Multiple-Group Analysis of Similarity in Latent Profile Solutions

By Alexandre J. S. Morin, John P. Meyer, Jordane Creusier, Franck Biétry. 2016

623 citations recorded by the source corpus.

Subfields: Applied Psychology, Experimental and Cognitive Psychology, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/8dbcce4841004b335b758ef46e52fdb11a3c0193

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Applied Psychology

1. Richard M. Ryan
2. J. Reeve
3. Marylène Gagné
4. Nicolas Gillet
5. Robert J. Vallerand
6. Alexandre J. S. Morin
7. Nikos Ntoumanis
8. Edward L. Deci

In Experimental and Cognitive Psychology

1. Marylène Gagné
2. Richard M. Ryan
3. J. Reeve
4. Alexandre J. S. Morin
5. Robert J. Vallerand
6. Nicolas Gillet
7. John P. Meyer
8. Nikos Ntoumanis

In Sociology and Political Science

1. Alexandre J. S. Morin
2. Marylène Gagné
3. Émilie Lapointe
4. Nicolas Gillet
5. Russell E. Johnson
6. Margarita Olivera‐Aguilar
7. Samuel Rikoon
8. John P. Meyer

Citations in the corpus

• Fuzzy Profiles (2018, background): “Likewise, although it is clear that larger sample sizes increase the ability to detect more complex profile solutions in LPA, there is limited evidence surrounding optimal sample size (Marsh, Lüdtke, Trautwein, & Morin, 2009; Morin et al., 2016).”
• Latent profile analysis: A review and “how to” guide of its application within vocational behavior research (2020, methodology): “…introduced approach is to apply multiple-groups LPA to simultaneously evaluate the similarity of latent profiles across groups based on statistical fit parameters (Morin, Meyer, Creusier, & Biétry, 2016), which was done by two articles in our review (Gillet et al., 2018; Perera & McIlveen, 2018).”
• Assessing Measurement Invariance in Multiple-Group Latent Profile Analysis (2018, methodology): “Although there are other fit statistics used in the comparison of models with different numbers of classes, when testing for invariance information criteria such as the AIC, BIC, and aBIC are most commonly used (Morin et al., 2016).”
• Evidence of a continuum structure of academic self-determination: A two-study test using a bifactor-ESEM representation of academic motivation (2017, methodology): “These guidelines have been established for CFA, and used in previous ESEM applications (e.g., Marsh et al., 2009, 2014; Morin et al., 2013, 2016a).”
• Stability, change, and implications of students’ motivation profiles: A latent transition analysis (2017, methodology): “, measurement invariance) better than scale scores (Morin et al., 2016).”
• A person‐centered approach to commitment research: Theory, research, and methodology (2016, methodology): “A first involves the stability of the profile structure within a sample, over time (i.e.,within-sample stability), and can be assessed using the same set of procedures described previously for the assessment of profile similarity (Morin et al., 2016).”
• Emotional labour profiles: Associations with key predictors and outcomes (2018, methodology): “Factor scores were saved from these measurement models and used as inputs for the main analyses (for details on the advantages of factor scores, see Meyer & Morin, 2016; Morin et al., 2016).”
• Finding latent groups in observed data: A primer on latent profile analysis in Mplus for applied researchers (2020, background): “, 2017), employees’ commitment mindsets (Morin et al., 2016), adolescents’ coping strategies Aldridge & Roesch, 2008), and school climate (Eck, Johnson, Bettencourt, & Johnson, 2017) are examples of other psychological and behavioral phenomena that have been examined.”