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Abstract
We believe that in last two decades perception regarding socio-religious values had been changed in our society. Survey has been carried out on “changes in social values and their acceptance” in year 2011. Respondents have asked 74 questions (marked on Likert-scale) regarding educational system, political and religious affiliations and their impact on social values. Among these we have selected only those questions related to socio-religious issues (based on of individual and collective perceptions about the prevailing standard of the society in comparison with Islamic standards). Similar surveys using the same questionnaire had had conducted in year 1994 and 2001. Respondents, at each time of survey, were young students (youth acquiring education) from different colleges (Karachi region) and Karachi University. Perception can be explained more appropriately through latent class model (LCM). Through LCM we can explore structures in the data in term of different opinion groups. The modeling is done on the selected set of similar questions from each year. Conditional probabilities for year 2011, 2001 and 1994 are then compared in search of presence of any difference of opinion between the respondents. It is observed that by the passage of time, due to the influence of the electronic media there is a change in the opinion about the values of the society among the youth. Although, there is a reduction in the proportion of “Dissatisfied group” within the society but negative perception is penetrating among our young generation specifically about Ulmah and Imam’s role and women’s due rights toward society.
References
Lazarsfeld PF, & Henry NW. Latent structure analysis. Boston: Houghton Mifflin; 1968.
Goodman LA. Exploratory latent structure analysis using both identifiable and unidentifiable models. Biometrika 1974a; 61(2): 215-231. https://doi.org/10.1093/biomet/61.2.215
Clogg CC. Latent class models: recent developments and prospects for the future. In Arminger G, Clogg CC, Sobel ME. (Eds.): Handbook of statistical modeling for the social and behavioral sciences, New York, NY: Plenum Press; 1995; 311-359. https://doi.org/10.1007/978-1-4899-1292-3_6
Aitkin M, Anderson D, Hinde J. Statistical modeling of data on teaching styles. Journal of the Royal Statistical Society. Series A (General): 1981; 144(4): 419-461. https://doi.org/10.2307/2981826
Bergan JR. Latent-class models in educational research: In Gordon EW. (ed.), Review of Research in Education 10, American Educational Research Association: Washington; DC; 1983. https://doi.org/10.2307/1167140
Dayton CM. Educational applications of latent class analysis. Measurement and Evaluation in Counseling and Development 1991; 24: 131-141.
Keel P, Fichter M, Quadflieg N, Bulik C, Baxter M, Thornton L, et al. Application of a latent class analysis to empirically define eating disorder phenotypes. Archives of General Psychiatry 2004; 61: 192-200. https://doi.org/10.1001/archpsyc.61.2.192
Eaton WW, Dryman A, Sorenson A, McCutcheon A. DSM-III major depressive disorder in the community: A latent class analysis of data from the NIMH epidemiologic catchment-area program. British Journal of Psychiatry 1989; 155: 48-54. https://doi.org/10.1192/bjp.155.1.48
Goldstein JM, Santangelo SL, Simpson JC, Tsuang MT. The role of gender in identifying subtypes of schizophrenia--A latent class analytic approach. Schizophrenia Bulletin 1990; 16: 263-275. https://doi.org/10.1093/schbul/16.2.263
Kendler KS, Eaves LJ, Walters EE, Neale MC, Heath AC, Kessler RC. The identification and validation of distinct depressive syndromes in a population-based sample of female twins. Archives of General Psychiatry 1996; 53: 391-399. https://doi.org/10.1001/archpsyc.1996.01830050025004
