Journal of Bisexuality

ISSN: 1529-9716 (Print) 1529-9724 (Online) Journal homepage: https://www.tandfonline.com/loi/wjbi20

Cluster Analysis of the Klein Sexual Orientation

Grid in Clinical and Nonclinical Samples: When
Bisexuality Is Not Bisexuality

James D. Weinrich, Fritz Klein, J. Allen McCutchan, Igor Grant & The HNRC
Group

To cite this article: James D. Weinrich, Fritz Klein, J. Allen McCutchan, Igor Grant & The HNRC
Group (2014) Cluster Analysis of the Klein Sexual Orientation Grid in Clinical and Nonclinical
Samples: When Bisexuality Is Not Bisexuality, Journal of Bisexuality, 14:3-4, 349-372, DOI:
10.1080/15299716.2014.938398

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Journal of Bisexuality, 14:349–372, 2014
Copyright © Taylor & Francis Group, LLC
ISSN: 1529-9716 print / 1529-9724 online
DOI: 10.1080/15299716.2014.938398

Cluster Analysis of the Klein Sexual

Orientation Grid in Clinical and Nonclinical
Samples: When Bisexuality Is Not Bisexuality

JAMES D. WEINRICH
HIV Neurobehavioral Research Center, Department of Psychiatry, University of California,
San Diego, San Diego, California, USA

FRITZ KLEIN1
Private Practice, San Diego, California, USA
J. ALLEN MCCUTCHAN, IGOR GRANT, and THE HNRC GROUP
HIV Neurobehavioral Research Center, University of California, San Diego, San Diego,
California, USA

A cluster analysis of the Klein Sexual Orientation Grid (KSOG)

in three samples (Internet-recruited men and women; HIV study
men) resulted in objectively determined 4- or 5-cluster classifica-
tions (such as “bi-heterosexual” or “bi-bisexual”). Group means
and standard deviations on the KSOG’s 21 items revealed that
overtly erotic items (sexual fantasies, sexual behavior, sexual at-
traction) and self-identification items were more uniform within
groups than social items (emotional preference, socialize with,
lifestyle) were. The bisexual cluster in the HIV sample was distinctly
different from all of the bisexual main sample clusters. Attempts to
generalize from this clinical bisexual group to a larger population
would be doomed to failure. This underscores the importance of re-
cruiting nonclinical samples if one wants insight into the nature of
bisexuality in the population at large. Our data empirically confirm
many previous nonempirical warnings against clinical samples in
studies of sexual orientation.

1
Deceased 2006.
Affiliations and financial support are those in effect when this article was first prepared.
Address correspondence to James D. Weinrich, P.O. Box 2421, El Cajon, CA 92021, USA.
E-mail: journalbi@gmail.com

349
350 Journal of Bisexuality

KEYWORDS sexual orientation, bisexuality, cluster analysis,

Internet, Klein Sexual Orientation Grid

INTRODUCTION

Sexual orientation is one of sexology’s thorniest concepts (Weinrich,

1987/2013). Seemingly simple—is someone sexually attracted to or aroused
by members of her or his own sex, or by the other sex?—it is philosophically
extremely complex (Klein, 1993). How are ‘homosexuality,’ ‘heterosexuality,’
and ‘bisexuality’ properly defined? Using self-report? Genital plethysmog-
raphy? Interviewer ratings? Taking into account fantasies? Behaviors?
Ideals?
This complexity is rarely embedded in the design of scientific experi-
ments. Scientists are aware of the philosophical difficulties of a dichotomous
division into “homosexual” and “heterosexual” categories, yet many scien-
tists (even, at times, the senior author of this article) press ahead with the
analysis of their data using dichotomous categories in spite of this aware-
ness. Sometimes this could be done with some justification—for example,
when researchers divide a sample into “tall people” and “short people” even
though they know that the distinction is relative, and that any dichotomiza-
tion is arbitrary. But sometimes such a strategy is undertaken without serious
comprehension of the consequences.
Understanding bisexuality is the key to understanding sexual orien-
tation. On many sexual orientation measures, bisexuality is intermediate
between homosexuality and heterosexuality, as it is in the widely used
“Kinsey scale” (a bipolar scale ranging from 0–6; see Kinsey, Pomeroy, &
Martin, 1948; McWhirter, Reinisch, & Sanders, 1990). In a bipolar measure
the two poles can only be averaged, not combined (e.g., no one can be
tall and short with respect to the same measure at the same time). With
other measures, bisexuality is a category applied to individuals who have
high levels of sexual interest in men and women. (This is analogous to the
“androgynous” category of Bem’s masculinity and femininity scales: Bem,
1981.) In this latter view, bisexuality is the “combination” of homosexuality
and heterosexuality, not a compromise between the two.
This article uses the exploratory statistical technique of cluster analy-
sis to empirically assign individual participants to discrete sexual orientation
categories. It is one in a series of reports that use this clustering solution
(e.g., Klein & Weinrich, 2014/this issue; Weinrich & Klein, 2002). The two
main study samples (male and female) were drawn from two sources (see
Method): bisexual meetings and support groups, and through electronic in-
terest groups and newsgroups (including private commercial networks such
as CompuServe and public ones such as Usenet). These participants certainly
do not constitute a random sample of any population. We chose this method
 J. D. Weinrich et al. 351

