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David Sánchez-Oliva, Antonio L. Palmeira, Eliana V. Carraça, Pedro J. Teixeira, David Markland, and Marlene N. Silva

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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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

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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

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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

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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

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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

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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.

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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.

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Kathryn A. Coniglio and Edward A. Selby

psychopathology. The purpose of this study is to identify latent profiles of individuals both with and at risk for eating psychopathology based on their motivation for exercise using SDT. As such, we have chosen to examine motivation for exercise using latent profile analysis (LPA) in two distinct samples

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John C. K. Wang, Woon Chia Liu, Nikos L. D. Chatzisarantis, and Coral B. S. Lim

The purpose of the current study was to examine the influence of perceived motivational climate on achievement goals in physical education using a structural equation mixture modeling (SEMM) analysis. Within one analysis, we identified groups of students with homogenous profiles in perceptions of motivational climate and examined the relationships between motivational climate, 2 × 2 achievement goals, and affect, concurrently. The findings of the current study showed that there were at least two distinct groups of students with differing perceptions of motivational climate: one group of students had much higher perceptions in both climates compared with the other group. Regardless of their grouping, the relationships between motivational climate, achievement goals, and enjoyment seemed to be invariant. Mastery climate predicted the adoption of mastery-approach and mastery-avoidance goals; performance climate was related to performance-approach and performance-avoidance goals. Mastery-approach goal had a strong positive effect while performance-avoidance had a small negative effect on enjoyment. Overall, it was concluded that only perception of a mastery motivational climate in physical education may foster intrinsic interest in physical education through adoption of mastery-approach goals.