Fayers, P. M., & Machin, D. (2016). Quality of life: The assessment, analysis and reporting of patient-reported outcomes (3rd ed.). Chichester, UK: Wiley.
Google Scholar
Streiner, D. L., Norman, G. R., & Cairney, J. (2015). Health measurement scales: A practical guide to their development and use (5th ed.). Oxford: Oxford University Press.
Book
Google Scholar
Teresi, J. A., Ramirez, M., Jones, R. N., Choi, S., & Crane, P. K. (2012). Modifying measures based on differential item functioning (DIF) impact analyses. Journal of Aging and Health, 24(6), 1044–1076. doi:10.1177/0898264312436877.
Article
PubMed Central
PubMed
Google Scholar
Walker, C. M. (2011). What’s the DIF? Why differential item functioning analyses are an important part of instrument development and validation. Journal of Psychoeducational Assessment, 29(4), 364–376. doi:10.1177/0734282911406666.
Article
Google Scholar
Finch, W. H., & Finch, M. E. H. (2013). Investigation of specific learning disability and testing accommodations based differential item functioning using a multilevel multidimensional mixture item response theory model. Educational and Psychological Measurement, 73(6), 973–993. doi:10.1177/0013164413494776.
Article
Google Scholar
Teresi, J. A. (2006). Different approaches to differential item functioning in health applications: Advantages, disadvantages and some neglected topics. Medical Care, 44(11 Suppl 3), S152–170. doi:10.1097/01.mlr.0000245142.74628.ab.
Article
PubMed
Google Scholar
Sawatzky, R., Ratner, P. A., Kopec, J. A., & Zumbo, B. D. (2012). Latent variable mixture models: A promising approach for the validation of patient reported outcomes. Quality of Life Research, 21(4), 637–650. doi:10.1007/s11136-011-9976-6.
Article
PubMed
Google Scholar
Sawatzky, R., Ratner, P. A., Kopec, J. A., Wu, A. D., & Zumbo, B. D. (2016). The accuracy of computerized adaptive testing in heterogeneous populations: A mixture item-response theory analysis. PLoS ONE, 11(3), e0150563. doi:10.1371/journal.pone.0150563.
Article
PubMed Central
CAS
PubMed
Google Scholar
Wu, X., Sawatzky, R., Hopman, W., Mayo, N., Sajobi, T. T., Liu, J., et al. (2017). Latent variable mixture models to test for differential item functioning: A population-based analysis. Health and Quality of Life Outcomes, 15, 102. doi:10.1186/s12955-017-0674-0.
Article
PubMed Central
PubMed
Google Scholar
Zumbo, B. D. (2009). Validity as contextualized and pragmatic explanation, and its implications for validation practice. In R. W. Lissitz (Ed.), The concept of validity: Revisions, new directions and applications (pp. 65–82). Charlotte, NC: Information Age Publishing.
Google Scholar
Zumbo, B. D. (2007). Validity: Foundational issues and statistical methodology. In C. R. Rao & S. Sinharay (Eds.), Handbook of statistics (Vol. 26, pp. 45–79). Amsterdam: Elsevier.
Google Scholar
Hambleton, R. K., Swaminathan, H., & Rogers, H. J. (1991). Fundamentals of item response theory. London: Sage.
Google Scholar
Embretson, S., & Reise, S. P. (2000). Item response theory for psychologists. Mahwah, NJ: Lawrence Erlbaum Associates.
Google Scholar
Reise, S. P., & Gomel, J. N. (1995). Modeling qualitative variation within latent trait dimensions: Application of mixed-measurement to personality assessment. Multivariate Behavioral Research, 30(3), 341–358. doi:10.1207/s15327906mbr3003_3.
Article
CAS
PubMed
Google Scholar
Sawatzky, R., Chan, E. K. H., Zumbo, B. D., Ahmed, S., Bartlett, S. J., Bingham III, C. O., et al. (2016). Modern perspectives of measurement validation emphasize justification of inferences based on patient-reported outcome scores: Seventh paper in a series on patient reported outcomes. Journal of Clinical Epidemiology. doi:10.1016/j.jclinepi.2016.12.002.
Article
PubMed
Google Scholar
DeVellis, R. F. (2012). Scale development: Theory and applications (3ed., vol. 26). Newbury Park, CA: Sage.
