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Operational Definitions of Sexual Orientation
and Estimates of Adolescent Health Risk Behaviors
Derrick D. Matthews, PhD, MPH,
1
John R. Blosnich, PhD, MPH,
2,3
Grant W. Farmer, MPH, MA,
4
and Brian J. Adams, MPH
1
Abstract
Purpose: Increasing attention to the health of lesbian, gay, and bisexual (LGB) populations comes with requisite
circumspection about measuring sexual orientation in surveys. However, operationalizing these variables also re-
quires considerable thought. This research sought to document the consequences of different operational defini-
tions of sexual orientation by examining variation in health risk behaviors.
Methods: Using Massachusetts Youth Risk Behavior Survey data, we examined how operational definitions of
sexual behavior and sexual identity influenced differences among three health behaviors known to disparately
affect LGB populations: smoking, suicide risk, and methamphetamine use. Sexual behavior and sexual identity
were also examined together to explore if they captured unique sources of variability in behavior.
Results: Estimates of health disparities changed as a result of using either sexual behavior or sexual identity.
Youth who reported their sexual identity as ‘‘not sure’’ also had increased odds of health risk behavior. Disaggre-
gating bisexual identity and behavior from same-sex identity and behavior frequently resulted in the attenuation
or elimination of health disparities that would have otherwise been attributable to exclusively same-sex sexual
minorities. Finally, sexual behavior and sexual identity explained unique and significant sources of variability
in all three health behaviors.
Conclusion: Researchers using different operational definitions of sexual orientation could draw different
conclusions, even when analyzing the same data, depending upon how they chose to represent sexual orienta-
tion in analyses. We discuss implications that these manipulations have on data interpretation and provide
specific recommendations for best-practices when analyzing sexual orientation data collected from adolescent
populations.
Key words: adolescents, data analysis, health behavior, measurement, sexual orientation.
Introduction
The health needs of lesbian, gay, and bisexual (LGB)
individuals have gained increasing attention in the
United States.
1–4
A recent report from the Institute of Medi-
cine (IOM) notes several health disparity issues for LGB
populations, such as increased self-directed violence and sub-
stance use.
1
The report also makes several recommendations
for research, a major one being assessing sexual orientation in
health surveillance surveys.
1
Though there is increasing con-
sensus on best practices to measure sexual orientation in sur-
vey research (e.g., item wording, response options),
5
less
attention is given on how to operationalize these data in empir-
ical analysis (e.g., which response categories to aggregate,
what constitutes a missing value). Several studies highlight
the theoretical and empirical consequences of using only
one dimension of sexual orientation in analysis (i.e., attrac-
tion, behavior, and identity).
6–12
Even still, researchers vary
with respect to how each of these dimensions is operational-
ized. Research with adolescents may be particularly sensitive
to issues of measurement and operationalization, as this pop-
ulation is in the midst of several developmental changes,
1
Department of Health Behavior, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill,
North Carolina.
2
Department of Psychiatry, University of Rochester, Rochester, New York.
3
VISN-2 Center of Excellence for Suicide Prevention, U.S. Department of Veterans Affairs, Canandaigua, New York.
4
Department of Epidemiology, Saint Louis University College for Public Health & Social Justice, St. Louis, Missouri.
LGBT Health
Volume 1, Number 1, 2014
ªMary Ann Liebert, Inc.
DOI: 10.1089/lgbt.2013.0002
42
including various stages of sexual experience and sexual
identity formation.
10,13–15
Furthermore, risk behaviors such
as smoking, illicit drug use, and self-directed violence in-
crease during adolescence, with particularly high prevalence
of risk behaviors noted among LGB youth.
2
The variability of outcomes based on operational defini-
tions of sexual orientation among adolescents is highlighted
by Kann et al.
