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Arch Sex Behav (2007) 36:385–394
DOI 10.1007/s10508-006-9088-5
ORIGINAL PAPER
Prevalence and Stability of Sexual Orientation Components
During Adolescence and Young Adulthood
Ritch C. Savin-Williams ·Geoffrey L. Ream
Received: 9 November 2004 / Revised: 27 May 2005 and 6 March 2006 / Accepted: 18 June 2006 / Published online: 29 December 2006
C
Springer Science+Business Media, Inc. 2006
Abstract Analyses of three waves (6 years) of the National
Longitudinal Survey of Adolescent Health data explored the
prevalence and stability of sexual orientation and whether
these two parameters varied by biologic sex, sexual orien-
tation component (romantic attraction, sexual behavior, sex-
ual identity), and degree of component. Prevalence rates for
nonheterosexuality varied between 1 and 15% and depended
on biologic sex (higher among females), sexual orientation
component (highest for romantic attraction), degree of com-
ponent (highest if “mostly heterosexual” was included with
identity), and the interaction of these (highest for nonhetero-
sexual identity among females). Although kappa statistics
testing for temporal stability across waves were significant,
they failed to reach acceptable levels of agreement and could
be largely attributable to the stability of opposite-sex rather
than same-sex attraction and behavior. Migration over time
among sexual orientation components was in both direc-
tions, from opposite-sex attraction and behavior to same-sex
attraction and behavior and vice versa. To assess sexual ori-
entation, investigators should measure multiple components
over time or abandon the general notion of sexual orientation
and measure only those components relevant for the research
question.
R. C. Savin-Williams ()
Department of Human Development, MVR Hall,
Cornell University,
Ithaca, New York 14853-4401
e-mail: rsw36@cornell.edu
G. L. Ream
School of Social Work, Adelphi University,
Garden City, New York
Keywords Gay .Homosexuality .Romantic attraction .
Sexual behavior .Sexual identity .Sexual orientation
Introduction
Sexual orientation is generally defined by whether one is
erotically attracted to males, females, or both (LeVay &
Valente, 2006). Assumed to be present from birth, either
because of genetics or prenatal hormones (Ellis, 1996;
Mustanski, Chivers, & Bailey, 2002), sexual orientation
is discernible through verbal and nonverbal indicators of
sexual and romantic attractions, erotic fantasies, sexual
behaviors, romantic relationships, and sexual identity labels.
Based on responses to inquiries about these components or
markers of sexual orientation, researchers group individuals
into sexual populations. The questions of who belongs in
a particular sexual group, on what basis, and for how long
are central for any viable paradigm for research on sexual
orientation (Diamond, 2003a; McConaghy, 1999;Savin-
Williams, 1990,2001,2005). Without these answers, it is
difficult to characterize with much confidence individuals
within sexual groups.
Prevalence rate of nonheterosexuality
The cross-cultural empirical literature reveals wide varia-
tions regarding the prevalence of sexual orientation groups,
based largely on definitional considerations. One review of
same-sex populations over the past decade (Black, Gates,
Sanders, & Taylor, 2000) found that the proportion of those
identifying as gay/bisexual was far smaller—often by a factor
of at least one half—than those engaging in same-sex behav-
ior. In the United States, the United Kingdom, and France,
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386 Arch Sex Behav (2007) 36:385–394
exclusive same-sex behavior rarely characterized more than
1% of the adult population (Sell, Wells, & Wypij, 1995).
If the definition was broadened, however, to include indi-
viduals who had sex with both sexes, had some degree of
same-sex attraction, or had at least one of the two, then the
proportion of “gays” expanded to nearly one fifth of the na-
tional population. In a representative survey of U.S. adults,
8% reported at least some degree of same-sex attraction,
7% had at least one same-sex behavior since puberty, and
2% identified as gay/lesbian/bisexual (Laumann, Gagnon,
Michael, & Michaels, 1994). In adolescent and young adult
populations, the data are consistent with studies of older
adults. Sixteen percent of Norwegian adolescents had some
degree of same-sex attraction, 7% if counting same-sex sex-
ual contact, and 3% if identifying those with a bisexual/gay
self-label (Wichstrøm & Hegna, 2003). Among Turkish uni-
versity students, 7% had sexual desire for the same sex but
only 2% had an orgasmic same-sex relationship and 2%
considered themselves to be homosexual or bisexual (Eskin,
Kaynak-Demir, & Demir, 2005).
