Women Managers, 2003
Method of Analysis
Using only the data obtained at the third wave, we added the responses to various questions to comprise a number of indices, or measures, of job and health experiences. We then computed the averages for these indices and compared male and female averages on:
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nine measures of Job Affect and Work Experiences, Table 1,
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eight different Health Outcomes, Table 2, and
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eight separate Health Behaviors, Table 3.
Each of these three tables presents the possible score range, the average score for men and women, and a test of whether or not these group averages are "statistically significant." Significance refers to whether or not the average difference between men and women can be attributed to chance or not; when the finding is significant, the probability of this result being incorrect is provided in parentheses. (When a finding is not significant, this means that although the averages are not exactly the same, they are not different enough for us to rule out the possibility that this is just random error variation between the two groups.)
General Findings
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Table 1: Wherever there are significant group differences, we find that women report more of everything, both positive and negative. For example, women report higher levels of job satisfaction, trust in top management, organizational commitment, as well as job stress, and being treated disrespectfully, and isolated/ excluded at work.
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Table 2: The gender differences in health outcomes are similar to those found in other studies. Women report more health problems, emotional exhaustion, depression, and sleeping problems while men report a greater number of alcohol problems and number of drinks consumed (unadjusted for body weight).
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Table 3: Compared to men, women report eating more and eating more junk food, exercising less, and using more over the counter drugs and herbal remedies in the last year.
Table 1. Comparison of Men and Women: Job Affect and Work Experiences
| Job Measure |
Possible Range |
Men |
Women |
Significant Gender Difference? |
| Job Satisfaction |
3-15 |
10.4 |
10.9 |
Yes (p < .01) |
| Intent to Quit |
3-15 |
7.8 |
7.6 |
No |
| Job Security |
3-12 |
6.6 |
6.4 |
No |
| Trust in Management |
4-20 |
9.4 |
9.9 |
Yes (p < .01) |
| Organizational Commitment |
3-15 |
8.9 |
9.2 |
Yes (p < .05) |
| Job Stress |
0-18 |
10.8 |
11.7 |
Yes (p < .01) |
| Work to Family Conflict |
2-8 |
4.6 |
4.7 |
No |
| Treated Disrespectfully At work |
0-18 |
4.2 |
5.0 |
Yes (p < .001) |
| Isolated/ Excluded At Work |
0-10 |
3.2 |
3.7 |
Yes (p < .01) |
Table 2. Comparison of Health Outcomes between Women and Men
| Health Outcome |
Possible Range |
Women |
Men |
Significant Gender Difference? |
| Health Problems |
0-6 |
2.27 |
2.03 |
Yes (p < .001) |
| Burnout - Depersonalization |
3-15 |
7.20 |
7.28 |
No |
| Burnout - Emotional Exhaustion |
3-15 |
9.57 |
8.68 |
Yes (p < .001) |
| Depression |
0-49 |
9.87 |
7.56 |
Yes (p >001) |
| Alcohol Problems |
0-4 |
.29 |
.41 |
Yes (p < .02) |
| Number of days In last 30 Drunk to Intoxication |
0 -30 |
.74 |
1.06 |
No |
| Alcohol Consumption (No. drinks last 6 months) |
0-1440 |
80.4 |
152.6 |
Yes (p < .001) |
| Sleep Problems |
4-20 |
11.13 |
9.97 |
Yes (p < .001) |
Table 3. Comparison of Health Behaviors between Women and Men
| Health Behaviors |
Women |
Men |
Significant Gender Difference? |
| % increase Eating last year |
27.4 |
12.9 |
Yes (p < .001) |
| % increaseSmoking last year |
4.7 |
3.5 |
No |
| % increase Alcoholic Beverages last year |
9.1 |
8.6 |
No |
| % decrease Exercise last year |
38.5 |
21.6 |
Yes (p < .001) |
| % increase Consumption of "Junk Food"last year |
25.3 |
10.9 |
Yes (p < .001) |
| % increase Prescription drugs last year |
11.9 |
11.7 |
No |
| % increase Over the counter Drugs last year |
11.0 |
7.4 |
Yes (p < .02) |
| % increase use of Herbal Remedies last year |
14.8 |
5.6 |
Yes (p < .001) |
Method of Analysis
Using the same measures reported in Tables 1 -3, we then considered the effect of paycode on male and female responses. Similar to before, we computed the averages for men and women, but did so separately for engineers, production/maintenance workers, managers, salaried-nonexempt, and salaried - exempt workers. These averages are shown in Tables 4, 5, and 6. The number of men and women (N = number) is provided at the top of each column.
Tables 4, 5, and 6 each show the results of several analyses where we made the following comparisons:
- Analysis 1. For each measure, for each paycode separately, we compared averages between men and women (e.g., for job satisfaction, we compared managerial men to managerial women and tested the significance level). Where the average difference is significant, the probability level is shown in the given cell, just under the average for men and women. Cells without probability levels reflect averages that are not significantly different from each other. (Again, this means that although the averages are not exactly the same, they are not different enough for us to rule out the possibility that this is just random error variation between the two groups.)
