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Response to Sijtsma and van der Ark (2015): "Conceptions of reliability revisited and practical recommendations"
Byron Gajewski1, Larry R Price, Marjorie Bott
1Byron Gajewski, PhD, is Professor of Biostatistics, Department of Biostatistics, University of Kansas Medical Center. Larry R. Price, PhD, is Professor of Psychometrics & Statistics, Department of Counseling, Leadership, Adult Education and School Psychology, College of Education, and Department of Mathematics, College of Science, and Director, Interdisciplinary Initiative for Research Design & Analysis, Texas State University, San Marcos. Marjorie Bott, RN, PhD, is Associate Professor and Associate Dean for Research, University of Kansas School of Nursing.
Sijtsma and van der Ark present a broad set of models and methods for reliability estimation, and their discussion of similarities and differences provides clear information for nurse researchers to move forward in their instrument development projects. In particular, we applaud the authors' clear exposition of the factor analytic model and its utility for providing a framework for unifying reliability and validity. However, we do not want to be constrained only to the point estimates. We also need to ascertain the uncertainty in the point estimate-usually in the form of a 95% confidence interval-or, as the Bayesians refer to, a credible interval. Another issue not discussed by Sijtsma and van der Ark is conditional standard errors of measurement along the score scale measuring latent traits or true scores. In our response, practical tools for estimating intervals and a brief discussion of conditional standard errors of measurement are presented.
Sijtsma and van der Ark present a broad set of models and methods for reliability estimation, and their discussion of similarities and differences provides clear information for nurse researchers to move forward in their instrument development projects. In particular, we applaud the authors' clear exposition of the factor analytic model and its utility for providing a framework for unifying reliability and validity. However, we do not want to be constrained only to the point estimates. We also need to ascertain the uncertainty in the point estimate-usually in the form of a 95% confidence interval-or, as the Bayesians refer to, a credible interval. Another issue not discussed by Sijtsma and van der Ark is conditional standard errors of measurement along the score scale measuring latent traits or true scores. In our response, practical tools for estimating intervals and a brief discussion of conditional standard errors of measurement are presented.