1932

Abstract

Adaptive approaches to measurement and assessment have been useful in organizational science for more than 100 years. Advances in psychometric theory and inexpensive computing power have propelled the field into a renaissance for every type of construct and level of analysis imaginable. Exciting innovations include the use of mobile computer-adaptive testing (CAT); expert systems (e.g., automatic item generation); and unobtrusive adaptive measurement in social media, intelligent tutoring systems, and virtual worlds. Adaptive approaches are setting the stage to better embed measurement and intervention into naturalistic organizational settings and portend substantial improvements in cross-level and longitudinal tests of organizational psychology and organizational behavior (OP/OB) hypotheses.

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2016-03-21
2024-03-29
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Literature Cited

  1. Adams RJ, Wilson M, Wang W-C. 1997. The multidimensional random coefficients multinomial logit model. Appl. Psychol. Meas. 21:11–23 [Google Scholar]
  2. Akrich M, Callon M, Latour B. 2002. The key to success in innovation Part I: the art of interessement. Int. J. Innov. Manag 6:2187–206 [Google Scholar]
  3. Andersen EB. 1977. Sufficient statistics and latent trait models. Psychometrika 42:169–81 [Google Scholar]
  4. Andersen EB. 1999. Sufficient statistics in educational measurement. See Masters & Keeves 1999 122–25
  5. Anseel F, Lievens F, Schollaert E, Choragwicka B. 2010. Response rates in organizational science, 1995–2008: a meta-analytic review and guidelines for survey researchers. J. Bus. Psychol. 25:3335–49 [Google Scholar]
  6. Arendasy M, Sommer M, Gittler G, Hergovich A. 2006. Automatic generation of quantitative reasoning items: A pilot study. J. Individ. Diff. 27:12–14 [Google Scholar]
  7. Arthur W Jr, Glaze RM, Villado AJ, Taylor JE. 2009. Unproctored internet-based tests of cognitive ability and personality: magnitude of cheating and response distortion. Ind. Org. Psychol. Perspect. Sci. Pract. 2:39–45 [Google Scholar]
  8. Barney MF. 2010a. Inverted computer-adaptive Rasch measurement: prospects for virtual and actual reality Conf. Int. Assoc. Computer Adaptive Test., 1st, Arnhem, Neth. http://www.iacat.org
  9. Barney MF. 2010b. Leadership @ Infosys Gurgaon, India: Penguin
  10. Barney MF. 2013a. Leading Value Creation: Organizational Science, Bioinspiration and the Cue See Model New York: Palgrave MacMillan
  11. Barney MF. 2013b. Method and apparatus for rapid metrological calibration, intervention assignment, evaluation, forecasting and reinforcement. US Patent Appl. No. 20150112766 [Google Scholar]
  12. Barney MF. 2015. LeaderAmp Manual http://www.leaderamp.com/manual Send requests for access to the online manual to [email protected].
  13. Baruch Y, Holtom BC. 2008. Survey response rate levels and trends in organizational research. Hum. Relat. 61:81139–60 [Google Scholar]
  14. Beaty JC, Dawson CR, Fallaw SS, Kantrowitz TM. 2009. Recovering the scientist-practitioner model: how I-Os should respond to unproctored Internet testing. Ind.-Org. Psychol. Perspect. Sci. Pract. 2:58–63 [Google Scholar]
  15. Bergstrom BA, Lunz ME. 1992. Confidence in pass/fail decisions for computer adaptive and paper and pencil examinations. Eval. Health Prof. 15:4453–64 [Google Scholar]
  16. Bergstrom BA, Lunz ME. 1999. CAT for certification and licensure. Innovations in Computerized Assessment F Drasgow, JB Olson-Buchanan 67–91 Mahwah, New Jersey: Lawrence Erlbaum Associates, Inc., Publishers [Google Scholar]
  17. Birnbaum A. 1968. Some latent trait models and their use in inferring an examinee's ability. Statistical Theories of Mental Test Scores FM Lord, MR Novick 397–472 Reading, MA: AddisonWesley [Google Scholar]
  18. Black P, Wilson M, Yao S. 2011. Road maps for learning: a guide to the navigation of learning progressions. Meas. Interdiscip. Res. Perspect. 9:1–52 [Google Scholar]
  19. Boake C. 2002. From the Binet-Simon to the Wechsler-Bellevue: tracing the history of intelligence testing. J. Clin. Exp. Neuropsychol. 24:3383–405 [Google Scholar]
  20. Bond T, Fox C. 2015. Applying the Rasch Model: Fundamental Measurement in the Human Sciences New York: Routledge, 3rd ed..
