可靠性(半导体)
等级间信度
考试(生物学)
有效性
质量(理念)
心理学
一致性(知识库)
度量(数据仓库)
过程(计算)
计算机科学
应用心理学
心理测量学
评定量表
数据挖掘
临床心理学
人工智能
古生物学
发展心理学
功率(物理)
物理
哲学
认识论
量子力学
生物
操作系统
作者
Carole L. Kimberlin,Almut G. Winterstein
摘要
Purpose. Issues related to the validity and reliability of measurement instruments used in research are reviewed. Summary. Key indicators of the quality of a measuring instrument are the reliability and validity of the measures. The process of developing and validating an instrument is in large part focused on reducing error in the measurement process. Reliability estimates evaluate the stability of measures, internal consistency of measurement instruments, and interrater reliability of instrument scores. Validity is the extent to which the interpretations of the results of a test are warranted, which depends on the particular use the test is intended to serve. The responsiveness of the measure to change is of interest in many of the applications in health care where improvement in outcomes as a result of treatment is a primary goal of research. Several issues may affect the accuracy of data collected, such as those related to self-report and secondary data sources. Self-report of patients or subjects is required for many of the measurements conducted in health care, but self-reports of behavior are particularly subject to problems with social desirability biases. Data that were originally gathered for a different purpose are often used to answer a research question, which can affect the applicability to the study at hand. Conclusion. In health care and social science research, many of the variables of interest and outcomes that are important are abstract concepts known as theoretical constructs. Using tests or instruments that are valid and reliable to measure such constructs is a crucial component of research quality.
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