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Soysal, Sumeyra; Karaman, Haydar; Dogan, Nuri – Eurasian Journal of Educational Research, 2018
Purpose of the Study: Missing data are a common problem encountered while implementing measurement instruments. Yet the extent to which reliability, validity, average discrimination and difficulty of the test results are affected by the missing data has not been studied much. Since it is inevitable that missing data have an impact on the…
Descriptors: Sample Size, Data Analysis, Research Problems, Error of Measurement
Warne, Russell T. – Journal of Advanced Academics, 2011
Reliability generalization (RG) is a meta-analysis that combines and synthesizes reliability coefficients from different studies to ascertain the average observed reliability across studies. An RG study was conducted on previously reported data from 16 samples of the Overexcitability Questionnaire--Two (OEQII) with a combined "N" of 5,275.…
Descriptors: Measures (Individuals), Error of Measurement, Psychometrics, Generalization
Hughes, Gail D. – Research in the Schools, 2009
The impacts of incorrect responses to reverse-coded survey items were examined in this simulation study by reversing responses to traditional Likert-format items from 700 administrators in randomly selected schools in a 7-county region in central Arkansas that were obtained from an archival dataset. Specifically, the number of reverse-coded items…
Descriptors: Surveys, Coding, Context Effect, Measures (Individuals)
Henson, Robin K.; Thompson, Bruce – 2001
Given the potential value of reliability generalization (RG) studies in the development of cumulative psychometric knowledge, the purpose of this paper is to provide a tutorial on how to conduct such studies and to serve as a guide for researchers wishing to use this methodology. After some brief comments on classical test theory, the paper…
Descriptors: Coding, Error of Measurement, Psychometrics, Reliability

Dijkers, Marcel P. J. M.; Creighton, Cynthia L. – Occupational Therapy Journal of Research, 1994
Presents general principles for cleaning data sets. Suggests techniques: check for column shifts, data completeness, marginal frequencies, and selected cross-tabulations; examines a sample of cases. Gives strategies for preventing data processing errors. (SK)
Descriptors: Coding, Data Analysis, Educational Research, Error of Measurement