90
Views
0
CrossRef citations to date
0
Altmetric
Teacher's Corner

Handling Measurement Error and Omitted Confounders Considering Informativeness of the Confounding Effect under Mediation Modeling

Received 01 Apr 2023, Accepted 09 Oct 2023, Published online: 10 Apr 2024

References

  • Albert, J. M. (2008). Mediation analysisvia potential outcomes models. Statistics in Medicine, 27, 1282–1304. https://doi.org/10.1002/sim.3016
  • Albert, J. M., Geng, C., & Nelson, S. (2016). Causal mediation analysis with a latent mediator. Biometrical Journal. Biometrische Zeitschrift, 58, 535–548. https://doi.org/10.1002/bimj.201400124
  • Asparouhov, T., & Muthén, B. (2010). Bayesian analysis using mplus: Technical implementation. Tech. Rep.
  • Bader, M., Jobst, L. J., & Moshagen, M. (2022). Sample size requirements for bifactor models. Structural Equation Modeling, 29, 772–783. https://doi.org/10.1080/10705511.2021.2019587
  • Baron, R. M., & Kenny, D. A. (1986). The moderator-mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51, 1173–1182. https://doi.org/10.1037//0022-3514.51.6.1173
  • Bollen, K. A. (1989). Structural equations with latent variables. Wiley.
  • Bradley, J. V. (1978). Robustness? British Journal of Mathematical and Statistical Psychology, 31, 144–152. https://doi.org/10.1111/j.2044-8317.1978.tb00581.x
  • Brand, J., Moore, R., Song, X., & Xie, Y. (2019). Why does parental divorce lower children’s educational attainment? a causal mediation analysis. Sociological Science, 6, 264–292. https://doi.org/10.15195/v6.a11
  • Chen, C., Finne, E., Kopp, A., & Jekauc, D. (2020). Can positive affective variables mediate intervention effects on physical activity? a systematic review and meta-analysis. Frontiers in Psychology, 11, 587757. https://doi.org/10.3389/fpsyg.2020.587757
  • Chen, F. F., West, S. G., & Sousa, K. H. (2006). A comparison of bifactor and second-order models of quality of life. Multivariate Behavioral Research, 41, 189–225. https://doi.org/10.1207/s15327906mbr4102_5
  • Cheng, C., Spiegelman, D., & Li, F. (2023). Mediation analysis in the presence of continuous exposure measurement error. Statistics in Medicine, 42, 1669–1686. https://doi.org/10.1002/sim.9693
  • Cheung, M. W. (2007). Comparison of approaches to constructing confidence intervals for mediating effects using structural equation models. Structural Equation Modeling, 14, 227–246. https://doi.org/10.1080/10705510709336745
  • Cole, D. A., & Preacher, K. J. (2014). Manifest variable path analysis: potentially serious and misleading consequences due to uncorrected measurement error. Psychological Methods, 19, 300–315. https://doi.org/10.1037/a0033805
  • Cox, M. G., Kisbu-Sakarya, Y., Miočević, M., & MacKinnon, D. P. (2013). Sensitivity plots for confounder bias in the single mediator model. Evaluation Review, 37, 405–431. https://doi.org/10.1177/0193841X14524576
  • DeMars, C. E. (2006). Application of the bi-factor multidimensional item response theory model to testlet-based tests. Journal of Educational Measurement, 43, 145–168. https://doi.org/10.1111/j.1745-3984.2006.00010.x
  • Depaoli, S. (2021). Bayesian structural equation modeling. GUILFORD PUBN.