Kendler KS, Karkowski LM, Walsh D. The structure of psychosis: latent class analysis of probands from the roscommon family study. Arch Gen Psychiatry 1998; 55: 492-499. https://doi.org/10.1001/archpsyc.55.6.492
Maier W, Philipp M. Construct validity of the DSM-III and RDC classification of melancholia (endogenous depression). Journal of Psychiatric Research 1986; 20(4): 289-299. https://doi.org/10.1016/0022-3956(86)90032-4
Solomon A, Haaga DA, Arnow B. Is clinical depression distinct from sub threshold depressive symptoms? A review of the continuity issue in depression research. J Nerv Ment Dis 2001; 189(8): 498-506. https://doi.org/10.1097/00005053-200108000-00002
Sullivan PF, Kessler RC, Kendler KS. Latent class analysis of lifetime depressive symptoms in the national comorbidity survey. Am J Psychiatry 1998; 155(10): 1398-1406. https://doi.org/10.1176/ajp.155.10.1398
Young MA. Evaluating diagnostic criteria: A latent class paradigm. Journal of Psychiatric Research 1983; 17: 285-296. https://doi.org/10.1016/0022-3956(82)90007-3
Young MA, Scheftner WA, Klerman G, Andreasen NC, Hirschfeld RM. The endogenous sub-type of depression: a study of its internal construct validity. British Journal of Psychiatry 1986; 148: 257-267. https://doi.org/10.1192/bjp.148.3.257
Dillon WR, Kumar A. Latent Structure and Other Mixture Models in Marketing: An Integrative Survey and Overview: Bagozzi RP. Ed. Advanced methods of Marketing Research. Cambridge: Blackwell Publishers. 1994; p. 352- 388.
Rindskopf R, Rindskopf W. The value of latent class analysis in medical diagnosis. Statistics in Medicine 1986; 5: 21-27. https://doi.org/10.1002/sim.4780050105
Guarnera U, Varriale R. Estimation and Editing for Data from Different Sources. An Approach Based on Latent Class Model. Working Paper No. 32: UN/ECE Work Session on Statistical Data Editing. Budapest. 2015.
Oberski DL. Estimating Error Rates in an Administrative Register and Survey Questions Using a Latent Class Model. In: Biemer P, West B, Eckman S, Edwards B, Tucker C editors. Total survey error. New York: Wiley. 2015.
Pavlopoulos D, Vermunt JK. Measuring Temporary Employment. Do Survey or Register Data Tell the Truth? Survey Methodology 2015; 41: 197-214.
Everitt BS and Dunn G. Applied multivariate data analysis. London: Edward Arnold; 1991.
Agresti A. Categorical data analysis. 2nd ed. New York: John Wiley & Sons; 2002.
Goodman LA. Analyzing qualitative/categorical data. Log-linear models and latent structure analysis. MA: Abt Books: Cambridge; 1978.
Oberski D. Beyond the number of classes: Separating substantive from non-substantive dependence in latent class analysis. Advances in Data Analysis and Classification 2015; 10(2): 171-182. https://doi.org/10.1007/s11634-015-0211-0
Bartholomew DJ. Factor Analysis for Categorical Data. Journal of the Royal Statistical Society: Series B (Methodological). 1980; 42: 293-321.
McLachlan GJ, Krishnan T. The EM algorithm and extensions. N.J: Hoboken: Wiley-Interscience; 2008. https://doi.org/10.1002/9780470191613
Hartley HO. Maximum likelihood estimation from incomplete data. Biometrics 1958; 14: 174-194. https://doi.org/10.2307/2527783
Dempster AP, Laird NM, Rubin DB. Maximum-likelihood from incomplete data via the EM algorithm (with Discussion). Journal of Royal Statistical Society B 1977; 39: 1-38.
Wu CFJ. On the convergence properties of the EM algorithm. The Annals of Statistics 1983; 11(1): 95-103. https://doi.org/10.1214/aos/1176346060
Everitt BS, Hand D J. Finite mixture distributions. London: Chapman and Hall; 1981. https://doi.org/10.1007/978-94-009-5897-5
Linzer DA, Lewis JB. poLCA: An R package for polytomous variable latent class analysis. Journal of Statistical Software 2011; 42(10): 1-29. https://doi.org/10.18637/jss.v042.i10
UCLA Academic Technology Services. Statistical Consulting Group. Available from http://www.ats.ucla.edu/stat/stata/ seminars/count_presentation/count.htm.
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