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The moderator-mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations.
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… Expand
Dyadic Data Analysis
Basic Definitions and Overview. The Measurement of Nonindependence. Analyzing Between- and Within-Dyads Independent Variables. Using Multilevel Modeling to Study Dyads. Using Structural Equation… Expand
Interpersonal Perception: A Social Relations Analysis
- D. A. Kenny
- 1 May 1988
Seven basic research questions in interpersonal perception are posed concerning issues of consensus, assimilation, reciprocity, accuracy, congruence, assumed similarity and self—other agreement. All… Expand
The Actor–Partner Interdependence Model: A model of bidirectional effects in developmental studies
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… Expand
Models of Non-Independence in Dyadic Research
- D. A. Kenny
- 1 May 1996
In dyadic research, the responses of the two members of the dyad are likely to be non-independent. Statistical estimation for three different processes that bring about non-independence are… Expand
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… Expand
Effect of the Number of Variables on Measures of Fit in Structural Equation Modeling
There has been relatively little systematic investigation of the effect of the number of variables on measures of model fit in structural equation modeling. There is conflicting evidence as to… Expand
Estimating the nonlinear and interactive effects of latent variables.
The Performance of RMSEA in Models With Small Degrees of Freedom
We show 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. Expand