Latent class/profile analysis

Applied Quantitative Analysis in Education and the Social Sciences

Volumn , Issue , 2013, Pages 304-328

  Samuelsen, Karen ^a   Raczynski, Katherine ^b  

^a PIEDMONT COLLEGE   (United States)

^b UNIVERSITY OF GEORGIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84876038472     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.4324/9780203108550     Document Type: Chapter

References (60)

1. Akaike, H. (1987). Factor analysis and AIC. Psychometrika, 52, 317–332.
2. Albert, P. S., McShane, L. M., Shih, J. H., & the U.S. National Cancer Institute (NCI) Bladder Tumor Marker Network (2001). Latent class modeling approaches for assessing diagnostic error without a gold standard: With applications to p53 immunohistochemical assays in bladder tumors. Biometrics, 57(2), 610–619.
3. Asparouhov T., & Muthén, B. (2007). Multilevel mixture models. In C. R. Hancock & K. M. Samuelsen (Eds.), Advances in latent variable mixture models (pp. 27–51). Charlotte, NC: Information Age.
4. Bartholomew, D. (1987). Latent variable models and factor analysis. London, England: Charles Griffi n.
5. Bolck, A., Croon, M. A., & Hagenaars, J. A. P. (2004). Estimating latent structure models with categorical variables: One-step versus three-step estimators. Political Analysis, 12, 3–27.
6. Bozdogan, H. (1987). Model selection and Akaike’s information criterion (AIC): The general theory and its analytical extensions. Psychometrika, 52, 345–370.
7. Celeux, G., & Soromenho, G. (1996). An entropy criterion for assessing the number of clusters in a mixture model. Journal of Classifi cation, 13, 195–212.
8. Clark, S. & Muthén, B. (2009). Relating latent class analysis results to variables not included in the analysis. Manuscript submitted for publication. Retrieved from http://www.statmodel.com/ papers.shtml
9. Clogg, C. C. (1979). Some latent structure models for the analysis of Likert-type data. Social Science Research, 8, 287–301.
10. Clogg, C. C., & Goodman, L. A. (1984). Latent structure analysis of a set of multi-dimensional contingency tables. Journal of the American Statistical Association, 79, 762–771.
11. Collins, L. M., Fidler, P. L., Wugalter, S. E., & Long, J. (1993). Goodness-of-fi t testing for latent class models. Multivariate Behavioral Research, 28, 375–389.
12. Dayton, C. M. (1999). Latent Class Scaling Analysis (Quantitative Applications in the Social Sciences, Vol. 126). Thousand Oaks, CA: Sage.
13. Dayton, C. M., & Macready, G. B. (1976). A probabilistic model for validation of behavioral hierarchies. Psychometrika, 41, 189–204.
14. Dayton, C. M., & Macready, G. B. (1988). Concomitant-variable latent class models. Journal of the American Statistical Association, 83, 173–178.
15. Dayton, C. M., & Scheers, N. J. (1997). Latent class analysis of survey data dealing with academic dishonesty. In J. Rost & R. Langeheine (Eds.) Application of latent trait and latent class models in the social sciences (pp. 172–180). Münster, Germany, and New York, NY: Waxmann.
16. Finch, W. H., & Bronk, K. C. (2011). Conducting confi rmatory latent class analysis using Mplus. Structural Equation Modeling, 18, 132–151.
17. Flaherty, B. P. (2007). Examining contingent discrete change over time with associative latent transition analysis. In G. R. Hancock, & K. M. Samuelsen (Eds.), Advances in Latent Variable Mixture Models (pp. 299–316). Charlotte, NC: Information Age.
18. Formann, A. K. & Kohlmann, T. (1996). Latent class analysis in medical research. Statistical Methods in Medical Research, 5(2), 179–211.
19. Francis, B. (2008, August). Latent class and latent transition analysis [PDF document]. Lecture notes from a mini-course at the NCRM Research Methods Festival. Retrieved from http://www. maths.lancs.ac.uk
