Abstract
A feature common to most models considered in the first part of this book may be easily depicted by Figure 1. In latent trait models, the point of departure is a set of manifest categorical variables (indicators, items, say A, B, and C), which may be related to each other in some way (cf. the curved lines). The crucial assumption now is that these relationships are conceived as being due to some continuous latent variable X. That is, if the model holds, the relationships between the manifest variables will vanish and the structure will be depicted by the straight lines going from X to A, B, and C. Several people, however, have questioned whether this procedure is advisable in all instances. Latent trait models strive for a relatively sophisticated scaling property of the latent variable (most models aim at least at an interval scale) which often remains unused for subsequent interpretation of the data. In fact, we are often simply interested in certain groups or types of persons (see Rost, Chapter 7, this volume), that means that we need no more than a categorical or nominal latent variable. This is exactly what latent class models assume.
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References
Andersen, E. B. (1982). Latent structure analysis: A survey. Scandinavian Journal of Statistics, 9, 1–12.
Anderson, T. W. (1954). On estimation of parameters in latent structure analysis. Psychometrika, 19, 1–10.
Bergan, J. R. (1983). Latent-class models in educational research. In E. W. Gordon (Ed.), Review of research in education 10. Washington, D.C.: American Educational Research Association.
Bergan, J. R., and Stone, C. A. (1985). Latent class models for knowledge domains. Psychological Bulletin, 98, 166–184.
Blumen, I. M., Kogan, M., and McCarthy, P. J. (1955). The industrial mobility of labor as a probability process. Ithaca: Cornell University Press.
Bye, B. V., and Schechter, E. S. (1986). A latent Markov model approach to the estimation of response errors in multivariate panel data. Journal of the American Statistical Association, 81, 375–380.
Clogg, C. C. (1977). Unrestricted and restricted maximum likelihood latent structure analysis: A manual for users (Working Paper 1977–09 ). University Park: Population Issues Research Center.
Clogg, C. C. (1979). Some latent structure models for the analysis of Likert-type data. Social Science Research, 8, 287–301.
Clogg, C. C. (1981a). New developments in latent structure analysis. In D. J. Jackson & E. F. Borgatta (Eds.), Factor analysis and measurement in sociological research. London: Sage.
Clogg, C. C. (1981b). Latent structure models of mobility. American Journal of Sociology, 86, 836–868.
Clogg, C. C. (1984). Some statistical models for analyzing why surveys disagree. In C. F. Turner & E. Martin (Eds.), Surveying subjective phenomena (Vol. 2 ). New York: Russell Sage Foundation.
Clogg, C. C., and Goodman, L. A. (1984). Latent structure analysis of a set of multidimensional contingency tables. Journal of the American Statistical Association, 79, 762–771.
Clogg, C. C., and Goodman, L. A. (1985). Simultaneous latent structure analysis in several groups. In N. B. Tuma (Ed.), Sociological methodology 1985. San Francisco: Jossey-Bass.
Clogg, C. C., and Sawyer, D. O. (1981). A comparison of alternative models for analyzing the scalability of response patterns. In S. Leinhardt (Ed.), Sociological methodology 1981. San Francisco: Jossey-Bass.
Dayton, C. M., and Macready, G. B. (1976). A probabilistic model for validation of behavioral hierarchies. Psychometrika, 41, 189–204.
Dayton, C. M., and Macready, G. B. (1980). A scaling model with response errors and intrinsically unscalable respondents. Psychometrika, 45, 343–356.
Dayton, C. M., and Macready, G. B. (1983). Latent structure analysis of repeated classifications with dichotomous data. British Journal of Mathematical and Statistical Psychology, 36, 189–201.
Dillon, W. R., and Mulani, N. (1984). A probabilistic latent class model for assessing inter-judge reliability. Multivariate Behavioral Research, 19, 438–458.
Dillon, W. R., Madden, T. J., and Kumar, A. (1983). Analyzing sequential categorical data on dyadic interaction: A latent structure approach. Psychological Bulletin, 94, 564–583.
Formann, A. K. (1976a). Schätzung der Parameter in Lazarsfeld’s Latent-Class-Analysis (Research Bulletin No. 18 ). Wien: Institut für Psychologie der Universität Wien.
Formann, A. K. (1976b). Latent-Class-Analyse polychotomer Daten (Research Bulletin No. 19 ). Wien: Institut für Psychologie der Universität Wien.
Formann, A. K. (1978a). A note on parameter estimation for Lazarsfeld’s latent class analysis. Psychometrika, 43, 123–126.
Formann, A. K. (1978b). The latent class analysis of polytomous data. Biometrical Journal, 20, 755–771.
Formann, A. K. (1982). Linear logistic latent class analysis. Biometrical Journal, 24, 171–190.
