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AnaG
AnaG
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The data can be found on OSF at this link: https://osf.io/as6rj/?view_only=142b7297f6bf422699a1a99bf5d75652 [.sav format]

Any advice would be appreciated, I'm quite new in those analyses. Thanks!!

The data can be found on OSF at this link: https://osf.io/as6rj/?view_only=142b7297f6bf422699a1a99bf5d75652 [.sav format]

Any advice would be appreciated, I'm quite new in those analyses. Thanks!!

Any advice would be appreciated, I'm quite new in those analyses. Thanks!!

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AnaG
AnaG
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What can explain large Odds Ratios 95% confidence intervals in multilevel binomial logistic regression (glmer)?

I conducted a multilevel binomial regression (glmer) and I obtain quite wide confidence intervals for my odds ratio. What could be causing such large 95% CI and what can be done to provide more precise CI? Is it right to assume that odds ratio can be interpreted in the same way for a multilevel logistic regression as for a normal logistic regression?

formal_c <- glmer(formal~type_opld_binary2+gender_binary2+pfeat2+(1 | opleader), data=political_data, family=binomial(link="logit"), control = glmerControl(optimizer = "bobyqa"), nAGQ=1)
summary(formal_c)

ORformal_c <- exp(fixef(formal_c))

CIformal_c<-exp(confint(formal_c, parm= "beta_", method="Wald"))

ORformal_c.CIformal_c <- rbind (cbind(ORformal_c, CIformal_c))
ORformal_c.CIformal_c

I tried a bootstrapping method below but no difference was found...

CIformal_c<-exp(confint.merMod(formal_c, method="boot"))

Here is the output:

Formula: 
formal ~ type_opld_binary2 + gender_binary2 + pfeat2 + (1 | opleader)
   Data: political_data
Control: glmerControl(optimizer = "bobyqa")

     AIC      BIC   logLik deviance df.resid 
   312.0    334.7   -150.0    300.0      322 

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-3.8863 -0.3788 -0.1705  0.4527  3.4420 

Random effects:
 Groups   Name        Variance Std.Dev.
 opleader (Intercept) 2.865    1.693   
Number of obs: 328, groups:  opleader, 48

Fixed effects:
                            Estimate Std. Error z value Pr(>|z|)
(Intercept)                  0.92427    0.66839   1.383 0.166714
type_opld_binary2athlete    -3.47398    0.93910  -3.699 0.000216
type_opld_binary2influencer -3.67788    0.84842  -4.335 1.46e-05
gender_binary2female         1.50796    0.68908   2.188 0.028642
pfeat2story                  0.09136    0.45331   0.202 0.840274
(Intercept)                    
type_opld_binary2athlete    ***
type_opld_binary2influencer ***
gender_binary2female        *  
pfeat2story                    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
               (Intr) typ_pld_bnry2t typ_pld_bnry2n gndr_2
typ_pld_bnry2t -0.474                                     
typ_pld_bnry2n -0.480  0.525                              
gndr_bnry2f    -0.398 -0.185         -0.103               
pfeat2story    -0.351  0.036         -0.144         -0.091

                            ORformal_c       2.5 %     97.5 %
(Intercept)                 2.52003814 0.679946015  9.3398477
type_opld_binary2athlete    0.03099351 0.004919372  0.1952683
type_opld_binary2influencer 0.02527639 0.004792244  0.1333187
gender_binary2female        4.51749331 1.170456269 17.4357183
pfeat2story                 1.09566606 0.450625842  2.6640375

The data can be found on OSF at this link: https://osf.io/as6rj/?view_only=142b7297f6bf422699a1a99bf5d75652 [.sav format]

Any advice would be appreciated, I'm quite new in those analyses. Thanks!!