This article was downloaded by: [University of New Hampshire] On: 08 February 2015, At: 09:13 Publisher: Routledge Informa Ltd Registered in England and Wales Registered Number: 1072954 Registered office: Mortimer House, 37-41 Mortimer Street, London W1T 3JH, UK
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Gender Differences in Depression in an Employment Setting Patricia A. Maffeo , Thomas W. Ford & Patrick F. Lavin Published online: 22 Jun 2011.
To cite this article: Patricia A. Maffeo , Thomas W. Ford & Patrick F. Lavin (1990) Gender Differences in Depression in an Employment Setting, Journal of Personality Assessment, 55:1-2, 249-262, DOI: 10.1080/00223891.1990.9674064 To link to this article: http://dx.doi.org/10.1080/00223891.1990.9674064
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JOURNAL OF PERSONALITY ASSESSMENT, 1990, 55(1&2),249-262 Copyright o 1990, Lawrence Erlbaum Associates, Inc.
Gender Differences in Depression in an Employment Setting Patricia A. Maffeo, Thomas W. Ford, and Patrick F. Lavin Downloaded by [University of New Hampshire] at 09:13 08 February 2015
Tennessee Valley Authority
This study extends the literature on sex differences in depression to an employment setting, using Minnesota Multiphasic Personality Inventory (MMPI; T and raw scores) and Depression (D)30 measures. In contrast to previous findings, no gender differences remained on any of the measures after the effects of salary, age, education, and job classification had been taken into account. Findings replicated earlier results showing depressed males to have greater difficulty with concentration and motivation than depressed females. Data suggest that MMPI sex-based T-scores may overcorrect for sex differences in raw scores. Possible explanationsfor the findings are discussed, including a general improvement in women's well-being associated with changes in social conditions such as employment, or the possibility of a self-selection bias in our sample.
It is widely believed that proportionately more women than men become depressed. Disproportionate numbers of depressed wonnen are found among treated as well as untreated populations (Belle & Goldman, 1980; Goldman & Ravid, 1980). Conclusions of various reviews have been questioned, however, due to methodological ~roblemsin the studies cited (Goldman & Ravid, 1900; Hammen, 1982) as well as to overinterpretive reviewing ((Parker,1979). The National Institute of Mental Health Epidemiologic Catchment Area program attempted to address problems in previous vvork by using similar methodology across five sites, including the application of a case identification instrument yielding Diagnostic and Statistical Manual of Mental Disorders (3rd ed. [DSM-Ill]; American Psychiatric Association, 1980) diagnoses (Regier et al., 1984). Results from three of the five sites have found higher lifetime prevalence rates for major depressive episode for women in all three sites (Robins et al., 1984), higher 6-months prevalence rates for women at all three sites for both major depression and dysthymia (Myers et al., 1984), and higher lifetime
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prevalence rates of dysthymia for women at all five sites (Weissman, Leaf, Bruce, & Florio, 1988). Certain research findings, however, indicate that social conditions (e.g., marital status, education, occupational level, salary, and age) may modify or reverse the relationship between gender and other forms of mental illness (Gove & Geerken, 1977; Ilfeld, 1977; Radloff, 1980). Significant sex differences in depressive symptoms are frequently not observed in homogeneous populations such as young, middle-class college students (Hammen & Padesky, 1977; Oliver & Burkham, 1979; Padesky & Hammen, 1981; Parker, 1979),and they were not observed in a current point prevalence measure of depression from a recent community survey (Weissman & Myers, 1978). Our study extends the literature on sex differences in depression to an employment setting. As in the studies based on a university student population, subjects generally can be assumed to be emotionally healthy and well functioning, but differ from those in the just-mentioned studies in being more heterogeneous on other socially relevant variables, such as age, education, occupation, and salary level. The Depression (D) scale of the MMPI, the psychological test most widely used for clinical and employment screening purposes, was employed in our study. Standard score values on the D scale have been found to track 5-point clinical ratings indicating minimal to extreme depression (Endicott & Jortner, 1966), providing evidence for absolute scaling as well as criterion and construct validity. Although not providing information sufficient for formal diagnosis, this scale is a measure of level of depressive symptomatology similar to those employed in studies by Craig and Van Natta (1979), Frerichs, Aneshensel, and Clark (1981), Radloff and Rae (1979), and Padesky and Hammen (1981). During the development of the MMPI, items with large gender differences in endorsement were eliminated (Hathaway & McKinley, 1942). Remaining gender differences were treated as measurement artifacts as opposed to valid differential measurements (Dahlstrom, Welsh, & Dahlstrom, 1975). Raw scores are converted to gender-based T-scores with a mean of 50 and standard deviation of 10. Males earned a mean raw score of 16.63 with a standard deviation of 4.18, and females earned a mean raw score of 19.26 with a standard deviation of 5.18 in the normative sample (Hathaway & Briggs, 1957), which results in females with higher raw scores attaining similar T-scores as males with lower raw scores, However, if gender differences represent valid measurement on the trait in question, single-sex norms may have been more appropriate. Similar total scores on the MMPI D scale could be obtained through endorsement of different individual items (Wiggins, 1966).Evidence exists which suggests that males and females achieve similar scores through endorsement of different items. For example, Lushene (1967/1975) found different factor structures for male and female patterns of item endorsement in the total MMPI item pool. Padesky and Hammen (1981) similarly found a different pattern of item
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endorsement among male and female college students with similar overall scores on the 030 subscale of the MMPI D scale. Because gender-based T-scores may obscure real differences on MMM D, we examined D raw scores and T-scores from a subset (D30) of the D scale items that uses same-sex norms (Dempsey, 1964) in addition to D ?'-scores. Relationships between D scores and validity Scales L and K on the MMPI and 030 were also examined to evaluate the extent of defensiveness reflected in the data. High scores on the L scale measure a deliberate and unsophisticated attempt to present oneself in a positive light by denying common faults, whereas high scores on the K scale reflect a more subtle and sophisticated attempt to deny psychopathology and present a favorable impression (Graham, 1977). Moderating effects of age, education, salary, and job level on the relationship between gender and depression were also examined. These variables have typicallybeen considered to function as social conditions that influence susceptibility to depression (Amenson & Lewinsohn, 1981; Hammen, 1982; Ilfeld, 1977). Tro determine if males and females exhibited a different pattern of responding, gender differences on individual item endorsement were also investigated.
