Mixed Mode Latent Class Analysis: An Examination of Fit Index Performance for Classification

By Grant B. Morgan. 2015

181 citations recorded by the source corpus.

Subfields: Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/eadf76ce19825d0f00ae5c039e237548e3ab8163

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In Statistics and Probability

1. E. W. G. Moore
2. Kevin Delucchi
3. D. Hull
4. Sarah Ferguson
5. P. Sinha
6. C. Calfee

Citations in the corpus

• Latent profile analysis: A review and “how to” guide of its application within vocational behavior research (2020, unknown): “M a y b e s l i g h t l y s u p e r i o r t o s e l e c t t h e c o rr e c t n u m b e r o f p r o fi l e w i t h s m a ll s a m p l e s ( 500 ) o r w h e n r a r e p r o fi l e s a r e p r e s e n t ( M o r g a n , 2015 ) .”
• Ten Frequently Asked Questions About Latent Class Analysis (2018, unknown): “It has been shown through simulation studies of a variety of different types of mixture models (Depaoli, 2013; Morgan, 2015; Morovati, 2014; Tofighi & Enders, 2008; Tueller & Lubke, 2010) that small or “rare” classes are generally difficult to recover at small sample sizes and when class…”
• Motivation profiles at work: A self-determination theory approach (2016, unknown): “…ABIC, and the BLRT in choosing the model which best recovers the sample's true parameters (e.g., Henson, Reise, & Kim, 2007; McLachlan & Peel, 2000; Morgan, 2015; Nylund, Asparouhov, & Muthén, 2007; Peugh & Fan, 2012, 2013, 2015; Tein et al., 2013; Tofighi & Enders, 2008; Tolvanen, 2007; Yang,…”
• Finding latent groups in observed data: A primer on latent profile analysis in Mplus for applied researchers (2020, methodology): “SABIC has been supported as the most accurate information criteria index in simulation studies, particularly with smaller samples and low class separation (Kim, 2014; Morgan, 2015):”
• Practitioner’s Guide to Latent Class Analysis: Methodological Considerations and Common Pitfalls. (2020, background): “In the case where there is a mix of categorical and continuous variables and the continuous ones are normally distributed, Morgan (53) found that BIC worked best at identifying the correct number of classes, particularly, in instances when most of the variables are continuous.”