Multilevel Modeling Programs

David A. Kenny

January 23, 2014

Presumed Background

• Multilevel Modeling

• Nested

Example Kashy (1991) Study of Gender and Intimacy

respondents completed a survey each night for two weeks

outcome is the average intimacy rating of each interaction partner(from 1 to 7, bigger numbers more intimacy)

Levels level 1: intimacy of the interaction (1-7),

partner gender (-1=male; 1=female) level 2: respondent gender (-1=male;

1=female)   3

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Syntax

MIXED

intimacy WITH resp_gender partner_gender

/FIXED = resp_gender partner_gender resp_gender*partner_gender

/PRINT = SOLUTION TESTCOV

/RANDOM INTERCEPT partner_gender | SUBJECT(id) COVTYPE(UNR).

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Random Effects

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Example: Fixed Effects

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Output from Other Programs

HLM SAS R: lmerMLwiNnot included: Stata 8

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HLM: Formulation

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HLM

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SAS: Syntax

PROC MIXED COVTEST;CLASS ID;MODEL INTIMACY = Part_Gen Resp_Gen Resp_Gen*Part_Gen

/ DDFM=SATTERTH SOLUTION;RANDOM INTERCEPT Part_Gen

/ TYPE=UN SUB=ID ;RUN; QUIT;

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SAS: Output

library(foreign);library(lme4);library(lmerTest)ifilename="c:/kashy.sav"OrDa = read.spss (ifilename,use.value.labels=FALSE,max.value.labels=Inf,to.data.frame=TRUE)OrDa$int= OrDa$resp_gender*OrDa$partner_gendermodel <- lmer(intimacy ~ 1 + resp_gender + partner_gender + int + (partner_gender|id), data=OrDa)modelA <- lmer(intimacy ~ 1 + resp_gender + partner_gender + int + ((1)|id) + (0+partner_gender|id), data=OrDa)modelB <- lmer(intimacy ~ 1 + resp_gender + partner_gender + int + ((1)|id), data=OrDa)modelanova(model)anova(model,modelB)anova(modelA,modelB)

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R: lmer

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REML criterion at convergence: 5181.537 Random effects: Groups Name Std.Dev. Corr id (Intercept) 0.9236 partner_gender 0.1444 -0.12 Residual 1.3751

Analysis of Variance Table of type 3 with Satterthwaite approximation for degrees of freedom Df Sum Sq Mean Sq F value Denom Pr(>F) resp_gender 1 9.1697 9.1697 5.0353 77.372 0.0276940 * partner_gender 1 0.4822 0.4822 1.3104 77.166 0.2558666 int 1 30.0729 30.0729 15.9047 77.166 0.0001503 ***--- Df logLik deviance Chisq Chi Df Pr(>Chisq)..1 6 -2584.5 5168.9 object 7 -2584.0 5168.0 0.9498 1 0.3298

R: lmer

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MLwiN

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More WebinarsReferences

Growth Curve

Repeated Measures

Two-Intercept Model

Crossed Design

Other Topics