The utility of item response modeling in marketing research

Journal of the Academy of Marketing Science

Volumn 42, Issue 4, 2014, Pages 337-360

  Raykov, Tenko ^a   Calantone, Roger J ^a  

^a MICHIGAN STATE UNIVERSITY   (United States)

Author keywords

Dimensionality; Factor analysis; Generalized linear model; Item characteristic curve; Item response modeling; Latent variable modeling; Local independence; Non linear factor analysis

Indexed keywords

EID: 84901825006     PISSN: 00920703     EISSN: None     Source Type: Journal    
DOI: 10.1007/s11747-014-0391-8     Document Type: Article

References (52)

1. Agresti, A. (2012). Categorical data analysis. New York: Wiley.
2. Baker, F. B. (2001). The basics of item response theory. Washington: ERIC Clearinghouse on Assessment and Evaluation.
3. Baker, F. B., & Kim, S.-H. (2004). Item response theory. parameter estimation techniques. Boca Raton: Taylor & Francis.
4. Bartholomew, D. J., Knott, M., & Moustaki, I. (2011). Factor analysis and latent variable models. New York: Wiley.
5. Birnbaum, A. (1968). Some latent trait models and their use in inferring an examinee's ability. In F. M. Lord & M. R. Novick (Eds.), Statistical theories of mental test scores. Reading, MA: Addison-Wesley.
6. Bock, R. D. (1972). Estimating item parameters and latent ability when responses are scores in two or more nominal categories. Psychometrika, 37, 29-51.
7. Bock, R. D. (1997a). A brief history of item response theory. Educational Measurement: Issues and Practice, 16, 21-33.
8. Bock, R. D. (1997b). The nominal categories model. In W. van der Linden & R. K. Hambleton (Eds.), Handbook of modern item response theory (pp. 32-49). New York: Springer.
9. Cai, L., Thissen, D., & duToit, S. H. C. (2011). IRTPRO user's guide. Lincolnwood: Scientific Software International, Inc.
10. Casella, G., & Berger, J. O. (2002). Statistical inference. Monterey: Wadsworth.
11. Cox, E. P. (1980). The optimal number of response alternatives for a scale: a review. Journal of Marketing Research, 17, 407-422.
12. De Jong, M. G., Steenkamp, J.-B. E. M., Fox, J.-P., & Baumgartner, H. (2008). Using item response theory to measure extreme response style in marketing research: a global investigation. Journal of Marketing Research, 45, 104-115.
13. de Leeuw, J., & Verhelst, N. (1986). Maximum-likelihood estimation in generalized rasch models. Psychometrika, 11, 183-196.
14. Diamantopoulos, A., Sarstedt, M., Fuchs, C., Wilchynski, P., & Kaiser, S. (2012). Guidelines for choosing between multi-item and single-item scales for construct measurement: a predictive validity perspective. Journal of the Academy of Marketing Science, 40, 434-449.
15. Ewing, M. T., Salzberger, T., & Sunkovic, R. R. (2005). An alternate approach to assessing cross-cultural measurement equivalence in advertising research. Journal of Advertising, 34, 17-36.
16. Fischer, G. (1974). Einführung in die theorie psychologischer tests. Bern: Huber.
17. Goldstein, H. (2011). Multilevel statistical models. London: Arnold.
18. Green, P. E. (1984). Hybrid models for conjoint analysis: an expository review. Journal of Marketing Research, 21, 155-170.
19. Kamakura, W. A., & Balasubramanian, S. (1989). Tailored interviewing: an application of item response theory for personality measurement. Journal of Personality Assessment, 53, 502-519.
20. Lancaster, T. (2000). The incidental parameter problem since 1948. Journal of Econometrics, 95, 391-413.
21. Lawley, D. N. (1943). On problems connected with item selection and test construction. Proceedings. Royal Society of Edinburgh, 61, 273-287.
22. Lord, F. M. (1980). Applications of item response theory to practical testing problems. Hillside: Erlbaum.
23. Messick, S. (1995). Validation of inferences from person's responses and performances as scientific inquiry into score meaning. American Psychologist, 50, 741-749.
24. Muthén, L. K., & Muthén, B. O. (2012). Mplus user's guide. Los Angeles: Muthén & Muthén.
