Performance of Factor Mixture Models as a Function of Model Size, Covariate Effects, and Class-Specific Parameters.

By Gitta H. Lubke, B. Muthén. 2007

411 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Genetics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/39c8f2eb01fb825dc28f92c283d9f0a9b368cc4c

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Citations in the corpus

Covariates and Mixture Modeling: Results of a Simulation Study Exploring the Impact of Misspecified Effects on Class Enumeration (2016, background): “Some recommendations suggest including covariates at the onset of the modeling process, arguing that the additional information provided by the covariates might aid in reliable class enumeration (Li & Hser, 2011; Lubke & Muthén, 2007; Muthén, 2002).”
Modeling Unobserved Heterogeneity Using Latent Profile Analysis: A Monte Carlo Simulation (2013, methodology): “Consistent with previous research (e.g., Lubke & Muthén, 2007), the default number of sets of starting values in Mplus for both LPA parameter estimation (i.e., using the Mplus ‘STARTS’ command) and likelihood ratio test computation (i.e., using the Mplus ‘LRTSTARTS’ command) were both doubled for…”
General Growth Mixture Analysis of Adolescents' Developmental Trajectories of Anxiety: The Impact of Untested Invariance Assumptions on Substantive Interpretations (2011, methodology): “…were compared on the basis of the information criteria and of LRTs.4 Direct inclusion of the predictors in the model is known to result in a more precise estimation of their effects (Bolck, Croon, & Hagenaars, 2004; Clark & Muthén, 2010) and in more accurate classifications (Lubke & Muthén, 2007).”
Statistical Power to Detect the Correct Number of Classes in Latent Profile Analysis (2013, background): “Lubke and colleagues (Lubke & Muthén, 2007; Lubke & Neale, 2006) investigated the relation between class separation and model performance.”
How Well Does Growth Mixture Modeling Identify Heterogeneous Growth Trajectories? A Simulation Study Examining GMM's Performance Characteristics (2012, background): “Entropy is known to be impacted by between-trajectory separation distance and within-trajectory variance (eg, Lubke & Muthen, 2007 ; Muthen, 2004 ).”
Latent Class Analysis With Distal Outcomes: A Flexible Model-Based Approach (2013, methodology): “These include latent profile analysis (where the indicators are continuous variables), growth mixture modeling (Muthén & Sheddon, 1999; Nagin, 2005), factor mixture modeling (Lubke & Muthén, 2007; McLachlan & Peel, 2000), and mixture regression modeling (Kaplan, 2005).”
A Multifoci Person-Centered Perspective on Workplace Affective Commitment: A Latent Profile/Factor Mixture Analysis (2011, background): “Thus, even though classifications that include covariates should be more accurate (Lubke & Muthén, 2007), the substantive interpretation of the latent profiles should remain unbiased by omission of the class predictors.”
Multiple-Group Analysis of Similarity in Latent Profile Solutions (2016, methodology): “Although the entropy should not be used to determine the optimal number of profiles (Lubke & Muthén, 2007), it provides a useful summary of classification accuracy.”
Stability, change, and implications of students’ motivation profiles: A latent transition analysis (2017, neutral): “The entropy should not be used to determine the optimal number of profiles (Lubke & Muthén, 2007), but it provides a useful summary of the classification accuracy (0 to 1), with higher values indicating more accuracy.”
Understanding the Determinants of Political Ideology: Implications of Structural Complexity (2014, positive): “To address this issue, we estimated a factor-mixture model (Lubke & Muthén, 2007): A model with a single continuous latent factor of ideology but with parameters that can differ across discrete latent classes.”
Performance of the entropy as an index of classification accuracy in latent profile analysis: A Monte Carlo simulation study (2017, neutral): “…的情况。尽管有研究发现指标数越多(指标数 = 6, 10, 15), Entropy 的结果越好(N = 250, 500, 1000; Tein et al., 2013), 但本研究发现这一规律只适用于小样本的情况(N = 50~100)。随着样本量的增大, 先是 8 个指标数的情况出现与这一结果的不一致现象, 然后到 12 个指标的组合情况。即中等以上的样本 (在本研究中为 N ≥ 500)与指标数交互影响分类精确性的表现, 所以不能忽略样本量的影响而单纯通过增多指标数来获得好的分类精确性。 4.2 类别距离对 Entropy…”
Ten Frequently Asked Questions About Latent Class Analysis (2018, positive): “…studies have shown that beyond sample size per se, the likelihood of accurately recovering the correct number of mixture classes depends heavily on class separation (Depaoli, 2013; Lubke & Muthén, 2007; Lubke & Neale, 2006; Tein et al., 2013; Tofighi & Enders, 2008; Tueller & Lubke, 2010).”
Disentangling Shape from Level Effects in Person-Centered Analyses: An Illustration Based on University Teachers’ Multidimensional Profiles of Effectiveness (2015, methodology): “Although the entropy should not be used to determine the optimal number of profiles (Lubke & Muthén, 2007), it provides a useful summary of classification accuracy.”
Teachers' engagement in professional learning: Exploring motivational profiles (2014, methodology): “When considering the inclusion of covariates in LPA models, Lubke and Muthén (2005) note that the assigned class membership of a”
Motivation profiles at work: A self-determination theory approach (2016, background): “However, the entropy should not be used in itself to determine the optimal number of profiles (Lubke & Muthén, 2007; Peugh & Fan, 2012, 2013, 2015; Tein, Coxe, & Cham, 2013).”