Analytic Approaches to Handle Missing Data in Simple Matrix Sampling Planned Missing Designs

References

• Allison, P. D. (1987). Estimation of linear models with incomplete data. Sociological Methodology, 17, 71. https://doi.org/10.2307/271029
   Google Scholar
• Allison, P. D. (2003). Missing data techniques for structural equation modeling. Journal of Abnormal Psychology, 112(4), 545–557. https://doi.org/10.1037/0021-843X.112.4.545
   PubMed Web of Science ®Google Scholar
• Anderson, T. W. (1957). Maximum likelihood estimates for a multivariate normal distribution when some observations are missing. Journal of the American Statistical Association, 52(278), 200–203. https://doi.org/10.1080/01621459.1957.10501379
   Web of Science ®Google Scholar
• Bandalos, D. L., & Leite, W. (2013). The role of simulation in structural equation modeling. In Structural equation modeling: A second course (2nd ed., pp. 625–666). Information Age Publishing, Inc.
   Google Scholar
• Boomsma, A. (2013). Reporting Monte Carlo studies in structural equation modeling. Structural Equation Modeling: A Multidisciplinary Journal, 20(3), 518–540. https://doi.org/10.1080/10705511.2013.797839
   Web of Science ®Google Scholar
• Brown, T. A., & Moore, M. T. (2012). Confirmatory factor analysis. In R. H. Hoyle (Ed.), Handbook of structural equation modeling (pp. 361–379). Guilford Publications.
   Google Scholar
• Cromley, J. G., Dai, T., Fechter, T. S., Nelson, F. E., Van Boekel, M., & Du, Y. (2021). Development of a tool to assess inference-making and reasoning in biology. Journal of Microbiology & Biology Education, 22(2), e00159–21. https://doi.org/10.1128/jmbe.00159-21
   PubMed Web of Science ®Google Scholar
• Cudeck, R. (2000). An estimate of the covariance between variables which are not jointly observed. Psychometrika, 65(4), 539–546. https://doi.org/10.1007/BF02296344
   Web of Science ®Google Scholar
• Davey, A., & Savla, J. (2010). Statistical power analysis with missing data: A structural equation modeling approach. Routledge.
   Google Scholar
• Enders, C. K. (2003). Using the expectation maximization algorithm to estimate coefficient alpha for scales with item-level missing data. Psychological Methods, 8(3), 322–337. https://doi.org/10.1037/1082-989X.8.3.322
   PubMed Web of Science ®Google Scholar
• Enders, C. K. (2022). Applied missing data analysis. (Second Edition) The Guilford Press.
   Google Scholar
• Fechter, T., Dai, T., Cromley, J. G., Nelson, F. E., Van Boekel, M., & Du, Y. (2021). Developing a validity argument for an inference-making and reasoning measure for use in higher education. Frontiers in Education, 6, 727539. https://doi.org/10.3389/feduc.2021.727539
   Google Scholar
• Feng, Y., & Hancock, G. R. (2021). Oh no! They cut my funding! Using “post hoc” planned missing data designs to salvage longitudinal research. Child Development, 92(3), 1199–1216. https://doi.org/10.1111/cdev.13501
   PubMed Web of Science ®Google Scholar
• Goldenberg, K., & Ryan, J. (2009). Evolution and change in the consumer expenditure surveys: Adapting methodologies to meet changing needs. National Bureau of Economic Research, Summer Institute 2009–Conference on Research on Income and Wealth, Cambridge, Massachusetts, July).
   Google Scholar
• Gonzalez, J. M., Eltinge, J. L. (2008, August). Adaptive matrix sampling for the consumer expenditure quarterly interview survey. JSM Proceedings.
   Google Scholar
• Graham, J. W. (2009). Missing data analysis: Making it work in the real world. Annual Review of Psychology, 60(1), 549–576. https://doi.org/10.1146/annurev.psych.58.110405.085530
   PubMed Web of Science ®Google Scholar
• Graham, J. W. (2012). Missing data: Analysis and design. Springer. https://doi.org/10.1007/978-1-4614-4018-5
   Google Scholar
• Graham, J. W., & Coffman, D. L. (2012). Structural equation modeling with missing data.
   Google Scholar
• Graham, J. W., Taylor, B. J., Olchowski, A. E., & Cumsille, P. E. (2006). Planned missing data designs in psychological research. Psychological Methods, 11(4), 323–343. https://doi.org/10.1037/1082-989X.11.4.323
   PubMed Web of Science ®Google Scholar
• Gullisken, H. (1950). Theory of mental tests. Wiley.