In these studies, the sexes seldom differed regarding the
proportion who reported same-sex behavior or identity, usu-
ally in the 2–4% range and consistent with an earlier cross-
cultural review of adolescents and adults (Diamond, 1993).
When considering sexual/romantic attraction, however, sex
differences were more marked. Whereas males seldom re-
ported prevalence rates of sexual interest or attraction to
other males above 5%, females were far more likely to en-
dorse same-sex attraction: 21% of Norwegian adolescent fe-
males were interested in, attracted to, or had fantasies about
other females (Wichstrøm & Hegna, 2003); 23% of New
Zealand young females were sexually attracted to other fe-
males (Dickson, Paul, & Herbison, 2003); and 14% of U.S.
young adult females reported having some sexual attraction
to other females (Mosher, Chandra, & Jones, 2005).
Stability of nonheterosexuality
Although Laumann et al. (1994) expressed doubt about
the extent to which nonheterosexual sexual categories, be-
haviors, and attractions remained stable over time, most
investigators presume the stability of sexual orientation and
thus assess it at one point in time. This might be a particularly
problematic tactic with adolescent and young adult popula-
tions, a time in which individuals experiment with their sexu-
ality, deceive themselves or others about their unconventional
sexuality because of the stigma attached to nonheterosexual-
ity, or lack the prerequisite experience to know the long-term
directionality of their sexuality. Yet, researchers readily ac-
knowledge the existence of such sexual groups (“gay youth”)
with little evidence that these individuals will be in the same
group a month, a year, or a decade henceforth.
Evidence to support sexual orientation stability among
nonheterosexuals is surprisingly meager. In a short-term
longitudinal study among pre- and early adolescents, a
Cronbach’s alpha coefficient of .75 was reported over a
school year in “sexual questioning,” which was defined
as having low expectations in the future of participating
or experiencing heterosexual attractions and heterosexual
relationships (Carver, Egan, & Perry, 2004). Support for the
instability of sexual orientation is far more prevalent–in both
adult and adolescent populations. Among the 14% of Dutch
adult males who reported ever having physical attraction to
other males, about half noted that these feelings disappeared
later in life (Sandfort, 1997). Comparing sexual attraction in
a New Zealand birth cohort at two time periods, first when
participants were 21-year olds and then later as 26-year olds,
Dickson et al. (2003) found that the proportion of males who
reported at least occasional same-sex attraction increased
50% (from 4–6%) and 78% among females (from 9–16%).
Nearly all heterosexual males (98%) kept their opposite-sex
attraction; 12% of heterosexual females experienced at
least occasional same-sex attraction. Migration was in both
directions–from heterosexuality to homosexuality and vice
versa. Only 38% of exclusive same-sex attracted females
stayed in this group with the rest moving into “occasional”
same-sex attraction (38%) or exclusive opposite-sex at-
traction (25%). One half of female and one third of male
21-year olds with occasional same-sex attraction only had
opposite-sex attraction as 26-year olds.
Stability of sexual orientation depends on which compo-
nent is assessed. Consistent with the assertions of adoles-
cent focus groups (Friedman et al., 2004), sexual identity
and behavior are subject to considerable change during ado-
lescence and young adulthood for reasons that range from
sexual behavior opportunities that are more available in col-
lege and the work world than in high school to develop-
mental changes in the meaning of sexual feelings for a sex-
ual identity (Rodr´
ıguez Rust, 2002; Savin-Williams, 1998;
Weinberg, Williams, & Pryor, 1994). Diamond’s (2003b)
longitudinal study of young females is one of the few to
assess long-term stability differences in sexual orientation
components. Most (62%) young women changed their iden-
tity labels at least once because sexual identity categories did
not adequately represent the diversity of their circumstantial
sexual and romantic feelings. Over time, lesbian and bisexual
identities lost the most adherents and heterosexual and un-
labeled identities gained the most. What remained relatively
unchanged were reports of sexual and romantic attraction.
That is, a young woman might change her identity from
bisexual to heterosexual without undergoing a comparable
change in her attraction to females.