  21. Borman WC, Buck DE, Hanson MA, Motowidlo SJ, Stark S, Drasgow F. 2001. An examination of the comparative reliability, validity, and accuracy of performance ratings made using computerized adaptive rating scales. J. Appl. Psychol. 86:5965–73 [Google Scholar]
  22. Boyd RL, Wilson SR, Pennebaker JW, Kosinski M, Stillwell D, Mihalcea R. 2015. Values in words: using language to evaluate and understand personal values. Proc. Int. AAAI Conf. Web Soc. Media, 9th, Oxford May 26–29, pp. 31–40 Palo Alto, CA: AAAI Press
  23. Buhrmester M, Kwang T, Gosling SD. 2011. Amazon's Mechanical Turk: a new source of inexpensive, yet high-quality data?. Persp. Psychol. Sci. 6:13–5 [Google Scholar]
  24. Camargo FR, Henson B. 2015. Conceptualising computerized adaptive testing for measurement of latent variables associated with physical objects. J. Phys. Conf. Ser. 588:1012012 [Google Scholar]
  25. Chen CM, Chung CJ. 2008. Personalized mobile English vocabulary learning system based on item response theory and learning memory cycle. Comp. Educ. 51:2624–45 [Google Scholar]
  26. Chen CM, Duh LJ. 2008. Personalized web-based tutoring system based on fuzzy item response theory. Expert Syst. Appl. 34:42298–315 [Google Scholar]
  27. Cialdini R. 2008. Influence: Science and Practice Boston: Allyn and Bacon, 5th ed..
  28. Connelly BS, Ones DS. 2010. Another perspective on personality: meta-analytic integration of observer's accuracy and predictive validity. Psychol. Bull. 136:61092–122 [Google Scholar]
  29. Coulby C, Hennessey S, Davies N, Fuller R. 2011. The use of mobile technology for work-based assessment: the student experience. Br. J. Educ. Technol. 42:2251–65 [Google Scholar]
  30. Craig SB, Kaiser RB. 2003. Applying item response theory to multisource performance ratings: What are the consequences of violating the independent observations assumption?. Org. Res. Methods 6:44–60 [Google Scholar]
  31. Crump MJC, McDonnell JV, Gureckis TM. 2013. Evaluating Amazon's Mechanical Turk as a tool for experimental behavioral research. PLoS ONE 8:3e57410 [Google Scholar]
  32. Dawson TL, Fischer KW, Stein Z. 2006. Reconsidering qualitative and quantitative research approaches: a cognitive developmental perspective. N. Ideas Psychol. 24:229–39 [Google Scholar]
  33. Day DV. 2011. Integrative perspectives on longitudinal investigations of leader development: from childhood through adulthood. Leadersh. Q. 22:561–71 [Google Scholar]
  34. Day DV, Barney MF. 2012. Personalizing global leader development @ Infosys. Advances in Global Leadership 7 WH Mobley, Y Wang, M Li 173–95 London: Emerald [Google Scholar]
  35. Day DV, Harrison MM, Halpin SM. 2009. An Integrative Approach to Leader Development: Connecting Adult Development, Identity, and Expertise New York: Routledge
  36. Day DV, Sin HP. 2011. Longitudinal tests of an integrative model of leader development: charting and understanding developmental trajectories. Leadersh. Q. 22:545–60 [Google Scholar]
  37. Deville C. 1993. Flow as a testing ideal. Rasch Meas. Trans. 7:3308 [Google Scholar]
  38. Diop B, Pascot D, Mbibi SMA. 2013. Theoretical framework of human capital development of SMEs: the context of an ERP project. J. Enterp. Resour. Plan. Stud. 2013:256196 [Google Scholar]
  39. Dumas G, Nadel J, Soussignan R, Martinerie J, Garnero L. 2010. Inter-brain synchronization during social interaction. PLoS ONE 5:81–10 [Google Scholar]
  40. Embretson SE. 1996. Item Response Theory models and spurious interaction effects in factorial ANOVA designs. Appl. Psychol. Meas. 20:3201–12 [Google Scholar]
  41. Engelhard G Jr. 2012. Invariant Measurement: Using Rasch Models in the Social, Behavioral, and Health Sciences. New York: Routledge Acad.
