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The HGLM was a population-averaged logistic regression model using penalized quasi-likelihood (PQL) estimation because the outcome variable was binary (whether or not students completed the assessment).
The two different types of HLM were necessary because the outcome variable was binary in the participation models (students did or did not participate) and continuous in the performance models (RBA score).
Because the outcome variable is the average rating of a trait of the subject either by the 20 female raters or by the 20 male raters, we include two observations for each subject one observation for the rating by female raters, and one observation for the rating by male raters.
We chose logistic regression modeling because the outcome variable "acceptance of medication" is binary.
Because the outcome variable was a count variable, poisson regression was performed [ 25].
A logistic regression was used because the outcome variable was dichotomous.
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Finally, models of partnership, mobility and access to the healthcare job market could be estimated as non-linear models because the outcome variables are binary.
Because the outcome variables were continuous and linear, ordinary least squares regression was chosen to model the independent effect of SSI on cost outcomes (Table 2).
We used a logit link function, because the distribution of the outcome variable (14-day mortality) was binary.
A negative log-log link function was selected because lower values of the outcome variable are more likely to occur.
Of the 641 adolescents who reported sexual initiation, eight respondents were excluded from the analysis because their responses on the outcome variable were incomplete.
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