# Divergence between JASP and SPSS ouptuts when computing polynomial contrasts for within-sjts designs

I am trying to reproduce David Howell's SPSS analysis of the aiport dataset (attached) in JASP. Dave's analysis is available at https://www.uvm.edu/~dhowell/StatPages/More_Stuff/RepMeasMultComp/RepMeasMultComp.html. When performing the polynomial contrast analysis of the effect of time on stress level (condition near aiport only), SPSS returns F(1, 99) = 1.721 p = .193 for the cubic trend while JASP returns t = 1.261 p = .208:

Obviously the t statistic returned by JASP is not equal to the square root of F returned by SPSS. Could you please clarify why?

Best,

Mat

## Comments

Hi Mat,

I just ran the analysis in R to check those results. Here is the code I wrote:

So this is the same result as JASP provides.

I did some digging into how these discrepancies arise, and found this post on StackExchange that provides some great insight:

``While the "t-test approach" tests each contrast against its own separate error term, the RM-ANOVA tests each contrast against a pooled error term (hence DF=14), which assumes that the variances of the contrast scores are approximately equal (i.e., sphericity assumption)."

It seems SPSS has the "t-test approach" without the pooled error term, and JASP/aov have the approach with the pooled error term. We have recently implemented the option to switch between pooled/nonpooled error terms for the post-hoc tests, and I do think it's prudent to add the same option to contrasts for those cases where the sphericity assumption has been violated. Thanks for pointing this out to us!

Kind regards,

Johnny