  42. Feinstein AR. 1995. Meta-analysis: statistical alchemy for the 21st century. J. Clin. Epidemiol. 48:171–79 [Google Scholar]
  43. Feng M, Heffernan N, Koedinger K. 2009. Addressing the assessment challenge with an online system that tutors as it assesses. User Model. User-Adapted Interact. 19:3243–66 [Google Scholar]
  44. Feng M, Heffernan NT, Koedinger KR. 2006. Predicting state test scores better with intelligent tutoring systems: developing metrics to measure assistance required. Intell. Tutoring Syst. 4053:31–40 [Google Scholar]
  45. Fetzer M, Dainis A, Lambert S, Meade A. 2011. Computer adaptive testing (CAT) in an employment context White Pap., SHLPreVisor. http://central.shl.com/SiteCollectionDocuments/White%20Papers,%20Guidelines%20and%20Other/White%20Papers/SHL%20PreVisor%20white%20paper%20-%20CAT%20in%20an%20employment%20context%20-%202011.pdf [Google Scholar]
  46. Finkelstein L. 2009. Widely-defined measurement—an analysis of challenges. Measurement 42:91270–77 [Google Scholar]
  47. Fischer GH. 1973. The linear logistic test model as an instrument in educational research. Acta Psychol. 37:359–74 [Google Scholar]
  48. Fischer GH. 1981. On the existence and uniqueness of maximum-likelihood estimates in the Rasch model. Psychometrika 46:159–77 [Google Scholar]
  49. Fisher WP Jr. 2010. The standard model in the history of the natural sciences, econometrics, and the social sciences. J. Phys. Conf. Ser. 238:1012016 [Google Scholar]
  50. Fisher WP Jr. 2011. Bringing human, social, and natural capital to life: practical consequences and opportunities. J. Appl. Meas. 12:49–66 [Google Scholar]
  51. Fisher WP Jr. 2012a. Measure and manage: intangible assets metric standards for sustainability. Business Administration Education: Changes in Management and Leadership Strategies J Marques, S Dhiman, S Holt 43–63 New York: Palgrave Macmillan [Google Scholar]
  52. Fisher WP Jr. 2012b. What the world needs now: a bold plan for new standards. Stand. Eng. 64:31–5 [Google Scholar]
  53. Fisher WP Jr, Stenner AJ. 2013a. On the potential for improved measurement in the human and social sciences. Pacific Rim Objective Measurement Symposium 2012 Conference Proceedings Q Zhang, H Yang 1–11 Berlin, Ger.: Springer-Verlag
  54. Fisher WP Jr, Stenner AJ. 2013b. Overcoming the invisibility of metrology: a reading measurement network for education and the social sciences. J. Phys. Conf. Ser. 459:012024 [Google Scholar]
  55. Fisher WP Jr, Stenner AJ. 2015. The role of metrology in mobilizing and mediating the language and culture of scientific facts. J. Phys. Conf. Ser. 588:012043 [Google Scholar]
  56. Fisher WP Jr, Wilson M. 2015. Building a productive trading zone in educational assessment research and practice. Pensam. Educ. Rev. Investig. Educ. Latinoam. 52(2)55–78
  57. Gierl MJ, Haladyna TM. 2013. Automatic Item Generation: Theory and Practice New York: Routledge
  58. Gorin J, Embretson SE. 2013. Using cognitive psychology to generate items and predict item characteristics. See Gierl & Haladyna 2013 136–56
  59. Grant AM. 2013. Rethinking the extraverted sales ideal: the ambivert advantage. Psychol. Sci. 24:61024–30 [Google Scholar]
  60. Green KE, Smith RM. 1987. A comparison of two methods of decomposing item difficulties. J. Educ. Stat. 12:4369–81 [Google Scholar]
  61. Guastello SJ. 2007. Non-linear dynamics and leadership emergence. Leadersh. Q. 18:4357–69 [Google Scholar]
  62. Guion RM. 2011. Assessment, Measurement, and Prediction for Personnel Decisions. New York: Routledge, 2nd ed.. [Google Scholar]
  63. Hambleton RK, Swaminathan H, Rogers L. 1991. Fundamentals of Item Response Theory Newbury Park, CA: Sage
  64. Häusler J, Sommer M. 2008. The effect of success probability on test economy and self-confidence in computerized adaptive tests. Psychol. Sci. Q. 50:175–87 [Google Scholar]
