Participants

From February through May 2005, the British Broadcasting Corporation (BBC) conducted an Internet survey on human sex differences for use in its documentary, Secrets of the Sexes. A total of 255,114 people responded to at least some items in each of the six sections of the survey. A number of published studies have used the BBC data to investigate cross-cultural variations in various traits, and the results of these studies have been consistent with other cross-cultural studies (Lippa, 2009, 2010; Lippa, Collaer, & Peters, 2010).

A number of methodological features of the BBC survey encouraged participants to respond honestly. The survey was taken online and was anonymous, and participants were informed that their data would be used in bona fide academic research. Except for a few questions (such as those inquiring about gender and age, which were used to channel participants to or away from subsequent questions), most questions could be left unanswered by participants who chose not to respond. The BBC survey was long and had multiple sections and, in the current analyses, we used data only from those participants who completed the entire survey. This excluded from the analysis the majority of casual participants who wanted to see “what the survey was like” without necessarily providing conscientious responses. The final question of the BBC survey asked, “Have you answered the questions on this site honestly?” Only one percent of participants responded “no” to this question. The survey language was English. A total of 462,859 participants completed demographic information (Section 1 of the survey) and 255,116 participants completed the entire survey (55.11%) which include sexual orientation items (Reimers, 2007). Only participants who were 18 years of age or older were able to respond to the sexuality-related questions in the survey. It was not possible to know how many people simply clicked on or opened the survey and did not progress any further.

To generate reasonably stable estimates of the prevalence of heterosexuality, bisexuality, and homosexuality in various nations, we restricted our analyses to 28 nations that yielded at least 150 men and 150 women who responded to sexual identity questions: Australia, Austria, Belgium, Bulgaria, Canada, Denmark, Finland, France, Germany, Greece, India, Ireland, Italy, Japan, Malaysia, the Netherlands, New Zealand, Norway, the Philippines, Poland, Romania, Singapore, Spain, Sweden, Switzerland, Turkey, the UK, and the U.S. A total of 191,088 individuals were included in the analysis (about 75% of the 255,116 participants mentioned above). Samples of men and women ranged in size across the 28 nations, with median sample sizes of 541 men and 397 women.

The majority (68%) of participants in the survey were young adults 18 to 40 years of age (median age = 27). Participants tended to be educated—13.5% reported completing postgraduate or professional school, 35.5% university, 9.3% technical or vocational college, 11.5% other colleges, and 29.2% primary or high school. Participants were relatively affluent, with 37.1% reporting an annual income of 0 to 10,000 British pounds, 28.7% an income of 10,000 to 25,000 British pounds, 25.4% an income of 25,000 to 50,000 British pounds, and 8.8% an income of 50,000 British pounds or greater. Participants reported a variety of occupations: 32.1% were students, 64.9% worked in various occupations, and 3.0 reported being unemployed. Participants had to be able to respond to the survey written in English (for further information about the demographics of the BBC sample, see Reimers, 2007).

Measures

Sexual Orientation

Most participants in the BBC study reported their sex and also responded to three questions related to sexual orientation: “What is your sexual orientation? (Response options: “Heterosexual (straight),” “Homosexual (gay/lesbian),” or “Bisexual.” “How sexually attracted are you to men?” and 3) “How sexually attracted are you to women?” (Participants were asked, in response to these items, to rate their degree of attraction on a 7-point scale that ranged from “1—not at all” to “7—very”). Thus, the BBC items tapped identity and attraction but not behavioral components of sexual orientation.

In our analysis, we used each participant’s ordinal responses to the sexual identity question. For participants’ responses to same-sex attraction items, we used both the continuous response on the seven-point scale, and an ordinal measure computed as being “predominantly not sexually attracted to the same-sex” (scoring “1” and “2” on the same-sex attraction scales), “moderately sexually attracted to the same-sex” (scoring “3,” “4,” or “5” on the same-sex attraction scales), and “predominantly sexually attracted to the same-sex” (scoring “6” or “7” on the same-sex attraction scales).

National Indices of Gender Equality, Economic Development, and Individualism Traits

National statistics for gender-related development and gender empowerment were taken from the United Nations 2005 and 2001 Human Development Reports (available at http://hdr.undp.org/en/content/human-development-report-2001 and http://hdr.undp.org/en/content/human-development-report-2005). The UN gender-related development index (termed “gender equality” here) assessed nations’ gender equity on three dimensions: health and longevity, standard of living, and knowledge and education. The UN gender empowerment (termed “gender power” here) measure assessed nations’ gender equity on three power dimensions: power over economic resources, participation in economic decision making, and participation in political decision making. In several cases, when 2005 statistics were not available for given nations, we used the 2001 statistics instead. United Nations gender empowerment statistics were not available for two of the 28 nations studied (France and India). Two indices of economic development were also obtained from the UN Human Development reports above (these figures are given for 2003 in those reports): nations’ per capita GDP income in US dollars and life expectancy in years. National scores for individualism-collectivism were taken from Hofstede (1991), and these scores were missing for three of the 28 nations (Bulgaria, Poland, and Romania).