25. Nelder, J. A., & Wedderburn, R. W. (1972). Generalized linear models. Journal of the Royal Statistical Society, Series A, 135, 370-384.
26. Neyman, J., & Scott, E. L. (1948). Consistent estimation from partially consistent observations. Econometrica, 16, 1-32.
27. Ostini, R., & Nering, M. L. (2006). Polytomous item response theory models. Thousand Oaks: Sage.
28. Rasch, G. (1960). Probabilistic models for some intelligence and attainment tests. Copenhagen: Danish Institute of Educational Research.
29. Raykov, T. (2007). Reliability if deleted, not "alpha if deleted": evaluation of scale reliability following component deletion. British Journal of Mathematical and Statistical Psychology, 60, 201-216.
30. Raykov, T. (2008). "Alpha if item deleted": a note on loss of criterion validity in scale development if maximising coefficient alpha. British Journal of Mathematical and Statistical Psychology, 61, 275-285.
31. Raykov, T. (2012). Scale development using structural equation modeling. In R. Hoyle (Ed.), Handbook of structural equation modeling (pp. 472-492). New York: Guilford Press.
32. Raykov, T. (2014). A course in item response modeling (Lecture Notes). Michigan State University, Measurement and Quantitative Methods. MI: East Lansing.
33. Raykov, T., & Marcoulides, G. A. (2006). A first course in structural equation modeling (second edition). Mahwah: Erlbaum.
34. Raykov, T., & Marcoulides, G. A. (2008). An introduction to applied multivariate analysis. New York: Taylor & Francis.
35. Raykov, T., & Marcoulides, G. A. (2011). Introduction to psychometric theory. New York: Taylor & Francis.
36. Raykov, T., & Marcoulides, G. A. (2012). Basic statistics: an introduction with R. New York: Rowman & Littlefield.
37. Raykov, T., Marcoulides, G. A. (2014). On examining the underlying normal variable assumption in latent variable models with categorical indicators. Structural Equation Modeling (in press).
38. Raykov, T., & Pohl, S. (2013a). On studying common factor variance in multiple component measuring instruments. Educational and Psychological Measurement, 73, 191-209.
39. Raykov, T., & Pohl, S. (2013b). Essential unidimensionality in multiple component measuring instruments: a correlation decomposition approach. Educational and Psychological Measurement, 73, 581-600.
40. Raykov, T., Rodenberg, C., Narayanan, A. (2015). On optimal shortenng of psychometric scales. Structural Equation Modeling (in press).
41. Reckase, M. D. (2009). Multidimensional item response theory. New York: Springer.
42. Samejima, F. (1969). Estimation of latent ability using a response pattern of graded scores. Psychometric Monograph, No. 17.
43. Samejima, F. (1997). Graded response model. In W. van der Linden & R. K. Hambleton (Eds.), Handbook of modern item response theory (pp. 85-100). New York: Springer.
44. Schultz, C., Salomo, S., & Talke, K. (2013). Measuring new product portfolio innovativeness: how differences in scale width and evaluator perspectives affect its relationship with performance. Journal of Product Innovation Management, 30, 93-109.
45. Stout, W. F. (1990). A new item response theory modeling approach with applications to unidimensionality assessment and ability estimation. Psychometrika, 55, 293-325.
46. Swaminathan, H., & Guifford, J. A. (1982). Bayesian estimation in the rasch model. Journal of Educational Statistics, 7, 175-192.
47. Takane, Y., & de Leeuw, J. (1987). On the relationship between item response theory and factor analysis of discretized variables. Psychometrika, 52, 393-408.
48. Thissen, D. (1982). Marginal maximum-likelihood estimation for the one-parameter logistic model. Psychometrika, 47, 175-186.
49. Timm, N. H. (2002). Multivariate analysis. New York: Springer.
50. van der Linden, W., & Hambleton, R. K. (Eds.). (1997). Handbook of modern item response theory. New York: Springer.
51. Verhelst, N., & Molenaar, I. (1988). Logit-based parameter estimation in the rasch model. Statistica Neerlandica, 42, 273-295.
52. Wechsler, D. (1958). The measurement and appraisal of adult intelligence. Baltimore: William & Wilkins.