Teorías y modelos reactivas o ambientalistas

Mechanical Engineering Prism C&EN Averages:

C&EN = 52.3 Prism = 31.5

Mechanical Engineering = 27.5

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Although the number of visuals available (those with and without people in them) were high among the Mechanical Engineering and C&EN full trade journals (Table 4c, column 4), the proportions with people in them (i.e. those visuals used) were lower (column 5), resulting in similar amounts of data collected (column 6). C&EN could have also been truncated, but the extent of data available was not appreciated until data collection was deeply in progress.

To determine the effect of truncating Prism on results obtained, comparison of the existing data, which included the first 30 pages of Prism and the full issues of Mechanical Engineering and C&EN, were developed, where significant differences in results were found, indicated in Table 4d. Women were over-represented numerically in Mechanical Engineering in the front portion, and under-over-represented in the latter portion; C&EN showed the opposite effect. These differences were significant; thus, truncating Prism likely affected results of this study. Given the variety shown in the differences in early and latter

sections in the other two trade journals, it was hard to predict effect of Prism's truncation.

This study could have truncated all of the data at the first 30 pages of all the trade journals for analysis. However, Mechanical Engineering would have shown so little data the power of the statistics would have been in jeopardy. Thus, the data was analyzed with Prism's first 30 pages and Mechanical Engineering's and C&EN's full issues.

Table 4d. Proportions of women found in two parts of each trade journal. Difference in proportions of each part and the full trade journal were shown in parentheses. *All results were significant, p<0.05.

Trade journal Proportion of Women in First 30 pages *

Proportion of Women in Remainder of Trade journal (page numbers

31+) *

Proportion of Women in Full Trade journal *

Prism 32.9% n/a n/a

ME 27.4% (+7.9%) 14.9% (-4.6%) 19.5%

C&EN 21.4% (-6.0%) 32.1% (+3.8%) 27.4%

4.2 Inferential Results

Stata statistical software was used for all inferential statistics, including chi-squared and

logged-odds analyses (Statacorp, 2015). Chi-squared analyses were run with a 'tabulate' command in which frequencies and their gendered proportions were reported along with significance. Logged-odds analyses were run with a 'logit' command appropriate for time (interval-level variable) as the independent variable

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and a binary dependent variable (Pollock, 2011, p. 137). Most of the dependent variables were multi- or bi-variate ordinal or interval values. For the logged odds analysis, some of the ordinal variables were transformed into bi-variate (0 or 1) variables by grouping all the selections except the one variable (or the range of variables compressed into one variable) selected for investigation. For example, the variable 'age' was coded by general decades of estimated subject age, such as childhood (coded 1) to 50+ years old (coded 5). A preference to understand temporal trends of 'older' workers led to combining multiple variables into two for the logged-odds analysis: one for ages under 40 and one for older subjects.

The term 'over-representation' in chi-squared analyses referred to a higher proportion found in the dependent variable than in the independent variable; and vice versa for 'under-representation'. For example, the category 'gender' was nearly always the independent variable in this study, and its

proportions varied depending on the view (or slice) of data, which are summarized in Table 4a. Categories were coded for images of men and women, thus the data of a single category was comprised of gender (female (0), male (1), or unknown (2)) and the value of the category feature (i.e. focused (1) or blurred (0)).

Chi-square analysis provided comparisons between two gender counts: that of gender within the category's assignment (i.e. female and focused) and that of the population of gender as its own category (independent variable). If the differences were large enough, they were 'significant' and thus reported.

The level of over- or under-representation was relative to the gender proportions of the data, not the proportions preferred (i.e. 50% equality). Though this latter analysis could have been developed, it was irrelevant with the data collected. Table 4a showed women represented less often than their 50% general population in society. This low level of females in visuals was enough to initially express the amount of gender representation of in the dataset. These proportions detailed in Table 4a were also the standard by which all other categories (dependent variables) were judged. Given the skewed gender proportions found in the trade journals, the data collected could not be expected to show equality in the dependent variables.

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4.2.1 Study 1: Comparisons Between Editorial Content and Advertisements

Editorial content comprised 1067 (63.8%) profiled subjects while 605 (36.2%) subjects were in advertisements. Advertisements developed by the publishing professional society were counted as editorial content since such content was editor-controlled. For example, editorial content was counted for ASEE's eGFI (Engineering Go For It!) campaign in Prism to encourage girls into science and math in K-12 and ASME's extensive conference brochure included in many issues of Mechanical Engineering.

Figure 4e indicated women's representation in both ads and editorial content improving over the 15 year period. Ads showed an overall greater improvement than editorial content, indicated by the linear trendline showing a steeper slope of growth (1.44% proportion of women per year) than editorial content (0.85% proportion of women per year).

Figure 4e. Graph of genders profiled in advertisements and content over time for all trade journals.

Stereotypes were representations of subjects that were found to be relevant in prejudging a worker in the engineering workplace. Stereotypes were typically gendered, where women's stereotypes

y = 0.0144x - 28.578

y = 0.0085x - 16.77

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