Structural equation modeling for high school principals' data-driven decision making: An analysis of information use environments

Educational Administration Quarterly

Volumn 44, Issue 5, 2008, Pages 603-634

  Mingchu, L ^a  

^a Emporia State University   (United Kingdom)

Author keywords

Data; Data driven decision making; Information use environment; Leadership dimension; Principal

Indexed keywords

EID: 57149134784     PISSN: 0013161X     EISSN: 15523519     Source Type: Journal    
DOI: 10.1177/0013161X08321506     Document Type: Article

References (72)

1. Agor, W.H. (1986). The logic of intuitive decision making: A research-based approach for top management. New York: Quorum.
2. American Association of School Administrators (AASA). (2002). Using data to improve schools: What's working. Retrieved May 20, 2003, from http://www.aasa.org/cas/UsingDataToImproveSchools.pdf
3. Armstrong, J., & Anthes, K. (2001). Identifying the factors, conditions, and policies that support schools' use of data for decision making and school improvement: Summary of findings. Retrieved June 24, 2004, from http://www.ecs.org/clearinghouse/30/69/3069.doc
4. Baker, B.D., & Richards, C.E. (2004). The ecology of educational systems: Data, models, and tools for improvisational leading and learning. Upper Saddle River, NJ: Pearson Education.
5. Bennet, A. (2004). Alive with the fire of shared understanding: Implementing knowledge management in the Department of the Navy. In M. E. D. Koenig and T. K. Srikantaiah (Eds.), Knowledge management lesson learned: What works and what doesn't (pp. 279-292). Medford, NJ: Information Today.
6. Bentler, P.M. (1992). On the fit of models to covariances and methodology to the Bulletin. Psychological Bulletin, 112, 400-404.
7. Bernhardt, V.L. (1998). Data analysis for comprehensive schoolwide improvement. Larchmont, NY: Eye on Education.
8. Bernhardt, V.L. (2003). No schools left behind. Educational Leadership, 60 (5). 26-30.
9. Choo, C.W. (1998). The knowing organization: How organizations use information to construct meaning, create knowledge, and make decisions. New York: Oxford University Press.
10. Choo, C.W. (2002). Information management for the intelligence organization: The art of scanning the environment (3 rd ed.). Medford, NJ: Information Today.
11. Choppin, J. (2002, April). Data use in practice: Examples from the school level. Paper presented at the annual meeting of the American Educational Research Association, New Orleans.
12. Creighton, T.B. (2001). Schools and data: The educator's guide for using data to improve decision making. Thousand Oaks, CA: Corwin Press.
13. Davernport, T.H., & Prusak, L. (1998). Working knowledge: How organizations manage what they know. Boston: Harvard Business School Press.
14. Davis, S.H., & Davis, P.B. (2003). The intuitive dimensions of administrative decision making. Lanham, MD: Scarecrow.
15. Deming, W.E. (1986). Out of the Crisis. Cambridge, MA: Massachusetts Institute of Technology Press.
16. Ediger, M. (2002). Scope in the reading curriculum. Experiments in Education, 30 (3). 44-49.
17. Educational Leadership Constituent Council (ELCC). (2002). Standards for advanced programs in educational leadership for principals, superintendents, curriculum directors, and supervisors. Washington, DC: National Policy Board for Educational Administration.
18. Fink, A. (2003). The survey toolkit: How to ask survey questions (2 nd ed.). Thousand Oaks, CA: Sage.
19. Fowler, F.J., Jr. (1995). Improving survey questions. Thousand Oaks, CA: Sage.
20. Gerstenberger, P.G., & Allen, T.J. (1968). Criteria used by research and development engineers in the selection of an information source. Journal of Applied Psychology, 52 (4). 272-279.
21. Glasman, N.S. (1994). Making better decisions about school problems: How administrators use evaluation to find solutions. Thousand Oaks, CA: Corwin.
