Abstract
Abstract. The consequences of speeded testing for the structure and validity of a numerical reasoning scale (NRS) were investigated. Confirmatory factor models including an additional factor for representing working speed and models without such a representation were employed for investigating reasoning data collected in speeded paper-and-pencil testing and in only slightly speeded testing. For achieving a complete account of the data, the models also accounted for the item-position effect. The results revealed the factor representing working speed as essential for achieving a good fit in data originating from speeded testing. The reasoning factors based on data due to speeded and slightly speeded testing showed a high correlation among each other. The factor representing working speed was independent of the other factors derived from reasoning data but related to an external score representing processing speed.
References
1990). Comparative fit indexes in structural models. Psychological Bulletin, 107, 238–246. https://doi.org/10.1037/0033-2909.107.2.238
(2017). Beyond the intellect: Complexity and learning trajectories in Raven’s Progressive Matrices depend on self-regulatory processes and conative dispositions. Intelligence, 61, 63–77. https://doi.org/10.1016/j.intell.2017.01.005
(2002). Item parameter estimation under conditions of test speededness: Application of a mixture Rasch model with ordinal constraints. Journal of Educational Measurement, 39, 331–348. https://doi.org/10.1111/j.1745-3984.2002.tb01146.x
(1959). Convergent and discriminant validation by the multitrait-multimethod matrix. Psychological Bulletin, 56, 81–105. https://doi.org/10.1037/h0046016
(2000). Item sequencing effects on the measurement of fluid intelligence. Intelligence, 28, 145–160. https://doi.org/10.1016/S0160-2896(00)00034-9
(2002). Evaluating goodness-of-fit indexes for testing measurement invariance. Structural Equation Modeling, 9, 233–255. https://doi.org/10.1207/S15328007SEM0902_5
(2013). Modeling item-position effects within an IRT framework. Journal of Educational Measurement, 50, 164–185. https://doi.org/10.1111/jedm.12009
(2016).
(Examining fit with structural equation models . In K. SchweizerC. DiStefanoEds., Principles and methods of test construction. Standards and recent advances (pp. 166–193). Göttingen, Germany: Hogrefe.1991). A multidimensional latent trait model for measuring learning and change. Psychometrika, 56, 495–515. https://doi.org/10.1007/BF02294487
(2017). Separating power and speed components of standardized intelligence measures. Intelligence, 61, 159–168. https://doi.org/10.1016/j.intell.2017.02.002
(2016).
(Overview on estimation methods and preconditions for their application with structural equation modeling . In K. SchweizerC. DiStefanoEds., Principles and methods of test construction. Standards and recent advances (pp. 166–193). Göttingen, Germany: Hogrefe.2008). A speeded item response model with gradual process change. Psychometrika, 73, 65–87. https://doi.org/10.1007/s11336-007-9031-2
(2006). Congeneric and (essentially) tau-equivalent estimates of score reliability. What they are and how to use them. Educational and Psychological Measurement, 66, 930–944. https://doi.org/10.1177/0013164406288165
(1950).
(Speed versus power tests . In H. GulliksenEd., Theory of mental tests (pp. 230–244). New York, NY: John Wiley & Sons.2007). A confirmatory analysis of item reliability trends (CAIRT): Differentiating true score and error variance in the analysis of item context effects. Multivariate Behavioral Research, 42, 157–183. https://doi.org/10.1080/00273170701341266
(1983). Leistungsprüfsystem (LPS)
([Performance testing system] (2nd ed.). Göttingen, Germany: Hogrefe.1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6, 1–55. https://doi.org/10.1080/10705519909540118
(1971). Statistical analysis of sets of congeneric tests. Psychometrika, 36, 109–133. https://doi.org/10.1007/BF02291393
(2006). LISREL 8.80. Lincolnwood, IL: Scientific Software International.
(1968). Statistical theories of mental test scores. Reading, MA: Addison-Wesley.
(2007). Validity issues in test speededness. Educational Measurement, 26, 29–37. https://doi.org/10.1111/j.1745-3992.2007.00106.x
(2005). The α and the ω of Congeneric Test Theory: An extension of reliability and internal consistency to heterogeneous tests. Applied Psychological Measurement, 29, 65–81. https://doi.org/10.1177/0146621604270882
(2005). What do Raven’s Matrices measure? An analysis in terms of sex differences. Intelligence, 33, 663–674. https://doi.org/10.1016/j.intell.2005.03.004
(1986). Latent variable growth within behavior genetic models. Behavior Genetics, 16, 163–200. https://doi.org/10.1007/BF01065485
(1974). Difficulty factors in binary data. British Journal of Mathematical and Statistical Psychology, 27, 82–99. https://doi.org/10.1111/j.2044-8317.1974.tb00530.x
(2013). Changes in test-taking patterns over time. Intelligence, 41, 780–790. https://doi.org/10.1016/j.intell.2013.04.005
(1994). The effect of speededness on parameter estimation in item response theory. Journal of Educational Measurement, 31, 200–219. https://doi.org/10.1111/j.1745-3984.1994.tb00443.x
(1980). Probabilistic models for some intelligence and attainment tests (expand. ed.). Chicago, IL: University of Chicago Press.
(1997). Raven’s progressive matrices and vocabulary scales. Edinburgh, UK: J. C. Raven Ltd.
(2013). The sources of the relationship between sustained attention and reasoning. Intelligence, 41, 51–58. https://doi.org/10.1016/j.intell.2012.10.006
(2018). Speeded testing in the assessment of intelligence gives rise to a speed factor. Intelligence, 66, 64–71. https://doi.org/10.1016/j.intell.2017.11004
(2008). Investigating experimental effects within the framework of structural equation modeling: an example with effects on both error scores and reaction times. Structural Equation Modeling, 15, 327–345. https://doi.org/10.1080/1070551080192262
(2011). Scaling variances of latent variables by standardizing loadings: Applications to working memory and the position effect. Multivariate Behavioral Research, 46, 938–955. https://doi.org/10.1080/00273171.2011.625312
(2013). A threshold-free approach to the study of the structure of binary data. International Journal of Statistics and Probability, 2, 67–75. https://doi.org/10.5539/ijsp.v2n2p67
(2013). The position effect in tests with a time limit: The consideration of interruption and working speed. Psychological Test and Assessment Modelling, 55, 62–78.
(2015).
(A comparison of confirmatory factor analysis of binary data on the basis of tetrachoric correlations and of probability-based covariances: A simulation study . In R. E. MillsapD. M. BoltL. A. van der ArkW.-C. WangEds., Springer Proceedings in Mathematics & Statistics. Quantitative Psychology Research (Vol. 89, pp. 273–292). Heidelberg, Germany: Springer International Publishing.2011). Test design and speededness. Journal of Educational Measurement, 48, 44–60. https://doi.org/10.1111/j.1745-3984.2010.00130.x
(2013). Speededness and adaptive testing. Journal of Educational and Behavioral Statistics, 38, 418–438. https://doi.org/10.3102/1076998612466143
(2000). A Rasch model for detecting learning while solving an intelligence test. Applied Psychological Measurement, 24, 151–162. https://doi.org/10.1177/01466210022031589
(2002). The relation of speeded and unspeeded reasoning with mental speed. Intelligence, 30, 537–554. https://doi.org/10.1016/j.intell.2017.11004
(