Abstract
In the USA, trends in educational accountability have driven several models attempting to provide quality data for decision making at the national, state, and local levels, regarding the success of schools in meeting standards for competence. Statistical methods to generate data for such decisions have generally included (a) status models that examine simple indications of number of students meeting a criterion level of achievement, (b) growth models that explore change over the course of one or more years, and (c) value-added models that attempt to control for factors deemed relevant to student achievement patterns. This study examined a new strategy for student and school achievement modeling that augments the field through the use of the probit model to estimate the likelihood of students meeting an established level standard and estimating the proportion of individuals within a school meeting the standard. Results of the study showed that the probit model was an effective tool both for providing such adjustments, as well as for adjusting them based upon salient demographic variables. Implications of these results and suggestions for further use of the model are discussed.
Similar content being viewed by others
Explore related subjects
Discover the latest articles and news from researchers in related subjects, suggested using machine learning.References
Agresti, A. (2002). Categorical data analysis. New York: Wiley.
Aud, S., Wilkinson‐Flicker, S., Kristapovich, P., Rathbun, A., Wang, X., and Zhang, J. (2013). The Condition of Education 2013 (NCES 2013‐037): U.S. department of education, national center for education statistics. Washington, DC. http://nces.ed.gov/pubsearch.
Azen, R., & Walker, C. M. (2011). Categorical data analysis for the behavioral and social sciences. New York: Routledge.
Baker, M., & Johnston, P. (2010). The impact of socioeconomic status on high stakes testing reexamined. Journal of Instructional Psychology, 37(3), 193–199.
Ballou, D., Sanders, W., & Wright, P. (2004). Controlling for student background in value-added assessment of teachers. Journal of Educational and Behavioral Statistics, 21, 37–66.
Barton, P. E. (2008). The right way to measure growth. Educational Leadership, 65, 70–73.
Braun, H. (2005). Using student progress to evaluate teachers: a primer to value-added models. Princeton: ETS.
Briggs, D. C., & Weeks, J. P. (2009). The sensitivity of value-added modeling to the creation of a vertical score scale. Education Finance and Policy, 4(4), 385–414.
Capraro, R. M., Young, J. R., Lewis, C. W., Yetkiner, Z. E., & Woods, M. N. (2009). An examination of mathematics achievement and growth in a midwestern urban school district: implications for teachers and administrators. Journal of Urban Mathematics Education, 2(2), 46–65.
Chiang, H. (2009). How accountability pressure on failing schools affects student achievement. Journal of Public Economics, 93, 1045–1057.
Choi, K., & Goldschmidt, P. (2012). A multilevel latent growth curve approach to predicting student proficiency. Asia Pacific Education Review, 13(2), 199–208.
IBM Corp. (2010). IBM SPSS Statistics for Windows, version 19.0. Armonk: IBM Corp.
Darling-Hammond, L., Amerin-Beardsley, A., Haertel, E., & Rothstein, J. (2012). Evaluating teacher evaluation. Phi Delta Kappan, 93(6), 8–15.
De Lisle, J., Smith, P., & Jules, V. (2010). Evaluating the geography of gendered achievement using large-scale assessment data from the primary school system of the Republic of Trinidad and Tobago. International Journal of Educational Development, 30(4), 405–417.
Downey, D. B., von Hippel, P. T., & Hughes, M. (2008). Are “failing” schools really failing? Using seasonal comparison to evaluate school effectiveness. Sociology of Education, 81(3), 242–270.
Fox, J. (2008). Applied regression analysis and generalized linear models. Thousand Oaks: Sage.
Franco, M. S., & Seidel, K. (2012). Evidence for the need to more closely examine school effects in value-added modeling and related accountability policies. Education and Urban Society. doi:10.1177/0013124511432306.
Goldschmidt, P., Roschewski, P., Choi, L., Auty, W., Hebbler, S., Blank, R., & Williams, A. (2005). Policymakers’ guide to growth models for school accountability: How do accountability models differ? Washington DC: The Council of Chief State School Officers. http://www.ccsso.org/Resources/Publications/Policymakers%E2%80%99_Guide_to_Growth_Models_for_School_Accountability_How_Do_Accountability_Models_Differ.html. Accessed 14 Jan 2014.
Goldschmidt, P., Choi, K., Martinez, F., & Novak, J. (2010). Using growth models to monitor school performance: comparing the effect of the metric and the assessment. School Effectiveness and School Improvement: An International Journal of Research, Policy, and Practice, 21(2), 337–357.
Gorard, S. (2011). Now you see it, now you don’t: school effectiveness as conjuring? Research in Education, 86, 39–45.
Lee, J. (2010). Tripartite growth trajectories of reading and math achievement: tracking national academic progress at primary, middle, and high school levels. American Educational Research Journal, 47(4), 800–832.
Linn, R. L. (2000). Assessments and accountability. Educational Researcher, 29, 4–16.
Lockwood, J. R., McCaffrey, D. F., Hamilton, L. S., Stecher, B., Le, V.-N., & Martinez, F. (2006). The sensitivity of value-added teacher effect estimates to different mathematics achievement measures. Santa Monica: RAND.
Martineau, J. A. (2006). Distorting value added: the use of longitudinal, vertically scaled student achievement data for value-added accountability. Journal of Educational and Behavioral Statistics, 31, 35–62.
McCaffrey, D.F. (2013). Do value-added methods level the playing field for teachers? Carnegie Knowledge Network. http://carnegieknowledgenetwork.org/briefs/value-added/level-playing-field/.
McCaffrey, D. F., Lockwood, J. R., Koretz, D., Louis, T. A., & Hamilton, L. (2004). Models for value-added modeling of teacher effects. Journal of Educational and Behavioral Statistics, 29(1), 67–101.
Northwest Evaluation Association. (2003). Technical manual for use with measures of academic progress and achievement level tests. Portland: Northwest Evaluation Association.
Olson, L. (2004). Value added models gain in popularity. Education Week, 24(12), 14–15.
Perry, L. B., & McConney, A. (2010). Does the SES of the school matter? An examination of socioeconomic status and student achievement using PISA 2003. Teachers College Record, 112(4), 1137–1162.
Scherrer, J. (2012). What’s the value of VAM (value-added modeling)? Phi Delta Kappan, 93(8), 58–60.
Schmidt, W. H., Houang, R. T., & McKnight, C. C. (2005). Value-added research: right idea but wrong solution? In R. Lissitz (Ed.), Value added models in education: theory and practice (pp. 272–297). Maple Grove: JAM.
Sidak, Z. (1967). Rectangular confidence regions for the means of multivariate normal distributions. Journal of American Statistical Association, 67(62), 626–633.
Tong, H., & Lim, K. S. (1980). Threshold autoregression, limit cycles, and cyclical data. Journal of the Royal Statistical Society, Series B, 42, 245–292.
Wainer, H. (2000). Computer adaptive testing: a primer. Mahwah: Lawrence Erlbaum.
Weiss, M. J., & May, H. (2012). A policy analysis of the federal growth model pilot program’s measures of school performance: the Florida case. Association for Education Finance and Policy, 7(1), 44–73. doi:10.1162/EDFP_a_00053.
Wiggan, G. (2007). Race, school achievement, and educational inequality: toward a student-based inquiry perspective. Review of Educational Research, 77(3), 310–333.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Finch, W.H., Cassady, J.C. Use of the probit model to estimate school performance in student attainment of achievement testing standards. Educ Asse Eval Acc 26, 177–201 (2014). https://doi.org/10.1007/s11092-013-9186-6
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11092-013-9186-6