Sentences with phrase «effect of all predictor variables»

allows for predictor variables to be added in sequence to determine the total direct effect and, by subtraction, the indirect effect of all predictor variables (Cohen et al., 2003).

Not exact matches

A common goal for a statistical research project is to investigate causality, and in particular to draw a conclusion on the effect of changes in the values of predictors or independent variables on response or dependent variables.

In place of reporting probit coefficients, we report the marginal effect of the variable on the award probability, which is the change in the award probability due to each predictor separately, with other variables evaluated at their mean values.

To assess the potential effect of missing data (ie, ignorable vs informative missing data), a pattern - mixture analysis was implemented using 2 - tailed tests.51 We defined patterns using a binary completer status variable, which was entered as a predictor in the RRM and MMANOVA.

To put the effect sizes for the hypothesized associations on wave 6 reckless driving into perspective, we re-ran the final model using logistic regressions (for the connections between the wave 6 indicators and the wave 6 latent variables) to obtain odds ratios (OR) for the indirect effects of wave 1 predictors on the individual wave 6 reckless driving items.

For the logit - based analyses and the t tests of differences in means, 1 - tailed tests of significance were conducted (α =.05) because we had an a priori prediction about the direction of the effect for each predictor variable.

To test this potential indirect effect, we used a non-parametric Monte Carlo simulation method, in which the indirect effect obtained from the a (the link between the predictor variable and the indirect effect variable) and b (the link between the indirect effect variable and the dependent variable, controlling for the remaining predictors) paths in a series of regression analyses is simulated k number of times using the slopes and standard errors obtained from the data (we used k = 50,000).

We also report whether there were significant indirect effects on 18 - month outcomes of any demographic baseline predictors mediated through the baseline latent variables.

A third model with the same predictors and random effects was performed with the FaceReader measure of disgust as dichotomous outcome variable.

When one tests for the presence of a moderational effect with multiple regression, one examines whether an interaction between two variables (one independent variable and a moderator) is a significant predictor of an outcome variable, after controlling for the effect of the two predictors.

For example, if one were interested in whether the association between a parenting variable (e.g., father psychological control; Holmbeck, Shapera, & Hommeyer, in press) and an outcome (e.g., school grades) is moderated by group status (e.g., spina bifida vs. an able - bodied comparison sample), one would test the interaction of psychological control and group as a predictor of school grades after controlling for the parenting and group main effects.