Open Collections

Dewsnap, Kyle

University of British Columbia

Survey-driven research frequently relies on the analysis of differences between subpopulations that share social identities, such as gender or ethnicity. One method of gathering identity information from research participants is the identity item, which I define as a closed-form, single response survey item that prompts a respondent to endorse an option that corresponds to the identity group they belong to. A potential concern for researchers using these items is “identity item mismatch,” where a respondent’s social identity does not fully conform to any of the options available on an identity item. Here, I examine whether this mismatch between the social identities of respondents and the response options on an identity item can bias the multiple regression analysis of survey response data. This thesis relies upon established literature in the cognitive aspects of survey methodology and social identity theory to first build a working theory of how identity item mismatch is produced through an interaction between respondents, the social identities of the respondents, and identity items. This theory then motivates a Monte Carlo simulation of a survey-driven research study, which uses a two-stage beta-Bernoulli event to characterise the response process necessary to answer a gender identity item. In two studies, I use this simulation to demonstrate that an increase in identity item mismatch behaviour biases estimates of population-level differences between identity groups. This bias also emerges when certain social identities within a sample of respondents are not listed as a response category on an identity item. Ultimately, this research illustrates how the data generated through identity items could impact the quality of the inferences generated from survey responses, which motivates the development of revised or alternative survey methods for the capturing of social identity.

Text

2022-05-12

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/81542

Measurement, Evaluation and Research Methodology

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/