2
in analyses of Youth Risk Behavior Survey
(YRBS) data pooled across 9 years from those states and cities
that assessed either sexual behavior or sexual identity. For ex-
ample, the prevalence of skipping school due to feeling un-
safe differed significantly between students who identified
as lesbian or gay and students who indicated same-sex sexual
experience (median 21.1% and 15.2%, respectively). Brewster
and Tillman
10
note similar constellations of difference in an
analysis of 15- to 24-year-olds from the National Survey of
Family Growth. For instance, 44% of female respondents
who indicated a lesbian or bisexual identity reported smok-
ing, whereas only 14% of females who indicated any same-
sex sexual experiences reported smoking. Though both of
these studies note variation across the three dimensions of
sexual orientation, Kann et al. did not examine differences
by sex, and Brewster & Tillman did not separate lesbian/
gay and bisexual in analysis. Further research is needed to ex-
plore both the sex-specific and sexual orientation dimension-
specific nuances that may impact results across definitions of
sexual orientation.
In addition to using different dimensions of sexual orienta-
tion, the implications of combining lesbian/gay and bisexual
groups in analyses (most often for the sake of statistical
power) are becoming clearer. Studies suggest that combining
lesbian/gay and bisexual groups together can mask unique
differences between groups and exaggerate risk to one
group that may, in actuality, be significantly lower in the
other. Recalling Kann and colleagues’ YRBS analysis,
2
they
report the median percentage of lesbian or gay students
that made suicide plans was 21.2%, but the percentage was
35.7% among bisexual-identified students. Additionally,
Ford and Jasinski
16
found that bisexual female college stu-
dents had a significantly higher prevalence of illicit drug
use than both their heterosexual and lesbian peers.
As large federal surveys begin to add measures of sexual
orientation,
3
circumspection about the consequences of
using different definitions and combinations of sexual orien-
tation is a necessary exercise not only to appraise epidemio-
logic results accurately but strategize interventions as well.
With a focus on previously documented risk behaviors
known to disproportionately occur among LGB youth (i.e.,
smoking,
17
methamphetamine use,
2,18
and suicide risk
19–21
),
the purpose of the present study is to explore the sensitivity
of estimates in these three behaviors as a function of varying
operational definitions of sexual orientation.
Methods
We analyzed a combined dataset of three independent
years (i.e., 2003, 2005, and 2007) of YRBS data from Massa-
chusetts (MA). Though a majority of states conduct the
YRBS, MA was among the first to include questions assessing
sexual behavior and sexual identity. By stratifying all public
high schools by enrollment size, randomly selecting schools
within each stratum, and then randomly selecting classrooms
within selected schools, the MA YRBS is designed to be rep-
resentative of all public high school students within the
state. The MA YRBS is biannual and employs an anonymous
survey; school participation rates were 88% in 2003
22
and 87%
in both 2005 and 2007.
23,24
In all, 82%, 78%, and 85% of eligi-
ble students completed the questionnaire in 2003, 2005, and
2007, respectively.
22–24
More detailed information about the
MA YRBS sampling and weighting methodology is available
elsewhere.
25–27
Twenty-four students missing data on sex
were excluded from analysis, resulting in an analytic sample
of 10,253 students, of which 9,869 (96.3%) provided responses
to both the sexual identity and sexual behavior questions.
Measures
Health risk behaviors. We selected three health risk be-
haviors a priori to illustrate the variation from manipulating
operational definitions of sexual orientation. We examined
current smoking habits (i.e., smoked a cigarette in the past
30 days), having ever made a plan to commit suicide, and
having ever used methamphetamines. We selected these be-
haviors among available health risk behaviors in the YRBS
for three primary reasons: 1) published research suggested
we would observe disparities by sexual orientation in these
behaviors among this population;
2,17–21,28
2) these behaviors
ranged from somewhat common to rare, increasing confi-
dence that any variation in estimates were due to operational
manipulations and not specific to any one behavior; and 3) by
not examining sexual health risk behaviors, we minimized
definitional overlap with the sexual behavior dimension of
sexual orientation.
