Social categorization and attitude change

Two experiments were carried out investigating the effect of categorization on attitude change. It was predicted that the division of a number of individuals into two subgroups (categorization), in such a way that initial attitudes correlate with subgroup membership, would lead to accentuation of attitudinal differences between subgroups. It was further predicted that an identical distribution of initial attitudes without superimposed categorization would lead to convergence of attitude positions. In experiment 1, the effect of a male-female classification on attitude change was studied. It was indeed found that subjects changed their attitudes in the direction opposite to the position of the outgroup (intergroup attitude differentiation), but only for groups who were initially more extreme than the comparison group. In the control condition (no categorization), conformity effects were observed. In experiment 2, an antagonistic intergroup setting was induced. In this situation, strong intergroup attitude differentiation effects were observed, which were not affected by the magnitude of the initial intergroup discrepancy. In the control condition, subjects did not show conformity to the overall group mean, but maintained their initial noncentral attitude position.     

Examining differential item functioning due to item difficulty and alternative attractiveness

A method for analyzing test item responses is proposed to examine differential item functioning (DIF) in multiple-choice items through a combination of the usual notion of DIF, for correct/incorrect responses and information about DIF contained in each of the alternatives. The proposed method uses incomplete latent class models to examine whether DIF is caused by the attractiveness of the alternatives, difficulty of the item, or both. DIF with respect to either known or unknown subgroups can be tested by a likelihood ratio test that is asymptotically distributed as a chi-square random variable.     

Alternating least squares algorithms for simultaneous components analysis with equal component weight matrices in two or more populations

Millsap and Meredith (1988) have developed a generalization of principal components analysis for the simultaneous analysis of a number of variables observed in several populations or on several occasions. The algorithm they provide has some disadvantages. The present paper offers two alternating least squares algorithms for their method, suitable for small and large data sets, respectively. Lower and upper bounds are given for the loss function to be minimized in the Millsap and Meredith method. These can serve to indicate whether or not a global optimum for the simultaneous components analysis problem has been attained.Financial support by the Netherlands organization for scientific research (NWO) is gratefully acknowledged.     

In-session gaming as a tool in treating adolescent problematic gaming

For some adolescent gamers, playing online games may become problematic, impairing functioning in personal, family, and other life domains. Parental and family factors are known to influence the odds that adolescents may develop problematic gaming (PG), negative parenting and conflictual family dynamics increasing the risk, whereas positive parenting and developmentally supportive family dynamics protecting against PG. This suggests that a treatment for adolescent PG should not only address the gaming behaviors and personal characteristics of the youth, but also the parental and family domains. An established research-supported treatment meeting these requirements is multidimensional family therapy (MDFT), which we adapted for use as adolescent PG treatment. We report here on one adaptation, applying in-session gaming. In-session demonstration of the “problem behavior” is feasible and informative in PG. In the opening stage of therapy, we use in-session gaming to establish an alliance between the therapist and the youth. By inviting them to play games, the therapist demonstrates that they are taken seriously, thus boosting treatment motivation. Later in treatment, gaming is introduced in family sessions, offering useful opportunities to intervene in family members' perspectives and interactional patterns revealed in vivo as the youth plays the game. These sessions can trigger strong emotions and reactions from the parents and youth and give rise to maladaptive transactions between the family members, thus offering ways to facilitate new discussions and experiences of each other. The insights gained from the game demonstration sessions aid the therapeutic process, more so than mere discussion about gaming.     

Penalized optimal scaling for ordinal variables with an application to international classification of functioning core sets

Ordinal data occur frequently in the social sciences. When applying principal component analysis (PCA), however, those data are often treated as numeric, implying linear relationships between the variables at hand; alternatively, non-linear PCA is applied where the obtained quantifications are sometimes hard to interpret. Non-linear PCA for categorical data, also called optimal scoring/scaling, constructs new variables by assigning numerical values to categories such that the proportion of variance in those new variables that is explained by a predefined number of principal components (PCs) is maximized. We propose a penalized version of non-linear PCA for ordinal variables that is a smoothed intermediate between standard PCA on category labels and non-linear PCA as used so far. The new approach is by no means limited to monotonic effects and offers both better interpretability of the non-linear transformation of the category labels and better performance on validation data than unpenalized non-linear PCA and/or standard linear PCA. In particular, an application of penalized optimal scaling to ordinal data as given with the International Classification of Functioning, Disability and Health (ICF) is provided.     

An alternating least squares method for the weighted approximation of a symmetric matrix

Bailey and Gower examined the least squares approximationC to a symmetric matrixB, when the squared discrepancies for diagonal elements receive specific nonunit weights. They focussed on mathematical properties of the optimalC, in constrained and unconstrained cases, rather than on how to obtainC for any givenB. In the present paper a computational solution is given for the case whereC is constrained to be positive semidefinite and of a fixed rankr or less. The solution is based on weakly constrained linear regression analysis.The authors are obliged to John C. Gower for stimulating this research.     

Procedural fairness and self-esteem

We argue that people's self-esteem is affected by the fairness of procedures to which they are subjected; unfair treatment will lower self-esteem. Moreover, since this influence on self-esteem is presumably due to the implicit evaluation expressed by the choice of procedure and hence by the evaluation expressed by the person implementing the procedure, people's concern with the fairness of treatment will be focused on the interactional aspects of the procedure. In two experiments designed to test these hypotheses subjects received either a high or a low grade on an ability test on the basis of either fair or unfair grading procedures. The results of Experiment 1 indicated that subjects' self-esteem was lower after unfair treatment, and this influence was only apparent when subjects received high test feedback. Additionally, ratings of the fairness of the interaction were lower following unfair grading procedures. Experiment 2 also manipulated level of involvement with the test. Self-esteem was affected by procedural fairness and procedural fairness influenced perceived fairness of the interaction only in the high involvement condition.