Multilevel Modeling Programs
This study utilizes multilevel modeling to explore the relationship between gender and intimacy in interactions. Respondents completed nightly surveys over two weeks, providing intimacy ratings from 1 to 7 with partner gender marked as -1 for male and 1 for female. The analysis leverages mixed-effects models to assess fixed and random effects, accounting for respondent and partner genders. Key findings from Kashy (1991) shed light on the dynamics of intimacy influenced by gender. The study employs various software tools like R and SAS for statistical analyses.
Multilevel Modeling Programs
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Presentation Transcript
Multilevel Modeling Programs David A. Kenny
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)
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).
Output from Other Programs HLM SAS R: lmer MLwiN not included: Stata
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;
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_gender model <- 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) model anova(model) anova(model,modelB) anova(modelA,modelB) R: lmer
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
More Webinars References Growth Curve Repeated Measures Two-Intercept Model Crossed Design Other Topics
