Testing measurement invariance using item response theory in longitudinal data: An introduction

Child Development Perspectives

Volumn 4, Issue 1, 2010, Pages 5-9

  Millsap, Roger E ^a  

^a ARIZONA STATE UNIVERSITY   (United States)

Author keywords

Item response theory; Measurement invariance

Indexed keywords

EID: 77951661334     PISSN: 17508592     EISSN: 17508606     Source Type: Journal    
DOI: 10.1111/j.1750-8606.2009.00109.x     Document Type: Article

References (33)

1. Baker F B. Item response theory: Parameter estimation techniques 1992, New York, Marcel Dekker
2. Bock R D, Muraki E, Pfeiffenberger W. Item pool maintenance in the presence of item parameter drift. Journal of Educational Measurement 1988, 25:275-285.
3. Cudeck R, MacCallum R C. Factor analysis at 100: Historical developments and future directions 2007, Mahwah, NJ, Erlbaum
4. Curran P J, Hussong A M, Cai L, Huang W, Chassin L, Sher K J. Pooling data from multiple longitudinal studies: The role of item response theory in integrative data analysis. Developmental Psychology 2008, 44:365-380.
5. Embretson S E, Reise S P. Item response theory for psychologists 2000, Mahwah, NJ, Erlbaum
6. Rasch models: Foundations, recent developments, and applications 1995, Fischer G HMolenaar I W. Eds, New York, Springer
7. Hambleton R K, Swaminathan H, Rogers H J. Fundamentals of item response theory 1991, Newbury Park, CA, Sage
8. Hedeker D, Mermelstein R J, Flay B R. Application of item response theory models for intensive longitudinal data. Models for intensive longitudinal data 2006, 84-108. Walls T A, Schafer J L, In, Eds, pp, New York, Oxford University Press
9. Hulin C L, Lissak R I, Drasgow F. Recovery of two- and three-parameter logistic item response characteristic curves: A Monte Carlo study. Applied Psychological Measurement 1982, 6:249-260.
10. Jöreskog K G, Sörbom D. LISREL 8 user's reference guide 1996, Chicago, Scientific Software International
11. Kamata A, Bauer D J. A note on the relation between factor analytic and item response theory models. Structural Equation Modeling 2008, 15:136-153.
12. Lord F M. Applications of item response theory to practical testing problems 1980, Hillsdale, NJ, Erlbaum
13. Lord F M, Novick M E. Statistical theories of mental test scores 1968, Reading, MA, Addison-Wesley
14. Masters G N. A Rasch model for partial credit scoring. Psychometrika 1982, 47:149-174.
15. McDonald R P. Test theory: A unified treatment 1999, Mahwah, NJ, Erlbaum
16. Meade A W, Lautenschlager G J, Hecht J E. Establishing measurement equivalence and invariance in longitudinal data with item response theory. International Journal of Testing 2005, 5:279-300.
17. Mellenbergh G J. Item bias and item response theory. International Journal of Educational Research 1989, 13:127-143.
18. Meredith W, Millsap R E. On the misuse of manifest variables in the detection of measurement bias. Psychometrika 1992, 57:289-311.
19. Millsap R E, Kwok O-M. Evaluating the impact of partial factorial invariance on selection in two populations. Psychological Methods 2004, 9:93-115.
20. Muthén L K, Muthén B O. Mplus user's guide 1998-2006, 4th ed., Los Angeles, Author
21. Patz R J, Junker B W. A straightforward approach to Markov chain Monte Carlo methods for item response models. Journal of Educational and Behavioral Statistics 1999a, 24:146-178.
22. Patz R J, Junker B W. Applications and extensions of MCMC in IRT: Multiple item types, missing data, and rated responses. Journal of Educational and Behavioral Statistics 1999b, 24:342-366.
23. Raju N S. The area between two item characteristic curves. Psychometrika 1988, 53:495-502.
24. Raju N S. Determining the significance of estimated signed and unsigned areas between two item response functions. Applied Psychological Measurement 1990, 14:197-207.
25. Raju N S, van der Linden W J, Fleer P F. IRT-based internal measures of differential functioning of items and tests. Applied Psychological Measurement 1995, 19:353-368.
26. Rasch G. Probabilistic models for some intelligence and attainment tests 1960, Copenhagen, Danish Institute for Educational Research
27. Reise S P, Yu J Y. Parameter recovery in the graded response model using MULTILOG. Journal of Educational Measurement 1990, 27:133-144.
28. Rensvold R B, Cheung G W. Testing for metric invariance using structural equation models: Solving the standardization problem. Research in Management 2001, 1:25-50.
29. Samejima F. Estimation of ability using a response pattern of graded scores. Psychometrika Monograph 1969, 17.
30. Samejima F. The graded response model. Handbook of modern item response theory 1996, 85-100. van der Linden W JHambleton R K. In, Eds, pp, New York, Springer
31. Skaggs G, Stevenson J. A comparison of pseudo-Bayesian and joint maximum likelihood procedures for estimating item parameters in the three-parameter IRT model. Applied Psychological Measurement 1989, 13:391-402.
32. Thissen D, Chen W-H, Bock R D. Multilog (version 7). 2003, Lincolnwood, IL, Scientific Software
33. van der Linden W J, Hambleton R K. Handbook of modern item response theory 1997, New York, Springer