Latent variable mixture models: A promising approach for the validation of patient reported outcomes

Quality of Life Research

Volumn 21, Issue 4, 2012, Pages 637-650

  Sawatzky, Richard ^a   Ratner, Pamela A ^b   Kopec, Jacek A ^b   Zumbo, Bruno D ^b  

^a TRINITY WESTERN UNIVERSITY   (Canada)

^b UNIVERSITY OF BRITISH COLUMBIA   (Canada)

Author keywords

Measurement validity; Physical function; Psychometrics; Self report measurement

Indexed keywords

ADOLESCENT; ADULT; AGED; ARTICLE; CANADA; CHILD; FEMALE; HEALTH STATUS; HUMAN; MALE; METHODOLOGY; MIDDLE AGED; OUTCOME ASSESSMENT; PATIENT; PSYCHOMETRY; QUESTIONNAIRE; STANDARD; STATISTICAL MODEL; VALIDATION STUDY;

ADOLESCENT; ADULT; AGED; AGED, 80 AND OVER; CANADA; CHILD; FEMALE; HEALTH STATUS; HUMANS; MALE; MIDDLE AGED; MODELS, STATISTICAL; OUTCOME ASSESSMENT (HEALTH CARE); PATIENTS; PSYCHOMETRICS; QUESTIONNAIRES; YOUNG ADULT;

EID: 84863614435     PISSN: 09629343     EISSN: None     Source Type: Journal    
DOI: 10.1007/s11136-011-9976-6     Document Type: Article

References (80)