Vandenberg, R. J., & Lance, C. E. (2000). A review and synthesis of the measurement invariance literature: Suggestions, practices, and recommendations for organizational research. Organizational Research Methods, 3(1), 4–69.
Article
Google Scholar
Byrne, B. M. (1998). Structural equation modeling with LISREL, PRELIS, and SIMPLIS: Basic concepts, applications, and programming. Mahwah, NJ: L. Erlbaum.
Google Scholar
Holland, P. W., & Thayer, D. T. (1988). Differential item functioning and the Mantel Haenszel procedure. In H. Wainer & H. I. Braun (Eds.), Test validity (pp. 129–145). Hillsdale, NJ: L. Erlbaum Associates.
Swaminathan, H., & Rogers, H. J. (1990). Detecting differential item functioning using logistic regression procedures. Journal of Educational Measurement, 27(4), 361–370. doi:10.1111/j.1745-3984.1990.tb00754.x.
Article
Google Scholar
Crane, P. K., Gibbons, L. E., Jolley, L., & van Belle, G. (2006). Differential item functioning analysis with ordinal logistic regression techniques: DIFdetect and difwithpar. Medical Care, 44(11 Suppl 3), S115–123. doi:10.1097/01.mlr.0000245183.28384.ed.
Article
PubMed
Google Scholar
Zumbo, B. D. (1999). A handbook on the theory and methods of differential item functioning (DIF): Logistic regression modeling as a unitary framework for binary and Likert-type (ordinal) item scores. Ottawa, ON: Directorate of Human Resources Research and Evaluation, Department of National Defense.
Google Scholar
Roussos, L., & Stout, W. (1996). A multidimensionality-based DIF analysis paradigm. Applied Psychological Measurement, 20(4), 355–371. doi:10.1177/014662169602000404.
Article
Google Scholar
Shealy, R., & Stout, W. (1993). A model-based standardization approach that separates true bias/DIF from group ability differences and detects test bias/DTF as well as item bias/DIF. Psychometrika, 58(2), 159–194. doi:10.1007/bf02294572.
Article
Google Scholar
Muthén, B., Kao, C.-F., & Burstein, L. (1991). Instructionally sensitive psychometrics: Application of a new IRT-based detection technique to mathematics achievement test items. Journal of Educational Measurement, 28, 1–22. doi:10.1111/j.1745-3984.1991.tb00340.x.
Article
Google Scholar
Steinberg, L., & Thissen, D. (2006). Using effect sizes for research reporting: Examples using item response theory to analyze differential item functioning. Psychological Methods, 11(4), 402–415. doi:10.1037/1082-989x.11.4.402.
Article
PubMed
Google Scholar
Zumbo, B. D. (2007). Three generations of DIF analyses: Considering where it has been, where it is now, and where it is going. Language Assessment Quarterly, 4(2), 223–233.
Article
Google Scholar
Morales, L. S., Flowers, C., Gutierrez, P., Kleinman, M., & Teresi, J. A. (2006). Item and scale differential functioning of the mini-mental state exam assessed using the differential item and test functioning (DFIT) framework. Medical Care, 44(11 Suppl 3), S143–151. doi:10.1097/01.mlr.0000245141.70946.29.
Article
PubMed Central
PubMed
Google Scholar
Cohen, A. S., & Bolt, D. M. (2005). A mixture model analysis of differential item functioning. Journal of Educational Measurement, 42(2), 133–148. doi:10.1111/j.1745-3984.2005.00007.
Article
Google Scholar
De Ayala, R. J., Kim, S.-H., Stapleton, L. M., & Dayton, C. M. (2002). Differential item functioning: A mixture distribution conceptualization. International Journal of Testing, 2(3–4), 243–276. doi:10.1080/15305058.2002.9669495.
Article
Google Scholar
Samuelsen, K. M. (2008). Examining differential item functioning from a latent mixture perspective. In G. R. Hancock & K. M. Samuelsen (Eds.), Advances in latent variable mixture models (pp. 177–198). Charlotte, NC: Information Age Publishing.
Google Scholar
Reise, S. P., & Waller, N. G. (2009). Item response theory and clinical measurement. Annual Review of Clinical Psychology, 5, 27–48. doi:10.1146/annurev.clinpsy.032408.153553.