of recruitment because we wanted male and female samples spanning the

sexual orientation spectrum that would be roughly uniformly distributed
across that spectrum—and for that purpose such convenience sampling is
adequate (sampling issues are addressed in the Discussion). Moreover, this
method is relatively inexpensive.
For some analyses, we compare our findings to those obtained in an
even larger sample for which sexual orientation information is available: the
HIV Neurobehavioral Research Center (HNRC) at the University of Califor-
nia, San Diego. This HIV study sample consists of men only and contains
only a modest proportion of heterosexuals. Although a homosexual sexual
orientation was not a prerequisite for participation in this study—and bi-
sexuals were welcome—it was recruited in a context in which it would be
reasonably accurate to call it a predominantly gay sample.
In this article, we describe the sampling and cluster analyses in de-
tail. Then we calculate the mean (M) and standard deviation (SD) of each
cluster’s scores on the 21 items used in the cluster analysis itself. Next, we
present each cluster’s scores on demographic variables. Finally, we discuss
the meaning of these results and draw conclusions about the nature of sexual
orientation variability in our samples. We believe that this report is one of the
first to use cluster analysis to discern discrete sexual orientation categories
empirically.

METHOD
Participants and Procedures: Main Study (Women and Men)
The first participants were recruited at a meeting of a social discussion group
aimed at bisexuals in a large city in the U.S. Southwest. The first two authors
attended the meeting, explained the purposes of the study, and distributed
questionnaires. Responses were entirely voluntary and confidential. Most
participants completed the questionnaire before leaving; a few returned them
by mail. Although we did not calculate a precise response rate, the study was
received enthusiastically; we estimate that more than 90% of those attending
the meeting completed the study.
The second batch was recruited in New York by the second author, who
attended a large 1994 political event in which many bisexuals participated.
One day of this event was devoted to a series of workshops and speeches
on bisexual issues. Questionnaires were made available near the site of these
events, and this author encouraged passersby to complete the questionnaire.
Most who accepted the questionnaire completed it and dropped it into a
specified box; a few questionnaires were returned later by mail. Again, there
was no formal return rate calculated, because it would be difficult to decide
what is the proper base rate upon which to base this percentage. If it were to
be calculated with respect to attendance as a whole, response rate would be
352 Journal of Bisexuality

very low; if with respect to the number of those accepting a questionnaire,

it was probably more than 75%.
We decided we wanted a larger sample of bisexuals and hit upon the
idea of recruiting more participants through the Internet (an idea which a
few thousand academics seemed to hit upon independently at approximately
the same time). Of course, the Internet contains several electronic meeting
places through which bisexuals can contact each other and discuss issues
pertaining to bisexuality. The Internet, we thought, might also permit us to
contact heterosexuals and homosexuals roughly comparable to the bisexual
sample with little expenditure of resources on questionnaire distribution,
data entry, and so on.
Accordingly, we prepared a brief description of the topics covered by,
and the purpose of, this study and distributed it through several commercial
online services’ special interest groups for sexual topics (a Human Sexuality
Forum, Gay and/or Bisexual Interest Groups, etc.). This description was also
posted to several Usenet (i.e., Internet) newsgroups (soc.singles, soc.couples,
soc.motss [members of the same sex], soc.bi, alt.homosexuality, alt.bi, and
alt.polyamory). The description invited readers to e-mail a request for the
questionnaire to the first author who examined the request and verified that
it contained a statement that the sender wished to receive the questionnaire
and was age 18 or older. Those whose e-mail did not contain this information,
or who did not specify their sex, were sent a reminder to return a complete
request. We did not reply to the one person who stated that he was not yet
age 18. These recruitment materials, as well as the questionnaires themselves,
are provided online as supplemental data.
The questionnaire materials began with a description of the study and
then moved to a lengthy discussion of confidentiality issues. Participants
were warned that returning their completed questionnaires through the In-
ternet or the commercial service would not by any means guarantee their
confidentiality. (Most Internet devotees know that their e-mail messages are
about as private as postcards sent through the mail; most arrive unread,
but outsiders can probably read one if they are highly motivated to do so.)
Nevertheless, most respondents chose this route. A somewhat more secure
alternative, used by about a dozen respondents, was to print their completed
questionnaire and then fax it back to the senior author. Participants had been
warned that this, too, was risky if complete anonymity was desired. The rest
of the questionnaires (fewer than 10) were returned anonymously by mail.
To ensure confidentiality and to prevent identification based upon iden-
tifiers linked to the participants, we gathered only a minimum of demo-
graphic information from each person. When the earliest few respondents
turned out to be predominantly male, we waited to e-mail questionnaires
to women until a sufficient number of requests had accumulated so that
the arrival of a completed female questionnaire would not betray its origins.
Once the data were safe in the databanks, all identifiers (e-mail addresses,
etc.) were removed from the electronic forms and destroyed; we can no
 J. D. Weinrich et al. 353

longer identify which questionnaire recipients actually returned responses.