METHOD Subjects were 1,902 male and 491 female employees of the Tennessee Valley Authority who took the MMPI as part of their evaluation for a clearance for a sensitive position in the nuclear industry between September 1983 and December 1985. Following common practice, males were eliminated who left mare than 30 items on the MMPI unanswered (Graham, 1977), and 72 males and 20 females were eliminated for whom demographic data was missing, leaving a usable total of 1,819 males and 471 females. Eighty-eight percent of the sample had between 12 and 16 years of education. Females were underrepresented, relative to their numbers, among those with less than a high school education and among those with a college education or better and especially among those with a postgraduate education, X2(4,N = 2290) = 47.12, p < .001. Eighty-seven percent of the sample was between 20 and 50 years of age, with females overrepresented relative to their numbers in the 20 to 40 age group and underrepresented in the over 50 age group, X2(5,N = 2290) = 68.85, p < .OOl. Twenty-seven percent of the sample was employed in trades and labor, 21% in public safety, 14% in administrative-technical, and 13%in clerical occupations. Males and females were proportionately represented among the various schedules. Forty-five percent of the sample earned less than $15,000 a year, and 37% earned between $15,000 and $30,000 a year. Males and females were proportionately represented among the defined salary levels.
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RESULTS MMPI D T-scores were significantly correlated with L scale scores, r = .13, p < .001, but not with K scale scores. MMPI D raw scores were significantly correlated with L scale scores, r = .12, p < .001, and with K scale scores, r = -.06, p < .001. Corresponding correlations were significant for males: r (D T-scores, L) = .14, p < .001; r (D T-scores, K) = - .05, p < .05; r (D raw scores, L) = .14, p < .001; r (D raw scores, K) = -.05, p < .05. They were not significantly correlated for females. D30 T-scores were not significantly correlated with either L or K scores. Scales L and K were themselves significantly correlated, r = .36, p < .001. A t test on L scale raw scores for males (M = 4.77, SD = 2.15) and females (M = 4.5 1, SD = 1.94) revealed significantly higher L scores among males, t (714.4) = -2.59, p < .01. A t test on K scale raw scores for males (M = 17.27, SD = 3.98) and females (M = 16.73, SD = 4.22) revealed significantly higher K scores among males, t(2288) = - 2.59, p < .01. Thirty-nine (2.14%) of the males and 6 (1.27%) of the female (1.97% overall) produced an L raw score as high as 10 (T-score = 70). One hundred thirty (7.15%) of the males and 27 (1.18%) of the females (6.86% overall) produced a K raw score as high as 23 (T-score = 70). A t test on MMPI D T-scores for males (M = 52.70, SD = 8.64) and females (M = 47.90, SD = 7.11) revealed significantly higher depressive symptoms among males, t(864) = - 12.43, p < .001. A t test on MMPI D raw scores for males (M = 17.80, SD = 3.60) and females (M = 18.22, SD = 3.75) revealed significantly higher depressive symptoms among females, t(2288) = 2.27, p < .05. A t test on D30 T-scores for males (M = 45.17, SD = 6.92) and females (M = 45.07, SD = 6.36) revealed no significant differences. For 4.12% (n = 75) of the males and 1.27% (n = 6) of the females, MMPI D T-scores in the clinically significant range (T r 70) were obtained. This sex difference was statistically significant, X2(1,N = 2290) = 8.90, P < .01. For 4.12% (n = 75) of the males and 4.25% (n = 20) of the females, raw scores in the clinically significant range (D raw score > 24) were obtained, which is the equivalent raw score for T = 70 from male norms. This sex difference was not statistically significant. For 1.04% (n = 19) of the males and .64% (n = 3) of the females, D30 T-scores in the clinically significant range (T 1 70) were obtained. This sex difference was not statistically significant. Although males and females were disproportionately represented in the upper ranges defined by D T-scores, there were no significant differences among means in the defined upper ranges. Table 1 reveals the results of generalized linear modeling analysis for MMPI T-scores. This procedure treats continuous independent variables (salary, education, and age) as covariates and categorical independent variables (schedule and sex) as main effects, Multiple runs are performed with nonsignificant effects gradually eliminated from the analysis. Significant differential linear regressions
GENDER DIFFERENCES IN DEPRESSION
TABLE 1
General Linear Models Procedure on MMPI T-Scores Source
Salary x sex Education x sex Age x sex Sex Error
df
Type I ss
F
Type 111ss
2 2 2 1 2280
6285.88 5207.71 3948.81 28.11
47.00* 38.94* 29.53* .42
164.10 2135.27 3634.34 28.11
253
F1.23 15.97* 27.17" .42
T for Hypothesis
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Paramem
Intercept Salary x sex: Education x sex: Age x sex:
Estimate
female male female male female male
Parameter = 0
54.42113403 - .0000190 - .0000230 - .33983678 - .47800648 .03291419 .I4404032
