Abstract
This paper concerns exploratory structural analysis of relations among binary variables. Some new methods are described and illustrated, methods that appear to hold promise for general improvements in binary structural analysis. Standard common factor theory and methods, including image analysis, are used in combination with methods for data smoothing, to construct an alternative data system at the outset of analysis. Most of the new algorithms are relatively fast, easy to explain and to program, and appear to work as intended, at least for initial applications with real and simulated data. Given various advances in statistical theory and methods for prediction, as well as increasingly powerful and convenient computing facilities, there are a number of ways to extend the methods discussed here beyond the current framework.
Presented at International Conference on Measurement and Multivariate Analysis (ICMMA), Banff, Alberta, CA May 2000
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Bock RD, Lieberman M. (1970). Fitting a response curve model for dichotomously scored items. Psychometrika, 35, 179–198.
Bock, R. D., Gibbons, R., and Muraki, E. (1988) Full-information item factor analysis. Applied Psychological Measurement, 12, 261–280.
Chen, C. (1979) Bayesian inference for a normal dispersion matrix and its applications to stochastic multiple regression. Journal of the Royal Statistical Society, Series B, 41, 235 —248.
Guttman, L. (1953) Image theory for the structure of quantitative variates. Psychometricka, 18, 273–285.
Harris, C.W. (1962). Some Rao-Guttman relationships. Psychometrika, 27, 247 –263.
Knol, D.L. and Berger, M.P.F. (1991). Empirical comparison between factor analysis and multidimensional item response models. ivvlultivariate Behavioral Research, 26, 457–477.
Mislevy R.J. (1986) Recent developments in the factor analysis of categorical variables. Journal of Educational Statistics, 11, 3–31.
Muthén, B. (1978) Contributions to factor analysis of dichotomized variables. Psychometrika, 43, 551–560.
Parry C.D. and McArdle J. J. (1991) An applied comparison of methods for least-squares factor analysis of dichotomous variables. Applied Psychological Measurement, 15, 35–46.
Pruzek, R.M. and Lepak, G. (1992) Weighted structural regression: A broad class of adaptive methods for improving linear prediction. Multivariate Behavioral Research, 27, 95 – 129.
Rabinowitz, S.N., Rule, D. and Pruzek, R.M. (1998) Some new regression methods for predictive and construct validation. Social Indicators Research, 45, 201– 231.
Tucker, L. R. (1983) Searching for structure in binary data. In H. Wainer and S. Messick (Eds.), Principals of Modern Psychological Measurement, 215 – 235. Erlbaum and Associates, Hillsdale, N.J.
Wailer, N. (2000) MicroFACT user’s manual 2.0. Assessment Systems Corp., St. Paul.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2002 Springer Japan
About this paper
Cite this paper
Pruzek, R.M. (2002). Borrowing Strength from Images to Facilitate Exploratory Structural Analysis of Binary Variables. In: Nishisato, S., Baba, Y., Bozdogan, H., Kanefuji, K. (eds) Measurement and Multivariate Analysis. Springer, Tokyo. https://doi.org/10.1007/978-4-431-65955-6_17
Download citation
DOI: https://doi.org/10.1007/978-4-431-65955-6_17
Publisher Name: Springer, Tokyo
Print ISBN: 978-4-431-65957-0
Online ISBN: 978-4-431-65955-6
eBook Packages: Springer Book Archive