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Table 4 presents the results of multi-level mixed effects ordered logistic models.
Expression (5) is the log-likelihood function for the random effects ordered probit model.
For each case, two models are presented: the finally selected ordered probit and the respective random effects ordered probit model.
The process is based on state-of-the-art econometric models (ordered probit and random effects ordered probit models), using data from a stated preference survey.
The estimated parameter coefficients of the random effects ordered probit model are very similar to those obtained by the ordered probit model.
The methodology uses data from a stated-preference questionnaire survey to specify and estimate state-of-the-art econometric models (ordered probit and random effects ordered probit).
To account for clustering of men within EAs, we used a random effects ordered regression model.
Random effects ordered regression models were used to assess bi-variable associations between rape categories and continuous variables.
A random effects ordered probit model was used to estimate the effect of cost and time on investigators' willingness to adhere to data standards.
Moreover, the effect order of above parameters was analyzed.
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