Course:PSYC305/2013ST2/ClassProject/2.2.1 Introduction - GD Description

Gender Diagnosticity

Gender Diagnosticity is the Bayesian probability that an individual is classified as male or female on the basis of an assortment of gender-related diagnostic indicators (Lippa & Connelly, 1990). The concept of gender diagnosticity is also theoretically linked to the diagnostic ratio approach to measuring stereotypes (McCauley & Stitt, 1978; McCauley, Stitt, & Segal, 1980).

Gender diagnosticity (GD) asks the question of, "Given a certain trait that is self-descriptive, what is the probability that the individual is female or male?" (Lippa & Connelly, 1990, p. 1053). If members of a population report having a given trait, we can figure out the probability of whether an individual is male or female by applying Bayes’ theorem. The calculations must be made with information across a group of respondents, and proposes only to ascertain those probabilities of being “male-like” or “female-like” within a particular group during a particular time, as “a given behavioral indicator may vary over time and across different populations of men and women” (Ibid.).

Helgeson states succinctly that “GD is a weighted combination of the indicators that discriminate men from women,” and the “indicators may include occupational preferences, personality characteristics, attitudes, cognitive abilities, and leisure interests” (Helgeson, 2005, p. 61). GD does not render maleness or femaleness as static or concrete concepts ("Reification," n.d.); as it is “not a statictical category” (Helgeson, 2005, p. 62) it is flexible and can be used as a measure of particular groups at particular times to highlight relative gender discriminations.

The word ‘stereotype’ has evolved to have a pejorative meaning, insofar as stereotypes may represent false generalizations and “may or may not accurately reflect reality” (“Stereotype,” n.d.). The authors of this report not only acknowledge that gender stereotypes are prevalent but that they may lead us to ask questions about gender-related traits and highlight empirical correlations using data harvested from this questionnaire and others.

Lippa (2005) has pointed out that generalizations of behaviour, preferences and attitudes often fail to highlight particular expressions of a trait; males may be more apt to express aggression through direct insult, while females express aggression more commonly through indirect or “relational aggression” (ex. ostracization, spreading rumors). Lippa also points out differences in helping behaviours, moral behaviours, conformity behaviours and, of special interest to us, occupational preferences. Lippa cited Holland’s six types of occupations: realistic, investigative, artistic, social, enterprising and conventional” (Lippa, 2005, p. 30-32). Holland created an intersecting polarized bi-axial scale for occupations graphically depicted with “things” and “people” on one axis and “data” and “ideas” on the other. Holland found that men show a distinct preference for “realistic occupations” such as machinery, engineering and computer science(d=1.06) while women for occupations which involve tangible “things”; the “ideas-data scale” shows little sex difference (Lippa, 2005. p. 30-32).