The response rate from the electronically distributed questionnaires was ap-
proximately 50%.
Faxed responses were reconverted to computer-readable ASCII charac-
ters by using optical character recognition software from Expervision, Inc.
Electronic responses were converted to database format automatically by
a parsing program written by the first author. Printed questionnaires were
entered into the databanks by hand.
Initial recruitment produced a sample that was roughly 75% male and
25% female. We had been warned that the ratio would probably be about
90/10, so we were gratified, but we wished to boost the percentage of women
respondents even more. We reposted the invitation on the same commer-
cial and Internet interest groups (adding a new newsgroup, soc.women),
summarizing our recruitment results to date and specifically asking for more
women to respond. This improved the sex ratio substantially.
For the analyses, men and women were separated, and will be referred
to as main study men (MS men) and main study women (MS women). How-
ever, everyone in these samples was recruited at the same times, using the
same procedures, as described above. Demographic and other information
about the participants will be presented in the Results.

Participants and Procedures: HIV Study

The HIV sample had already been recruited from the membership of a previ-
ous longitudinal study of HIV in San Diego, by advertisements placed in local
newspapers, by word of mouth, and by referral from the HIV care facilities as-
sociated with large local hospitals. Inclusion criteria were: Male, age 18 to 49
at entry, and having more than 9 years of education. Exclusion criteria were:
History of alcohol or other psychoactive substance use disorders in the past
12 months, intravenous drug use (ever), clinical diagnosis of AIDS dementia
complex, chronic medical illness (e.g., chronic obstructive pulmonary dis-
ease), or neurologic disorder unrelated to HIV (e.g., head trauma). Because
this was an HIV study, most of the participants were HIV positive, but about
25% were recruited as HIV negative controls. Participants were screened
in a face-to-face interview and signed a consent form. Most—but far from
all—participants reported a sexual orientation of homosexual or bisexual.
Participants used in the present study were the 620 who had completed the
Klein Sexual Orientation Grid (KSOG; Klein, Sepekoff, & Wolf, 1985).

Instruments
Participants in both samples completed the KSOG (Klein et al., 1985). As
shown in Figure 1, this questionnaire consists of 21 items grouped into
seven variables (labeled A through G). For each variable participants rate
themselves three times (for present, past, and ideal) on a scale from 1 to 7,
354 Journal of Bisexuality

Klein Variable Present Past Ideal

A Sexual Attraction
B Sexual Behavior
C Sexual Fantasies
D Emotional Preference
(whom you love and like)
E Socialize With
(men vs. women)
F Lifestyle (sexual orientation
of people with whom
you spend time)
G Self-Identification

Definitions of rating scale values

Value Scale for A – E Scale for F – G

1 Other sex only Heterosexual only

2 Other sex mostly Heterosexual mostly

3 Other sex somewhat more Heterosexual somewhat more

4 Both sexes equally Heterosexual/homosexual equally

5 Same sex somewhat more Homosexual somewhat more

6 Same sex mostly Homosexual mostly

7 Same sex only Homosexual only

FIGURE 1 Klein Sexual Orientation Grid.

where 1 indicates the most other-sex or heterosexual response and 7 represents

the most same-sex or homosexual response. (Note that on our questionnaire
and in our tabulations, these three time-related aspects are listed in the order
of present, past, and ideal. This is different order than the one used on the
original KSOG, namely past, present, and ideal.)
Participants in the MS completed a few additional questionnaires whose
items will be analyzed in future articles. The full set of questionnaires is
available online as supplemental data.
Demographic information was also gathered: in all samples, age, educa-
tion, and (heterosexual) marital status; in the MS (both sexes), employment
status, and homosexual relationship status (coupled or uncoupled).

Analyses
Data were entered into computers, scored, and analyzed using JMP version
3.0.1 from the SAS Institute.
 J. D. Weinrich et al. 355

One of the major goals of this study was to devise an empirical method
to assess sexual orientation categories using the KSOG in a statistically more
sophisticated way than has been done in the past. Correlations among some
KSOG items are typically very high, especially in samples of a predominant
sexual orientation (e.g., in male AIDS samples such as that described by
Weinrich et al., 1993). In this case they were high enough that we were
unable to perform a factor analysis of KSOG scores due to the high statistical
interdependence of some of these items.
Instead, we performed a cluster analysis of the 21 KSOG items, using
hierarchical agglomerative clustering by participant. Clustering was done
separately for each of the three samples: MS women, MS men, and HIV
study men. In agglomerative clustering, each participant begins in his/her
own, single-member “cluster.” The two clusters (individuals) with the most
similar scores on the KSOG are then agglomerated into a single cluster, and
cluster distances are recomputed. Then the two next most similar clusters are
combined and distances recomputed. This process continues until all clusters
are combined. Finally, a dendrogram (cluster tree) is generated showing the
history of how clusters were combined.
This type of clustering is an exploratory data technique. It does not
generate any statistical test, and interpretation of the tree is not always un-
ambiguous. However, certain qualitative characteristics of the dendrogram
can be informative and suggest further analyses—some of which might reveal
important relationships.
If the underlying distribution is bimodal (or even discrete), then the ag-
glomeration pattern will reveal the existence of those subgroups empirically.
For example, a cluster analysis of the heights of all the human beings in an
elementary school building will reveal short people (students) and tall peo-
ple (teachers) segregating in two different clusters. In contrast, a stair-step
pattern of agglomeration, in which individuals are added mostly one at a
time to an increasingly large main cluster, strongly suggests that there is no
“graininess” or clumping in the underlying distribution.