- Analysis 2. For each measure, we divided the sample by gender and looked to see if there were differences between the paycodes (e.g., for men, do the 5 paycode groups differ on the measure of intent to quit?) These differences are reported in text following the tables.
- Analysis 3. Considering gender and paycode differences at the same time, do we find that gender, paycode, or both factors together have an impact on the job, health outcome, or health behavior measures? The findings from this analysis are reported in the last column under the "Effects" heading. Although similar to the previous two analyses, this last one allows us to see the relative impact of either gender or paycode on the index examined. It can also detect interaction effects, or the unique outcomes associated with the joint impact of gender and paycode simultaneously.
General Findings
- Analysis 1
- Comparisons between men and women, within paycode, reveals that there are a greater number of significant gender differences for the managerial category. However, this is very much influenced by the fact that the managerial group is the largest - when the group size is large, it takes less of a difference between the two averages for the finding to be deemed "significant."
- Compared to male managers, female managers, report poorer job affect and experiences (Table 4) and a greater number of health problems (Table 5), although managerial men report more alcohol-related problems and consumption. Women managers also report a greater number of health behavior changes (Table 6).
- For the health measures (Tables 5 and 6) the pattern for the salaried-exempt women was similar to that of the managerial women.
- Analysis 2
- For job affect and experiences (Table 4) there were many differences between the paycodes. Generally, the production/ maintenance group fared the worst as compared to other groups; they had the lowest satisfaction, job security, trust in management, organizational commitment, the highest intent to quit (along with engineers), and reported among the highest levels of disrespectful treatment at work. Managers, however, had the greatest stress, conflict, isolation/ exclusion at work and among the highest levels of disrespectful treatment. On the other hand, managers also reported the highest levels of satisfaction, trust in management, and organizational commitment.
- There were few paycode differences for the health outcome and health behaviors
- Analysis 3
- The effect of gender was greatest on the health outcome and health behavior measures (Tables 5 and 6, last columns) while the effect of paycode was greatest for the job affect and experience measures (Table 4)
- There were no significant interactions of gender on paycode.
Table 4. Comparison of Job Affect and Work Experiences between Men and Women: By Paycode
(Note: For each cell, average values are shown as Men / Women.)
| Job Measure |
Possible Range |
Engineers(N = 135 N = 29) |
Production Maintenance(N = 201 N = 34) |
Managers(N = 330 N = 220) |
Salaried - Non Exempt(N = 92 N = 88) |
Salaried Exempt(N = 119 N = 134) |
Effects Gender? Paycode? Interaction? |
| Job Satisfaction |
3-15 |
10.3 / 10.7 |
9.7 / 9.4 |
10.8 / 11.2 |
10.1 / 10.8 |
10.5 / 10.7 |
Paycode |
| Intent to Quit |
3-15 |
7.8 / 8.6 |
8.4 / 7.9 |
7.3 / 7.1 |
7.9 / 7.7 |
7.8 / 8.1 |
Paycode |
| Job Security |
3-12 |
7.6 / 6.6 (p < .05) |
5.3 / 4.7 |
7.2 / 7.0 |
6.4 / 5.8 |
6.6 / 6.1 |
Paycode Gender |
| Trust in Management |
4-20 |
8.5 / 8.9 |
7.4 / 7.8 |
11.0 / 11.0 |
8.5 / 8.9 |
9.8 / 9.7 |
Paycode |
| Organizational Commitment |
3-15 |
8.5 / 8.8 |
8.0 / 7.4 |
9.6 / 9.9 |
8.3 / 8.6 |
9.1 / 9.0 |
Paycode |
| Job Stress |
0-18 |
9.1 / 10.0 |
10.2 / 11.1 |
12.2 / 13.5 (p < .01) |
10.2 / 8.6 |
10.2 / 11.2 |
Paycode Interaction (p = .06) |
| Work to Family Conflict |
2-8 |
4.3 / 4.6 |
4.6 / 4.9 |
4.9 / 5.1 |
4.0 / 4.2 |
4.3 / 4.4 |
Paycode Gender |
| Treated Disrespectfully At Work |
0-18 |
3.2 / 4.3 |
4.8 / 4.9 |
4.2 / 5.4 (p < .001) |
4.1 / 4.3 |
4.0 / 4.9 |
Gender |
| Isolated/Excluded At Work |
0-10 |
2.7 / 2.9 |
3.2 / 3.0 |
3.5 / 4.2 (p < .01) |
3.0 / 2.9 |
2.9 / 3.4 |
Paycode |
Dividing the sample by Gender and comparing paycode groups revealed many differences. For men, only isolated / excluded at work failed to show paycode differences, while for women, disrespectful treatment was the only variable that failed to reveal paycode differences.