A retrospective study of adults assessed perceived
changes in multiple dimensions of sexuality, including sex-
ual fantasy, romantic attraction, sexual behavior, and sexual
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Arch Sex Behav (2007) 36:385–394 387
identity (Kinnish, Strassberg, & Turner, 2005). Change
scores were derived from participants’ ratings of their
sexuality for every 5-year period beginning with ages 16–20
years. Although most (97%) heterosexuals maintained their
heterosexual identity, nonheterosexuals frequently changed
their identity label over the life course: 39% of gay males,
65% of lesbians, 66% of male bisexuals, and 77% of female
bisexuals. The dimensional assessments of fantasy, attrac-
tion, and behavior reflected similar trends. Although roughly
90% of heterosexually identified individuals had none or
one point changes during their lifetime, the majority of gay
(52%), lesbian (80%), and bisexual (90%) identified individ-
uals had multiple changes on the dimensional variables. For
nonheterosexuals, sexual behavior changed more often than
romantic attraction and for all sexual identity categories,
except bisexuals, women changed more than men.
Research questions
Through secondary analyses of a longitudinal data set, the
present study examined the prevalence of sexual orienta-
tion components (romantic attraction, sexual behavior, sex-
ual identity) and the extent to which two components (ro-
mantic attraction, sexual behavior) were stable over time.
Consistent with the empirical literature, the prevalence of
same-sex behavior and identity was expected to be roughly
equal (2–4% range) and considerably less than that of same-
sex romantic attraction (greater than 10%). Females were
expected to evidence greater rates of nonheterosexuality than
males, especially when assessed by romantic attraction.
Although we predicted substantially greater agreement in
same-sex attraction and behavior between waves than would
be expected if raw prevalence was held constant and mi-
gration between categories was random, previous research
indicated that this stability would be considerably lower than
opposite-sex attraction and behavior. We expected same-sex
attraction would be more stable than sexual behavior if only
because behavior depends on opportunity while attraction
does not. Over time, migration in romantic attraction and
sexual behavior should be bi-directional, from heterosexual
to nonheterosexual and vice-versa although to what degree
was uncertain. Males and heterosexuals were expected to be
the most stable.
Cohen’s kappa inter-rater statistics were used as an in-
ferential test of temporal stability. Kappa is 0 if observed
and expected agreements are the same and approaches 1
as observed agreement exceeds expected agreement. The-
oretically, kappa can be negative if observed is lower than
expected agreement. Consistent with the interpretation scale
for standard levels of acceptable agreement for kappa statis-
tics provided by Landis and Koch (1977), in this study we
defined “good” stability as a level of .70.
Method
Participants
Data for this study were drawn from the first three waves
of the National Longitudinal Survey of Adolescent Health
(Add Health) (Udry & Bearman, 1998). The study was de-
signed to assess contextually mediated positive and negative
effects on adolescent (grades 7 through 12) health. The pri-
mary sampling frame was school-based, with a nationwide
sample of 80 high schools selected and a 70% response rate.
Comparable replacement schools were selected for schools
that declined to participate. If the recruited high school was
not for all grades 7 through 12, younger students were re-
cruited from middle and junior high schools that fed into
the sample high schools. A total of 132 public and private
schools in 80 communities participated.
In selecting students for the in-home interviews, the
within-school sample was split into sex by grade strata and a
random sample was taken within each stratum. Roughly 17
students per stratum per school pair were selected for a total
of 12,105 students in the core sample. Over-samples of Chi-
nese, Cuban, Puerto Rican, disabled, twins, and Black youth
with at least one parent holding a college degree were also
collected. Additionally, the entire student body of 16 diverse
schools was selected for in-home interviews to provide
data on peer networks. The total Wave 1 in-home interview
sample (Mage =15.8 years) included 20,747 individuals.
At Wave 2, one year later, 14,738 participants who had
not been in the 12th grade at Wave 1 were re-interviewed
(Mage =16.7 years). Wave 3 data were follow-up interviews
with 15,170 original Wave 1 respondents located by field
interviewers between August 2001 and April 2002 when
participants were between the ages of 18 and 26 years (M
age =21.7 years). To be included in this study’s analyses,
cases had to have valid data for all three waves plus a valid
“grand sample weight” value (n=18,924 for comparisons
using only Wave 1, n=13,570 for comparisons using only
Waves 1 and 2, n=14,322 for comparisons using only
Waves 1 and 3, n=10,828 for comparisons using Waves 2
and 3 or all three waves), indicating a positive probability,
however small, of inclusion in a national probability sample
of American adolescents.
Measures
Add Health interviews were conducted in two different
modes, one with interviewers asking questions and enter-
ing participants’ answers on a laptop computer and another
(audio computer-assisted self-interview, or ACASI) with par-
ticipants listening to recorded interview questions via head-
phones and entering their responses into the laptop. ACASI
was used for romantic attraction, sexual behavior, and sexual
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388 Arch Sex Behav (2007) 36:385–394
identity questions and when special candor was needed from
respondents.
