Abstract
In an age of telemarketers, spam emails, and pop-up advertisements, sociologists are finding it increasingly difficult to achieve high response rates for their surveys. Compounding these issues, the current political and social climate has decreased many survey respondents’ likelihood of responding to controversial questions, which are often at the heart of much research in the discipline. Here we discuss such implications for survey research in sociology using: a content analysis of the prevalence of missing data and survey research methods in the most cited articles in top sociology journals, a case study highlighting the extraction of meaningful information through an example of potential mechanisms driving the non-random missing data patterns in the Religion Among Academic Scientists dataset, and qualitative responses from non-responders in this same case. Implications are likely to increase in importance given the ubiquitous nature of survey research, missing data, and privacy concerns in sociological research.
Similar content being viewed by others
Notes
Harzig, A.W. 2011. Publish or Perish, version #3, available at www.harzing.com/pop.htm
In order to ensure an equal coverage across the three journals, the top 35 articles since 2000 were identified and downloaded for further examination of data collection techniques and handling of any noted missing data. We further standardized the selection process by total cites per year so not to give more weight to articles published earlier in the decade based on total cites. We do understand that there is still a lag in regards to publication and citation timing, but believe that we have collected a representative sample of the most visible sociology publications over the past decade.
After the RAAS study began, the “Top American Research Universities” project moved to Arizona State University. See http://mup.asu.edu/, accessed April 17, 2009.
Psyc 17, conducted January 3, 2006.
This individual did not participate in the survey.
See Ladd and Lipset, “The Politics of Academic Natural Scientists and Engineers.”
The 1998 GSS had 2,832 respondents, although only half of the sample was asked the expanded set of religion and spirituality questions. The 2004 GSS had 2,812 respondents. Where possible, we used data from the GSS 2006 for the comparisons of scientists with the general population. See Davis et al. (2007).
References
Abraham, K., Maitland, A., & Bianchi, S. (2006). Nonresponse in the American time use survey: who is missing from the data and how much does it matter? Public Opinion Quarterly, 70(5), 676–703.
Allison, P. (2002). Missing data (Sage quantitative applications in the social sciences series). Sage Publishing.
Alosh, M. (2009). The impact of missing data in a generalized integer-valued autoregression model for count data. Journal of Biopharmaceutical Statistics, 19, 1039–1054.
Babbie, E. (2007). Practice of social research (11th ed.). Thomson: Wadsworth Publishing.
Davis, J. A., Smith, T. W., Marsden, P. V. (2007). General social surveys, 1972–2006 [Cumulative File]. ICPSR Study Number 4697.
Dawid, A. P. (1984). Statistical theory: the prequential approach (with discussion). Journal of the Royal Statistical Society A, 147, 278–292.
DeSouza, C. M., Legedza, A. T. R., & Sankoh, A. J. (2009). An overview of practical approaches for handling missing data in clinical trials. Journal of Biopharmaceutical Statistics, 19, 1055–1073.
Ecklund, E. H. (2010). Science Vs. religion: What scientists really think. New York: Oxford University Press.
Ecklund, E. H., & Scheitle, C. (2007). Religion among academic scientists: distinctions, disciplines, and demographics. Social Problems, 54(2), 289–307.
Ecklund, E. H., Park, J. Z., & Veliz, P. T. (2008). Secularization and religious change among elite scientists: a cross-cohort comparison. Social Forces, 86(4), 1805–1840.
Edgell, P., Gerteis, J., & Hartmann, D. (2006). Atheists as ‘other’: moral boundaries and cultural membership in American Society. American Sociological Review, 71, 211–234.
Evans, J. H., & Evans, M. S. (2008). Religion and science: beyond the epistemological conflict narrative. Annual Review of Sociology, 34, 87–105.
Garcia, R. I., Ibrahim, J. G., & Zhu, H. (2010). Variable selection in the cox regression model with covariates missing at random. Biometrics, 66, 97–104.
Gelman, A., Meng, X. L., & Stern, H. S. (1996). Posterior predictive assessment of model fitness via realized discrepancies (with discussion). Statistica Sinica, 6, 733–807.
Gelman, A., Van Mechelen, I., Verbeke, G., Heitjan, D. F., & Meulders, M. (2005). Multiple imputation for model checking: completed data plots with missing and latent data. Biometrics, 61, 74–85.
Griliches, Z. (1986). Comment on Behrman and Taubman. Journal of Labor Economics, 4(3), S146–S150.
