Gender-related differential item functioning in vocational interest measurement

Journal of Individual Differences

Volumn 34, Issue 3, 2013, Pages 170-183

  Wetzel, Eunike ^a   Hell, Benedikt ^b  

^a UNIVERSITY OF BAMBERG   (Germany)

^b UNIVERSITY OF APPLIED SCIENCES AND ARTS NORTHWESTERN SWITZERLAND   (Switzerland)

Author keywords

AIST R; Differential item functioning; Differential test functioning; Gender differences; Vocational interests

Indexed keywords

EID: 84884264511     PISSN: 16140001     EISSN: 21512299     Source Type: Journal    
DOI: 10.1027/1614-0001/a000112     Document Type: Article

References (51)

1. American Educational Research Association, American Psychological Association, & National Council on Measurement in Education. (1999). Standards for educational and psychological testing. Washington, DC: American Education Research Association.
2. Andersen, E. B. (1973). A goodness of fit test for the Rasch model. Psychometrika, 38, 123-140. doi 10.1007/BF02291180
3. Armstrong, P. I., Hubert, L., & Rounds, J. (2003). Circular unidimensional scaling: A new look at group differences in interest structure. Journal of Counseling Psychology, 50, 297-308.
4. Aros, J. R., Henly, G. A., & Curtis, N. T. (1998). Occupational sextype and sex differences in vocational preference-measured interest relationships. Journal of Vocational Behavior, 53, 227-242. doi 10.1006/jvbe.1997.1612
5. Bergmann, C., & Eder, F. (2005). Allgemeiner Interessen-Struktur-Test mit Umwelt-Struktur-Test (AIST-R/UST-R) [General Interest Structure Test with Environment Structure Test]. Göttingen, Germany: Beltz Test.
6. Bock, R. D., & Aitkin, M. (1981). Marginal maximum likelihood estimation of item parameters: An application of the EM algorithm. Psychometrika, 46, 443-459. doi 10.1007/BF02294168
7. Borkenau, P., & Ostendorf, F. (1993). NEO-Fünf-Faktoren-Inventar (NEO-FFI) nach Costa und McCrae [NEO Five Factor Inventory (NEO-FFI) after Costa and McCrae]. Göttingen, Germany: Hogrefe.
8. Camilli, G., & Penfield, D. A. (1997). Variance estimation for differential test functioning based on Mantel-Haenszel statistics. Journal of Educational Measurement, 34, 123-139. doi 10.1111/j.1745-3984.1997.tb00510.x
9. Chang, H. H., Mazzeo, J., & Roussos, L. A. (1996). Detecting DIF for polytomously scored items: An adaptation of the SIBTEST procedure. Journal of Educational Measurement, 33, 333-353. doi 10.1111/j.1745-3984.1996.tb00496.x
10. Cohen, J. (1988). Statistical power analysis for the behavioral sciences. New York, NY: Erlbaum.
11. Costa, P., Jr., Terracciano, A., & McCrae, R. R. (2001). Gender differences in personality traits across cultures: Robust and surprising findings. Journal of Personality and Social Psychology, 81, 322-331. doi 10.1037//0022-3514.81.2.322
12. Costa, P. T., & McCrae, R. R. (1992). Revised NEO Personality Inventory (NEO-PI-R) and NEO Five-Factor Inventory (NEO-FFI). Odessa, FL: Psychological Assessment Resources.
13. Einarsdottir, S., & Rounds, J. (2000). Application of three dimensions of vocational interests to the Strong Interest Inventory. Journal of Vocational Behavior, 56, 363-379. doi 10.1006/jvbe.1999.1720
14. Einarsdottir, S., & Rounds, J. (2009). Gender bias and construct validity in vocational interest measurement: Differential item functioning in the Strong Interest Inventory. Journal of Vocational Behavior, 74, 295-307. doi 10.1016/j.jvb.2009.01.003
15. Feingold, A. (1994). Gender differences in personality: A meta-analysis. Psychological Bulletin, 116, 429-456. doi 10.1037//0033-2909.116.3.429
16. Gati, I. (1979). A hierarchical model for the structure of vocational interests. Journal of Vocational Behavior, 15, 90-106.
17. German Federal Statistical Office. (2009). Mikrozensus: Bevölkerung und Erwerbstätigkeit; Beruf, Ausbildung und Arbeitsbedingungen der Erwerbstätigen Deutschland [Microcensus: Population and gainful occupation: Occupation, education, and labor conditions in Germany]. Wiesbaden, Germany: Author.
18. Hambleton, R. K. (2006). Good practices for identifying differential item functioning. Medical Care, 44(11, Suppl. 3), 182-188. doi 10.1097/01.mlr. 0000245443.86671.c4
19. Hansen, J. C., & Campbell, D. P. (1985). Manual for the SVIB-SCII (4th ed.). Palo Alto, CA: Consulting Psychologist Press.
20. Holland, J. L. (1959). A theory of vocational choice. Journal of Counseling Psychology, 6, 35-45. doi 10.1037/h0040767
21. Holland, J. L. (1997). Making vocational choices: A theory of vocational personalities and work environments (3rd ed.). Odessa, FL: Psychological Assessment Resources.
22. Holland, P. W., & Wainer, H. (Eds.). (1993). Differential item functioning. Hillsdale, NJ: Erlbaum.
23. Hyde, J. S. (2005). The gender similarities hypothesis. American Psychologist, 60, 581-592. doi 10.1037/0003-066X.60.6.581