RESULTS

Demographic tabulations for the samples are presented in Table 1. The MS

was about two thirds male, young middle age, and very highly educated.
Most were employed full-time or were students; about one half had been
married at least once, and about one seventh were currently in a homosexual
relationship. About one third were recruited through the bisexual samples,
two thirds through the Internet. The MS women were on average about
4 years younger than the MS men, were twice as likely to be students, and
about two thirds as likely to be employed full time—the only statistically
significant demographic differences between the sexes.
 TABLE 1 Demographic Characteristics of the Samples

356
Main Study
Females Males HIV Males Females vs. Males Main vs. HIV
N 120 212 620 Statistic p Statistic p

Age M 32.0 36.2 32.5 12.4 F .0005 37.1 F .0001

SD ±10.0 ±10.5 ±7.3
Minimum 18 18 18
Median 30 35 31
Maximum 61 66 60
Education
Some high school 0% 1% 2% 2.2 χ 2 n.s. 133.6 χ 2 .0001
High school graduate 2% 4% 29%
Some college 31% 27% 40%
College graduate 35% 38% 22%
Higher degree 32% 31% 7%
Employment 30.0 χ 2 .0001
Full-time 48% 74%
Part-time 17% 7%
Retired 0% 2%
Student 28% 14%
Unemployed 8% 3%
Marital status 0.98 χ 2 n.s. 57.0 χ 2 .0001
Single 46% 51% 75%
Married 40% 35% 12%
Divorced 14% 14% 13%
Homosexual Coupled 14% 18% 0.80 χ 2 n.s.
relationship Not coupled 86% 82%
status
Recruitment Bisexual groups 32% 33% 0.03 χ 2 n.s.
source Internet 68% 67%
Note. Percentages may not add to 100% due to rounding error.
F = F ratio; χ 2 = Chi-squared.
 J. D. Weinrich et al. 357

Compared to the participants in the MS, the HIV sample was (by design)
100% male, about the same age as the MS women (i.e., about 4 years younger
than the MS men), more likely to have only a high school education, much
less likely to have a college or postgraduate degree, and much less likely to
have ever been married.
Most other studies of bisexuality have identified participants’ sexual ori-
entation by self-report. One of the 21 KSOG items closely resembles this
measure: a participant’s present self-identification as heterosexual, bisexual,
or homosexual on a 7-point Kinsey-like scale (ranging from 1–7 instead of
Kinsey’s 0–6). By this criterion, as shown in Table 2 we succeeded in attract-
ing MS men and women that were only moderately weighted toward het-
erosexual participants. Just over 40% of the participants were completely or
nearly completely heterosexual; the others were roughly evenly distributed
across the sexual orientation spectrum. The MS women were twice as likely
as the MS men to report that they were equally homosexual and heterosex-
ual (i.e., bisexual); the men were more than 4 times as likely to report that
they were entirely homosexual. This finding is consistent with previous sug-
gestions (see Weinrich, 1987/2013, pp. 41–42, for a review) that women are
more likely than men to report bisexuality and less likely to report homosex-
uality. Note that even though our sample cannot be considered random or
representative, none of our recruitment methods were biased toward such
a finding—that is, we did not recruit from sources known to have a dispro-
portion of homosexual men and/or bisexual women.
Table 2 also shows the distribution of the HIV sample’s KSOG scores
on the present identity question. Not surprisingly (due to its recruitment
methods), it is strongly U shaped, with peaks at the two ends (1 and 7). Less
than 20% of those men rated themselves as 1, 2, or 3 on this KSOG item
(corresponding to Kinsey’s 0, 1, and 2).
In cluster analysis, it is usually deemed unfortunate if the sample’s clus-
tering consists of a “stair-step” pattern in which each additional cluster splits
off only one or two more individuals from the main body of the sample. A
stair-step pattern typically suggests a continuous underlying distribution—as
would probably result, for example, if one were to cluster on the heights
of people attending a university. Instead, in satisfactory clustering one typ-
ically hopes that the top levels will reveal a pattern that divides the total
sample into discrete subgroups, each with a substantial number of members.
For example, clustering on the heights of people in an elementary school
would identify two nearly nonoverlapping subgroups: tall adults and short
children. It is especially intriguing if the clustering then turns out to cor-
relate significantly with some outside variables not used in the clustering
process, because this would suggest that the clustering is not merely a statis-
tical fiction, convenience, or quirk. The status distinction of “teacher” versus
“student” would be revealed in a cluster analysis by height in an elementary
school, but not in a university.
358
TABLE 2 Klein Sexual Orientation Grid Present Identity

Significance
Main Study HIV Study Females vs. Males vs.
Total Females Males HM Males Males (p) HM Males (p)