  65. Houston JS, Borman WC, Farmer WL, Bearden RM. 2006. Development of the enlisted computer adaptive personality scales (ENCAPS), renamed Navy Computer Adaptive Personality Scales (NCAPS). Tech. Rep. NPRST-TR-06–2, Navy Pers. Res., Stud., and Technol. [Google Scholar]
  66. Hunter JE, Schmidt FL. 2004. Methods of Meta-Analysis: Correcting Error and Bias in Research Findings Thousand Oaks, CA: Sage
  67. Jehn KA. 1995. A multimethod examination of the benefits and detriments of intragroup conflict. Admin. Sci. Q. 40:2256–82 [Google Scholar]
  68. Kantrowitz TM, Dawson CR, Fetzer MS. 2011. Computer adaptive testing (CAT): a faster, smarter, and more secure approach to pre-employment testing. J. Bus. Psychol. 26:2227–32 [Google Scholar]
  69. Kimura T, Ohnishi A, Nagaoka K. 2012. Moodle UCAT: a computer-adaptive test module for Moodle based on the Rasch model. Presented at Int. Conf. Probab. Models Meas., 5th, Perth
  70. Kosinski M, Stillwell DJ, Graepel T. 2013. Private traits and attributes are predictable from digital records of human behavior. PNAS 110:5802–5 [Google Scholar]
  71. Kozlowski SWJ, Klein KJ. 2000. A multilevel approach to theory and research in organizations: contextual, temporal, and emergent processes. Multilevel Theory, Research and Methods in Organizations: Foundations, Extensions, and New Directions KJ Klein, SWJ Kozlowski 3–90 San Francisco: Jossey-Bass [Google Scholar]
  72. Lance CE, Vandenberg RJ, Self RM. 2000. Latent growth models of intraindividual change: the case of newcomer adjustment. Org. Behav. Hum. Decis. Process. 83:1107–40 [Google Scholar]
  73. Landers RN, Behrend TS. 2015. An inconvenient truth: arbitrary distinctions between organizational, Mechanical Turk, and other convenience samples. Ind. Organ. Psychol. 8:142–64 [Google Scholar]
  74. Latour B. 2005. Reassembling The Social: An Introduction To Actor-Network-Theory Oxford, UK: Oxford Univ. Press
  75. Le H, Oh IS, Robbins SB, Ilies R, Holland E, Westrick P. 2011. Too much of a good thing: curvilinear relationships between personality traits and job performance. J. Appl. Psychol. 96:1113–33 [Google Scholar]
  76. Linacre JM. 1994. Many-Facet Rasch Measurement. Chicago: MESA Press, 2nd ed..
  77. Linacre JM. 1998. Estimating Rasch measures with known polytomous (or rating scale) item difficulties: anchored Maximum Likelihood Estimation (AMLE). Rasch Meas. Trans. 12:2638 [Google Scholar]
  78. Linacre JM. 2000. Computer-adaptive testing: a methodology whose time has come: MESA Memorandum No. 69. Development of Computerized Middle School Achievement Test S Chae, U Kang, E Jeon, JM Linacre. Seoul, S. Korea: Komesa Press. http://www.rasch.org/memo69.pdf Excellent introduction to Rasch measurement, including a BASIC computer program for a computer-adaptive test. [Google Scholar]
  79. Liu P, Tov W, Kosinski M, Stillwell DJ, Qiu L. 2015. Do Facebook status updates reflect subjective well-being? Valence and time matter. Cyberpsychol. Behav. Soc. Netw. 18:7373–79 [Google Scholar]
  80. Lord FM. 1983. Small N justifies Rasch model. New Horizons in Testing: Latent Trait Test Theory and Computerized Adaptive Testing DJ Weiss 51–61 New York: Academic [Google Scholar]
  81. Makransky G, Glas CAW. 2011. Unproctored internet test verification: using adaptive confirmation testing. Org. Res. Methods 14:608–30 [Google Scholar]
  82. Mason W, Suri S. 2012. Conducting behavioral research on Amazon's Mechanical Turk. Behav. Res. Methods 44:11–23 [Google Scholar]
  83. Masters GN, Keeves JP. 1999. Advances in Measurement in Educational Research and Assessment New York: Pergamon
  84. Meehl PE. 1967. Theory-testing in psychology and physics: a methodological paradox. Philos. Sci. 34:2103–15 [Google Scholar]
  85. Mehrabad MS, Brojeny MF. 2007. The development of an expert system for effective selection and appointment of the jobs applicants in human resource management. Comp. Industr. Engin. 53:306–12 [Google Scholar]
  86. Michell J. 1986. Measurement scales and statistics: a clash of paradigms. Psychol. Bull. 100:398–407 [Google Scholar]
