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signi cant, then, closer examination of the distribution of the DV is in order If it is markedly skewed, the sphericity test should be repeated after a normalizing transformation of the DV If the test is now nonsigni cant, the problem with the data set is probably nonnormality rather than failure of sphericity If the test is still signi cant, there is probably nonsphericity The Mauchly test also has low power for small samples and is overly sensitive with very large samples Thus, with large samples it is sometimes signi cant when departure from sphericity is slight It is always worthwhile, then, to consider the magnitude of the epsilon factor, even when the test of sphericity is explicitly provided.

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Clinical Psychology: Science and Practice, 4, 175 187 Bornstein, R F (2003) Behaviorally referenced experimentation and symptom validation: A paradigm for 21st-century personality disorder research Journal of Personality Disorders, 17, 1 18 Carson, R C (1969) Interaction concepts of personality Chicago: Aldine Carson, R C (1982) Self-fulfilling prophecy, maladaptive behavior, and psychotherapy In J C Anchin & D J Kielser (Eds), Handbook of interpersonal psychotherapy (pp 64 77) New York: Pergamon Press Clark, L A (1993) Manual for the schedule for nonadaptive and adaptive personality Minneapolis: University of Minnesota Press..

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There are ve options when the assumption of sphericity is not tenable: (a) use comparisons on the IV in question (usually trend analysis) instead of the omnibus test; (b) use an adjusted F test; (c) use a multivariate test of the within-subjects effects; (d) use a maximum likelihood procedure that lets you specify that the structure of the variance-covariance matrix is other than compound symmetry; or (e) use a multilevel modeling approach in which the multiple responses over time are the lowest level of analyses and are nested with subjects, the next higher level of analysis The rst option comparisons takes advantage of the fact that sphericity is not required when there is only one df for the within-subjects IV.

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/* Main program */ void main(int argc, char* argv[]) { int i; long counts[NC], flag = 0; char *output[NC]; for(i=0; i<NC; i++) { counts[i] = 0; output[i] = malloc(1024); if(!output[i]) { printf( Error: Out of memory \n ); exit(1); } output[i][0] = 0; } if(argc < 2) { Usage(); exit(1); } else { for(i=1; i<argc-1; i++) { if(argv[i][0] == - ) { switch(argv[i][1]) { case r : /* recursive subdirectories */ flag += 1; break; case v : /* verbose output */ flag += 2; break; case d : /* debug */ flag += 4; break; case s : /* small files */

This option, in the form of trend analysis, is often a good one because questions about trends in the DV over time are usually the ones that researchers want answered anyway, and the assumption of sphericity is most likely to be violated when the IV is time related Trend analysis asks, Does the DV increase (or decrease) steadily over time Does the DV rst increase and then decrease (or the reverse) Are both patterns present, superimposed on each other and Are there other, more complicated, patterns in the data Before the sophisticated software was available for other options, trend analysis (or other comparisons) was preferred on strictly computational grounds It is still preferred if the researcher has questions about the shape of the patterns in the DV over time.

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However, a disadvantage of trend analysis (or any set of comparisons) in within-subjects design is that each comparison develops its own error term This reduces the number of df for error and consequently the power available for the test of the comparison The second option is to use a more stringent F test of the IV in question Both Greenhouse-Geisser (1959) and Huynh-Feldt (1976) adjustments are offered by all three software packages that recognize within-subjects designs Both compute an adjustment factor, epsilon ( ), that is used to reduce df associated with both numerator and denominator of the F test Reducing df makes the F test more conservative Both Greenhouse-Geisser and Huynh-Feldt compute.

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