# Linear Mixed Model in SPSS Guidance

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## Linear Mixed Model in SPSS Guidance

 Dear List:  I am working on  pretest/post-test study with two between group factors, Treatment (Therapy A vs. Therapy B) and Age (younger vs. Older) on various outcome variables (all continuous). I originally considered doing a  repeated measures analysis but after reading up on the pros and cons of such an analysis I decided that a linear mixed model would be more appropriate given the correlation between the pre-test scores and the post-test scores. To further my understanding I reviewed the text by Verbeke and Molenberghs (Linear Mixed Models for Longitudinal Data). Getting through the text proved to be a challenge (many many equations beyond my pay grade). So I starting looking for some dummy downed explanations on how to set up my statistical model. So far that have not generated any comparable examples of my design  (2 x 2 x (2)). I am hoping there are some folks on the listserve that might be able to point me in some directions that will prove to be beneficial. I have googled but I have not found any helpful tutorials. Per chance if anyone has a good tutorial for my design I would appreciate hearing from you.  Thanks,  martin sherman Martin F. Sherman, Ph.D. Professor of Psychology Loyola University Maryland 4501 North Charles Street 222 B Beatty Hall Baltimore, MD 21210 ===================== To manage your subscription to SPSSX-L, send a message to [hidden email] (not to SPSSX-L), with no body text except the command. To leave the list, send the command SIGNOFF SPSSX-L For a list of commands to manage subscriptions, send the command INFO REFCARD
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## Re: Linear Mixed Model in SPSS Guidance

 It seems that your decision to use a mixed model is pushed by the pre-post correlations. What is it about those correlations that recommend a mixed model over repeated measures? Gene Maguin   From: SPSSX(r) Discussion <[hidden email]> On Behalf Of Martin Sherman Sent: Tuesday, April 23, 2019 9:00 AM To: [hidden email] Subject: Linear Mixed Model in SPSS Guidance   Dear List:  I am working on  pretest/post-test study with two between group factors, Treatment (Therapy A vs. Therapy B) and Age (younger vs. Older) on various outcome variables (all continuous). I originally considered doing a  repeated measures analysis but after reading up on the pros and cons of such an analysis I decided that a linear mixed model would be more appropriate given the correlation between the pre-test scores and the post-test scores. To further my understanding I reviewed the text by Verbeke and Molenberghs (Linear Mixed Models for Longitudinal Data). Getting through the text proved to be a challenge (many many equations beyond my pay grade). So I starting looking for some dummy downed explanations on how to set up my statistical model. So far that have not generated any comparable examples of my design  (2 x 2 x (2)). I am hoping there are some folks on the listserve that might be able to point me in some directions that will prove to be beneficial. I have googled but I have not found any helpful tutorials. Per chance if anyone has a good tutorial for my design I would appreciate hearing from you.  Thanks,  martin sherman   Martin F. Sherman, Ph.D. Professor of Psychology Loyola University Maryland 4501 North Charles Street 222 B Beatty Hall Baltimore, MD 21210 ===================== To manage your subscription to SPSSX-L, send a message to [hidden email] (not to SPSSX-L), with no body text except the command. To leave the list, send the command SIGNOFF SPSSX-L For a list of commands to manage subscriptions, send the command INFO REFCARD ===================== To manage your subscription to SPSSX-L, send a message to [hidden email] (not to SPSSX-L), with no body text except the command. To leave the list, send the command SIGNOFF SPSSX-L For a list of commands to manage subscriptions, send the command INFO REFCARD
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## Re: Linear Mixed Model in SPSS Guidance

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## Re: Linear Mixed Model in SPSS Guidance