were found for Salary x Sex, F(2, 2280) = 47.00, p < .001, Education X Sex, F(2, 2280) = 38.94, p < .001, and Age x Sex, F(2, 2280) = 29.53, p < .001. The Salary x Sex differential regression was no longer significant after other effects had been accounted form, and slopes did not differ significantly from zero for either males or females. The Education x Sex differential regression remained significant after all other effects had been accounted for, F(2,2280) = 15.97, p < .001, with slope differing significantly from zero only for the males. The Age x Sex differential regression also remained significant after all other effects had been accounted for, F(2, 2280) = 27.17, p < .001, with slope differing significantly from zero only for the males. No significant main effects for sex remained after these regressions had been accounted for. Total amount of variance accounted for by the model is summarized by R~ = .09. Table 2 reveals the results of generalized linear modeling analysis for MMPI raw scores. Significant differential linear regressions were found for Salary x Sex, F(2, 2280) = 3.59, p < .05, Education x Sex, F(2, 2280) = 22.56, p -< .001, and Age x Sex, F(2, 2280) = 24.48, p < .001. The Salary x Sex differential regression was no longer significant after other effects had been accounted for, and slopes did not differ significantly from zero for either males or females. The Education x Sex differential regression remained significant after all other effects had been accounted for, F(2, 2280) = 15.38, g < ,001, wirh slope differing significantly from zero only for the males. The Age x Sex differential regression also remained significant after all other effects had been accounted for, F(2, 2280) = 24.91, P < .001, with slope differing significantlly from zero only for the males. No significant main effects for sex remained after
TABLE 2 General Linear Models Procedure on MMPI Raw Scores Source Salary x sex Education x sex Age x sex Sex Error
df
Type I rs
F
Type I11 ss
F
2 2 2 1 2280
91.07 572.12 620.67 11.06
3.59* 22.56** 24.48** .87
29.49 390.04 63 1.63 11.06
1.16 15.38** 24.91** .87
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Parameter Intercept Salary x sex:
female male female male female male
Education x sex: Age
x sex:
Estimate
T for Hypothesis Parameter = 0
18.53675079 - .0000097 - .0000093 -.I747561 - .20089256 .01783516 .05985094
28.52** - .75 - 1.33 - 1.75 -5.26** .89 7.00**
*p < .05. **p < .Ol.
TABLE 3 General Linear Models Procedure on D30 T-Scores Source Salary x sex Education x sex Age x sex Schedule
Sex Schedule x sex Error
df
Type I ss
F
Type 111 ss
F
2 2 2 8 1 8 2280
392.33 263.15 47.29 1873.46 8.95 264.81
4.31* 2.89 .52 5.14** .20 .73
347.54 150.26 50.55 1379.73 30.20 264.81
3.82* 1.65 .56 3.79** .66 .73
Parameter Intercept Salary x sex: Education x sex: Age x sex:
*p
< .05. **p < .01.
female male female male female male
Estimate
T for Hypotksis Parameter = 0
45.48216522 .00013253 .0000414 - .I7900875 - .I1295439 .03575864 - .00788748
27.33** 2.36* 1.43 - .94 - 1.56 .94 - .49
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these regressions had been accounted for. Total amount of variance accounted for by the model is summarized by R2 = .042. Table 3 reveals the results of generalized linear modeling ~roceduresfor 0 3 0 T-scores. The Salary x Sex differential regression remained significant after all other effects had been accounted for, F(2, 2280) = 3.82, p < .05. Slope differed significantly from zero only for the females. The main effects for schedule also remained significant after all other effects had been accounted for, F(8, 2280) = 3.79, p < .001. Least squares means for the nine schedules are reported in Table 4. Sheffe's test for multiple post hoc comparisons was used to test a set of eight linearly independent contrasts, with experiment-wise alpha level set at .05. Higher level managers were compared with each of the other schedule groups. The analysis indicated no significant differences between higher level managers and lower level managers, operations personnel, professional, administrative-technical, or public safety employees. Higher level managers were found to have significantly fewer symptoms of depression than trades and labor, clerical, and janitorial employees. No significant main effects for sex were found for D3O T-scores. Total amount of variance accounted for by the model is summarized by R2 = .027. Stepwise discriminant analysis on the MMPI data yielded two items that significantly differentiated between males and females, Wilks's L = .995, F(2, 2287) = 5.71, p < .01, with probability of correct classification equal to the priors. These were MMPI Item 67, "I wish I could be as happy as others seem to be," with males more likely to score true than females; and MMPI Item 130, "I have never vomited blood or coughed up blood," with females more likely to score true than males. No items were found to differentiate between males and females on the overall D30 data. Stepwise discriminant analysis on the MMPI data yilelded eight items that significantly differentiated between males and females at the upper ranges using D T-scores (T-score 1 70), Wilks's L = S54, F(8,72) = 7.26, p 24), Wilks's L = .949, F(1,93) = 4.97, p < .05, with probability of correct classification = .80. This was MMPI Item 285, "Once in a while I laugh at a dirty joke," with females more likely to score true than males. No items were found to differentiate between males and females at the upper ranges on the D30 data (no items were selected).