Sexual orientation. We assessed sexual orientation using
two separate questions that asked respondents about sexual
behavior and sexual identity. Sexual behavior was assessed
with the item, ‘‘During your life, with whom have you had
sexual contact? 1) I have never had sexual contact, 2) Females,
3) Males, or 4) Females and Males.’’ Sexual identity was
assessed with the item, ‘‘Which of the following best describes
you? 1) Heterosexual (straight), 2) Gay or lesbian, 3) Bisexual,
or 4) Not sure.’’
Demographic characteristics. We used student grade
(9th to 12th) and year of data collection (2003, 2005, or
2007) to control for both developmental and cohort effects.
We also controlled for race/ethnicity, however its distribu-
tion led us to collapse race/ethnicity into four categories:
Black or African American, Hispanic or Latino, White, and
all others. Following previous research, we stratified analyses
by sex.
10,12,29
Operational manipulations
We created six comparisons of operational definitions of
sexual orientation. The first three definitions were manipula-
tions of the sexual behavior variable. Definition 1 compared
those with any same-sex sexual behavior to those with ex-
clusively opposite-sex sexual behavior. Definition 2 dis-
aggregated any same-sex sexual behavior into exclusively
same-sex sexual behavior and sexual behavior with both
women and men. In both definitions 1 and 2, persons report-
ing ‘‘no sexual contact’’ were excluded from analyses. For def-
inition 3, students who indicated ‘‘no sexual contact’’ were
OPERATIONAL DEFINITIONS OF SEXUAL ORIENTATION 43
added as a group alongside the three other previously de-
fined sexual behavior groups. Exclusively opposite-sex sexual
behavior was the referent category for all analyses.
The last three definitions similarly manipulated the sexual
identity variable. Definition 4 compared students with any
sexual minority identity (lesbian, gay, or bisexual) to students
who identified as heterosexual/straight. Definition 5 disag-
gregated the sexual minority group into bisexual identity
and lesbian/gay identity. Definition 6 built upon the previ-
ous definition by including ‘‘not sure’’ as a sexual identity
rather than excluding it as missing data. Heterosexual/
straight identity was the referent category for each of these
definitions.
Data analysis
We utilized logistic regression to analyze the association of
sexual orientation definitions with each of the three health
risk behaviors. Each analysis of health risk behavior consisted
of six models, corresponding to one of the previously de-
scribed operational definitions. All analyses were weighted
to accommodate the survey design.
We also tested if adding sexual identity explained a statis-
tically significant amount of additional and unique variability
beyond sexual behavior for each of the health risk behaviors.
Using a chi-square test with 3 degrees of freedom, we com-
pared the 2 log likelihood value of a fully saturated model
with that of Model 6 in order to test the ‘‘value added’’ of sex-
ual behavior to a model already including sexual identity.
30
Analyses were conducted using SAS software for Windows,
version 9.3 (SAS Institute Inc., Cary, NC).
Results
Demographic characteristics are presented in Table 1. Con-
sistent with the demographic makeup of Massachusetts, most
students in the sample were white (75%). Almost 20% of the
sample had smoked a cigarette in the past 30 days, 7.4% had
ever made a plan to commit suicide, and 4.8% had ever used
methamphetamines. The majority of the sample (52.9%)
reported only having sexual contact with someone of the op-
posite sex, and a minority reported either exclusively same-
sex (2.1%) or both-sex sexual behavior (3.5%). Most students
identified as heterosexual or straight (93.7%), approximately
1% identified as lesbian or gay, 3% identified as bisexual,
and 2% were ‘‘not sure’’ about their sexual identity. Seven
percent of the sample reported any same-sex sexual behavior
or sexual minority identity. Table 2 summarizes the distribu-
tion of sexual behavior by sexual identity for females and
males. The results of sexual orientation operational manipula-
tions are presented for current smoking status (Table 3), form-
ing a suicide plan (Table 4), and methamphetamine use
(Table 5).
