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Measurement, Statistics, and Research Design

Integrating Bifactor Models into a Generalizability Theory Based Structural Equation Modeling Framework

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References

  • Ark, T. K. (2015). Ordinal generalizability theory using an underlying latent variable framework (Doctoral dissertation). University of British Columbia.
  • Bader, M., Jobst, L. J., & Moshagen, M. (2022). Sample size requirements for bifactor models. Structural Equation Modeling: A Multidisciplinary Journal. Advance online publication. https://doi.org/10.1080/10705511.2021.2019587
  • Becker, G. (2000). How important is transient error in estimating reliability? Going beyond simulation studies. Psychological Methods, 5(3), 370–379. https://doi.org/10.1037/1082-989x.5.3.370
  • Brennan, R. L. (2001a). Generalizability theory. Springer-Verlag.
  • Brennan, R. L. (2001b). Manual for mGENOVA. Iowa Testing Programs, University of Iowa.
  • Brennan, R. L. (2010). Generalizability theory and classical test theory. Applied Measurement in Education, 24(1), 1–21. https://doi.org/10.1080/08957347.2011.532417
  • Cronbach, L. J., Gleser, G. C., Nanda, H., & Rajaratnam, N. (1972). The dependability of behavioral measurements: Theory of generalizability for scores and profiles. Wiley.
  • Cronbach, L. J., Rajaratnam, N., & Gleser, G. C. (1963). Theory of generalizability: A liberalization of reliability theory. British Journal of Statistical Psychology, 16(2), 137–163. https://doi.org/10.1111/j.2044-8317.1963.tb00206.x
  • DeYoung, C. G., Quilty, L. C., & Peterson, J. B. (2007). Between facets and domains: 10 aspects of the Big Five. Journal of Personality and Social Psychology, 93(5), 880–896. https://doi.org/10.1037/0022-3514.93.5.880
  • Eid, M., Geiser, C., Koch, T., & Heene, M. (2017). Anomalous results in G-factor models: Explanations and alternatives. Psychological Methods, 22(3), 541–562. https://doi.org/10.1037/met0000083
  • Eid, M., Krumm, S., Koch, T., & Schulze, J. (2018). Bifactor models for predicting criteria by general and specific factors: Problems of nonidentifiability and alternative solutions. Journal of Intelligence, 6(3), 42. https://doi.org/10.3390/jintelligence6030042
  • Graham, J. M. (2006). Congeneric and (essentially) tau-equivalent estimates of score reliability. Educational and Psychological Measurement, 66(6), 930–944. https://doi.org/10.1177/0013164406288165
  • Holzinger, K. J., & Harman, H. H. (1938). Comparison of two factorial analyses. Psychometrika, 3(1), 45–60. https://doi.org/10.1007/BF02287919
  • Holzinger, K. J., & Swineford, F. (1937). The bi-factor method. Psychometrika, 2(1), 41–54. https://doi.org/10.1007/BF02287965
  • Jeon, M.-J., Lee, G., Hwang, J.-W., & Kang, S.-J. (2009). Estimating reliability of school-level scores using multilevel and generalizability theory models. Asia Pacific Education Review, 10(2), 149–158. https://doi.org/10.1007/s12564-009-9014-3
  • John, O. P., Donahue, E. M., & Kentle, R. L. (1991). The Big Five Inventory—versions 4a and 54. University of California, Berkeley, Institute of Personality and Social Research.
  • Jorgensen, T. D. (2021). How to estimate absolute-error components in structural equation models of generalizability theory. Psych, 3(2), 113–133. https://doi.org/10.3390/psych3020011
  • Koch, T., Holtmann, J., Bohn, J., & Eid, M. (2018). Explaining general and specific factors in longitudinal, multimethod, and bifactor models: Some caveats and recommendations. Psychological Methods, 23(3), 505–523. https://doi.org/10.1037/met0000146
  • Le, H., Schmidt, F. L., & Putka, D. J. (2009). The multifaceted nature of measurement artifacts and its implications for estimating construct-level relationships. Organizational Research Methods, 12(1), 165–200. https://doi.org/10.1177/1094428107302900
  • Marcoulides, G. A. (1996). Estimating variance components in generalizability theory: The covariance structure analysis approach. Structural Equation Modeling, 3(3), 290–299. https://doi.org/10.1080/10705519609540045
  • McCrae, R. R., & Costa, P. T. (2010). NEO Inventories professional manual. Psychological Assessment Resources.
  • McDonald, R. P. (1999). Test theory: A unified approach. Erlbaum.
  • Morris, C. A. (2020). Optimal methods for disattenuating correlation coefficients under realistic measurement conditions with single-form, self-report instruments (Publication No. 27668419) [Doctoral dissertation]. University of Iowa. ProQuest Dissertation and Theses database.
  • Raykov, T., & Marcoulides, G. A. (2006). Estimation of generalizability coefficients via a structural equation modeling approach to scale reliability evaluation. International Journal of Testing, 6(1), 81–95. https://doi.org/10.1207/s15327574ijt0601_5
  • Reeve, C. L., Heggestad, E. D., & George, E. (2005). Estimation of transient error in cognitive ability scales. International Journal of Selection and Assessment, 13(4), 316–332. https://doi.org/10.1111/j.1468-2389.2005.00328.x
  • Reise, S. P. (2012). The rediscovery of bifactor measurement models. Multivariate Behavioral Research, 47(5), 667–696. https://doi.org/10.1080/00273171.2012.715555
  • Reise, S. P., Bonifay, W. E., & Haviland, M. G. (2013). Scoring and modeling psychological measures in the presence of multidimensionality. Journal of Personality Assessment, 95(2), 129–140. https://doi.org/10.1080/00223891.2012.725437.