  • Derogatis, L. R., Lipman, R. S., Rickels, K., Uhlenhuth, E. H., & Covi, L. (1974). The hopkins symptom checklist (HSCL): A self-report symptom inventory. Behavioral Science, 19, 1–15. https://doi.org/10.1002/bs.3830190102
  • Emsley, R., Dunn, G., & White, I. R. (2010). Mediation and moderation of treatment effects in randomised controlled trials of complex interventions. Statistical Methods in Medical Research, 19, 237–270. https://doi.org/10.1177/0962280209105014
  • Forster, D. E., Billingsley, J., Burnette, J. L., Lieberman, D., Ohtsubo, Y., & McCullough, M. E. (2021). Experimental evidence that apologies promote forgiveness by communicating relationship value. Scientific Reports, 11, 13107. https://doi.org/10.1038/s41598-021-92373-y
  • Fritz, M. S., Kenny, D. A., & MacKinnon, D. P. (2016). The combined effects of measurement error and omitting confounders in the single-mediator model. Multivariate Behavioral Research, 51, 681–697. https://doi.org/10.1080/00273171.2016.1224154
  • Fukuhara, H., & Kamata, A. (2011). A bifactor multidimensional item response theory model for differential item functioning analysis on testlet-based items. Applied Psychological Measurement, 35, 604–622. https://doi.org/10.1177/0146621611428447
  • Fung, J., Kim, J. J., Jin, J., Chen, G., Bear, L., & Lau, A. S. (2019). A randomized trial evaluating school-based mindfulness intervention for ethnic minority youth: Exploring mediators and moderators of intervention effects. Journal of Abnormal Child Psychology, 47, 1–19. https://doi.org/10.1007/s10802-018-0425-7
  • Gallop, R., Small, D. S., Lin, J. Y., Elliott, M. R., Joffe, M., & Have, T. R. T. (2009). Mediation analysis with principal stratification. Statistics in Medicine, 28, 1108–1130. https://doi.org/10.1002/sim.3533
  • Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2013). Bayesian data analysis (3rd ed.). Chapman & Hall/CRC.
  • Geramipour, M. (2021). Rasch testlet model and bifactor analysis: how do they assess the dimensionality of large-scale iranian efl reading comprehension tests? Language Testing in Asia, 11, 1–23. https://doi.org/10.1186/s40468-021-00118-5
  • Gibbons, R. D., & Hedeker, D. R. (1992). Full-information item bi-factor analysis. Psychometrika, 57, 423–436. https://doi.org/10.1007/BF02295430
  • Gonzalez, O., & MacKinnon, D. P. (2018). A bifactor approach to model multifaceted constructs in statistical mediation analysis. Educational and Psychological Measurement, 78, 5–31. https://doi.org/10.1177/0013164416673689
  • Gonzalez, O., & MacKinnon, D. P. (2021). The measurement of the mediator and its influence on statistical mediation conclusions. Psychological Methods, 26, 1–17. https://doi.org/10.1037/met0000263
  • Gunnell, K. E., & Gaudreau, P. (2015). Testing a bi-factor model to disentangle general and specific factors of motivation in self-determination theory. Personality and Individual Differences, 81, 35–40. https://doi.org/10.1016/j.paid.2014.12.059
  • Hafeman, D. M. (2011). Confounding of indirect effects: A sensitivity analysis exploring the range of bias due to a cause common to both the mediator and the outcome. American Journal of Epidemiology, 174, 710–717. https://doi.org/10.1093/aje/kwr173
  • Hand, E. D., & Lonigan, C. J. (2022). Examining the relations between preschooler’s externalizing behaviors and academic performance using an s-1 bifactor model. Research on Child and Adolescent Psychopathology, 50, 577–589. https://doi.org/10.1007/s10802-021-00861-6
  • Hastings, W. K. (1970). Monte carlo sampling methods using markov chains and their applications. Biometrika, 57, 97–109. https://doi.org/10.1093/biomet/57.1.97
  • Hu, L., & Bentler, P. M. (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
  • Huh, D., Li, X., Zhou, Z., Walters, S. T., Baldwin, S. A., Tan, Z., Larimer, M. E., & Mun, E.-Y. (2022). A structural equation modeling approach to meta-analytic mediation analysis using individual participant data: Testing protective behavioral strategies as a mediator of brief motivational intervention effects on alcohol-related problems. Prevention Science, 23, 390–402. https://doi.org/10.1007/s11121-021-01318-4
  • Imai, K., Keele, L., & Tingley, D. (2010). A general approach to causal mediation analysis. Psychological Methods, 15, 309–334. https://doi.org/10.1037/a0020761
  • Imai, K., Keele, L., & Yamamoto, T. (2010). Identification, inference, and sensitivity analysis for causal mediation effects. Statistical Science, 25, 51–71. https://doi.org/10.1214/10-STS321
  • Jo, B. (2008). Causal inference in randomized experiments with mediational processes. Psychological Methods, 13, 314–336. https://doi.org/10.1037/a0014207
  • Kang, H.-A., Han, S., Kim, D., & Kao, S.-C. (2022). Polytomous testlet response models for technology-enhanced innovative items: Implications on model fit and trait inference. Educational and Psychological Measurement, 82, 811–838. https://doi.org/10.1177/00131644211032261
  • Kaplan, R. M., & Saccuzzo, D. P. (2009). Psychological testing: Principles, applications, and issues (7th ed.). Thompson Wadsworth.