Operational manipulations of sexual behavior
Manipulations of the sexual behavior variable are repre-
sented in Models 1–3 (Tables 3–5). For all health risk behav-
iors, once any same-sex sexual behavior was disaggregated
into its two component categories (i.e., having sex with
both men and women, and having sex exclusively with
same-sex partners) disparities previously attributed to
same-sex behavior appeared to be largely driven by persons
who were behaviorally bisexual. This manipulation (i.e.,
moving from Model 1 to Model 2) was so large that effects as-
sociated with exclusively same-sex sexual behavior were no
longer statistically significant for males or females. For exam-
ple, the adjusted odds ratio (AOR) for smoking among fe-
males with any same-sex sexual behavior was 3.2 (95%
confidence interval [CI]: 2.4, 4.3), but in the disaggregated
model, the AOR for women who only had sex with women
was 1.0 (95% CI: .76, 1.4), while the AOR for women who
had sex with both women and men was 3.8 (95% CI: 2.4,
6.3). The addition of students who were not sexually active
did not change the findings from the previous model, though
they were significantly less likely to engage in all health risk
behaviors.
Operational manipulations of sexual identity
Manipulations of the sexual identity variable are repre-
sented in Models 4–6 (Tables 3–5). In contrast to the results
of disaggregating the sexual behavior variable, disaggregat-
ing the sexual identity variable from Model 4 into its compo-
nent categories (i.e., lesbian/gay and bisexual) in Model 5
neither erased nor attenuated associations of exclusive
same-sex identity with the outcomes. All sexual minority
identities were associated with increased likelihood of engag-
ing in all health risk behaviors for both males and females.
Table 1. Demographic Characteristics
of Massachusetts Youth Risk Behavioral
Surveillance System Respondents, 2003–2007
n
a
% (SE)
b
Sex
Female 5233 49.4 (0.71)
Male 5020 50.6 (0.71)
Grade
9th 2923 28.5 (2.1)
10th 2785 25.6 (1.6)
11th 2430 23.9 (1.2)
12th 2033 22.0 (1.1)
Race/ethnicity
Black or African American 707 8.7 (1.0)
Hispanic or Latino 876 8.7 (1.0)
White 6834 75.0 (1.9)
Other 1594 7.6 (0.35)
Health risk behaviors
Current smoker 1935 19.7 (0.67)
Ever made suicide plan 675 7.4 (0.32)
Ever used methamphetamines 509 4.8 (0.27)
Sexual behavior
Exclusively same sex 201 2.1 (0.15)
Both sexes 356 3.5 (0.23)
No sexual contact 4218 41.5 (0.72)
Exclusively opposite sex 5147 52.9 (0.74)
Sexual identity
Lesbian or gay 120 1.2 (0.12)
Bisexual 320 3.1 (0.18)
Not sure 219 2.1 (0.21)
Heterosexual 9460 93.7 (0.32)
Any same-sex behavior or identity 740 7.2 (0.32)
a
Unweighted frequencies.
b
Weighted proportions.
44 MATTHEWS ET AL.
However, lesbians were just as, if not more, likely to engage in
health risk behaviors compared to bisexual females, a result
contrary to comparable manipulations for sexual behavior.
Furthermore, compared to their heterosexual counterparts,
lesbian and bisexual females appeared to have greater odds
of engaging in health risk behaviors than gay and bisexual
men. Disaggregating sexual identity among sexual minority
males revealed that disparities for gay and bisexual males
were relatively similar to each other, with the exception that
gay males appeared to have greater odds of having ever
made a suicide plan. Finally, the addition of those students
who were unsure about their sexual identity revealed that
they too were consistently more likely to engage in health
risk behaviors than heterosexual students. Being ‘‘not sure’’
of one’s sexual identity appeared to result in a greater dispar-
ity for males than females.
Sexual behavior and sexual identity
We ran a fully saturated model that included the most
expansive definitions of both sexual behavior and sexual
identity to determine if these variables were redundant in
modeling the association between sexual orientation and
health risk behavior. The addition of sexual identity was sta-
tistically significant for both females and males for all three
behaviors (p <0.001; data not shown). These results indicate
that both sexual identity and sexual behavior explain signifi-
cant and unique sources of variability in health disparities by
sexual orientation.
