Zastosowanie analizy klas ukrytych w badaniach ekonomicznych

• Autorzy: Justyna Brzezińska
• Języki publikacji: PL

Abstrakty

Niniejsza monografia poświęcona jest analizie klas ukrytych (latent class analysis). Metoda ta zostanie zaprezentowana w kontekście badań społecznych, w których podstawowe narzędzie badawcze to zazwyczaj test lub kwestionariusz, stąd też znaczną część pracy poświęcono teorii testów, genezie tej metody na gruncie badań psychometrycznych, a także badaniom praktycznym. Z uwagi na ogromny potencjał oraz możliwości aplikacyjne w badaniach społecznych, jakie dają właśnie metody analizy zmiennych niemetrycznych z uwzględnieniem zmiennych ukrytych, niniejsza monografia dotyczyć będzie właśnie tego obszaru. Metoda ta uwzględnia w modelu zarówno zmienne obserwowalne, jak i zmienne ukryte, dzięki czemu pozwala na znacznie szersze oraz dokładniejsze opisanie badanych zjawisk. (fragment tekstu)

Słowa kluczowe

PL

Metody statystyczne; Statystyka w naukach społecznych; Analiza danych statystycznych; Analiza klas ukrytych; Badania społeczno-ekonomiczne; Testy

EN

Statistical methods; Statistics in the social sciences; Statistical data analysis; Latent class analysis; Socio-economic research; Tests

• Czasopismo: Prace Naukowe / Uniwersytet Ekonomiczny w Katowicach
• Rocznik: 2021
• Strony: 123

