The aim of the present paper is to examine the contribution of evaluative conditioning (EC) to attitude formation theory in social psychology. This aim is pursued on two fronts. First, evaluative conditioning is analysed for its relevance to social psychological research. We show that conditioned attitudes can be acquired through simple co‐occurrences of a neutral and a valenced stimulus. Moreover, we argue that conditioned attitudes are not confined to direct contact with a valenced stimulus, but can be formed and dynamically (...) reformed indirectly, through association chains. Second, social research is examined in an effort to identify evaluative learning mechanisms. We suggest that several important phenomena in social psychology (e.g., ingroup favouritism, prejudice, name letter effect) are at least partly due to simple mechanisms of evaluative learning. The implications for attitude formation theory and for applied settings are discussed. (shrink)

Conventionally, multilevel analysis of covariance (ML-ANCOVA) has been the recommended approach for analyzing treatment effects in quasi-experimental multilevel designs with treatment application at the cluster-level. In this paper, we introduce the generalized ML-ANCOVA with linear effect functions that identifies average and conditional treatment effects in the presence of treatment-covariate interactions. We show how the generalized ML-ANCOVA model can be estimated with multigroup multilevel structural equation models that offer considerable advantages compared to traditional ML-ANCOVA. The proposed model takes into account measurement (...) error in the covariates, sampling error in contextual covariates, treatment-covariate interactions, and stochastic predictors. We illustrate the implementation of ML-ANCOVA with an example from educational effectiveness research where we estimate average and conditional effects of early transition to secondary schooling on reading comprehension. (shrink)

Item context effects refer to the impact of features of a test on an examinee’s item responses. These effects cannot be explained by the abilities measured by the test. Investigations typically focus on only a single type of item context effects, such as item position effects, or mode effects, thereby ignoring the fact that different item context effects might operate simultaneously. In this study, two different types of context effects were modeled simultaneously drawing on data from an item calibration study (...) of a multidimensional computerized test (N = 1,632) assessing student competencies in mathematics, science, and reading. We present a generalized linear mixed model (GLMM) parameterization of the multidimensional Rasch model including item position effects (distinguishing between within-block position effects and block position effects), domain order effects, and the interactions between them. Results show that both types of context effects played a role, and that the moderating effect of domain orders was very strong. The findings have direct consequences for planning and applying mixed domain assessment designs. (shrink)