DISCUSSION Our findings are consistent with those of studies reporting no sex differences in depression or depressive symptoms in homogeneous populations and do not support the general stereotype that disproportionately more women than men are depressed. Although males in our study were significantly more likely to be found in the clinical range using MMPI D T-scores, this difference may be artifactual due to use of sex-based norms. No differences were found using either D raw scores or D30 scores, which do not use gender-related scoring. Although significant sex differences were found for mean MMPI D T-scores and raw scores, mean scores in each case remained quite close and within the normal range. These findings should, therefore, be considered to represent sex differences in depressive symptoms within the normal range, rather than sex differences in clinical depression or diagnosed depression. No sex differences in depressive symptoms remained among employees after effects of salary, age, education, and schedule had been accounted for. This result suggests that overall sex differences observed on MMPI D T and raw scores were moderated by social conditions and parallels Greene's (1987)finding in the context of ethnicity that MMM differences for target groups are less likely
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to be observed the more rigorously moderator variables are controlled. In our study, increases in depressive symptoms are assumed to result from adverse social conditions, which represent aetiology rather than measurement distortion. Depressive symptoms resulting from adverse social conditions appear worthy of investigation in their own right and, in sufficient magnitude or in interaction with predisposing conditions, could be assumed to lead to clinical depression. Our findings contrast with those of Radloff and Rae (1979) and Amenson and Lewinsohn (1981), who found that controlling for various social variables did not eliminate sex differences in depressive symptoms (females scored more depressed).This discrepancy may be due to differences in the particular variables controlled and the dependent measure employed. The Radloff and Amenson studies used the Center for Epidemiologic Studies-Depression (CES-D) scade, which is heavily loaded with affective items to the relative neglect of other depressive symptoms (Hammen, 1982) and may artifactually produce higher scores for females. A fuller range of symptoms (including those tending to be endorsed more frequently by each sex) appear to be represented in the MkPI and the D30. Amenson and Lewinsohn did not find social variables to be related to diagnosis of depression. Social conditions were found in our study to relate differently to level of depressive symptoms by sex. Although the reasons for these diierences are not readily apparent, similar differential relationships have been observed by other investigators (Ilfeld, 1977; Radloff & Rae, 1979). For MMPI D T and raw scores, salary did not appear to contribute independently to the variance in depressive symptoms. For males, depressive symptoms increased significantly with increasing age but decreased with higher education. Education and age were not related to depressive symptoms for females. The significant association between age and depression scores for males, with older men reporting more symptoms, has been found by other investigators (see Colligan, Osborne, Swenson, & Offord, 1983; Levitt, Lubin, & Brooks, 19133; Weissman & Myers, 1978, for current point prevalence) although the reverse relationship has also been found (see Craig & Van Natta, 1979; Myers et al., 1984; Radloff, 1980; Robins et al., 1984; Weissman et al., 1988; Weissman & Myers, 1978, for lifetime prevalence). It may be that bath younger and older individuals are at greater risk for depression or for stressful circumstances that precede depression. The significant negative association between education and depressive symptoms found here for males has been reliably found in previous work (Colligan et al., 1983; Craig & Van Natta, 1979; Levitt et al., 1983; Radloff, 1980). For the D30 measure, occupational level (schedule)was significantly related to depressive symptoms for both sexes after the effects of age, education, salary, and sex had been accounted for. Higher status jobs were generally associated with lower depression. Other researchers (Levitt et al., 1983; Radloff, 1980)have
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found a negative relationship between occupational status and depressive symptoms, similar to the one obtained presently on the D30 measure. Salary contributed significantly to the variance only for females on D30, with females revealing higher depressive symptoms with increasing salary. (Although salary did not contribute significantlyto depression on the MMM measures after other effects had been accounted for, the trend was in the inverse direction for both sexes, as expected.) The positive relationship between salary and depressive symptoms obtained for women on D30 is counterintuitive and discrepant from previous research (Amenson & Lewinsohn, 1981; Frerichs et al., 1981; Radloff, 1980; Radloff & Rae, 1979). Several previous studies examined the effects of family income rather than that of personal income, as assessed here. Although low family income might contribute to depression through a mechanism of financial stress, low personally earned income could contribute to depression through low self-esteem. In two of these studies, income was treated as a categorical (blocking) variable, with differentiation neither specified above $12,000 in the Amenson study nor above $4,000 in the Radloff study. Radloff, Amenson and Lewinsohn, and Frerichs et al. found lower income to be associated with higher depression. In our study, income was treated as a continuous variable extending into the $70,000 range. Females at upper salary levels may be under heightened pressure to produce due to high visibility. (Males, n = 161, outnumber females, n = 49, by more than 3 to 1 among employees in our study earning more than $40,000.) Heightened visibility may lead to increased levels of depressive symptoms as a stress response. The two items found to differentiatebetween males and females on the overall MMPI data, reflecting comparative unhappiness for males and denial of somatic symptomatologyfor females, could be considered reflective of gender-stereotypic responding in general. Items differentiating between males and females only in the clinical range are more likely to reflect differential expression of depression between the sexes. For the clinical range defined by MMPI T-score 2 70, males were more likely than females to endorse items reflecting obsessive worry, denial of excitement, poor health, difficulty with concentration, and lack of motivation and energy. Females more frequently endorsed an item reflective of embarrassment. Three of these items-'? have difficulty in starting to do things," "I have had periods of days, weeks, months when I couldn't get going," and "I find it hard to keep my mind on a task or job"-werd also found by Padesky and Hammen (1981) to differentiatebetween males and females zit the upper ranges of the D30 scale. Replication of these items thus strengthens their conduaion that cognitive and motivational deficits are more typical of depressed males than females. The tendency of females in the clinical range, defined by M W I raw score >24, to endorse more frequently laughing at a dirty joke appears to be a more singular finding. Both Dempsey (1964) and Padesky and Hammen (1981) found endorsement
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2:59
of the item "Icry easily" to be strongly associated with gender in both depres~~ed and overall samples. The failure of this item to differentiate between males and females in any of these analyses (the item was endorsed very infrequently in our study by both males and females) may be due to the context of testing. Both males and females may interpret endorsement of the item1 as reflecting adversely on their suitability for a nuclear clearance. Another possibility is that women in our study cry less than women in the general population. There is a possibility that defensiveness due to testing context could account for the overall lack of sex differences, because both sexes might be motivated to minimize psychopathology. However, as noted, protocols with 30 or more unanswered items were omitted from the analyses, thus eliminating cases of obvious invalidity. Defensive styles reflected by L and K do not appear to contribute to D30 scores. MMPI D T and raw scores tend to be positively associated with L scale scores but negatively associated with K scale scores. Because males tend to score higher on both L and K than do females, the scores of males would appear to be both elevated to a greater extent by L and lowered to a greater extent by K than would the scores of females. It seems reasonable: to assume that these processes might cancel each other out. D scores are not K-corrected during standard scoring of the MMPI. K lhas actually been found to reduce the discrimination between normative and criterion groups for the D scale (McKinley, Hathaway, & Meehl, 1948). Because L and K may reflect personality styles that contribute in substantive ways to level of depression, no attempt was made in our study to exclude or control on the basis of these variables. In any event, high scorers on L and K represent very small percentages of the total sample. It is important to note in general that, due to the large sample size, relationships reported here as significant accounted for a very small proportion of variance. Most differences, although statistically significant, do not appear to be clinically significant. The 4.8 point MMPI D T-score difference between males and females approaches the effect size considered by Greene (1987) to be clinically relevant, but it appears that this difference is created artifactually by the use of sex-based norms. Use of MMPI T-score norms produces higher mean scores for males and a disproportion of males in the clinical range, although no differences were observed with either MMPI raw scores or D30 T-scores, which do not use gender-based transformations. Our study, however, provides no evidence as to whether MMPI T-scores, MMPI raw scores, or D30 T-scores represent the better measure of depression, because no independent criterion variable was available. Dempsey (1964) maintained that the D30 is a purer measure which eliminates items not related to the depression construct. We were able to find little research on this scale bearing upon its construct validity. The different pattern of relationships observed with covariates for the MMM and the D30 underscores the lack of consistency between the two measures.
