We present a three-dimensional taxonomy of achievement emotions that considers valence, arousal, and object focus as core features of these emotions. By distinguishing between positive and negative emotions (valence), activating and deactivating emotions (arousal), and activity emotions, prospective outcome emotions, and retrospective outcome emotions (object focus), the taxonomy has a 2 × 2 × 3 structure representing 12 groups of achievement emotions. In four studies across different countries (N = 330, 235, 323, and 269 participants in Canada, the United States, Germany, and the U.K., respectively), we investigated the empirical robustness of the taxonomy in educational (Studies 1-3) and work settings (Study 4). An expanded version of the Achievement Emotions Questionnaire was used to assess 12 key emotions representing the taxonomy. Consistently across the four studies, findings from multilevel facet analysis and structural equation modeling documented the importance of the three dimensions for explaining achievement emotions. In addition, based on hypotheses about relations with external variables, the findings show clear links of the emotions with important antecedents and outcomes. The Big Five personality traits, appraisals of control and value, and context perceptions were predictors of the emotions. The 12 emotions, in turn, were related to participants' use of strategies, cognitive performance, and self-reported health problems. Taken together, the findings provide robust evidence for the unique positions of different achievement emotions in the proposed taxonomy, as well as unique patterns of relations with external variables. Directions for future research and implications for policy and practice are discussed. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Research involving early adolescents highlights systematic declines in motiva- tion for learning as students progress from elementary school to secondary school. Students' attributions or explanations for past achievement outcomes often are important determinants of future activity choice, investment and persistence. In this article, we critique prior music attribution research and report findings from our empirical study of American 7th-graders' attributional beliefs about success and failure in classroom music. Our results demonstrated that secondary students do not attribute success and failure to the same factors, and that many of the most salient reasons for music-related outcomes (family-, teacher- and peer-influence) are not addressed in traditional attribution research. Attributional beliefs, particularly those concerning music ability, were strongly linked to students' music self-concept and achievement test scores, and the magnitude of those linkages was typically greater when students reflected upon past failures. Based on these findings, we recommend that music practitioners increase their awareness of students' attributional beliefs (particularly the tendency to attribute failure to lack of ability and/or negative family influence), encourage students to consider the r6le that less stable and more controllable factors (effort, persistence, strategy use, metacognition) play in determining achievement outcomes, and employ instructional or evaluative strategies that promote more expansive and developmental views of music ability among all students.

In this article, we illustrate how generalizability theory (G-theory) can extend traditional assessment methods for designing, improving, and evaluating results from both objectively and subjectively scored measures of individual differences. Our illustrations include quantification of multiple sources of measurement error, derivation of unique indexes of consistency for norm- and criterion-referenced interpretations of scores, estimation of score consistency when changing a measurement procedure, and disattenuation of correlation coefficients for measurement error. We also expand G-theory analyses beyond the item level to include parcels and split measures and highlight linkages among G-theory, classical test theory, and structural equation modeling. Computer code and sample data are provided in online supplements to help readers apply the demonstrated techniques to their own assessments.

Our goal in this investigation was to evaluate the reliability of scores from the Balanced Inventory of Desirable Responding (BIDR) more comprehensively than in prior research using a generalizability-theory framework based on both dichotomous and polytomous scoring of items. Generalizability coefficients accounting for specific-factor, transient, and random-response error ranged from .64 to .75 for the BIDR's Self-Deception Enhancement (SDE) and Impression Management (IM) subscale scores, and these values were systematically lower than corresponding alpha (.66 to .83) and 1-week test-retest (.78 to .86) coefficients. Polytomous scoring provided higher reliability than dichotomous scoring on nearly all indexes reported. Random-response (8%-17%) and specific factor error (11%-17%) exceeded transient error (3%-6%) for both subscales and scoring methods. Doubling the number of items on a single occasion provided greater improvements in generalizability (.76-.83) than aggregating scores across 2 administrations (.72-.81). Both scoring methods provided reasonably high indexes of consistency (φ coefficients≥.91) at cut scores on the IM scale for detecting faked responses when all sources of error were taken into account. Implications of these results for common uses of the BIDR are discussed.

We used structural equation modeling techniques to expand traditional generalizability theory (G-theory) models to allow for congeneric relationships among item responses while accounting for the primary sources of measurement error that affect results from objectively scored, self-report measures. Data came from 919 respondents who completed the Agreeableness, Conscientiousness, Extraversion, Neuroticism, and Openness subscales of the Big Five Inventory (BFI; John et al., 1991 John, O. P. , Donahue, E. M. , & Kentle, R. L. (1991). The Big Five Inventory – Versions 4a and 54 . University of California, Berkeley, Institute of Personality and Social Research. [Google Scholar] ) on two occasions. When compared to traditional and factor-based essential tau-equivalent G-theory models, congeneric models on average yielded superior fit statistics, higher estimates of reliability, and lower estimates of transient and specific-factor measurement error. Essential tau-equivalent and congeneric factor models also were configured to allow for simultaneous partitioning of systematic and measurement error variance at both total score and individual item levels. We provide detailed guidelines, examples, and computer code in R for all models discussed in an extended online supplement to enable readers to apply the demonstrated techniques.

Over recent years, latent state-trait theory (LST) and generalizability theory (GT) have been applied to a wide variety of situations in numerous disciplines to enhance understanding of the reliability and validity of assessment data. Both methodologies involve partitioning of observed score variation into systematic and measurement error components. LST theory is focused on separating state, trait, error, and sometimes method effects, whereas generalizability theory is concerned with distinguishing universe score effects from multiple sources of measurement error. Despite these fundamental differences in focus, LST and GT share much in common. In this article, we use data from a widely used personality measure to illustrate similarities and differences between these two frameworks and show how the same data can be readily interpreted from both perspectives. We also provide comprehensive instructional online supplemental materials to demonstrate how to analyze data using the R package for all LST models and GT designs discussed. (PsycInfo Database Record (c) 2022 APA, all rights reserved).