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The generalized internal/external frame of reference (GI/E) model assumes social and dimensional achievement comparisons to form self-perceptions. These domain-specific comparisons have been shown to shape two facets of test anxiety (i.e., worry and emotionality) both directly and indirectly through academic self-concepts. However, examinations of such domain-specific relations have rarely integrated general components, although the hierarchical nature of both test anxiety and academic self-concept is well-known. Thus, the present study implemented a nested factor modeling approach. We examined social and dimensional comparison effects on worry and emotionality as well as mediation effects of academic self-concepts in the math and verbal domains while controlling for general components. We contrasted this approach with the conventionally used first-order factor model where general components were not considered. Data from N  = 348 German secondary school students ( M age  = 15.3 years, Grades 9–10) were analyzed using structural equation models. Direct negative within-domain and positive cross-domain achievement-anxiety relations emerged, yet, the pattern of cross-domain relations changed across modeling approaches. Only the nested factor model showed indirect cross-domain mediation relations. Our findings suggest the importance of structural representations of hierarchical constructs. The nested factor model approach enhanced predictions within the GI/E model, particularly those related to dimensional comparisons.

Background Medically unexplained symptoms are the hallmark of somatoform disorders and functional somatic syndromes. Purpose Although medically unexplained symptoms represent a common phenomenon both in the general population as well as in medical settings, the exact latent structure of somatic symptoms remains largely unclear. Method We examined the latent structure of medically unexplained symptoms by means of the Patient Health Questionnaire-15 (PHQ-15) questionnaire (i.e., a popular symptom checklist) and provide support for the construct validity of our model. The data were analyzed using confirmatory factor analysis in a general population sample (study 1; N  = 414) and in a sample of primary care patients (study 2; N  = 308). We compared four different latent structure models of medically unexplained symptoms: a general factor model, a correlated group factor model, a hierarchical model, and a bifactor model. Results In study 1, a bifactor model with one general factor and four independent specific symptom factors (i.e., gastrointestinal, pain, fatigue, and cardiopulmonary symptoms) showed the best model fit. This bifactor model was confirmed in the primary care sample (study 2). Additionally, the model explained 59 % of the variance of the irritable bowel syndrome (IBS). In this structural equation model, both the general factor (14 %) as well as the gastrointestinal symptom factor (42 %) significantly predicted the IBS. Conclusion The findings of both studies help to clarify the latent structure of somatic symptoms in the PHQ-15. The bifactor model outperformed alternative models and demonstrated external validity in predicting IBS.

The purpose of the current study is to compare the extent to which general and specific abilities predict academic performances that are also varied in breadth (i.e., general performance and specific performance). The general and specific constructs were assumed to vary only in breadth, not order, and two data analytic approaches (i.e., structural equation modeling [SEM] and relative weights analysis) consistent with this theoretical assumption were compared. Conclusions regarding the relative importance of general and specific abilities differed based on data analytic approaches. The SEM approach identified general ability as the strongest and only significant predictor of general academic performance, with neither general nor specific abilities predicting any of the specific subject grade residuals. The relative weights analysis identified verbal reasoning as contributing more than general ability, or other specific abilities, to the explained variance in general academic performance. Verbal reasoning also contributed to most of the explained variance in each of the specific subject grades. These results do not provide support for the utility of predictor-criterion alignment, but they do provide evidence that both general and specific abilities can serve as useful predictors of performance.

A frequently reported finding is that general mental ability (GMA) is the best single psychological predictor of job performance. Furthermore, specific abilities often add little incremental validity beyond GMA, suggesting that they are not useful for predicting job performance criteria once general intelligence is accounted for. We review these findings and their historical background, along with different approaches to studying the relative influence of g and narrower abilities. Then, we discuss several recent studies that used relative importance analysis to study this relative influence and that found that specific abilities are equally good, and sometimes better, predictors of work performance than GMA. We conclude by discussing the implications of these findings and sketching future areas for research.

The relative value of specific versus general cognitive abilities for the prediction of practical outcomes has been debated since the inception of modern intelligence theorizing and testing. This editorial introduces a special issue dedicated to exploring this ongoing “great debate”. It provides an overview of the debate, explains the motivation for the special issue and two types of submissions solicited, and briefly illustrates how differing conceptualizations of cognitive abilities demand different analytic strategies for predicting criteria, and that these different strategies can yield conflicting findings about the real-world importance of general versus specific abilities.

A common occurrence in practical design of experiments is that one factor, called a nested factor, can only be varied for some but not all the levels of a categorical factor, called a branching factor. In this case, it is possible, but inefficient, to proceed by performing two experiments. One experiment would be run at the level(s) of the branching factor that allow for varying the second, nested, factor. The other experiment would only include the other level(s) of the branching factor. It is preferable to perform one experiment that allows for assessing the effects of both factors. Clearly, the effect of the nested factor then is conditional on the levels of the branching factor for which it can be varied. For example, consider an experiment comparing the performance of two machines where one machine has a switch that is missing for the other machine. The investigator wants to compare the two machines but also wants to understand the effect of flipping the switch. The main effect of the switch is conditional on the machine. This article describes several example situations involving branching factors and nested factors. We provide a model that is sensible for each situation, present a general method for constructing appropriate models, and show how to generate optimal designs given these models.

In many areas of scientific research, complex experimental designs are now routinely employed. The statistical analysis of data generated when using these designs may be carried out by a statistician; however, modern statistical software packages allow such analyses to be performed by non-statisticians. For the non-statistician, failing to correctly identify the structure of the experimental design can lead to incorrect model selection and misleading inferences. A procedure, which does not require expert statistical knowledge, is described that focuses the non-statistician's attention on the relationship between the experimental material and design, identifies the underlying structure of the selected design, and highlights any potential weaknesses it may have. These are important precursors to the randomization and subsequent statistical analysis and can be easily overlooked by a non-statistician. The process is illustrated using a generalization of the Hasse diagram and has been implemented in a program written in R.

This article introduces a new class of experimental designs, called split factorials, which allow for the estimation of both response surface effects (fixed effects of crossed factors) and variance components arising from nested random effects. With an economical run size, split factorials provide flexibility in dividing the degrees of freedom among the different estimations. For a split factorial design, it is shown that the OLS estimators for the fixed effects are BLUE and that the variance component estimators from the mean squared errors on the ANOVA table are minimum variance among unbiased quadratic estimators. An application involving concrete mixing demonstrates the use of a split factorial experiment.

This chapter contains sections titled: Null hypotheses Linear models Analysis of variance Assumptions Other issues Further reading Key for partly nested ANOVA Worked examples of real biological data sets

This chapter contains sections titled: Linear models Null hypotheses Analysis of variance Variance components Assumptions Pooling denominator terms Unbalanced nested designs Linear mixed effects models Robust alternatives Power and optimisation of resource allocation Nested ANOVA in R Further reading Key for nested ANOVA Worked examples of real biological data sets