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The Analysis of Infant Habituation DataDistributional Problems and Solutions

William Gardner, University of Virginia

The repeated measures analysis of variance issensitive to problems concerning the distribu­tion of the residuals. These problems arepervasive in infant habituation data, butthe solutions proposed in the methodologicalliterature are usually inadequate. First, thevariances of the residuals must be equalacross groups. This is of particular concern ininfant habituation studies, where there areoften substantial differences in the variabi­lityof looking-time data at different ages.Second, the residuals are assumed to benormally distributed. The distributions oflooking times, however, are always truncated atthe bottom. The effects of these Violations onthe repeated measures design are investigatedusing monte-carlo simulations reflectingdistributional problems found in the analysisof real habituation data. The commonly knownremedies (e.g., MANOVA) for such problems areof little help here. An alternative solutionis nonparametric analysis using Efron's boot­strapping algorithm, a technique which requiresno assumptions about the distributionalproperties of the data. Unlike classical non­parametric analyses, however, the data are notconverted to ranks and no information islost. Infant habituation data sets exhibitingnon-normality and heterogeneous residualvariance are reanalyzed using the bootstrappingalgorithm, and the reSUlts of bootstrapped andconventional analyses are compared.