结构效度
差异(会计)
心理学
生产力
解释的变化
构造(python库)
再现性
生活质量研究
预测效度
逻辑回归
医学
工作效率
工作(物理)
临床心理学
多元统计
测试有效性
线性回归
回归分析
老年学
多元分析
心理测量学
外部有效性
标准效度
电话采访
日常生活活动
统计
活动监视器
重复措施设计
呈现主义
多元方差分析
作者
Margaret C. Reilly,Arthur S. Zbrozek,Ellen Dukes
标识
DOI:10.2165/00019053-199304050-00006
摘要
The construct validity of a quantitative work productivity and activity impairment (WPAI) measure of health outcomes was tested for use in clinical trials, along with its reproducibility when administered by 2 different methods. 106 employed individuals affected by a health problem were randomised to receive either 2 self-administered questionnaires (self administration) or one self-administered questionnaire followed by a telephone interview (interviewer administration). Construct validity of the WPAI measures of time missed from work, impairment of work and regular activities due to overall health and symptoms, were assessed relative to measures of general health perceptions, role (physical), role (emotional), pain, symptom severity and global measures of work and interference with regular activity. Multivariate linear regression models were used to explain the variance in work productivity and regular activity by validation measures. Data generated by interviewer-administration of the WPAI had higher construct validity and fewer omissions than that obtained by self-administration of the instrument. All measures of work productivity and activity impairment were positively correlated with measures which had proven construct validity. These validation measures explained 54 to 64% of variance (p less than 0.0001) in productivity and activity impairment variables of the WPAI. Overall work productivity (health and symptom) was significantly related to general health perceptions and the global measures of interference with regular activity. The self-administered questionnaire had adequate reproducibility but less construct validity than interviewer administration. Both administration methods of the WPAI warrant further evaluation as a measure of morbidity.
科研通智能强力驱动
Strongly Powered by AbleSci AI