22. Glickman, C.D. (1993). Renewing America's schools. San Francisco: Jossey-Bass.
23. Goldstein, M.S., Marcus, A.C., & Rausch, M.P. (1978). The nonutilization of evaluation research. Pacific Sociological Review, 21, 21-44.
24. Hogarth, R.M. (2001). Educating intuition. Chicago, IL: University of Chicago Press.
25. Holcomb, E.L. (1999). Getting excited about data: How to combine people, passion, and proof. Thousand Oaks, CA: Corwin.
26. Hoy, W.K., & Miskel, C.G. (1996). Educational administration: Theory, research, and practice (5 th ed.). New York: McGraw-Hill.
27. Hoyle, J.R., English, F.W., & Steffy, B.E. (1994). Skills for successful school leaders (2 nd ed.). Arlington, VA: American Association of School Administrators.
28. Hoyle, R. H. (Ed.). (1995). Structural equation modeling: Concepts, issues, and applications. Thousand Oaks, CA: Sage.
29. Hu, L., & Bentler, P.M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6, 1-55.
30. Jamentz, K. (2001). Beyond data mania. Leadership, 31 (2). 8-12.
31. Kirby, P.C., & Bogotch, I. (1993, April). Information utilization in restructuring schools: The role of the beginning principal. Paper presented at the annual meeting of the American Educational Research Association, Atlanta. (ERIC Document Reproduction Service No. 359646)
32. Kline, R.B. (2005). Principles and practice of structural equation modeling. New York: Guilford.
33. LaFee, S. (2002). Data-driven districts. School Administrator, 59 (11). 6-15.
34. Leithwood, K., & Aitken, R. (1995). Making schools smarter: A system for monitoring school and district progress. Thousand Oaks, CA: Corwin.
35. Leithwood, K., Aitken, R., & Jantzi, D. (2001). Making schools smarter: A system for monitoring school and district progress (2 nd ed.). Thousand Oaks, CA: Corwin.
36. Leithwood, K., & Steinbach, R. (1995). Expert problem solving: Evidence from school and district leaders. Albany, NY: State University of New York Press.
37. Litwin, M.S. (2003). The survey toolkit: How to assess and interpret survey psychometrics (2 nd ed.). Thousand Oaks, CA: Sage.
38. Lord, R.G., & Hall, R.J. (1992). Contemporary views of leadership and individual differences. Leadership Quarterly, 3 (3). 137-157.
39. Lord, R.G., & Maher, K.J. (1991). Leadership and information processing. Boston: Unwin Hyman.
40. MacCallum, R.C., Browne, M.W., & Sugawara, H.M. (1996). Power analysis and determination of sample size for covariance structure modeling. Psychological Methods, 1, 130-149.
41. Mandinach, E.B., Honey, M., & Light, D. (2006, April). A theoretical framework for data-driven decision making. Paper presented at the annual meeting of American Educational Research Association, San Francisco. Retrieved November 14, 2006, from http://cct.edc.org/admin/publications/speeches/DataFrame-AERA06. pdf
42. Marsh, D.D. (1992). Enhancing instructional leadership: Lessons from the California school leadership academy. Education and Urban Society, 24 (3). 386-409.
43. Maslow, A. (1954). Motivation and personality. New York: Harper & Row.
44. Mason, S. (2002). Turning data into knowledge: Lessons from six Milwaukee public schools (Working Paper No. 2002-3). Madison: University of Wisconsin, Wisconsin Center for Education Research.
45. Mathews, C.V.F. (2002). Principals and data-driven decision making (Virginia). Unpublished doctoral dissertation, University of Virginia.
46. McClure, C.R. (1978). The information rich employee and information for decision making: Review and comments. Information Processing and Management, 14, 381-394.
47. McColskey, W.H., Altschuld, J.W., & Lawton, R.W. (1985). Predictors of principals' reliance on formal and informal sources of information. Educational Evaluation and Policy Analysis, 7 (4). 427-436.
