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Factor Indeterminacy as Metrological Uncertainty: Implications for Advancing Psychological Measurement

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References

  • Asparouhov, T., Muthén, B., & Morin, A. J. S. (2015). Bayesian structural equation modeling with cross-loadings and residual covariances: Comments on Stromeyer. Journal of Management, 41(6), 1561–1577. doi: 10.1177/0149206315591075.
  • Babin, B. J., Hair, J. F., & Boles, J. S. (2008). Publishing research in marketing journals using structural equation modeling. Journal of Marketing Theory & Practice, 16(4), 279–285. doi: 10.2753/MTP1069-6679160401.
  • Bechtoldt, H. P. (1959). Construct validity: A critique. The American Psychologist, 14(10), 619–629. doi: 10.1037/h0040359.
  • Bell, S. (1999). Good practice guide #12: A beginner’s guide to uncertainty of measurement. Teddington, UK: National Physical Laboratory.
  • Bentler, P. M. (1986). Structural modeling and Psychometrika: An historical perspective on growth and achievements. Psychometrika, 51(1), 35–51. doi: 10.1007/BF02293997.
  • Bollen, K. A. (1989). Structural equations with latent variables. New York, NJ: John Wiley & Sons.
  • Boker, S. M. (2002). Consequences of continuity: The hunt for intrinsic properties within parameters of dynamics in psychological processes. Multivariate Behavioral Research, 37(3), 405–422. doi: 10.1207/S15327906MBR3703_5.
  • Borsboom, D. (2005). Measuring the mind: Conceptual issues in contemporary psychometrics. Cambridge, UK: Cambridge University.
  • Borsboom, D., Mellenbergh, G. J., & van Heerden, J. (2003). The theoretical status of latent variables. Psychological Review, 110(2), 203–219. doi: 10.1037/0033-295X.110.2.203.
  • Bridgman, P. W. (1927). The logic of modern physics. New York: MacMillan.
  • Bucher, J. L. (2012). The metrology handbook. Milwaukee, WS: Quality Press.
  • Campbell, D. T. (1960). Recommendations for APA test standards regarding construct, trait, or discriminant validity. The American Psychologist, 15(8), 546–553. doi: 10.1037/h0048255.
  • Campbell, D. T., & Fiske, D. W. (1959). Convergent and discriminant validation by the multitrait-multimethod matrix. Psychological Bulletin, 56(2), 81–105. doi: 10.1037/h0046016.
  • Chakravartty, A. (2007). A metaphysics for scientific realism: Knowing the unobservable. Cambridge, MA: Cambridge University.
  • Chakravartty, A. (2015). Scientific realism. In E. N. Zalta (Ed.), Stanford encyclopedia of philosophy. Retrieved from http://plato.stanford.edu/archives/fall2015/entries/scientific-realism/. Accessed 02/10/2017.
  • Chang, H. (2009). Operationalism. In E. N. Zalta (Ed.), Stanford encyclopedia of philosophy. Retrieved from http://plato.stanford.edu/archives/fall2009/entries/operationalism/. Accessed 02/10/2017.
  • Chou, C. W., Hume, D. B., Rosenband, T., & Wineland, D. J. (2010). Optical clocks and relativity. Science, 329(5999), 1630–1632. doi: 10.1126/science.1192720.
  • Cliff, N. (1983). Some cautions concerning the application of causal modeling methods. Multivariate Behavioral Research, 18(1), 115–126. doi: 10.1207/s15327906mbr1801_7.
  • Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2003). Applied multiple regression/correlation analysis for the behavioral sciences (3rd ed.). Mahwah, NJ: LEA.
  • Creath, R. (2014). Logical empiricism. In E. N. Zalta (Ed.), Stanford encyclopedia of philosophy. Retrieved from http://plato.stanford.edu/archives/spr2014/entries/logical-empiricism/. Accessed 2/10/2017.
  • Cronbach, L. J. (1989). Construct validation after thirty years. In R. L. Linn (Ed.), Intelligence: Measurement, theory and public policy (pp. 147–171). Urbana, IL: University of Illinois.
  • Cronbach, L. J., Gleser, G. C., Nanda, H., & Rajaratnam, N. (1972). The dependability of behavioral measurements: Theory of generalizability for scores and profiles. New York, NY: Wiley.
  • Cronbach, L. J., & Meehl, P. E. (1955). Construct validity in psychological tests. Psychological Bulletin, 52(4), 281–302. doi: 10.1037/h0040957.
  • de Leeuw, J., & Nishisato, S. (1985). Reviews. Psychometrika, 50(3), 371–375. doi: 10.1207/S15328007SEM0701_08.
  • Dicken, P. (2016). A critical introduction to scientific realism. London: Bloomsbury.
  • Feigl, H. (1970). The 'orthodox' view of theories: Remarks in defense as well as critique. In M. Radner, & S. Winokur (Eds.), Minnesota studies in the philosophy of science (Vol. 4, pp. 3–16). Minneapolis, MN: University of Minnesota Press.
