Supplemental Tables
Creators: name: Anonymous
Publisher: name: Zenodo
Year: 2024

description: Supplemental Tables for the Manuscript: 

Heterogeneity in Criminal Violent Victimization within the LGBT Population:Estimates from the U.S. National Crime Victimization Study

Guide to Supplementary Tables

A.    Table S1 provides the descriptive statistics for the weighted, survey-adjusted dataset. The NCVS is designed to be a representative sample of individuals aged 12 and older residing in households in the USA. Rather than the sample statistics, this table displays the descriptive statistics for the sample, weighted to represent the US population. As noted in the text, our sample is restricted to individuals aged 16 or older, given that respondents younger than 16 were not asked the sexual orientation or gender identity questions.

B.    Table S2 displays the full results for the logistic regression models presented in the text in Table 3. Specifically, here we include the coefficients associated with the interview controls not shown in the main text given space constraints. Additionally, Table S2 presents the results for other forms of violent crime: simple assaults and violent threats without a weapon. 

C.    Table S3 displays the results of our models testing whether biological sex moderates the effects of LGBT status on increased risk of violent victimization. As can be seen, we did not find sufficient evidence to reject the null hypothesis of no difference in the effect of LGBT status by sex. This can be seen by the non-significant interaction terms.

D.    Table S4 displays the results where the logistic regression models from Table 3 were estimated with the alternative measure of transgender status and including those who respond “I don’t know the answer” to the sexual orientation questions. Following others, in all other analyses, we classify respondents as transgender if they identify as transgender or if they report a mismatch between their self-reports of “sex assigned at birth” and “how you think about yourself” (intending to capture gender identity) and subsequently confirm this “mismatch” in a follow-up question. For this model, we code those as transgender only those who self-identify as “thinking of themselves as transgender” and exclude “sex-gender mismatches” from the model. As can be seen, the pattern of results with this alternative measure is generally the same, although the point estimates and upper confidence interval are a bit higher using the alternative measure of transgender status.

E.    Table S5 displays the results of another robustness test of the logistic regression results presented in the main text. Specifically, the sample in the main analyses in Table 3 excluded ~1% of respondents who were missing information on marital status and educational attainment. Thus, this model excludes these two variables to assess whether the pattern of findings is robust to the inclusion of those roughly 5200 respondents, including some LGBT individuals, lost to listwise deletion. As can be seen, the pattern of findings is not dependent on the exclusion of these respondents.

F.    Whereas the rates presented in the main text are prevalence rates, Table S6 examines the incidence rate of violent victimization by LGBT status for both time periods: the 2017-2019.5 dataset, using Taylor’s series linearization (TSL) and the 2017-2021 data using generalized variance functions and population estimates provided by the BJS (see Couzens et al., 2015; Truman & Morgan, 2022). Of course, because incidents count repeat victimizations, the incidence rates are higher than the prevalence rates.

Three differences stand out for the 2017 – 2019.5 incidence estimates (compared to Table 4). First, the disparities between bisexual respondents and their non-LGBT counterparts are heightened for incidence versus prevalence. Second, while transgender respondents had the highest prevalence rate of verbal threats (as shown in Table 4), bisexual individuals have a higher incidence rate of verbal threats, suggesting a heightened rate of repeat victimization against bisexual individuals. Finally, while bisexual individuals had the highest prevalence rates of total and severe violent crime victimization as well as simple assault, bisexual individuals have the highest incidence rates of total violence, simple assault, and verbal threats. The highest incidence rate of severe violence was observed among those who report “something else” sexual orientation. Notably, however, the estimates of violence for those reporting “something else” sexual orientation have a coefficient of variation greater than 50% (indicating low reliability) and thus should be interpreted with caution. 

For the 2017 – 2021 data, the pattern of findings is again similar, with one notable difference from the prevalence data. As with the 2017-2019.5 data, whereas the prevalence rate of verbal threats was highest for the transgender subgroup, the incidence rates of all four violent crime categories are highest for the bisexual subgroup. 

In sum, the general pattern of findings—that the rate of violent victimization is highest for bisexual individuals—is found whether measuring incidence or prevalence. The pattern of findings for threats of violence varies between incidence and prevalence.

G.    Table S7 displays the full generalized variance functions (GVF) prevalence and incidence estimates for violent crime types by LGBT status using the 2017 – 2021 data. 

H.    Table S8 displays our coding of violent victimization and, for comparison, displays the coding of violent victimization in a cited study by Flores et al. (2020) using the NCVS and exploring differences by LGBT status.

I.    Table S9 displays the results of re-estimating the prevalence rates in Table 2 using respondent reports of their sex at birth (V3085), rather than the sex allocated measure used throughout the paper.

J.    Table S10 expands up Table 4 in the text and displays prevalence estimates in four ways: (1) Using the study sample (2017-2019.5 data using listwise deletion) and TSL estimation [presented in the manuscript], (2) Using the full 2017-2019.5 sample (only missing on sex allocated or LGBT status) and TSL estimation, (3) Using the full 2017-2019.5 sample (only missing on sex allocated or LGBT status) and GVF estimation, and (4) Using 2017-2021 data (only missing on sex allocated or LGBT status) and GVF estimation [results presented in main text].

K.    Table S11 provides more detail on the parameters used in the GVF prevalence and incidence estimations for the 2017-2019.5 data.

L.    Tables S12A and S12B uses the alternative violent victimization coding used by Bender and Lauritsen (2021) to estimate prevalence rates. These estimates are provided to test the robustness of our findings to alternative operationalizations of violent victimization. As can be seen, the pattern of findings is consistent with Bender & Lauritsen’s general pattern of findings using incidence rates, albeit lower given our focus on prevalence, and our pattern of findings.