In this article, we attempt to distinguish between the properties of moderator and mediator variables at a number of levels. First, we seek to make theorists and researchers aware of the importance of not using the terms moderator and mediator interchangeably by carefully elaborating, both conceptually and strategically, the many ways in which moderators and mediators differ. We then go beyond this largely pedagogical function and delineate the conceptual and strategic implications of making use of such distinctions with regard to a wide range of phenomena, including control and stress, attitudes, and personality traits. We also provide a specific compendium of analytic procedures appropriate for making the most effective use of the moderator and mediator distinction, both separately and in terms of a broader causal system that includes both moderators and mediators.

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The moderator–mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations.

Interest in the problem of method biases has a long history in the behavioral sciences. Despite this, a comprehensive summary of the potential sources of method biases and how to control for them does not exist. Therefore, the purpose of this article is to examine the extent to which method biases influence behavioral research results, identify potential sources of method biases, discuss the cognitive processes through which method biases influence responses to measures, evaluate the many different procedural and statistical techniques that can be used to control method biases, and provide recommendations for how to select appropriate procedural and statistical remedies for different types of research settings.

/pdf/common-method-biases-in-behavioral-research-a-critical-53ocysaemj.pdf

Common method biases in behavioral research: a critical review of the literature and recommended remedies.

Hypotheses involving mediation are common in the behavioral sciences. Mediation exists when a predictor affects a dependent variable indirectly through at least one intervening variable, or mediator. Methods to assess mediation involving multiple simultaneous mediators have received little attention in the methodological literature despite a clear need. We provide an overview of simple and multiple mediation and explore three approaches that can be used to investigate indirect processes, as well as methods for contrasting two or more mediators within a single model. We present an illustrative example, assessing and contrasting potential mediators of the relationship between the helpfulness of socialization agents and job satisfaction. We also provide SAS and SPSS macros, as well as Mplus and LISREL syntax, to facilitate the use of these methods in applications.

https://link.springer.com/content/pdf/10.3758/BRM.40.3.879.pdf

Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models

Factor analysis, path analysis, structural equation modeling, and related multivariate statistical methods are based on maximum likelihood or generalized least squares estimation developed for covariance structure models. Large-sample theory provides a chi-square goodness-of-fit test for comparing a model against a general alternative model based on correlated variables. This model comparison is insufficient for model evaluation: In large samples virtually any model tends to be rejected as inadequate, and in small samples various competing models, if evaluated, might be equally acceptable. A general null model based on modified independence among variables is proposed to provide an additional reference point for the statistical and scientific evaluation of covariance structure models. Use of the null model in the context of a procedure that sequentially evaluates the statistical necessity of various sets of parameters places statistical methods in covariance structure analysis into a more complete framework. The concepts of ideal models and pseudo chi-square tests are introduced, and their roles in hypothesis testing are developed. The importance of supplementing statistical evaluation with incremental fit indices associated with the comparison of hierarchical models is also emphasized. Normed and nonnormed fit indices are developed and illustrated.

Significance tests and goodness of fit in the analysis of covariance structures

From the Publisher:
Probabilistic Reasoning in Intelligent Systems is a complete andaccessible account of the theoretical foundations and computational methods that underlie plausible reasoning under uncertainty. The author provides a coherent explication of probability as a language for reasoning with partial belief and offers a unifying perspective on other AI approaches to uncertainty, such as the Dempster-Shafer formalism, truth maintenance systems, and nonmonotonic logic. The author distinguishes syntactic and semantic approaches to uncertaintyand offers techniques, based on belief networks, that provide a mechanism for making semantics-based systems operational. Specifically, network-propagation techniques serve as a mechanism for combining the theoretical coherence of probability theory with modern demands of reasoning-systems technology: modular declarative inputs, conceptually meaningful inferences, and parallel distributed computation. Application areas include diagnosis, forecasting, image interpretation, multi-sensor fusion, decision support systems, plan recognition, planning, speech recognitionin short, almost every task requiring that conclusions be drawn from uncertain clues and incomplete information.
Probabilistic Reasoning in Intelligent Systems will be of special interest to scholars and researchers in AI, decision theory, statistics, logic, philosophy, cognitive psychology, and the management sciences. Professionals in the areas of knowledge-based systems, operations research, engineering, and statistics will find theoretical and computational tools of immediate practical use. The book can also be used as an excellent text for graduate-level courses in AI, operations research, or applied probability.

Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference

1. Basic Definitions and Overview Nonindependence Basic Definitions Data Organization A Database of Dyadic Studies 2. The Measurement of Nonindependence Interval Level of Measurement Categorical Measures Consequences of Ignoring Nonindependence What Not to Do Power Considerations 3. Analyzing Between- and Within-Dyads Independent Variables Interval Outcome Measures and Categorical Independent Variables Interval Outcome Measures and Interval Independent Variables Categorical Outcome Variables 4. Using Multilevel Modeling to Study Dyads Mixed-Model ANOVA Multilevel-Model Equations Multilevel Modeling with Maximum Likelihood Adaptation of Multilevel Models to Dyadic Data 5. Using Structural Equation Modeling to Study Dyads Steps in SEM Confirmatory Factor Analysis Path Analyses with Dyadic Data SEM for Dyads with Indistinguishable Members 6. Tests of Correlational Structure and Differential Variance Distinguishable Dyads Indistinguishable Dyads 7. Analyzing Mixed Independent Variables: The Actor-Partner Interdependence Model The Model Conceptual Interpretation of Actor and Partner Effects Estimation of the APIM: Indistinguishable Dyad Members Estimation of the APIM: Distinguishable Dyads Power and Effect Size Computation Specification Error in the APIM 8. Social Relations Designs with Indistinguishable Members The Basic Data Structures Model Details of an SRM Analysis Model Social Relations Analyses: An Example 9. Social Relations Designs with Roles SRM Studies of Family Relationships Design and Analysis of Studies The Model Application of the SRM with Roles Using Confirmatory Factor Analysis The Four-Person Design Illustration of the Four-Person Family Design The Three-Person Design Multiple Perspectives on Family Relationships Means and Factor Score Estimation Power and Sample Size 10. One-with-Many Designs Design Issues Measuring Nonindependence The Meaning of Nonindependence in the One-with-Many Design Univariate Analysis with Indistinguishable Partners Univariate Estimation with Distinguishable Partners The Reciprocal One-with-Many Design 11. Social Network Analysis Definitions The Representation of a Network Network Measures The p1 12. Dyadic Indexes Item Measurement Issues Measures of Profile Similarity Mean and Variance of the Dyadic Index Stereotype Accuracy Differential Endorsement of the Stereotype Pseudo-Couple Analysis Idiographic versus Nomothetic Analysis Illustration 13. Over-Time Analyses: Interval Outcomes Cross-Lagged Regressions Over-Time Standard APIM Growth-Curve Analysis Cross-Spectral Analysis Nonlinear Dynamic Modeling 14. Over-Time Analyses: Dichotomous Outcomes Sequential Analysis Statistical Analysis of Sequential Data: Log-Linear Analysis Statistical Analysis of Sequential Data: Multilevel Modeling Event-History Analysis 15. Concluding Comments Specialized Dyadic Models Going Beyond the Dyad Conceptual and Practical Issues The Seven Deadly Sins of Dyadic Data Analysis The Last Word

Dyadic Data Analysis

Data analysis in social psychology.

This article presents the rationale and procedures for conducting a process analysis in evaluation research. Such an analysis attempts to identify the process that mediates the effects of some treatment, by estimating the parameters of a causal chain between the treatment and some outcome variable. Two different procedures for estimating mediation are discussed. In addition we present procedures for examining whether a treatment exerts its effects, in part, by altering the mediating process that produces the outcome. Finally, the benefits of process analysis in evaluation research are underlined.

David A. Kenny

Papers

The moderator–mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations.

Dyadic Data Analysis

Data analysis in social psychology.

Process Analysis: Estimating Mediation in Treatment Evaluations

Correlation and causality