  87. Michell J. 2000. Normal science, pathological science and psychometrics. Theory Psychol. 10:5639–67 [Google Scholar]
  88. Molenaar P. 2004. A manifesto on psychology as idiographic science: bringing the person back into scientific psychology, this time forever. Meas. Interdisc. Res. Perspect. 2:4201–18 [Google Scholar]
  89. Mulder J, van der Linden WJ. 2010. Multidimensional adaptive testing with Kullback-Leibler information item selection. Elements of Adaptive Testing WJ van der Linden, CAW Glas 77–102 New York: Springer [Google Scholar]
  90. Newby VA, Conner GR, Grant CP, Bunderson CV. 2009. The Rasch model and additive conjoint measurement. J. Appl. Meas. 10:4348–54 [Google Scholar]
  91. Newton PE, Shaw SD. 2013. Standards for talking and thinking about validity. Psychol. Methods 18:3301–19 [Google Scholar]
  92. Oh IS, Berry CM. 2009. The five-factor model of personality and managerial performance: validity gains through the use of 360 degree performance ratings. J. Appl. Psychol. 94:61498–513 [Google Scholar]
  93. Oh IS, Wang G, Mount MK. 2011. Validity of observer ratings of the five-factor model of personality traits: a meta-analysis. J. Appl. Psychol. 96:4762–73 [Google Scholar]
  94. Ortner TM, Caspers J. 2011. Consequences of test anxiety on adaptive versus fixed item testing. Eur. J. Psychol. Assess. 27:3157–63 [Google Scholar]
  95. Park G, Schwartz HA, Eichstaedt JC, Kern M, Kosinski M. et al. 2015. Automatic personality assessment through social media language. J. Personal. Soc. Psychol. 108:6934–52 [Google Scholar]
  96. Peña-Suárez E, Muñiz J, Campillo-Álvarez Á, Fonseca-Pedrero E, García-Cueto E. 2013. Assessing organizational climate: psychometric properties of the CLIOR Scale. Psicothema 25:1137–44 [Google Scholar]
  97. Pendrill L, Fisher WP Jr. 2015. Counting and quantification: comparing psychometric and metrological perspectives on visual perceptions of number. Measurement 71:46–55 [Google Scholar]
  98. Pennebaker JW, Mehl MR, Niderhoffer KG. 2003. Psychological aspects of natural language use: our words, our selves. Annu. Rev. Psychol. 54:547–77 [Google Scholar]
  99. Peterson SJ, Luthans F, Avolio BJ, Walumbwa FO, Zhang Z. 2011. Psychological capital and employee performance: a latent growth modeling approach. Pers. Psychol. 64:2427–50 [Google Scholar]
  100. Rasch G. 1980. Probabilistic Models for Some Intelligence and Attainment Tests Chicago: Univ. Chicago PressSeminal book on objective measurement built via analogy from the mathematical structure of natural laws in the physical and biological sciences.
  101. San Martin E, Gonzalez J, Tuerlinckx F. 2015. On the unidentifiability of the fixed-effects 3PL model. Psychometrika 80:2450–67 [Google Scholar]
  102. Salas E, Sims DE, Burke SE. 2005. Is there a “Big Five” in teamwork?. Small Group Res. 36:555–99 [Google Scholar]
  103. Schmidt FL, Hunter JE. 1977. Development of a general solution to the problem of validity generalization. J. Appl. Psychol. 62:5529–40 [Google Scholar]
  104. Shyamsunder A, Barney MF. 2012. Others know best—a test of socioanalytic theory Presented at Annu. Meet. Acad. Manag., Boston (poster)
  105. Smith RM. 1996. A comparison of methods for determining dimensionality in Rasch measurement. Struct. Equation Model. 3:125–40 [Google Scholar]
  106. Stark S, Chernyshenko OS, Guenole N. 2011. Can subject matter expert ratings of statement extremity be used to streamline the development of unidimensional pairwise preference scales?. Org. Res. Methods 14:256–78 [Google Scholar]
  107. Stark S, Chernyshenko OS, Drasgow F, White LA. 2012. Adaptive testing with multidimensional pairwise preference items: improving the efficiency of personality and other noncognitive assessments. Org. Res. Methods 15:3463–87 [Google Scholar]
  108. Stenner AJ, Fisher WP Jr, Stone MH, Burdick DS. 2013. Causal Rasch models. Front. Psychol. Quant. Psychol. Meas. 4:5361–14 [Google Scholar]
