The Analysis of Systems of Qualitative Variables When Some of the Variables Are Unobservable. Part I-A Modified Latent Structure Approach

This article presents methods for analyzing the relationships among a set of qualitative variables when some of these variables are specified manifest (i.e., observed) variables and others are latent (i.e., unobserved or unobservable) variables. We shall show how to estimate the magnitude of the various effects represented in pathdiagram models that include both the manifestand latent variables, and also how to test whether this kind of path-diagram model is congruent with the observed data. These methods can be applied in order to analyze data obtained in various kinds of surveys (including panel studies), and also in order to construct tests and indices for purposes of measurement and prediction. To illustrate their wide applicability and flexibility, we shall use these methods to reanalyze several different sets of data which were analyzed earlier by Coleman (1964), Lazarsfeld (1948, 1970), Goodman (1973a), and others. Except for some related conclusions in Goodman (1973a), the methods introduced herein lead to conclusions that are very different from those presented by the other researchers who had analyzed these data earlier.

[1]  C. F. Schumacher,et al.  TESTS FOR CREATIVE ABILITY IN MACHINE DESIGN , 1953 .

[2]  T. W. Anderson On estimation of parameters in latent structure analysis , 1954 .

[3]  W. Gibson An extension of Anderson's solution for the latent structure equations , 1955 .

[4]  R. McHugh Efficient estimation and local identification in latent class analysis , 1956 .

[5]  Richard B. McHugh Note on “efficient estimation and local identification in latent class analysis” , 1958 .

[6]  A. Madansky Determinantal methods in latent class analysis , 1960 .

[7]  W. Gibson Extending latent class solutions to other variables , 1962 .

[8]  J. Coleman Introduction to Mathematical Sociology , 1965 .

[9]  L. A. Goodman The Analysis of Cross-Classified Data: Independence, Quasi-Independence, and Interactions in Contingency Tables with or without Missing Entries , 1968 .

[10]  Paul F. Lazarsfeld,et al.  Latent Structure Analysis. , 1969 .

[11]  L. A. Goodman The Multivariate Analysis of Qualitative Data: Interactions among Multiple Classifications , 1970 .

[12]  L. A. Goodman Partitioning of Chi-Square, Analysis of Marginal Contingency Tables, and Estimation of Expected Frequencies in Multidimensional Contingency Tables , 1971 .

[13]  L. A. Goodman The Analysis of Multidimensional Contingency Tables: Stepwise Procedures and Direct Estimation Methods for Building Models for Multiple Classifications , 1971 .

[14]  Leo A. Goodman,et al.  A Modified Multiple Regression Approach to the Analysis of Dichotomous Variables , 1972 .

[15]  Leo A. Goodman,et al.  A General Model for the Analysis of Surveys , 1972, American Journal of Sociology.

[16]  Leo A. Goodman,et al.  Causal Analysis of Data from Panel Studies and Other Kinds of Surveys , 1973, American Journal of Sociology.

[17]  Leo A. Goodman,et al.  The analysis of multidimensional contingency tables when some variables are posterior to others: a modified path analysis approach , 1973 .

[18]  S. Haberman,et al.  The analysis of frequency data , 1974 .

[19]  S. Haberman Log-Linear Models for Frequency Tables Derived by Indirect Observation: Maximum Likelihood Equations , 1974 .