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Seeking a Balance Between the Statistical and Scientific Elements in Psychometrics

Published online by Cambridge University Press:  01 January 2025

Mark Wilson
Mark Wilson*
Affiliation:
University of California, Berkeley
*
Requests for reprints should be sent to Mark Wilson, University of California, Berkeley, Berkeley, CA, USA. E-mail: markw@berkeley.edu
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Abstract

In this paper, I will review some aspects of psychometric projects that I have been involved in, emphasizing the nature of the work of the psychometricians involved, especially the balance between the statistical and scientific elements of that work. The intent is to seek to understand where psychometrics, as a discipline, has been and where it might be headed, in part at least, by considering one particular journey (my own). In contemplating this, I also look to psychometrics journals to see how psychometricians represent themselves to themselves, and in a complementary way, look to substantive journals to see how psychometrics is represented there (or perhaps, not represented, as the case may be). I present a series of questions in order to consider the issue of what are the appropriate foci of the psychometric discipline. As an example, I present one recent project at the end, where the roles of the psychometricians and the substantive researchers have had to become intertwined in order to make satisfactory progress. In the conclusion I discuss the consequences of such a view for the future of psychometrics.

Keywords

psychometricstest theorytest construction

Information

Type
Original Paper
Information
Psychometrika , Volume 78 , Issue 2 , April 2013 , pp. 211 - 236
Copyright
Copyright © 2013 The Psychometric Society