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With respect to the MMI'I, overall results from t tests suggest that D scale sex-based T-scores overcorrect for observed raw score differences. It is tempting to speculate about changes in MMPI D scores relative to the original normative sample. Scores for males were found to be slightly higher, paralleling trends (more pronounced for males) observed by Colligan et al. (1983)for higher scores in general relative to the normative sample. MMM scores were slightly lower in our study than normative data for females. Although the lower scores for females in relation to male scores could be attributed to a self-selection bias, the same pattern was found by Colligan, who used a random community sample matched to the 1980 U.S. census for age and sex among Whites. These changes may, in part, represent changes over time in the meaning of MMPI items, as suggested by Colligan et al., or could be due to the sharply reduced frequency of omitted items in more recent protocols (Greene, 1987). Another possibility is that our results reflect decreased pathology in females over time. Pathology such as depression might be expected to decrease in females over time due to positive mental health benefits resulting from increased employment rates (Bebbington, Hurry, Tennant, Sturt, & Wing, 1981; Birnbaum, 1975; Brown & Harris, 1978; Gove & Geerken, 1977; Ilfeld, 1977). Other investigators have obtained data supportive of the hypothesis that women's mental health has improved in the recent past. Weissman and Myers's (1978) failure to find sex differences on a current point prevalence measure of depression, although finding increased rates for women on a lifetime prevalence measure, is consistent with the improvement hypothesis. Also consistent is Srole and Fischer's (1980)finding that the younger generation of like-age female cohorts showed improvement in mental health to the level of similar-age males, for whom rates remained stable across like-age intergenerational cohorts. However, although not looking at intergenerational cohort differences by sex, the NIMH Epidemiologic Catchment Area studies found higher rates for major depressive episode and dysthymia among younger participants (Myers et al., 1984.; Robins et al., 1984; Weissman et al., 1988), which would appear to contradict an improvement hypothesis. These authors noted that these findings are counter to their predictions and possibly artifactual. But given the pattern of finding increased rates at younger ages, Weissman et al. (1988), looking at lifetime prevalence for dysthytnia across five sites, found the diierence between the sexes to be greatest in the 45 to 64 age group, This finding is consistent with improvement of younger women possibly due to changed social conditions. (Howevw, the 65 or older age group showed the lowest rates of all.) Inferences to an improvement hypothesis in our data as well as in the just-mentioned studies remain highly speculative. The possibility exists that women in our study are a self-selected, healthy sample of a population in which women are generally more depressed than men. It may be that a more select sample of women versus men choose or are chosen to work in this somewhat high-powered and male-dominated occupational setting.
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It is clear that increased rates of depression for women continue to be found in the general population. Generali~abilit~ of our results should be considered limited by the particular characteristics of the occupational setting studiled (subjects were not scientifically selected) and by the fact that diagnosable cases of depression were not identified.
ACKNOWLEDGMENT
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We thank Paul G. Cooper for his helpful consultation and advice concerning the statistical analyses.