  • Rodriguez, A., Reise, S. P., & Haviland, M. G. (2016a). Applying bifactor statistical indices in the evaluation of psychological measures. Journal of Personality Assessment, 98(3), 223–237. https://doi.org/10.1080/00223891.2015.1089249
  • Rodriguez, A., Reise, S. P., & Haviland, M. G. (2016b). Evaluating bifactor models: Calculating and interpreting statistical indices. Psychological Methods, 21(2), 137–150. https://doi.org/10.1037/met0000045
  • Rosseel, Y. (2012). lavaan: An R package for structural equation modeling. Journal of Statistical Software, 48(2), 1–36. https://doi.org/10.18637/jss.v048.i02
  • Saucier, G. (1994). Mini-markers: A brief version of Goldberg’s unipolar Big-Five markers. Journal of Personality Assessment, 63(3), 506–516. https://doi.org/10.1207/s15327752jpa6303_8
  • Schmidt, F. L., Le, H., & Ilies, R. (2003). Beyond alpha: An empirical investigation of the effects of different sources of measurement error on reliability estimates for measures of individual differences constructs. Psychological Methods, 8(2), 206–224. https://doi.org/10.1037/1082-989X.8.2.206
  • Shavelson, R. J., & Webb, N. M. (1991). Generalizability theory: A primer. Sage.
  • Smith, P. L. (1978). Sampling error of variance components in small sample multifacet generalizability designs. Journal of Educational Statistics, 3(4), 319–346. https://doi.org/10.2307/1164776
  • Soto, C. J., & John, O. P. (2017). The next Big Five Inventory (BFI-2): Developing and assessing a hierarchical model with 15 facets to enhance bandwidth, fidelity, and predictive power. Journal of Personality and Social Psychology, 113(1), 117–143. https://doi.org/10.1037/pspp0000096
  • ten Hove, D., Jorgensen, T. D., & van der Ark, L. A. (2021). Interrater reliability for multilevel data: A generalizability theory approach. Psychological Methods. Advance online publication. https://doi.org/10.1037/met0000391
  • Vispoel, W. P., Hong, H., Lee, H., Xu, G., & Jorgensen, T. R. (2022). Assessing relative and absolute differences in scores within G-theory designs using structural equation modeling versus conventional procedures. Manuscript submitted for publication.
  • Vispoel, W. P., Lee, H., & Hong, H. (2022a). Applications of multivariate generalizability theory with psychological assessments. Manuscript submitted for publication.
  • Vispoel, W. P., Lee, H., & Hong, H. (2022b). Analyzing multivariate generalizability theory designs within structural equation modeling frameworks. Manuscript submitted for publication.
  • Vispoel, W. P., Lee, H., Xu, G., & Hong, H. (in press). Expanding bifactor models of psychological traits to account for multiple sources of measurement error. Psychological Assessment.
  • Vispoel, W. P., Morris, C. A., & Kilinc, M. (2018a). Applications of generalizability theory and their relations to classical test theory and structural equation modeling. Psychological Methods, 23(1), 1–26. https://doi.org/10.1037/met0000107
  • Vispoel, W. P., Morris, C. A., & Kilinc, M. (2018b). Practical applications of generalizability theory for designing, evaluating, and improving psychological assessments. Journal of Personality Assessment, 100(1), 53–67. https://doi.org/10.1080/00223891.2017.1296455
  • Vispoel, W. P., Morris, C. A., & Kilinc, M. (2018c). Using generalizability theory to disattenuate correlation coefficients for multiple sources of measurement error. Multivariate Behavioral Research, 53(4), 481–501. https://doi.org/10.1080/00273171.2018.1457938
  • Vispoel, W. P., Morris, C. A., & Kilinc, M. (2018d). Using G-theory to enhance evidence of reliability and validity for common uses of the Paulhus Deception Scales. Assessment, 25(1), 69–83. https://doi.org/10.1177/1073191116641182
  • Vispoel, W. P., Morris, C. A., & Kilinc, M. (2019). Using generalizability theory with continuous latent response variables. Psychological Methods, 24(2), 153–178. https://doi.org/10.1037/met0000177. [PMC][30080056]
  • Vispoel, W. P., & Tao, S. (2013). A generalizability analysis of score consistency for the Balanced Inventory of Desirable Responding. Psychological Assessment, 25(1), 94–104. https://doi.org/10.1037/a0029061
  • Vispoel, W. P., Xu, G., & Kilinc, M. (2021). Expanding G-theory models to incorporate congeneric relationships: Illustrations using the Big Five Inventory. Journal of Personality Assessment, 103(4), 429–442. https://doi.org/10.1080/00223891.202.1808474.
  • Vispoel, W. P., Xu, G., & Schneider, W. S. (2021). Interrelationships between latent state-trait theory and generalizability theory in a structural equation modeling framework. Psychological Methods. Advance online publication. https://doi.org/10.1037/met0000290
  • Vispoel, W. P., Xu, G., & Schneider, W. S. (2022). Using parallel splits with self-report and other measures to enhance precision in generalizability theory analyses. Journal of Personality Assessment, 104(3), 303–319. https://doi.org/10.1080/00223891.2021.1938589.
  • Zinbarg, R. E., Revelle, W., Yovel, I., & Li, W. (2005). Cronbach’s α, Revelle’s β, and McDonald’s ω H: Their relations with each other and two alternative conceptualizations of reliability. Psychometrika, 70(1), 123–133. https://doi.org/10.1007/s11336-003-0974-7

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