Behav Genet (2008) 38:257–265 1989) and Adult Gender Nonconformity (seven items; Finn 1987, The structure of masculinity and femininity self rat- ings, Unpublished manuscript). A principal components analysis of these 47 items (performed in SPSS-13.0 for Windows) revealed nine factors for males and 10 for females. The most apparent factor for both sexes, explaining 16% of the total variance for both sexes, was a factor regarding attitudes to homosexuality, comprising all 10 items of the Attitudes to Homosexuality scale created by Klassen et al. (1989). Each item showed a high factor loading for both males and females (between 0.55 and 0.87). Accordingly, in the present study homophobia was measured using the 10 items of this Attitudes to Homo- sexuality scale, which comprised statements like ‘Homosexuality is obscene and vulgar’, and ‘Homosexual men should be allowed to work as schoolteachers’ (see Appendix A for the total scale). Twins were asked to either tick YES if they agreed with the statement or NO if they disagreed. If a participant ﬁlled out both answers on an item (both YES and NO), the item was coded as missing. To enhance unbiased answering, a preamble to the section explained that there are no right or wrong answers and no trick questions and they were encouraged to work quickly and not to think too long about the exact meaning of questions. To maximise the number of twin pairs available for genetic analysis, missing item responses were imputed where possible by using the imputation methods in PRELIS 2.30 (Jo¨ reskog and So¨rbom 1999). Missing values were substituted with values from other cases with similar response patterns, but without missing data. Note that it was only possible to impute items in PRELIS if partici- pants had only one missing value and that imputation depended on the response patterns from other cases. A total of 109 responses were imputed (0.2% of the total item responses), increasing the available twins for structural equation modelling from 4,442 to 4,551, a gain of 2.5% in the total available sample size. A total homophobia score was calculated by summing the answers on all 10 statements, where a homophobic response was counted as one and a non-homophobic response as zero. To further maximise the number of twin pairs available for genetic analysis, participants missing one (N = 116) or two (N = 101) responses were included in further analyses. Their homophobia score was corrected by adding their mean item score for each missing item. This resulted in another gain of 4.8% of the available sample size. As the distribution of the homophobia data showed signiﬁcant skewness and kurtosis, scores were grouped into four ordinal categories with roughly equal sample sizes. Individuals scoring 0 on the continuous scale were given an 259 ordinal score of 0, while individuals scoring 1 or 2 were given score 1, individuals scoring 3–6 score 2, and indi- viduals scoring 7–10 score 3. To determine the participants’ sexual orientation, twins were asked the following question in another part of the questionnaire, called ‘Sexual Feelings and Behaviour’: Do you consider yourself to be heterosexual (straight), bisex- ual or homosexual (lesbian or gay)? For the present study, sexual orientation was only subdivided into heterosexual and non-heterosexual. The latter includes both bisexual and homosexual individuals. Data analysis As the homophobia scale analysed in this study is an ordinal scale, the data were analysed using a threshold model (Falconer 1989). For these analyses it is assumed that there is an underlying continuum of liability which is normally distributed in the population. Upon this normal distribution three thresholds are placed to delimit the four categories. Variation in liability can be modelled as a function of genetic and environmental inﬂuences. Descriptive statistics for the homophobia scores are obtained using SPSS-13.0 for Windows. Subsequently, Structural Equation Modelling (SEM) is applied to esti- mate thresholds, polychoric correlations between twins and components of variance. First, the effects of gender, age and sexual orientation on the thresholds is tested. Next, heterogeneity of polychoric twin pair correlations was assessed as a function of sex and zygosity, and ﬁnally the importance of different sources of familial aggregation (i.e. shared genes and shared environment) was tested. SEM is conducted using Mx 1.65b, a matrix algebra calculator that ﬁts structural equation models to observed data (Neale et al. 2003). All analyses were conducted using maximum- likelihood estimation for raw ordinal data in Mx. Models were compared with a likelihood ratio v2 test. To determine the effects of age, sexual orientation and sex on the thresholds, different (nested) submodels are tested against a full model, in which all parameters were estimated. By looking for differences in model ﬁt after constraining these effects to zero in a stepwise manner, the signiﬁcance of parameters that contribute to explaining differences in thresholds can be determined. Heterogeneity of twin pair correlations is tested by determining the change in model ﬁt when twin pair cor- relations (for sex and/or zygosity) were equated. Genetic model ﬁtting of twin data allows for decom- position of the observed variance in homophobia scores into its genetic and environmental components. Additive genetic variance (A) denotes the variance resulting from the sum of allelic effects across multiple genes, while D symbolizes non-additive genetic effects including 123