Discussion
The accumulating documentation of health disparities
among sexual minority populations has received national at-
tention in the last several years through high-profile docu-
ments such as the IOM consensus report.
1
With the
Department of Health and Human Services expressing public
intent to add sexual orientation to federal surveys,
3
data
about sexual minority populations will grow. We should be
circumspect about how sexual orientation is operationalized;
two researchers with the same data but different operational
definitions could reach two different conclusions. Just as
studies illustrate the empirical consequences of making dis-
tinctions between dimensions of sexual orientation on the
prevalence of studied outcomes,
6–12
so too can the operation-
alization of these dimensions influence results. Rather than la-
beling these definitional issues as limitations, we suggest they
provide opportunities to investigate how identity changes
across the life course and to explore mechanisms through
which sexual orientation becomes associated with health
inequities.
We highlight three primary findings. The first is that aggre-
gating all sexual minorities into one category, while increas-
ing statistical power, obscures important differences. In our
sample, bisexual behavior was associated with disparities in
health risk behaviors to such a degree that it effectively
masked the lack of significant differences in health risk be-
haviors between those who engaged in exclusively same-
sex sexual behavior and their opposite-sex counterparts
until these groups were examined separately.
The second primary finding concerns the inability to align
sexual behavior and sexual identity, confirming work done
by others.
6–12
As shown in Table 2, there is a sizeable ‘‘discor-
dance’’ between sexual identity and sexual behavior, indica-
tive that distinct dimensions of sexual orientation should
not be misinterpreted as proxies for each other. This lack of
concordance may signify the rapid development in adoles-
cence, which makes equating sexual identity and sexual
behavior even more problematic. For instance, among
sexually active adolescents, there may be limited opportuni-
ties to engage in same-sex sexual behavior due to external in-
fluences, such as identifying same-sex romantic partners or
being pressured to comply with heterosexist narratives of op-
posite-sex sexual behavior. Such factors may contextualize
why one in five self-identified gay men reported having
had sex only with women or why one-fourth of self-identified
bisexual women reported sex only with men. When possible,
Table 2. Reported Measures of Sexual Orientation: Sexual Behavior by Sexual Identity,
Massachusetts Youth Risk Behavioral Surveillance System Respondents, 2003–2007
Sexual identity, n (%)
a
Women
Sexual behavior Lesbian (n =41) Bisexual (n =246) Heterosexual (n =4,665) Not sure (n =112)
Exclusively same sex 16 (36.6) 14 (6.4) 53 (1.1) 3 (2.2)
Both sexes 19 (47.9) 124 (51.5) 94 (2.1) 22 (21.0)
No sexual contact 5 (13.5) 45 (16.9) 2127 (44.3) 55 (47.2)
Exclusively opposite sex 1 (2.0) 63 (25.3) 2391 (52.5) 32 (29.7)
Men
Gay (n =75) Bisexual (n =66) Heterosexual (n =4,568) Not sure (n =96)
Exclusively same sex 23 (33.5) 13 (20.2) 69 (1.6) 5 (6.5)
Both sexes 17 (20.4) 24 (37.3) 37 (0.9) 16 (16.9)
No sexual contact 20 (25.1) 12 (19.2) 1883 (40.4) 40 (43.3)
Exclusively opposite sex 15 (21.1) 17 (23.3) 2579 (57.2) 35 (33.4)
Note: Individuals were excluded from analysis if data was missing on either their sexual behavior or sexual identity.
a
Column percentages are weighted proportions.
