This article discusses the meta-analysis of raw mean differences. It presents a rationale for cumulating psychological effects in a raw metric and compares raw mean differences to standardized mean differences. Some limitations of standardization are noted, and statistical techniques for raw meta-analysis are described. These include a graphical device for decomposing effect sizes. Several illustrative data sets are analyzed.
Several models of relationship dissolution imply a sequence of steps or stages, for which there might exist a cultural script. Previous research has identified a script for first dates. The present research attempted to identify a relationship dissolution script by asking men and women to list the steps that typically occur when a couple breaks up. Analysis of their 1480 responses indicated a 16-step ordered script for relation-ship dissolution. The relationship dissolution script is discussed in terms of approach-avoidance theories of conflict and relevant relationship dissolution theories.
Many social psychologists believe that if research results are obvious, they are unimportant and uninteresting. The current study evaluated lay perceptions of social psychological research findings. Results from three studies reveal differences between lay evaluations of research and scientific evaluations. In Study 1, students with no prior exposure to social psychology judge the most obvious research findings to be the most important. In Study 2, students can predict findings, and the most predictable findings are judged most important. In Study 3, students judge the most obvious findings to be most important to establish with research. Results address the accuracy of lay judgments of research, judgmental strategies, and the process by which social psychologists select research topics.
Researchers have debated whether laypeople can detect covariation and have tried to identify conditions that might facilitate or retard this ability. Language, especially linguistic representation of variables, seems important to consider since misrepresentation appears to be relatively common in linguistic exchanges. In the present theory-based experiment, 16 subjects were asked to make judgments about the relationship between height and either weight (heavy or light) or bodyfat (fat or thin). Data provided evidence of a powerful illusory association, that is, if tall, then thin; if short, then fat, and there was no compelling evidence to suggest that subjects understood the relationship between height and weight.
To analyze variance in a triadic variable, Bond, Horn, and Kenny (1997) have proposed a Triadic Relations Model. Here we extend this model to analyze the covariances between triadic variables. A bivariate version of the Triadic Relations Model is specified, and estimation methods are presented. These can be used to decompose the covariance between two triadic variables into thirty-three covariance components. Interpretations and an example of this analysis are offered. Applications of this model and alternative techniques are noted.
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