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Genetically Adjusted Propensity Score Matching: A Proposal of a Novel Analytical Tool to Help Close the Gap between Non-experimental Designs and True Experiments in the Social Sciences

Silver, Ian
http://orcid.org/0000-0001-8233-4378
http://rave.ohiolink.edu/etdc/view?acc_num=ucin1561996445863208

Abstract Details

2019, PhD, University of Cincinnati, Education, Criminal Justice, and Human Services: Criminal Justice.
Objectives: Social scientists employ various statistical techniques to approximate the causal association between two interrelated constructs. Although these methodologies have been useful for the advancement of knowledge, the limitations associated with preceding statistical techniques limit the ability of scholars to approximate causal associations within some conditions. As such, the current study provides a new statistical technique designed to approximate causal associations independent of observed genetic and environmental confounders. Methods: Genetically adjusted propensity score matching (GAPSM) represents an innovative iteration of propensity score matching (PSM) designed to integrate environmental and genetic factors into the matching process. By using polygenic risk scores, future scholars can estimate genetically adjusted propensity scores (GAPS) through the implementation of two distinct statistical processes. To demonstrate the validity of the GAPSM approach, the current study employs simulation analyses to compare the point estimates derived from a post-GAPSM model to the point estimates derived from a post-PSM model and an MZ difference score model. Results: The results of the simulation analyses demonstrated that when environmental measures that explain a larger portion of the variance in a treatment condition are introduced into the GAPSM approach, post-GAPSM models approach the true point estimate more closely than the point estimates derived from a post-PSM model and an MZ difference score model. Conclusions: Overall, the findings demonstrate that the GAPSM approach can be useful when assessing the causal effects of treatment conditions on subsequent phenotypes by adjusting for observed environmental and genetic factors. Within the social sciences, this method could provide substantive advancements in our understanding of causal effects. Specifically, GAPSM represents another tool social scientists can use to conduct rigorous genetically sensitive examinations of the etiological influence of environmental factors on human behavior.
Joseph Nedelec, Ph.D. (Committee Chair)
J.C. Barnes, Ph.D. (Committee Member)
Joshua Cochran, Ph.D. (Committee Member)
George Richardson, Ph.D. (Committee Member)
263 p.
Criminology
Polygenic Risk Scores; Propensity Score Matching; Criminology; Social Sciences

Recommended Citations

Citations

  • Silver, I. (2019). Genetically Adjusted Propensity Score Matching: A Proposal of a Novel Analytical Tool to Help Close the Gap between Non-experimental Designs and True Experiments in the Social Sciences [Doctoral dissertation, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1561996445863208

    APA Style (7th edition)

  • Silver, Ian. Genetically Adjusted Propensity Score Matching: A Proposal of a Novel Analytical Tool to Help Close the Gap between Non-experimental Designs and True Experiments in the Social Sciences. 2019. University of Cincinnati, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ucin1561996445863208.

    MLA Style (8th edition)

  • Silver, Ian. "Genetically Adjusted Propensity Score Matching: A Proposal of a Novel Analytical Tool to Help Close the Gap between Non-experimental Designs and True Experiments in the Social Sciences." Doctoral dissertation, University of Cincinnati, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1561996445863208

    Chicago Manual of Style (17th edition)

ucin1561996445863208
288
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This open access ETD is published by University of Cincinnati and OhioLINK.