Romantic attraction
Based on responses to the questions “Have you ever had a
romantic attraction to a female” and “Have you ever had a
romantic attraction to a male” at Wave 1 and “Since [time of
Wave 1 or 2 interview], have you had a romantic attraction
to a [male/female]” at Waves 2 and 3, participants’ attraction
was classified as opposite-sex, same-sex, both-sex, or none.
Sexual behavior
In Waves 1 and 2, participants could list up to six romantic
relationships that they had in the past 18 months, three ro-
mantic relationships and three non-romantic sexual relation-
ships, plus additional catch-all questions at the end. They
were asked for their partner’s sex and were given a stack
of cards with various relationship activities listed on them
and invited to keep or reject cards indicating events that
had happened in their relationships. Romantic relationships
were coded as sexual based on whether participants kept or
rejected the card “We had sexual intercourse.” Because par-
ticipants had read a definition of sexual intercourse that was
limited to penile-vaginal penetration in a previous section,
this was not an ideal operationalization of same-sex sexual
behavior. Non-romantic sexual relationships were classified
as sexual based on whether participants listed them as such.
Third, participants were asked about the biologic sex of ad-
ditional sex partners from the past 18 months not listed on
the previous two rosters. Youth were classified according
to whether their sex partners in the three assessments were
male, female, or both.
In Wave 3, participants reported information about all ro-
mantic relationships since the beginning of Wave 1, including
the question, “Please indicate whether [initials of person] is
male or female” and “Have you ever had sexual relations
with [initials of person].” Sexual relations were defined as
“vaginal intercourse (a man inserts his penis into a woman’s
vagina), oral sex (a person puts his or her mouth on another
person’s sex organs), or anal sex (a man inserts his penis into
his partner’s anus or asshole).” Although this assessment
of the sexual nature of a relationship was not substantially
different from the other waves, its greater sensitivity and
specificity may have contributed to Wave 3 results.
Sexual identity
In Wave 3 only, participants were given six possible re-
sponses to the question “Please choose the description that
best fits how you think about yourself:” “100% heterosexual
(straight),” “mostly heterosexual (straight), but somewhat
attracted to people of your own sex,” “bisexual—that is, at-
tracted to men and women equally,” “mostly homosexual
(gay), but somewhat attracted to people of the opposite sex,”
“100% homosexual (gay),” and “not sexually attracted to
either males or females.”
Statistical procedures
The Add Health data set was designed as a survey data set
with four sampling strata (north, south, east, and west re-
gions), 132 primary sampling units (schools), and a prob-
ability of each student within any school to be included in
the sample (grand sample weight). When possible, survey
procedures in STATA 8/SE (Statacorp, 2001)wereusedto
adjust for clustering and unequal probability of inclusion in
the sample. Survey estimation procedures weigh the data and
adjust the denominator df of F-tests to be more realistically
strict. Because survey-adjusted kappa is not available in Stata
8/SE, it was based on unweighted counts, although the cell
proportions were survey-adjusted.
Results
Table 1presents the prevalence rates of romantic attraction
and sexual behavior at all three waves and of sexual iden-
tity at Wave 3. Reports of having some degree of same-sex
romantic attraction ranged from 4.5–12.9%; the proportion
of participants reporting same-sex behavior was lower and
more constricted (1 to 3%). In Wave 3, 5.6% of males and
14.5% of females reported nonexclusive heterosexuality; the
majority of these individuals did not identify as gay/bisexual
but as “mostly heterosexual.” Thus, 97.2% of males and
95.8% of females self-labeled as predominantly or exclusive
heterosexual.
Tables 2and 3report stability across waves in attraction
and behavior, separately by sex. Analyses of attraction in
Table 2included all participants but Table 3included only
participants who had been sexually active both before Wave
1 and between Wave 1 and Wave 2. Confidence intervals of
kappas were thus wider in Table 3than in Table 2because
the valid N was smaller.
