Hostname: page-component-5f745c7db-6bmsf Total loading time: 0 Render date: 2025-01-06T06:48:16.276Z Has data issue: true hasContentIssue false

Statistical Evaluation of Measures of Fit in the Lingoes-Borg Procrustean Individual Differences Scaling

Published online by Cambridge University Press:  01 January 2025

Rolf Langeheine*
Affiliation:
Institute for Science Education at the University of Kiel
*
Requests for reprints and/or the volume of the simulation norms should be sent to: Rolf Langeheine, IPN at the University of Kiel, Olshausenstr. 40-60, D-2300 Kiel 1, Federal Republic of Germany.

Abstract

PINDIS, as recently presented by Lingoes and Borg [1978] not only marks the latest development within the scope of individual differences scaling, but, may be of benefit in some closely related topics, such as target analysis. Decisions on whether the various models available from PINDIS fit fallible data are relatively arbitrary, however, since a statistical theory of the fit measures is lacking. Using Monte Carlo simulation, expected fit measures as well as some related statistics were therefore obtained by scaling sets of 4(4)24 random configurations of 5(5)30 objects in 2, 3, and 4 dimensions (individual differences case) and by fitting one random configuration to a fixed random target for 5(5)30 objects in 2, 3, and 4 dimensions (target analysis case). Applications are presented.

Type
Original Paper
Copyright
Copyright © 1982 The Psychometric Society

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

The author wishes to thank Ingwer Borg and Peter Schönemann, along with several anonymous reviewers, for a number of suggestions concerning this paper.

References

Reference Note

Langeheine, R. Approximate norms and significance tests for the Lingoes-Borg procrustean individual differences scaling (PINDIS), 1980, Kiel: Institute for Science Education.Google Scholar

