Handling Missing Values in Information Systems Research: A Review of Methods and Assumptions

Published Online:https://doi.org/10.1287/isre.2022.1104

References

  • Adjerid I, Peer E, Acquisti A (2018) Beyond the privacy paradox: Objective vs. relative risk in privacy decision making. MIS Quart. 42(2):465–488.CrossrefGoogle Scholar
  • Allison PD (2009) Missing data. Millsap RE, Maydeu-Olivares A, eds. The Sage Handbook of Quantitative Methods in Psychology (Sage, Thousand Oaks, CA), 72–89.CrossrefGoogle Scholar
  • Azen SP, van Guilder M, Hill MA (1989) Estimation of parameters and missing values under a regression model with non–normally distributed and non–randomly incomplete data. Statist. Medicine 8(2):217–228.CrossrefGoogle Scholar
  • Baird A, Davidson E, Mathiassen L (2017) Reflective technology assimilation: Facilitating electronic health record assimilation in small physician practices. J. Management Inform. Systems 34(3):664–694.CrossrefGoogle Scholar
  • Ballou D, Madnick S, Wang R (2003) Assuring information quality. J. Management Inform. Systems 20(3):9–11.Google Scholar
  • Booth JG, Hobert JP (1999) Maximizing generalized linear mixed model likelihoods with an automated Monte Carlo EM algorithm. J. Royal Statist. Soc. Ser. B. Statist. Methodology 61(1):265–285.CrossrefGoogle Scholar
  • Brick JM, Kalton G (1996) Handling missing data in survey research. Statist. Methods Medical Res. 5(3):215–238.CrossrefGoogle Scholar
  • Burton-Jones A, Boh WF, Oborn E, Padmanabhan B (2021) Advancing research transparency at MIS Quarterly: A pluralistic approach. MIS Quart. 45(2):iii–xviii.Google Scholar
  • Cappiello C, Francalanci C, Pernici B (2003) Time-related factors of data quality in multichannel information systems. J. Management Inform. Systems 20(3):71–92.CrossrefGoogle Scholar
  • Carpenter JR, Kenward MG (2007) Missing Data in Randomised Controlled Trials: A Practical Guide. (Health Technology Assessment Methodology Programme, Birmingham, UK).Google Scholar
  • Chen H, Chiang RH, Storey VC (2012) Business intelligence and analytics: From big data to big impact. MIS Quart. 36(4):1165–1188.CrossrefGoogle Scholar
  • Chen S, Miao B, Shevlin T (2015) A new measure of disclosure quality: The level of disaggregation of accounting data in annual reports. J. Accounting Res. 53(5):1017–1054.CrossrefGoogle Scholar
  • Chiang RHL, Grover V, Liang T-P, Zhang DS (2018) Strategic value of big data and business analytics. J. Management Inform. Systems 35(2):383–387.CrossrefGoogle Scholar
  • Dempster AP, Laird NM, Rubin DB (1977) Maximum likelihood from incomplete data via the EM algorithm. J. Royal Statist. Soc. Ser. B. Statist. Methodology 39(1):1–38.Google Scholar
  • Dinev T, McConnell AR, Smith HJ (2015) Informing privacy research through information systems, psychology, and behavioral economics: Thinking outside the “APCO” box. Inform. Systems Res. 26(4):639–655.LinkGoogle Scholar
  • Downey RG, King CV (1998) Missing data in Likert ratings: A comparison of replacement methods. J. General Psych. 125(2):175–191.CrossrefGoogle Scholar
  • Enders CK (2001) The impact of nonnormality on full information maximum-likelihood estimation for structural equation models with missing data. Psych. Methods 6(4):352–370.CrossrefGoogle Scholar
  • Enders CK (2011) Missing not at random models for latent growth curve analyses. Psych. Methods 16(1):1–16.CrossrefGoogle Scholar
  • Galimard JE, Chevret S, Curis E, Resche-Rigon M (2018) Heckman imputation models for binary or continuous MNAR outcomes and MAR predictors. BMC Medical Res. Methodology 18:90.CrossrefGoogle Scholar