48. McIntire, T. (2002). The administrator's guide to data-driven decision making. Technology and Learning, 22 (11). 18-19.
49. Murphy, J., & Shipman, N.J. (1998). The Interstate School Leaders Licensure Consortium: A standards-based approach to strengthening educational leadership. Paper presented at the annual conference of the American Educational Research Association, San Diego, CA.( ERIC Document Reproduction Service No. 420698)
50. Murphy, J., Yff, J., & Shipman, N.J. (2000). Implementation of the Interstate School Leaders Licensure Consortium standards. International Journal of Leadership in Education, 3 (1). 17-39.
51. National Council for the Accreditation of Teacher Education (NCATE). (2002). Educational leadership program standards. Retrieved June 30, 2003, from http://www.ncate.org/institutions/programStandards.asp?ch=4
52. No Child Left Behind Act of 2001, 20 U.S.C. § 6301 et seq. U.S. Department of Education. Retrieved March 22, 2003, from http://www. NoChildLeftBehind.gov
53. Noyce, P., Perda, D., & Traver, R. (2000). Creating data-driven schools. Educational Leadership, 57 (5). 52-56.
54. O'Day, J.A. (2002). Complexity, accountability, and school improvement. Harvard Educational Review, 72 (3). 293-329.
55. O'Reilly, C.A. (1983). The use of information in organizational decision making: A model and some propositions. Research in Organizational Behavior, 5, 103-139.
56. Parsons, B.A. (2003). A tale of two schools' data. Educational Leadership, 60 (5). 66-68.
57. Pearl, J. (2000). Causality: Models, reasoning, and inference. Cambridge, UK: Cambridge University Press.
58. Pfeffer, J. (1992). Managing with power: Politics and influence in organizations. Boston: Harvard Business School Press.
59. Reeves, D.B. (2002). The leader's guide to standards: A blueprint for educational equity and excellence. San Francisco: Jossey-Bass.
60. Reichardt, R. (2000). The state's role in supporting data-driven decision making: A view of Wyoming. Aurora, CO: Mid-continent Research for Education and Learning. (ERIC Document Reproduction Service No. 449186)
61. Rosenbaum, H. (1993). Information use environments and structuration: Toward an integration for Taylor and Giddens. In S. Bonzi (Ed.), Proceedings of the 56th annual meeting of the American Society for Information Science (pp. 235-245). Columbus, OH: Learned Information.
62. Rudy, D.W., & Conrad, W.H. (2004). Breaking down the data. American School Board Journal, 191 (2). 39-41.
63. Salpeter, J. (2004). Data: Mining with a mission. Technology & Learning, 24 (8). 30-36.
64. Simon, H.A. (1997). Administrative behavior: A study of decision making processes in administrative organizations (4 th ed.). New York: The Free Press.
65. Streifer, P.A. (2002). Using data to make better educational decisions. Lanham, MD: Scarecrow.
66. Taylor, R.S. (1986). Value-added process on information systems. Norwood, NJ: Ablex.
67. Taylor, R.S. (1991). Information use environments. In B. Dervin and M. J. Voigt (Eds.), Progress in communication science (pp. 217-254). Norwood, NJ: Ablex.
68. Thornton, B., & Perreault, G. (2002). Becoming a data-based leader: An introduction. NASSP Bulletin, 86 (630). 86-96.
69. Wallace, R.C. (1985). Data driven educational leadership. Spectrum, 3 (4). 9-15.
70. Wang, R.Y., & Strong, D.M. (1996). Beyond accuracy: What data quality means to data consumers. Journal of Management Information Systems, 12 (4). 5-34.
71. Yukl, G., Lepsinger, R., & Lucia, T. (1992). Preliminary report in the development and validation of the influence behavior questionnaire. In K. Clark and M. Clark (Eds.), The impact of leadership (pp. 417-427). Greensboro, NC: Center for Creative Leadership.
72. Zmuda, A., Kuklis. R., & Kline, E. (2004). Transforming schools: Creating a culture of continuous improvement. Alexandria, VA: Association for Supervision and Curriculum Development.