Twórcy

autor

Justyna Brzezińska

• Uniwersytet Ekonomiczny w Katowicach

Bibliografia

• Achenwall G. (1749), Abris der neuesten Statistik, Göttingen.
• Agresti A. (1990), Categorical Data Analysis, John Wiley & Sons, New York.
• Agresti A. (2002), Categorical Data Analysis, John Wiley & Sons, Hoboken, New Jersey.
• Agresti A. (2007), An Introduction to Categorical Data Analysis, John Wiley & Sons, Hoboken, New Jersey.
• Agresti A. (2010), Analysis of Ordinal Categorical Data, John Wiley & Sons, Hoboken, New Jersey.
• Aitkin M., Anderson D., Hinde J. (1981), Statistical Modeling Of Data On Teaching Styles, "Journal of the Royal Statistical Society", Series A, Vol. 144, s. 419-461.
• Akaike H. (1973), Information Theory and an Extension of the Maximum Likelihood Principle [w:] B.N. Petrov, F. Csaki (eds.), Proceedings of the 2nd International Symposium on Information Theory, Akademiai Kiado, Budapest, s. 267-281.
• Akaike H. (1987), Factor Analysis and AIC, "Psychometrika", Vol. 52, s. 317-332.
• Anastasi A., Urbina S. (1999), Testy psychologiczne, Pracownia Testów Psychologicznych Polskiego Towarzystwa Psychologicznego, Warszawa.
• Andersen E.B. (1970), Asymptotic Properties of Conditional Maximum Likelihood Estimators, "Journal of Royal Statistical Society", Ser. B. Statistical Methods, Vol. 34, s. 283-301.
• Andersen E.B. (1972), The Numerical Solution of a Set of Conditional Estimation Equations, "Journal of Royal Statistical Society", Ser. B. Statistical Methods, Vol. 34, s. 42-54.
• Andersen E.B. (1973), Conditional Inference and Models for Measuring, Mentalhygiejnisk Forlag, Copenhagen.
• Andersen E.B. (1977), Sufficient Statistics and Latent Trait Models, "Psychometrika", Vol. 42, s. 69-81.
• Anderson T.W. (1954), On Estimation of Parameters in Latent Structure Analysis, "Psychometrika", Vol. 19, s. 1-10.
• Andrich D. (1978), A Rating Formulation for Ordered Response Categories, "Psychometrika", Vol. 34, s. 561-573.
• APA [American Psychological Association] (1985), Standardy dla testów stosowanych w psychologii i pedagogice, Polskie Towarzystwo Psychologiczne, Laboratorium Technik Diagnostycznych, Warszawa.
• van der Ark L.A., Croon M.A., Sijtsma K., eds. (2005), New Developments in Categorical Data Analysis for the Social and Behavioral Sciences, Lawrence Erlbaum, Mahwah.
• Asparouhov T., Muthén B. (2014), Auxiliary Variables in Mixture Modeling: Three-Step Approaches Using Mplus, "Structural Equation Modeling", Vol. 21, s. 329-341.
• Auerbach K.J., Collins L.M. (2006), A Multidimensional Developmental Model of Alcohol Use During Emerging Adulthood, "Journal of Studies on Alcohol", Vol. 67, s. 917-925.
• Baker F.B. (1964), An Intersection of Test Score Interpretation and Item Analysis, "Journal of Educational Measurement", Vol. 1, s. 23-28.
• Balicki A. (2009), Statystyczna analiza wielowymiarowa i jej zastosowania społeczno-ekonomiczne, Wydawnictwo Uniwersytetu Gdańskiego, Gdańsk.
• Bandeen-Roche K., Miglioretti D.L., Zeger S.L., Rathouz P.J. (1997), Latent Variable Regression for Multiple Discrete Outcomes, "Journal of the American Statistical Association", Vol. 92, s. 1375-1386.
• Banfield J.D., Raftery A.E. (1993), Model-Based Gaussian And Non-Gaussian Clustering, "Biometrics", Vol. 49, s. 803-821.
• Bartholomew D. (1987), Latent Variable Models and Factor Analysis, Griffin, London.
• Bartholomew D.J., Knott M. (1999), Latent Variable Models and Factor Analysis, 2nd ed., Arnold, London.
• Barthlomew D.J., Knott M., Moustaki I. (2011), Latent Variable Models and Factor Analysis. A Unified Approach, 3rd edition, Wiley Series in Probability and Statistics, Wiley & Sons, Hoboken.
• Bartlett M.S. (1935), Contingency Table Interactions, "Journal of the Royal Statistical Society", Vol. 2, s. 248-252.
• Bartolucci F., Farcomeni A., Pennoni F. (2013), Latent Markov Models for Longitudinal Data, CRC Press, Taylor & Francis Group.
• Basilevsky A. (1994), Factor Analysis, Wiley Series in Probability and Statistics, Wiley & Sons, Hoboken.
• Bauer D.J., Curran P.J. (2004), The Integration of Continuous and Discrete Latent Variable Models: Potential Problems and Promising Opportunities, "Psychological Methods", Vol. 9, s. 3-29.
• Beath K.J. (2017), randomLCA: An R Package for Latent Class with Random Effects Analysis, "Journal of Statistical Software", Vol. 81(13), s. 1-25.
• Birnbaum A. (1957), Efficient Design and Use of Tests of a Mental Ability for Various Decision Making Problems, Series Rep. No. 58-16, Project No. 7755-23, Randolph Air Force Base, Tx: USAF School of Aviation Medicine.
• Birnbaum A. (1958), On the Estimation of Mental Ability, Series Rep. No.15, Project No.7755-23, Randolph Air Force Base, Tx: USAF School of Aviation Medicine.
