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28 - Psychometrics

from General Methods

Published online by Cambridge University Press:  27 January 2017

John T. Cacioppo
Affiliation:
University of Chicago
Louis G. Tassinary
Affiliation:
Texas A & M University
Gary G. Berntson
Affiliation:
Ohio State University
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Publisher: Cambridge University Press
Print publication year: 2016

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References

Algina, J. & Penfield, R. D. (2009). Classical test theory. In Millsap, R. E. & Maydeu-Olivares, A. (eds.), The Sage Handbook of Quantitative Methods in Psychology (pp. 93122). Thousand Oaks, CA: Sage.Google Scholar
Boucsein, W., Fowles, D. C., Grimnes, S., Ben-Shakhar, G., Roth, W. T., Dawson, M. E., & Filion, D. L. (2012). Publication recommendations for electrodermal measurements. Psychophysiology, 49: 10171034.Google Scholar
Brennan, R. L. (1992). Elements of Generalizability Theory, rev. edn. Iowa City, IA: American College Testing.Google Scholar
Brennan, R. L. (1995). The conventional wisdom about group mean scores. Journal of Educational Measurement, 32: 385396.Google Scholar
Brennan, R. L. (2001). Generalizability Theory. New York: Springer.Google Scholar
Brennan, R. L. (ed.) (2006). Educational Measurement, 4th edn. Lanham, MD: Rowman & Littlefield.Google Scholar
Brennan, R. L., Gao, X., & Colton, D. A. (1995). Generalizability analyses of work keys listening and writing tests. Educational and Psychological Measurement, 55: 157176.Google Scholar
Brennan, R. L. & Kane, M. T. (1977). An index of dependability for mastery tests. Journal of Educational Measurement, 14: 277289.Google Scholar
Burgess, A. P. & Gruzelier, J. H. (1996). The reliability of event-related desynchronisation: a generalisability study analysis. International Journal of Psychophysiology, 23: 163169.CrossRefGoogle ScholarPubMed
Burt, K. B. & Obradović, J. (2013). The construct of psychophysiological reactivity: statistical and psychometric issues. Developmental Review, 33: 2957.Google Scholar
Bush, N. R., Alkon, A., Obradović, J., Stamperdahl, J., & Boyce, W. T. (2011). Differentiating challenge reactivity from psychomotor activity in studies of children’s psychophysiology: considerations for theory and measurement. Journal of Experimental Child Psychology, 110: 6279.CrossRefGoogle ScholarPubMed
Cacioppo, J. T. & Tassinary, L. G. (1990a). Inferring psychological significance from physiological signals. American Psychologist, 45: 1628.Google Scholar
Cacioppo, J. T. & Tassinary, L. G. (eds.) (1990b). Principles of Psychophysiology. Cambridge University Press.Google Scholar
Campbell, D. T. & Fiske, D. W. (1959). Convergent and discriminant validation by the multitrait-multimethod matrix. Psychological Bulletin, 56: 81105.Google Scholar
Campbell, N. R. (1957). Foundations of Science: The Philosophy of Theory. New York: Dover.Google Scholar
Cardinet, J., Johnson, S., & Pini, G. (2009). Applying Generalizability Theory Using EduG. New York: Routledge.Google Scholar
Cardinet, J., Tourneur, Y., & Allal, L. (1976). The symmetry of generalizability theory: application to educational measurement. Journal of Educational Measurement, 13: 119135.Google Scholar
Cardinet, J., Tourneur, Y., & Allal, L. (1981). Extension of generalizability theory and its application in educational measurement. Journal of Educational Measurement, 18: 183204.CrossRefGoogle Scholar
Clayson, P. E. & Larson, M. J. (2013). Psychometric properties of conflict monitoring and conflict adaptation indices: response time and conflict N2 event-related potentials. Psychophysiology, 50: 12091219.Google Scholar
