|
1. J. Douglas Williams, Monitoring
School Performance: A Guide for Educators.
Philadelphia: Falmer Press, 1992; Center for Educational
Research and Innovation, Measuring the Quality of
Schools. Paris, France: Organization for Economic
Co-operation and Development, 1995; Center for
Educational Research and Innovation, Measuring What
Students Learn. Paris, France: Organization for
Economic Co-operation and Development, 1995; Sandra
Black, “Measuring the value of better schools,” Economic
Policy Review, 4(1): 87-94, 1998.
2. Cf. Daniel Koretz, “Indicators of educational
achievement,” In Indicators of Children’s
Well-Being, Eds. Robert M. Hauser, Brett V. Brown,
William R. Prosser. New York: Russell Sage Foundation,
1997, pp. 208-234; Stephen P. Klein et al., “Gender
and racial/ethnic differences on performance assessments
in science,” Educational Evaluation and Policy
Analysis, 19(2): 83-98, 1997; Bonnie L.
Halpern-Felsher et al., “Neighborhood and family
factors predicting educational risk and attainment in
African American and white children and adolescents,”
in Neighborhood Poverty: Context and Consequences for
Children, Eds. Jeanne Brooks-Gunn, Greg J. Duncan,
J. Lawrence Aber. New York: Russell Sage Foundation,
1997, Volume 1, pp. 146-173; Jeanne-Brooks Gunn, Greg J.
Duncan, “The effects of poverty on children,” Children
and Poverty, 7(2): 55-71, 1997.
3. David Kaplan, Pamela R. Elliott, “A model-based
approach to validating education indicators using
multilevel structural equation modeling,” Journal
of Educational and Behavioral Statistics, 22(3):
323-347, 1997; Garrett K. Mandeville, “The South
Carolina experience with incentives,” In Midwest
Approaches to School Reform, Eds. Thomas A. Downes,
William A. Testa. Chicago: Federal Reserve Bank of
Chicago, 1994, pp. 69-91; Robert D. Felner et al., “The
impact of school reform for the middle years:
Longitudinal study of a network engaged in Turning
Points-based comprehensive school transformation,”
Phi Delta Kappan, 78(7): 528-532, 541-550,
1997.
4. Details on the statistical terminology used in the
Rhode Island Model are presented in Appendix A.
5. For further information consult George A. Marcoulides,
Scott L. Hershberger, Multivariate Statistical
Methods: A First Course. Mahwah, NJ: Lawrence
Erlbaum Associates, 1997; J. Scott Long, Regression
Models for Categorical and Limited Dependent
Variables, Thousand Oaks, CA: SAGE Publications,
1997; and one of the classics in the field, Jacob Cohen,
Patricia Cohen, Applied Multiple
Regression/Correlation Analysis for the Behavioral
Sciences. Hillsdale, NJ: Lawrence Erlbaum
Associates, 2nd ed., 1983. A good general discussion of
modeling is David W. Britt, A Conceptual Introduction
to Modeling: Qualitative and Quantitative Perspectives.
Mahwah, NJ: Lawrence Erlbaum Associates, 1997.
6. Confounding occurs when more than one variable
affects the response variable and the effects of the
variables cannot be distinguished from each other.
7. Reproduced from Statistics: The Easy Way,
Douglas Downing, Jeffrey Clark, Barron’s Educational
Series Inc., Hauppauge, NY, 1997, p.256.
8. Reproduced from Statistics: The Easy Way,
Douglas Downing, Jeffrey Clark, Barron’s Educational
Series Inc., Hauppauge, NY, 1997, p.259.
9. Example obtained from
http://www.bath.ac.uk/~pssiw/stats/week4ohp/sld012.htm
|
