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Complex Samples and Regression-Based Inference: Considerations for Consumer Researchers




type="main" xml:id="joca12038-abs-0001"> This article demonstrates that researchers who treat data collected via complex sampling procedures as if they were collected via simple random sample (SRS) may draw improper inferences when estimating regression models. Using complex sample data from the 2004 panel of the Survey of Income and Program Participation (SIPP) two models—one ordinary least squares (OLS) regression and one logistic regression—were estimated using three methods: SRS with and without population weights, Taylor series linearization, and Fay's Balanced Repeated Replication (BRR). The results of the alternative models demonstrate that depending on the variables of interest, authors who fail to incorporate sample design information or fail to consider the effects of weighting may draw improper inferences from their regression models. Reasons why researchers continue to neglect complex sample-based variance are proposed and discussed, and example SAS and Stata code is offered to encourage adoption by the consumer research community.

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  • Robert B. Nielsen & Martin C. Seay, 2014. "Complex Samples and Regression-Based Inference: Considerations for Consumer Researchers," Journal of Consumer Affairs, Wiley Blackwell, vol. 48(3), pages 603-619, October.
  • Handle: RePEc:bla:jconsa:v:48:y:2014:i:3:p:603-619

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    References listed on IDEAS

    1. Alisha Coleman-Jensen, 2011. "Working for Peanuts: Nonstandard Work and Food Insecurity Across Household Structure," Journal of Family and Economic Issues, Springer, vol. 32(1), pages 84-97, March.
    2. Cynthia Sanders & Shirley Porterfield, 2010. "The Ownership Society and Women: Exploring Female Householders’ Ability to Accumulate Assets," Journal of Family and Economic Issues, Springer, vol. 31(1), pages 90-106, March.
    3. Alastair Scott & Chris Wild, 2002. "On the robustness of weighted methods for fitting models to case-control data," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(2), pages 207-219.
    4. Christopher Tamborini & Howard Iams & Gayle Reznik, 2012. "Women’s Earnings Before and After Marital Dissolution: Evidence from Longitudinal Earnings Records Matched to Survey Data," Journal of Family and Economic Issues, Springer, vol. 33(1), pages 69-82, March.
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    1. repec:bla:jconsa:v:51:y:2017:i:3:p:682-702 is not listed on IDEAS
    2. repec:bla:jconsa:v:51:y:2017:i:2:p:433-447 is not listed on IDEAS

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