Analysis of Latent Class Models in Economic Research

• Autorzy: Justyna Brzezińska

Warianty tytułu

Analiza modeli zmiennych ukrytych w badaniach ekonomicznych

• Języki publikacji: EN

Abstrakty

EN

Latent variable models are used and applied in many areas of the social and behavioral sciences. The increasing availability of computer packages for fitting such models makes latent variable models popular, known and applied in many scientific areas. Latent variable models have a very wide range of applications, especially in the presence of repeated observations, longitudinal data, and multilevel data. The basic model postulates an underlying categorical latent variable; within any category of the latent variable the manifest or observed categorical variables are assumed independent of one another (the axiom of conditional independence). The observed relationships between the manifest variables are thus assumed to result from the underlying classification of the data produced by the categorical latent variable. In this paper we present the theoretical and methodological aspects of latent variable models, as well as their application in R software in the field of economic research.(original abstract)

Modele zmiennych ukrytych są z powodzeniem stosowane w wielu obszarach badawczych, szczególnie w naukach społecznych. Wzrost dostępności nowoczesnych programów komputerowych pozwalających na budowę oraz dopasowanie złożonych modeli statystycznych sprawił, że modele te stają się coraz bardziej popularne w wielu innych dyscyplinach naukowych. Modele oparte na zmiennych ukrytych mają szerokie zastosowanie, szczególnie w przypadku analizy danych panelowych czy też wielopoziomowych. Podstawowy model bazuje na założeniu, że zmienna ukryta ma charakter jakościowy, a dla każdej kategorii zmiennej ukrytej jakościowe zmienne obserwowalne są niezależne od siebie (aksjomat lokalnej niezależności). W niniejszym artykule przedstawione zostaną teoretyczne i metodologiczne aspekty modeli opartych na zmiennych ukrytych wraz z ich zastosowaniem w naukach ekonomicznych przy wykorzystaniu programu R.(abstrakt oryginalny)

Słowa kluczowe

EN

Latent class analysis; Data analysis; Programing languages

PL

Analiza klas ukrytych; Analiza danych; Języki programowania

• Czasopismo: Ekonometria / Uniwersytet Ekonomiczny we Wrocławiu
• Rocznik: 2016
• Numer: nr 4 (54)
• Strony: 36--47

