Trait Perfectionism and Dance Goals Among Young Female Dancers: An Application of the 2 × 2 Model of Perfectionism

This study provided the first test of the 2 × 2 model of perfectionism with respect to dancers’ goals for dancing in competitive dance. Four hundred twenty-five young female North American competitive dancers (M = 11.33 years; SD = 2.14) completed questionnaires assessing multidimensional perfectionism and goals for participation in dance. The latent moderated structural equations approach along with procedures outlined by Gaudreau indicated partial support for the 2 × 2 model of perfectionism. Pure Evaluative Concerns Perfectionism was associated with fewer intrinsic goals for dance and greater extrinsic goals for dance relative to nonperfectionism. Pure Personal Standards Perfectionism was related to less endorsement of extrinsic goals relative to nonperfectionism. Findings were complex with respect to mixed perfectionism, with this form of perfectionism being related to greater endorsement of both intrinsic and extrinsic goals for dance. Results provide partial support for the 2 × 2 model in youth dance.

Latent Variable Interactions With Ordered-Categorical Indicators: Comparisons of Unconstrained Product Indicator and Latent Moderated Structural Equations Approaches

Methods to handle ordered-categorical indicators in latent variable interactions have been developed, yet they have not been widely applied. This article compares the performance of two popular latent variable interaction modeling approaches in handling ordered-categorical indicators: unconstrained product indicator (UPI) and latent moderated structural equations (LMS). We conducted a simulation study across sample sizes, indicators’ distributions and category conditions. We also studied four strategies to create sets of product indicators for UPI. Results supported using a parceling strategy to create product indicators in the UPI approach or using the LMS approach when the categorical indicators are symmetrically distributed. We applied these models to study the interaction effect between third- to fifth-grade students’ social skills improvement and teacher–student closeness on their state English language arts test scores.

Analyser l’interaction de variables latentes : une exemplification méthodologique de la méthode d’équations structurelles avec interaction latente1

Le présent article est une exemplification méthodologique de la méthode LMS (Latent Moderated Structural Equations) disponible dans le logiciel Mplus. Des données recueillies pour étudier la motivation d’adolescentes (n = 434) en éducation physique serviront à présenter la méthodologie à suivre pour évaluer l’interaction de variables latentes dans des modèles d’équations structurelles. Le texte focalise sur la compréhension générale du lecteur quant à l’application de cette méthode et un accent est mis sur la présentation et l’interprétation des résultats. En terminant, les avantages de la méthode LMS sont mis de l’avant et des pistes d’exemplifications méthodologiques sont proposées.

Multicollinearity and Missing Constraints

Multicollinearity complicates the simultaneous estimation of interaction and quadratic effects in structural equation modeling (SEM). So far, approaches developed within the Kenny-Judd (1984 ) tradition have failed to specify additional and necessary constraints on the measurement error covariances of the nonlinear indicators. Given that the constraints comprise, in part, latent linear predictor correlations, multicollinearity poses a problem for such approaches. Klein and Moosbrugger’s (2000 ) latent moderated structural equations approach (LMS) approach does not utilize nonlinear indicators and should therefore not be affected by this problem. In the context of a simulation study, we varied predictor correlation and the number of nonlinear effects in order to compare the performance of three approaches developed for the estimation of simultaneous nonlinear effects: Ping’s (1996 ) two-step approach, a correctly extended Jöreskog-Yang (1996 ) approach, and LMS. Results show that in contrast to the Jöreskog-Yang approach and LMS, the two-step approach produces biased parameter estimates and false inferences under heightened multicollinearity. Ping’s approach resulted in overestimated interaction effects and underestimated quadratic effects.