information criterion (BIC), and the sample-size adjusted BIC. This plan of analysis had 2 objectives: (1) to analyze the factorial validity of the Portuguese versions of the measures and (2) to use the factor scores from the retained measurement models for the latent profile analysis (LPA). Using the factor
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David Sánchez-Oliva, Antonio L. Palmeira, Eliana V. Carraça, Pedro J. Teixeira, David Markland, and Marlene N. Silva
Stephen Shannon, Garry Prentice, and Gavin Breslin
relationships between people on the aforesaid interactions between needs-supportive and needs-controlling perceptions ( Myers, Ntoumanis, Gunnell, Gucciardi, & Lee, 2018 ). Using latent profile analysis (LPA), the interaction effects in Figure 1 are calculated in a mixture model to extract unobserved latent
Barbara E. Bechter, James A. Dimmock, Joshua L. Howard, Peter R. Whipp, and Ben Jackson
analytic techniques (e.g., Aelterman, Vansteenkiste, Soenens, & Haerens, 2016 ; Haerens et al., 2010 ; Jackson, Gucciardi, & Dimmock, 2011 ; Wang & Biddle, 2001 ). However, model-based methods—such as the use of latent profile analysis (LPA)—allow for a more sophisticated approach to person
Annette Lohbeck, Andreas Hohmann, Philipp von Keitz, and Monika Daseking
early childhood and the relations of those profiles to certain individual characteristics and physical achievement when using person-centered approaches like latent profile analysis (LPA). LPA focuses on the identification of relatively homogenous subgroups of persons (i.e., latent profiles) that differ
Collin A. Webster, Diana Mindrila, Chanta Moore, Gregory Stewart, Karie Orendorff, and Sally Taunton
loadings were sequentially removed until the models reached an optimal fit to the data. The internal consistency of the two scales was estimated by computing Cronbach’s alpha coefficient. Latent profile analysis Latent profile analysis allows the estimation of an error-free categorical latent variable ( C
David Trouilloud, Sandrine Isoard-Gautheur, and Valentin Roux
coaches see them (i.e., SA > RA). A negative discrepancy score indicates that athletes evaluate themselves more negatively than they think their coaches see them (i.e., SA < RA). Next, a three-step latent profile analysis (LPA) was conducted in Mplus (version 7.31; Los Angeles, CA, Muthén & Muthén, 1998
J.D. DeFreese and Alan L. Smith
( Tabachnick & Fidell, 2013 ) using IBM SPSS (version 19; IBM Corp., Chicago, IL). Descriptive statistics were then calculated for all study variables. Data were analyzed using latent profile analysis, also referred to as latent class analysis (see Muthén & Muthén, 2000 ; Nylund, 2007 ), with Mplus software
Laura C. Healy, Nikos Ntoumanis, and Calum A. Arthur
-athletes pursue their goals relate to important outcomes in the goal-striving process. To the best of our knowledge, only one study has used a person-centered approach in relation to the motives for goal pursuit. Specifically, Healy, Ntoumanis, and Duda ( 2016 ) used latent profile analysis to create profiles
John C.K. Wang, Alexandre J.S. Morin, Richard M. Ryan, and W.C. Liu
The purpose of the current study is to test the self-determination theory (SDT) continuum hypothesis of motivation using latent profile analysis (LPA). A total of 3,220 school students took part in the study. We compared LPA solutions estimated using the four motivation types versus the two higher-order dimensions to assess their degree of correspondence to the SDT continuum hypothesis. To examine the concurrent validity of the profiles, we also verified their associations with three predictors (age, gender, perception of physical education teachers’ autonomy-supportive behaviors) and two outcomes variables (perceived competence and intentions to be physically active). The results showed that profiling using the four motivation types provides more differentiated and meaningful description of responses to the Perceived Locus of Causality Scale, compared with profiling using two higher-order factors. In general, the results of the current study were consistent with the SDT continuum hypothesis of human motivation.
Collin Webster, Diana Mîndrilă, and Glenn Weaver
Affective learning is a major focus of the national K-12 physical education (PE) content standards (National Association for Sport and Physical Education [NASPE, 2004]). Understanding how students might fit into different affective learning subgroups would help extend affective learning theory in PE and suggest possible intervention strategies for teachers wanting to increase students’ affective learning. The present study used cluster analysis (CA) and latent profile analysis (LPA) to develop a two-level affective learning-based typology of high school students in compulsory PE from an instructional communication perspective. The optimal classification system had ten clusters and four latent profiles. A comparison of students’ class and cluster memberships showed that the two classification procedures yielded convergent results, thus suggesting distinct affective learning profiles. Students’ demographic and biographical characteristics, including gender, race, body mass index, organized sport participation, and free time physical activity, were helpful in further characterizing each profile.
