Avoidance Responses to Alternative Alcohol Warning Labels1

A preference-based measure of responses to warning labels was used to study alternative alcohol warning labels that differed in: (a) length, (b) presence of qualifier words, (c) alternative content, and (d) specific beverage. The specific risks mentioned were more important than the overall label length, qualifier words reduced avoidance responses, various alternative warnings elicited more avoidance responses than the existing alcohol warning label, and more avoidance responses were made to labels on a whiskey bottle than on a beer bottle. Warnings with the words “poison,”“cancer,” or “health problems” were the most powerful. The results for college students in Study 1 were replicated in a high-school sample in Study 2.     

Contrasting strategic and Milan therapies

Three related models of therapy are often grouped together as the strategic therapies. These are the brief therapy model associated with the Mental Reseach Institute, approaches developed by Jay Haley and Cloë Madanes, and the model developed by the Milan associates. Controversy exists, however, as to whether the Milan model should be included as a strategic therapy. It appears that the similarities among the three models can mask deeper differences, thus confounding the confusion. This paper contrasts the models in their development, theory, and practice.     

Commentary on “Mediation analysis and categorical variables: The final frontier” by Dawn Iacobucci

The commentary discusses several topics in Dr. Iacobucci's article on mediation analysis with categorical variables including the lack of equivalency of mediated effect measures, ab, and c–c′, that are algebraically equivalent in ordinary least squares regression models. The commentary raises the issue of correlated mediation coefficients and presents formulas to include the correlation in the test of significance. References to new developments in causal mediation analysis for categorical variables are given and the importance of programs of research to establish a mediation theory is emphasized.     

Negative internal causal attributions of a specific offense and forgiveness

Causal attributions are important social‐cognitive predictors of forgiveness. This article presents the Transgression Attribution Questionnaire (TAQ), a measure of one's negative internal causal attributions of a specific offense. In 4 studies, scores on the TAQ showed initial evidence of estimated internal consistency, temporal stability, and construct validity. Negative internal attributions for the cause of a transgression predicted lower levels of empathy and forgiveness. Furthermore, scores on the TAQ predicted forgiveness over and above the hurtfulness of the offense, relationship commitment, and a general measure of internal causal attributions in relationships. The current research bridges research on internal causal attributions and forgiveness. Implications for the social‐cognitive study of forgiveness and the measurement of causal attributions are discussed.     

Countertheses: Communication,material conditions,and future sociocultural development

Chaos in brain function. Edited by Erol Basar Springer‐Verlag, 1990, Berlin. Softcover, 176 pp., 66 figures, index.     

Explanation of Two Anomalous Results in Statistical Mediation Analysis

Previous studies of different methods of testing mediation models have consistently found two anomalous results. The first result is elevated Type I error rates for the bias-corrected and accelerated bias-corrected bootstrap tests not found in nonresampling tests or in resampling tests that did not include a bias correction. This is of special concern as the bias-corrected bootstrap is often recommended and used due to its higher statistical power compared with other tests. The second result is statistical power reaching an asymptote far below 1.0 and in some conditions even declining slightly as the size of the relationship between X and M, a, increased. Two computer simulations were conducted to examine these findings in greater detail. Results from the first simulation found that the increased Type I error rates for the bias-corrected and accelerated bias-corrected bootstrap are a function of an interaction between the size of the individual paths making up the mediated effect and the sample size, such that elevated Type I error rates occur when the sample size is small and the effect size of the nonzero path is medium or larger. Results from the second simulation found that stagnation and decreases in statistical power as a function of the effect size of the a path occurred primarily when the path between M and Y, b, was small. Two empirical mediation examples are provided using data from a steroid prevention and health promotion program aimed at high school football players (Athletes Training and Learning to Avoid Steroids; Goldberg et al., 1996), one to illustrate a possible Type I error for the bias-corrected bootstrap test and a second to illustrate a loss in power related to the size of a. Implications of these findings are discussed.