M ± SD 3.56 ± 2.26 3.20 ± 1.87 3.76 ± 2.44 5.73 ± 2.19 4.81 F (.03)
Breakdown by category (%) 27.2 χ 2 146.4 χ 2
(.0001) (.0001)
1: Heterosexual only 30.7 28.3 32.1 14.7
2: Heterosexual mostly 10.8 12.5 9.9 1.6
3: Heterosexual somewhat 8.1 12.5 5.7 1.1
more
4: Heterosexual/homosexual 16.9 24.2 12.7 3.1
equally
5: Homosexual somewhat 6.6 8.3 5.7 3.6
more
6: Homosexual mostly 9.0 8.3 9.4 9.7
7: Homosexual only 17.8 5.8 24.5 66.3
Note. Percentages may not add to 100% due to rounding error.
HM = homosexual males; F = F ratio; χ 2 = Chi-squared.
 J. D. Weinrich et al. 359

The cluster analysis for the MS women participants was highly satis-
factory, showing, at the top end, an easily interpreted division into four
subgroups (which is actually the bottom-most part of the bottom left corner
of Figure 2). Figure 2 also shows the women’s scores, averaged by cluster,
on the 21 KSOG items. Examination of these patterns led us to name the four
female subgroups lesbian, bi-lesbian, bi-heterosexual, and heterosexual.
The cluster analysis for the MS men participants likewise showed
an easily interpreted division into five subgroups (bottom left corner of
Figure 3). Figure 3 also shows the men’s scores, averaged by cluster, on
the 21 KSOG items. Examination of these patterns led us to name the five
male subgroups homosexual, bi-homosexual, bisexual, bi-heterosexual, and
heterosexual.
The cluster analysis for the HIV-study participants was (1) conducted
on a total of 620 men who were (2) far more likely to be homosexual
than were our MS men. These two facts combined to produce a five-cluster
solution with little detail on the bisexual and heterosexual end of the spec-
trum and a much finer division of the homosexual end. Figure 4 shows
the average KSOG scores in a way corresponding to Figures 2 and 3. One
cluster was clearly heterosexual; another bisexual (to a degree that might
correspond to the bi-homosexual group in the MS). The remaining three
groups were labeled homosexual; two of those three were more consis-
tently homosexual than the third (and are labeled GG1 and GG2 in the
charts). These three groups differ in ways that will be discussed further
below.
Figures 5, 6, and 7 present the SDs for participants in the three samples.
In the Discussion, we address the importance of looking not only at the
cluster means, but also at the variability about each mean.

DISCUSSION
Initial Observations
Examine Figures 2 and 3. As expected, for MS women and MS men, each
of the 21 KSOG bar charts shows a monotonic increasing pattern by cluster
membership. Almost without exception, as one moves from the heterosexual
cluster through the bisexual clusters to the homosexual cluster, the average
value of the KSOG dependent variable increases. Almost always, each such
difference is statistically significant (statistical tests not shown).
The only exceptions occur in the socialize with variable (E). None
of these group differences is statistically significantly different by cluster
membership for either women or men. For men, at least, this confirms our
previous factor analysis of the KSOG using the HIV sample (Weinrich et al.,
1993), in which the three socialize with items loaded onto a different factor
than the sexual attraction, sexual fantasy, and sexual behavior items.
360 Journal of Bisexuality

FIGURE 2 Klein Sexual Orientation Grid item means by sexual orientation clusters (main
study women).
 J. D. Weinrich et al. 361

FIGURE 3 Klein Sexual Orientation Grid item means by sexual orientation clusters (main
study men).
362 Journal of Bisexuality

FIGURE 4 Klein Sexual Orientation Grid item means by sexual orientation clusters (HIV
men).
 J. D. Weinrich et al. 363

FIGURE 5 Klein Sexual Orientation Grid standard deviations by sexual orientation clusters
(main study women).

The corresponding comparisons in the HIV study are interesting. Just

as before, the emotional preference and socialize with variables show little
variation among the subgroups. The three homosexual groups differ scarcely
at all on self-identification and scarcely at all on all three of the sexual vari-
ables (attraction, behavior, fantasies). A similar pattern holds for the lifestyle
items. The heterosexual group is, of course, completely different. Although
364 Journal of Bisexuality

FIGURE 6 Klein Sexual Orientation Grid standard deviations by sexual orientation clusters
(main study men).

the bisexual group’s means are almost always intermediate between those of
the heterosexuals on the one hand and the three G groups on the other, their
means usually are quite a bit closer to the homosexual groups’ means than
to the heterosexual group’s. (The minor exceptions are those pesky present
and past emotional preference and socialize with items.) Interestingly,
 J. D. Weinrich et al. 365

FIGURE 7 Klein Sexual Orientation Grid standard deviations by sexual orientation clusters
(HIV men).

the bi means are almost exactly halfway between the heterosexual and gay
groups’ means (and thus not closely resembling the homosexual groups)
for three ideal variables: sexual behavior, sexual fantasies, and sexual self-
identification.
In this sample, then, there is some empirical support for a particularly
durable stereotype: that bisexual men are (pardon the expression) “really”
366 Journal of Bisexuality

gay men whose erotic ideal scores are more heterosexual than gay men’s
are—more bluntly, that bisexual men are (pardon again) “really” gay men
who just want to be straight. This stereotype is emphatically not reflected
in the MS men; the means of the bisexual men in that sample are clearly
distinguishable from those of the gay men. Those data do not support an
interpretation that the bisexual men are mostly homosexually oriented indi-
viduals who want to become more heterosexual or bisexual in their future
Ideal. These differences between the two male samples are addressed in a
later section of this Discussion.