Coan, J. A., Allen, J. J. B., & McKnight, P. E. (2006). A capability model of individual differences in frontal EEG asymmetry. Biological Psychology, 72: 198207.Google Scholar
Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2002). Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences, 3rd edn. New York: Routledge.Google Scholar
Cole, D. A., Howard, G. S., & Maxwell, S. E. (1981). Effects of mono- versus multiple-operationalization in construct validation efforts. Journal of Consulting and Clinical Psychology, 49: 395405.Google Scholar
Crocker, L. & Algina, J. (2006). Introduction to Classical and Modern Test Theory. Independence, KY: Cengage.Google Scholar
Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16: 292334.Google Scholar
Cronbach, L. J., Gleser, G. C., Nanda, H., & Rajaratnam, N. (1972). The Dependability of Behavioral Measurements: Theory of Generalizability of Scores and Profiles. New York: John Wiley.Google Scholar
Cronbach, L. J. & Meehl, P. E. (1955). Construct validity in psychological tests. Psychological Bulletin, 52: 281302.CrossRefGoogle ScholarPubMed
de Ayala, R. J. (2008). The Theory and Practice of Item Response Theory. New York: Guilford Press.Google Scholar
Di Nocera, F., Ferlazzo, F., & Borghi, V. (2001). G theory and the reliability of psychophysiological measures: a tutorial. Psychophysiology, 38: 796806.Google Scholar
Embretson, S. & Reise, S. P. (2000). Item Response Theory for Psychologists. Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar
Fahrenberg, J., Foerster, F., Schneider, H. J., Müller, W., & Myrtek, M. (1986). Predictability of individual differences in activation processes in a field setting based on laboratory measures. Psychophysiology, 23: 323333.Google Scholar
Feldt, L. S. & Brennan, R. L. (1989). Reliability. In Lin, R. L. (ed.), Educational Measurement, 3rd edn. (pp. 105146). New York: Macmillan.Google Scholar
Fiske, D. W. (1987). Construct invalidity comes from method effects. Educational and Psychological Measurement, 47: 285307.CrossRefGoogle Scholar
Gao, X. & Harris, D. J. (2012). Generalizability theory. In Cooper, H., Camic, P. M., Long, D. L., Panter, A. T., Rindskopf, D., & Sher, K. J. (eds.), APA Handbook of Research Methods in Psychology, vol. 1: Foundations, Planning, Measures, and Psychometrics (pp. 661681). Washington, DC: American Psychological Association.Google Scholar
Garćia-Vera, M. & Sanz, J. (1999). How many self-measured blood pressure readings are needed to estimate hypertensive patients’ “true” blood pressure? Journal of Behavioral Medicine, 22: 93113.Google Scholar
Ghiselli, E. E., Campbell, J. P., & Zedeck, S. (1981). Measurement Theory for the Behavioral Sciences. San Francisco, CA: Freeman.Google Scholar
Guion, R. M. (1978). Scoring of content domain samples. Journal of Applied Psychology, 63: 449506.Google Scholar
Hambleton, R. K., Swaminathan, H., & Rogers, H. J. (1991). Fundamentals of Item Response Theory. Newbury Park, CA: Sage.Google Scholar
Hammond, K. R., Hamm, R. M., & Grassia, J. (1986). Generalizing over conditions by combining the multitrait–multimethod matrix and the representative design of experiments. Psychological Bulletin, 100: 257269.Google Scholar
Hecimovich, M. D., Peiffer, J. J., & Harbaugh, A. G. (2014). Development and psychometric evaluation of a post exercise exhaustion scale utilizing the Rasch measurement model. Psychology of Sports and Exercise, 15: 569579.Google Scholar