  • Feynman, R. (1965/1994). The character of physical law. New York, NY: The Modern Library.
  • Fine, A. (1998). Scientific realism and antirealism, 1998. doi: 10.4324/9780415249126-Q094-1. Routledge Encyclopedia of Philosophy, Taylor and Francis. Retrieved from https://www.rep.routledge.com/articles/thematic/scientific-realism-and-antirealism/v-1.
  • Gibney, E. (2015). Atomic clocks face off. Nature, 522(7554), 16–17. doi: 10.1038/522016a.
  • Goldsmith, M. (2010). Good practice guide #118: A beginner’s guide to measurement. Teddington, UK: National Physical Laboratory.
  • Grice, J. W. (2001). Computing and evaluating factor scores. Psychological Methods, 6(4), 430–450. doi: 10.1037/1082-989X.6.4.430.
  • Guttman, L. (1955). The determinacy of factor score matrices with implications for five other basic problems of common-factor theory. British Journal of Statistical Psychology, 8(2), 65–81. doi: 10.1111/j.2044-8317.1955.tb00321.x.
  • Haig, B. D., & Evers, C. W. (2016). Realist inquiry in social science. London: Sage.
  • Hair, J. F., Hult, G. T. M., Ringle, C. M., Sarstedt, M., & Thiele, K. O. (2017). Mirror, mirror on the wall: a comparative evaluation of composite-based structural equation modeling methods. Journal of the Academy of Marketing Science, 45(5), 616–632. doi: 10.1007/s11747-017-0517-x.
  • Hair, J. F., Sarstedt, M., Ringle, C. M., & Gudergan, S. P. (2018). Advanced issues in partial least squares structural equation modeling. Thousand Oaks, CA: Sage.
  • Hair, J. F., Sarstedt, M., Ringle, C. M., & Mena, J. A. (2012). An assessment of the use of partial least squares structural equation modeling in marketing research. Journal of the Academy of Marketing Science, 40(3), 414–433. doi: 10.1007/s11747-011-0261-6.
  • Higgins, K., Miner, D., Smith, C. N., & Sullivan, D. B. (2004). A walk through time (version 1.2.1). [Online] Retrieved from http://physics.nist.gov/time [2010, July 12]. Gaithersburg, MD: National Institute of Standards and Technology.
  • Hoyle, R. H. (Ed.). (2014). Handbook of structural equation modeling. New York: Guilford Press.
  • Hunt, S. D. (1991). Modern marketing theory: Critical issues in the philosophy of marketing science. Cincinnati, OH: South-Western.
  • Hwang, H., & Takane, Y. (2015). Generalized structured component analysis: A component-based approach to structural equation modeling. Boca Ratron, FL: CRC Press.
  • Hwang, H., Takane, Y., & Jung, K. (2017). Generalized structured component analysis with uniqueness terms for accommodating measurement error. Frontiers in Psychology, 8, 21–37. doi: 10.3389/fpsyg.2017.02137.
  • Joint Committee for Guides in Metrology/Working Group on the Expression of Uncertainty in Measurement (JCGM/WG1). (2008). Evaluation of measurement data—Guide to the expression of uncertainty in measurement. Retrieved from http://www.iso.org/sites/JCGM/GUM-introduction.htm. Accessed 02/10/2017.
  • Jöreskog, K. G. (1969). A general approach to confirmatory maximum likelihood factor analysis. Psychometrika, 34(2), 183–202. doi:10.1007/BF02289343
  • Keat, R. (1998). Scientific realism and social science, 1998, doi: 10.4324/9780415249126-R025-1. Routledge Encyclopedia of Philosophy, Taylor and Francis. Retrieved from https://www.rep.routledge.com/articles/thematic/scientific-realism-and-social-science/v-1.
  • MacCallum, R. C., Browne, M. W., & Sugawara, H. M. (1996). Power analysis and determination of sample size for covariance structure modeling. Psychological Methods, 1(2), 130–149. doi: 10.1037/1082-989X.1.2.130.
  • MacCorquodale, K., & Meehl, P. E. (1948). On a distinction between hypothetical constructs and intervening variables. Psychological Review, 55(2), 95–107. doi: 10.1037/h0056029.
  • Maraun, M. D. (1996). Metaphor taken as math: Indeterminacy in the factor analysis model. Multivariate Behavioral Research, 31(4), 517–538. doi: 10.1207/s15327906mbr3104_6.
  • Maraun, M. D., & Gabriel, S. M. (2013). Illegitimate concept-equating in the partial fusion of construct validation theory and latent variable modeling. New Ideas in Psychology, 31(1), 32–42. doi: 10.1016/j.newideapsych.2011.02.006.
  • Markus, K. A., & Borsboom, D. (2013). Frontiers in test validity theory. Measurement, causation, and meaning. New York, NY: Routledge.
  • Mayo, D. G. (1981). In defense of the Neyman-Pearson theory of confidence intervals. Philosophy of Science, 48(2), 269–280. doi: 10.1086/288996.