 Randomization was used with 100 per treatment group. The two groups were equated on all demos and on the pretest. The age variable was equal between the two treatment groups before creating the binary age variable (of special interest to the PI-younger vs. older).  All pretest score distributions and post test scores distribution are bell shaped with some outliers for both groups.  From: Rich Ulrich <[hidden email]> Sent: Tuesday, April 23, 2019 12:00 PM To: [hidden email]; Martin Sherman Subject: Re: Linear Mixed Model in SPSS Guidance   Books have been written about the analysis of change scores. The choices may be described as "change" (repeated measures), "regressed change" (ANCOVA), and "other" (special, awkward considerations that hopefully do not arise).   The problems for inference that are most frequent arise when the initial groups are not matched on the Outcome score -- And you have Age as a factor, which is very often correlated with everything. Is that a problem for your data? (Of course, there also should be Random assignment to the treatments shown by similar means for those groups, or the inference problem is even worse.)   When initial scores are not matched, THEN, especially, you  need to worry that the "scaling" of an outcome might be "wrong" so that it introduces apparent effects that are artifacts of scoring.    Artifacts: For instance, if everybody doubles their score from Pre to Post on a scale where you should have taken the logs, then the initially-higher scoring group will show greater change. Or, the opposite, for a scale with a max:  If there is a "ceiling effect", then the initially-higher group has little room to improve and will show less change.   -- Rich Ulrich   From: SPSSX(r) Discussion <[hidden email]> on behalf of Martin Sherman <[hidden email]> Sent: Tuesday, April 23, 2019 9:00 AM To: [hidden email] Subject: Linear Mixed Model in SPSS Guidance   Dear List:  I am working on  pretest/post-test study with two between group factors, Treatment (Therapy A vs. Therapy B) and Age (younger vs. Older) on various outcome variables (all continuous). I originally considered doing a  repeated measures analysis but after reading up on the pros and cons of such an analysis I decided that a linear mixed model would be more appropriate given the correlation between the pre-test scores and the post-test scores. To further my understanding I reviewed the text by Verbeke and Molenberghs (Linear Mixed Models for Longitudinal Data). Getting through the text proved to be a challenge (many many equations beyond my pay grade). So I starting looking for some dummy downed explanations on how to set up my statistical model. So far that have not generated any comparable examples of my design  (2 x 2 x (2)). I am hoping there are some folks on the listserve that might be able to point me in some directions that will prove to be beneficial. I have googled but I have not found any helpful tutorials. Per chance if anyone has a good tutorial for my design I would appreciate hearing from you.  Thanks,  martin sherman   Martin F. Sherman, Ph.D. Professor of Psychology Loyola University Maryland 4501 North Charles Street 222 B Beatty Hall Baltimore, MD 21210 ===================== To manage your subscription to SPSSX-L, send a message to [hidden email] (not to SPSSX-L), with no body text except the command. To leave the list, send the command SIGNOFF SPSSX-L For a list of commands to manage subscriptions, send the command INFO REFCARD ===================== To manage your subscription to SPSSX-L, send a message to [hidden email] (not to SPSSX-L), with no body text except the command. To leave the list, send the command SIGNOFF SPSSX-L For a list of commands to manage subscriptions, send the command INFO REFCARD
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## Re: Linear Mixed Model in SPSS Guidance

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## Re: Linear Mixed Model in SPSS Guidance

 There are times, like in this instance, there are different approaches such as covariance vs change scores, or ordinal vs interval assumptions on level of measurement. In my experiencing,  it is often informative to try both approaches and compare the substantive conclusions.  If the substantive conclusions differ, a report would need to discuss the differences and the implications for further reasoning.  If the substantive conclusions are quite similar, i have usually suggested using the approach most familiar to the wider audience and mentioning that the alternative approach led to similar thinking. ----- Art Kendall Social Research Consultants -- Sent from: http://spssx-discussion.1045642.n5.nabble.com/===================== To manage your subscription to SPSSX-L, send a message to [hidden email] (not to SPSSX-L), with no body text except the command. To leave the list, send the command SIGNOFF SPSSX-L For a list of commands to manage subscriptions, send the command INFO REFCARD Art Kendall Social Research Consultants
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## Re: Linear Mixed Model in SPSS Guidance

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## FW: Linear Mixed Model in SPSS Guidance

 Further to Gene Maguin’s suggestion, you *can* find workedexamples for SPSS and other packages at the UCLA website  Tony Babinec From: SPSSX(r) Discussion <[hidden email]> On Behalf Of Maguin, EugeneSent: Tuesday, April 23, 2019 1:23 PMTo: [hidden email]Subject: Re: Linear Mixed Model in SPSS Guidance I suggest that you look at Singer and Willett’s book Applied Longitudinal Data Analysis. It’s not an spss-based book, so you won’t get spss command language examples but I do think that you will get a good introduction to working through the model building process. Given where you seem to be at this will help with whatever you do next.Gene Maguin   ===================== To manage your subscription to SPSSX-L, send a message to [hidden email] (not to SPSSX-L), with no body text except the command. To leave the list, send the command SIGNOFF SPSSX-L For a list of commands to manage subscriptions, send the command INFO REFCARD
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## Re: Linear Mixed Model in SPSS Guidance