   Google Scholar
• Hallquist, M. N., & Wiley, J. F. (2018). MplusAutomation: An R package for facilitating large-scale latent variable analyses in Mplus. Structural Equation Modeling: A Multidisciplinary Journal, 25(4), 621–638. https://doi.org/10.1080/10705511.2017.1402334
   PubMed Web of Science ®Google Scholar
• Hayduk, L. A. (1996). LISREL issues, debates and strategies. JHU Press.
   Google Scholar
• Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55. https://doi.org/10.1080/10705519909540118
   Web of Science ®Google Scholar
• Jewsbury, P., Finnegan, R., Xi, N., Jia, Y., Rust, K., & Burg, S. (2020). 2017 NAEP transition to digitally based assessments in mathematics and reading at grades 4 and 8: Mode evaluation study ETS.
   Google Scholar
• Johnson, E. G. (1992). The design of the national assessment of educational progress. Journal of Educational Measurement, 29(2), 95–110. https://doi.org/10.1111/j.1745-3984.1992.tb00369.x
   Web of Science ®Google Scholar
• Kaplan, D., & Su, D. (2016). On matrix sampling and imputation of context questionnaires with implications for the generation of plausible values in large-scale assessments. Journal of Educational and Behavioral Statistics, 41(1), 57–80. https://doi.org/10.3102/1076998615622221
   Web of Science ®Google Scholar
• Kyriazos, T. A. (2018). Applied psychometrics: Sample size and sample power considerations in factor analysis (EFA, CFA) and SEM in general. Psychology, 09(08), 2207–2230. https://doi.org/10.4236/psych.2018.98126
   Google Scholar
• Little, R. J. A., & Rubin, D. B. (2019). Statistical analysis with missing data (3rd ed.). Wiley.
   Google Scholar
• Little, T. D., Jorgensen, T. D., Lang, K. M., & Moore, E. W. G. (2014). On the joys of missing data. Journal of Pediatric Psychology, 39(2), 151–162. https://doi.org/10.1093/jpepsy/jst048
   PubMed Web of Science ®Google Scholar
• Little, T. D., & Rhemtulla, M. (2013). Planned missing data designs for developmental researchers. Child Development Perspectives, 7(4), 199–204. https://doi.org/10.1111/cdep.12043
   Web of Science ®Google Scholar
• Logan, J. A. R. (2020). Missing School-Based Data due to COVID-19: Some Guidelines. EdArXiv. https://doi.org/10.35542/osf.io/24bsu
   Google Scholar
• Lord, F. M. (1955). Estimation of parameters from incomplete data. Journal of the American Statistical Association, 50(271), 870–876. https://doi.org/10.1080/01621459.1955.10501972
   Web of Science ®Google Scholar
• Lord, F.M. (1980). Applications of item response theory to practical testing problems (1st ed.). Routledge. https://doi.org/10.4324/9780203056615
   Google Scholar
• McArdle, J. J. (1994). Structural factor analysis experiments with incomplete data. Multivariate Behavioral Research, 29(4), 409–454. https://doi.org/10.1207/s15327906mbr2904_5
   PubMed Web of Science ®Google Scholar
• McArdle, J. J., & Woodcock, R. W. (1997). Expanding test–retest designs to include developmental time-lag components. Psychological Methods, 2(4), 403–435. https://doi.org/10.1037/1082-989X.2.4.403
   Web of Science ®Google Scholar
• Mullis, I. V. S. (1990). The NAEP guide: A description of the content and methods of the 1990 and 1992 assessments (NAEP-21-TR-01).
   Google Scholar
• Muthén, L. K., & Muthén, B. O. (2002). How to use a Monte Carlo study to decide on sample size and determine power. Structural Equation Modeling: A Multidisciplinary Journal, 9(4), 599–620. https://doi.org/10.1207/S15328007SEM0904_8
   Web of Science ®Google Scholar
• Muthén, L. K., & Muthén, B. O. (2017). Mplus user’s guide (8th ed.) Muthén & Muthén.
   Google Scholar
• Nassiri, V., Lovik, A., Molenberghs, G., & Verbeke, G. (2018). On using multiple imputation for exploratory factor analysis of incomplete data. Behavior Research Methods, 50(2), 501–517. https://doi.org/10.3758/s13428-017-1013-4
   PubMed Web of Science ®Google Scholar
• National Academies of Sciences, Engineering, and Medicine. (2016). Reducing response burden in the American Community survey: Proceedings of a workshop (T. J. Plewes, Ed.; p. 23639). National Academies Press. https://doi.org/10.17226/23639
   Google Scholar
• National Center for Education Statistics. (1999). The NAEP guide (NCES 2000-456).