Article
PubMed
Google Scholar
Kelderman, H., & Macready, G. B. (1990). The use of loglinear models for assessing differential item functioning across manifest and latent examinee groups. Journal of Educational Measurement, 27(4), 307–327. doi:10.1111/j.1745-3984.1990.tb00751.x.
Article
Google Scholar
Leite, W. L., & Cooper, L. A. (2010). Detecting social desirability bias using factor mixture models. Multivariate Behavioral Research, 45(2), 271–293. doi:10.1080/00273171003680245.
Article
PubMed
Google Scholar
Pohl, S., Südkamp, A., Hardt, K., Carstensen, C. H., & Weinert, S. (2016). Testing students with special educational needs in large-scale assessments—Psychometric properties of test scores and associations with test taking behavior. Frontiers in Psychology, 7, 154. doi:10.3389/fpsyg.2016.00154.
Article
PubMed Central
PubMed
Google Scholar
Allan, N. P., Korte, K. J., Capron, D. W., Raines, A. M., & Schmidt, N. B. (2014). Factor mixture modeling of anxiety sensitivity: A three-class structure. Psychological Assessment, 26(4), 1184–1195. doi:10.1037/a0037436.
Article
PubMed
Google Scholar
Bernstein, A., Stickle, T. R., Zvolensky, M. J., Taylor, S., Abramowitz, J., & Stewart, S. (2010). Dimensional, categorical, or dimensional-categories: Testing the latent structure of anxiety sensitivity among adults using factor-mixture modeling. Behavior Therapy, 41(4), 515–529. doi:10.1016/j.beth.2010.02.003.
Article
PubMed
Google Scholar
Roberson-Nay, R., Latendresse, S. J., & Kendler, K. S. (2012). A latent class approach to the external validation of respiratory and non-respiratory panic subtypes. Psychological Medicine, 42(3), 461–474. doi:10.1017/S0033291711001425.
Article
CAS
PubMed
Google Scholar
Clark, S. L., Muthén, B., Kaprio, J., D’Onofrio, B. M., Viken, R., & Rose, R. J. (2013). Models and strategies for factor mixture analysis: An example concerning the structure underlying psychological disorders. Structural Equation Modeling: A Multidisciplinary Journal, 20(4), 681–703. doi:10.1080/10705511.2013.824786.
Article
Google Scholar
Wu, L. T., Woody, G. E., Yang, C., Pan, J. J., & Blazer, D. G. (2011). Abuse and dependence on prescription opioids in adults: A mixture categorical and dimensional approach to diagnostic classification. Psychological Medicine, 41(3), 653–664. doi:10.1017/S0033291710000954.
Article
PubMed
Google Scholar
Lee, H., & Beretvas, S. N. (2014). Evaluation of two types of differential item functioning in factor mixture models with binary outcomes. Educational and Psychological Measurement, 74(5), 831–858. doi:10.1177/0013164414526881.
Article
Google Scholar
Lubke, G. H., & Muthen, B. (2005). Investigating population heterogeneity with factor mixture models. Psychological Methods, 10(1), 21–39. doi:10.1037/1082-989X.10.1.21.
Article
PubMed
Google Scholar
Lubke, G., & Neale, M. (2008). Distinguishing between latent classes and continuous factors with categorical outcomes: Class invariance of parameters of factor mixture models. Multivariate Behavioral Research, 43(4), 592–620. doi:10.1080/00273170802490673.
Article
PubMed Central
PubMed
Google Scholar
Maij-de Meij, A., Kelderman, H., & van der Flier, H. (2010). Improvement in detection of differential item functioning using a mixture item response theory model. Multivariate Behavioral Research, 45(6), 975–999.
Article
PubMed
Google Scholar
Kopec, J. A., Sayre, E. C., Davis, A. M., Badley, E. M., Abrahamowicz, M., Sherlock, L., et al. (2006). Assessment of health-related quality of life in arthritis: Conceptualization and development of five item banks using item response theory. Health Quality of Life Outcomes, 4(1), 33. doi:10.1186/1477-7525-4-33.