  • Kenny, D. A., & Judd, C. M. (1984). Estimating the nonlinear and interactive effects of latent variables. Psychological Bulletin, 96, 201–210. https://doi.org/10.1037/0033-2909.96.1.201
  • Lauriola, M., & Iani, L. (2017). Personality, positivity and happiness: A mediation analysis using a bifactor model. Journal of Happiness Studies, 18, 1659–1682. https://doi.org/10.1007/s10902-016-9792-3
  • Le Cessie, S., Debeij, J., Rosendaal, F. R., Cannegieter, S. C., & Vandenbroucke, J. P. (2012). Quantification of bias in direct effects estimates due to different types of measurement error in the mediator. Epidemiology (Cambridge, Mass.), 23, 551–560. https://doi.org/10.1097/EDE.0b013e318254f5de
  • Lin, Q., Nuttall, A. K., Zhang, Q., & Frank, K. A. (2023). How do unobserved confounding mediators and measurement error impact estimated mediation effects and corresponding statistical inferences? introducing the r package ConMed for sensitivity analysis. Psychological Methods, 28, 339–358. https://doi.org/10.1037/met0000567
  • Little, T. D. (2013). Longitudinal structural equation modeling. Guilford.
  • Liu, X., & Wang, L. (2021). The impact of measurement error and omitting confounders on statistical inference of mediation effects and tools for sensitivity analysis. Psychological Methods, 26, 327–342. https://doi.org/10.1037/met0000345
  • MacKinnon, D. P. (2000). Contrasts in multiple mediator models. In J. S. Rose, L. Chassin, C. C. Presson, & S. J. Sherman (Eds.), Multivariate applications in substance use research: New methods for new questions (pp. 141–160). Lawrence Erlbaum Associates Publishers.
  • MacKinnon, D. P. (2008). Introduction to statistical mediation analysis. Taylor & Francis.
  • Marsh, H. W., Hau, K. T., Wen, Z., Nagengast, B., & Morin, A. J. S. (2013). Moderation. In The oxford handbook of quantitative methods (vol 2): Statistical analysis (p. 361–386). Oxford University Press.
  • Mauro, R. (1990). Understanding l.o.v.e. (left out variables error): A method for estimating the effects of omitted variables. Psychological Bulletin, 108, 314–329. https://doi.org/10.1037/0033-2909.108.2.314
  • McDonald, R. P. (1999). Test theory: A unified treatment. Erlbaum.
  • Merkle, E., Fitzsimmons, E., Uanhoro, J., & Goodrich, B. (2020). Efficient bayesian structural equation modeling in stan. arxiv. arXiv preprint arXiv:2008.07733.
  • Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H., & Teller, E. (1953). Equation of state calculations by fast computing machines. The Journal of Chemical Physics, 21, 1087–1092. https://doi.org/10.1063/1.1699114
  • Miočević, M., Levy, R., & MacKinnon, D. P. (2021). Different roles of prior distributions in the single mediator model with latent variables. Multivariate Behavioral Research, 56, 20–40. https://doi.org/10.1080/00273171.2019.1709405
  • Morse, G. A., Calsyn, R. J., Allen, G., & Kenny, D. A. (1994). Helping homeless mentally ill people: What variables mediate and moderate program effects? American Journal of Community Psychology, 22, 661–683. https://doi.org/10.1007/BF02506898
  • Muthén, B., & Asparouhov, T. (2012). Bayesian structural equation modeling: A more flexible representation of substantive theory. Psychological Methods, 17, 313–335. https://doi.org/10.1037/a0026802
  • Nunnally, J. C. (1978). Psychometric theory (2nd ed.). McGraw-Hill.
  • Olatunji, B. O., Ebesutani, C., & Kim, E. H. (2015). Examination of a bifactor model of the three domains of disgust scale: Specificity in relation to obsessive-compulsive symptoms. Psychological Assessment, 27, 102–113. https://doi.org/10.1037/pas0000039
  • Pearl, J. (2001). Direct and indirect effects. In Proceedings of the seventeenth conference on uncertainty in artificial intelligence (p. 411–420). Morgan Kaufmann Publishers Inc.
