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Dong, Nianbo – American Journal of Evaluation, 2015
Researchers have become increasingly interested in programs' main and interaction effects of two variables (A and B, e.g., two treatment variables or one treatment variable and one moderator) on outcomes. A challenge for estimating main and interaction effects is to eliminate selection bias across A-by-B groups. I introduce Rubin's causal model to…
Descriptors: Probability, Statistical Analysis, Research Design, Causal Models
Briggs, Derek C. – Partnership for Assessment of Readiness for College and Careers, 2011
There is often confusion about distinctions between growth models and value-added models. The first half of this paper attempts to dispel some of these confusions by clarifying terminology and illustrating by example how the results from a large-scale assessment can and will be used to make inferences about student growth and the value-added…
Descriptors: Value Added Models, Language Usage, Measurement, Inferences
Mulvenon, Sean W.; Stegman, Charles E. – Journal of Educational Research & Policy Studies, 2006
As part of No Child Left Behind (NCLB) legislation, many states are using confidence intervals to determine a range of scores for evaluating a school system. More specifically, the states are employing confidence intervals to help minimize measurement error in determining a school system's performance. The methodology and techniques employed in…
Descriptors: Federal Legislation, Computation, Intervals, Error of Measurement

Kirsch, Irwin S.; And Others – 1992
A comprehensive assessment of the literacy proficiencies of Job Training Partnership Act (JTPA) and Employment Service/Unemployment Insurance (ES/UI) participants was conducted by the Department of Labor. The survey responses of a sample of 2,501 JTPA applicants and 3,277 ES/UI participants were scored, weighted, analyzed, and used to develop a…
Descriptors: Adult Literacy, Comparative Analysis, Correlation, Data Collection