Article
PubMed Central
PubMed
Google Scholar
Kopec, J. A., Badii, M., McKenna, M., Lima, V. D., Sayre, E. C., & Dvorak, M. (2008). Computerized adaptive testing in back pain: Validation of the CAT-5D-QOL. Spine, 33(12), 1384–1390. doi:10.1097/BRS.0b013e3181732a3b.
Article
PubMed
Google Scholar
Muthén, B., & Muthén, L. (2015). MPlus (version 7.4). Los Angeles, CA: Statmodel.
IBM Corp. (2016). IBM SPSS Statistics for Windows, Version 24.0. Armonk, NY: IBM.
Fabrigar, L. R., Wegener, D. T., MacCallum, R. C., & Strahan, E. J. (1999). Evaluating the use of exploratory factor analysis in psychological research. Psychological Methods, 4, 272–299. doi:10.1037/1082-989X.4.3.272.
Article
Google Scholar
Hattie, J. (1984). Methodology review: Assessing unidimensionality of tests and items. Applied Psychological Measurement, 20, 1–14. doi:10.1177/014662168500900204.
Article
Google Scholar
Slocum-Gori, S. L., & Zumbo, B. D. (2011). Assessing the unidimensionality of psychological scales: Using multiple criteria from factor analysis. Social Indicators Research, 102(3), 443–461.
Article
Google Scholar
Kline, R. B. (2010). Principles and practice of structural equation modeling (3rd ed.). New York: Guilford.
Google Scholar
Samejima, F. (1997). Graded response model. In W. J. Linden & R. K. Hambelton (Eds.), Handbook of modern item response theory (pp. 85–100). New York: Springer.
Li, F., Cohen, A. S., Kim, S.-H., & Cho, S.-J. (2009). Model selection methods for mixture dichotomous IRT models. Applied Psychological Measurement, 33(5), 353–373. doi:10.1177/0146621608326422.
Article
Google Scholar
Ram, N., & Grimm, K. J. (2009). Growth mixture modeling: A method for identifying differences in longitudinal change among unobserved groups. International Journal of Behavioral Development, 33(6), 565–576. doi:10.1177/0165025409343765.
Article
PubMed Central
PubMed
Google Scholar
Wang, C. P., Brown, C. H., & Bandeen-Roche, K. (2005). Residual diagnostics for growth mixture models: Examining the impact of a preventive intervention on multiple trajectories of aggressive behavior. Journal of the American Statistical Association, 100, 1054–1076. doi:10.1198/016214505000000501.
Article
CAS
Google Scholar
Muthén, B., & Muthén, L. (2007, November 16). Wald test of mean equality for potential latent class predictors in mixture modeling. Los Angeles: Statmodel. Retrieved http://www.statmodel.com/download/MeanTest1.pdf
Scott, N. W., Fayers, P. M., Aaronson, N. K., Bottomley, A., de Graeff, A., Groenvold, M., et al. (2010). Differential item functioning (DIF) analyses of health-related quality of life instruments using logistic regression. Health Quality of Life Outcomes, 8, 81. doi:10.1186/1477-7525-8-81.
PubMed Central
Article
PubMed
Google Scholar
Jodoin, M. G., & Gierl, M. J. (2001). Evaluating type I error and power rates using an effect size measure with the logistic regression procedure for DIF detection. Applied Measurement in Education, 14(4), 329–349. doi:10.1207/S15324818AME1404_2.
Article
Google Scholar
Clark, S. L., Muthén, B., Kaprio, J., D’Onofrio, B. M., Viken, R., & Rose, R. J. (2013). Models and strategies for factor mixture analysis: An example concerning the structure underlying psychological disorders. Structural Equation Modeling, 20(4), 681–703. doi:10.1080/10705511.2013.824786.
Article
Google Scholar
Roussos, L. A., & Stout, W. (2004). Differential item functioning analysis: Detecting DIF items and testing. In D. Kaplan (Ed.), The SAGE handbook of quantitative methodology for the social sciences. Thousand Oaks, CA: SAGE Publications.
Google Scholar
Ackerman, T. A. (1992). A didactic explanation of item bias, item impact, and item validity from a multidimensional perspective. Journal of Educational Measurement, 29(1), 67–91. doi:10.1111/j.1745-3984.1992.tb00368.x.
Article
Google Scholar