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 Post subject: Multilevel models and intra-level interactionPosted: Wed Apr 23, 2008 12:21 am
I am currently using SPSS 15.0.

My study has one dependent variable (quantitative),
one categorical indipendent variable at level 1, individual(gender, value are 0 and 1)
one categorical indipendent variable at level 2, groups (nature of team, value can be 0,1,2).

Each group is composed by six individuals. My hypotheses ISthat, according to the nature of group, male and females scores different values of the dependent variable (in some groups men are better than women, in other group women are better than men). Since observation are dependant (Intra Class Correlation is about 30%), I have to use MultiLevel models for a study.

I am not sure of the correct form of the syntax for my models (which should take into account the interaction between team nature and gender).

I used this code:

Code:
MIXED outcome BY gender WITH condition
/PRINT solution testcov
/METHOD = REML
/FIXED = intercept gender condition gender*condition
/RANDOM=intercept | SUBJECT(group)

The outcomes are strange because if I run the simpler model

Code:
MIXED outcome WITH condition
/PRINT solution testcov
/METHOD = REML
/FIXED = intercept condition
/RANDOM=intercept condition | SUBJECT(group)

condition factor is significative, while in the most complex model it is not.

Thank you in advance for any suggestion or correction you can provide me.

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 Post subject: Re: Multilevel models and intra-level interactionPosted: Wed Apr 23, 2008 3:03 pm

Joined: Wed Apr 23, 2008 12:01 am
Posts: 8
I am the author of the previous post (sorry if I figured out as a guest).
I modified the code because I cannot use condition as a covariate (it is a categorical variable). So, the code I run is the following:

Code:
MIXED outcome BY gender condition
/PRINT solution testcov
/METHOD = REML
/FIXED = intercept gender condition gender*condition
/RANDOM=intercept condition| SUBJECT(group)

Is it the right code? My hypotheses are specifically addressed to estimate if gender behaviour is different accordin to the nature of the group.
Do you know I can also made post-hoc comparisons among different condition (e.g. there is no gender difference in condition 1 while women performs better in condition 2), in case interaction is significative?

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 Post subject: Re: Multilevel models and intra-level interactionPosted: Tue Feb 14, 2012 5:01 pm

Joined: Thu May 19, 2011 2:13 pm
Posts: 3
Hi,

I know this is a really old post but it relates to my question so I was hoping you might have found the answer in the mean time.

In my experiment I measure pupil dilation when subjects observe different stimuli.
between subject factor:
species_subject=2 (0-1)
within subject factor:
same_different=2 (0-1)
increase_decrease_stimulus=2 (0-1)

For my experiment, I asked adivice from a statistician. He proposed the model below but I myself would have chosen for 'by' instead of 'with'. But I am only a beginner to multilevel, so perhaps you could give me an advice?

mixed pupilsize_subject with int lin quadr species_subject same_different increase_decrease_stimulus
/ fixed int luminance_of_stimulus
species_subject same_different increase_decrease_stimulus
species_subject*lin same_different*lin increase_decrease_stimulus*lin
species_subject*same_different
species_subject*increase_decrease_stimulus
same_different*increase_decrease_stimulus
species_subject *same_different*increase_decrease_stimulus
species_subject*same_different*lin
species_subject*increase_decrease_stimulus*lin
same_different*increase_decrease_stimulus*lin
species_subject *same_different*increase_decrease_stimulus*lin
| noint
/ random int lin | subject(id) covtype(DIAG)
/ random int lin | subject(id*condition) covtype(DIAG)
/ repeated = time | subject(id*condition) covtype(AR1)
/ print solution testcov r.

best wishes,
Mariska

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