Although the between wave agreement data were high
(usually around 70% for attraction and 95% for sexual be-
havior), this was largely because of the stability of opposite-
sex attraction and behavior. The kappa statistics, reflect-
ing similarity between observed and expected levels of
agreement, were notably below the .70 level considered
acceptable. The data in Tables 2and 3also highlight the
high proportion of participants with same- and both-sex at-
traction and behavior that migrated into opposite-sex cat-
egories between waves. A much smaller percentage (but
larger total number) of opposite-sex attracted and behaving
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Arch Sex Behav (2007) 36:385–394 389
Table 1 Prevalence of romantic attraction, sexual behavior, and sexual identity across Waves by sex
Male Female
Wave 1 Wave 2 Wave 3 Wave 1 Wave 2 Wave 3
Adj. % Real nAdj. % Real nAdj. % Real nAdj. % Real nAdj. % Real nAdj. % Real n
Attraction
None 15.5 1283 23.4 1478 3.8 255 12.4 1115 16.9 1183 3.7 270
Both-sex 6.3 560 3.1 205 4.3 297 3.9 374 3.5 258 12.3 904
Same-sex 0.9741.4851.0771.4 130 1.0720.645
Opposite-sex 77.4 7371 72.1 4786 91.1 6138 82.3 8015 78.6 5407 83.3 6336
Behavior
None 67.8 6074 64.4 4113 23.8 1666 68.8 6519 64.4 4416 19.9 1568
Both-sex 0.7650.6351.0620.8750.5262.5 168
Same-sex 0.4380.7591.3930.4420.5400.547
Opposite-sex 31.2 3111 34.3 2405 74.0 4946 30.1 2998 34.6 2474 77.1 5772
Identity
Heterosexual 94.0 6314 85.1 6392
Mostly heterosexual 3.2 207 10.7 761
Bisexual 0.641 2.6 188
Mostly homosexual 0.648 0.745
Homosexual 1.286 0.539
No attraction 0.423 0.545
Note. “Adj. %” refers to sample proportions weighted using survey procedures in Stata/SE 9.1 to reflect, as accurately as possible, actual population
proportions. They do not correspond exactly to proportions based on the unweighted sample.
individuals migrated to nonheterosexual categories; a similar
migration occurred for those without attraction and/or sexual
behavior.
Explaining the variance in stability was undertaken in a
series of logistic regressions. Tables 4and 5report results
predicting, from biologic sex and the response in the ear-
lier wave, the likelihood of a consistent response in the later
wave. They each show the results of nine models, three for
each between-wave comparison. One model was run with
only biologic sex as the independent variable (reference cat-
egory =male), one was run with only sexual orientation
as the independent variable (reference category =hetero-
sexual), and one was run with the interaction between sexual
orientation and biologic sex. Each included the overall, main,
and interaction effects of sexual orientation component and
biologic sex.
Analyses reported in Table 4revealed significant biologic
sex, romantic attraction, and biologic sex by attraction differ-
ences. All attraction categories other than opposite-sex were
associated with a lower likelihood of stability over time. That
is, individuals reporting any same-sex attractions were more
likely to report subsequent shifts in their attractions than
were individuals without any same-sex attractions. Female
sex had an overall effect of greater stability between Wave 1
and Wave 2 but no overall effect on stability between either
Wave 1 or Wave 2 with Wave 3. Female sex had a main effect
for lower stability. Combined with the null overall effects,
this indicated that Wave 1 and Wave 2 opposite-sex attracted
females were less likely than opposite-sex attracted males to
give a consistent report of opposite-sex attraction in Wave 3,
but both-sex and no-sex attracted females were more likely
than comparable males to give a consistent report in Wave 3.
Analyses in Table 5included youth who reported sexual
activity to test hypotheses about change (not the initiation) in
sexual activity. Same- and both-sex behavior was collapsed
into one category because exclusively same-sex behavior was
so rare in all three waves (usually <1%) that an adequate cell
number to perform analyses could not be achieved. During
Wave 1 and Wave 2, same/both-sex behavior was the only
important predictor of an instability report; there was no
effect for biologic sex. Those who engaged in same/both-
sex behavior during the first two waves were more likely
to report Wave 3 exclusive opposite-sex behavior than those
who engaged in opposite-sex behavior were to later report
same/both-sex behavior.
Discussion
Prevalence rates
The prevalence rate for nonheterosexuality varied between
1% (Wave 1 youth engaging in same-sex behavior) and
15% (Wave 3 females with any nonheterosexual identity)
and depended on biologic sex (higher among females),
sexual orientation component (highest for romantic attrac-
tion), degree of component (highest if “mostly hetero-
sexual” is included with identity), and the interaction of
these (highest for nonheterosexual identity among females).
