Abstract
In this article we have tried to explore, "multiple intelligence" in the educated youth through questionnaire items by applying latent class models. A questionnaire consists of 50 questions. These questions have constructed in the light of Howard Gardner theory of multiple intelligence to explore "multiple intelligence". A survey was conducted on 399 adult students from different regions of Karachi. For statistical analysis we have selected three sets with seven variables, and one set with 4 variables each with binary response. On these four sets up to three classes latent class models were applied. The Probability of positive response (?iy) in each class were estimated by using E.M algorithm and interpreted the class as on the basis of ?iy values. By assessed goodness of fit latent classes/ groups were identified. Two class (two groups of people) model was found in all four data sets. A group (class) consists of the people who think that they have strong verbal expressions abilities, effectively use language to express himself/herself theoretically and poetically, they have good ability to recognize musical pitches, tones and rhythms, we may call this class as "self competence and self esteem" as "musically talented" as "socialize" (having high interpersonal ability).
References
Gardner H. Frames of Mind: The Theory of Multiple Intelligences. New York 1983.
Spearman C. General intelligence, objectively determine and measured. An J Psycol 1904; 15: 201-93.
Bartholomew DJ. Latent Variable Models and Factor Analysis. Monograph no 40 Oxford University New York 1987.
Jansen PGW, Roskam EE. Latent trait models and dichotomization of graded responses. Psychometrica 1986; 51: 69-91. http://dx.doi.org/10.1007/BF02294001
Marcouledes GA, Schumacker RE. New development and Techniques In Structural equation modeling 2001.
Lazarsfeld PF, Henry NW. Latent structure Analysis. New York: Houghton-Miffin 1968.
Clogg CC. Latent class models for measuring. In Langeheine R, Rost J, Eds. Latent trait and latent class models. New York: Plenum 1981.
Hagenars JA. Categorical Longitudinal Data-Loglinear Analysis of Panel, Trend and Cohort Data. Newbury Park: Sage 1990.
Vermunt JK, Magidson J. Latent class cluster analysis. In Hagenaars JA, McCutcheon AL. Eds. Applied latent class models. Cambridge, UK: Cambridge University Press 2002; pp. 89-106. http://dx.doi.org/10.1017/CBO9780511499531.004
Rabe-Hesketh S, Skrondal A, Zheng X. Multilevel structural equation modeling. In Handbook of latent variable and related models. Lee SY, Ed. Elsevier, Msterdam 2007; pp. 209-227.
Mavridis I, Moustaki Irini. The forward search algorithm for detecting aberrant response patterns in factor analysis for binary data. J Computat Graph Statist 2009; 18(4): 1016-34. ISSN 1061-8600
Giorgio E, Montanari M, Ranalli G, Eusebi P. Latent variable modeling of disability people aged 65 or more journal of statistical method and applications 2010; 20(1): 49-63. Dol:10,1007/10260-010-0148-6
Demirhan H. Latent Class Analysis for Models with Error of Measurement Using Log-Linear Models and An Application to Women’s Liberation Data Journal of Data Science 2011; 9(2011): 43-54.
Goodman LA, Clogg CC. The analysis of cross-classified data having ordered categories Harvard University Press, Cambridge, Mass 1984.
Waller GN. LCA 1.1: An R Package for Exploratory Latent Class Analysis journal of the applied Psychological Measurement 2004; 28(2): 141-42.
Angelou M. 2007. Multiple Intelligence Questionnaire-Teacher vision.com (Available from http://www.teachervison.fen.com/parents-and-school/intelligence/3678.html)
Linzer AD, Levis. R development core Team, 2007 poLCA: Polytomous Variable Latent Class Analysis Version 1.1 2007.