REFERENCES Amenson, C. S., & Lewinsohn, P. M. (1981). An investigation into the observed sex differenm in prevalence of unipolar depression. Journal of Abnormal Psychology, 90, 1-13. American Psychiatric Association. (1980). Diagnostic and statistical manual of mental disorders (3rd ed.). Washington, DC: Author. Bebbington, P., Hurry, J., Tennant, C., Sturt, E., & Wing, J. K. (1901). Epidemiology of mental disorders in Camberwell. Psychological Medicine, 11, 561-579. Belle, D., & Goldman, N. (1980). Patterns of diagnoses received by men and women. In M. Guttentag, S. Salasin, & D. Belle (Eds.), The mental health of women (pp. 21-30). New York: Academic. Birnbaum, J. A. (1975). Life patterns and self-esteem in gifted farnily-oriented and career-committed women. In M. T. S. Medick, S. S. Tangri, & L. W. Hoffman (Eds.), Women and achievement: Social and motivntional analyses (pp. 396-419). New York: Halsted. Brown, G. W., &Harris, T. (1978). Social origins of depresston: A study of psychiatric disorders in wonaen. London: Tavistock. Colligan, R. C., Osborne, D., Swenson, W. M., & Offord, K. P. (1983). The MMPI: A contemporary normative study. New York: Praeger. Craig, T. J., &Van Natta, P. A. (1979). Influence of demographic characteristics on two measures of depressive symptoms. Archives of General Psychlatry, 36, 149-154. Dahlstrom, W. G., Welsh, G. S., & Dahlstrom, L. E. (1975). An MMPI handbook: Research applications Wol. 2, rev. ed.). Minneapolis: University of Minnesota Press. Dempsey, P. (1964). A unidimensional scale for the MMPI. Journal of Consulting Psychology, 28, 364-370. Endicott, N. A., & Jortner, S. (1966). Objective measures of depression. Archives of General Psychiatry, 15, 249-255. Frerichs, R. R., Aneshensel, C. S., &Clark, V. A. (1981). Prevalence of depression in Los Angeles County. American Journal of Epidemiology, 113, 691-699. Goldman, N., & Ravid, R. (1980). Community surveys: Sex differences in mental illness. In M. Guttentag, S. Salasin, & D. Belle (Eds.), The mend health of women (pp. 31-56). New York: Academic. Gove, W. R., &Geerken, M. R. (1977). The effect of children and employment on the mental health of married men and women. Social Forces, 56, 66-76. Graham, J. R. (1977). The MMPI: A practical guide. New York: Oxford University Press. Greene, R. L. (1987). Ethnicity and MMPI performance: A review. Journal of Consulting B Clinical Psychology, 55, 497-512. Hammen, C. L. (1982). Gender and depression. In I. Al-Issa (Ed.), Gender and psychopathology (pp. 134-153). New York: Academic.
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Hammen, C. L., & Padesky, C. (1977). Sex differences in the expression of depressive responses on the Beck Depression Inventory. Journal of Abnormal Psychology, 86, 609-614. Hathaway, S. R., & Briggs, P. F. (1957). Some normative data on new MMPI scales. Journal of Clinical Psychology, 13, 364-368. Hathaway, S. R., & McKinley, J. C. (1942). A multiphasic personality schedule (Minnesota): 111. The measurement of symptomatic depression. Journal of Psychology, 14, 73-84. Ilfeld, F. W. (1977, August). Sex differences in psychiatric symptomatology. Paper presented at the meeting of the American Psychological Association, San Francisco. Levitt, E. E., Lubin, B., &Brooks, J. M. (1983). Depression: Concepts, controversies,and some new facts (2nd ed.). Hillsdale, NJ: Lawrence Erlbaum Associates, Inc. Lushene, R. E. (1975). Factor structure of the MME'I item pool. In W. G . Dalhstrom, G . S. Welsh, & L. E. Dahlstrom (Eds.), A n MMPI handbook: Research applicanons (Vol. 2, rev. ed., pp. 126-127). Minneapolis: University of Minnesota Press. (Original work published 1967) Myers, J. K., Weissman, M. M., Tischler, G . L., Holzer, C. E., Leaf, P. J., Orvaschel, H., Anthony, J. C., Boyd, J. H., Burke, J. D., Kramer, M., & Stoltzman, R. (1984). Six-month prevalence of psychiatric disorders in three communities. Archives of General Psychiatry, 41, 959-967. McKinley, J. D., Hathaway, S. R., & Meehl, P. E. (1948). The MMPI: VI. The K scale. Journal of Consultmg Psychology, 12, 20-3 1. Oliver, J. M., & Burkham, R. (1979). Depression in university students: Duration, relation to calendar time, prevalence, and demographic correlations. Journal of A b n d Psychology, 88, 667-670. Padesky, C . H., & Hammen, C. L. (1981). Sex differences in depressive symptom expression and help-seeking among college students. Ser Roles, 7, 309-320. Parker, G . (1979). Sex differences in non-clinical depression. Australian B New Zealand Journal of Psychiaty, 13, 127-132. Radloff, L. S. (1980). Risk factors for depression: What do we learn from them? In M. Guttentag, S. Salasin, & D. Belle (Eds.), The mental health of women (pp. 93-1 10). New York: Academic. Radloff, L. S., & Rae, D. S. (1979). Susceptibility and precipitating factors in depression: Sex differences and similarities. Journal of A b n d Psychology, 88, 174-181. Regier, D. A., Myers, J. K., Kramer, M., Robins, L. N., Blazer, D. G., Hough, R. L., Eaton, W. W., & Locke, B. Z. (1984). The NIMH Epidemiologic Catchman Area program. Archives of General Psychiatry, 41, 934-941. Robins, L. N., Helzer, J. E., Weissman, M. M., Orvaschel, H., Gruenberg, E., Burke, J. D., & Regier, D. A. (1984). Lifetime prevalence of specific psychiatric disorders in three sites. Archives of General Psychiatry, 41, 949-958. Srole, L., & Fischer, A. K. (1980). The Mldtown Manhattan longitudinal study vs. 'The Mental Paradise Lost' doctrine. Archives of General Psychiaty, 37, 209-221. Weissman, M. M., Leaf, P. J., Bruce, M. L., & Florio, L. (1988). The epidemiology of d~sthymiain five communities: Rates, risks, comorbidity, and treatment. American Journal of Psychiatry, 145, 815-819. Weissman, M. M., &Myers, J. K. (1978). Affective disorders in a U.S. urban community. Archives of General Psychiatry, 35, 1304-131 1. Wiggins, J. S. (1966). Substantive dimensions of self-report in the MMPI item pool. Psychologzcal Monogfaehr, 80(22, Whole No. 630).
Patricia A. Maffeo 4160 Towanda Trail Knoxville, TN 379 19 Received August 22, 1989 Revised January 22, 1990
Journal of Personality Assessment Publication details, including instructions for authors and subscription information: http://www.tandfonline.com/loi/hjpa20
Gender Differences in Depression in an Employment Setting Patricia A. Maffeo , Thomas W. Ford & Patrick F. Lavin Published online: 22 Jun 2011.
