The analysis in this study uses linear regression, Poisson regression analysis, and a logistic regression model depending on the different dependent variables.
Principal component analysis was performed by using the different dependent variables to summarize the most relevant differences among treatments.
Second, fixed effects regression analyses on the four different dependent variables were made.
First, the means of the four different dependent variables were shown by group.
Mixed finite elements use different approximation spaces for different dependent variables.
Although all screens are shot in the same place and time, what they reveal is often very different, dependent on the direction of each participants head.
Because the different dependent measures collected in this research were recorded on different scales, each was transformed to a standardized score.
For examples testing dummy-variable party indicators with 12 different dependent variables, see Figure 3 and Table 2 in Hamilton and Saito (2015).
Because the analyses consider different dependent variables (i.e., upselling behavior, contract terminations, and service contacts), three different propensity score matchings are needed (Table 2).