 In reply to this post by Art Kendall I've used that approach that Art suggests - If both analyses get the same results - using raw data, and also rank-transformed data (for "ordinal") - then I've given the results one way, commenting that the choice does not make a difference. Also, I have reported results using that strategy where the choice was raw versus log-transformed, or whatever.  I do not care much for rank-transformed analyses for several reasons.  One of those reasons is that they do not behave well (in fact, may introduce artifacts) when the analysis is multi-variable.  An /appropriate/ power-transform (log, root, reciprocal)  will always serve better. Martin Sherman - (a) leaves open the chance that Age is correlated with the Outcome score; (b) reports that Age is of special interest to the PI; and (c) says that the Age-dichotomy is created from a continuous score. If Age /is/  a confounder for the score, I am sure that I would want to look at the scattergrams for raw age vs Score, separately for Pre and Post, to confirm that the "bell shape" holds up and that nothing peculiar is going on at any extremes.  (How extreme are those outliers?) I think I would try to do the analyses with Age as continuous, though I can see that a dichotomy can sometimes make it easier to display what you want to display. -- Rich Ulrich From: SPSSX(r) Discussion <[hidden email]> on behalf of Art Kendall <[hidden email]> Sent: Tuesday, April 23, 2019 1:59 PM To: [hidden email] Subject: Re: Linear Mixed Model in SPSS Guidance   There are times, like in this instance, there are different approaches such as covariance vs change scores, or ordinal vs interval assumptions on level of measurement. In my experiencing,  it is often informative to try both approaches and compare the substantive conclusions.  If the substantive conclusions differ, a report would need to discuss the differences and the implications for further reasoning.  If the substantive conclusions are quite similar, i have usually suggested using the approach most familiar to the wider audience and mentioning that the alternative approach led to similar thinking. ----- Art Kendall Social Research Consultants -- Sent from: http://spssx-discussion.1045642.n5.nabble.com/ ===================== To manage your subscription to SPSSX-L, send a message to [hidden email] (not to SPSSX-L), with no body text except the command. To leave the list, send the command SIGNOFF SPSSX-L For a list of commands to manage subscriptions, send the command INFO REFCARD ===================== To manage your subscription to SPSSX-L, send a message to [hidden email] (not to SPSSX-L), with no body text except the command. To leave the list, send the command SIGNOFF SPSSX-L For a list of commands to manage subscriptions, send the command INFO REFCARD
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## Re: Linear Mixed Model in SPSS Guidance

 In reply to this post by msherman No need for multilevel modeling  here. Your data do not violate the assumption of independence of observations. David Greenberg On Tue, Apr 23, 2019 at 9:00 AM Martin Sherman <[hidden email]> wrote: > > Dear List:  I am working on  pretest/post-test study with two between group factors, Treatment (Therapy A vs. Therapy B) and Age (younger vs. Older) on various outcome variables (all continuous). I originally considered doing a  repeated measures analysis but after reading up on the pros and cons of such an analysis I decided that a linear mixed model would be more appropriate given the correlation between the pre-test scores and the post-test scores. To further my understanding I reviewed the text by Verbeke and Molenberghs (Linear Mixed Models for Longitudinal Data). Getting through the text proved to be a challenge (many many equations beyond my pay grade). So I starting looking for some dummy downed explanations on how to set up my statistical model. So far that have not generated any comparable examples of my design  (2 x 2 x (2)). I am hoping there are some folks on the listserve that might be able to point me in some directions that will prove to be beneficial. I have googled but I have not found any helpful tutorials. Per chance if anyone has a good tutorial for my design I would appreciate hearing from you.  Thanks,  martin sherman > > Martin F. Sherman, Ph.D. > Professor of Psychology > Loyola University Maryland > 4501 North Charles Street > 222 B Beatty Hall > Baltimore, MD 21210 > ===================== To manage your subscription to SPSSX-L, send a message to [hidden email] (not to SPSSX-L), with no body text except the command. To leave the list, send the command SIGNOFF SPSSX-L For a list of commands to manage subscriptions, send the command INFO REFCARD ===================== To manage your subscription to SPSSX-L, send a message to [hidden email] (not to SPSSX-L), with no body text except the command. To leave the list, send the command SIGNOFF SPSSX-L For a list of commands to manage subscriptions, send the command INFO REFCARD
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## Re: Linear Mixed Model in SPSS Guidance

 In reply to this post by Anthony Babinec Thank you all.  I will start my journey.  Thanks again,   From: SPSSX(r) Discussion <[hidden email]> On Behalf Of Anthony Babinec Sent: Tuesday, April 23, 2019 2:35 PM To: [hidden email] Subject: FW: Linear Mixed Model in SPSS Guidance   Further to Gene Maguin’s suggestion, you *can* find worked examples for SPSS and other packages at the UCLA website     Tony Babinec   From: SPSSX(r) Discussion <[hidden email]> On Behalf Of Maguin, Eugene Sent: Tuesday, April 23, 2019 1:23 PM To: [hidden email] Subject: Re: Linear Mixed Model in SPSS Guidance   I suggest that you look at Singer and Willett’s book Applied Longitudinal Data Analysis. It’s not an spss-based book, so you won’t get spss command language examples but I do think that you will get a good introduction to working through the model building process. Given where you seem to be at this will help with whatever you do next. Gene Maguin     ===================== To manage your subscription to SPSSX-L, send a message to [hidden email] (not to SPSSX-L), with no body text except the command. To leave the list, send the command SIGNOFF SPSSX-L For a list of commands to manage subscriptions, send the command INFO REFCARD ===================== To manage your subscription to SPSSX-L, send a message to [hidden email] (not to SPSSX-L), with no body text except the command. To leave the list, send the command SIGNOFF SPSSX-L For a list of commands to manage subscriptions, send the command INFO REFCARD
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## Re: Linear Mixed Model in SPSS Guidance

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## Re: Linear Mixed Model in SPSS Guidance

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## Re: Linear Mixed Model in SPSS Guidance

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## Re: Linear Mixed Model in SPSS Guidance

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