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skip navigation About the Conference
Commissioned Papers
Barbara J. Burns, Ph.D.
Scott N. Compton, Ph.D.
Helen L. Egger, M.D.
Elizabeth M.Z. Farmer, Ph.D.
E. Jane Costello
Tonya D. Armstrong
Alaattin Erkanli
Paul E. Greenbaum
Chi-Ming Kam
Linda M. Collins
Selected Bibliography
Program Contacts

Annotated Bibliography on Research Methods

Kam & Collins

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A. Measurement Theories and Procedures

  1. Embretson, S.E., & Hershberger, S.L. The New Rules of Measurement: What Every Psychologist and Educator Should Know. Mahwah, NJ: Lawrence Erlbaum Associates, 1999.

This volume brings together leading measurement researchers and practitioners to discuss new developments in test development, administration, scoring, and interpretation. With topics ranging from intelligence testing and personality assessment to validity issues in testing theory and practice, psychometricians and clinical and educational measurement specialists alike will find this book a useful tool.

  1. Fuller, W.A. Measurement Error Models. New York: John Wiley & Sons, 1987.

This volume addresses the issue of response error with a book-length treatment of the theory and applications. It aims to promote the use of statistical techniques that explicitly recognize the presence of measurement error. The book begins with an introduction to techniques for simple models, such as measurement variance known, instrumental variable estimation, factor analysis, and others. Subsequent chapters examine, in detail, vector explanatory variables, extensions of the single relation model, and multivariate models.

  1. McLellan, A.T. Measurement issues in the evaluation of experimental treatment intervention. In: Kilbey, M.M., & Asghar, K., eds. Methodological Issues in Epidemiological, Prevention, and Treatment Research on Drug-Exposed Women and Their Children. National Institute on Drug Abuse Research Monograph 117. DHHS Pub. No. (ADM)92-1881. Washington, DC: Supt. of Docs., U.S. Govt. Print. Off., 1992. pp. 18-30.

The chapter focuses on issues of patient and treatment measurement that would be encountered in an evaluation of an experimental treatment or a novel therapeutic program. The first part of the chapter deals with the rationale and methods associated with collecting patient information at the start of a treatment intervention; the middle part deals with the measurement of the intervention; and the last part deals with the rationale for and the methodological issues in measuring patient outcome following an intervention.

  1. Wilson, M. Objective Measurement: Theory into Practice. Norwood, NJ: Ablex Publishing, 1992.

The chapters in this volume were selected from papers presented at the Fifth International Objective Measurement Workshop, held at UC Berkeley in 1989. The papers are grouped into three themes: 1. Measurement practice: how objective measurement methods are applied to a variety of fields; 2. Measurement theory: development of new measurement models that extend objective measurement into new domains; and 3. Mathematical and statistical applications to measurement: mathematical programming techniques, parameter estimation, and generalizability theory.

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B. Reliability and Validity

  1. Harrison, L., & Hughes, A., eds. The Validity of Self-Reported Drug Use: Improving the Accuracy of Survey Estimates. National Institute on Drug Abuse Research Monograph 167. NIH Pub. No. (ADM)97-4147. Washington, DC: Supt. of Docs., U.S. Govt. Print. Off., 1997.

The monograph arises from a technical review that was conducted on September 8 and 9, 1994, in Gaithersburg, MD, where papers were presented by 25 leading U.S. researchers on various aspects pertaining to the validity of self-reported drug use. It reviews a number of studies that use some presumably more accurate measure of drug use to validate self-reported use. In addition, evolving methods to improve a wide variety of procedures used in survey designs are explored, including computer-assisted interviewing, predictors of response propensity, measurement error models, and improved prevalence estimation techniques. Experimental manipulations of various survey conditions and situational factors also show promise in improving the validity of drug prevalence estimates in self-report surveys.

  1. Marsh, H.W., & Bailey, M. Confirmatory factor analyses of multitrait-multimethod data: A comparison of alternative models. Appl Psychol Meas 15:47-70, 1991.

The paper briefly describes the use of the CFA approach in analyzing MTMM data and the inherent problems in such an approach. The authors then present results of two studies, one with real data and one with simulated data, that were set up to evaluate the performance of the CFA methods for analyzing different types of MTMM data. The correlated uniqueness model converges most of the time but the general model only converges about one quarter of the times. The authors then presents their recommendations for using the CFA approach in dealing with MTMM problem.

  1. Traub, R.E. Reliability for Social Sciences: Theory and Application. Thousand Oaks, CA: Sage Publications, 1994.

The author provides a careful and illustrative review of the principles of classical reliability theory. He also explores some general strategies for improving measurement procedures. The book begins with a presentation of random variables and the expected values of a random variable. It then covers topics like the definition of reliability as a coefficient and possible uses of a coefficient, the notion of parallel tests so as to make possible the estimation of a reliability coefficient for a set of measurements, what to do when parallel tests are not available, what factors affect the reliability coefficient, and how to estimate the standard error of measurement.

  1. Zimmerman, D.W., & Williams, R.H. Note on the reliability of experimental measures and the power of significance tests. Psychol Bull 100:123-124, 1986.

The paper deals with the paradox that the power of a statistical test sometimes increases and sometimes decreases as the reliability coefficient of a dependent variable increases. The author points out the relation between statistical power and the reliability coefficient is not a functional relation unless another variable—either true variance or error variance—remain constant.

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C. Special Topics in Measurement

  1. Shavelson, R.J., & Webb, N.M. Generalizability Theory: A Primer. Newbury Park, CA: Sage Publications, 1991.

The book offers an intuitive development of generalizability theory, a technique for estimating the relative magnitudes of various components of error variation and for indicating the most efficient strategy for achieving desired measurement precision. The text covers a variety of topics such as generalizability studies with nested facets and with fixed facets, measurement error and generalizability coefficients, and decision studies with same and with different designs.

  1. Hambleton, R.K., Swaminathan, H., & Rogers, H.J. Fundamentals of Item Response Theory. Newbury Park, CA: Sage Publications, 1991.

The book provides a lucid but rigorous introduction to the fundamental concepts of item response theory, followed by thorough, accessible descriptions of the application of IRT methods to problems in test construction, identification of potential biased test items, test equating, and computerized-adaptive testing. A summary of new directions in IRT research and development completes the book.

  1. Camilli, G., & Shepard, L.A. Methods for Identifying Biased Test Items. Thousand Oaks, CA: Sage Publications, 1994.

Aimed at helping researchers understand how item bias methods work, this book provides practical advice and specific details on the most useful methods for particular testing situations. Beginning with a review of early bias methods and the fairness issues associated with the topic of test bias, the authors explain the logic of each method in terms of how differential item functioning (DIF) is defined by the method–and how well the method can be expected to work in various situations. In addition, chapters include a summary of findings regarding the behavior of the various indices in empirical studies, especially their reliability, correlation with known bias criteria, and correlations with other bias methods. The book concludes with a set of principles for deciding when DIF should be interpreted as evidence of bias.

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