Matrix Summaries Improve Research Reports

Secondary Analyses Using Published Literature

Dados Bibliográficos

ID9730877
AutoresLinda Reichwein Zientek (0000-0001-9741-0257, Texas A&M University System, autor correspondente), Bruce Thompson (0000-0002-5885-0652, Texas A&M University System)
Ano2009
Volume38
Fascículo5
Páginas343-352
Data de publicação2009-06-01
Peer ReviewedSim
Open AccessSim
TipoARTICLE
PeriódicoEducational Researcher (JOURNAL)
Identificadores do periódicoISSN: 0013-189X • E-ISSN: 1935-102X
EditoraAmerican Educational Research Association (AERA) (PUBLISHER)
DOI10.3102/0013189x09339056
OpenAlexW2108588644
IdiomaEN
Citações recebidas10
Referências citadas43

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

Correlation · Covariance matrix · Econometrics · Educational research · Inclusion (mineral · Management science · Mathematics education · Meta-analysis · Multivariate analysis · Multivariate statistics · Statistics · Univariate · Behavioral and Psychological Studies · Computer Science · Mathematics · Medicine · Psychology · Psychometric Methodologies and Testing · Social Psychology · Statistical Methods and Applications