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Date:         Fri, 18 May 2012 11:34:23 -0700
Reply-To:     David Marso <david.marso@gmail.com>
Sender:       "SPSSX(r) Discussion" <SPSSX-L@LISTSERV.UGA.EDU>
From:         David Marso <david.marso@gmail.com>
Subject:      Re: Subject specific information
In-Reply-To:  <7DE1AA9F-BFAA-4FA0-88E0-EA50B0ED2836@PHHP.UFL.EDU>
Content-Type: text/plain; charset=UTF-8

How do you get 32 rows from 3 dichotomies? I suspect there would be 8 (or you have two other dichotomous variables you are not talking about). I do hope you have counterbalanced or randomized your target1..targetk combinations over time or you have a big mess. I will not provide specific syntax but I would direct you to study the MIXED procedure. However I believe it is designed to fit univariate responses but it permits flexibility in specifying error structures unlike GLM.

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Craggs,Jason G wrote > > Sorry for the confusion. I was trying to convey that the same subject > rates multiple dimensions of all possible combinations of the dichotomous > variables. For example: > Time ID Target1 Target2 Target3 rate1 > 1 1 0 0 0. 30 > 2 1. 0. 0. 1. 62 > 3. 1. 0. 1. 0. 15 > > Etc. etc. > > I will look into the split file option. Thank for the suggestion. > > Jason > > On May 17, 2012, at 10:57 PM, "Rich Ulrich" > &lt;rich-ulrich@&lt;mailto:rich-ulrich@&gt;> wrote: > > 1. Oh. That is contrary to your example, which shows all > the dichotomous values as the same for a subject, row 1, 2, [ ,], 32. > > I think you can get the information you want on parameter > estimates from repeated measures, but I never have. > > 2. Split Files certainly generates the sets more readily than > using a bunch of Select If's. The new problem might be in > preserving the IDs for the sets. > > -- > Rich Ulrich > > ________________________________ > From: jcraggs@.UFL&lt;mailto:jcraggs@.UFL&gt; > To: rich-ulrich@&lt;mailto:rich-ulrich@&gt; > CC: spssx-l@.uga&lt;mailto:spssx-l@.uga&gt; > Subject: Re: Subject specific information > Date: Fri, 18 May 2012 02:41:34 +0000 > > Hi Rich, > > Thanks for the reply. > > 1. All data are nested within subject. The three dichotomous variables > refer to characteristics of a target in a vignette (e.g., young/female/AA) > that may influence the subsequent ratings. Basically we're looking for > idiographic decision policy/rating biases. > > 2. The current analysis is scripted to basically "select if" for each > subject and perform a regression. However, there are now several subject > groups and will eventually compare these as well. Using an MLM approach, > with ID being a random effect, I can get some of the information I want, > but am stuck as how to get the rest. Getting subject specific betas and > looking at interaction effects being the two biggest conundrums at the > moment. > > Cheers, > Jason > > On May 17, 2012, at 10:18 PM, "Rich Ulrich" > &lt;rich-ulrich@&lt;mailto:rich-ulrich@&gt;> wrote: > > 1. All three of your dichotomous IVs are between-subject > variables; the within-subject trials are irrelevant to their > main effects and interactions. So you will simplify this > analysis if you aggregate across rows, and look at the > easy 2x2x2 ANOVA. > > It is more complicated if you also want to look at effects > and interactions with "32 observations" in some fashion. > > 2. For subject-specific effects across the 32 observations, > it appears that you are looking for the linear trend/ linear > regression. I would probably use Split Files and Regression, > but if you are looking for immediate tests on the within-effects, > there probably are direct ways to get them. Is Regression > enough for you? > > -- > Rich Ulrich > > > ________________________________ > Date: Thu, 17 May 2012 20:36:51 +0000 > From: jcraggs@.UFL&lt;mailto:jcraggs@.UFL&gt; > Subject: Subject specific information > To: SPSSX-L@.UGA&lt;mailto:SPSSX-L@.UGA&gt; > > > Greetings, > > > > I’ve a dataset organized for HLM/MLM analyses (each subject spans several > row, 1 row per observation, for 32 observations). > > Specifically, the data are organized as such: > > Time ID TargetAge TargetSex > TargetRace Rate1 Rate2 … > > 1 1 [0,1] [0,1] > [0,1] [0-100] [0-100] … > > 2 1 [0,1] [0,1] > [0,1] [0-100] [0-100] … > > … 1 [0,1] [0,1] > [0,1] [0-100] [0-100] … > > 32 1 [0,1] [0,1] > [0,1] [0-100] [0-100] … > > 1 2 [0,1] [0,1] > [0,1] [0-100] [0-100] … > > 2 2 [0,1] [0,1] > [0,1] [0-100] [0-100] … > > … 2 [0,1] [0,1] > [0,1] [0-100] [0-100] … > > 32 2 [0,1] [0,1] > [0,1] [0-100] [0-100] … > > 1 … [0,1] [0,1] > [0,1] [0-100] [0-100] … > > … … [0,1] [0,1] > [0,1] [0-100] [0-100] … > > 1 300 [0,1] [0,1] > [0,1] [0-100] [0-100] … > > … 300 [0,1] [0,1] > [0,1] [0-100] [0-100] … > > 32 300 [0,1] [0,1] > [0,1] [0-100] [0-100] … > > > > TargetAge coded: young/old > > TargetSex coded: male/female > > TargetSex coded: AA/Cauc > > > > The goal is to use the first three “target” variables as the independent > variables and the subsequent ‘Rate…’ variables as the dependent variables. > > While I am familiar with running fixed and random effects analyses, and > saving and plotting the estimated predicted values, I am struggling to > accomplish two goals using SPSS-20. > > 1. I would like to test the main- and interaction- effects of the > IV’s. Testing the main effects seems fairly straightforward, but I am > unclear as to the best way to estimate and test the interaction terms. > > 2. I would like to save subject specific values for: intercepts, > slope, and a standardized beta weight from each of the tests mentioned > above. > > > > > > Any thoughts, suggestions, and or comments are greatly appreciated. > > > > Best regards, > > Jason >

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