1. van der Linden, W. J., & Hambleton, R. K. (1997). Handbook of modern item response theory. New York: Springer.
2. Embretson, S. E., & Reise, S. P. (2000). Item response theory for psychologists. New Jersey: Lawrence Erlbaum.
3. Hambleton, R. K., Swaminathan, H., & Rogers, H. J. (1991). Fundamentals of item response theory. London: Sage.
4. Streiner, D. L., & Norman, G. R. (2008). Health measurement scales: A practical guide to their development and use (4th ed.). Oxford: Oxford University Press.
5. Fayers, P., & Machin, D. (2007). Quality of life: The assessment, analysis and interpretation of patient-reported outcomes. Chichester, West Sussex: Wiley.
6. Zumbo, B. D. (2007). Validity: Foundational issues and statistical methodology. In C. R. Rao & S. Sinharay (Eds.), Handbook of statistics (vol. 26: Psychometrics) (pp. 45-79). Amsterdam: Elsevier Science.
7. Reise, S. P., & Gomel, J. N. (1995). Modeling qualitative variation within latent trait dimensions: Application of mixed-measurement to personality assessment. Multivariate Behavioral Research, 30, 341-358.
8. Zumbo, B. D. (2009). Validity as contextualized and pragmatic explanation, and its implications for validation practice. In R. W. Lissitz (Ed.), The concept of validity: Revisions, new directions and applications (pp. 65-82). Charlotte, NC: Information Age Publishing.
9. Zumbo, B. D. (2007). Three generations of DIF analyses: Considering where it has been, where it is now, and where it is going. Language Assessment Quarterly, 4, 223-233.
10. Sawatzky, R., Ratner, P. A., Johnson, J. L., Kopec, J., & Zumbo, B. D. (2009). Sample heterogeneity and the measurement structure of the Multidimensional Students' Life Satisfaction Scale. Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, 94, 273-296.
11. Vandenberg, R. J., & Lance, C. E. (2000). A review and synthesis of the measurement invariance literature: Suggestions, practices, and recommendations for organizational research. Organizational Research Methods, 3, 4-69.
12. Byrne, B. M. (1998). Structural equation modeling with LISREL, PRELIS, and SIMPLIS: Basic concepts, applications, and programming. Mahwah, NJ: L. Erlbaum.
13. Holland, P. W., & Thayer, D. T. (1988). Differential item functioning and the Mantel Haenszel procedure. In H. Wainer, H. I. Braun, & Educational Testing Service (Eds.), Test validity (pp. 129-145). Hillsdale, NJ: L. Erlbaum Associates.
14. Swaminathan, H., & Rogers, H. J. (1990). Detecting differential item functioning using logistic regression procedures. Journal of Educational Measurement, 27, 361-370.
15. Crane, P. K., Gibbons, L. E., Jolley, L., & van Belle, G. (2006). Differential item functioning analysis with ordinal logistic regression techniques. DIFdetect and difwithpar. Medical Care, 44(11 Suppl 3), S115-S123. (Pubitemid 44632037)
16. Zumbo, B. D. (1999). A handbook on the theory and methods of differential item functioning (DIF): Logistic regression modeling as a unitary framework for binary and Likert-type (ordinal) item scores. Ottawa, ON: Directorate of Human Resources Research and Evaluation, Department of National Defense.
17. Roussos, L., & Stout, W. (1996). A multidimensionality-based DIF analysis paradigm. Applied Psychological Measurement, 20, 355-371. (Pubitemid 126332317)
18. Shealy, R., & Stout, W. (1993). A model-based standardization approach that separates true bias/DIF from group ability differences and detects test bias/DTF as well as item bias/DIF. Psychometrika, 58, 159-194.
19. Muthén, B., Kao, C.-F., & Burstein, L. (1991). Instructionally sensitive psychometrics: Application of a new IRT-based detection technique to mathematics achievement test items. Journal of Educational Measurement, 28, 1-22.
20. Steinberg, L., & Thissen, D. (2006). Using effect sizes for research reporting: Examples using item response theory to analyze differential item functioning. Psychological Methods, 11, 402-415. (Pubitemid 44950901)
21. Morales, L. S., Flowers, C., Gutierrez, P., Kleinman, M., & Teresi, J. A. (2006). Item and scale differential functioning of the Mini-Mental State Exam assessed using the differential item and test functioning (DFIT) framework. Medical Care, 44(11 Suppl 3), S143-S151. (Pubitemid 44632040)
22. Cohen, A. S., & Bolt, D. M. (2005). A mixture model analysis of differential item functioning. Journal of Educational Measurement, 42, 133-148. (Pubitemid 40937934)
23. De Ayala, R. J., Kim, S. H., Stapleton, L. M., & Dayton, C. M. (2002). Differential item functioning: A mixture distribution conceptualization. International Journal of Testing, 2, 243.
24. Samuelsen, K. M. (2008). Examining differential item functioning from a latent mixture perspective. In G. R. Hancock & K. M. Samuelsen (Eds.), Advances in latent variable mixture models (pp. 177-198). Charlotte, NC: Information Age Publishing.
