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Eliminating Aggregation Bias in Experimental Research: Random Coefficient Analysis as an Alternative to Performing a ‘by-subjects’ and/or ‘by-items’ ANOVA

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Doi: 10.20982/tqmp.04.1.p021

Thompson, Glenn L.
21-34
Keywords: Statistics , ANOVA
(no sample data)   (Appendix)

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 ers (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.


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