OPERATIONAL DEFINITIONS OF SEXUAL ORIENTATION 45
Table 3. Adjusted Odds Ratios of Current Smoking for Sexual Minorities: Six Operational Definitions of Sexual Behavior and Sexual Identity,
Massachusetts Youth Risk Behavioral Surveillance System Respondents, 2003–2007
Currently smoking Currently smoking
AOR (95%CI) AOR (95%CI)
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6
Women Women
Sexual behavior Sexual identity
Any same sex (n =349) 3.2 (2.4, 4.3) Lesbian/bisexual (n =295) 6.4 (5.1, 8.0)
Only same sex (n =89) — 1.0 (0.76, 1.4) 1.0 (0.76, 1.4) Lesbian (n =43) — 9.9 (4.8, 20.6) 9.9 (4.8, 20.4)
Sex with both (n =260) — 3.8 (2.4, 6.3) 3.8 (2.4, 6.3) Bisexual (n =252) — 6.0 (4.6, 7.9) 6.0 (4.6, 7.9)
No sex (n =2249) — 0.23 (0.16, 0.33) Not sure (n =112) — — 1.2 (0.73, 2.0)
Only opposite sex (n =2492) ref ref ref Heterosexual (n =4743) ref ref ref
Men Men
Sexual behavior Sexual Identity
Any same sex (n =208) 2.2 (1.5, 3.2) Gay/bisexual (n =143) 2.9 (2.0, 4.2)
Only same sex (n =112) — 1.4 (0.88, 2.2) 1.4 (0.87, 2.2) Gay (n =76) — 2.8 (1.5, 5.2) 2.8 (1.5, 5.2)
Sex with both (n =96) — 3.8 (2.2, 6.6) 3.8 (2.2, 6.6) Bisexual (n =67) — 3.0 (1.6, 5.3) 3.0 (1.6, 5.3)
No sex (n =1969) — — 0.23 (0.19, 0.28) Not sure (n =104) — — 2.0 (1.2, 3.3)
Only opposite sex (n =2655) ref ref ref Heterosexual (n =4703) ref ref ref
All analyses are weighted and adjusted for year of data collection, race/ethnicity, and high school grade.
AOR, adjusted odds ratio; CI, confidence interval.
Table 4. Adjusted Odds Ratios of Planned Suicide Attempt for Sexual Minorities: Six Operational Definitions of Sexual Behavior and Sexual Identity,
Massachusetts Youth Risk Behavioral Surveillance System Respondents, 2003–2007
Planned suicide attempt Planned suicide attempt
AOR (95%CI) AOR (95%CI)
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6
Women Women
Sexual behavior Sexual Identity
Any same sex (n =349) 2.9 (2.2, 3.7) Lesbian/bisexual (n =295) 4.6 (3.3, 6.2)
Only same sex (n =89) — 1.4 (0.78, 2.6) 1.4 (0.78, 2.6) Lesbian (n =43) — 5.9 (2.9, 12.2) 5.9 (2.9, 12.3)
Sex with both (n =260) — 3.5 (2.6, 4.7) 3.5 (2.6, 4.7) Bisexual (n =252) — 4.3 (3.1, 6.1) 4.3 (3.1, 6.1)
No sex (n =2249) — — 0.56 (0.46, 0.68) Not sure (n =112) — — 1.9 (1.1, 3.2)
Only opposite sex (n =2492) ref ref ref Heterosexual (n =4743) ref ref ref
Men Men
Sexual behavior Sexual Identity
Any same sex (n =208) 2.8 (1.8, 4.3) Gay/bisexual (n =143) 4.6 (3.0, 7.1)
Only same sex (n =112) — 1.3 (0.67, 2.4) 1.3 (0.66, 2.4) Gay (n =76) — 5.4 (3.4, 8.6) 5.4 (3.4, 8.7)
Sex with both (n =96) — 5.4 (2.9, 10.4) 5.4 (2.9, 10.4) Bisexual (n =67) — 3.7 (1.9, 7.1) 3.7 (1.9, 7.1)
No sex (n =1969) — — 0.59 (0.45, 0.77) Not sure (n =104) — — 2.6 (1.3, 5.1)
Only opposite sex (n =2655) ref ref ref Heterosexual (n =4703) ref ref ref
All analyses are weighted and adjusted for year of data collection, race/ethnicity, and high school grade.
46
these dimensions of sexual orientation should be examined
independently.
Our last primary finding is an extension of this ‘‘discor-
dance.’’ The inclusion of both identity and behavior in the sat-
urated regression model indicated that these two separate
dimensions explain unique and significant sources of vari-
ability in behaviors, which highlights the robustness of dis-
parities across multiple dimensions of sexual orientation.
