Table 2.

Responses to frequently asked questions about the study of sex differences

What if data from males and females are collected in different cohorts or at different times?	Under these conditions it is not appropriate to directly compare males and females. Similarities and differences between the samples may be discussed at a conceptual level. Potential sex differences should be investigated with simultaneous testing of both sexes in future studies.
What if the trait of interest occurs predominantly or only in one sex? Should both sexes be studied?	After a sex difference in the incidince of a trait (i.e., a population difference) has been established, additional studies may be sex-specific. Especially for biomedical research, mechanisms underlying relevant outcomes in both male and female subjects should be studied.
Is it always necessary to compare females across the estrous or menstrual cycle because of potential cycle-dependent variability in females?	No. Female rodents are not more variable than males, and ovarian cycles do not introduce greater variability in females. If estrous or menstrual cyclicity is part of the research question, then cycle stage is a valid variable to consider. Ways to incorporate these variables into a scientific design have been discussed previously [14].
Is it appropriate to “control” for sex in studies with humans?	No. In the human literature, it is not uncommon to include both males and females in studies, yet discount or disregard effects of sex in analyses. This is typically done by “controlling for” sex or using sex as a covariate (of no interest) in statistical tests. Doing this relies on two assumptions: (1) once sex-related variance is removed, sex does not “confound” results; and (2) study findings are then valid for both sexes. Both assumptions, however, are faulty (for a discussion of the perils of statistically controlling for non-random group differences in analyses, see [8]). The first assumes that sex differences are quantitative, linear, and without interactions, which is rarely the case (discussed above and in Becker et al. [15]). The second assumes individuals are “averagely-sexed” or “sex-less,” and very rarely is this true of the populations to which generalizations are being made. Instead, sex should be explicitly considered as a variable of interest (see Fig. 1).
Can sex differences change over time? How should repeated measures data related to sex be analyzed?	Yes. All types of sex differences can change over proximal or distal time. For instance, a sex difference may be present at one measurement occasion, but not the next, or there may be sex differences in patterns of change, including variability. Depending upon the type of difference, data can be analyzed separately by sex or by including sex as a variable of interest in planned analyses. These differences can be detected using longitudinal analysis approaches, such as repeated measures analyses of variance (e.g., for quantitative differences), growth curve analyses (e.g., for qualitative and latent differences), and time series analyses (e.g., of intensive longitudinal data to examine differences in variability and patterns of covariation among variables of interest).
What if some individuals are not well-represented by the average for their sex? For instance, what if a female exhibits a “male-like” trait (e.g., high spatial ability) or a male exhibits a “female-like” trait (e.g., major depression)?	Most studies of sex differences and between-subject analyses (e.g., t tests, regression, factorial designs) assume homogeneity within each sex, so that results can be generalized to all subjects of that sex. This assumption is often violated, especially in human research or research on multi-determined traits. To address this, individuals should be studied using within-subject analyses (e.g., based on intensive longitudinal designs, or studies in which many data points are collected from the same individual on the same variables) that characterize subject-specific patterns (see [11]). This also suggests that analysis of population differences should be investigated for the measure.
I am confused about the different types of sex differences. How do I tell them apart?	The easiest sex differences to discern are qualitative differences, wherein males and females cannot be measured on the same scale; these differences can emerge as a function of sexual differentiation. Population differences resemble qualitative differences in that the traits measured are not overlapping, but both males and females exhibit the traits, albeit in different proportions. These traits may emerge as a function of life events (e.g., early life stress or experience with drugs of abuse). For quantitative differences the trait in males and females is measured on the same scale but the normal distribution around the mean is different for the two sexes. Quantitative and population differences may seem difficult to dissociate, especially if the trait is measured on a scale developed for only one sex. If the distribution of values for the one or both of the sexes is skewed with a disproportion of values at one end of the scale, for example, this may indicate that there is an underlying population difference that could be explored. Finally, latent sex differences in a trait are differences between the sexes that emerge as a function of challenge, such as stress, or when investigating mechanism (e.g., intracellular signaling or gene expression); in this case, studies of a trait might identify quantitative differences, while studies of the mechanism underlying it might identify qualitative differences.