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Motivational Strategies Used by Exercise Professionals: A Latent Profile Analysis

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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Athletes’ Psychological Needs and Coaches’ Interpersonal Behaviors: A Within-Person Latent Profile Analysis

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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Student Motivation in High School Physical Education: A Latent Profile Analysis Approach

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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Children’s Motivation Profiles in Sports and Physical Activities: A Latent Profile Analysis and Self-Determination Theory Approach

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

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Exploring the Role of Physical Education Teachers’ Domain-Specific Innovativeness, Educational Background, and Perceived School Support in CSPAP Adoption

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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“Mirror, Mirror, Am I as My Coach Sees Me?”: Discrepancy Between Athlete Self-Appraisals and Reflected Appraisals of Their Coach, and Relations With Athlete Burnout

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

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A Person-Centered Exploration of Athlete Burnout Across a Competitive Season

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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A Resource-Based View of Organizational Sustainability in Sport for Development

Wonjun Choi, Mi Ryoung Chung, Wonju Lee, Gareth J. Jones, and Per G. Svensson

variables were log-transformed ( Eggert et al., 2014 ) and the resulting skewness and kurtosis values for all log-transformed variables fell within acceptable ranges ( Kline, 2015 ). To address the first RQ, latent profile analysis (LPA) was utilized to identify subgroups based on similarities in resource

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Goal Motives and Well-Being in Student-Athletes: A Person-Centered Approach

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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Students’ Motivational Profiles in the Physical Education Context

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.