• Bock R.D. (1972), Estimating Item Parameters and Latent Ability When Responses are Scored in Two or More Nominal Categories, "Psychometrika", Vol. 37, s. 29-51.
• Bock R.D., Aitkin M. (1981), Marginal Maximum Likelihood Estimation of Item Parameters: Application of an EM Algorithm, "Psychometrika", Vol. 46, s. 443-459.
• Bollen K.A. (1989), Structural Equations with Latent Variables, John Wiley, New York.
• Bollen K.A., Curran P.J. (2004), Autoregressive Latent Trajectory (ALT) Models: A Synthesis of Two Traditions, "Sociological Methods & Research", Vol. 32, s. 336-383.
• Bozdogan H. (1987), Model Selection and Akaike's Information Criterion (AIC): The General Theory and Its Analytical Extensions, "Psychometrika", Vol. 52, s. 345-370.
• Brunden M.N. (1972), The Analysis of Non-Independent 2  2 Tables from 2  C Using Rank Sums, "Biometrics", Vol. 28, s. 603-607.
• Brzezińska J. (2015), Analiza logarytmiczno-liniowa. Teoria i zastosowanie w programie R, C.H. Beck, Warszawa.
• Brzezińska J. (2020), Modele odpowiedzi na pozycje testowe (IRT) w badaniach ekonomiczno-społecznych, Wydawnictwo Uniwersytetu Ekonomicznego w Katowicach, Katowice.
• Brzeziński J. (2006), Metodologia badań psychologicznych, Wydawnictwo Naukowe PWN, Warszawa.
• 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.
• Cattell J.M. (1890), Mental Tests and Measurements, "Mind", Vol. 15, s. 373-380.
• Cattell R.B. (1941), Some Theoretical Issues in Adult Intelligence Testing, "Psychological Bulletin", Vol. 38, s. 592.
• Chen C.F. (1981), The EM Approach to the Multiple Indicators and Multiple Causes Model Via the Estimation of the Latent Variable, "Journal of the American Statistical Association", Vol. 76, s. 704-708.
• Choynowski M. (1971), Podstawy i zastosowania teorii rzetelności testów psychologicznych [w:] J. Kozielecki (red.), Problemy psychologii matematycznej, PWN, Warszawa.
• Christensen R. (1997), Log-linear Models and Logistic Regression, Springer-Verlag, New York.
• Chung H., Anthony J.C., Schafer J.L. (2009), Latent Class Profile Analysis: An Application to Stage-Sequential Proces in Early-Onset Drinking Behaviours, "Journal of the Royal Statistical Society Series A (Statistics in Society)", Vol. 174(3), s. 689-712.
• Chung H., Flaherty B.P., Schafer J.L. (2006), Latent Class Logistic Regression: Application to Marijuana Use and Attitudes Among High School Seniors, "Journal of the Royal Statistical Society", Series A, Vol. 169, s. 723-743.
• Chung H., Park Y., Lanza S.T. (2005), Latent Transition Analysis with Covariates: Pubertal Timing and Substance Use Behaviours in Adolescent Females, "Statistics in Medicine", Vol. 24, s. 2895-2910.
• Clogg C.C. (1979), Some Latent Structure Models for the Analysis of Likert-Type Data, "Social Science Research", Vol. 8, s. 287-301.
• Clogg C.C. (1981a), Latent Structure Models of Mobility, "American Journal of Sociology", Vol. 86, s. 836-868.
• Clogg C.C. (1981b), New Developments in Latent Structure Analysis [w:] D.M. Jackson, E.F. Borgatta (eds.), Factor Analysis and Measurement in Sociological Research, Sage, Beverly Hills, CA, s. 214-280.
• Clogg C.C. (1995), Latent Class Models [w:] G. Arminger, C.C. Clogg, M.E. Sobel (eds.), Handbook of Statistical Modeling for the Social and Behavioral Sciences, Plenum, New York.
• Cochran W.G. (1954), Some Methods for Strengthening the Common Chi-Square Test, "Biometrics", Vol. 10, s. 417-451.
• Coffman D., Patrick M.E., Palen L., Rhoades B.L., Ventura A. (2007), Why Do High School Seniors Drink? Implications for a Targeted Approach, "Prevention Science", Vol. 8, s. 241-248.
• Collins L., Lanza S.T. (2010), Latent Class and Latent Transition Analysis, Wiley, Hoboken, NJ.
• Conniff R. (2011), Poszukiwacze gatunków. Bohaterowie, głupcy i szalony pościg, by zrozumieć życie na Ziemi, Prószyński Media, Warszawa.
• Coombs C.H., Dawes A., Tversky A. (1977), Wprowadzenie do statystyki matematycznej, PWN, Warszawa.
• Cressie N., Read T.R.C. (1989), Pearson's χ^2 and the Loglikelihood Ratio Statistic G2: A Comparative Review, "International Statistical Review", Vol. 57, s. 19-43.
• Cronbach L.J. (1990), Essentials of Psychological Testing, 5, Harper Collins Publishers, New York.
• Croon M. (1990), Latent Class Analysis with Ordered Latent Classes [w:] R. Langeheine, J. Rost (eds.), Latent Trait and Latent Class Models, Plenum, New York, s. 173-205.
• Croon M.A. (1991), Investigating Mokken Scalability of Dichotomous Items By Means of Ordinal Latent Class Analysis, "British Journal of Mathematical and Statistical Psychology", Vol. 44, s. 315-331.
• Croon M.A. (1993), Ordinal Latent Class Analysis for Single-Peaked Items, "Kwantitatieve Methodes", Vol. 14, s. 128-142.