As young adults, females reported the highest rates of
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390 Arch Sex Behav (2007) 36:385–394
Table 2 Across-wave agreement in romantic attraction by sex
Male (%) Female (%)
Wave 2 Both Same Opposite Wave 2 Both Same Opposite
Wave 1 Wave 3 Wave 3
None/missing 47.12.61.948.440.62.00.856.6
Both-sex 19.714.54.561.416.020.35.458.2
Same-sex 32.55.310.351.919.813.76.759.8
Opposite-sex 18.72.20.978.113.32.90.783.2
Percent agree 68.7% Exp. % agree 60.2% Cohen’s kappa
.212
95% CI (.193, .230) Percent agree 74.3% Exp. % agree 66.8% Cohen’s
kappa .225
95% CI (.206, .243)
Wave 1 Wave 3 Wave 3
None/missing 12.04.01.083.011.55.40.282.9
Both-sex 4.18.74.882.43.041.13.252.6
Same-sex 1.222.94.371.75.036.53.255.3
Opposite-sex 2.23.70.693.52.611.60.585.3
Percent agree 75.6% Exp. % agree 72.6% Cohen’s kappa
.109
95% CI (.094, .124) Percent agree 74.4% Exp. % agree 71.0% Cohen’s
kappa .114
95% CI (.100, .129)
Wave 2 Wave 3 Wave 3
None/missing 8.63.60.787.17.87.40.684.2
Both-sex 2.718.78.770.02.343.54.449.9
Same-sex 4.88.217.269.812.645.21.141.1
Opposite-sex 2.03.90.593.62.211.60.585.7
Percent agree 70.8% Exp. % agree 67.5% Cohen’s kappa
.104
95% CI (.088, .120) Percent agree 71.0% Exp. % agree 67.6% Cohen’s
kappa .107
95% CI (.091 .123)
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Arch Sex Behav (2007) 36:385–394 391
Table 3 Across-wave agreement in sexual behavior by sex
Male Female
Both Same Opposite Both Same Opposite
Wave 1 Wave 2
Both-sex 8.8% 17.3% 73.9% 16.5% 0.1% 83.4%
Same-sex 0.0% 39.0% 61.0% 26.1% 18.8% 55.0%
Opposite-sex 1.0% 0.6% 98.4% 1.1% 1.7% 97.3%
Percent Agree
95.71%
Exp. % Agree
93.93%
Cohen’s kappa
.293
95% CI (.254,.332) Percent Agree
93.76%
Exp. % Agree
92.88%
Cohen’s kappa
.124
95% CI (.087,.161)
Wave 1 Wave 3
Both-sex 2.1% 10.2% 87.7% 9.0% 2.4% 88.7%
Same-sex 0.0% 28.4% 71.6% 22.9% 0.3% 76.8%
Opposite-sex 1.4% 0.5% 98.1% 3.2% 0.3% 96.5%
Percent Agree
95.63%
Exp. % Agree
95.05%
Cohen’s kappa
.116
95% CI (.081,.151) Percent Agree
93.24%
Exp. % Agree
92.60%
Cohen’s kappa
.086
95% CI (.051,.120)
Wave 2 Wave 3
Both-sex 4.3% 0.0% 95.7% 0.0% 3.9% 96.1%
Same-sex 5.2% 48.1% 46.8% 0.6% 6.1% 93.4%
Opposite-sex 1.3% 0.4% 98.3% 3.7% 0.6% 95.7%
Percent Agree
95.84%
Exp. % Agree
93.85%
Cohen’s kappa
.324
95% CI (.285,.362) Percent Agree
94.31%
Exp. % Agree
93.93%
Cohen’s kappa
.064
95% CI (.030,.097)
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392 Arch Sex Behav (2007) 36:385–394
Table 4 Binary logistic
regressions predicting likelihood
of stability between attraction in
a later Wave and attraction in an
earlier Wave from attraction in
the earlier wave with biologic
sex and attraction as predictors
Independent variable
Romantic attraction during a Wave Wave 1 Wave 2 Wave 1
Dependent variable
Stability with attraction during a Wave Wave 2 Wave 3 Wave 3
Female 0.297∗∗∗ −0.024 −0.091
Constant 0.774∗∗∗ 0.906∗∗∗ 1.118∗∗∗
Model F(1,128) 31.49∗∗∗ 0.19 2.76
No attraction −1.665∗∗∗ −4.548∗∗∗ −4.138∗∗∗
Both-sex attraction −3.055∗∗∗ −2.896∗∗∗ −3.460∗∗∗
Same-sex attraction −3.860∗∗∗ −4.206 −5.399∗∗∗
Constant 1.431∗∗∗ 2.143∗∗∗ 2.124∗∗∗
Model F(3,126) 248.55∗∗∗ 347.27∗∗∗ 340.10∗∗∗
Female 0.324∗∗∗ −0.894∗∗∗ −4.658∗∗∗
No attraction −1.389∗∗∗ −5.044∗∗∗ −5.017∗∗∗
Both-sex attraction −3.048∗∗∗ −4.155∗∗∗ −5.770∗∗∗
Same-sex attraction −3.434∗∗∗ −4.252∗∗∗ −0.909∗∗∗
Female ×no attraction −0.589∗∗∗ 0.785∗∗ 0.864∗∗∗
Female ×both-sex attraction 0.087 2.102∗∗∗ 2.903∗∗∗
Female ×same-sex attraction −0.792 −2.063∗0.593
Constant 1.272∗∗∗ 2.683∗∗∗ 2.664∗∗∗
Model F(7,122) 119.95∗∗∗ 196.51∗∗∗ 164.57∗∗∗
N 13474 10766 14322
∗p<.05, ∗∗p<.01, ∗∗∗p<.001.