To cite this article: Patricia A. Maffeo , Thomas W. Ford & Patrick F. Lavin (1990) Gender Differences in Depression in an Employment Setting, Journal of Personality Assessment, 55:1-2, 249-262, DOI: 10.1080/00223891.1990.9674064 To link to this article: http://dx.doi.org/10.1080/00223891.1990.9674064
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JOURNAL OF PERSONALITY ASSESSMENT, 1990, 55(1&2),249-262 Copyright o 1990, Lawrence Erlbaum Associates, Inc.
Gender Differences in Depression in an Employment Setting Patricia A. Maffeo, Thomas W. Ford, and Patrick F. Lavin Downloaded by [University of New Hampshire] at 09:13 08 February 2015
Tennessee Valley Authority
This study extends the literature on sex differences in depression to an employment setting, using Minnesota Multiphasic Personality Inventory (MMPI; T and raw scores) and Depression (D)30 measures. In contrast to previous findings, no gender differences remained on any of the measures after the effects of salary, age, education, and job classification had been taken into account. Findings replicated earlier results showing depressed males to have greater difficulty with concentration and motivation than depressed females. Data suggest that MMPI sex-based T-scores may overcorrect for sex differences in raw scores. Possible explanationsfor the findings are discussed, including a general improvement in women's well-being associated with changes in social conditions such as employment, or the possibility of a self-selection bias in our sample.
It is widely believed that proportionately more women than men become depressed. Disproportionate numbers of depressed wonnen are found among treated as well as untreated populations (Belle & Goldman, 1980; Goldman & Ravid, 1980). Conclusions of various reviews have been questioned, however, due to methodological ~roblemsin the studies cited (Goldman & Ravid, 1900; Hammen, 1982) as well as to overinterpretive reviewing ((Parker,1979). The National Institute of Mental Health Epidemiologic Catchment Area program attempted to address problems in previous vvork by using similar methodology across five sites, including the application of a case identification instrument yielding Diagnostic and Statistical Manual of Mental Disorders (3rd ed. [DSM-Ill]; American Psychiatric Association, 1980) diagnoses (Regier et al., 1984). Results from three of the five sites have found higher lifetime prevalence rates for major depressive episode for women in all three sites (Robins et al., 1984), higher 6-months prevalence rates for women at all three sites for both major depression and dysthymia (Myers et al., 1984), and higher lifetime
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prevalence rates of dysthymia for women at all five sites (Weissman, Leaf, Bruce, & Florio, 1988). Certain research findings, however, indicate that social conditions (e.g., marital status, education, occupational level, salary, and age) may modify or reverse the relationship between gender and other forms of mental illness (Gove & Geerken, 1977; Ilfeld, 1977; Radloff, 1980). Significant sex differences in depressive symptoms are frequently not observed in homogeneous populations such as young, middle-class college students (Hammen & Padesky, 1977; Oliver & Burkham, 1979; Padesky & Hammen, 1981; Parker, 1979),and they were not observed in a current point prevalence measure of depression from a recent community survey (Weissman & Myers, 1978). Our study extends the literature on sex differences in depression to an employment setting. As in the studies based on a university student population, subjects generally can be assumed to be emotionally healthy and well functioning, but differ from those in the just-mentioned studies in being more heterogeneous on other socially relevant variables, such as age, education, occupation, and salary level. The Depression (D) scale of the MMPI, the psychological test most widely used for clinical and employment screening purposes, was employed in our study. Standard score values on the D scale have been found to track 5-point clinical ratings indicating minimal to extreme depression (Endicott & Jortner, 1966), providing evidence for absolute scaling as well as criterion and construct validity. Although not providing information sufficient for formal diagnosis, this scale is a measure of level of depressive symptomatology similar to those employed in studies by Craig and Van Natta (1979), Frerichs, Aneshensel, and Clark (1981), Radloff and Rae (1979), and Padesky and Hammen (1981). During the development of the MMPI, items with large gender differences in endorsement were eliminated (Hathaway & McKinley, 1942). Remaining gender differences were treated as measurement artifacts as opposed to valid differential measurements (Dahlstrom, Welsh, & Dahlstrom, 1975). Raw scores are converted to gender-based T-scores with a mean of 50 and standard deviation of 10. Males earned a mean raw score of 16.63 with a standard deviation of 4.18, and females earned a mean raw score of 19.26 with a standard deviation of 5.18 in the normative sample (Hathaway & Briggs, 1957), which results in females with higher raw scores attaining similar T-scores as males with lower raw scores, However, if gender differences represent valid measurement on the trait in question, single-sex norms may have been more appropriate. Similar total scores on the MMPI D scale could be obtained through endorsement of different individual items (Wiggins, 1966).Evidence exists which suggests that males and females achieve similar scores through endorsement of different items. For example, Lushene (1967/1975) found different factor structures for male and female patterns of item endorsement in the total MMPI item pool. Padesky and Hammen (1981) similarly found a different pattern of item
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endorsement among male and female college students with similar overall scores on the 030 subscale of the MMPI D scale. Because gender-based T-scores may obscure real differences on MMM D, we examined D raw scores and T-scores from a subset (D30) of the D scale items that uses same-sex norms (Dempsey, 1964) in addition to D ?'-scores. Relationships between D scores and validity Scales L and K on the MMPI and 030 were also examined to evaluate the extent of defensiveness reflected in the data. High scores on the L scale measure a deliberate and unsophisticated attempt to present oneself in a positive light by denying common faults, whereas high scores on the K scale reflect a more subtle and sophisticated attempt to deny psychopathology and present a favorable impression (Graham, 1977). Moderating effects of age, education, salary, and job level on the relationship between gender and depression were also examined. These variables have typicallybeen considered to function as social conditions that influence susceptibility to depression (Amenson & Lewinsohn, 1981; Hammen, 1982; Ilfeld, 1977). Tro determine if males and females exhibited a different pattern of responding, gender differences on individual item endorsement were also investigated.
