Keywords
How to Cite
Abstract
In this paper Latent Class Analysis is applied on two different data sets. One of which is of elections of Karachi University Teacher Society (KUTS) in year 1993-1994. Members of two (Rightist and Mix) groups were competing for the post of President, Vice president, Secretary and Treasurer. The second data is about the study of parenting style on rearing children along with the factors self esteem and thoughts of suicide. From both the data set we will be able to come across the groups prevailing in our society and be able to assign conditional probability to individual, to which group they belong.
References
Anderson TW. An introduction to multivariate statistical analysis. 2nd ed. John Wiley: New York; 1984.
Lazarsfeld PF, Henry NW. Latent structure analysis. Boston: Houghton Mi_in; 1968.
Bartholomew DJ. Factor analysis for categorical data. Journal of the Royal Statistical Society, Series B (Methodological) 1980; 42(3): 293-321.
Goodman LA, Kruskal WH. Measures of association for cross classifications. J. Amer. Statist. Assoc. 1954; 49(268): 732-764. http://dx.doi.org/10.2307/2281536
Clogg CC, Goodman LA. Latent structural analysis of a set of multidimensional contingency tables. J. Amer. Statist. Assoc 1984; 79: 762-71.http://dx.doi.org/10.2307/2288706
Clogg CC. Some latent structure models for analysis of likert-type data. Social Science Research 1979; 8: 287-301. http://dx.doi.org/10.1016/S0049-089X(79)80001-0
McCutcheon AL. Latent class analysis. Newbury Park: Sage Publications; 1987.
Vermunt JK. Multilevel latent class models. Sociological Methodology 2003; 33: 213-39. http://dx.doi.org/10.1111/j.0081-1750.2003.t01-1-00131.x
Muthen BO. Beyond SEM: General latent variable modeling. Behaviormetrika 2002; 29(1): 81-117. http://dx.doi.org/10.2333/bhmk.29.81
Agresti A. Categorical data analysis. 2nd ed. New York: Wiley; 2002. http://dx.doi.org/10.1002/0471249688
Dempster AP, Laird NM, et al. Maximum likelihood from incomplete data via the em algorithm. Journal of the Royal Statistical Society, Series B (Methodological) 1977; 39 (1): 1-38.
Linzer DA, Lewis J. poLCA: An R package for polytomous variable latent class analysis. Journal of Statistical Software 2011; 42(10): 1-29.
Linzer DA, Lewis J. poLCA: Polytomous variable latent class analysis. R package version 1.3, 2011. (URL http://CRAN.R-project.org/package=poLCA).
R Development Core Team 2010. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL http://www.R-project.org.
Goodman L. The multivariate analysis of qualitative data: Interactions among Multiple Classifications. J. Amer. Statist. Assoc 1970; 65: 226-56. http://dx.doi.org/10.2307/2283589
Akaike H. 1973. Information theory as an extension of the maximum likelihood principle. Pages 267-281 in B. N. Petrov and F. Csaki, editors. Second international symposium on information theory. Akademiai Kiado, Budapest, Hungary.
Schwarz GE. Estimating the dimension of a model. Annals of Statistics 1978; 6(2): 461–4. http://dx.doi.org/10.1214/aos/1176344136
Buri, J.R. 1991. Parental authority questionnaire. University of St. Thomas, 2115 Simmit Avenue, St. Paul, MN 55105.
Baumrind D. Effects of authoritative parental control on child behavior, Child Development 1966; 37(4): 887-907. http://dx.doi.org/10.2307/1126611
Baumrind, D. Child care practices anteceding three patterns of preschool behavior. Genetic Psychology Monographs. 1967; 75(1): 43-88.
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.