Building a Model to Predict Caseworker and Supervisor Turnover Using a Neural Network and Logistic Regression

Journal of Technology in Human Services

Volumn 19, Issue 4, 2002, Pages 65-85

  Quinn, Andrew ^a,b,c   Rycraft, Joan R ^a   Schoech, Dick ^a  

^a UNIVERSITY OF TEXAS AT ARLINGTON   (United States)

^b UNIVERSITY OF VERMONT   (United States)

^c CESS   (United States)

Author keywords

Child welfare worker turnover; Logistic regression; Neural network

Indexed keywords

EID: 0242359271     PISSN: 15228835     EISSN: None     Source Type: Journal    
DOI: 10.1300/J017v19v04_05     Document Type: Article

References (29)

1. Acock, A. C. (n.d.). Working with missing data. Retrieved on July 25, 2000 from the World Wide Web: http://www.orst.edu/instruct/hdfs632/MissingData.html#Missing %20Data%20Paper.
2. Brodzinski, J. D., Crable, E. A., & Scherer, R. F. (1994). Using artificial intelligence to model juvenile recidivism patterns. Computers in Human Services, 10 (4) 1-18.
3. Cabena, P., Hadjinian, P., Stadler, R., Verhees, J., & Zanazi, A. (1998). Discovering data mining. New Jersey: Prentice Hall.
4. Cohen, I., Sudhalter, V., Landon-Jiminez, D., & Keogh, M. (1994). Diagnosing autism: A neural net-based tool. PC AI, 8(3), 22-25.
5. Cone, E. (1998). Managing that churning sensation. Informationweek, 680, 50-67.
6. Desantis, V. S. & Durst, S. L. (1996). Comparing job satisfaction among public and private-sector employees. American Review of Public Administration, 26(3), 327-343.
7. Ewalt, P. L. (1991). Trends affecting recruitment and retention of social work staff in human services agencies. Social Work, 36(3), 214-217.
8. Garson, G. D. (1998). Neural networks: An introductory guide for social scientists. Thousand Oaks, CA: Sage.
9. Garson, G. D. (n.d.). PA 765 Statnotes: An Online Textbook. Retrieved July 25, 2000 from the World Wide Web: http://www2.chass.ncsu.edu/garson/pa765/statnote.htm.
10. Helfgott, K. P. (1991). Staffing the child welfare agency: Recruitment and retention. Washington, DC: Child Welfare League of America.
11. Hosmer, D. W. & Lemeshow, S. (1989). Applied logistic regression. New York, NY: John Wiley & Sons.
12. Lee, T. W. (1996). Why employees quit. In L. R. Beach (Ed.), Decision making in the workplace: A unified perspective (pp. 73-90). Mahwah, NJ: Lawrence Erlbaum Associates.
13. Liou, K. T. (1998). Employee turnover intention and professional orientation: A study of detention workers. Public Administration Quarterly, 22(2), 161-175.
14. Marshall, D. B. & English, D.J (2000). Neural network modeling of risk assessment in child protective services. Children & Youth Services Review, 21(4), 1-23.
15. MedCal (2001). Statistics for biomedical research: Software manual. Retrieved June 28, 2001 from the World Wide Web: http://www.medcalc.be/manual/index.html.
16. Packard, T. (1989). Participation in decision making, performance, and job satisfaction in a social work bureaucracy. Administration in Social Work, 13(1), 59-73.
17. Patterson, D. A (2000). Personal computer applications in the social services. Boston: Allyn and Bacon.
18. Patterson, D. A. & Cloud, R. (1999). The application of artificial neural networks for outcome prediction in a cohort of severely mentally ill outpatients. Journal of Technology in Human Services, 16(2/3), 47-61.
19. Pinkovitz, W. H., Moskal, J., & Green, G. (1997). How much does your employee turnover cost? Retrieved June 12, 2000 from the World Wide Web: http://www.usex.edu.
20. Schoech, D., Quinn, A. & Rycraft, J. R. (2000). Data mining in child welfare, Child Welfare 64(5), 633-650.
21. Somers, M. J. (1999). Application of two neural network paradigms to the study of voluntary employee turnover. Journal of Applied Psychology, 84(2), 177-185.
22. SPSS Inc. (1999). SPSS regression models 9.0. Chicago: SPSS Inc.
23. SPSS Inc. (1997). Neural connection 2.0 user's guide. Chicago: SPSS Inc.
24. Statistica (1998). Neural networks. Tulsa, OK: Statsoft.
25. Statistica (1999). Neural networks: Addendum for version 4. Tulsa, OK: Statsoft.
26. Stevens, R., Ikeda, J., Casillas, A., Palacio-Cayetano, J., & Clyman, S. (1999). Artificial neural network based performance assessments. Computers in Human Behavior, 15, 295-313.
27. Steyaert, J. (1994). Soft computing for soft technologies: Artificial neural networks and fuzzy set theory for human services. Computers in Human Services, 10(4), 55-67.
28. Vinokur-Kaplan, D. & Harman, A. (1986). A national profile of child welfare workers and supervisors. Child Welfare, 65(4), 323-335.
29. Wiley, C. (1998). Employee turnover: What quitting costs. Society for Human Resource Management. Retrieved June 12, 2000 from the World Wide Web: http://careers.altavista.com.