Covariates

Serving as possible control variables (Tables 1, 2), participant age and education level (primary or high school, technical or vocational college, other college, university, and postgraduate) were assessed as individual-level covariates, and national sex ratios and dominant religion (Protestant, Catholic, Eastern Orthodox, Muslim, Buddhist, mixed Christian, and mixed) served as nation-level covariates in multilevel models (see below). National sex ratios for people 15–64 years old were obtained from the World Fact Book 2005, a publication of the United States Central Intelligence Agency (https://www.cia.gov/library/publications/download/download-2005/index.html). Information regarding the dominant religion of a country was obtained from the US Department of State International Religious Freedom Report 2004 (for more details, see Lippa, 2009).

Table 1 National indices (including covariates, sex ratio, and religion) by nation Full size table

Table 2 Mean age and education level (proportions) by nation Full size table

In addition, we computed men’s and women’s mean age and education levels for each national sample. Education was assessed in terms of the percent of men and women who reported completing at least a high school education in each nation. These measures proved to be highly correlated with gender equality, economic development, and individualism-collectivism, across nations. When correlations were computed between the four demographic factors (men’s mean age, women’s mean age, men’s education, women’s education) and the five overlapping national indices (gender equality, gender power, income, life expectancy, and individualism–collectivism), 18 of 20 correlations were significant, with a median correlation of .56. In general, higher mean ages and higher education levels in both males and females were associated with greater gender equality, economic development, and individualism. Analysis of the demographic factors showed that male and female samples, across nations, tended to be well matched. The correlation between men’s and women’s mean age, across nations, was r(28) = .94, p < .001, and the corresponding correlation for education level was r(28) = .93, p < .001.

Statistical Analysis

Missing Data

The variables included in the current study had 0.002–16.81% missing information within the analysis sample. These missing data were handled by a technique called multiple imputation which is useful for large datasets such as ours. It quantifies uncertainty about the missing data by creating different imputed data sets and combining results obtained from them (Sterne et al., 2009). This increases power and overcomes some possible biases as incomplete data is included in analyses. Multiple imputation was used because the commonly used complete-cases analysis approach causes a substantial loss of precision and power (Sterne et al., 2009). Complete-cases analysis may also cause bias when data are missing at random instead of missing completely at random (Sterne et al., 2009).

In our data, there were at least 359 individuals within each cluster (nation) and 28 clusters. Statisticians generally recommend that missing data be imputed separately within each cluster (Graham, 2009). Individual-level missing variables were imputed separately within each cluster using individual-level and observed cluster-level variables (Gelman & Hill, 2007). Then the cluster-level missing variables were imputed using the cluster-level variables and aggregated forms (national means) of the individual-level variables (Gelman & Hill, 2007).

Prior to imputation, we examined, using logistic regression, whether the observed variables predicted “missingness.” For the imputation model, recommendations for multilevel studies suggest that all variables in the analysis model should be included (White, Royston, & Wood, 2011). Thus, the outcome variable (sexual orientation), predictors (national indices), and covariates were included. In addition, it is recommended that the number of imputations is at least as large as the percentage of missing data (White et al., 2011). Thus, we used 17 imputations. We used the chained equations algorithm (MICE) model since we had a combination of continuous and categorical variables. We used predictive mean matching for continuous variables since this approach makes no distributional assumption. Trace plots and other diagnostics indicated no cause for concern regarding the imputed values. The main multilevel models (see below) were based on the imputed data. For completeness, we compare those results with those based on complete-cases (see Supplemental tables).

Principal Component Analysis for Intercorrelations Between National Indices

We expected national indices to be highly intercorrelated. However, it would not be theoretically meaningful to compute a single composite measure of all these variables. Since gender power and gender equality were highly correlated, r(26) = .84, p < .01, and life expectancy and income were also highly correlated, r(28) = .79, p < .01, summary scores incorporating these indicators were constructed to generate two more meaningful composites: gender development and economic development. We applied principal component analysis and used the loadings on the first principal component as item weightings to generate a summary score for gender development and economic development separately. The first component explained 91.93% and 89.27% of the variation gender development and economic development, respectively. We compared the results for multilevel models using the individual national indices and those using these summary measures.

Multilevel Models

Because participants (Level 1 units) were nested within nations (Level 2 units), there were dependencies in the data. Therefore, we used a multilevel modeling approach to the main research question (to test whether national indices of gender equality, economic development, and individualism predict sexual orientation). Data were analyzed in STATA version 15 using multilevel models with random intercepts and fixed slopes (since the nation-level variables did not vary across individuals within a country). Given the variation in age and education level between participants, and in sex ratio and dominant religion between nations, we used age and education as Level 1 covariates and sex ratio and religion as Level 2 covariates in the models. Sexual identity was treated as an ordinal outcome variable, and same-sex attractions as both ordinal and continuous outcome variables. Analyses were stratified by sex. Results show the odds ratios and 95% confidence intervals for ordinal outcome variable, and regression coefficients (beta) and 95% confidence intervals for continuous outcome. The model also provided the variance partition coefficient which refers to the proportion of unexplained variance (after accounting for predictors and covariates in the model) in the outcome due to differences between nations.