Handbook on the Temporal Dynamics of Organizational Behavior 2020
DOI: 10.4337/9781788974387.00031
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Mixture modeling for organizational behavior research

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Cited by 49 publications
(89 citation statements)
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“…We used latent profile analysis (LPA) to identify latent profiles using the individual facets of the three work characteristics as indicators, and using a robust maximum likelihood estimator as implemented in M plus 8.4 (Muthén & Muthén, 1998–2017). We identified the optimal profile solution by specifying a single-profile model and then adding profiles in subsequent models (see Hofmans et al, 2020; McLarnon & O’Neill, 2018; Morin et al, 2020; for further applications of this procedure, see also O’Neill et al, 2016, 2018).…”
Section: Methodsmentioning
confidence: 99%
“…We used latent profile analysis (LPA) to identify latent profiles using the individual facets of the three work characteristics as indicators, and using a robust maximum likelihood estimator as implemented in M plus 8.4 (Muthén & Muthén, 1998–2017). We identified the optimal profile solution by specifying a single-profile model and then adding profiles in subsequent models (see Hofmans et al, 2020; McLarnon & O’Neill, 2018; Morin et al, 2020; for further applications of this procedure, see also O’Neill et al, 2016, 2018).…”
Section: Methodsmentioning
confidence: 99%
“…Following the definition of profiles, we investigated how the profile membership relates to covariates. According to Morin, McLarnon and Litalien (2020), covariates should only be included in the analysis after the optimal unconditional profile solution has been obtained. We used the Bolck, Croon and Hagenaars (BCH; Bolck, Croon & Hagenaars, 2004) method to investigate how the coping profiles relate to covariates (Asparouhov & Muthén, 2014;.…”
Section: Discussionmentioning
confidence: 99%
“…We executed a two-step analytical approach that reflects contemporary recommendations for mixture modeling (Asparouhov and Muthén, 2018;Morin et al, 2020). In the first step, we identified profiles of participants who share commonalities in their reflection scores using latent profile analysis (LPA) with a robust maximum likelihood estimator (MLR).…”
Section: Data Analyses and Rigormentioning
confidence: 99%
“…In so doing, LPA incorporates measurement error directly into the statistical model and quantifies via posterior probabilities the likelihood that cases belong to one profile rather than the other profiles. We used factor scores of seven latent reflection variables obtained from a correlated seven-factor measurement model as indicators of the LPA to control partially for measurement error (i.e., more weight to items that are most reliable; Skrondal and Laake, 2001 ) and maximize interpretability via standardization with a mean of zero and variance of one ( Morin et al, 2020 ). We calculated McDonald’s Omega (ω) as an estimate of internal reliability using Hayes and Coutts’s (2020) SPSS macro.…”
Section: Study 2: Coach Reflective Practice Profiles and Differences mentioning
confidence: 99%
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