• Croon M.A. (2002), Using Predicted Latent Scores in General Latent Structure Models [w:] G.A. Marcoulides, I. Moustaki (eds.), Latent Variable and Latent Structure Models, Lawrence Erlbaum, Mahwah, NJ, s. 195-223.
• Czekanowski J. (1909), Zur differentialdiagnose der Neandertalegruppe, Korrespondentblatt der Deutchen Gesellschaft für Anthropologie, "Ethnologie und Urgeschichte", vol. XL, nr 6/7, s. 44-47.
• Dayton C.M. (1998), Latent Class Scaling Analysis, Sage Publications, Thousand Oaks - London - New Delhi.
• Dayton C.M., Macready G.B. (1988), Concomitant-Variable Latent Class Models, "Journal of the American Statistical Association", Vol. 83, s. 173-178.
• Dayton C.M., Macready G.B. (2002), Use of Categorical and Continuous Covariates in Latent Class Analysis [w:] J.A. Hagenaars, A.L. McCutcheon (eds.), Advances in Latent Class Modeling, Cambridge University Press, Cambridge, MA, s. 213-233.
• Dempster A.P., Laird N.M., Rubin D.B. (1977), Maximum Likelihood Fromincomplete Data Via the EM Algorithm, "Journal of the Royal Statistical Society", Series B, Vol. 39, s. 1-38.
• Dempster A.P., Rubin D.B., Tsutakawa R.D. (1981), Estimation in Covariance Components Models, "Journal of the American Statistical Association", Vol. 76, s. 341-353.
• Dewilde C. (2004), The Multidimensional Measurement of Poverty in Belgium and Britain: A Categorical Approach, "Social Indicators Research", Vol. 68, s. 331-369.
• Edwards J.R., Bagozzi R.P. (2000), On the Nature and Direction of Relationships Between Constructs and Measures, "Psychological Methods", Vol. 5, s. 155-174.
• Everitt B.S. (1977), The Analysis of Contingency Tables, Chapman and Hall, London.
• von Eye A., Clogg C.C., eds. (1994), Latent Variables Analysis: Applications for Developmental Research, Sage Publications, Thousand Oaks, CA.
• Fienberg S.E. (1968), The Geometry of an r  c Contingency Table, "Annals of the Mathematical Statistics", Vol. 39, s. 1186-1190.
• Fischer G.H. (1974), Einfuhrung in die theorie psychologischer tests, Huber Bern.
• Fisher R.A. (1922), On the Interpretation of Chi-Square from Contingency Tables, and the Calculations of P, "Journal of the Royal Statistical Society", Vol. 85, s. 87-94.
• Florek K., Łukaszewicz J., Perkal J., Steinhaus H., Zubrzycki S. (1951), Taksonomia wrocławska, "Przegląd Antropologiczny", t. 17.
• Fop M., Murphy T.B. (2018), Variable Selection Methods for Model-based Clustering, "Statistical Survey", Vol. 12, s. 1-48.
• Formann A.K. (1984), Die Latent-Class-Analyse, Beltz Verlag, Weinheim and Basel.
• Formann A.K. (1985), Constrained Latent Class Models: Theory and Applications, "British Journal of Mathematical and Statistical Psychology", Vol. 38, s. 87-111.
• Formann A.K. (1992), Linear Logistic Latent Class Analysis for Polytomous Data, "Journal of the American Statistical Association", Vol. 87(418), s. 476-486.
• Fraley C., Raftery A.E. (1999), MCLUST: Software for Model-Based Cluster Analysis, "Journal of Classification", Vol. 16(2), s. 297-306.
• Galton F. (1892), Finger prints, Macmillan, London, http://galton.org/books/finger-prints/galton-1892-fingerprints-1up.pdf (data dostępu: 15.11.2020).
• Gibson W.A. (1955), An Extension of Anderson's Solution for the Latent Structure Equations, "Psychometrika", Vol. 20, s. 69-73.
• Goodman L.A. (1974), Exploratory Latent Structure Analysis Using Both Identifiable and Unidentifiable Models, "Biometrika", Vol. 61, s. 215-231.
• Goodman L.A. (1978), Analyzing Qualitative/Categorical Data, Abt Books, Cambridge, MA.
• Goodman L.A. (1979), The Analysis of Qualitative Variables Using More Parsimonious Quasi-Independence Models, Scaling Models, and Latent Structure Models [w:] R.K. Merton, J.S. Coleman, P.H. Rossi (eds.), Qualitative and Quantitative Social Research: Papers in Honor of Paul F. Lazarsfeld, Free Press, New York, s. 119-137.
• Goodman L.A. (2002), Latent Class Analysis: The Empirical Study of Latent Types, Latent Variables, and Latent Structures [w:] J.A. Hagenaars, A.L. McCutcheon (eds.), Applied Latent Class Analysis, Cambridge University Press, Cambridge, s. 3-55.
• Gorsuch R.L. (1983), Factor Analysis, Lawrence Erlbaum Associates, Hillsdale, NJ.
• Grabiński T., Wydymus S., Zeliaś A. (1989), Metody taksonomii numerycznej w modelowaniu zjawisk społeczno-gospodarczych, PWN, Warszawa.
• Graunt J. (1662), Natural and Political Observations Made Upon the Bills of Mortality, http://www.edstephan.org/Graunt/bills.html (data dostępu: 20.11.2020).
• Green B.F. (1951), A General Solution for the Latent Class Model of Latent Structure Analysis, "Psychometrika", Vol. 16, s. 151-166.
• Green P.E., Tull D.S., Albaum G. (1988), Research for Marketing Decisions, Prentice- -Hall, Englewood Cliffs.
• Guilford J.P. (1936), Psychometric Methods, McGraw-Hill, New York - London.
• Gulliksen H. (1950), The Reliability of Speed Tests, "Psychometrika", Vol. 10, s. 255-282.