The Importance of Standard Deviations

As hinted above, however, cluster means are not the whole story. The SDs
(or standard errors, which support essentially the same conclusions in our
data set) help identify which of the 21 KSOG items were quintessential to the
classification scheme computed by the analysis and which were peripheral.
A cluster with a high SD on a particular item is composed of members who
tend to differ from each other on that item much more than members of
clusters with low SDs. Statistically speaking, such facts are elementary, but
they are frequently overlooked in the rush to compare means.
If the SD in a cluster is low then members of that cluster are quite
similar to each other on that item, and the mean value of that item more or
less represents the members of that cluster. It involves only a slight degree
of stereotyping to use the mean value in describing the cluster’s character-
istics. If on the other hand the SD for an item is high, the cluster’s mean
score on that item tells us little about each member (i.e., there are a lot
of “exceptions”). Using the mean value to describe that cluster involves a
significant amount of stereotyping (albeit a stereotype with a grain of truth
in it).
For example, consider the HIV study’s Ms and SDs for the present sexual
attraction item (Figures 4 and 7). According to the Ms (upper left corner of
Figure 4), the three gay groups are virtually identical, and the bi group
almost the same. But the SDs (upper left corner of Figure 7) reveal that the
bi cluster is far more variable than any of the other four. This implies that the
heterosexual and homosexual groups are composed of individuals who are
much more similar to each other in terms of their present sexual attractions
than the members of the bi group are. On this item, some of the members
of the bi cluster are indeed bisexual, but a fair number rated themselves
as homosexual 7s or even heterosexual 1s. Most of the members of the Ht
cluster are heterosexual 1s and 2s, and most of the members of the two GG
clusters are homosexual 6s and 7s.
The meaning of this pattern is discussed below. Then we move on to a
more detailed examination of the results in each sample.
 J. D. Weinrich et al. 367

The Female Main Study Sample

Begin with the heterosexual cluster in this sample (Ht). Examination of Fig-
ures 2 and 5 show that these women are very similar to each other in
the strongly heterosexual nature of their sexual attractions, behaviors, and
self-identification—throughout life and in their ideal. They also preferred to
socialize fairly equally with men and women in the present and past. Note
that although these women on average have heterosexual fantasies, lead a
heterosexual lifestyle, and emotionally tend to like and love men, they are
quite varied on these items (i.e., the SDs in Figure 5 are high). This sug-
gests that many heterosexual women have a higher capacity for bisexual or
homosexual responsivity than the word ‘heterosexual’ would imply.
Even though the lesbian cluster’s members on average clearly score near
the lesbian end of the spectrum on each of the 21 KSOG items, they resemble
each other closely (i.e., their scores have low SDs) on just three items: present
sexual attraction, present sexual fantasies, and self-identification. Moreover,
this cluster has the lowest SD on present sexual fantasies; all three of the other
clusters (even the heterosexual cluster) are more variable on this important
item. It appears that the lesbian women are more uniform in their sexual
object choices than the heterosexual women are.
The two bisexual clusters have mean scores intermediate to the het-
erosexual and homosexual clusters on all 21 of the KSOG items, but SDs
are relatively high for nearly all items, too. There are only two exceptions:
the bi-lesbian cluster is almost as uniform in its present sexual fantasies as
the lesbian cluster is, and the bi-heterosexual cluster is quite uniform in
their ideal emotional preference (which is for men and women equally).
Apparently the bisexual women are more varied in their preferences than
the heterosexuals or lesbians are.

The Male Main Study Sample

In this sample, note first that the emotional preference and socialize with
variables have fairly high SDs (Figure 6) regardless of cluster membership;
this shows that the clustering procedure tended to ignore these variables
in making its clustering assignments. Therefore, in our remarks below we
mostly ignore the clusters’ mean scores on these items.
We see in Figures 3 and 6 that the heterosexual cluster is very consis-
tently heterosexual in all five of the other KSOG variables: sexual attractions,
behaviors, fantasies, lifestyle, and self-identification. The gay cluster is almost
as consistent in four of the same five KSOG variables—lifestyle being the
exception. Although the homosexual cluster reports the most homosexual
lifestyle on average, it is quite varied on this parameter.
The three bisexual clusters’ means on the 21 items are almost always
intermediate between those of the heterosexuals and the homosexuals. The
368 Journal of Bisexuality

bi-bisexual cluster seems to resemble the bi-gay and the gay clusters in
their high (mean) levels of sexual attraction to and sexual fantasies about
men, but their actual sexual behaviors with men average about the same as
the heterosexual and bi-heterosexual clusters. However, because the SDs in
these three clusters are almost always high, such generalizations overlook
individual variations.