  • Preacher, K. J. (2015). Advances in mediation analysis: A survey and synthesis of new developments. Annual Review of Psychology, 66, 825–852. https://doi.org/10.1146/annurev-psych-010814-015258
  • Preacher, K. J., & Hayes, A. F. (2008). Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behavior Research Methods, 40, 879–891. https://doi.org/10.3758/brm.40.3.879
  • Ranby, K. W., Aiken, L. S., MacKinnon, D. P., Elliot, D. L., Moe, E. L., McGinnis, W., & Goldberg, L. (2009). A mediation analysis of the ATHENA intervention for female athletes: Prevention of athletic-enhancing substance use and unhealthy weight loss behaviors. Journal of Pediatric Psychology, 34, 1069–1083. https://doi.org/10.1093/jpepsy/jsp025
  • Robert, C. P., & Casella, G. (2004). Monte carlo statistical methods (vol. 2). Springer.
  • Robins, J. (1986). A new approach to causal inference in mortality studies with a sustained exposure period—application to control of the healthy worker survivor effect. Mathematical Modelling, 7, 1393–1512. https://doi.org/10.1016/0270-0255(86)90088-6
  • Rowe, R., Roman, G. D., McKenna, F. P., Barker, E., & Poulter, D. (2015). Measuring errors and violations on the road: A bifactor modeling approach to the driver behavior questionnaire. Accident; Analysis and Prevention, 74, 118–125. https://doi.org/10.1016/j.aap.2014.10.012
  • Ryu, E. (2015). Multiple-group analysis approach to testing group difference in indirect effects. Behavior Research Methods, 47, 484–493. https://doi.org/10.3758/s13428-014-0485-8
  • Satorra, A., & Bentler, P. M. (1994). Corrections to test statistics and standard errors in covariance structure analysis. In A. von Eye & C. C. Clogg (Eds.), Latent variables analysis: Applications for developmental research (pp. 399–419). Sage.
  • Schafer, J. L., & Kang, J. (2008). Average causal effects from nonrandomized studies: A practical guide and simulated example. Psychological Methods, 13, 279–313. https://doi.org/10.1037/a0014268
  • Scopel Hoffmann, M., Moore, T. M., Kvitko Axelrud, L., Tottenham, N., Zuo, X.-N., Rohde, L. A., Milham, M. P., Satterthwaite, T. D., & Salum, G. A. (2022). Reliability and validity of bifactor models of dimensional psychopathology in youth. Journal of Psychopathology and Clinical Science, 131, 407–421. https://doi.org/10.1037/abn0000749
  • Shrout, P. E., & Bolger, N. (2002). Mediation in experimental and nonexperimental studies: new procedures and recommendations. Psychological Methods, 7, 422–445.
  • Sobel, M. E. (1982). Asymptotic confidence intervals for indirect effects in structural equation models. Sociological Methodology, 13, 290–312. https://doi.org/10.2307/270723
  • Soysal, S., & Koğar, E. Y. (2022). Item parameter recovery via traditional 2pl, testlet and bi-factor models for testlet-based tests. International Journal of Assessment Tools in Education, 9, 254–266. https://doi.org/10.21449/ijate.948182
  • Spiegelman, D., Rosner, B., & Logan, R. (2000). Estimation and inference for logistic regression with covariate misclassification and measurement error in main study/validation study designs. Journal of the American Statistical Association, 95, 51–61. https://doi.org/10.1080/01621459.2000.10473898
  • Stagaki, M., Nolte, T., Feigenbaum, J., King-Casas, B., Lohrenz, T., Fonagy, P., … Montague, P. R, Personality and Mood Disorder Research Consortium. (2022). The mediating role of attachment and mentalising in the relationship between childhood maltreatment, self-harm and suicidality. Child Abuse & Neglect, 128, 105576. https://doi.org/10.1016/j.chiabu.2022.105576
  • Ten Have, T. R., & Joffe, M. M. (2012). A review of causal estimation of effects in mediation analyses. Statistical Methods in Medical Research, 21, 77–107. https://doi.org/10.1177/0962280210391076
  • Tofighi, D., Hsiao, Y.-Y., Kruger, E. S., MacKinnon, D. P., Horn, M. L. V., & Witkiewitz, K. (2019). Sensitivity analysis of the no-omitted confounder assumption in latent growth curve mediation models. Structural Equation Modeling, 26, 94–109. https://doi.org/10.1080/10705511.2018.1506925