Table 5. Comparison of Men and Women by Paycode: Health Measures
(Note: For each cell average values are shown as Men / Women.)
| Health Outcome |
Possible Range |
Engineers (N = 135 N = 29) |
Production Maintenance (N = 201 N = 34) |
Managers (N = 330 N = 220) |
Salaried - Non Exempt (N = 92 N = 88) |
Salaried Exempt (N = 119 N = 134) |
Effects Gender? Paycode? Interaction? |
| Health Problems |
0-6 |
1.89 / 1.96 |
2.39/ 2.32 |
1.89/ 2.24 (p < .001) |
2.06 / 2.35 |
.93 / 2.34 (p < .01) |
Gender |
| Burnout - Depersonalization |
3-15 |
7.06/ 6.75 |
8.08/ 8.68 |
7.16/ 7.12 |
6.96 / 6.84 |
6.76 / 7.28 |
Paycode |
| Burnout - Emotional Exhaustion |
3-15 |
8.45/ 9.90 (p < .05) |
8.93/ 10.54 (p < .01) |
8.85/ 9.60 (p < .01) |
8.39 / 8.82 |
8.29 / 9.66 (p < .0001) |
Gender Paycode |
| Depression |
0-49 |
7.05/ 10.72 |
9.23/ 12.4 |
7.14/ 9.56 (p < .01) |
7.09 / 9.14 |
6.85 / 10.02 (p < .01) |
Gender |
| Alcohol Problems |
0-4 |
.36/ .21 |
.38/ .28 |
.43/ .28 (p < .05) |
.35 / .32 |
.46 / .32 |
Gender |
| Number of days In last 30 Drunk to Intoxication |
0 -30 |
.91 / .09 |
1.24 / .76 |
.86 / .50 (p < .05) |
1.49 / .77 |
1.20 / 1.29 |
Gender |
| Alcohol Consumption (No. drinks last 6 months) |
0-1440 |
138 / 30 (p < .0001) |
165 / 47 (p < .0001) |
149 / 93 (p < .0001) |
128 / 69 |
178 / 81 (p < .01) |
Gender |
| Sleep Problems |
4-20 |
9.44 / 10.79 |
10.46 / 12.38 (p < .05) |
9.97 / 10.77 (p < .05) |
10.11 / 11.29 (p < .05) |
9.67 / 11.37 (p < .001) |
Gender |
Dividing the sample by Gender and examining paycode differences revealed:
For Men: Health Problems --The male production/ maintenance group reported significantly more health problems than did male engineers, managers, and salaried exempt
For Men: Burnout Depersonalization -- The male production / maintenance group reported significantly higher levels than all other male groups.
For Women: Burnout Depersonalization --The male production / maintenance group reported significantly higher levels than male managers and salaried nonexempt.
Table 6. Comparison of Men and Women by Paycode: Health Behaviors
(Note: For each cell average values are shown as Men / Women.)
| Health Behaviors |
Engineers (N = 135 N = 29) |
Production Maintenance (N = 201 N = 34) |
Managers (N = 330 N = 220) |
Salaried - Non Exempt (N = 92 N = 88) |
Salaried Exempt (N = 119 N = 134) |
Effects Gender? Paycode? Interaction? |
| % increase Eating last year |
14.6 / 27.6 |
9.9 / 8.6 |
14.5 / 31.1 p < .001 |
11.7 / 19.5 |
12.6 / 31.1 (p < .001) |
Gender Paycode |
| % increase Smoking last year |
2.2 / 0 |
4 .0 / 2.9 |
4.5 / 3.6 |
3.2 / 5.7 |
1.7 / 7.5 (p < .05) |
|
| % increase Alcoholic Beverages Last year |
11.7 / 3.6 |
7.4 / 5.7 |
9.1 / 13.5 |
8.5 / 5.7 |
6.8 / 5.9 |
|
| % decrease Exercise last year |
17.5 / 41.4 (p < .02) |
19.4 / 37.1 (p < .05) |
25.1 / 40.5 (p < .001) |
17.0 / 28.4 |
24.4/ 41.5 (p < .01) |
Gender |
| % increase Consumption of "Junk Food" last year |
10.9 / 27.6 |
8.4 / 8.5 |
14.2 / 30.6 (p < .001) |
8.5 / 15.9 |
7.6 / 26.7 (p < .001) |
Gender Paycode |
| % increase Prescription Drugs last year |
9.5 / 6.9 |
13.9 / 20.0 |
12.4 / 10.8 |
10.6 / 13.6 |
9.2 / 11.9 |
|
| % increase Over the counter Drugs last year |
9.5 / 13.8 |
8.9 / 8.6 |
7.6 / 11.7 |
1.1 / 10.2 (p < .01) |
6.7 / 10.4 |
Gender |
| % increase use of Herbal Remedies last year |
12.4 / 20.7 |
4.9 / 17.1 |
4.5 / 12.6 (p < .001) |
1.1 / 18.2 (p < .001) |
5.1 / 14.8 (p < .001) |
Gender |
Dividing the sample by Gender and examining paycode differences revealed:
For Men: Herbal Remedies The engineer group is significantly greater than production/maintenance, managers, and salaried nonexempt
For Women: Eat and Junk Food - The managerial group is significantly greater than the production / maintenance group.
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