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References

Adams, R.J., Wilson, M., Wu, M. (1997). Multilevel item response models: an approach to errors in variables regression. Journal of Educational and Behavioral Statistics, 22(1), 4776CrossRefGoogle Scholar
Adams, R.J., Wilson, M., Wang, W.C. (1997). The multidimensional random coefficients multinomial logit. Applied Psychological Measurement, 21, 1–23CrossRefGoogle Scholar
Adams, R.J., Wu, M., & Wilson, M. (2012). ConQuest 3.0 [computer program]. Hawthorn, Australia: ACER. Google Scholar
Acton, G.S., Kunz, J.D., Wilson, M., Hall, S.M. (2005). The construct of internalization: conceptualization, measurement, and prediction of smoking treatment outcome. Psychological Medicine, 35, 395–408CrossRefGoogle ScholarPubMed
American Educational Research Association, American Psychological Association, National Council for Measurement in Education (AERA, APA, NCME) (1999). Standards for educational and psychological testing, Washington: American Educational Research AssociationGoogle Scholar
American Institutes for Research (2000). Voluntary national test, cognitive laboratory report, year 2, Palo Alto: American Institutes for ResearchGoogle Scholar
Biggs, J.B., Collis, K.F. (1982). Evaluating the quality of learning: the SOLO taxonomy, New York: Academic PressGoogle Scholar
Borsboom, D. (2006). The attack of the psychometricians. Psychometrika, 71(3), 425440CrossRefGoogle ScholarPubMed
Brown, N.J.S., Wilson, M. (2011). Model of cognition: the missing cornerstone of assessment. Educational Psychology Review, 23(2), 221234CrossRefGoogle Scholar
Corcoran, T., Mosher, F.A., & Rogat, A. (2009). Learning progressions in science: an evidence-based approach to reform (CPRE Research Report #RR-63). New York: Center on Continuous Instructional Improvement, Teachers College—Columbia University. Google Scholar
De Boeck, P., Wilson, M., Acton, G.S. (2005). A conceptual and psychometric framework for distinguishing categories and dimensions. Psychological Review, 112(1), 129158CrossRefGoogle ScholarPubMed
Demetriou, A., Efklides, A. (1989). The person’s conception of the structures of developing intellect: early adolescence to middle age. Genetic, Social, and General Psychology Monographs, 115, 371–423Google ScholarPubMed
Demetriou, A., Kyriakides, L. (2006). The functional and developmental organization of cognitive developmental sequences. British Journal of Educational Psychology, 76(2), 209242CrossRefGoogle ScholarPubMed
Dempster, A.P., Laird, N.M., Rubin, D.B. (1977). Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society. Series B, 39, 1–38CrossRefGoogle Scholar
Diakow, R., & Irribarra, D.T. (2011). Developing assessments of data modeling and mapping a learning progression using a structured constructs model. Paper presented at the international meeting of the psychometric society, Hong Kong, July 2011. Google Scholar
Diakow, R., Irribarra, D.T., & Wilson, M. (2011). Analyzing the complex structure of a learning progression: structured construct models. Paper presented at the annual meeting of the national council of measurement in education, New Orleans, LA, April 2011. Google Scholar
Diakow, R., Irribarra, D.T., & Wilson, M. (2012a). Analyzing the complex structure of a learning progression: structured construct models. Paper presented at the national council on measurement in education annual meeting, Vancouver, Canada, April 2012. Google Scholar
Diakow, R., Irribarra, D.T., & Wilson, M. (2012b). Evaluating the impact of alternative models for between and within construct relations. Paper presented at the international meeting of the psychometric society, Lincoln, Nebraska, July 2012. Google Scholar
Draney, K. (1996). The polytomous saltus model: a mixture model approach to the diagnosis of developmental differences. Unpublished doctoral dissertation, University of California, Berkeley. Google Scholar
Draney, K., Jeon, M. (2011). Investigating the saltus model as a tool for setting standards. Psychological Test and Assessment Modeling, 53(4), 486498Google Scholar
Draney, K., Wilson, M. (2004). Application of the polytomous saltus model to stage-like data. In van der Ark, A., Croon, M., Sijtsma, K. (Eds.), New developments in categorical data analysis for the social and behavioral sciences, Mahwah: ErlbaumGoogle Scholar
Falmagne, J.-C., Doignon, J.-P. (2011). Learning spaces, Heidelberg: SpringerCrossRefGoogle Scholar
Fischer, K.W., Pipp, S.L., Bullock, D. (1984). Detecting discontinuities in development: methods and measurement. In Emde, R.N., Harmon, R. (Eds.), Continuities and discontinuities in development, Norwood: AblexGoogle Scholar
Irribarra, D.T., Diakow, R., & Wilson, M. (2012). Alternative specifications for structured construct models. Paper presented at the IOMW 2012 conference, Vancouver, April 2012. Google Scholar
Lehrer, R., Kim, M.-J., Ayers, E., & Wilson, M. (2013, in press). Toward establishing a learning progression to support the development of statistical reasoning. In J. Confrey & A. Maloney (Eds.), Learning over time: learning trajectories in mathematics education. Charlotte: Information Age Publishers. CrossRefGoogle Scholar
Marton, F. (1981). Phenomenography: describing conceptions of the world around us. Instructional Science, 10, 177–200CrossRefGoogle Scholar
Marton, F. (1986). Phenomenography—a research approach to investigating different understandings of reality. Journal of Thought, 21, 29–49Google Scholar
Marton, F. (1988). Phenomenography—exploring different conceptions of reality. In Fetterman, D. (Eds.), Qualitative approaches to evaluation in education, New York: Praeger 176205Google Scholar
Masters, G.N. (1982). A Rasch model for partial credit scoring. Psychometrika, 47, 149–174CrossRefGoogle Scholar
Mislevy, R.J., Steinberg, L.S., Almond, R.G. (2003). On the structure of educational assessments. Measurement Interdisciplinary Research & Perspective, 1, 3–67CrossRefGoogle Scholar
Mislevy, R.J., Wilson, M. (1996). Marginal maximum likelihood estimation for a psychometric model of discontinuous development. Psychometrika, 61, 41–71CrossRefGoogle Scholar
National Research Council (2001). Knowing what students know: the science and design of educational assessment. Committee on the Foundations of Assessment, J. Pellegrino, N. Chudowsky, & R. Glaser (Eds.), Washington: National Academy Press. Google Scholar
Nunnally, J.C., Bernstein, I.H. (1994). Psychometric theory, (3rd ed.). New York: McGraw-HillGoogle Scholar
Patton, M.Q. (1980). Qualitative evaluation methods, Beverly Hills: SageGoogle Scholar
Pirolli, P., Wilson, M. (1998). A theory of the measurement of knowledge content, access, and learning. Psychological Review, 105(1), 5882CrossRefGoogle Scholar
Rasch, G. (1961). On general laws and the meaning of measurement in psychology. Proceedings of the fourth Berkeley symposium on mathematical statistics and probability, 321334Google Scholar
Rasch, G. (1980). Probabilistic models for some intelligence and attainment tests, Chicago: University of Chicago Press (original work published 1960)Google Scholar
Rost, J. (1990). Rasch models in latent classes: an integration of two approaches to item analysis. Applied Psychological Measurement, 14, 271–282CrossRefGoogle Scholar
Rupp, A.A., Templin, J., Henson, R. (2010). Diagnostic measurement: theory, methods, and applications, New York: The Guilford PressGoogle Scholar
Scalise, K., & Gifford, B.R. (2008). Innovative item types: intermediate constraint questions and tasks for computer-based testing. Paper presented at the national council on measurement in education (NCME), session on ‘Building adaptive and other computer-based tests’, in New York, May 2008. Google Scholar
Schwartz, R., Ayers, E., & Wilson, M. (2010). Modeling a multi-dimensional learning progression. Paper presented at the annual meeting of the American educational research association, Denver, CO, April 2010. Google Scholar
Siegler, R.S. (1981). Developmental sequences within and between concepts. Monograph of the Society for Research in Child Development, 46(2, Serial No. 189). CrossRefGoogle Scholar
Spiegelhalter, D.J., Best, N.G., Carlin, B.P., van der Linde, A. (2002). Bayesian measures of model complexity and fit. Journal of the Royal Statistical Society, Series B, 64, 583–616CrossRefGoogle Scholar
Vermunt, J.K., & Magidson, J. (2007). Latent GOLD 4.5 syntax module (computer program). Belmont, MA: Statistical Innovations. Google Scholar
Wilson, M. (1989). Saltus: a psychometric model of discontinuity in cognitive development. Psychological Bulletin, 105(2), 276289CrossRefGoogle Scholar
Wilson, M. (2005). Constructing measures: an item response modeling approach, Mahwah: Lawrence Erlbaum AssociatesGoogle Scholar
Wilson, M. (2009). Measuring progressions: assessment structures underlying a learning progression. Journal for Research in Science Teaching, 46(6), 716730CrossRefGoogle Scholar
Wilson, M. (2012). Responding to a challenge that learning progressions pose to measurement practice: hypothesized links between dimensions of the outcome progression. In Alonzo, A.C., Gotwals, A.W. (Eds.), Learning progressions in science, Rotterdam: Sense PublishersGoogle Scholar