25. Mislevy, R. J., Levy, R., Kroopnick, M., & Rutstein, D. (2008). Evidentiary foundations of mixture item response theory models. In G. R. Hancock & K. M. Samuelsen (Eds.), Advances in latent variable mixture models (pp. 149-176). Charlotte, NC: Information Age Publishing.
26. De Ayala, R. J. (2009). The theory and practice of item response theory. New York: Guilford Press.
27. Vermunt, J. K. (2001). The use of restricted latent class models for defining and testing nonparametric and parametric item response theory models. Applied Psychological Measurement, 25, 283-294.
28. Rost, J. (1990). Rasch models in latent classes: An integration of two approaches to item analysis. Applied Psychological Measurement, 14, 271-282.
29. Maij-de Meij, A. M., Kelderman, H., & van der Flier, H. (2010). Improvement in detection of differential item functioning using a mixture item response theory model. Multivariate Behavioral Research, 45(6), 975-999.
30. Muthén, B. (2008). Latent variables hybrids. In G. R. Hancock & K. M. Samuelsen (Eds.), Advances in latent variable mixture models (pp. 1-24). Charlotte, NC: Information Age Publishing.
31. Muthén, B. (2001). Latent variable mixture modeling. In G. A. Marcoulides & R. E. Schumacker (Eds.), New developments and techniques in structural equation modeling (pp. 1-33). Mahwah, NJ: Lawrence Erlbaum.
32. Muthén, B. (2002). Beyond SEM: General latent variable modeling. Behaviormetrika, 29(1), 81-117.
33. Muthén, B., & Muthén, L. (2008). MPlus (version 5.2). Los Angeles, CA: Statmodel.
34. Lubke, G., & Muthén, B. (2005). Investigating population heterogeneity with factor mixture models. Psychological Methods, 10, 21-39. (Pubitemid 40551477)
35. von Davier, M., & Carstensen, C. H. (2007). Multivariate and mixture distribution Rasch models: Extensions and applications. New York, NY: Springer.
36. Bolt, D. M. (2000). A SIBTEST approach to testing DIF hypotheses using experimentally designed test items. Journal of Educational Measurement, 37, 307-327.
37. Rost, J. (1991). A logistic mixture distribution model for polychotomous item responses. British Journal of Mathematical and Statistical Psychology, 44, 75-92.
38. von Davier, M., & Yamamoto, K. (2007). Mixture-distribution and HYBRID Rasch models. In M. von Davier & C. H. Carstensen (Eds.), Multivariate and mixture distribution Rasch models: Extensions and applications (pp. 99-115). New York, NY: Springer Science ? Business Media.
39. Muthén, B., & Asparouhov, T. (2006). Item response mixture modeling: Application to tobacco dependence criteria. Addictive Behaviors, 31, 1050-1066. (Pubitemid 43775959)
40. Kamata, A., & Bauer, D. J. (2008). A note on the relation between factor analytic and item response theory. Structural Equation Modeling: A Multidisciplinary Journal, 15, 136-153.
41. Takane, Y., & de Leeuw, J. (1987). On the relationship between item response theory and factor analysis of discretized variables. Psychometrika, 52, 393-408.
42. McDonald, R. P. (1999). Test theory: A unified treatment. Mahwah, NJ: L. Erlbaum Associates.
43. Samejima, F. (1997). Graded response model. In W. J. Van der Linden & R. K. Hambleton (Eds.), Handbook of modern item response theory (pp. 85-100). New York: Springer.
44. Reeve, B. B., Hays, R. D., Bjorner, J. B., Cook, K. F., Crane, P. K., Teresi, J. A., et al. (2007). Psychometric evaluation and calibration of health-related quality of life item banks: Plans for the patient-reported outcomes measurement information system (PROMIS). Medical Care, 45(5 Suppl 1), S22-S31.
45. McLachlan, G. J., & Peel, D. (2000). Finite mixture models. New York: Wiley.
46. Hagenaars, J. A., & McCutcheon, A. L. (2002). Applied latent class analysis. Cambridge, NY: Cambridge University Press.
47. Asparouhov, T., & Muthén, B. (2008). Multilevel mixture models. In G. R. Hancock & K. M. Samuelsen (Eds.), Advances in latent variable mixture models (pp. 27-51). Charlotte, NC: Information Age Publishing.
48. Chen, W. H., & Thissen, D. (1997). Local dependence indexes for item pairs using item response theory. Journal of Educational and Behavioral Statistics, 22, 265-289. (Pubitemid 127472138)
49. Agresti, A. (2002). Categorical data analysis (2nd ed.). New York: Wiley-Interscience.
50. Sclove, S. L. (1987). Application of model-selection criteria to some problems in multivariate analysis. Psychometrika, 52, 333-343.
51. Henson, J. M., Reise, S. P., & Kim, K. H. (2007). Detecting mixtures from structural model differences using latent variable mixture modeling: A comparison of relative model fit statistics. Structural Equation Modeling: A Multidisciplinary Journal, 14, 202-226.
52. Yang, C. C. (2006). Evaluating latent class analysis models in qualitative phenotype identification. Computational Statistics & Data Analysis, 50, 1090-1104.
53. Nylund, K. L., Asparoutiov, T., & Muthén, B. (2007). Deciding on the number of classes in latent class analysis and growth mixture modeling: A Monte Carlo simulation study. Structural Equation Modeling: A Multidisciplinary Journal, 14, 535-569.