Hoyt, W. T. (2000). Rater bias in psychological research: when is it a problem and what can we do about it? Psychological Methods, 5: 6486.Google Scholar
Kamarck, T. W., Debski, T. T., & Manuck, S. B. (2000). Enhancing the laboratory-to-life generalizability of cardiovascular reactivity using multiple occasions of measurement. Psychophysiology, 37: 533542.Google Scholar
Kane, M. T. & Brennan, R. L. (1977). The generalizability of class means. Review of Educational Research, 47: 267292.Google Scholar
Kelley, T. L. (1927). Interpretation of Educational Measurements. New York: Macmillan.Google Scholar
Kenny, D. A. (1995). The multitrait–multimethod matrix: design, analysis, and conceptual issues. In Shrout, P. E. & Fiske, S. T. (eds.), Personality, Research, Methods, and Theory: A Festschrift Honoring Donald W. Fiske (pp. 111124). Hillsdale, NJ: Lawrence Erlbaum Associates.Google Scholar
Llabre, M. M., Ironson, G. H., Spitzer, S. B., Gellman, M. D., Weidler, D. J. & Schneiderman, N. (1988). How many blood pressure measurements are enough? An application of generalizability theory to the study of blood pressure reliability. Psychophysiology, 25: 97106.Google Scholar
Llabre, M. M., Spitzer, S. B., Saab, P. G, Ironson, G. H., & Schneiderman, N. (1991). The reliability and specificity of delta versus residualized change as measures of cardiovascular reactivity to behavioral challenges. Psychophysiology, 28: 701711.Google Scholar
Marcoulides, G. A. (1994). Selecting weighting schemes in multivariate generalizability studies. Educational and Psychological Measurement, 54: 37.Google Scholar
Marcoulides, G. A. & Goldstein, Z. (1990). The optimization of generalizability studies with resource constraints. Educational and Psychological Measurement, 50: 761768.Google Scholar
Marsh, H. W. & Grayson, D. (1995). Latent variable models of multitrait–multimethod data. In Hoyle, R. H. (ed.), Structural Equation Modeling: Concepts, Issues, and Applications (pp. 117198). Thousand Oaks, CA: Sage.Google Scholar
Maxwell, S. E. & Delaney, H. D. (2003). Designing Experiments and Analyzing Data: A Model Comparison Perspective, 2nd edn. New York: Routledge.Google Scholar
Messick, S. (1981). Constructs and their vicissitudes in educational and psychological measurement. Psychological Bulletin, 89: 575588.CrossRefGoogle Scholar
Messick, S. (1989). Validity. In Linn, R. L. (ed.), Educational Measurement, 3rd edn. (pp. 13103). New York: Macmillan.Google Scholar
Myers, J. E., Well, A. D., & Lorch, R. F. Jr. (2010). Research Design and Statistical Analysis, 3rd edn. New York: Routledge.Google Scholar
Nunnally, J. C. & Bernstein, I. H. (1994). Psychometric Theory, 3rd edn. New York: McGraw-Hill.Google Scholar
Nussbaum, A. (1984). Multivariate generalizability theory in educational measurement: an empirical study. Applied Psychological Measurement, 8: 219230.Google Scholar
Pennebaker, J. W. (1982). The Psychology of Physical Symptoms. New York: Springer-Verlag.Google Scholar
Pickering, T. G., Harshfield, G. A., Kleinert, H. D., Blank, S., & Laragh, J. H. (1982). Blood pressure during normal daily activities, sleep, and exercise. Journal of the American Medical Association, 247: 992996.Google Scholar
Raykov, T. & Marcoulides, G. A. (2010). Introduction to Psychometric Theory. New York: Routledge.Google Scholar
Sarter, M., Berntson, G. G., & Cacioppo, J. T. (1996). Brain imaging and cognitive neuroscience: toward strong inference in attributing function to structure. American Psychologist, 51: 1321.Google Scholar
Schmidt, F. L. & Hunter, J. E. (1996). Measurement error in psychological research: lessons from 26 research scenarios. Psychological Methods, 1: 199223.Google Scholar