• Haberman S.J. (1974a), Log-Linear Models for Frequency Tables Derived by Indirect Observations, "Annals of Statistics", Vol. 2, s. 911-924.
• Haberman S.J. (1974b), The Analysis of Frequency Data, University of Chicago Press, Chicago.
• Haberman S. (1979), Analysis of Qualitative Data, Vol. 2: New Developments, Academic Press, New York.
• Hagenaars J.A. (1978), Latent Probability Models with Direct Effects Between Indicators, "Quality and Quantity", Vol. 12, s. 205-221.
• Hagenaars J.A. (1993), Loglinear Models with Latent Variables, Newbury Park, Sage, CA.
• Hagenaars J.A. (1994), Latent Variables in Log-Linear Models of Repeated Observations [w:] A. von Eye, C.C. Clogg (eds.), Latent Variables Analysis: Applications for Developmental Research, Sage Publications, Thousands Oaks, s. 329-352.
• Hagenaars J.A., Luijkx R. (1987), LCAG: Latent Class Models And Other Loglinear Models with Latent Variables. User's Manual, Working Papers Series, Department of Sociology Tilburg University, Tilburg.
• Hagenaars J.A., McCutcheon A.L. (2002), Applied Latent Class Analysis, Cambridge University Press, Cambridge.
• Hair J.F., Anderson R.E., Tatham R.L., Black W.C. (1995), Multivariate Data Analysis, Prentice-Hall, Englewood Cliffs, NJ.
• Hair J.F., Anderson R.E., Tatham R.L., Black W.C. (1998), Multivariate Data Analysis, Prentice-Hall, Englewood Cliffs.
• Hambleton R.K., Swaminathan H. (1985), Item Response Theory: Principles and Applications, Kluwer Academic Publishers, Boston.
• Harmann H.H. (1976), Modern Factor Analysis, 3rd edition revised, The University of Chicago Press, Chicago, IL.
• Hägglund G. (2001), Milestones in the History of Factor Analysis [w:] Structural Equation Modeling: Present and Future, R. Cudeck, S. du Toit, D. Sörbom (eds.), Scientific Software Int, Lincolnwood, IL.
• Heijden P.G.M., Dressens J., Bockenholt U. (1996), Estimating the Concomitant-Variable Latent-Class Model with the EM Algorithm, "Journal of Educational and Behavioral Statistics", Vol. 21, s. 215-229.
• Heinen T. (1996), Latent Class and Discrete Latent Trait Models: Similarities and Differences, Sage Publications, Thousand Oaks, CA.
• Hellwig Z. (1968), Zastosowania metody taksonomicznej do typologicznego podziału krajów ze względu na poziom ich rozwoju i strukturę wykwalifikowanych kadr, "Przegląd Statystyczny", nr 4, s. 307-327.
• Hotelling H. (1933), Analysis of a Complex of Statistical Variables Into Principal Components, "Journal of Educational Psychology", Vol. 24, s. 417-441.
• Irwin J.O. (1949), A Note on the Subdivision of 2 Into Components, "Biometrika", Vol. 36, s. 130-134.
• Jackson K.M., Sher J.J., Gotham H.J., Wood P.K. (2001), Transitioning Into and Out of Large-Effect Drinking in Young Adulthood, "Journal of Abnormal Psychology", Vol. 100, s. 378-391.
• Jajuga K. (1987), Statystyka ekonomicznych zjawisk złożonych - wykrywanie i analiza niejednorodnych rozkładów wielowymiarowych, "Prace Naukowe Akademii Ekonomicznej we Wrocławiu", nr 371.
• Jöreskog K.G. (1970), Estimation and Testing of Simplex Models, "British Journal of Mathematical and Statistical Psychology", Vol. 23(2), s. 121-145.
• Jöreskog K.G. (1971), Simultaneous Factor Analysis in Several Populations, "Psychometrika", Vol. 36(4), s. 409-426.
• Jöreskog K.G. (1990), New Developments in LISREL: Analysis of Ordinal Variables Using Polychoric Correlations and Weighted Least Squares, "Quality and Quantity", Vol. 24, s. 387-404.
• Kendall M.G. (1975), Multivariate Analysis, Griffin, London.
• Kim J., Mueller C. (1978), Introduction to Factor Analysis: What It Is and How to Do It, Sage Publications, Beverly Hills, CA.
• Kimball A.W. (1954), Short-cut Formulae for the Exact Partition of Chi-Square in Contingency Tables, "Biometrics", Vol. 10, s. 452-458.
• Kinnear T.C., Taylor J.R. (1991), Marketing Research. An Applied Approach, McGraw-Hill, New York.
• Koopmans T.C. (1951), An Analysis of Production as Efficient Combination of Activities [w:] Cowles Commission for Research in Economics, Monograph no. 13, Activity Analysis of Production and Allocation, T.C. Koopmans (ed.), John Wiley & Sons, Chapman & Hall, New York, London, s. 33-97.
• Kwiatek Ł. (2016), Dziedzictwo Linneusza, https://www.tygodnikpowszechny.pl/dziedzictwo-linneusza-68447 (data dostępu: 20.05.2020).
• Lancaster H.O. (1949), The Derivation and Partition of 2 in Certain Discrete Distributions, "Biometrika", Vol. 36, s. 117-129.
• Langeheine R., Rost J. (1988), Latent Trait and Latent Class Models, Springer Science & Business Media.
• Lanza S.T., Collins L.M. (2002), Pubertal Timing and the Onset of Substance Use in Females During Early Adolescence, "Prevention Science", Vol. 3, s. 69-82.
