Whereas situational judgment tests (SJTs) have traditionally been conceptualized as low-fidelity simulations with an emphasis on contextualized situation descriptions and context-dependent knowledge, a recent perspective views SJTs as measures of more general domain (context-independent) knowledge. In the current research, we contrasted these 2 perspectives in 3 studies by removing the situation descriptions (i.e., item stems) from SJTs. Across studies, the traditional contextualized SJT perspective was not supported for between 43% and 71% of the items because it did not make a significant difference whether the situation description was included or not for these items. These results were replicated across construct domains, samples, and response instructions. However, there was initial evidence that judgment in SJTs was more situational when (a) items measured job knowledge and skills and (b) response options denoted context-specific rules of action. Verbal protocol analyses confirmed that high scorers on SJTs without situation descriptions relied upon general rules about the effectiveness of the responses. Implications for SJT theory, research, and design are discussed.

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The bifactor model is a widely applied model to analyze general and specific abilities. Extensions of bifactor models additionally include criterion variables. In such extended bifactor models, the general and specific factors can be correlated with criterion variables. Moreover, the influence of general and specific factors on criterion variables can be scrutinized in latent multiple regression models that are built on bifactor measurement models. This study employs an extended bifactor model to predict mathematics and English grades by three facets of intelligence (number series, verbal analogies, and unfolding). We show that, if the observed variables do not differ in their loadings, extended bifactor models are not identified and not applicable. Moreover, we reveal that standard errors of regression weights in extended bifactor models can be very large and, thus, lead to invalid conclusions. A formal proof of the nonidentification is presented. Subsequently, we suggest alternative approaches for predicting criterion variables by general and specific factors. In particular, we illustrate how (1) composite ability factors can be defined in extended first-order factor models and (2) how bifactor(S-1) models can be applied. The differences between first-order factor models and bifactor(S-1) models for predicting criterion variables are discussed in detail and illustrated with the empirical example.