{"id":65,"date":"2024-03-26T14:16:01","date_gmt":"2024-03-26T14:16:01","guid":{"rendered":"https:\/\/routledgelearning.com\/evaluatingresearch\/?post_type=content&p=65"},"modified":"2024-03-26T15:43:22","modified_gmt":"2024-03-26T15:43:22","slug":"chapter-8-evaluating-measures","status":"publish","type":"content","link":"https:\/\/routledgelearning.com\/evaluatingresearch\/student-resources\/chapter-8-evaluating-measures\/","title":{"rendered":"Chapter 8 – Evaluating Measures"},"content":{"rendered":"\n\n
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Chapter 8 – Evaluating Measures<\/h1>\n\n\n

Besides decisions about how to select the sample, researchers\u2019 decisions about how to measure key variables in their study are some of the most influential for the study results. When it comes to accurately measuring complex concepts common in the social sciences (such characteristics or traits as well-being, achievement, impulsivity, self-doubt, etc.), there are many important considerations. The evaluation questions and examples in this chapter can help you make preliminary assessments of researchers\u2019 measurement procedures by learning more about some of the tricky matters that contribute to the validity of measures. Most of the chapter content applies to both qualitative and quantitative (as well as mixed methods) studies, where measurement can include surveys, interviews, observations, tests, and so on. This chapter is organized into three main sections: (1) Details of the Measures, (2) Efforts to Reduce Biases, and (3) Reliability and Validity of the Measures. It covers a range of issues\u2014from assessing the accuracy of data gathered on sensitive topics to considering interrater reliability and internal consistency, from using data triangulation for accurate measurement to distinctions between face and empirical validity. The end-of-chapter exercises help reinforce key takeaways.<\/p>\n<\/div>\n<\/div>\n\n<\/div>\n <\/div>\n<\/section>\n\n\n\n

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Multiple Choice Questions<\/h2>\n\n\n