54. Li, F. M., Cohen, A. S., Kim, S. H., & Cho, S. J. (2009). Model selection methods for mixture dichotomous IRT models. Applied Psychological Measurement, 33, 353-373.
55. Vuong, Q. H. (1989). Likelihood ratio tests for model selection and non-nested hypotheses. Econometrica, 57, 307.
56. Lo, Y. T., Mendell, N. R., & Rubin, D. B. (2001). Testing the number of components in a normal mixture. Biometrika, 88, 767-778. (Pubitemid 33771022)
57. Muthén, B., Brown, C. H., Masyn, K., Jo, B., Khoo, S. T., Yang, C. C., et al. (2002). General growth mixture modeling for randomized preventive interventions. Biostatistics, 3, 459-475.
58. Lubke, G., & Muthén, B. (2007). Performance of factor mixture models as a function of model size, covariate effects, and classspecific parameters. Structural Equation Modeling: A Multidisciplinary Journal, 14, 26-47.
59. Holland, P. W. (2007). A framework and history for score linking. In N. J. Dorans, M. Pommerich, & P. W. Holland (Eds.), Linking and aligning scores and scales (pp. 5-30). New York: Springer.
60. Kolen, M. J., & Brennan, R. L. (2004). Test equating, scaling, and linking: Methods and practices (2nd ed.). New York: Springer.
61. Thomas, D. R., Zhu, P., Zumbo, B. D., & Dutta, S. (2008). On measuring the relative importance of explanatory variables in a logistic regression. Journal of Modern Applied Statistical Methods, 7, 21-38.
62. Muthén, B., & Muthén, L. (2010). IRT in Mplus. http://www.statmodel.com/download/MplusIRT2.pdf. Accessed 15 Jan 2011.
63. Maij-de Meij, A. M., Kelderman, H., & van der Flier, H. (2008). Fitting a mixture item response theory model to personality questionnaire data: Characterizing latent classes and investigating possibilities for improving prediction. Applied Psychological Measurement, 32, 611-631.
64. Vermunt, J. K. (2010). Latent class modeling with covariates: Two improved three-step approaches. Political Analysis, 18(4), 450-469.
65. Tofighi, D., & Enders, C. K. (2008). Identifying the correct number of classes in growth mixture models. In G. R. Hancock & K. M. Samuelsen (Eds.), Advances in latent variable mixture models (pp. 317-342). Charlotte, NC: Information Age Publishing.
66. Hosmer, D. W., & Lemeshow, S. (2000). Applied logistic regression (2nd ed.). New York: Wiley.
67. Bolck, A., Croon, M., & Hagenaars, J. (2004). Estimating latent structure models with categorical variables: One-step versus three-step estimators. Political Analysis, 12, 3-27.
68. Wang, C. P., Brown, C. H., & Bandeen-Roche, K. (2005). Residual diagnostics for growth mixture models: Examining the impact of a preventive intervention on multiple trajectories of aggressive behavior. Journal of the American Statistical Association, 100, 1054-1076. (Pubitemid 41276349)
69. Bandeen-Roche, K., Miglioretti, D. L., Zeger, S. L., & Rathouz, P. J. (1997). Latent variable regression for multiple discrete outcomes. Journal of the American Statistical Association, 92(440), 1375-1386.
70. Muthén, B., & Muthén, L. (2007). Wald test of mean equality for potential latent class predictors in mixture modeling. http://www.statmodel.com/download/MeanTest1.pdf. Accessed on 20 Oct. 2010.
71. Ware, J. E., Snow, K. K., Kosinski, M., & Gandek, B. (1993). SF-36 health survey: Manual and interpretation guide. Boston, MA: The Health Institute, New England Medical Center.
72. Canada, Statistics. (2005). Canadian Community Health Survey Cycle 2.1: User guide for the public use microdata file. Ottawa, ON: Statistics Canada: Health Statistics Division.
73. Dayton, C. M. (1998). Latent class scaling analysis. Thousand Oaks, CA: Sage.
74. Finney, S. J., & DiStefano, C. (2006). Non-normal and categorical data in structural equation modeling. In G. R. Hancock & R. O. Mueller (Eds.), Structural equation modeling: A second course (pp. 269-314). Greenwich, CT: Information Age Publishing.
75. Jöreskog, K. G. (1990). New developments in LISREL: Analysis of ordinal variables using polychoric correlations and weighted least squares. Quality & Quantity, 24, 387-404.
76. Jöreskog, K. G., & Moustaki, I. (2001). Factor analysis of ordinal variables: A comparison of three approaches. Multivariate Behavioral Research, 36, 347-387. (Pubitemid 33571332)
77. Rigdon, E. E., & Ferguson, C. E., Jr. (1991). The performance of the polychoric correlation coefficient and selected fitting functions in confirmatory factor analysis with ordinal data. Journal of Marketing Research, 28, 491-497.
78. Lord, F. M. (1980). Applications of item response theory to practical testing problems. Hillsdale, NJ: L. Erlbaum Associates.
79. Bauer, D. J., & Curran, P. J. (2004). The integration of continuous and discrete latent variable models: Potential problems and promising opportunities. Psychological Methods, 9, 3-29. (Pubitemid 38516435)
80. Canada, Statistics. (2005). Canadian Community Health Survey: Questionnaire for cycle 2.1. Ottawa, ON: Statistics Canada: Health Statistics Division.