  • McDonald, R. P. (1999). Test theory: A unified treatment. Mahwah, NJ: LEA.
  • Michell, J. (1999). Measurement in psychology: A critical history of a methodological concept. Cambridge: Cambridge University Press.
  • Michell, J. (2013). Constructs, inferences and mental measurement. New Ideas in Psychology, 31(1), 13–21. doi: 10.1016/j.newideapsych.2011.02.004
  • Mulaik, S. A. (2010). Foundations of factor analysis (2nd ed.). Boca Raton, FL: Chapman & Hall.
  • Mulaik, S. A., & McDonald, R. P. (1978). The effect of additional variables on factor indeterminacy in models with a single common factor. Psychometrika, 43(2), 177–192. doi: 10.1007/BF02293861.
  • Passmore, J. (1967). Logical positivism. In P. Edwards (Ed.), The encyclopedia of philosophy (pp. 52–57). New York, NY: Macmillan.
  • Pointer, M. R. (2003). New directions—soft metrology requirements for support from mathematics, statistics and software. Teddigton: National Physical Laboratory. Retrieved from http://publications.npl.co.uk/npl_web/pdf/cmsc20.pdf. Accessed 29/12/2017.
  • Preacher, K. J., & Merkle, E. C. (2012). The problem of model selection uncertainty in structural equation modeling. Psychological Methods, 17(1), 1–14. doi: 10.1037/a0026804.
  • Psillos, S. (1999). Scientific realism: How science tracks truth. Abingdon: Routledge.
  • Rigdon, E. E. (1994). Demonstrating the effects of unmodeled random measurement error. Structural Equation Modeling, 1(4), 375–380. doi: 10.1080/10705519409539986.
  • Rigdon, E. E. (2013). Partial least squares path modeling. In G. Hancock & R. Mueller (Eds.), Structural equation modeling: A second course (2nd ed., pp. 81–116). Charlotte, NC: Information Age.
  • Rosenband, T., Hume, D. B., Schmidt, P. O., Chou, C. W., Brusch, A., Lorini, L., … Bergquist, J. C. (2008). Frequency ratio of Al+ and Hg+ single-ion optical clocks: metrology at the 17th decimal place. Science, 319(5871), 1808–1812. doi: 10.1126/science.1154622.
  • Schönemann, P. H., & Steiger, J. H. (1976). Regression component analysis. British Journal of Mathematical and Statistical Psychology, 29(2), 175–189. doi: 10.1111/j.2044-8317.1976.tb00713.x.
  • Schönemann, P. H., & Steiger, J. H. (1978). On the validity of indeterminate factor scores. Bulletin of the Psychonomic Society, 12(4), 287–290. doi: 10.3758/BF03329685.
  • Schönemann, P. H., & Wang, M.-M. (1972). Some new results on factor indeterminacy. Psychometrika, 37(1), 61–91. doi: 10.1007/BF02291413.
  • Schwab, D. P. (1999). Research methods for organizational studies. Mahwah, NJ: Lawrence Erlbaum Associates.
  • Slaev, V. A., Chunovkina, A. G., & Mironovsky, L. A. (2013). Metrology and theory of measurement. Berlin: Walter de Gruyter.
  • Spearman, C. (1904). “General intelligence,” objectively determined and measured. The American Journal of Psychology, 15(2), 201–292. doi: 10.2307/1412107.
  • Steiger, J. H. (1979a). The relationship between external variables and common factors. Psychometrika, 44(1), 93–97. doi: 10.1007/BF02293788.
  • Steiger, J. H. (1979b). Factor indeterminacy in the 1930's and the 1970's some interesting parallels. Psychometrika, 44(2), 157–167. doi: 10.1007/BF02293967.
  • Tal, E. (2011). How accurate is the standard second? Philosophy of Science, 78(5), 1082–1096. doi: 10.1086/662268.
  • Velicer, W. F., & Jackson, D. N. (1990). Component analysis versus common factor analysis: Some issues in selecting an appropriate procedure. Multivariate Behavioral Research, 25(1), 1–28. doi: 10.1207/s15327906mbr2501_1.
  • Voorhees, C. M., Brady, M. K., Calantone, R., & Ramirez, E. (2016). Discriminant validity testing in marketing: An analysis, causes for concern and proposed remedies. Journal of the Academy of Marketing Science, 44(1), 119–134. doi: 10.1007/s11747-015-0455-4.
  • Widaman, K. F. (1985). Hierarchically nested covariance structure models for multitrait-multimethod data. Applied Psychological Measurement, 9(1), 1–26. doi: 10.1177/014662168500900101.
  • Widaman, K. F. (2018). On common factor and principal component representations of data: Implications for theory and for confirmatory replications. Structural Equation Modeling: A Multidisciplinary Journal, 25(6), 829–847. doi: 10.1080/10705511.2018.1478730
  • Wilson, E. B. (1928). The abilities of man: Their nature and measurement. Science, 67(1731), 244–248. doi: 10.1126/science.68.1750.38-a.

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