  • Valeri, L., & VanderWeele, T. J. (2013). Mediation analysis allowing for exposure–mediator interactions and causal interpretation: Theoretical assumptions and implementation with SAS and SPSS macros. Psychological Methods, 18, 137–150. https://doi.org/10.1037/a0031034
  • van Erp, S., Mulder, J., & Oberski, D. L. (2018). Prior sensitivity analysis in default bayesian structural equation modeling. Psychological Methods, 23, 363–388. https://doi.org/10.1037/met0000162
  • VanderWeele, T. J. (2009). Marginal structural models for the estimation of direct and indirect effects. Epidemiology (Cambridge, Mass.), 20, 18–26. https://doi.org/10.1097/EDE.0b013e31818f69ce
  • VanderWeele, T. J., & Robinson, W. R. (2014). On the causal interpretation of race in regressions adjusting for confounding and mediating variables. Epidemiology (Cambridge, Mass.), 25, 473–484. https://doi.org/10.1097/EDE.0000000000000105
  • VanderWeele, T. J., & Vansteelandt, S. (2009). Conceptual issues concerning mediation, interventions and composition. Statistics and Its Interface, 2, 457–468. https://doi.org/10.4310/SII.2009.v2.n4.a7
  • VanderWeele, T. J., Valeri, L., & Ogburn, E. L. (2012). The role of measurement error and misclassification in mediation analysis. Epidemiology (Cambridge, Mass.), 23, 561–564. https://doi.org/10.1097/EDE.0b013e318258f5e4
  • VanderWeele, T., & Vansteelandt, S. (2014). Mediation analysis with multiple mediators. Epidemiologic Methods, 2, 95–115. https://doi.org/10.1515/em-2012-0010
  • Vinokur, A. D., & Schul, Y. (1997). Mastery and inoculation against setbacks as active ingredients in the JOBS intervention for the unemployed. Journal of Consulting and Clinical Psychology, 65, 867–877. https://doi.org/10.1037//0022-006x.65.5.867
  • Vinokur, A. D., Price, R. H., & Schul, Y. (1995). Impact of the JOBS intervention on unemployed workers varying in risk for depression. American Journal of Community Psychology, 23, 39–74. https://doi.org/10.1007/BF02506922
  • Wang, C., Guo, J., Zhou, X., Shen, Y., & You, J. (2023). The dark triad traits and suicidal ideation in chinese adolescents: Mediation by social alienation. Journal of Research in Personality, 102, 104332. https://doi.org/10.1016/j.jrp.2022.104332
  • Wang, L., & Preacher, K. J. (2015). Moderated mediation analysis using Bayesian methods. Structural Equation Modeling: A Multidisciplinary Journal, 22, 249–263. https://doi.org/10.1080/10705511.2014.935256
  • Warner, E., Nannarone, M., Manuel, D., Lashewicz, B., Patten, S., Schmitz, N., & Wang, J. (2021). Self-help behaviors partially mediate the relationship between personalized depression risk disclosure and psychological distress: A mediation analysis using data from a randomized controlled trial. Journal of Psychiatric Research, 140, 7–14. https://doi.org/10.1016/j.jpsychires.2021.05.047
  • Weierich, M. R., & Nock, M. K. (2008). Posttraumatic stress symptoms mediate the relation between childhood sexual abuse and nonsuicidal self-injury. Journal of Consulting and Clinical Psychology, 76, 39–44. https://doi.org/10.1037/0022-006X.76.1.39
  • Xia, F., & Chan, K. C. G. (2023). Identification, semiparametric efficiency, and quadruply robust estimation in mediation analysis with treatment-induced confounding. Journal of the American Statistical Association, 118, 1272–1281. https://doi.org/10.1080/01621459.2021.1990765
  • Yeo, Z. Z., & Suárez, L. (2022). Validation of the mental health continuum-short form: The bifactor model of emotional, social, and psychological well-being. PloS One, 17, e0268232. https://doi.org/10.1371/journal.pone.0268232
  • Yuan, Y., & MacKinnon, D. P. (2009). Bayesian mediation analysis. Psychological Methods, 14, 301–322. https://doi.org/10.1037/a0016972
  • Zhang, Q., & Yang, Y. (2020). Autoregressive mediation models using composite scores and latent variables: Comparisons and recommendations. Psychological Methods, 25, 472–495. https://doi.org/10.1037/met0000251
  • Zwaanswijk, W., Veen, V. C., & Vedder, P. (2017). The youth psychopathic traits inventory: a bifactor model, dimensionality, and measurement invariance. Assessment, 24, 932–944. https://doi.org/10.1177/1073191116632340

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.