20. Galindo-Garre, F., & Vermunt, J. K. (2006). Avoiding boundary estimates in latent class analysis by Bayesian posterior mode estimation. Behaviormetrika, 33, 43–59.
21. Gelfand, A. E., & Solomon, H. (1974). Modeling jury verdicts in the American legal system. Journal of the American Statistical Association, 69, 32–37.
22. Goodman, L. A. (1974). Exploratory latent structure analysis using both identifi able and unidentifi - able models. Biometrika, 61, 215–231.
23. Heinen, T. (1996). Latent class and discrete trait models (Advanced Quantitative Techniques in the Social Sciences Series 6). Thousand Oaks, CA: Sage.
24. Henry, K. L., & Muthén, B. (2010). Multilevel latent class analysis: An application of adolescent smoking typologies with individual and contextual predictors. Structural Equation Modeling, 17(2): 193–215. doi: 10.1080/10705511003659342
25. Herman, K. C., Ostramder, R., Walkup, J. T., Silva, S. G., & March, J. S. (2007). Empirically derived subtypes of adolescent depression: Latent profi le analysis of co-occurring symptoms in the Treatment for Adolescents with Depression Study (TADS). Journal of Counseling and Clinical Psychology, 75, 715–728. doi: 10.1037/0022-006X.75.5.716
26. Huh, J., Riggs, N. R., Spruijt-Metz, D., Chou, C-P., Huang, Z., & Pentz, M. (2011). Identifying patterns of eating and physical activity in children: A latent class analysis of obesity risk. Obesity, 19, 652–658.
27. IBM Corporation. (2012). IBM SPSS Statistics for Windows (Version 21). Armonk, NY: Author.
28. Jansen, B. R. J., & van der Maas, H. L. J. (1997). Statistical test of the rule assessment methodology by latent class analysis. Developmental Review, 17, 321–357.
29. Kankaras, M., Moors, G. B. D., & Vermunt, J. K. (2010). Testing for measurement invariance with latent class analysis. In E. Davidov, P. Schmidt, & J. Billiet (Eds.), Cross-cultural analysis. Methods and applications (pp. 359–384). New York, NY: Taylor & Francis Group/Routledge.
30. Kendler, K. S., Karkowski, L. M., & Walsh, D. (1998). The structure of psychosis: Latent class analysis of probands from the Roscommon Family Study. Archives of General Psychiatry, 55, 492–499.
31. Lazarsfeld, P. F., & Henry, N. W. (1968). Latent structure analysis. Boston, MA: Houghton Miffl in.
32. Lindsay, B., Clogg, C. C, (1991). Semiparametric estimation in the Rasch model and related exponential response models, including a simple latent class model for item analysis. Journal of the American Statistical Association, 86, 96–107.
33. Lo, Y., Mendell, N., & Rubin, D. (2001). Testing the number of components in a normal mixture. Biometrika, 88, 767–778.
34. McCutcheon, A. L. (1987). Latent class analysis. Beverly Hills, CA: Sage.
35. McCutcheon, A. L. (2002). Basic concepts and procedures in single-and multiple-group latent class analysis. In J. A. Hagenaars & A. L. McCutcheon (Eds.), Applied latent class analysis (pp. 57– 88). Cambridge, England: Cambridge University Press.
36. McLachlan, G., & Peel, D. (2000). Finite mixture models. New York, NY: Wiley.
37. Muthén, B. O. (2001). Latent variable mixture modeling. In G. A. Marcoulides & R. E. Schumacker (Eds.), New developments and techniques in structural equation modeling (pp. 1–34). Mahwah, NJ: Erlbaum.
38. Muthén, B. O. (2007). Latent variable hybrids: Overview of old and new models. In G. R. Hancock, & K. M. Samuelsen (Eds.), Advances in latent variable mixture models (pp. 1–24). Charlotte, NC: Information Age.
39. Muthén, B., & Asparouhov, T. (2006). Growth mixture analysis: Models with non-Gaussian random effects. In G. Fitzmaurice, M. Davidian, G. Verbeke, & G. Molenberghs (Eds.), Advances in longitudinal data analysis (pp. 143–166). Boca Raton, FL: Chapman & Hall/CRC Press.
