Hello,
Im doing two way ANOVA with SPSS 13.0. I would like to do post hoc test. In the post hoc subcommand, I can select my two main factors, but the interaction is not present. Does anybody know how to make post hoc test on interaction with SPSS 13.0? Thanks, Sébastien. |
What do you mean by post-hoc tests for interaction? Do you mean comparing means of factor A separately at each level of Factor B? if yes, then this can be done using /EMMEANS = TABLES(A*B) COMPARE (A) subcommand when running GLM. Let me know if you need more specific syntax.
Bozena Bozena Zdaniuk, Ph.D. University of Pittsburgh UCSUR, 6th Fl. 121 University Place Pittsburgh, PA 15260 Ph.: 412-624-5736 Fax: 412-624-4810 email: [hidden email] -----Original Message----- From: SPSSX(r) Discussion [mailto:[hidden email]] On Behalf Of Sebastien Sent: Wednesday, June 28, 2006 6:28 AM To: [hidden email] Subject: post hoc test on interaction SPSS 13.0 Hello, I'm doing two way ANOVA with SPSS 13.0. I would like to do post hoc test. In the post hoc subcommand, I can select my two main factors, but the interaction is not present. Does anybody know how to make post hoc test on interaction with SPSS 13.0? Thanks, Sébastien. |
In reply to this post by Sebastien-6
How can I use the /EMMEANS = TABLES(A*B) COMPARE (A) subcommand? I have
used SPSS for only few weeks and I know nothing about syntax. |
In reply to this post by Sebastien-6
Try This :
GLM Y BY X1 x2 /EMMEANS = TABLES(X1*X2) COMPARE(X1) /DESIGN. The output of this includes a parwise comparison table for the dependent variable Y. Regards. Edward -----Original Message----- From: SPSSX(r) Discussion [mailto:[hidden email]]On Behalf Of Sebastien Sent: Wednesday, June 28, 2006 1:58 PM To: [hidden email] Subject: Re: post hoc test on interaction SPSS 13.0 How can I use the /EMMEANS = TABLES(A*B) COMPARE (A) subcommand? I have used SPSS for only few weeks and I know nothing about syntax. |
In reply to this post by Sebastien-6
Sebastien,
First of all, you needed to inform the list serve group that you know nothing about syntax. Using syntax in spss to a large extent can be done using the paste function. In order to test interactions in the GLM modelling in spss, you will unfortunately need to use syntax. Here is a crash course in syntax for use in the glm approach Please see the link below on how to do this. http://www.utexas.edu/its/rc/answers/spss/spss50.html HTH Paul > Sebastien <[hidden email]> wrote: > > How can I use the /EMMEANS = TABLES(A*B) COMPARE (A) subcommand? I have > used SPSS for only few weeks and I know nothing about syntax. |
All,
I have some questions about understanding how ICCs are computed. The context for my question is multilevel analyses and not ICCs between raters and items. Peugh and Enders, in a 2005 article on how to use the spss Mixed command for several simple types of analyses, compute the ICC as the ratio of the intercept variance to the sum of the intercept and residual (within subject) variances. (Every computation in this email is for an unconditional means model.) My estimate is .000326. I was also googling ICC and found a different definition. That one is that ICC is [MS(between) minus MS(error)] divided by [MS(between) plus MS(error)]. This formula was developed for unordered pairs such as would be found in a twin study. Using the values from a oneway run This estimate is .134. (Unianova with the 'by' variable declared as random gives the same component values.) I also ran Varcomp and the error variance matches the Mixed residual variance number but the numerator printed .000, which is probably ok because the Mixed estimate of the intercept variance is .0003. I quite confused because, no doubt, there's something I don't understand. I hope that someone can set me straight because I want to make some analytical decision on the basis of the (correct) ICC value. Thanks, Gene Maguin |
gene--
a good description of ICCs can be found here: http://www.uvm.edu/~dhowell/StatPages/More_Stuff/icc/icc.html specifically, this derivation applies to your question: http://www.uvm.edu/~dhowell/StatPages/More_Stuff/icc/icc.ht12.gif your first equation is: [var(intercept)]/[SUM(var(intercept)) + var(residual)] (1) while your second equation is: [MS(between) - MS(error)]/[MS(between) + MS(error)] (2) we see that the relationship between these is based on: MS(between) = k*var(between) + var(residual) where k = number of levels of your "factor" or number of observations (i.e., time points in a multilevel model). ...these are essentially two approaches to the same problem. typically, ICCs are used to answer the question of "what proportion of variance is accounted for by X?". therefore, for hand-calculation and ease of interpretation, i use (1). the results are easily described in terms of the original question. and it requires easily-accessed variance components for calculation. good luck! --matthew * * * * * * * * * * * * * * * * * * * * matthew m. gushta -- research associate computer & statistical sciences center american institutes for research [hidden email] -- 202.403.5079 -----Original Message----- From: SPSSX(r) Discussion [mailto:[hidden email]] On Behalf Of Gene Maguin Sent: Thursday, July 06, 2006 5:04 PM To: [hidden email] Subject: Intraclass correlation (ICC) confusion All, I have some questions about understanding how ICCs are computed. The context for my question is multilevel analyses and not ICCs between raters and items. Peugh and Enders, in a 2005 article on how to use the spss Mixed command for several simple types of analyses, compute the ICC as the ratio of the intercept variance to the sum of the intercept and residual (within subject) variances. (Every computation in this email is for an unconditional means model.) My estimate is .000326. I was also googling ICC and found a different definition. That one is that ICC is [MS(between) minus MS(error)] divided by [MS(between) plus MS(error)]. This formula was developed for unordered pairs such as would be found in a twin study. Using the values from a oneway run This estimate is .134. (Unianova with the 'by' variable declared as random gives the same component values.) I also ran Varcomp and the error variance matches the Mixed residual variance number but the numerator printed .000, which is probably ok because the Mixed estimate of the intercept variance is .0003. I quite confused because, no doubt, there's something I don't understand. I hope that someone can set me straight because I want to make some analytical decision on the basis of the (correct) ICC value. Thanks, Gene Maguin |
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