relationship with the independent variable. Other possible causal routes from the IV to the dependent variable are exclusion-restriction violations and invalidate the instrument. Weather has been widely used as an instrumental variable in social science to predict many different variables. The use of weather to instrument different independent variables represents strong prima facie evidence of exclusion violations for all studies using weather IVs. A review of 288 studies reveals 192 variables previously linked to weather: all representing potential exclusion violations. Using sensitivity analysis, I show that the magnitude of many of these violations is sufficient to overturn numerous existing IV results. I conclude with practical steps to systematically review existing literature to identify possible exclusion violations when using IV designs.

Background: Motivation in learning behaviour and education is well-researched in general education, but less in medical education.Aim: To answer two research questions, ‘How has the literature studied motivation as either an independent or dependent variable? How is motivation useful in predicting and understanding processes and outcomes in medical education?’ in the light of the Self-determination Theory (SDT) of motivation.Methods: A literature search performed using the PubMed, PsycINFO and ERIC databases resulted in 460 articles. The inclusion criteria were empirical research, specific measurement of motivation and qualitative research studies which had well-designed methodology. Only studies related to medical students/school were included.Results: Findings of 56 articles were included in the review. Motivation as an independent variable appears to affect learning and study behaviour, academic performance, choice of medicine and specialty within medicine and intention to continue medical study. Motivation as a dependent variable appears to be affected by age, gender, ethnicity, socioeconomic status, personality, year of medical curriculum and teacher and peer support, all of which cannot be manipulated by medical educators. Motivation is also affected by factors that can be influenced, among which are, autonomy, competence and relatedness, which have been described as the basic psychological needs important for intrinsic motivation according to SDT.Conclusion: Motivation is an independent variable in medical education influencing important outcomes and is also a dependent variable influenced by autonomy, competence and relatedness. This review finds some evidence in support of the validity of SDT in medical education.

Qualitative and multimethod scholars face a wide and often confusing array of alternatives for case selection using the results of a prior regression analysis. Methodologists have recommended alternatives including selection of typical cases, deviant cases, extreme cases on the independent variable, extreme cases on the dependent variable, influential cases, most similar cases, most different cases, pathway cases, and randomly sampled cases, among others. Yet this literature leaves it substantially unclear which of these approaches is best for any particular goal. Via statistical modeling and simulation, I argue that the rarely considered approach of selecting cases with extreme values on the main independent variable, as well as the more commonly discussed deviant case design, are the best alternatives for a broad range of discovery-related goals. By contrast, the widely discussed and advocated typical case, extreme-on- Y, and most similar cases approaches to case selection are much less valuable than scholars in the qualitative and multimethods research traditions have recognized to date.