Twórcy

autor

Justyna Brzezińska

• University of Economics in Katowice, Poland

Bibliografia

• Agresti A., 2002, Categorical Data Analysis, second edition, John Wiley & Sons, Hoboken.
• Andersen E.B., 1982, Latent structure analysis: a survey, Scand. J. Statist. 9, pp. 1-12.
• Anderson T.W., 1954, On estimation of parameters in latent structure analysis, Psychometrika, 19, pp. 1-10.
• Carroll R., Ruppert D., Stefanski L., Crainiceanu C., 2006, Measurement error in nonlinear models: a modern perspective, vol. 105, CRC Monographs on Statistics & Applied Probability, Chapman & Hall, Boca Raton, FL.
• Chung H., Anthony J.C., Schafer J.L., 2011, Latent class profile analysis: an application to stage sequential processes in early onset drinking behaviours, Journal of the Royal Statistical Society: Series A, 174, s. 689-712.
• Clogg C.C., 1981, Latent structure models of mobility, American Journal of Sociology, 86, pp. 836-868.
• Clogg C.C., Sawyer D.O., 1981, A Comparison of Alternative Models for Analyzing the Scalability of Response Patterns, [in:] S. Leinhardt (ed.), Sociological Methodology 1981, Jossey-Bass, San Francisco, s. 240-280.
• Coull B.A., Agresti A., 1999, The use of mixed logit models to reflect heterogeneity in capture-recapture studies, Biometrics, 55, pp. 294-301.
• Dempster A.P., Laird N.M., Rubin D.B., 1977, Maximum likelihood from incomplete data via the EM algorithm, J. Royal Statist. Soc. B, 39, pp. 1-38.
• Edwards J.R., Bagozzi R.P., 2000, On the nature and direction of relationships between constructs and measures, Psychol. Methods, 5, pp. 155-74.
• Gibson W.A., 1955, An extension of Anderson's solution for the latent structure equations, Psychometrika, 20, pp. 69-73.
• Green B.F., 1951, A general solution for the latent class model of latent structure analysis, Psychometrika, 16, pp. 151-166.
• Haberman S.J., 1974, Log-linear models for frequency tables derived by indirect observation: maximum likelihood equations, Annals of Statistics, 2, pp. 911-924.
• Haberman S.J., 1976, Generalized residuals for log-linear models, Proceedings of the ninth International Biometrics Conference, vol. 1 Biometrics Society, Raleigh, NC, pp. 104-172.
• Haberman S.J., 1979, Analysis of Qualitative Data, vol. 2: New Developments, Academic Press, New York.
• Hägglund G., 2001, Milestones in the History of Factor Analysis, [in:] Structural Equation Modeling: Present and Future, ed. R. Cudeck, S. du Toit, D. Sörbom, IL: Scientific Software Int, Lincoln- wood.
• Hougaard P., 2000, Analysis of Multivariate Survival Data, Springer, New York.
• Koopmans T.C., 1951, An Analysis of Production as Efficient Combination of Activities, [in:] Activity Analysis of Production and Allocation, T.C. Koopmans (ed.), Cowles Commission for Research in Economics, Monograph no. 13, New York.
• Lazarsfeld P.F., Dudman J., 1951, The General Solution of the Latent Class Case, [in:] P.F. Lazarsfeld (ed.), The Use of Mathematical Models in the Measurement of Attitudes, RAND Corporation, Santa Monica.
• Lo Y., Mendell N., Rubin D., 2001, Testing the number of components in a normal mixture, Biometrika, 88, pp. 767-778.
• Madansky A., 1959, The fitting of straight lines when both variables are subject to error, Journal of the American Statistical Association, 54, pp. 173-205.
• Madansky A., 1960, Determinantial methods in latent class analysis, Psychometrica, 25, pp. 183-198.
• McDonald R.P., 1999, Test Theory: A Unified Treatment, Erlbaum, Mahwah, NJ.
• McHugh R.B.,1956, Efficient estimation and local identification in latent class analysis, Psychometrika, 21, pp. 331-347.
• Neale M.C., Cardon L.R., 1992, Methodology for Genetic Studies of Twins and Families, Kluwer Aca- demic Publishers, Dordrecht.
• Rue H., Held L., 2005, Gaussian Markov Random Fields: Theory and Applications, vol. 104 of Monographs on Statistics and Applied Probability, Chapman & Hall, London.
• Shen J., 2009, Latent class model or mixed logit model? A comparison by transport mode choice data, Applied Economics, 41, pp. 2915-2924.
• Sobel M.E., 1994, Causal inference in latent variable models, See von Eye and Clogg, pp. 3-35.
• Sutton S., 2000, Interpreting cross-sectional data on stages of change, Psychology & Health, 15, pp. 163-171.
• Train K.E., 2003, Discrete Choice Methods with Simulation, Cambridge University Press, Cambridge.
• Verbeke G., Molenberghs G., 2000, Linear Mixed Models for Longitudinal Data, Springer.
• Vermunt J., 1999, On the use of order-restricted latent class models for defining and testing non-parametric IRT models, Methods of Psychological Research, 4, 71.
• Vermunt J.K., 1997, Log-linear models for event histories, Advanced Quantitative Techniques in the Social Sciences Series, vol. 8, Sage Publication, Thousand Oaks.
• Wedel M., Kamakura W.A., 2000, Market segmentation: Conceptual and Methodological Foundations (second Edition), Dordrecht Kluwer.

Identyfikatory

DOI

10.15611/ekt.2016.4.02