• Lanza S.T., Collins L.M. (2006), A Mixture Model of Discontinuous Development in Heavy Drinking from Ages 18 to 30: The Role of College Enrollment, "Journal of Studies on Alcohol", Vol. 67, s. 552-561.
• Lanza S.T., Bray B.C., Collins L.M. (2013), An Introduction to Latent Class and Latent Transition Analysis [w:] J.A. Schinka, W.F. Velicer, I.B. Weiner (eds.), Handbook of Psychology, 2nd ed., Vol. 2, Wiley, Hoboken, NJ, s. 691-716.
• Lanza T.S., Tan X., Bray C.B. (2013), Latent Class Analysis with Distal Outcomes: A Flexible Model-Based Approach, "Structural Equation Modeling", Vol. 20(1), s. 1-26.
• Laplace P. (1812), Théorie analytique des probabilités, Courcier, Paris.
• Laplace P. (1814), Essai philosophique sur les probabilités, Courcier, Paris.
• Lattin J.M., Caroll J.D., Green P.E. (2003), Analyzing Multivariate Data, Brooks/Cole, Pacific Grove.
• Lawley D.N., Maxwell A.E. (1971), Factor Analysis as a Statistical Method, Second ed., Butterworth, London.
• Lazarsfeld P.F. (1950), The Obligations of the 1950 Pollster to the 1984 Historian, "Public Opinion Quarterly", Vol. 14, s. 618-638.
• Lazarsfeld P.F., Dudman J. (1951), The General Solution of the Latent Class Case [w:] P.F. Lazarsfeld (ed.), The Use of Mathematical Models in the Measurement of Attitudes, RAND Corporation, Santa Monica.
• Lazarsfeld P.F., Henry N.W. (1968), Latent Structure Analysis, Houghton Mifflin, New York.
• Lee Y., Nedler J.A. (2009), Likelihood Inference for Models with Unobservables: Another View, "Statistical Science", Vol. 24(3), s. 255-269.
• Lindquist E.F., ed. (1951), Educational Measurement, American Council on Education, Washington, D.C.
• Linzer D.A., Lewis J.B. (2011), poLCA: An R Package for Polytomous Variable Latent Class Analysis, "Journal of Statistical Software", Vol. 42(10), s. 3-29.
• Linzer D.A., Lewis J.B. (2013), poLCA: Polytomous Variable Latent Class Analysis, R package version 1.4, http://dlinzer.github.com/poLCA.
• Little T.D. (2013), The Oxford Handbook of Quantitative Methods, Volume 2: Statistical Analysis, Oxford University Press, Oxford.
• Lord F.M. (1952), A Theory of Test Scores, "Psychometric Monograph", No. 7.
• Lord F.M., Novick M.R. (1968), Statistical Theories of Mental Test Scores, Reading, Addison-Wesley, MA.
• Madansky A. (1959), The Fitting of Straight Lines When Both Variables Are Subject to Error, "Journal of the American Statistical Association", Vol. 54, s. 173-205.
• Madansky A. (1960), Determinantial Methods in Latent Class Analysis, "Psychometrica", Vol. 25, s. 183-198.
• Magidson J., Vermunt J.K. (2000), Biplots and Related Graphical Displays Based on Latent Class Factor and Cluster Models [w:] W. Jansen, J.G. Bethlehem (eds.), Proceedings in Computational Statistics 2000, Statistics Netherlands, s. 121-122.
• Mantel N., Haenszel W. (1959), Statistical Aspects of the Analysis of Data from Retrospective Studies of Disease, "Journal of National Cancer Institute", Vol. 22, s. 719-748.
• Maxwell A.E. (1977), Multivariate Analysis in Behavioural Research, Chapman & Hall, London.
• McCutcheon A.C. (1987), Latent Class Analysis, Sage, Beverly Hills, CA.
• McDonald R.P. (1985), Factor Analysis and Related Methods, Lawrence Erlbaum Associates, Hillsdale, NJ.
• McDonald R.P. (1996), Path Analysis with Composite Variables, "Multivariate Behavioral Research", Vol. 31, s. 239-270.
• McDonald R.P. (1997), Haldane's Lungs: A Case Study in Path Analysis, "Multivariate Behavioral Research", Vol. 32, s. 1-38.
• 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", Vol. 21, s. 331-347.
• McLachlan G., Krishnan T. (1997), The EM Algorithm and Extensions, John Willey & Sons, New York.
• McLachlan G., Peel D. (2000), Finite Mixture Models, John Willey & Sons, New York.
• McNemar Q. (1955), Psychological Statistics, Wiley, New York.
• Minelli A. (2009), Historical Review of Systematic Biology and Nomenclature [w:] A. Minelli, G. Contrafatto (eds.), Biological Science Fundamentals and Systematics, Vol. 2, Encyclopedia of Life Support Systems (EOLSS), Developed under the auspices of the UNESCO, France: EOLSS Pubishers, s. 27-39.
• Molenaar P.C.M., Eye A. von (1994), On the Arbitrary Nature of Latent Variables [w:] A. von Eye, C.C. Clogg (eds.), Latent Variables Analysis: Applications for Developmental Research, Sage Publications, Thousand Oaks, CA, s. 226-242.
• Muthén B., Asparouhov T. (2006), Item Response Mixture Modeling: Application to Tobacco Dependence Criteria, "Addictive Behaviors", Vol. 31(6), s. 1050-1066.
• Nagin D.S., Tremblay R.E. (2005), Developmental Trajectory Groups: Fact or Useful Statistical Fiction?, "Criminology", Vol. 43(4), s. 873-904.
• Neale M.C., Cardon L.R. (1992), Methodology for Genetic Studies of Twins and Families, Kluwer Academic Publishers, Dordrecht.
