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Dorie, Vincent; Hill, Jennifer; Shalit, Uri; Scott, Marc; Cervone, Daniel – Grantee Submission, 2018
Statisticians have made great progress in creating methods that reduce our reliance on parametric assumptions. However this explosion in research has resulted in a breadth of inferential strategies that both create opportunities for more reliable inference as well as complicate the choices that an applied researcher has to make and defend.…
Descriptors: Statistical Inference, Simulation, Causal Models, Research Methodology
Ali, Usama S.; Walker, Michael E. – ETS Research Report Series, 2014
Two methods are currently in use at Educational Testing Service (ETS) for equating observed item difficulty statistics. The first method involves the linear equating of item statistics in an observed sample to reference statistics on the same items. The second method, or the item response curve (IRC) method, involves the summation of conditional…
Descriptors: Difficulty Level, Test Items, Equated Scores, Causal Models
Sun, Shuyan; Pan, Wei – Journal of Experimental Education, 2013
Regression discontinuity design is an alternative to randomized experiments to make causal inference when random assignment is not possible. This article first presents the formal identification and estimation of regression discontinuity treatment effects in the framework of Rubin's causal model, followed by a thorough literature review of…
Descriptors: Regression (Statistics), Computation, Accuracy, Causal Models
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