Latent Class Analysis and Finite Mixture Modeling

By Katherine E. Masyn. 2013

1,377 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/f1cfedf6757d93c19310914e6d834776298de605

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In Artificial Intelligence

Citations in the corpus

Ten Frequently Asked Questions About Latent Class Analysis (2018, positive): “In this case, we recommend that fit for these candidate models be presented and that the solutions be studied closely (Masyn, 2013; B. O. Muthén, 2003; Ram & Grimm, 2009).”
Latent Class Analysis: A Guide to Best Practice (2020, methodology): “In addition to evaluating fit, researchers need to review classification diagnostics (Masyn, 2013).”
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): SABICðKÞ ¼ 2LðKÞ þ vðKÞlnn ðnþ 2Þ=24 ð7Þ Finally, AIC is an inconsistent model fit measure as it does not have strong ”
Measurement Invariance and Differential Item Functioning in Latent Class Analysis With Stepwise Multiple Indicator Multiple Cause Modeling (2017, methodology): “…random start values) and examining overall model convergence rates across the sets of random starts, the replication rate of the best maximum log likelihood values, the size (membership frequency and percent) of the smallest class, and the condition number (see Masyn, 2013, for more information).”
Latent class analysis and latent profile analysis (2016, methodology): “However, although LCA and LPA could be termed to be largely person centered in orientation, it has been argued that person-centered and variable-centered methods are rarely independent of each other (Masyn, 2013).”
Covariates and Mixture Modeling: Results of a Simulation Study Exploring the Impact of Misspecified Effects on Class Enumeration (2016, methodology): “Current best practices recommend the simultaneous consideration of multiple fit indexes (along with substantive criteria, e.g., interpretability) to make a final model selection regarding the number of latent classes (Masyn, 2013; Muthén, 2003).”
Understanding Why Consumers Don't Skip Pre-Roll Video Ads (2017, methodology): “As shown in Table 2, our Bayesian information criterion (BIC) results suggested adopting a three-group solution (Collins and Lanza 2010; Masyn 2013; Vermunt and Magidson 2005, 2013).”
Measuring well-being: A comparison of subjective well-being and PERMA (2018, background): “One latent profile was specified, and in subsequent iterations, the number of profiles increased by one until the model fails to converge or failed to replicate across starting values3 (Masyn, 2013; Nylund, Asparouhov, & Muthén, 2007).”
Seeing relationships through the lens of psychological contracts: the structure of consumer service relationships (2017, methodology): “To verify the stability of the class structure, we randomly split our full sample and used subsample A (N = 362) for calibration and subsample B for validation (N = 338) (Masyn 2013).”
Exploring patterns of Latino/a children's school readiness at kindergarten entry and their relations with Grade 2 achievement (2013, positive): “LCA also allowed for the inclusion of predictors to help understand who comprises the emergent classes, as well as for additional tests of whether these profiles predicted subsequent academic outcomes, such as performance or achievement measures (Masyn, 2012).”
Tweeting with the Stars: Automated Text Analysis of the Effect of Celebrity Social Media Communications on Consumer Word of Mouth (2019, positive): “We started by comparing the relative fit of each solution based on the Bayesian Information Criterion (henceforth BIC) statistic (Masyn 2013).”
Integrating the light and dark sides of student engagement using person-oriented and situation-specific approaches (2016, methodology): “…of model fit and parsimony) preferred over models with larger values for these criteria; 3) the Bayes Factor and Correct Model Probability (see Masyn, 2013) were used to identify the most likely correct model among the set of estimated models; and 4) models with better overall classification…”
Person-Centered Methodologies in the Organizational Sciences (2018, background): “…specific substantive areas (e.g., Mäkikangas & Kinnunen, 2016; Mäkikangas, Kinnunen, Feldt, & Schaufeli, 2016; Meyer & Morin, 2016) as well as of introductory papers aiming to review best practices in the estimation of person-centered models (e.g., Masyn, 2013; Meyer & Morin, 2016; Morin, 2016).”