   Google Scholar
• National Research Council. (1988). Improving indicators of the quality of science and mathematics education in grades K-12. The National Academies Press. https://doi.org/10.17226/988
   Google Scholar
• Noble, S. M., Hollingworth, W., & Tilling, K. (2012). Missing data in trial-based cost-effectiveness analysis: The current state of play. Health Economics, 21(2), 187–200. https://doi.org/10.1002/hec.1693
   PubMed Web of Science ®Google Scholar
• Noble, D. W. A., & Nakagawa, S. (2021). Planned missing data designs and methods: Options for strengthening inference, increasing research efficiency and improving animal welfare in ecological and evolutionary research. Evolutionary Applications, 14(8), 1958–1968. https://doi.org/10.1111/eva.13273
   PubMed Web of Science ®Google Scholar
• R Core Team. (2020). R: A language and environment for statistical computing [Manual]. https://www.R-project.org/
   Google Scholar
• Raykov, T. (2011). On testability of missing data mechanisms in incomplete data sets. Structural Equation Modeling: A Multidisciplinary Journal, 18(3), 419–429. https://doi.org/10.1080/10705511.2011.582396
   Web of Science ®Google Scholar
• Rhemtulla, M., & Hancock, G. R. (2016). Planned missing data designs in educational psychology research. Educational Psychologist, 51(3–4), 305–316. https://doi.org/10.1080/00461520.2016.1208094
   Web of Science ®Google Scholar
• Rhemtulla, M., Savalei, V., & Little, T. D. (2016). On the asymptotic relative efficiency of planned missingness designs. Psychometrika, 81(1), 60–89. https://doi.org/10.1007/s11336-014-9422-0
   PubMed Web of Science ®Google Scholar
• Richardson, H. A., & Simmering, M. J. (2020). Missing data in research. In H. A. Richardson & M. J. Simmering, Oxford research encyclopedia of business and management. Oxford University Press. https://doi.org/10.1093/acrefore/9780190224851.013.226
   Google Scholar
• Rioux, C., Lewin, A., Odejimi, O. A., & Little, T. D. (2020). Reflection on modern methods: Planned missing data designs for epidemiological research. International Journal of Epidemiology, 49(5), 1702–1711. https://doi.org/10.1093/ije/dyaa042
   PubMed Web of Science ®Google Scholar
• Rubin, D. B. (1976). Inference and missing data. Biometrika, 63(3), 581–592. https://doi.org/10.1093/biomet/63.3.581
   Web of Science ®Google Scholar
• Rubin, D. B., & Szatrowski, T. H. (1982). Finding maximum likelihood estimates of patterned covariance matrices by the EM algorithm. Biometrika, 69(3), 657–660. https://doi.org/10.1093/biomet/69.3.657
   Web of Science ®Google Scholar
• Rubin, D. B., & Thayer, D. (1978). Relating tests given to different samples. Psychometrika, 43(1), 3–10. https://doi.org/10.1007/BF02294084
   Web of Science ®Google Scholar
• Satorra, A., & Saris, W. E. (1985). Power of the likelihood ratio tests in covariance structure analysis. Psychometrika, 50(1), 83–90. https://doi.org/10.1007/BF02294150
   Web of Science ®Google Scholar
• Schoemann, A. M., Miller, P., Pornprasertmanit, S., & Wu, W. (2014). Using Monte Carlo simulations to determine power and sample size for planned missing designs. International Journal of Behavioral Development, 38(5), 471–479. https://doi.org/10.1177/0165025413515169
   Web of Science ®Google Scholar
• Shoemaker, D. M. (1973). Principles and procedures of multiple matrix sampling. Ballinger.
   Google Scholar
• Shoemaker, D. M., & Knapp, T. R. (1974). A note on terminology and notation in matrix sampling. Journal of Educational Measurement, 11(1), 59–61. https://doi.org/10.1111/j.1745-3984.1974.tb00972.x
   Web of Science ®Google Scholar
• Sirotnik, K., & Wellington, R. (1977). Incidence sampling: An integrated theory for “matrix sampling”. Journal of Educational Measurement, 14(4), 343–399. https://doi.org/10.1111/j.1745-3984.1977.tb00050.x
   Web of Science ®Google Scholar
• Thayer, D. (1983). Maximum likelihood estimation of the joint covariance matrix for sections of tests given to distinct samples with application to test equating. Psychometrika, 48(2), 293–297. https://doi.org/10.1007/BF02294023
   Web of Science ®Google Scholar
• Thurstone, L. L. (1947). Multiple factor analysis. University of Chicago Press.
   Google Scholar