The power of a paired t-test with a covariate.

Many researchers employ the paired t-test to evaluate the mean difference between matched data points. Unfortunately, in many cases this test in inefficient. This paper reviews how to increase the precision of this test through using the mean centered independent variable x, which is familiar to researchers that use analysis of covariance (ANCOVA). We add to the literature by demonstrating how to employ these gains in efficiency as a factor for use in finding the statistical power of the test. The key parameters for this factor are the correlation between the two measures and the variance ratio of the dependent measure on the predictor. The paper then demonstrates how to compute the gains in efficiency a priori to amend the power computations for the traditional paired t-test. We include an example analysis from a recent intervention, Families Preparing the New Generation (Familias Preparando la Nueva Generación). Finally, we conclude with an analysis of extant data to derive reasonable parameter values.

[1]  E Thomas Public policy 1 , 2015 .

[2]  Dana Peterson,et al.  HOW GREAT IS G.R.E.A.T.? RESULTS FROM A LONGITUDINAL QUASI‐EXPERIMENTAL DESIGN , 2001 .

[3]  G. H. Thomson,et al.  A formula to correct for the effect of errors of measurement on the correlation of initial values with gains. , 1924 .

[4]  W. Shadish,et al.  Experimental and Quasi-Experimental Designs for Generalized Causal Inference , 2001 .

[5]  Frederic M. Lord The Measurement of Growth , 1956 .

[6]  Edgar Erdfelder,et al.  G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences , 2007, Behavior research methods.

[7]  Andrew C. Porter,et al.  Analysis of Covariance: Its Model and Use in Psychological Research. , 1987 .

[8]  R. F. Garside,et al.  The regression of gains upon initial scores , 1956 .

[9]  R. L. Thorndike Regression fallacies in the matched groups experiment , 1942 .

[10]  David P MacKinnon,et al.  A Monte Carlo Comparison Study of the Power of the Analysis of Covariance, Simple Difference, and Residual Change Scores in Testing Two-Wave Data , 2013, Educational and psychological measurement.

[11]  D. Wayne Osgood,et al.  Gang Resistance Education and Training (Great): Results from the National Evaluation , 1999 .

[12]  J. M. Oakes,et al.  Statistical Power for Nonequivalent Pretest-Posttest Designs , 2001, Evaluation review.

[13]  Lela Rankin Williams,et al.  Efficacy of a Culturally Based Parenting Intervention: Strengthening Open Communication Between Mexican-Heritage Parents and Adolescent Children , 2012, Journal of the Society for Social Work and Research.

[14]  Donald B. Rubin,et al.  Measurement Error and Regression to the Mean in Matched Samples , 1971 .

[15]  Jacob Cohen Statistical Power Analysis for the Behavioral Sciences , 1969, The SAGE Encyclopedia of Research Design.

[16]  Jacob Cohen,et al.  A power primer. , 1992, Psychological bulletin.

[17]  William C. Guenther,et al.  Sample Size Formulas for Normal Theory T Tests , 1981 .

[18]  Steven Pedlow,et al.  Sample design, sample augmentation, and estimation for Wave 2 of the NSHAP. , 2014, The journals of gerontology. Series B, Psychological sciences and social sciences.

[19]  Quinn McNemar,et al.  On Growth Measurement , 1958 .

[20]  J. Arthur Woodward,et al.  Unreliability of difference scores: A paradox for measurement of change. , 1975 .

[21]  Flavio F Marsiglia,et al.  Familias: Preparando la Nueva Generación , 2014, Research on social work practice.

[22]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[23]  Mark W. Roosa,et al.  Examination of the Cross-cultural and Cross-language Equivalence of the Parenting Self-Agency Measure. , 1996 .

[24]  C. Aberson Applied Power Analysis for the Behavioral Sciences , 2010 .

[25]  Student,et al.  THE PROBABLE ERROR OF A MEAN , 1908 .

[26]  William Dale,et al.  Using and interpreting mental health measures in the National Social Life, Health, and Aging Project. , 2014, The journals of gerontology. Series B, Psychological sciences and social sciences.

[27]  W. G. Cochran Analysis of covariance: Its nature and uses. , 1957 .