The (Male) HIV Study

In this sample (see Figures 4 and 7), the emotional preference and socialize
with variables tend to have high SDs regardless of cluster membership—
though there is a trend for the homosexual clusters to have lower variance
in their emotional preference scores than the heterosexual clusters do. In
the present emotional preference item, for example, the scores indicate that
heterosexual men tend to like and love women, though many heterosexual
men deviate from this pattern. The gay men more consistently like and love
men.
The heterosexual cluster is very consistent in its heterosexual prefer-
ences for sexual attraction, behavior, fantasies, and self-identification in the
present, past, and ideal. The GG1 gay cluster is just as consistent in its cor-
responding homosexual preferences, and GG2 is nearly so—the exception
being past self-identification for the GG2 group, which is nearly twice as
variable as GG1 on this parameter. With this sole exception, the two GG
clusters are erotically nearly indistinguishable.
Prominent in many of the charts in Figure 7 is the bisexual cluster, which
usually has the highest SD of any of the clusters under discussion (emotional
preference and socialize with variables excluded). Although on average this
cluster resembles the three homosexual clusters more closely than it does
the heterosexual cluster, these high variances show that the members of this
cluster are very diverse.
The middle cluster of the five, labeled “G,” has mean scores that are very
close to those of the two GG clusters. But the pattern of SDs is intriguing.
Members of the G cluster resemble each other quite closely for present
sexual attraction, behavior, and fantasy (almost as much as the GG clusters
do), but they show as much variability on these items in the past as the
bisexual cluster did, and a bit more variability for the Ideal items than the
GG groups did. The G group, then, was in the past more ambivalently gay
than the GG1 or GG2 groups were, whereas the bisexual cluster had a more
bisexual ideal than the three gay clusters.

GENERAL DISCUSSION

With such a wealth of detail, seeking patterns might seem a futile exercise.
Yet there are many interesting generalizations that emerge from this dataset.
 J. D. Weinrich et al. 369

First consider the dendrograms (lower left corners of Figures 2, 3, and 4).
Working from the bottom up, the first split in each case is a division between
heterosexuals and homosexuals. In the female MS sample, each of these two
major groups then splits into a more bisexual cluster and a less bisexual
cluster. In the male MS bi/heterosexual cluster, an initially confusing first
split is followed by an easily interpretable second split; this in turn results in
a heterosexual cluster and two others. Each of those two others then splits
(as with the women) into a more bisexual and a less bisexual cluster. In
the HIV study, the heterosexual cluster does not divide further; instead, a
consistently gay cluster is split off on the homosexual side (GG2), then the
bisexual cluster (Bi) emerges, and finally the remaining cluster splits into
two homosexual clusters (one labeled G and the other GG1).
These general patterns of clustering result, we believe, from two factors:
some facts about the world and some details of our sample recruitment. The
facts about the world are fairly obvious: (1) there are many people who are
very consistently heterosexual, (2) there are people who are very consistently
homosexual, and (3) there are people whose behaviors and fantasies are not
accurately described by either group (1) or (2)’s patterns. A goal of this
study, obviously, is to better characterize group 3—the bi-heterosexuals, the
bi-bisexuals, and the bi-homosexuals in the MS, and the bisexuals in the HIV
study.
Here it is important to consider our sample recruitment procedures. Our
cluster analysis found two or three kinds of bisexual in the MS samples (male
and female), where we specifically tried to emphasize recruitment of bisexu-
als (and others) from the Internet at large, which is a nonclinical source. This
sample yielded bisexual clusters that were fairly simply interpretable based
on their mean scores on certain KSOG items. The HIV study sample, on
the other hand, was a clinical (AIDS-related) sample recruited with no such
special goal; in that sample, the bisexual cluster identified was so statistically
variable that no single KSOG item could be used to characterize the group.
Recall, from the initial Discussion, the fact that the bisexual men in the
HIV sample (the bi cluster) seemed to fit the stereotype of “behaviorally gay
men who have a bisexual ideal,” but the bisexuals in the MS men did not.
One is tempted to hypothesize that the gay men in our HIV sample were
fairly densely involved in the gay community, and that the bisexual men they
knew through this community were indeed more gay than bisexual—that is,
their bisexual friends involved in the gay community resembled the bisexual
cluster identified in the gay-community based HIV study. There are a lot of
“ifs” in this interpretation, but if it is correct, it would be consistent with the
common (albeit inaccurate) opinion about bisexuals held by many members
of the gay community.
The fact that the HIV sample was, of course, recruited through a study
of the medical effects of HIV, and that the MS men were not, very tentatively
suggests that the “more bisexual” bisexuals in the MS were not densely
connected to gay social networks likely to refer each other to an HIV study.
370 Journal of Bisexuality

