A Three-Dimensional Taxonomy of Achievement Emotions

We present a three-dimensional taxonomy of achievement emotions that considers valence, arousal, and object focus as core features of emotions occurring in achievement settings. 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 US, Germany, and the UK, 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

The music self-perception inventory: development of parallel forms a and b

Music self-concept integrates perceptions, beliefs, and self-schemas about a person's musical abilities and potential. Like other self-concept dimensions, it is multifaceted, hierarchically organized and has implications for motivation toward musical practice. The Music Self-Perception Inventory (MUSPI) is a theoretically-based instrument assessing six specific music self-concept dimensions, as well as global music self-concept. Nonetheless, its applicability is limited by its length (84 items) and by the fact that it does not provide a way to control for consistency biases in the context of repeated measurement. In this study, we developed and validated two parallel versions (A and B) of the MUSPI, and showed that both yielded equivalent psychometric properties to the original, and were fully equivalent to one another. We also tested whether the MUSPI-A and MUSPI-B psychometric properties generalized (were invariant) across gender and grade-differentiated subgroups. Finally, we examined the convergent validity of the MUSPI-A and MUSPI-B. Results highlighted the psychometric soundness, and equivalence, of the various MUSPI versions on all criteria, and showed that they presented patterns of associations with other constructs equivalent to that observed with the original MUSPI

Extending Applications of Generalizability Theory-Based Bifactor Model Designs

In recent years, researchers have described how to analyze generalizability theory (GT) based univariate, multivariate, and bifactor designs using structural equation models. However, within GT studies of bifactor models, variance components have been limited to those reflecting relative differences in scores for norm-referencing purposes, with only limited guidance provided for estimating key indices when making changes to measurement procedures. In this article, we demonstrate how to derive variance components for multi-facet GT-based bifactor model designs that represent both relative and absolute differences in scores for norm- or criterion-referencing purposes using scores from selected scales within the recently expanded form of the Big Five Inventory (BFI-2). We further develop and apply prophecy formulas for determining how changes in numbers of items, numbers of occasions, and universes of generalization affect a wide variety of indices instrumental in determining the best ways to change measurement procedures for specific purposes. These indices include coefficients representing score generalizability and dependability; scale viability and added value; and proportions of observed score variance attributable to general factor effects, group factor effects, and individual sources of measurement error. To enable readers to apply these techniques, we provide detailed formulas, code in R, and sample data for conducting all demonstrated analyses within this article

Using Structural Equation Modeling to Reproduce and Extend ANOVA-Based Generalizability Theory Analyses for Psychological Assessments

Generalizability theory provides a comprehensive framework for determining how multiple sources of measurement error affect scores from psychological assessments and using that information to improve those assessments. Although generalizability theory designs have traditionally been analyzed using analyses of variance (ANOVA) procedures, the same analyses can be replicated and extended using structural equation models. We collected multi-occasion data from inventories measuring numerous dimensions of personality, self-concept, and socially desirable responding to compare variance components, generalizability coefficients, dependability coefficients, and proportions of universe score and measurement error variance using structural equation modeling versus ANOVA techniques. We further applied structural equation modeling techniques to continuous latent response variable metrics and derived Monte Carlo-based confidence intervals for those indices on both observed score and continuous latent response variable metrics. Results for observed scores estimated using structural equation modeling and ANOVA procedures seldom varied. Differences in reliability between raw score and continuous latent response variable metrics were much greater for scales with dichotomous responses, thereby highlighting the value of doing analyses on both metrics to evaluate gains that might be achieved by increasing response options. We provide detailed guidelines for applying the demonstrated techniques using structural equation modeling and ANOVA-based statistical software

Analyzing Multivariate Generalizability Theory Designs within Structural Equation Modeling Frameworks

We demonstrate how to analyze complete multivariate generalizability theory (GT) designs within structural equation modeling frameworks that encompass both individual subscale scores and composites formed from those scores. Results from numerous analyses of observed scores obtained from respondents who completed the recently updated form of the Big Five Inventory (BFI-2) revealed that the lavaan SEM package in R produced results virtually identical to those obtained from the mGENOVA package, which historically has served as the gold standard for conducting multivariate GT analyses. We further extended lavaan analyses beyond what mGENOVA allows to produce Monte Carlo based confidence intervals for key GT parameters and correct score consistency and correlational indices for effects of scale coarseness characteristic of binary and ordinal data. Our comprehensive online Supplemental Material includes code for performing all illustrated analyses using lavaan and mGENOVA.</p

Music self-concept and self-esteem formation in adolescence: A comparison between individual and normative models of importance within a latent framework

We examined the possible effects of six dimensions of music self-concept on determination of self-esteem, through the application of models based on individual and normative-group importance. Previous studies have supported the individual model of importance in narrowly defined self-domains such as spiritual self-concept that might be unimportant for most people, but very important for some people. However, results from more recent studies of spiritual, academic, and physical self-concepts involving latent variable methodologies support the normative-group model. Here, we extended the use of latent variable methods to music self-concept using a sample of 512 junior high students (11–16 years old). Our results for music-reading skills supported the individual importance model rather than the normative-group importance model. Additional results revealed that singing, instrument playing, and the importance of instrument playing had direct rather than interactive linkages with self-esteem. Collectively, these results highlight differential effects of performance (singing, instrument playing) and knowledge (reading) on self-esteem, and imply that strategies to enhance self-esteem may vary within different domains of music instruction and participation. At a more general level, the findings together with those from previous studies indicate that interconnections between specific and global aspects of self-concept vary across domains and are more complex than previously thought