Loglinear rasch models for the analysis of stability and change

Psychometrika

Volumn 61, Issue 4, 1996, Pages 629-645

  Meiser, Thorsten ^a  

^a UNIVERSITY OF BONN   (Germany)

Author keywords

Goodness of fit testing; Measurement of change; Multidimensional Rasch model; Nonstandard loglinear model; Rasch model

Indexed keywords

EID: 0030356467     PISSN: 00333123     EISSN: None     Source Type: Journal    
DOI: 10.1007/BF02294040     Document Type: Article

References (52)

1. Agresti, A. (1993). Computing conditional maximum likelihood estimates for generalized Rasch models using simple loglinear models with diagonals parameters. Scandinavian Journal of Statistics, 20, 63-71.
2. Andersen, E. B. (1980). Discrete statistical models with social science applications. Amsterdam: North-Holland.
3. Andersen, E. B. (1982). Latent structure analysis: A survey. Scandinavian Journal of Statistics, 9, 1-12.
4. Andersen, E. B. (1985). Estimating latent correlations between repeated testings. Psychometrika, 50, 3-16.
5. Andersen, E. B. (1988). Comparison of latent structure models. In R. Langeheine & J. Rost (Eds.), Latent trail and latent class models (pp. 207-229). New York: Plenum Press.
6. Andersen, E. B. (1990). The statistical analysis of categorical data. Berlin: Springer.
7. Andersen, E. B. (1991). Longitudinal studies for discrete data based on latent structure models. In D. Magnusson, L. R. Bergman, G. Rudinger, & B. Törestad (Eds.), Problems and methods in longitudinal research: Stability and change (pp. 308-322). Cambridge: Cambridge University Press.
8. Andrich, D. (1978). A rating formulation for ordered response categories. Psychometrika, 43, 561-573.
9. Bergman, L. R., Eklund, G., & Magnusson, D. (1991). Studying individual development: Problems and methods. In D. Magnusson, L. R. Bergman, G. Rudinger, & B. Törestad (Eds.), Problems and methods in longitudinal research: Stability and change (pp. 1-27). Cambridge: Cambridge University Press.
10. Christensen, R. (1990). Log-linear models. New York: Springer.
11. Clogg, C. C. (1988). Latent class models for measuring. In R. Langeheine, & J. Rost (Eds.), Latent trait and latent class models (pp. 173-205). New York: Plenum Press.
12. Cressie, N., & Holland, P. W. (1983). Characterizing the manifest probabilities of latent trait models. Psychometrika, 48, 129-141.
13. Duncan, O. D. (1984). Rasch measurement: Further examples and discussion. In C. F. Turner & E. Martin (Eds.), Surveying subjective phenomena, Vol. 11 (pp. 367-403). New York: Russell Sage Foundation.
14. Embretson, S. E. (1991a). A multidimensional latent trait model for measuring learning and change. Psychometrika, 56, 495-515.
15. Embretson, S. E. (1991b). Implications of a multidimensional latent trait model for measuring change. In L. M. Collins & J. L. Horn (Eds.), Best methods for the analysis of change: Recent advances, unanswered questions, future directions (pp. 184-197). Washington, DC: American Psychological Association.
16. Erdfelder, E. (1990). Deterministic developmental hypotheses, probabilistic rules of manifestation, and the analysis of finite mixture distributions. In A. von Eye (Ed.), Statistical methods in longitudinal research. Vol. II: Time series and categorical longitudinal data (pp. 471-509). New York: Academic Press.
17. Fischer, G. H. (1983). Logistic latent trait models with linear constraints. Psychometrika, 48, 3-26.
18. Fischer, G. H. (1987). Applying the principles of specific objectivity and of generalizability to the measurement of change. Psychometrika, 52, 565-587.
19. Fischer, G. H. (1989). An IRT-based model for dichotomous longitudinal data. Psychometrika, 54, 599-624.
20. Fischer, G. H., & Formann, A. K. (1982). Some applications of logistic latent trait models with linear constraints on the parameters. Applied Psychological Measurement, 6, 397-416.
21. Fischer, G. H., & Parzer, P. (1991). An extension of the rating scale model with an application to the measurement of change. Psychometrika, 56, 637-651.
22. Fischer, G. H., & Ponocny, I. (1994). An extension of the partial credit model with an application to the measurement of change. Psychometrika, 59, 177-192.
23. Goodman, L. A. (1990). Total-score models and Rasch-type models for the analysis of a multidimensional contingency table, or a set of multidimensional contingency tables, with specified and/or unspecified order for response categories. In C. C. Clogg (Ed.), Sociological methodology 1990 (pp. 249-294). Oxford: Blackwell.
24. Haberman, S. J. (1978). Analysis of qualitative data. Volume 1: Introductory topics. New York: Academic Press.
