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AbstractBeyond assessing whether a treatment is effective, we need to understand why it is effective if we are to learn from evaluation research how treatments might be improved.

Interpersonal conflict usually involves conflict over attitudes as well as over scarce resources. It is hypothesized that the course of attitude conflicts will influence the subsequent view of the issue under dispute. Specifically, competitively oriented subjects will highly differentiate the opposing attitude positions while cooperatively oriented subjects will emphasize the similarity of the positions. These perceptual changes are expected to be cognitively mediated by differential weighting of the ways in which the position differ. In an experiment to test this hypothesis, pairs of subjects were assigned attitude positions on how National Health Insurance should be organized. Positions within any pair differed a great deal in some ways and less in others. Pairs then discussed the issue under either cooperative or competitive contingencies. The results of the experiment strongly confirmed the hypothesis: competition led to decreased perceived similarity between the positions and an emphasis upon those ways in which the positions differed the most. Cooperation had opposite effects.

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.

ABSTRACTTechniques used in decision sciences and business research to estimate interactions between latent variables are limited in controlling for measurement error. This article uses a latent structure modeling approach that substantially controls for measurement error in nonlinear relationships. The results of this technique are compared to the results obtained applying hierarchical regression analysis and the impact of measurement error is assessed. The paper provides a unique assessment of the validity of the multi‐attribute attitude model. The validity of the multiplicative rule in the model is supported.

This indispensable collection provides extensive, yet accessible, coverage of conceptual and practical issues in research design in personality and social psychology. Using numerous examples and clear guidelines, especially for conducting complex statistical analysis, leading experts address specific methods and areas of research to capture a definitive overview of contemporary practice. Updated and expanded, this third edition engages with the most important methodological innovations over the past decade, offering a timely perspective on research practice in the field. To reflect such rapid advances, this volume includes commentary on particularly timely areas of development such as social neuroscience, mobile sensing methods, and innovative statistical applications. Seasoned and early-career researchers alike will find a range of tools, methods, and practices that will help improve their research and develop new conceptual and methodological possibilities. Supplementary online materials are available on Cambridge Core

In a simple mediation model, the effect of a manipulated variable X on a dependent variable Y over and above the effect of the mediator Me can be estimated by regressing Y on X and Me. The impact of X on Y in such a model is adjusted for the relationship both between X and Me and between Me and Y. The authors examine the adjustment function in the context of a 2 × 2 design with two manipulated variables. In such a situation, the mediator could be affected by either one of the main effects and/or the interaction between two manipulated variables. To adjust for the impact of the mediator, a standard procedure has been to rely on an ANCOVA that includes only the mediator. The authors show, both analytically and with simulations, that this leads to improper control of the mediator and to biased estimates of the model parameters.

"Intended for graduate students in personality and social psychology, this indispensable collection provides clear, detailed coverage of conceptual and practical issues in research design. In this third edition, leading experts address specific methods and offer contemporary commentary, reflecting the rapid changes of this dynamic area of research"--

In light of current concerns with replicability and reporting false-positive effects in psychology, we examine Type I errors and power associated with 2 distinct approaches for the assessment of mediation, namely the component approach (testing individual parameter estimates in the model) and the index approach (testing a single mediational index). We conduct simulations that examine both approaches and show that the most commonly used tests under the index approach risk inflated Type I errors compared with the joint-significance test inspired by the component approach. We argue that the tendency to report only a single mediational index is worrisome for this reason and also because it is often accompanied by a failure to critically examine the individual causal paths underlying the mediational model. We recommend testing individual components of the indirect effect to argue for the presence of an indirect effect and then using other recommended procedures to calculate the size of that effect. Beyond simple mediation, we show that our conclusions also apply in cases of within-participant mediation and moderated mediation. We also provide a new R-package that allows for an easy implementation of our recommendations.

In light of current concerns with replicability and reporting false-positive effects in psychology, we examine Type I errors and power associated with 2 distinct approaches for the assessment of mediation, namely the component approach (testing individual parameter estimates in the model) and the index approach (testing a single mediational index). We conduct simulations that examine both approaches and show that the most commonly used tests under the index approach risk inflated Type I errors compared with the joint-significance test inspired by the component approach. We argue that the tendency to report only a single mediational index is worrisome for this reason and also because it is often accompanied by a failure to critically examine the individual causal paths underlying the mediational model. We recommend testing individual components of the indirect effect to argue for the presence of an indirect effect and then using other recommended procedures to calculate the size of that effect. Beyond simple mediation, we show that our conclusions also apply in cases of within-participant mediation and moderated mediation. We also provide a new R-package that allows for an easy implementation of our recommendations.