The phrase "assessment variables" is correct and usable in written English.
It can be used in contexts related to evaluation, measurement, or analysis, particularly in fields like education, psychology, or research.
Example: "In our study, we identified several key assessment variables that influenced the outcomes of the participants."
Alternatives: "evaluation factors" or "measurement variables."
Quality of life, psychologic profile, and personality assessment variables were similar among the 2 patient groups and the controls.
Duncan's multiple-ranged test was also performed to identify the homogenous type of the treatments for the various assessment variables (NPRS statistical package, version 9/98).
According to Kansei methodology, in order to evaluate a situation appropriately, the assessment variables must be adapted to the users' mental scheme.
One way, which proved successful in the NUSAP method, for reduction of assessment variables is a classification of input variables and a consequent sensitivity analysis to discern highly relevant input variables [25].
Assessment variables included incontinence episode frequency, the Incontinence Quality of Life Questionnaire results, fixed volume (400 mL) stress pad test results, and the Patient Global Impression of Improvement and of Severity question results.
As with assessment, variables were included in a stepwise fashion.
Efficiency has been used as an assessment variable in prior FCU performance studies [15] and is important for several reasons.
Continuous variables were analyzed using a restricted maximum likelihood analysis, based on repeated measures approach, including the independent 'treatment' variable, the repeated 'time of assessment' variable and their interaction as factors.
The incidence of the other primary assessment variable, peripheral oedema was low in both the treatment groups (n = 9, 5.8%).
The Rasch item response model was employed for item and test analysis and to empirically derive the substantive interpretation of the assessment variable.
JHLES detected significant differences across schools in 5/7 domains and had stronger correlations than DREEM to each global LE assessment variable.