25. Kelderman, H. (1984). Loglinear Rasch model tests. Psychometrika, 49, 223-245.
26. Kelderman, H. (1988). Common item equating using the loglinear Rasch model. Journal of Educational Statistics, 13, 319-336.
27. Kelderman, H. (1989). Item bias detection using loglinear IRT. Psychometrika, 54, 681-697.
28. Kelderman, H. (1992). Computing maximum likelihood estimates of loglinear models from marginal sums with special attention to loglinear Item Response Theory. Psychometrika, 57, 437-450.
29. Kelderman, H. (1993). Estimating and testing a multidimensional Rasch model for partial credit scoring of children's application of size concepts. In R. Steyer, K. F. Wender, & K. F. Widaman (Eds.), Psychometric methodology. Proceedings of the 7th European meeting of the Psychometric Society in Trier (pp. 209-212). Stuttgart: Fischer.
30. Kelderman, H., & Macready, G. B. (1990). The use of loglinear models for assessing differential item functioning across manifest and latent examinee groups. Journal of Educational Measurement, 27, 307-327.
31. Kelderman, H., & Rijkes, C. P. M. (1994). Loglinear multidimensional IRT models for polytomously scored items. Psychometrika, 59, 149-176.
32. Langeheine, R. (1993). Diagnosing incremental learning: Some probabilistic models for measuring change and testing hypotheses about growth. Studies in Educational Evaluation, 19, 349-362.
33. Langeheine, R., & van de Pol, F. (1990). Veränderungsmessung bei kategorialen Daten [Measurement of change in categorical data]. Zeitschrift für Sozialpsychologie, 21, 88-100.
34. Lazarsfeld, P. F., & Henry, N. W. (1968). Latent structure analysis. Boston: Houghton Mifflin.
35. Little, R. J. A., & Rubin, D. B. (1987). Statistical analysis with missing data. New York: Wiley.
36. Masters, G. N. (1982). A Rasch model for partial credit scoring. Psychometrika, 47, 149-174.
37. Masters, G. N. (1985). A comparison of latent trait and latent class analyses of Likert-type data. Psychometrika, 50, 69-82.
38. Masters, G. N. (1988). Measurement models for ordered response categories. In R. Langeheine & J. Rost (Eds.), Latent trait and latent class models (pp. 11-30). New York: Plenum Press.
39. Rindskopf, D. (1984). Linear equality restrictions and loglinear models. Psychological Bulletin, 96, 597-603.
40. Rindskopf, D. (1990). Nonstandard log-linear models. Psychological Bulletin, 108, 150-162.
41. Rost, J. (1988). Test theory with qualitative and quantitative latent variables. In R. Langeheine, & J. Rost (Eds.), Latent trait and latent class models (pp. 147-172). New York: Plenum Press.
42. Rost, J. (1989). Rasch models and latent class models for measuring change with ordinal variables. In R. Coppi, & S. Bolasco (Eds.). Multiway data analysis (pp. 473-483). Amsterdam: North Holland.
43. Rost, J. (1990). Rasch models in latent classes: An integration of two approaches to item analysis. Applied Psychological Measurement, 14, 271-282.
44. Rost, J. (1991). A logistic mixture distribution model for polychotomous item responses. British Journal of Mathematical and Statistical Psychology, 44, 75-92.
45. Rost, J., & Spada, H. (1983). Die Quantifizierung von Lerneffekten anhand von Testdaten [The quantification of learning effects with test data]. Zeitschrift für Differentielle und Diagnostische Psychologie, 4, 29-49.
46. SAS Institute. (1988). SAS/STAT user's guide, release 6.03 edition. Cary, NC: Author.
47. Schröder, E., Edelstein, W., & Hoppe-Graff, S. (1991). Qualitative analyses of individual differences in intraindividual change: Examples from cognitive development. In D. Magnusson, L. R. Bergman, G. Rudinger, & B. Törestad (Eds.), Problems and methods in longitudinal research: Stability and change (pp. 166-189). New York: Cambridge University Press.
48. Spada, H., & McGaw, B. (1985). The assessment of learning effects with linear logistic test models. In S. Embretson (Ed.), Test design: Developments in psychology and psychometrics (pp. 169-194). Orlando: Academic Press.
49. Stegelmann, W. (1983). Expanding the Rasch model to a general model having more than one dimension. Psychometrika, 48, 259-267.
50. Thomae, H. (1965). Objective socialization variables and personality development. Findings from a longitudinal study. Human Development, 8, 87-116.
51. Tjur, T. (1982). A connection between Rasch's item analysis model and a multiplicative Poisson model. Scandinavian Journal of Statistics, 9, 23-30.
52. Winship, C., & Mare, R. D. (1989). Loglinear models with missing data: A latent class approach. In C. C. Clogg (Ed.), Sociological Methodology 1989 (pp. 331-367). Oxford: Blackwell.