Mis(ver)standen in de selectiepraktijk

Literatuur

1. Ægisdóttir, S., White, M. J., Spengler, P. M., Maugherman, A. S., Anderson, L. A. et al. (2006). The metaanalysis of clinical judgment project: Fiftysix years of accumulated research on clinical versus statistical prediction. The Counseling Psychologist, 34, 341–382. doi:10.1177/0011000005285875
2. American Educational Research Association, American Psychological Association & the National Council on Measurement in Education. (2014). Standards for educational and psychological testing. Washington, Verenigde Staten: American Educational Research Association.
3. Bridgeman, B., Burton, N. & Cline, F. (2009). A note on presenting what predictive validity numbers mean. Applied Measurement in Education, 22, 109119. doi:10.1080/08957340902754577
4. Brooks, M.E., Dalal, D.K. & Nolan, K.P. (2014). Are common language effect sizes easier to understand than traditional effect sizes? Journal of Applied Psychology, 99, 332–340. doi:10.1037/a0034745
5. Cascio, W.F. & Aguinis, H. (2008) Industrial and Organizational Psychology 1963–2007: Changes, choices, and trends. Journal of Applied Psychology, 93, 1062–1081. doi:10.1037/00219010.93.5.1062
6. Clauser, A.L. & Wainer, H. (2016). A tale of two tests (and of two examinees). Educational Measurement: Issues and Practice, 35, 1928. doi:10.1111/emip.12110
7. Dana, J. & Dawes, R.M. (2004). The Superiority of simple alternatives to regression for social science predictions. Journal of Educational and Behavioral Statistics, 29, 317331.
8. Dana, J., Dawes, R. & Peterson, N. (2013). Belief in the unstructured interview: The persistence of an illusion. Judgment and Decision Making, 8, 512520.
9. Dawes, R.M. (1979). The robust beauty of improper linear models on decision making. American Psychologist, 34, 571582. doi:10.1037/0003066X.34.7.571.
10. Dawes, R.M. (2005). The ethical implications of Paul Meehl’s work on comparing clinical versus actuarial prediction methods. Journal of Clinical Psychology, 61, 12451255. doi:10.1002/jclp.20180
11. Dawes, R. M., Faust, D., & Meehl, P. E. (1989). Clinical versus actuarial judgment. Science, 243, 1668–1674. doi:10.1126/science.2648573
12. Dietvorst, B.J., Simmons, J.P. & Massey, C. (2015). Algorithm aversion: People erroneously avoid algorithms after seeing them err. Journal of Experimental Psychology: General, 144, 114–126. doi:10.1037/xge0000033.supp
13. Dietvorst, B.J., Simmons, J.P. & Massey, C. (2018). Overcomingalgorithm aversion: People will use imperfect algorithms if they can (even slightly) modify them. Management Science, 64, 11551170. doi:10.1287/mnsc.2016.2643
14. Drenth, P.J.D. (2008). Psychology: Is it applied enough? Applied Psychology: An International Review, 57, 524540. doi:10.1111/ j.14640597.2008.00337.x.
15. Highhouse, S. (2002). Assessing the candidate as a whole: A historical and critical analysis of individual psychological assessment for personnel decision making. Personnel Psychology, 55, 363–396. doi :10.1111/j.17446570.2002.tb00114.x
16. Highhouse, S. (2008). Stubborn reliance on intuition and subjectivity in employee selection. Industrial and Organizational Psychology: Perspectives on Science and Practice, 1, 333–342. doi:10.1111/j.17549434.2008.00058.x