Formann, A. K. (1984). Die Latent-Class-Analyse. Weinheim: Beltz.
Formann, A. K. (1985). Constrained latent class models: Theory and applications. British Journal of Mathematical and Statistical Psychology, 38, 87–111.
Gibson, W. A. (1955). An extension of Anderson’s solution for the latent structure equations. Psychometrika, 20, 69–73.
Gibson, W. A. (1962). Extending latent class solutions to other variables. Psychometrika, 27, 73–81.
Goodman, L. A. (1974a). The analysis of systems of qualitative variables when some of the variables are unobservable. Part I-A modified latent structure approach. American Journal of Sociology, 79, 1179–1259.
Goodman, L. A. (1974b). Exploratory latent structure analysis using both identifiable and unidentifiable models. Biometrika, 61, 215–231.
Goodman, L. A. (1975). A new model for scaling response patterns: An application of the quasi-independence concept. Journal of the American Statistical Association, 70, 755–768.
Guttman, L. (1950). The basis for scalogram analysis. In S. A. Stouffer, L. Guttman, E. A. Suchman, P. F. Lazarsfeld, S. A. Star, & J. A. Clausen (Eds.), Measurement and prediction: Studies in social psychology in World War II (Vol. IV ). Princeton: Princeton University Press.
Haberman, S. J. (1979). Analysis of qualitative data: Vol. 2. New developments. New York: Academic Press.
Haertel, E. (1984a). Detection of a skill dichotomy using standardized achievement test items. Journal of Educational Measurement, 21, 59–72.
Haertel, E. (1984b). An application of latent class models to assessment data. Applied Psychological Measurement, 8, 333–346.
Harper, D. (1972). Local dependence latent structure models. Psychometrika, 37, 53–59.
Langeheine, R. (1984). Neuere Entwicklungen in der Analyse latenter Klassen und latenter Strukturen. Zeitschrift für Sozialpsychologie, 15, 199–210.
Lazarsfeld, P. F. (1950). The logical and mathematical foundation of latent structure analysis. In S. A. Stouffer, L. Guttman, E. A. Suchman, P F Lazarsfeld, S. A. Star, and J. A. Clausen (Eds.), Measurement and prediction: Studies in social psychology in World War II (Vol. IV ). Princeton: Princeton University Press.
Lazarsfeld, P. F., and 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. Santa Monica: RAND Corporation.
Lazarsfeld, P. F., and Henry, N. W. (1968). Latent structure analysis. Boston: Houghton Mifflin
Macready, G. B., and Dayton, C. M. (1977). The use of probabilistic models in the assessment of mastery. Journal of Educational Statistics, 2, 99–120.
Macready, G. B., and Dayton, C. M. (1980). The nature and use of state mastery models. Applied Psychological Measurement, 4, 493–516.
Madansky, A. (1960). Determinantal methods in latent class analysis. Psychometrika, 25, 183–198.
Madden, T. J., and Dillon, W. R. (1982). Causal analysis and latent class models: An application to a communication hierarchy of effects model. Journal of Marketing Research, 19, 472–490.
McHugh, R. B. (1956). Efficient estimation and local identification in latent class analysis. Psychometrika, 21, 331–347.
McHugh, R. B. (1958). Note on “Efficient estimation and local identification in latent class analysis.” Psychometrika, 23, 273–274.
Poulsen, C. A. (1982). Latent structure analysis with choice modeling applications. Aarhus: Aarhus School of Business Administration and Economics.
Proctor, C. H. (1970). A probabilistic formulation and statistical analysis of Guttman scaling. Psychometrika, 35, 73–78.
Rasch, G. (1960). Probabilistic models for some intelligence and attainment tests. Copenhagen: Danish Institute for Educational Research.
Rindskopf, D. (1983). A general framework for using latent class analysis to test hierarchical and nonhierarchical learning models. Psychometrika, 48, 85–97.
Rindskopf, D. (1984). Linear equality restrictions in regression and loglinear models. Psychological Bulletin, 96, 597–603.
Taylor, M. C. (1983). The black-and-white model of attitude stability: A latent class examination of opinion and nonopinion in the American public. American Journal of Sociology, 89, 373–401.
Tuch, S. A. (1984). A multivariate analysis of response structure: Race attitudes, 1972–1977. Social Science Research, 13, 55–71.
Wiggins, L. M. (1955). Mathematical models for the analysis of multi-wave panels. Unpublished doctoral dissertation, Columbia University.
Wigging, L. M. (1973). Panel analysis. Amsterdam: Elsevier.
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Langeheine, R. (1988). New Developments in Latent Class Theory. In: Langeheine, R., Rost, J. (eds) Latent Trait and Latent Class Models. Springer, Boston, MA. https://doi.org/10.1007/978-1-4757-5644-9_5
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