nonheterosexuality. Both the component per se and the de-
gree to which each component must be present to count as
“gay” were critical. If a heterosexual identity included only
those individuals who characterized themselves as 100% het-
erosexual, then the prevalence rate of nonheterosexuality
tripled the number who identified as gay/lesbian/bisexual.
So, too, if having romantic attraction to both sexes counted
as same-sex oriented, then the prevalence rate was nine times
higher than if the criterion was exclusive same-sex attraction.
This “how much” consideration has seldom been ad-
dressed in previous reports of homosexuality, despite Kinsey,
Pomeroy, and Martin’s (1948) early argument, renewed by
McConaghy (1999), against a dichotomous construction of
sexual orientation. Dunne, Bailey, Kirk, and Martin (2000)
concluded that “there was little evidence for true bipolarity in
sexual orientation” (p. 556). If sexual orientation is a contin-
uous construct, then the relevant research question is, “How
frequent should sexual behavior with one or the other sex be,
and/or how strong or frequent should sexual attraction to one
or the other sex be in order to classify a respondent as homo-
sexual, bisexual, or heterosexual?” (Sell et al., 1995, p. 245).
Subjects who are “bisexual leaning toward the heterosexual
side” are nearly always excluded from gay/bisexual samples
(e.g., D’Augelli, Hershberger, & Pilkington, 2001). That is,
although individuals with a significant amount of homosexu-
ality (however defined) are usually treated as belonging to the
class identified as gay, the critical consideration is whether
having “any” same-sex sexuality qualifies as nonheterosex-
uality. How much of a dimension must be present to tip
the scales from one sexual orientation to another was not
Table 5 Binary logistic
regressions predicting likelihood
of stability between sexual
behavior in a later Wave and
sexual behavior in an earlier
Wave from sexual behavior in
the earlier Wave with biologic
sex and sexual behavior as
predictors
Independent variable
Sexual behavior during a Wave Wave 1 Wave 2 Wave 1
Dependent variable
Stability with sexual behavior during a Wave Wave 2 Wave 3 Wave 3
Female −0.323 −0.412 −0.422
Constant 3.161∗∗∗ 3.100∗∗∗ 3.064∗∗∗
Model F(1,123) 1.72 2.95 3.64
Same- or both-sex behavior −4.795∗∗∗ −4.675∗∗∗ −5.229∗∗∗
Constant 3.794∗∗∗ 3.461∗∗∗ 3.573∗∗∗
Model F(1,123) 201.91∗∗∗ 175.12∗∗∗ 217.09∗∗∗
Same- or both-sex behavior −4.868∗∗∗ −4.744∗∗∗ −5.488∗∗∗
Female −0.517 −0.960∗∗ −0.610∗
Female ×same- or both-sex behavior 0.068 −1.232 0.446
Constant 4.091∗∗∗ 4.068∗∗∗ 3.920∗∗∗
Model F(3,121) 68.69∗∗∗ 63.81∗∗∗ 72.58∗∗∗
N 2965 3337 3938
∗p<.05, ∗∗p<.01, ∗∗∗p<.001.
Springer
Arch Sex Behav (2007) 36:385–394 393
resolved with the present data, only that such decisions mat-
ter in terms of prevalence rates.