Such (pardon the expression) “truer” bisexuals would not have gotten in-
volved in the HIV study to the same extent to which they became involved
in the MS. If this speculation is correct and reflects a corresponding truth
about the social networks of gay and bisexual men in general, we may have
highlighted empirically a pattern often thought prevalent in early studies of
homosexuality: that clinical samples produce different results than nonclini-
cal samples do.
In the early days of the Gay Liberation movement, scientific studies were
often criticized for being biased, by virtue of being conducted using clinical
samples. Better studies using better samples were then proposed, funded,
and carried out. Rarely, however, were clinical samples directly compared
with nonclinical samples in the same study, using the same procedures. Thus,
the assertion that clinical samples were biased samples, although reasonable,
was rarely tested directly. This study constitutes an (admittedly tardy) empir-
ical verification of this clinical-bias hypothesis. This is not an earth-shattering
conclusion, but note that it is based on categories using empirically delin-
eated subgroups.
Moreover, note how misleading it would be to try to extrapolate, even
allowing for sample recruitment biases, from the HIV study to the population
of bisexual men at large (as represented, arguably, by the MS). Simply put,
HIV study bisexuality is not MS bisexuality. The bisexual group identified by
the cluster analysis of the HIV sample was distinctly different from any of
the bisexual groups identified by the clustering process in the Main Sample
men. Attempts to generalize (even cautiously) from the HIV sample’s bisexual
group to a larger population would be doomed to failure.
In the HIV study, the data seem to imply that the bisexual men with
HIV who were recruited to the HIV study were qualitatively different from
bisexual men who did not have HIV. That implies that there may be
another group of bisexual men with HIV disease who did not get recruited
in corresponding numbers to the HIV study. If this speculation is correct, it
suggests that HIV outreach workers might want to design separate outreach
programs to the gay and the bisexual communities with regard to medical
problems shared by both communities. And it suggests that the common
misperception that bisexual men are “really” gay men who want to be less
gay may have some limited validity in clinical samples.
Moving on, note that there are some remarkable patterns when we
compare results across all three of the samples. First, the socialize with
variable was almost never important in characterizing a cluster. Next, for all
three samples, sexual attraction, sexual behavior, and self-identification were
extremely useful in differentiating the clusters (as might be expected a priori).
It was remarkable how unvarying the heterosexual women were on sexual
attraction, behavior, and self-identification, but how much more variable
they were on sexual fantasies. In contrast, in the two male samples, patterns
of the means and variances of sexual attraction, behavior, and fantasy are
almost identical.
 J. D. Weinrich et al. 371

In all three samples, the bisexual clusters tended to vary much more
in their present attractions, behaviors, and fantasies than the heterosexual
or homosexual clusters. In theory, this could be due to a variety of factors,
but first let us note that this result is not necessarily a result of the defini-
tion of ‘bisexuality’ or of the clustering process. One could imagine a world
in which there were three sexual orientations—bisexual, heterosexual, and
homosexual—and in which each of these orientations would be unvarying:
heterosexuals would only be attracted to members of the other sex, homo-
sexuals to their own sex, and bisexuals to both sexes. If this were the case
(and adding some random error to our sampling and measurement instru-
ments), then the variation within each bisexual group would have been about
as low as the variation within the heterosexual and homosexual groups. Such
was not the case for even one of the bisexual groups. Although there may
indeed be some bisexuals in our bisexual clusters who fit this description,
there were others clustered with them whose characteristics on several of the
KSOG items were at odds with the ideal “type.” We cannot decide from the
present data whether the bisexual clusters contained many, few, or none of
these ideal bisexual types—only that many of the mixed type were present
in the samples.
For this reason, it seems that bisexuality can indeed be thought of as a
continuum. Although we found two female and three male bisexual groups
through cluster analysis, the higher variability in the bisexual groups shows
that individual bisexuals do not neatly fit into an ideal pattern of discrete
groups.
In an earlier paper on a related topic (Weinrich et al., 1993), we
described scientists studying sexual orientation as being either “lumpers”
or “splitters.” Lumpers tend to minimize individual differences on sexual
orientation parameters; they will be pleased to see a heterosexual/
homosexual dichotomy emerging as the first split in each of our three
samples. Splitters tend to revel in the differences; they will be pleased to
see that distinguishable subgroups of bisexuals emerged in the samples in
which we increased the probability of their participation. Further clarity in
this debate must await further articles in the present series, as we report
some continuities and some surprising discontinuities in the specific sexual
acts and scenarios that members of these clusters enjoy (Klein & Weinrich,
2014/this issue).

FUNDING

The principal support for Dr. Weinrich was from the Universitywide AIDS
Research Program of the University of California (grants R93-SD-062 and R95-
SD-123). The HIV Neurobehavioral Research Center (HNRC) was supported
by National Institute of Mental Health Center grant 5 P50 MH45294.
372 Journal of Bisexuality

SUPPLEMENTAL DATA

Supplemental data for this article can be accessed on the publisher’s website,
http://www.tandfonline.com/wjbi.

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James D. Weinrich, PhD, is currently the editor of the Journal of Bisexuality

and former principal investigator of the Sexology Project at the HNRC (HIV
Neurobehavioral Research Center). He collaborated with Fritz Klein on some
innovative studies of bisexuality in the early years of the journal.

Fritz Klein, MD, was the founding editor of the Journal of Bisexuality. He
passed away in 2006.

J. Allen McCutchan, MD, was the principal investigator of the Medical Core of
the HNRC at the time of the preparation of this article.

Igor Grant, MD, is the founding principal investigator of the HNRC.

The HNRC GROUP is a research arm of the Department of Psychia-

try at the School of Medicine at the University of California, San Diego
(http://hnrc.hivresearch.ucsd.edu/). A great many individuals at the HNRC vi-
tally contributed to aspects of this research.