  • Garg P (2013) Robustness of multiple imputation under missing at random (MAR) mechanism: A simulation study. Unpublished doctoral dissertation, Georgia Southern University.Google Scholar
  • Glynn RJ, Laird NM, Rubin DB (1993) Multiple imputation in mixture models for nonignorable nonresponse with follow-ups. J. Amer. Statist. Assoc. 88(423):984–993.CrossrefGoogle Scholar
  • Grover V, Chiang RH, Liang T-P, Zhang D (2018) Creating strategic business value from big data analytics: A research framework. J. Management Inform. Systems 35(2):388–423.CrossrefGoogle Scholar
  • Hall BL, Hirbe M, Yan Y, Khuri SF, Henderson WG, Hamilton BH (2007) Thyroid and parathyroid operations in Veterans Affairs and selected university medical centers: Results of the patient safety in surgery study. J. Amer. College Surgeons 204(6):1222–1234.CrossrefGoogle Scholar
  • Havakhor T, Sabherwal R, Steelman ZR, Sabherwal S (2019) Relationships between information technology and other investments: A contingent interaction model. Inform. Systems Res. 30(1):291–305.LinkGoogle Scholar
  • Heckman JJ (1979) Sample selection bias as a specification error. Econometrica 47(1):153–161.CrossrefGoogle Scholar
  • Honaker J, King G (2010) What to do about missing values in time series cross section data. Amer. J. Political Sci. 54(2):561–581.CrossrefGoogle Scholar
  • Hu N, Pavlou PA, Zhang J (2017) On self-selection biases in online product reviews. MIS Quart. 41(2):449–472.CrossrefGoogle Scholar
  • Ibrahim JG, Chen M-H, Lipsitz SR (2001) Missing responses in generalised linear mixed models when the missing data mechanism is nonignorable. Biometrika 88(2):551–564.CrossrefGoogle Scholar
  • Jöreskog K, Sörbom D (2007) Lisrel. V. 8.80. Scientific Software International, Chicago.Google Scholar
  • Kanat I, Hong YL, Raghu TS (2018) Surviving in global online labor markets for IT services: A geo-economic analysis. Inform. Systems Res. 29(4):893–909.LinkGoogle Scholar
  • Karanja E, Zaveri J, Ahmed A (2013) How do MIS researchers handle missing data in survey-based research: A content analysis approach. Internat. J. Inform. Management 33(5):734–751.CrossrefGoogle Scholar
  • King G, Zeng L (2001) Logistic regression in rare events data. Political Anal. 9(2):137–163.CrossrefGoogle Scholar
  • Koh P-S, Reeb DM (2015) Missing R&D. J. Accounting Econom. 60(1):73–94.CrossrefGoogle Scholar
  • Li X-B (2009) A Bayesian approach for estimating and replacing missing categorical data. J. Data Inform. Quality 1(1):1–11.CrossrefGoogle Scholar
  • Little RJ (1988) A test of missing completely at random for multivariate data with missing values. J. Amer. Statist. Assoc. 83(404):1198–1202.CrossrefGoogle Scholar
  • Little RJ (1992) Regression with missing X’s: A review. J. Amer. Statist. Assoc. 87(420):1227–1237.Google Scholar
  • Little RJ (1995) Modeling the drop-out mechanism in repeated-measures studies. J. Amer. Statist. Assoc. 90(431):1112–1121.CrossrefGoogle Scholar
  • Little RJ, Rubin DB (1989) The analysis of social science data with missing values. Sociol. Methods Res. 18(2-3):292–326.CrossrefGoogle Scholar
  • Little RJ, Rubin DB (2019) Statistical Analysis with Missing Data (Wiley, Hoboken, NJ).Google Scholar
  • Marlin BM, Zemel RS, Roweis S, Slaney M (2007) Collaborative filtering and the missing at random assumption. Proc. 23rd Conf. Uncertainty Artificial Intelligence, Washington, DC.Google Scholar
  • Meng X-L, Rubin DB (1991) Using EM to obtain asymptotic variance-covariance matrices: The SEM algorithm. J. Amer. Statist. Assoc. 86(416):899–909.CrossrefGoogle Scholar