40. Muthén, L. K., & Muthén, B. O. (2010). Mplus (6th Ed.) [Computer Software]. Los Angeles, CA: Muthén & Muthén.
41. Niederkrotenthaler, T., Voracek, M., Herberth, A., Till, B., Strauss, M., Etzersdorfer, E., Sonneck, G. (2010). Role of media reports in completed and prevented suicide: Werther v. Papageno effects. British Journal of Psychiatry, 197, 234–243. doi: 10.1192/bjp.bp.109.074633
42. Norman, G. J., Adams, M. A., Kerr, J., Ryan, S., Frank, L. D., & Roesch, S. C. (2010). A latent profi le analysis of neighborhood recreation environments in relation to adolescent physical activity, sedentary time, and obesity. Journal of Public Health Management and Practice, 16, 411–419. doi: 10.1097/PHH.0b013e3181c60e92
43. Nylund, K., Asparouhov, T., & Muthén, B. O. (2007). Deciding on the number of classes in latent class analysis and growth mixture modeling: A Monte Carlo simulation study. Structural Equation Modeling, 14(4), 535–569.
44. Orpinas, P., Murray, N., & Kelder, S. (1999). Parental infl uences on students’ aggressive behavior and weapon-carrying. Health Education and Behavior, 26(6), 774–787.
45. Owen, A. L., & Videras, J. (2009). Reconsidering social capital: A latent class approach. Empirical Economics, 37(3), 555–582.
46. Pastor, D. A., Barron, K. E., Miller, B. J., & Davis, S. L. (2007). A latent profi le analysis of college students’ achievement goal orientation. Contemporary Educational Psychology, 32, 8–47. doi: 10.1016/j.cedpsych.2006.10.003
47. Pierce, C. L., & Osmond, C. P. (1999). From workplace attitudes and values to a global pattern of nations: An application of latent class modeling. Journal of Management, 25, 759–778.
48. Ramaswamy, V., Desarbo, W. S., Reibstein, D. J., & Robinson, W. T. (1993). An empirical pooling approach for estimating marketing mix elasticities with PIMS data. Marketing Science, 12(1), 103–124.
49. Reboussin, B. A., Song, E.-Y., Shrestha, A., Lohman, K. K., & Wolfson, M. (2006). A latent class analysis of underage problem drinking: Evidence from a community sample of 16–20 year olds. Drug and Alcohol Dependence, 83, 199–209. doi: 10.1016/j.drugalcdep.2005.11.013
50. Samuelsen, K. M., & Dayton, C. M. (2010). Latent class analysis. In G. R. Hancock & R. O. Mueller (Eds.), The reviewer’s guide to quantitative methods in the social sciences (pp. 173–184). New York, NY: Routledge.
51. Schwartz, G. (1978). Estimating the dimension of a model. The Annals of Statistics, 6, 461–464.
52. Sclove, L. (1987). Application of model-selection criteria to some problems in multivariate analysis. Psychometrika, 52, 333–343.
53. Skrondal, A., & Rabe-Hesketh, S. (2004). Generalized latent variable modeling: Multilevel, longitudinal, and structural equation models. Boca Raton, FL: Chapman & Hall/CRC.
54. Uebersax, J. (2009). A brief study of local maximum solutions in latent class analysis. Retrieved from http://john-uebersax.com/stat/local.htm
55. Uebersax, J., & Grove, W. M. (1990). Latent class analysis of diagnostic agreement. Statistics in Medicine, 9(5), 559–572.
56. Vaughn, M. G., DeLisi, M., Beaver, K. M., & Howard, M. O. (2008). Toward a quantitative typology of burglars: A latent profi le analysis of career offenders. Journal of Forensic Science, 53(6), 1387–1392.
57. Vermunt, J. K. (2003). Multilevel latent class models. Sociological Methodology, 33, 213–239.
58. Vermunt, J. K. (2008). Latent class and fi nite mixture models for multilevel data sets. Statistical Methods in Medical Research, 17(1), 33–51.
59. Vermunt, J. K., & Magidson, J. (2008). Latent GOLD. Syntax user’s guide: Manual for Latent GOLD 4.5 syntax module. Belmont, MA: Statistical Innovations.
60. Wedel, M., & Kamakura, W. A. (2000). Market segmentation: Conceptual and methodological foundations (2nd ed.). Dordrecht, the Netherlands: Kluwer.