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Patterson, Brian F. – College Board, 2012
The main goal of this study was to illustrate and provide some direction for dealing with the complexities of propensity score matching within different multilevel contexts. Special attention is given to how procedures typically applied in a non-hierarchical setting may be modified to properly reduce the expected bias in the estimated treatment…
Descriptors: Probability, Scores, Statistical Bias, High School Students
Shaw, Emily J.; Kobrin, Jennifer L.; Patterson, Brian F.; Mattern, Krista D. – College Board, 2011
Presented at the Annual Meeting of the American Educational Research Association (AERA) in New Orleans, LA in April 2011. The current study examined the differential validity of the SAT for predicting cumulative GPA through the second-year of college by college major, as well as the differential prediction of cumulative GPA by college major among…
Descriptors: College Entrance Examinations, Predictive Validity, Grade Point Average, College Students
Patterson, Brian F.; Kobrin, Jennifer L. – College Board, 2011
This study presents a case for applying a transformation (Box and Cox, 1964) of the criterion used in predictive validity studies. The goals of the transformation were to better meet the assumptions of the linear regression model and to reduce the residual variance of fitted (i.e., predicted) values. Using data for the 2008 cohort of first-time,…
Descriptors: Predictive Validity, Evaluation Criteria, Regression (Statistics), College Freshmen
Kobrin, Jennifer L.; Patterson, Brian F. – College Board, 2010
There is substantial variability in the degree to which the SAT and high school grade point average (HSGPA) predict first-year college performance at different institutions. This paper demonstrates the usefulness of multilevel modeling as a tool to uncover institutional characteristics that are associated with this variability. In a model that…
Descriptors: Scores, Validity, Prediction, College Freshmen
Patterson, Brian F. – College Board, 2008
Presented at the quarterly meeting of the New York Area SAS Users-Group in December 2008. In virtually any research context, there is likely to be opportunity to apply mixed-models--of which repeated-measures, multilevel, hierarchical and random- and fixed-effect models are specific types-- that improve upon their traditional linear or generalized…
Descriptors: Hierarchical Linear Modeling, Statistics, Computer Software, College Entrance Examinations