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Matrix Summaries Improve Research Reports: Secondary Analyses Using Published Literature

LINDA REICHWEIN ZIENTEK is an assistant professor of mathematics education at Sam Houston State University, Department of Mathematics and Statistics, P.O. Box 2206, Huntsville, TX 77341; lrzientek{at}shsu.edu. Her research focuses on mathematics teacher preparation, teacher induction programs, and quantitative research methods.

BRUCE THOMPSON is Distinguished Professor and College Distinguished Research Fellow of Educational Psychology, and Distinguished Professor of Library Sciences, Texas A&M University, and adjunct professor of allied health sciences, Baylor College of Medicine (Houston), Department of Educational Psychology, 4225 TAMU, College Station, TX 77843; bruce-thompson{at}tamu.edu. His research interests include effect sizes, multivariate statistics, and LibQUAL+^TM.

Correlation matrices and standard deviations are the building blocks of many of the commonly conducted analyses in published research, and AERA and APA reporting standards recommend their inclusion when reporting research results. The authors argue that the inclusion of correlation/covariance matrices, standard deviations, and means can enhance findings in education and psychology by permitting secondary researchers to (a) conduct commonly utilized traditional univariate and multivariate analyses not initially performed in primary studies, (b) produce effect sizes and other statistics not included in prior published literature, and (c) conduct analyses once difficult to perform. Furthermore, meta-analytic thinking is encouraged when researchers have the ability to conduct the same analyses on multiple studies and then compare these findings across studies.

Key Words: correlation • general linear model • reporting standards • secondary data analyses

Educational Researcher, Vol. 38, No. 5, 343-352 (2009)
DOI: 10.3102/0013189X09339056


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