  • Miao W, Ding P, Geng Z (2016) Identifiability of normal and normal mixture models with nonignorable missing data. J. Amer. Statist. Assoc. 111(516):1673–1683.CrossrefGoogle Scholar
  • Neath RC (2013) On Convergence Properties of the Monte Carlo EM Algorithm. Jones G, Shen X, eds. Modern Statistical Theory and Applications: A Festschrift in Honor of Morris L. Eaton (Institute of Mathematical Statistics, Beachwood, OH), 43–62.CrossrefGoogle Scholar
  • Newman DA (2003) Longitudinal modeling with randomly and systematically missing data: A simulation of ad hoc, maximum likelihood, and multiple imputation techniques. Organ. Res. Methods 6(3):328–362.CrossrefGoogle Scholar
  • Newman DA (2014) Missing data: Five practical guidelines. Organ. Res. Methods 17(4):372–411.CrossrefGoogle Scholar
  • Pepinsky TB (2018) A note on listwise deletion vs. multiple imputation. Political Anal. 26(4):480–488.CrossrefGoogle Scholar
  • Roth PL, Switzer FS III, Switzer DM (1999) Missing data in multiple item scales: A Monte Carlo analysis of missing data techniques. Organ. Res. Methods 2(3):211–232.CrossrefGoogle Scholar
  • Rotnitzky A, Robins JM, Scharfstein DO (1998) Semiparametric regression for repeated outcomes with nonignorable nonresponse. J. Amer. Statist. Assoc. 93(444):1321–1339.CrossrefGoogle Scholar
  • Rubin DB (1976) Inference and missing data. Biometrika 63(3):581–592.CrossrefGoogle Scholar
  • Rubin DB (1987) Multiple Imputation for Nonresponse in Surveys (Wiley, New York).CrossrefGoogle Scholar
  • Rubin DB (1996) Multiple imputation after 18+ years. J. Amer. Statist. Assoc. 91(434):473–489.CrossrefGoogle Scholar
  • Schafer JL (1997) Analysis of Incomplete Multivariate Data (CRC Press, New York).CrossrefGoogle Scholar
  • Schafer JL (1999) Multiple imputation: A primer. Statist. Methods Medical Res. 8(1):3–15.CrossrefGoogle Scholar
  • Schafer JL, Graham JW (2002) Missing data: Our view of the state of the art. Psych. Methods 7(2):147–177.CrossrefGoogle Scholar
  • Schimert J, Schafer J, Hesterberg T, Fraley C, Clarkson D (2001) Analyzing Data with Missing Values in S-PLUS (Insightful Corporation, Seattle).Google Scholar
  • Schlomer GL, Bauman S, Card NA (2010) Best practices for missing data management in counseling psychology. J. Counseling Psych. 57(1):1–10.CrossrefGoogle Scholar
  • Shmueli G, Koppius OR (2011) Predictive analytics in information systems research. MIS Quart. 35(3):553–572.CrossrefGoogle Scholar
  • StataCorp (2013) Stata Multiple-Imputation Reference Manual, v. 13 (StataCorp LP, College Station, TX)Google Scholar
  • Sung YJ, Geyer CJ (2007) Monte Carlo likelihood inference for missing data models. Ann. Statist. 35(3):990–1011.CrossrefGoogle Scholar
  • Tsikriktsis N (2005) A review of techniques for treating missing data in OM survey research. J. Oper. Management 24(1):53–62.CrossrefGoogle Scholar
  • Wei GCG, Tanner MA (1990) A Monte Carlo implementation of the EM algorithm and the poor man’s data augmentation algorithms. J. Amer. Statist. Assoc. 85(411):699–704.CrossrefGoogle Scholar
  • White IR, Carlin JB (2010) Bias and efficiency of multiple imputation compared with complete-case analysis for missing covariate values. Statist. Med. 29(28):2920–2931.CrossrefGoogle Scholar
  • Wooldridge JM (2015) Introductory Econometrics: A Modern Approach, 6th ed. (Cengage Learning, Boston).Google Scholar
  • Ying Y, Feinberg F, Wedel M (2006) Leveraging missing ratings to improve online recommendation systems. J. Marketing Res. 43(3):355–365.CrossrefGoogle Scholar
  • Yuan YC (2010) Multiple Imputation for Missing Data: Concepts and New Development, v. 9.0 (SAS Institute Inc., Rockville, MD).Google Scholar
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