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Latent Class Analysis in M plus Version 3

Latent Class Analysis in M plus Version 3. Karen Nylund Social Research Methods Graduate School of Education & Information Studies knylund@ucla.edu. Overview of Session. General description of Latent Class Analysis (LCA) within a hypothetical example

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Latent Class Analysis in M plus Version 3

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  1. Latent Class Analysis in Mplus Version 3 Karen Nylund Social Research Methods Graduate School of Education & Information Studies knylund@ucla.edu

  2. Overview of Session • General description of Latent Class Analysis (LCA) within a hypothetical example • Two examples of LCA analysis using Mplus Version 3 • Anti-Social Behavior • Diabetes Diagnosis • Extensions of the LCA models • Resources and References

  3. Hypothetical Example: Identifying effective teachers • Setting: Unsure how to identify an effective teacher • Possible Indicators: • Credential or Not? • Promotes critical thinking • Reflective • Professional Development (P.D.)

  4. What would the data look like?

  5. Possible research questions: • Are there specific characteristics that identify an effective teacher? • Given known ideas of what an effective teacher is, what characteristics are important indicators? • Are there background characteristics of the teachers that help classify them as effective?

  6. What could LCA tell us? • To find groups of teacher that are similar based on observed characteristics • Identify and accurately enumerate the number of groups of teachers • Identify characteristics that indicate groups well • Estimate the prevalence of the groups • Classify teachers into classes

  7. The LCA Model • Observed Continuous (y’s) or Categorical Items (u’s) • Categorical Latent Class Variable (c) • Continuous or Categorical Covariates (x) Y1 Y2 Y3 Yp . . . C X

  8. How is this modelingprocess conducted? • Run through models imposing different numbers of classes • Estimation via the EM algorithm • Start with random split of people into classes. • Reclassify based on a improvement criterion • Reclassify until the best classification of people is found.

  9. Model Fit BIC and AIC X2 Statistic Lo-Mendell-Rubin Test (Tech 11) Standardized Residuals (Tech 10) Model Usefulness Substantive Interpretation Classification Quality Classification Tables Entropy Evaluating the Model

  10. 1st Data Example: Anti-Social Behavior • National Longitudinal Survey of Youth (NLSY) • Respondent ages between 16 and 23 • Background information: age, gender and ethnicity • N=7,326 17 antisocial dichotomously scored behavior items: • Damaged property • Fighting • Shoplifting • Stole <$50 • Stole >$50 • Use of force • Seriously threaten • Intent to injure • Use Marijuana • Use other drug • Sold Marijuana • Sold hard drugs • ‘Con’ somebody • Stole an Automobile • Broken into a building • Held stolen goods • Gambling Operation

  11. Anti Social Behavior Example Damage Property Fighting Shoplifting Stole <$50 Gambling . . . Male C Race Age

  12. Antisocial behavior Example in Mplus Version 3

  13. ASB Item Probabilities

  14. Relationship between class probabilities and covariate (AGE94) Females Males

  15. ASB Example Conclusions • Summary of four classes: • Property Offense Class (9.8%) • Substance Involvement Class (18.3%) • Person Offenses Class (27.9%) • Normative Class (44.1%) • Classification Table:

  16. 2nd Example: Diabetes Data • Three continuous variables: • Glucose (y1) • Insulin (y2) • SSPG (Steady-stage plasma glucose, y3) • N=145 • Data from Reaven and Miller (1979)

  17. Diabetes Example Glucose Insulin SSPG C

  18. Diabetes Example in Mplus Version 3

  19. Diabetes Results

  20. Diabetes Results

  21. Diabetes Example Conclusions • Summary of Three classes: • Class 1: Overt Diabetes group (52%) • Class 2: Chemical Diabetes group (19.6%) • Class 3: Normal Group (28.4%) • Classification Table:

  22. Extensions of the LCA Model • Confirmatory LCA • Constraints on Model Parameters • Multiple LCA variables • Multiple Measurement Instruments • Latent Transition Analysis • Multi-level LCA • Use Monte Carlo to explore sample size issues

  23. Resources • Mplus User Guide • http://www.statmodel.com • ATS Mplus Support • http://www.ats.ucla.edu/stat/mplus/ • http://www.ats.ucla.edu/stat/seminars/ed231e/ • Applied Latent Class Analysis, Edited by Hagenaars and McCutcheon (‘02)

  24. References • Hagenaars, J.A & McCutcheon, A. (2002).  Applied latent class analysis.  Cambridge: Cambridge University Press. • Muthén, B. (2001). Latent variable mixture modeling. In G. A. Marcoulides & R. E. Schumacher (eds.), New Developments and Techniques in Structural Equation Modeling (pp. 1-33). Lawrence Erlbaum Associates. (#86) • Muthén, L. & Muthén, B. (1998-2004). Mplus user’s guide. Los Angeles, CA: Muthén & Muthén. • Muthén, B. & Muthén, L. (2000). Integrating person-centered and variable-centered analysis: growth mixture modeling with latent trajectory classes. Alcoholism: Clinical and Experimental Research, 24, 882-891. • Reaven, G.M., & Miller., R.G.(1979). “An attempt to define the nature of chemical diabetes using multidimensional analysis,” Diabetologica, 16, 17-27.

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