• Paluchowski W.J. (1991), Diagnozowanie osobowości. Testowanie-interpretacja- -interwencja, Wydawnictwo Naukowe UAM, Nakom, Poznań.
• Pearson K. (1900), On a Criterion That a Given System of Deviations from the Probable in the Case of a Correlated System of Variables Is Such That It Can Be Reasonably Supposed to Have Arisen From Random Sampling, "Philosophical Magazine Series", Vol. 5(50), s. 157-175.
• Pearson K. (1901), On Lines and Planes of Closest Fit to Systems of Points in Space, "Philosophical Magazine", No. 2, s. 559-572.
• Pearson K. (1904), On the Theory of Contingency and Its Relation to Association and Normal Correlation, Biometric Series, Drapers' Co. Memoirs, London.
• Pearson K. (1914), The Life, Letters and Labours of Francis Galton, Vol. I: Birth 1822 to Marriage 1853, Cambridge University Press, Cambridge.
• Pearson K. (1924), The Life, Letters and Labours of Francis Galton, Vol. II: Researches of Middle Life, Cambridge University Press, Cambridge.
• Pearson K. (1930a), The Life, Letters and Labours of Francis Galton, Vol. IIIA: Correlation, Personal Identification and Eugenics, Cambridge University Press, Cambridge.
• Pearson K. (1930b), The Life, Letters and Labours of Francis Galton, Vol. IIIB: Characterisation, Especially by Letters, Index, Cambridge University Press, Cambridge.
• Peirce C.S., ed. (1883), Studies in Logic, John Benjamins Publishing Company, Amsterdam/Philadelphia.
• Piaget J. (1966), Studia z psychologii dziecka, PWN, Warszawa.
• Proust-Lima C., Philipps V., Liquet B. (2017), Estimation of Extended Mixed Models Using Latent Classes and Latent Processes: The R Package lcmm, "Journal of Statistical Software", Vol. 78(2), s. 1-56.
• Raftery A.E. (1993), Bayesian Model Selection in Structural Equation Models. Reprinted in Testing Structural Equation Models, K.A. Bollen, J.S. Long (eds.), Sage, Newbury Park, CA, s. 163-180.
• 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", Vol. 12(1), s. 103-124.
• Rasch G. (1960), Probabilistic Models for Some Intelligence and Attainment Tests, Danish Institute for Educational Research, Copenhagen, The University of Chicago Press, Chicago.
• Raudenbush S.W., Bryk A.S. (2002), Hierarchical Linear Models, Sage, Thousand Oaks, CA.
• Read T.R.C., Cressie N.A.C. (1988), Goodness of Fit Statistics for Discrete Multivariate Data, Springer Verlag.
• Rodger R.S. (1969), Linear Hypothesis in 2  a Frequency Tables, "The British Journal of Mathematical and Statistical Psychology", Vol. 22, s. 29-48.
• Rubin D.B., Stern H.S. (1994), Testing in Latent Class Models Using a Posterior Predictive Check Distribution [w:] A. von Eye, C.C. Clogg (eds.), Latent Variables Analysis: Applications for Developmental Research, Sage Publications, Thousand Oaks, CA, s. 420-438.
• Rubin D.B., Thayer D.T. (1982), EM Algorithms for ML Factor Analysis, "Psychometrika", Vol. 47, s. 69-76.
• Rudas T., Clogg C.C., Lindsay B.G. (1994), A New Index of Fit Based on Mixture Methods for the Analysis of Contingency Tables, "Journal of the Royal Statistical Society", Ser. B, Vol. 56, s. 623-639.
• Rue H., Held L. (2005), Gaussian Markov Random Fields: Theory and Applications, Monographs on Statistics and Applied Probability, Vol. 104, Chapman & Hall, London.
• Rybicka A., Jefmański B., Pełka M. (2012), Analiza klas ukrytych w badaniach satysfakcji studentów, "Prace Naukowe Uniwersytetu Ekonomicznego we Wrocławiu", nr 242, s. 247-255.
• Samejima F. (1969), Estimation of Latent Ability Using a Response Pattern of Graded Scores, "Psychometrika Monograph Supplement", Vol. 34(4).
• Samejima F. (1979), A New Family of Models for the Multiple-Choice Item (Research Report No. 79-4), Department of Psychology, University of Tennessee, Knoxville.
• Sagan A. (2000), Wybrane problemy identyfikacji i pomiaru struktur ukrytych, "Zeszyty Naukowe Akademii Ekonomicznej w Krakowie", nr 543, s. 53-64.
• Sagan A. (2003), Analiza rzetelności skal satysfakcji i lojalności, StatSoft Polska.
• Sagan A. (2005), Ocena ekwiwalencji skal pomiarowych w badaniach międzykulturowych, "Zeszyty Akademii Ekonomicznej w Krakowie", nr 659, s. 59-73.
• Sagan A. (2009), Podejście modelowe w segmentacji rynku, "Zeszyty Naukowe Uniwersytetu Ekonomicznego w Krakowie", nr 800, s. 21-35.
• Sagan A. (2013), Zmienne ukryte w badaniach ekonomicznych, Wydawnictwo Uniwersytetu Ekonomicznego, Kraków.
• Sagan A. (2014), Analiza inwariancji pomiaru w badaniach przekrojowych, "Problemy Zarządzania, Finansów i Marketingu", nr 33, s. 187-197.
• Sagan A. (2017), Wielopoziomowe modele klas ukrytych w badaniach międzynarodowych, "Marketing i Rynek", nr 9, s. 331-341.
• Schwarz G. (1978), Estimating the Dimension of a Model, "Annals of Statistics", Vol. 6, s. 461-464.