17. Kahneman, D. (2001). Thinking fast and slow. New York: Farrar, Straus & Giroux.
18. Kahneman, D., & Klein, G. (2009). Conditions for intuitive expertise: A failure to disagree. American Psychologist, 64, 515–526. doi:10.1037/a0016755
19. Kahneman, D., & Tversky, A. (1973). On the psychology of prediction. Psychological Review, 80, 237–251. Doi:10.1037/h0034747
20. Karelaia, N. & Hogarth, R. M. (2008). Determinants of linear judgment: A metaanalysis of lens model studies. Psychological Bulletin, 134, 404–426. doi:10.1037/00332909.134.3.404.supp
21. Kausel, E.E., Culbertson, S.S. & Madrid, H.P. (2016). Overconfidence in personnel selection: When and why unstructured interview information can hurt hiring decisions. Organizational Behavior and Human Decision Processes, 137, 27–44. doi:10.1016/j.obhdp.2016.07.005
22. König, C.J., Klehe, U.C., Berchtold, M. & Kleinmann, M. (2010). Reasons for being selective when choosing personnel selection procedures. International Journal of Selection and Assessment, 18, 1727. doi:10.1111/j.14682389.2010.00485.x
23. Kuncel, N.R. (2008). Some new (and old) suggestions for improving personnel selection. Industrial and Organizational Psychology, 1, 343,346. doi:10.1111/j.17549434.2008.00059.x
24. Kuncel, N.R. & Highhouse, S. (2011). Complex predictions and assessor mystique. Industrial and Organizational Psychology: Perspectives on Science and Practice, 4, 302–306. doi:10.1111/j.17549434.2011.01343.x
25. Kuncel, N.R., Klieger, D.M., Connelly, B.S. & Ones, D.S. (2013). Mechanical versus clinical data combination in selection and admissions decisions: A metaanalysis. Journal of Applied Psychology, 98, 10601072. doi:10.1037/a0034156
26. Lewis, M. (2016). The Undoing project: A friendship that changed our minds. New, York, Verenigde Staten: W. W. Norton.
27. McLellan, R. A. (1996). Theoretical expectancy calculator [online utiliteitscalculator]. Beschikbaar via: http://www.hrsoftware.net/cgi/TheoreticalExpectancy.cgi
28. Meehl. P.E. (1954). Clinical versus statistical prediction, a theoretical analysis and a review of the evidence. Minneapolis, Verenigde Staten: University of Minnesota Press
29. Morris, S.B., Daisley, R.L., Wheeler, M. & Boyer, P. (2014). A metaanalysis of the relationship between individual assessment and job performance. Journal of Applied Psychology, 100, 520. doi:10.1037/a0036938.
30. Nederlands Instituut van Psychologen (2017). Algemene Standaard Testgebruik.
31. Nolan, K.P. & Highhouse, S. (2014). Need for autonomy and resistance to standardized employee selection practices. Human Performance, 27, 328–346. doi:10.1080/08959285.2014.929691
32. Sackett, P.R., Dahlke, J.A., Shewach, O.R. & Kuncel, N.R. (2017). Effects of predictor weighting methods on incremental validity. Journal of Applied Psychology, 102, 14211434. doi:10.1037/apl0000235
33. Schmidt, F.L. & Hunter, J.E. (1998). The validity and utility of selection methods in personnel psychology: Practical and theoretical implications of 85 years of research findings. Psychological Bulletin, 124, 262–274. doi:10.1037/00332909.124.2.262
34. Silzer, R. & Jeanneret, R. (2011). Individual psychological assessment: A practice and science in search of common ground. Industrial and Organizational Psychology: Perspectives on Science and Practice, 4, 270–296. doi:10.1111/j.17549434.2011.01341.x
35. SIOP (2016). Guidelines for education and training in IndustrialOrganizational psychology. Bowling Green, Verenigde Staten. Opgevraagd via: http://www.siop.org/Instruct/SIOP_ET_Guidelines_2017.pdf
36. Slaughter, J.E. & Kausel, E.E. (2014). Employee selection decisions. In S. Highhouse, R.S. Dalal & E. Salas (Eds.), Judgment and decision making at work. (pp. 57–79). New York, Verenigde Staten: Routledge/Taylor & Francis Group
37. Swets, J.A., Dawes, R.M. & Monahan, J (2000). Better decisions through Science. Scientific American, 283, 82-87.
38. Yu, M.C. (2018). Viewing expert judgment in individual assessments through the lens model: Testing the limits of expert information processing (Dissertatie). Opgevraagd via: https://conservancy.umn.edu/handle/11299/199018
39. Zhang, D.C., Highhouse, S., Brooks, M.E. & Zhang, Y. (2018). Communicating the validity of structured job interviews with graphical visual aids. International Journal of Selection and Assessment. doi:10.1111/ijsa.12220