The theoretical reasoning for separation of these constructs
notwithstanding, post-hoc analyses of multicollinearity con-
firmed that sexual behavior and sexual identity were not so
highly correlated that, even empirically, one could be used
as a proxy for the other.
These operational manipulations provide opportunities for
additional explication of how sexual orientation ultimately
results in health disparities, since, to be clear, sexual minority
status does not, itself, cause poor health. The frequency with
which bisexual-identified adolescents reported the highest
odds of engaging in health risk behaviors lends support to
Zinik’s articulation of the ‘‘double closet,’’ where the effect
of stress for bisexual persons is enhanced because of hiding
same-sex activities from their heterosexual peers while simul-
taneously hiding heterosexual activities from their gay and
lesbian peers.
31
Bauer and Brennan
32
note that measurement
of bisexual behavior may cause an artificial association with
negative health outcomes. Unlike other response categories,
individuals must have had sex with at least two partners
over a time period and may be confounded with number
of sex partners, a variable which may reflect a general risk-
taking propensity irrespective of sexual orientation. Our re-
sults support this possibility, since respondents in the MA
YRBS who had not yet initiated sexual behavior were far
less likely to engage in any of the three health risk behaviors,
and bisexual identity did not diverge from gay or lesbian
identity to the extent that bisexual behavior diverged from
same-sex sexual behavior.
Disparities experienced by those who were ‘‘not sure’’ of
their sexual identity also provide additional insight. Austin
et al.
33
found that adolescents preferred sexual identity mea-
sures that allowed for intermediate options, such as ‘‘mostly
heterosexual;’’ reporting ‘‘not sure’’ may reflect a desire for
these intermediate categories. Additionally, ‘‘not sure’’ may
reflect the process in which an adolescent begins to reconcile
a newly discovered same-sex attraction with self-identity.
15,34
An examination of ‘‘not sure’’ among those who have had
more time to develop their sexual identity may yield very dif-
ferent results from our study of adolescents. Much remains to
be learned about what ‘‘not sure’’ substantively entails for
adolescents, though it appears to suggest a consequence of
being unable to benefit from heterosexual privilege.
The impending inclusion of sexual orientation on federal
health surveys necessitates consensus for best ways to opera-
tionalize and analyze these data to allow for meaningful
comparisons.
3,5
Acknowledging the previously mentioned
imprecision of ‘‘not sure’’ as a response option, we recom-
mend that when statistically viable researchers treat this
as a valid response group rather than recoding as missing
data. We also caution against aggregating bisexual and exclu-
sively same-sex response options when possible. Though
not the focus of this study, we recommend researchers
continue to stratify analyses by sex and formally test effect
modification by sex if sample size allows. Public health
Table 5. Adjusted Odds Ratios of Methamphetamine Use for Sexual Minorities: Six Operational Definitions of Sexual Behavior and Sexual Identity,
Massachusetts Youth Risk Behavioral Surveillance System Respondents, 2003–2007
Used methamphetamines Used methamphetamines
AOR (95%CI) AOR (95%CI)
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6
Women Women
Sexual behavior Sexual Identity
Any same sex (n =349) 4.1 (2.7, 6.3) Lesbian/bisexual (n =295) 6.8 (4.9, 9.4)
Only same sex (n =89) — 1.1 (0.40, 3.2) 1.1 (0.41, 3.2) Lesbian (n =43) — 10.4 (3.7, 29.0) 10.4 (3.8, 28.9)
Sex with both (n =260) — 5.3 (3.4, 8.2) 5.3 (3.4, 8.2) Bisexual (n =252) — 6.4 (4.7, 8.6) 6.4 (4.7, 8.6)
No sex (n =2249) — — 0.11 (0.06, 0.22) Not sure (n =112) — — 2.5 (1.1, 6.0)
Only opposite sex (n =2492) ref ref ref Heterosexual (n =4743) ref ref ref
Men Men
Sexual behavior Sexual identity
Any same sex (n =208) 4.1 (2.5, 6.7) Gay/bisexual (n =143) 5.2 (3.4, 7.9)
Only same sex (n =112) — 1.0 (0.45, 2.3) 1.0 (0.44, 2.3) Gay (n=76) — 5.2 (2.4, 11.2) 5.1 (2.4, 11.1)
Sex with both (n =96) — 10.2 (6.1, 16.9) 10.2 (6.1, 16.9) Bisexual (n =67) — 5.1 (2.5, 10.3) 5.1 (2.5, 10.3)
No sex (n =1969) — — 0.13 (0.08, 0.21) Not sure (n =104) — — 4.0 (2.4, 6.6)
Only opposite sex (n =2655) ref ref ref Heterosexual (n =4703) ref ref ref
All analyses are weighted and adjusted for year of data collection, race/ethnicity, and high school grade.
