Subject specific information
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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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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: [hidden email] Subject: Subject specific information To: [hidden email] 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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Re: Subject specific information
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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
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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: [hidden email] To: [hidden email] CC: [hidden email] 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
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Re: Subject specific information
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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
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Administrator
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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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Re: Subject specific information
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In reply to this post by Craggs,Jason G
Hi David,
There are actually 4 dichotomies, resulting in 16 unique scenarios, presented twice, which have been randomized and counterbalanced. Thanks for the recommendation. I will look at the MIXED procedure to see if I am able to estimate subject specific beta weights (e.g., a random effect) for my analysis. Cheers Jason On Fri, 18 May 2012 11:34:23 -0700, David Marso <[hidden email]> wrote: >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. > >---------------------------------- > >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" >> <rich-ulrich@<mailto:rich-ulrich@>> 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<mailto:jcraggs@.UFL> >> To: rich-ulrich@<mailto:rich-ulrich@> >> CC: spssx-l@.uga<mailto:spssx-l@.uga> >> 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" >> <rich-ulrich@<mailto:rich-ulrich@>> 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<mailto:jcraggs@.UFL> >> Subject: Subject specific information >> To: SPSSX-L@.UGA<mailto:SPSSX-L@.UGA> >> >> >> Greetings, >> >> >> >> Iâve 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 âtargetâ variables as the independent >> variables and the subsequent âRateâ¦â 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 >> IVâs. 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 >> > > >-- >View this message in context: >Sent from the SPSSX Discussion mailing list archive at 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. 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