• Sclove L. (1987), Application of Model-Selection Criteria to Some Problems in Multivariate Analysis, "Psychometrika", Vol. 52, s. 333-343.
• Seber G.A.F. (1984), Multivariate Observation, Wiley, New York.
• Shannon C.E. (1948), A Mathematical Theory of Communication, "Bell System Technical Journal", Vol. 27, s. 379-423.
• Skrondal A., Rabe-Hesketh S. (2004a), Generalized Latent Variable Modeling: Multilevel, Longitudinal and Structural Equation Models, Chapman & Hall/CRC, Boca Raton, FL.
• Skrondal A., Rabe-Hesketh S. (2004b), Generalized Linear Latent and Mixed Models with Composite Links and Exploded Likelihoods [w:] A. Biggeri, E. Dreassi, C. Lagazio, M. Marchi (eds.), Proceedings of the 19th International Workshop on Statistical Modeling, Firenze University Press, Florence, s. 27-39.
• Sneath P.H.A., Sokal R.R. (1973), Numerical Taxonomy. The Principles and Practice of Numerical Classification, W.H. Freeman & Company, San Francisco.
• Spearman C. (1904), General Intelligence, Objectively Determined and Measured, "American Journal of Psychology", Vol. 15, s. 201-293.
• Spearman C. (1907), Demonstration of Formulae for True Measurement of Correlation, "The American Journal of Psychology", Vol. 18(2), s. 161-169.
• Strenio J.L.F., Weisberg H.I., Bryk A.S. (1983), Empirical Bayes Estimation of Individual Growth Curve Parameters and Their Relations to Covariates, "Biometrics", Vol. 39, s. 71-86.
• Sutton M.A., Karanian D.A., Self D.W. (2000), Factors That Determine a Propensity for Cocaine-Seeking Behavior During Abstinence in Rats, "Neuropsychopharmacology", Vol. 22, s. 626-641.
• Swaminathan H., Gifford J.A. (1982), Bayesian Estimation in the Rasch Model, "Journal of Educational and Behavioral Statistics", Vol. 7, s. 175-191.
• Szymkowiak M., Klimanek T. (2018), Analiza klas ukrytych w badaniu niepełnosprawności, "Prace Naukowe Uniwersytetu Ekonomicznego we Wrocławiu", nr 507, s. 237-246.
• Thissen D. (1982), Marginal Maximum Likelihood Estimation for the One-Parameter Logistic Model, "Psychometrika", Vol. 47, s. 175-186.
• Thorndike R.L. (1904), An Introduction to the Theory of Mental and Social Measurement, Science Press, New York.
• Thorndike R.L. (1977), Measurement and Evaluation in Psychology and Education, Wiley, New York.
• Thurstone L.L. (1954), An Analytical Method for Simple Structure, "Psychometrika", Vol. 9, s. 173-182.
• 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-Verlag, New York.
• Velicer W.F., Colleen A.R., Anatchkova M.D., Fava J.L., Prochaska J.O. (2007), Identifying Cluster Subtypes for the Prevention of Adolescent Smoking Acquisition, "Addictive Behaviors", Vol. 32, s. 228-247.
• Velicer W.F., Peacock A.C., Jackson D.N. (1982), A Comparison of Component and Factor Patterns: A Monte Carlo Approach, "Multivariate Behavioral Research", Vol. 17, s. 371-388.
• Vermunt J.K. (1997), Log-linear Models for Event Histories, Advanced Quantitative Techniques in the Social Sciences Series, vol. 8, Sage Publication, Thousand Oaks, CA.
• Vermunt J.K. (2004), An EM Algorithm for the Estimation of Parametricand Nonparametric Hierarchical Nonlinear Models, "Statistica Neerlandica", Vol. 58, s. 220-233.
• Vermunt J.K. (2010), Latent Class Modeling with Covariates: Two Improved Three-Step Approaches, "Political Analysis", Vol. 18, s. 450-469.
• Vermunt J.K., Magidson J. (2000), Latent GOLD User's Manual, Statistical Innovations, Boston.
• Vermunt J.K., Magidson J. (2002), Latent Class Cluster Analysis [w:] J.A. Hagenaars, A.L. McCutcheon (eds.), Applied Latent Class Analysis, Cambridge University Press, Cambridge, s. 89-106.
• Vermunt J.K., Magidson J. (2005), Latent Gold 4.0.User's Guide, https://www. statisticalinnovations.com/wp-content/uploads/LGusersguide.pdf (data dostępu: 30.01.2021).
• Vermunt J.K., Magidson J. (2013), Technical Guide for Latent GOLD 5.0: Basic, Advanced, Andsyntax, Statistical Innovations Inc., Belmont, MA.
• White A., Murphy T.B. (2014), BayesLCA: An R Package for Bayesian Latent Class Analysis, "Journal of Statistical Software", Vol. 61(13), s. 1-28.
• Wiggins L.M. (1973), Panel Analysis, Elsevier, Amsterdam.
• Wright B.D. (1977), Misunderstanding the Rasch Model, "Journal of Educational Measurement", Vol. 14(3), s. 219-225.
• Wright B., Stone M. (1979), Best Test Design, MESA Press, Chicago, IL.
• Yates F. (1934), Contingency Tables Involving Small Numbers and the Chi-Square Test, "Journal of the Royal Statistical Society", Vol. 1, s. 217-235.
• Yule G.U. (1900), On the Association of Attributes in Statistics, Philosophical Transactions of the Royal Society of London, Series A, 194, s. 257-319.
• Zucchini W., MacDonald I.L. (2009), Hidden Markov Models for Time Series: An Introduction Using R, Monograph on Statistics and Applied Probability, Chapman & Hall, CRC, Boca Raton, FL.