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Keller, Bryan – Journal of Educational and Behavioral Statistics, 2020
Widespread availability of rich educational databases facilitates the use of conditioning strategies to estimate causal effects with nonexperimental data. With dozens, hundreds, or more potential predictors, variable selection can be useful for practical reasons related to communicating results and for statistical reasons related to improving the…
Descriptors: Nonparametric Statistics, Computation, Testing, Causal Models
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Mealli, Fabrizia; Pacini, Barbara; Stanghellini, Elena – Journal of Educational and Behavioral Statistics, 2016
Unless strong assumptions are made, nonparametric identification of principal causal effects can only be partial and bounds (or sets) for the causal effects are established. In the presence of a secondary outcome, recent results exist to sharpen the bounds that exploit conditional independence assumptions. More general results, though not embedded…
Descriptors: Graphs, Nonparametric Statistics, Causal Models, Statistical Analysis
Rosenthal, James A. – Springer, 2011
Written by a social worker for social work students, this is a nuts and bolts guide to statistics that presents complex calculations and concepts in clear, easy-to-understand language. It includes numerous examples, data sets, and issues that students will encounter in social work practice. The first section introduces basic concepts and terms to…
Descriptors: Statistics, Data Interpretation, Social Work, Social Science Research
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Mapuranga, Raymond; Dorans, Neil J.; Middleton, Kyndra – ETS Research Report Series, 2008
In many practical settings, essentially the same differential item functioning (DIF) procedures have been in use since the late 1980s. Since then, examinee populations have become more heterogeneous, and tests have included more polytomously scored items. This paper summarizes and classifies new DIF methods and procedures that have appeared since…
Descriptors: Test Bias, Educational Development, Evaluation Methods, Statistical Analysis