Groves, R. (2006). Nonresponse rates and nonresponse bias in household surveys. Public Opinion Quarterly, 70(5), 646–675.
Groves, R., Couper, M., Presser, S., Singer, E., Tourangeau, R., Piani, G., & Nelson, L. (2006). Experiments in producing nonresponse bias. Public Opinion Quarterly, 70(5), 646–675.
Haung, R., Liang, Y., & Carrierre, K. C. (2005). The role of proxy information in missing data analysis. Statistical Methods in Medical Research, 14, 457–471.
Khoshgoftaar, T. M., Van Hulse, J., Seiffert, C., & Zhao, L. (2007). The multiple imputation quantitative noise corrector. Intelligent Data Analysis, 11, 245–263.
Lenski, G. (1961). The religious factor. Garden City: Doubleday.
Little, R. J. A. (1995). Modeling the drop-out mechanism in repeated measures studies. Journal of the American Statistical Association, 90, 1112–1121.
Little, R. J. A., & Rubin, D. B. (1987). Statistical analysis with missing data. New York: Wiley.
Martinussen, T., Nord-Larsen, T., & Johannsen, V. K. (2008). Estimating forest cover in the presence of missing observations. Scandinavian Journal of Forest Research, 23, 266–271.
Maxim, P. (1999). Quantitative research methods in the social sciences. Oxford University Press.
Montiel-Overall, P. (2006). Implications of missing data in survey research. Canadian Journal of Information and Library Science, 30(3/4), 241–269.
Neuman, L. (2003). Social research methods: qualitative and quantitative approaches (5th ed.). Allyn and Bacon Publishing.
Olson, K. (2006). Survey participation, nonresponse bias, measurement error bias, and total bias. Public Opinion Quarterly, 70(5), 737–758.
Paik, M. C. (2004). Nonignorable missingness in matched case–control data analysis. Biometrics, 60, 306–314.
PEW Research Center for the People and Press. (2004). Polls face growing resistance, but still representative. Survey reports (April 20th).
Porter, J. R., Cossman, R., & James, W. L. (2009). Research note: imputing large group averages for missing data, using rural–urban continuum codes for density driven industry sectors. Journal of Population Research, 26, 273–278.
Rose, R. A., & Fraser, M. W. (2008). A simplified framework for using multiple imputation in social work research. Social Work Research, 32(3), 171–178.
Rubin, D. B. (1976). Inference and missing data. Biometrika, 63, 581–592.
Rubin, D. B. (1984). Multiple imputation for nonresponse in surveys. New York: Wiley.
Satten, G. A., & Carroll, R. J. (2000). Conditional and unconditional categorical regression models with missing covariates. Biometrics, 56, 384–388.
Schrecker, E. (2006). Worse than McCarthy. pp. B20, February 10, 2006, in The chronicle of higher education.
Southern, D. A., Norris, C. M., Quan, H., Shrive, F. M., Diane Galbraith, P., Humphries, K., Gao, M., Knudtson, M. L., & Ghali, W. A. (2008). An administrative data merging solution for dealing with missing data in a clinical registry: adaptation from ICD-9 to ICD-10. BMC Medical Research Methodology, 8(1), 1–9.
Verbeke, G., & Molenberghs, G. (2000). Linear mixed models for longitudinal data. New York: Springer.
Verbeke, G., & Molenberghs, G. (2010). Arbitrariness of models for augmented and coarse data, with emphasis on incomplete data and random effects models. Statistical Modeling, 10(4), 391–419.
Wright, J. D., & Marsden, P. V. (2010). In P. V. Marsden & J. D. Wright (Eds.), The Handbook of Survey Research (2nd ed.). United Kingdom: Emerald Press.
Author information
Authors and Affiliations
Corresponding author
Additional information
This research was supported by a grant from the John Templeton Foundation (grant #11299; Elaine Howard Ecklund, PI). The authors also wish to thanks and acknowledge that Kelsey Pedersen provided invaluable help with manuscript editing and formatting.
Rights and permissions
About this article
Cite this article
Porter, J.R., Ecklund, E.H. Missing Data in Sociological Research: An Overview of Recent Trends and an Illustration for Controversial Questions, Active Nonrespondents and Targeted Samples. Am Soc 43, 448–468 (2012). https://doi.org/10.1007/s12108-012-9161-6
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12108-012-9161-6