Schwerdtfeger, A. R., Schienle, A., Leutgeb, V., & Rathner, E. M. (2014). Does cardiac reactivity in the laboratory predict ambulatory heart rate? Baseline counts. Psychophysiology, 51: 565572.Google Scholar
Shadish, W. R., Cook, T. D., & Campbell, D. T. (2001). Experimental and Quasi-Experimental Designs for Generalized Causal Inference. Boston, MA: Houghton Mifflin.Google Scholar
Shavelson, R. J. & Webb, N. M. (1991). Generalizability Theory: A Primer. Newbury Park, CA: Sage.Google Scholar
Shavelson, R. J., Webb, N. M., & Rowley, G. L. (1989). Generalizability theory. American Psychologist, 44: 922932.Google Scholar
Stevens, J. P. (2009). Applied Multivariate Statistics for the Social Sciences, 5th edn. New York: RoutledgeGoogle Scholar
Strube, M. J. (1989). Assessing subjects’ construal of the laboratory situation. In Schneiderman, N., Weiss, S. M., & Kaufman, P. (eds.), Handbook of Research Methods in Cardiovascular Behavioral Medicine (pp. 527542). New York: Plenum Press.Google Scholar
Thomas, M. L., Brown, G. G., Thompson, W. K., Voyvodic, J., Greve, D. N., Turner, J. A., … & Potkin, S. G. (2013). An application of item response theory to fMRI data: prospects and pitfalls. Psychiatry Research: Neuroimaging, 212: 167174.Google Scholar
Thurston, R. C., Hernandez, J., Del Rio, J. M., & De La Torre, F. (2010). Support vector machines to improve physiologic hot flash measures: applications to the ambulatory setting. Psychophysiology, 48: 10151021.Google Scholar
Torrents-Rodas, D., Fullana, M. A., Bonillo, A., Andion, O., Molinuevo, B., Caseras, X., & Torrubia, R. (2014). Testing the temporal stability of individual differences in the acquisition and generalization of fear. Psychophysiology, 51: 697705.CrossRefGoogle ScholarPubMed
Vanleeuwen, D. M. & Mandabach, K. H. (2002). A note on the reliability of ranked items. Sociological Methods & Research, 31: 87105.Google Scholar
Webb, N. M. & Shavelson, R. J. (1981). Multivariate generalizability of general educational development ratings. Journal of Educational Measurement, 18: 1322.Google Scholar
Westen, D. & Rosenthal, R. (2003). Quantifying construct validity: two simple measures. Journal of Personality and Social Psychology, 84: 608618.Google Scholar
Whitley, B. E. Jr. & Kite, M. E. (2012). Principles of Research in Behavioral Science, 3rd edn. New York: Routledge.CrossRefGoogle Scholar
Winer, B. J., Brown, D. R., & Michels, K. M. (1991). Statistical Principles in Experimental Design, 3rd edn. New York: McGraw-Hill.Google Scholar
Wohlgemuth, W. K., Edinger, J. D., Fins, A. I., & Sullivan, R. J. Jr. (1999). How many nights are enough? The short-term stability of sleep parameters in elderly insomniacs and normal sleepers. Psychophysiology, 36: 233244.Google Scholar
Wothke, W. (1996). Models for multitrait-multimethod matrix analysis. In Marcoulides, G. A. & Schumacker, R. E. (eds.), Advanced Structural Equation Modeling: Issues and Techniques (pp. 756). Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar
Youngstrom, E. A. & De Los Reyes, A. (2015). Commentary. Moving toward cost-effectiveness in using psychophysiological measures in clinical assessment: validity, decision making, and adding value. Journal of Clinical Child & Adolescent Psychology, 44: 352361.Google Scholar
Zillmann, D. (1978). Attribution and misattribution of excitatory reactions. In Harvey, J. H., Ickes, W., & Kidd, R. F. (eds.), New Directions in Attribution Research, vol. 2 (pp. 335368). Hillsdale, NJ: Lawrence Erlbaum Associates.Google Scholar

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