  109. Stöhr K. 2003. A multicentre collaboration to investigate the cause of severe acute respiratory syndrome. Lancet 361:93701730–33 [Google Scholar]
  110. Stone MH, Wright BD, Stenner AJ. 1999. Mapping variables. J. Outcome Meas. 3:4308–22 [Google Scholar]
  111. Thompson NA, Weiss DJ. 2011. A framework for the development of computerized adaptive tests. Pract. Assess. Res. Eval. 16:11–9 [Google Scholar]
  112. Tonidandel S, Quiñones MA, Adams AA. 2002. Computer adaptive testing: the impact of test characteristics on perceived performance and test takers' reactions. J. Appl. Psychol. 87:320–32 [Google Scholar]
  113. Triantafillou E, Georgiadou E, Economides AA. 2008. The design and evaluation of a computerized adaptive test on mobile devices. Comput. Educ. 50:1319–30 [Google Scholar]
  114. van der Linden WJ. 2008. Some new developments in adaptive testing technology. J. Psychol. 216:13–11 [Google Scholar]
  115. van der Linden WJ, Glas CAW. 2010. Elements of adaptive testing. Statistics for Social and Behavioral Sciences SE Feinberg, WJ van der Linden 1–438 New York: SpringerComprehensive technical treatment of CAT topics, including areas not covered in this review such as item exposure control. [Google Scholar]
  116. van der Linden WJ, Ren H. 2015. Optimal Bayesian adaptive design for test-item calibration. Psychometrika 80:2263–88 [Google Scholar]
  117. Verhelst ND, Glas CAW. 1995. The one parameter logistic model. Rasch Models: Foundations, Recent Developments, and Applications GH Fischer, IW Molenaar 215–37 New York: Springer [Google Scholar]
  118. Vispoel WP, Wang T, Bleiler T. 2005. Computerized adaptive and fixed-item testing of music listening skill: a comparison of efficiency, precision and concurrent validity. J. Educ. Meas. 34:143–63 [Google Scholar]
  119. Vosk T. 2010. Trial by numbers: uncertainty in the quest for truth and justice. Champion 56:48–56 [Google Scholar]
  120. Vosk T, Forrest ARW, Emery A, McLane LD. 2014. The measurand problem in breath alcohol testing. J. Forensic Sci. 59:3811–15 [Google Scholar]
  121. Vygotsky LS. 1978. Mind and Society: The Development of Higher Mental Processes. Cambridge, MA: Harvard Univ. Press
  122. Wainer H. 2007. Testlet Response Theory and its Applications New York: Cambridge Univ. Press
  123. Weiner SP, Dalessio AT. 2006. Oversurveying: causes, consequences, and cures. Getting Action from Organizational Surveys: New Concepts, Technologies, and Applications AI Kraut 294–311 San Francisco: Jossey-Bass [Google Scholar]
  124. Wilson MR. 2005. Constructing Measures: An Item Response Modeling Approach Mahwah, NJ: Lawrence Erlbaum Assoc.
  125. Wilson MR. 2009. Measuring progressions: assessment structures underlying a learning progression. J. Res. Sci. Teach. 46:716–30 [Google Scholar]
  126. Wilson MR. 2013a. Seeking a balance between the statistical and scientific elements in psychometrics. Psychometrika 78:2211–36 [Google Scholar]
  127. Wilson MR. 2013b. Using the concept of a measurement system to characterize measurement models used in psychometrics. Measurement 46:3766–74 [Google Scholar]
  128. Wilson MR, Mari L, Maul A, Torres Irribarra D. 2015. A comparison of measurement concepts across physical science and social science domains: instrument design, calibration, and measurement. J. Phys. Conf. Ser. 588:012034 [Google Scholar]
  129. Wright BD. 1999. Fundamental measurement for psychology. The New Rules of Measurement: What Every Educator and Psychologist Should Know SE Embretson, SL Hershberger 65–104 Hillsdale, NJ: Lawrence Erlbaum Assoc. [Google Scholar]
  130. Wright BD, Stone MH. 1979. Best Test Design: Rasch Measurement Chicago: MESA Press
  131. Yao T, Linacre JM. 1991. CAT with a poorly calibrated item bank. Rasch Meas. Trans. 5:2141 [Google Scholar]
  132. Youyou W, Kosinski M, Stillwell D. 2015. Computer-based personality judgments are more accurate than those made by humans. PNAS 112:1036–40 [Google Scholar]
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