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Gender schematicity, gender identity salience, and gender-linked language use
Unformatted Document Text:  Gender schematicity, identity salience, and -linked language use 19 Gender-linked language use depends on an individual’s sex, gender schematicity, and GIS. Hypothesis 4 receives support—language use is prototypically gendered when individuals are GS and have a high GIS. The ability of the discriminant function to classify men and women into the eight groups is limited. Table 7 reports the classification results for the eight group discriminant function. The discriminant function was able to correctly classify only 18.2% of the participants. By chance alone, one can expect to predict accurately group membership 12.5% (proportional chance criterion) to 15% (maximum chance criterion) of the time. Discriminant functions typically must improve accuracy at least 25% above chance levels to be considered useful (Hair, Anderson, & Tatham, 1987), which in this case is 15.6% to 18.75%, levels barely reached and not exceeded. Knowing language use alone provides minimal utility in accurately identifying participants in terms of their sex, gender schematicity, and GIS simultaneously. Closer examination of Table 7 reveals that errors in classification are most common for men and NGS women. NGS men and NGS women are commonly misclassified as a member of the opposite sex. For example, NGS women with a high GIS are (mis)classified 39.1% of the time as NGS men with a high GIS. Problems in the current classification analysis are most likely due to NGS individuals who seem to be unaffected by the GIS manipulation. Interaction effects for gender schematic individuals. Given the difficulty in accurately classifying NGS individuals, an examination of language differences holding gender schematicity constant can prove meaningful in examining further the overall three-way interaction. A follow-up MANOVA, testing the effects of sex by GIS by language use for GS individuals, reveals the expected interaction between sex, GIS, and language use, F (56, 526) = 41.95, p < .001, 2 = .82. Figure 1(a) plots the discriminant function score centroids for the four groups of GS individuals to illustrate this interaction. For GS individuals, women compared to men vary in their gender-linked language use depending on their GIS. The language pattern of GS men with low GIS is similar to that

Authors: Palomares, Nicholas.
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Gender schematicity, identity salience, and -linked language use
19
Gender-linked language use depends on an individual’s sex, gender schematicity, and GIS.
Hypothesis 4 receives support—language use is prototypically gendered when individuals are GS
and have a high GIS.
The ability of the discriminant function to classify men and women into the eight groups is
limited. Table 7 reports the classification results for the eight group discriminant function. The
discriminant function was able to correctly classify only 18.2% of the participants. By chance alone,
one can expect to predict accurately group membership 12.5% (proportional chance criterion) to 15%
(maximum chance criterion) of the time. Discriminant functions typically must improve accuracy at
least 25% above chance levels to be considered useful (Hair, Anderson, & Tatham, 1987), which in
this case is 15.6% to 18.75%, levels barely reached and not exceeded. Knowing language use alone
provides minimal utility in accurately identifying participants in terms of their sex, gender
schematicity, and GIS simultaneously.
Closer examination of Table 7 reveals that errors in classification are most common for men
and NGS women. NGS men and NGS women are commonly misclassified as a member of the
opposite sex. For example, NGS women with a high GIS are (mis)classified 39.1% of the time as
NGS men with a high GIS. Problems in the current classification analysis are most likely due to NGS
individuals who seem to be unaffected by the GIS manipulation.
Interaction effects for gender schematic individuals. Given the difficulty in accurately
classifying NGS individuals, an examination of language differences holding gender schematicity
constant can prove meaningful in examining further the overall three-way interaction. A follow-up
MANOVA, testing the effects of sex by GIS by language use for GS individuals, reveals the
expected interaction between sex, GIS, and language use, F (56, 526) = 41.95, p < .001,
2
= .82.
Figure 1(a) plots the discriminant function score centroids for the four groups of GS individuals to
illustrate this interaction. For GS individuals, women compared to men vary in their gender-linked
language use depending on their GIS. The language pattern of GS men with low GIS is similar to that


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