The present data were also consistent with cross-cultural
investigations which reported that the proportion of non-
heterosexual participants dropped steeply as the inclusion
criteria became more singularly same-sex focused. The 16%
of Norwegian youth who reported some attraction to the
same sex was reduced to 5% if only those with bisexual and
same-sex attraction were included and to 1% if only those
with exclusive same-sex attraction was the criterion. Sim-
ilarly, the proportion dropped from 11% to 3% to .50% if
the criteria were “not exclusively heterosexual,” “bisexual
or homosexual identity,” and “exclusive homosexual iden-
tity,” respectively (Wichstrøm & Hegna, 2003). In a study
with New Zealander adults (Fergusson, Horwood, Ridder, &
Beautrais, 2005), the construction of nonheterosexual groups
depended on the stringency of the criteria applied and on bi-
ologic sex. Females were far more likely than males to be
included in the nonheterosexual groups: 14% versus 5% in
the predominantly heterosexual group and 4% versus 2% in
the predominantly homosexual group. The sex difference—
females more likely than males to report minor degrees of
same-sex sexuality—has been a common finding across var-
ious cultures and age groups, including Australia (Smith,
Rissel, Richters, Grulich, & de Visser, 2003), Great Britain
(Wellings, Field, Johnson, & Wadsworth, 1994), Thailand
(Van Griensven et al., 2004), and the United States (Lippa,
2000). Thus, the answer to the question “How many gays are
there?” depends on which component of sexual orientation
(behavior, attraction, identity) is used, how much of a com-
ponent must be present to determine the cut-off point, and
which biologic sex is being assessed.
Stability of sexuality
Although the stability of sexual orientation components over
time was relatively high, this was primarily due to the sta-
bility of opposite-sex attraction and behavior. Participants
indicating nonheterosexuality in Wave 1 were often not the
same individuals who indicated nonheterosexuality one and
five years later. Despite this heterosexual migration, non-
heterosexual prevalence rates did not decline, indicating that
even as individuals were abandoning the ranks of nonhetero-
sexuality to join the heterosexual majority, a small proportion
(but larger number) of opposite-sex attracted and behaving
individuals and those with no attraction or sexual behavior
were replacing them.
The instability of same-sex romantic attraction and behav-
ior (plus sexual identity in previous investigations) presents
a dilemma for sex researchers who portray nonheterosexu-
ality as a stable trait of individuals. This is problematic not
only in the Add Health data—which have been used to de-
fine “gay youth” in a large number of investigations (e.g.,
Russell & Joyner, 2001)—but also, as indicated in the lit-
erature reviewed in this article, for adults. That is, sexual
orientation instability may not be simply a “developmen-
tal” issue (adolescents as experimenters) but a conceptual or
measurement problem. Subjects might not understand what
constitutes “romantic attraction”—is it romantic attraction
to a specific male or female or to males or females in gen-
eral? Engaging in same-sex behavior depends on the time
frame that counts (“ever” or “last year”), opportunities to
find desired sexual partners, pressures to fit in and act het-
erosexually, and understandings about what constitutes sex.
Whether one identifies as gay might well be contingent on
perceived stereotypes of gays (“I’m not one of those!”) or
political inclinations (lower for traditionalists).
Given that most sex researchers define a sexual popula-
tion based on a single measure of sexual orientation at one
point of time, it is highly likely that some same-sex ori-
ented individuals are excluded and some heterosexuals (e.g.,
those with same-sex behavior) are misidentified as gay. Re-
searchers would be best assured of a nonheterosexual sample
if they included those with some degree of multiple sexual
orientation components over time. Another alternative is to
forsake the general notion of sexual orientation altogether
and assess only those components relevant for the research
question. For example, to assess HIV transmission, mea-
sure sexual behavior; to assess interpersonal attachments,
measure sexual/romantic attraction; and to assess political
ideology, measure sexual identity. Few data sets offer the
amenities suitable for either approach.
Acknowledgements This research uses data from Add Health, a
program project designed by J. Richard Udry, Peter S. Bearman,
and Kathleen Mullan Harris, and funded by a grant P01-HD31921
from the National Institute of Child Health and Human Develop-
ment, with cooperative funding from 17 other agencies. R. R. Rind-
fuss and B. Entwisle assisted in the original design. To obtain
data files, please contact Add Health, Carolina Population Center,
123 W. Franklin Street, Chapel Hill, North Carolina 27516-2524
(www.cpc.unc.edu/addhealth/contract.html).
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