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Performance simulations for categorical mediation: Analyzing khb estimates of mediation in ordinal regression models

Author

Listed:
  • E. Keith Smith

    (GESIS-Leibniz Institute for the Social Sciences)

  • Michael G. Lacy

    (Colorado State University)

  • Adam Mayer

    (Colorado State University)

Abstract
Standard mediation techniques for fitting mediation models cannot readily be translated to nonlinear regression models because of scaling issues. Methods to assess mediation in regression models with categorical and limited response variables have expanded in recent years, and these techniques vary in their approach and versatility. The recently developed khb technique purports to solve the scaling problem and produce valid estimates across a range of nonlinear regression models. Prior studies demonstrate that khb performs well in binary logistic regression models, but performance in other models has yet to be inves- tigated. In this article, we evaluate khb’s performance in fitting ordinal logistic regression models as an exemplar of the wider set of models to which it applies. We examined performance across 38,400 experimental conditions involving sample size, number of response categories, distribution of variables, and amount of medi- ation. Results indicate that under all experimental conditions, khb estimates the difference (mediation) coefficient and its associated standard error with little bias and that the nominal confidence interval coverage closely matches the actual. Our results suggest that researchers using khb can assume that the routine reasonably approximates population parameters.

Suggested Citation

  • E. Keith Smith & Michael G. Lacy & Adam Mayer, 2019. "Performance simulations for categorical mediation: Analyzing khb estimates of mediation in ordinal regression models," Stata Journal, StataCorp LP, vol. 19(4), pages 913-930, December.
    • Handle: RePEc:tsj:stataj:v:19:y:2019:i:4:p:913-930
    DOI: 10.1177/1536867X19893638
    Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj19-4/st0583/
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    Cited by:

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    2. Marja Eliisa Holm & Päivi Sainio & Jaana Suvisaari & Katri Sääksjärvi & Tuija Jääskeläinen & Suvi Parikka & Seppo Koskinen, 2022. "Differences in Unfavorable Lifestyle Changes during the COVID-19 Pandemic between People with and without Disabilities in Finland: Psychological Distress as a Mediator," IJERPH, MDPI, vol. 19(12), pages 1-15, June.
    3. Roxy Damen & Jaco Dagevos & Willem Huijnk, 2024. "Feeling at Home? A Dynamic Analysis of the Impact of Discrimination, Refugee-Specific, and Participation Characteristics on Recently Arrived Refugees’ Belonging," Journal of International Migration and Integration, Springer, vol. 25(3), pages 1547-1569, September.
    4. Terence Wood & Sabit Otor & Matthew Dornan, 2022. "Why are aid projects less effective in the Pacific?," Development Policy Review, Overseas Development Institute, vol. 40(3), May.
    5. Arthur Jacobs & Elsy Verhofstadt & Luc Van Ootegem, 2023. "Are more automatable jobs less satisfying?," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 23/1059, Ghent University, Faculty of Economics and Business Administration.
    6. Hua Guo & Yang Zhang & Yanling Peng & Tong Luo & Hong Wang, 2022. "Does COVID-19 Affect Household Financial Behaviors? Fresh Evidence From China," SAGE Open, , vol. 12(3), pages 21582440221, August.
    7. Li, Fanlue & He, Ke & Wang, Yuejie & Zhang, Junbiao, 2021. "Does Indoor Air Pollution from Solid Fuels Influence the Mental Health of Rural Residents? Evidence from China," 2021 Conference, August 17-31, 2021, Virtual 315024, International Association of Agricultural Economists.
    8. Rao, Smitha & Enelamah, Ngozi V., 2024. "Social protection and absorptive capacity: Disaster preparedness and social welfare policy in the United States," World Development, Elsevier, vol. 173(C).

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