References

Andrews, F. M. & Inglehart, R. F. The structure of subjective well-being in nine western societies. Social Indicators Research, 1979, 6, 7590.CrossRefGoogle Scholar
Borg, I. Geometric representations of individual differences. In Lingoes, J. C., Roskam, E. E. & Borg, I. (Eds.), Geometric representations of relational data. Readings in multidimensional scaling, 1979, Ann Arbor: Mathesis Press.Google Scholar
Borg, I. Anwendungsorientierte Multidimensionale Skalierung, 1981, Berlin: Springer.CrossRefGoogle Scholar
Borg, I. & Bergermaier, R. Die Ähnlichkeit von Einstellungsstrukturen zur Lebensqualität in elf westlichen Gesellschaften. Zeitschrift für Sozialpsychologie, 1979, 10, 253261.Google Scholar
Borg, I. & Bergermaier, R. Some comments on “The structure of subjective well-being in nine western societies” by Andrews & Inglehart. Social Indicators Research, 1981, 9, 265278.CrossRefGoogle Scholar
Borg, I. & Lingoes, J. C. Ein direkter Transformationsansatz der multidimensionalen Analyse dreimodaler Datenmatrizen: Theorie und Anwendungen. Zeitschrift für Sozialpsychologie, 1977, 8, 98114.Google Scholar
Borg, I., Lingoes, J. C. What weights should weights have in individual difference scaling?. Quality and Quantity, 1978, 12, 223237.CrossRefGoogle Scholar
Carroll, J. D. & Chang, J. J. Analysis of individual differences in multidimensional scaling via anN-way generalization of “Eckart-Young” decomposition. Psychometrika, 1970, 35, 283319.CrossRefGoogle Scholar
Cliff, N. Orthogonal rotation to congruence. Psychometrika, 1966, 31, 3342.CrossRefGoogle Scholar
Cohen, H. S. & Jones, L. E. The effects of random error and subsampling of dimensions on recovery of configurations by non-metric multidimensional scaling. Psychometrika, 1974, 39, 6990.CrossRefGoogle Scholar
Coxon, A. P. M. & Jones, C. Multidimensional scaling: Exploration to confirmation. Quality and Quantity, 1980, 14, 3173.CrossRefGoogle Scholar
Feger, H. Quantitative sociometry: Problems, methods and first results. In Hummell, H. J. & Ziegler, R. (Eds.), Anwendung mathematischer Verfahren zur Analyse sozialer Netzwerke, 1977, Duisburg: Verlag der Sozialwissenschaftlichen Kooperative.Google Scholar
Feger, H. Einstellungsstruktur und Einstellungsänderung: Ergebnisse, Probleme und ein Komponentenmodell der Einstellungsobjekte. Zeitschrift für Sozialpsychologie, 1979, 10, 331349.Google Scholar
Fischer, G. & Roppert, J. Ein Verfahren der Transformationsanalyse faktorenanlytischer Ergebnisse. In Roppert, J., Fischer, G. (Eds.), Lineare Strukturen in Mathematik und Statistik, 1965, Wien: Physika-Verlag.Google Scholar
Graef, J. & Spence, I. Using distance information in the design of large multidimensional scaling experiments. Psychological Bulletin, 1979, 86, 6066.CrossRefGoogle Scholar
Green, B. F. The orthogonal approximation of an oblique structure in factor analysis. Psychometrika, 1952, 17, 429440.CrossRefGoogle Scholar
Korth, B. & Tucker, L. R. The distribution of chance congruence coefficients from simulated data. Psychometrika, 1975, 40, 361372.CrossRefGoogle Scholar
Kristof, W. Die beste orthogonale Transformation zur gegenseitigen Überführung zweier Faktormatrizen. Diagnostica, 1964, 10, 8790.Google Scholar
Langeheine, R. The evaluation of classroom social structure by three-way multidimensional scaling of sociometric data. Studies in Educational Evaluation, 1978, 4, 185208.CrossRefGoogle Scholar
Langeheine, R. Strukturelle Stabilität—Ein neuer Ansatz zur Überprüfung der Reliabilität in der Soziometrie. Psychologische Beiträge, 1978, 20, 571588.Google Scholar
Langeheine, R. Strukturanalytische Untersuchungen der Schulklasse. Eine Analyse mit Hilfe multidimensionaler Skalierungsmodelle, 1979, Frankfurt: Lang.Google Scholar
Langeheine, R. Structural aspects reconsidered: A reanalysis of two studies in educational evaluation. Studies in Educational Evaluation, 1980, 6, 329341.CrossRefGoogle Scholar
Langeheine, R. & Andresen, N. Strukturelle Stabilität des AFS. Zeitschrift für Empirische Pädagogik, 1980, 4, 203212.Google Scholar
Levine, M. S. Canonical analysis and factor comparison, 1977, Beverly Hills: Sage.CrossRefGoogle Scholar
Lingoes, J. C. & Borg, I. A direct approach to individual differences scaling using increasingly complex transformations. Psychometrika, 1978, 43, 491519.CrossRefGoogle Scholar
MacCallum, R. C. & Cornelius, E. T. III A Monte Carlo investigation of recovery of structure by ALSCAL. Psychometrika, 1977, 42, 401428.CrossRefGoogle Scholar
Maimon, Z., Venezia, I. & Lingoes, J. C. How similar are different results?. Quality and Quantity, 1980, 14, 727742.CrossRefGoogle Scholar
Mulaik, S. A. The foundations of factor analysis, 1972, New York: McGraw-Hill.Google Scholar
Nesselroade, J. R. & Baltes, P. B. On a dilemma of comparative factor analysis: A study of factor matching based on random data. Educational and Psychological Measurement, 1970, 30, 935948.CrossRefGoogle Scholar
Poor, D. D. S. & Wherry, R. J. Invariance of multidimensional configurations. British Journal of Mathematical and Statistical Psychology, 1976, 29, 114125.CrossRefGoogle Scholar
Roskam, E. E. Contributions of multidimensional scaling to social science research. In Borg, I. (Eds.), Multidimensional data representations: When and why, 1981, Ann Arbor: Mathesis Press.Google Scholar
Schönemann, P. H. A generalized solution to the orthogonal procrustes problem. Psychometrika, 1966, 31, 110.CrossRefGoogle Scholar
Schönemann, P. H. & Carroll, R. M. Fitting one matrix to another under choice of a central dilation and a rigid motion. Psychometrika, 1970, 35, 245255.CrossRefGoogle Scholar
Schönemann, P. H., James, W. L. & Carter, F. S. COSPA: Common space analysis—A program for fitting and testing Horan's subjective metric model. Journal of Marketing Research, 1978, 15, 268272.Google Scholar
Schönemann, P. H., James, W. L. & Carter, F. S. Statistical inference in multidimensional scaling: A method for fitting and testing Horan's model. In Lingoes, J. C., Roskam, E. E. & Borg, I. (Eds.), Geometric representations of relational data. Readings in multidimensional scaling, 1979, Ann Arbor: Mathesis Press.Google Scholar
Spence, I. A Monte Carlo evaluation of three nonmetric multidimensional scaling algorithms. Psychometrika, 1972, 37, 461486.CrossRefGoogle Scholar
Tagiuri, R. Relational analysis: An extension of sociometric method with emphasis upon social perception. Sociometry, 1952, 15, 91104.CrossRefGoogle Scholar
Tagiuri, R. Social preference and its perception. In Tagiuri, R. & Petrullo, L. (Eds.), Person perception and interpersonal behavior, 1958, Stanford: Stanford University Press.Google Scholar
Tagiuri, R. Perceptual sociometry. In Moreno, J. L. (Eds.), The sociometry reader, 1960, New York: The Free Press.Google Scholar
Takane, Y., Young, F. W. & De Leeuw, J. Nonmetric individual differences multidimensional scaling: An alternating least squares method with optimal scaling features. Psychometrika, 1977, 42, 767.CrossRefGoogle Scholar
Weeks, D. G. & Bentler, P. M. A comparison of linear and monotone multidimensional scaling models. Psychological Bulletin, 1979, 86, 349354.CrossRefGoogle Scholar