OPERATIONAL DEFINITIONS OF SEXUAL ORIENTATION 47
intersectionality research illustrates the varying effects of race
and class by sex,
35
so too should modeling strategies accommo-
date this perspective with sexual orientation when thedata can
support it. Notably, other research has indicated ‘‘discordance’’
between sexual behavior and sexual identity may be more
likely among racial and ethnic minority individuals.
36,37
There are notable limitations in this study, one of which is
the relatively small sample of sexual minority respondents in
the sample, despite the large overall sample size. Further-
more, the use of one state’s YRBS data hampers generalizabil-
ity of results. We strongly suspect changing the operational
definition of sexual orientation will have consequences in
any sample of LGB adolescents. However, we caution that
the specific nature of effects documented in this study will
vary across regions within the United States and countries
over the world given the diverse and changing sociocultural
and temporal contexts of sexual orientation. Social desirabil-
ity bias may be enhanced among students fearing disclosure
of their sexual orientation because the YRBS is administered
at school. The MA YRBS did not assess sexual attraction, so
we were unable to include this dimension in our operational
manipulations. We also acknowledge the limits of quantita-
tive methods to explore the heterogeneity captured by the
‘‘not sure’’ response option. Despite controlling for several de-
mographic factors to reduce the likelihood of confounding,
there are other unmeasured variables for which we could
not control (e.g., the extent of sexual identity disclosure).
This study makes notable contributions by empirically
demonstrating the consequences of operational definitions
in the study of LGB health disparities among adolescents.
Though the nature of secondary data analysis precludes the
ability to directly impact how sexual orientation is assessed,
we hope that by making an explicit distinction between mea-
surement and operationalization that we encourage research-
ers to spend additional time thinking about how to analyze
data and interpret results. Finally, it should be made clear
that we do not advocate for abandoning a great analysis for
a perfect one when the latter can never be. All statistical mod-
eling necessitates decisions that are not conceptually ideal.
Instead of impeding quantitative research with sexual minor-
ity adolescents, we intend the opposite: to enable the produc-
ers and consumers of research to engage in a more informed
contextualization of reported findings. The thoughtful analy-
sis of sexual orientation in public health data can help to elu-
cidate mechanisms and better inform those interventions and
policies aimed at severing the links between sexual orienta-
tion and poor health.
Acknowledgments
This research project was partially supported by a training
fellowship from the Summer Institute in LGBT Population
Health to D.D.M., J.R.B., and G.W.F. under award number
R25HD064426 from the Eunice Kennedy Shriver National
Institute of Child Health and Human Development
(NICHD). The project was also supported by a post-doctoral
fellowship to J.R.B. in an Institutional National Research
Service Award from the National Institute of Mental Health
(5T32MH020061).
Author Disclosure Statement
No competing financial interests exist.
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Address correspondence to:
Derrick D. Matthews, PhD, MPH
Department of Health Behavior
Gillings School of Global Public Health
The University of North Carolina at Chapel Hill, Campus Box 7440
Chapel Hill, NC 27599-7440
E-mail: derrick.matthews@unc.edu
OPERATIONAL DEFINITIONS OF SEXUAL ORIENTATION 49
