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.The purpose of this analysis is to distinguish between the properties of moderator and mediator variables in such a way as to clarify the different ways in which conceptual variables may account for differences in peoples' behavior. Specifically, we differentiate between two often-confused functions of third variables: (a) the moderator function of third variables, which partitions a focal independent variable into subgroups that establish its domains of maximal effectiveness in regard to a given dependent variable, and (b) the mediator function of a third variable, which represents the generative mechanism through which the focal independent variable is able to influence the dependent variable of interest.Although these two functions of third variables have a relatively long tradition in the social sciences, it is not at all uncommon for social psychological researchers to u, the terms moderator and mediator interchangeably. For example, Harkins, Latan6, and Williams 0980) first summarized the impact of identifiability on social loafing by observing that it "moderates social loafing" (p. 303) and then within the same paragraph proposed "that identifiability is an important mediator of social loafing: ' Similarly, Findley and Cooper (1983), intending a moderator interpretation, labeled gender, age, race, and socioeconomic level as mediators of the relation between locus of control and academic achievement. Thus, one largely pedagogiThis research was supported in part by National Science Foundation Grant BNS-8210137 and National Institute of Mental Health Grant R01 MH-40295-01 to the second author. Support was also given to him during his sabbatical year by the MacArthur Foundation at the Center for Advanced Studies in the Behavioral Sciences, Stanford, California.Thanks are due to Judith Harackiewicz, Charles Judd, Stephen West, and Harris Cooper, who provided comments on an earlier version of this article. Stephen P. Needel was instrumental in the beginning stages of this work.Correspondence concerning this article should be addressed to Reuben M. Baron, Department of Psychology U-20, University of Connecticut, Storrs, Connecticut 06268. cal functi...

Seven basic research questions in interpersonal perception are posed concerning issues of consensus, assimilation, reciprocity, accuracy, congruence, assumed similarity and self—other agreement. All questions can be addressed at the individual level, and three at the dyadic level. It is shown how the Social Relations Model can be used to answer the questions.

The actor–partner interdependence model (APIM) is a model of dyadic relationships that integrates a conceptual view of interdependence with the appropriate statistical techniques for measuring and testing it. In this article we present the APIM as a general, longitudinal model for measuring bidirectional effects in interpersonal relationships. We also present three different approaches to testing the model. The statistical analysis of the APIM is illustrated using longitudinal data on relationship specific attachment security from 203 mother–adolescent dyads. The results support the view that interpersonal influence on attachment security is bidirectional. Moreover, consistent with a hypothesis from attachment theory, the degree to which a child’s attachment security is influenced by his or her primary caregiver is found to diminish with age.

Given that the root mean square error of approximation (RMSEA) is currently one of the most popular measures of goodness-of-model fit within structural equation modeling (SEM), it is important to know how well the RMSEA performs in models with small degrees of freedom (df). Unfortunately, most previous work on the RMSEA and its confidence interval has focused on models with a large df. Building on the work of Chen et al. to examine the impact of small df on the RMSEA, we conducted a theoretical analysis and a Monte Carlo simulation using correctly specified models with varying df and sample size. The results of our investigation indicate that when the cutoff values are used to assess the fit of the properly specified models with small df and small sample size, the RMSEA too often falsely indicates a poor fitting model. We recommend not computing the RMSEA for small df models, especially those with small sample sizes, but rather estimating parameters that were not originally specified in the model.

Throughout social and cognitive psychology, participants are routinely asked to respond in some way to experimental stimuli that are thought to represent categories of theoretical interest. For instance, in measures of implicit attitudes, participants are primed with pictures of specific African American and White stimulus persons sampled in some way from possible stimuli that might have been used. Yet seldom is the sampling of stimuli taken into account in the analysis of the resulting data, in spite of numerous warnings about the perils of ignoring stimulus variation (Clark, 1973; Kenny, 1985; Wells & Windschitl, 1999). Part of this failure to attend to stimulus variation is due to the demands imposed by traditional analysis of variance procedures for the analysis of data when both participants and stimuli are treated as random factors. In this article, we present a comprehensive solution using mixed models for the analysis of data with crossed random factors (e.g., participants and stimuli). We show the substantial biases inherent in analyses that ignore one or the other of the random factors, and we illustrate the substantial advantages of the mixed models approach with both hypothetical and actual, well-known data sets in social psychology (Bem, 2011; Blair, Chapleau, & Judd, 2005; Correll, Park, Judd, & Wittenbrink, 2002).