24. Jörin, S., Stoll, F., Bergmann, C., & Eder, F. (2003). Explorix: Das Werkzeug zur Berufswahl und Laufbahnplanung [Explorix: A tool for occupational choices and career planning]. Bern, Switzerland: Huber.
25. Le, L. (2009). Investigating gender differential item functioning across countries and test languages for PISA science items. International Journal of Testing, 9, 122-133. doi 10.1080/15305050902880769
26. Liu, I.-M., & Agresti, A. (1996). Mantel-Haenszel-Type inference for cumulative odds ratios with a stratified ordinal response. Biometrics, 52(4), 1223-1234. doi:10.2307/2532838
27. Mair, P., Hatzinger, R., & Maier, M. (2010). eRm: Extended Rasch modeling: R package version 0.13-0. Retrieved fromhttp://CRAN.R-project.org/ package=eRm
28. Mantel, N., & Haenszel, W. (1959). Statistical aspects of the analysis of data from retrospective studies of disease. Journal of the National Cancer Institute, 22, 719-748.
29. Masters, G. N. (1982). A Rasch model for partial credit scoring. Psychometrika, 47, 149-174. doi 10.1007/BF02296272
30. McDonald, R. P. (1999). Test theory: A unified treatment. Mahwah, NJ: Erlbaum.
31. Mount, M., Barrick, M., Scullen, S., & Rounds, J. (2005). Higher-order dimensions of the Big Five personality traits and the Big Six vocational interest types. Personnel Psychology, 58, 447-478. doi 10.1111/j.1744-6570.2005.00468.x
32. Nagy, G., Trautwein, U., & Lüdtke, O. (2010). The structure of vocational interests in Germany: Different methodologies, different conclusions. Journal of Vocational Behavior, 76, 153-169. doi 10.1016/j.jvb.2007.07.002
33. OECD. (2009). PISA 2009 assessment framework - key competencies in reading, mathematics, and science. Paris, France: Author.
34. OECD. (2012). Closing the gender gap: Act now(online document). OECD Publishing. Retrieved fromhttp://dx.doi.org/10.1787/9789264179370-en
35. Penfield, R. D. (2009). DIFAS: Differential item functioning analysis system (Version 5.0) [Computer software].
36. Penfield, R. D., & Algina, J. (2003). Applying the Liu-Agresti Estimator of the cumulative common odds ratio to DIF detection in polytomous Items. Journal of Educational Measurement, 40, 353-370. doi 10.1111/j.1745-3984. 2003.tb01151.x
37. Penfield, R. D., & Algina, J. (2006). A generalized DIF effect variance estimator for measuring unsigned differential test functioning in mixed format tests. Journal of Educational Measurement, 43, 295-312. doi 10.1111/j.1745-3984.2006. 00018.x
38. Penfield, R. D., & Camilli, G. (2007). Differential item functioning and item bias. In C.Rao & S.Sinharay (Eds.), Handbook of statistics 26: Psychometrics (pp. 125-168). Amsterdam, The Netherlands: Elsevier.
39. Prediger, D. J. (1982). Dimensions underlying Holland-s hexagon: Missing link between interests and occupations. Journal of Vocational Behavior, 21, 259-287. doi 10.1016/0001-8791(82)90036-7
40. R Development Core Team. (2010). R: A language and environment for statistical computing (Version 2.14.1)[Computer software]. Retrieved fromhttp://www.R-project.org/.Vienna, Austria: R Foundation for Statistical Computing.
41. Rasch, G. (1960). Probabilistic models for some intelligence and attainment tests. Copenhagen, Denmark: Danmarks Paedagogiske Institut.
42. Reise, S. P., Smith, L., & Furr, R. M. (2001). Invariance on the NEO-PI-R Neuroticism scale. Multivariate Behavioral Research, 36(1), 83-110. doi 10.1207/S15327906MBR3601-04
43. Rost, J. (2004). Lehrbuch Testtheorie - Testkonstruktion [Textbook test theory - test construction] (2nd ed.). Bern, Switzerland: Huber.
44. 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. doi 10.1007/BF02294572
45. Shepard, L., Camilli, G., & Averill, M. (1981). Comparison of procedures for detecting test-item bias with both internal and external ability criteria. Journal of Educational and Behavioral Statistics, 6, 317-375.
46. Strobl, C. (2012). Das Rasch-Modell: Eine verständliche Einführung für Studium und Praxis [The Rasch-Modell: A comprehensible introduction]. Munich, Germany: Rainer Hampp.
47. Su, R., Rounds, J., & Armstrong, P. I. (2009). Men and things, women and people: A meta-analysis of sex differences in interests. Psychological Bulletin, 135, 859-884. doi 10.1037/a0017364
48. Wang, W.-C. (2008). Assessment of differential item functioning. Journal of Applied Measurement, 9, 387-408.
49. Wetzel, E., Hell, B., & Pässler, K. (2012). Comparison of different test construction strategies in the development of a gender fair interest inventory using verbs. Journal of Career Assessment, 20(1), 88-104. doi 10.1177/1069072711417166
50. Wu, M. L., Adams, R. J., Wilson, M. R., & Haldane, S. (2007). ConQuest (Version 2.0)[Computer software]. Camberwell, Australia: Australian Council for Educational Research.
51. Zieky, M. (1993). Practical questions in the use of DIF statistics in item development. In P. W.Holland & H.Wainer (Eds.), Differential item functioning (pp. 337-364). Hillsdale, NJ: Erlbaum.