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List:       r-sig-mixed-models
Subject:    [R-sig-ME] Interpreting GLMM output and is this the right model?
From:       Gabriella Kountourides <gabriella.kountourides () sjc ! ox ! ac ! uk>
Date:       2020-12-07 17:09:35
Message-ID: LNXP265MB05387D448BFA6431EAC91F7AE8CE0 () LNXP265MB0538 ! GBRP265 ! PROD ! OUTLOOK ! COM
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Hi everyone,

I emailed a few weeks ago, but am still struggling with this data.
The description of the question below, and model/code/output at the bottom. Many \
thanks for reading.


I want to look at whether there is a relationship between the way a question is asked \
(positive, negative, neutral wording) and the sentiment of the response. I have 2638 \
people asked a question about symptoms. 1/3 of the people were asked it with a \
negative wording, 1/3 with a neutral one, 1/3 with a positive one. From this, I did \
sentiment analysis (using Trincker's package) to see whether their responses were \
more positive or negative, depending on the wording of the question. Sentiment \
analysis breaks down responses into sentences, so I have 2638 people, but 7924 \
sentences, so I would assume to fit ID as a random effect.

The big question is: does the way the question is asked (primetype) affect the \
polarity/sentiment of the response? My data is negatively skewed, and has a lot of 0s \
(this is because some people felt 'neutral'  and so they scored '0'.

Model using the dataframe DF, to see how primetype (this is the way the question is \
asked) predicts sentiment (the polarity score, which is negatively skewed with lots \
of 0s), fixed effect is age, and random effect is ID

```
glmmTMB(sentiment ~ primetype + age + (1|id), data=DF)
```


Output:

```
Family: gaussian  ( identity )
Formula:          sentiment ~ primetype + age + (1 | id)
Data: DF

     AIC      BIC                 logLik        deviance       df.resid
  7254.9   7296.5          -3621.4        7242.9         7556

Random effects:

Conditional model:
 Groups          Name                Variance           Std.Dev.
 id                   (Intercept)            8.732e-11     9.344e-06
 Residual                                   1.526e-01         3.906e-01
Number of obs: 7562, groups:  id, 2520

Dispersion estimate for gaussian family (sigma^2): 0.153

Conditional model:
                         Estimate           Std. Error            z value           \
Pr(>|z|) (Intercept).   -0.1655972         0.0204310         -8.105           \
5.27e-16 *** primetype2   0.0907564         0.0114045          7.958           \
1.75e-15 *** primetype3   0.0977533         0.0115802         8.441           < 2e-16 \
*** age                -0.0020644          0.0006483       -3.184           0.00145 \
                **
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
> 
```
How can I interpret whether the model is a good one for my data, is there something \
else I should be doing? I'm not sure how to interpret the output at all. Would be \
immensely grateful for any insight


Thanks all


Gabriella Kountourides

DPhil Student | Department of Anthropology

Evolutionary Medicine and Public Health Group

St. John’s College, University of Oxford

gabriella.kountourides@sjc.ox.ac.uk

Tweet me: https://twitter.com/GKountourides

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