%0 Journal Article
%T Eliminating Aggregation Bias in Experimental Research: Random Coefficient Analysis as an Alternative to Performing a  by-subjects  and/or  by-items  ANOVA
%A Glenn L. Thompson
%J Tutorials in Quantitative Methods for Psychology
%D 2008
%I Universit¨¦ de Montr¨¦al
%X Experimental psychologists routinely simplify the structure of their data by computing means for each experimental condition so that the basic assumptions of regression/ANOVA are satisfied. Typically, these means represent the performance (e.g. reaction time or RT) of a participant over several items that share some target characteristic (e.g. Mean RT for high-frequency words). Regrettably, analyses based on such aggregated data are biased toward rejection of the null hypothesis, inflating Type-I error beyond the nominal level. A preferable strategy for analyzing such data is random coefficient analysis (RCA), which can be performed using a simple method proposed by Lorch and Myers (1990). An easy to use SPSS implementation of this method is presented using a concrete example. In addition, a technique for evaluating the magnitude of potential aggregation bias in a dataset is demonstrated. Finally, suggestions are offered concerning the reporting of RCA results in empirical articles.
%K Statistics
%K ANOVA
%U http://www.tqmp.org/Content/vol04-1/p021/p021.pdf