METHOD Subjects were 1,902 male and 491 female employees of the Tennessee Valley Authority who took the MMPI as part of their evaluation for a clearance for a sensitive position in the nuclear industry between September 1983 and December 1985. Following common practice, males were eliminated who left mare than 30 items on the MMPI unanswered (Graham, 1977), and 72 males and 20 females were eliminated for whom demographic data was missing, leaving a usable total of 1,819 males and 471 females. Eighty-eight percent of the sample had between 12 and 16 years of education. Females were underrepresented, relative to their numbers, among those with less than a high school education and among those with a college education or better and especially among those with a postgraduate education, X2(4,N = 2290) = 47.12, p < .001. Eighty-seven percent of the sample was between 20 and 50 years of age, with females overrepresented relative to their numbers in the 20 to 40 age group and underrepresented in the over 50 age group, X2(5,N = 2290) = 68.85, p < .OOl. Twenty-seven percent of the sample was employed in trades and labor, 21% in public safety, 14% in administrative-technical, and 13%in clerical occupations. Males and females were proportionately represented among the various schedules. Forty-five percent of the sample earned less than $15,000 a year, and 37% earned between $15,000 and $30,000 a year. Males and females were proportionately represented among the defined salary levels.
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RESULTS MMPI D T-scores were significantly correlated with L scale scores, r = .13, p < .001, but not with K scale scores. MMPI D raw scores were significantly correlated with L scale scores, r = .12, p < .001, and with K scale scores, r = -.06, p < .001. Corresponding correlations were significant for males: r (D T-scores, L) = .14, p < .001; r (D T-scores, K) = - .05, p < .05; r (D raw scores, L) = .14, p < .001; r (D raw scores, K) = -.05, p < .05. They were not significantly correlated for females. D30 T-scores were not significantly correlated with either L or K scores. Scales L and K were themselves significantly correlated, r = .36, p < .001. A t test on L scale raw scores for males (M = 4.77, SD = 2.15) and females (M = 4.5 1, SD = 1.94) revealed significantly higher L scores among males, t (714.4) = -2.59, p < .01. A t test on K scale raw scores for males (M = 17.27, SD = 3.98) and females (M = 16.73, SD = 4.22) revealed significantly higher K scores among males, t(2288) = - 2.59, p < .01. Thirty-nine (2.14%) of the males and 6 (1.27%) of the female (1.97% overall) produced an L raw score as high as 10 (T-score = 70). One hundred thirty (7.15%) of the males and 27 (1.18%) of the females (6.86% overall) produced a K raw score as high as 23 (T-score = 70). A t test on MMPI D T-scores for males (M = 52.70, SD = 8.64) and females (M = 47.90, SD = 7.11) revealed significantly higher depressive symptoms among males, t(864) = - 12.43, p < .001. A t test on MMPI D raw scores for males (M = 17.80, SD = 3.60) and females (M = 18.22, SD = 3.75) revealed significantly higher depressive symptoms among females, t(2288) = 2.27, p < .05. A t test on D30 T-scores for males (M = 45.17, SD = 6.92) and females (M = 45.07, SD = 6.36) revealed no significant differences. For 4.12% (n = 75) of the males and 1.27% (n = 6) of the females, MMPI D T-scores in the clinically significant range (T r 70) were obtained. This sex difference was statistically significant, X2(1,N = 2290) = 8.90, P < .01. For 4.12% (n = 75) of the males and 4.25% (n = 20) of the females, raw scores in the clinically significant range (D raw score > 24) were obtained, which is the equivalent raw score for T = 70 from male norms. This sex difference was not statistically significant. For 1.04% (n = 19) of the males and .64% (n = 3) of the females, D30 T-scores in the clinically significant range (T 1 70) were obtained. This sex difference was not statistically significant. Although males and females were disproportionately represented in the upper ranges defined by D T-scores, there were no significant differences among means in the defined upper ranges. Table 1 reveals the results of generalized linear modeling analysis for MMPI T-scores. This procedure treats continuous independent variables (salary, education, and age) as covariates and categorical independent variables (schedule and sex) as main effects, Multiple runs are performed with nonsignificant effects gradually eliminated from the analysis. Significant differential linear regressions
GENDER DIFFERENCES IN DEPRESSION
TABLE 1
General Linear Models Procedure on MMPI T-Scores Source
Salary x sex Education x sex Age x sex Sex Error
df
Type I ss
F
Type 111ss
2 2 2 1 2280
6285.88 5207.71 3948.81 28.11
47.00* 38.94* 29.53* .42
164.10 2135.27 3634.34 28.11
253
F1.23 15.97* 27.17" .42
T for Hypothesis
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Paramem
Intercept Salary x sex: Education x sex: Age x sex:
Estimate
female male female male female male
Parameter = 0
54.42113403 - .0000190 - .0000230 - .33983678 - .47800648 .03291419 .I4404032
were found for Salary x Sex, F(2, 2280) = 47.00, p < .001, Education X Sex, F(2, 2280) = 38.94, p < .001, and Age x Sex, F(2, 2280) = 29.53, p < .001. The Salary x Sex differential regression was no longer significant after other effects had been accounted form, and slopes did not differ significantly from zero for either males or females. The Education x Sex differential regression remained significant after all other effects had been accounted for, F(2,2280) = 15.97, p < .001, with slope differing significantly from zero only for the males. The Age x Sex differential regression also remained significant after all other effects had been accounted for, F(2, 2280) = 27.17, p < .001, with slope differing significantly from zero only for the males. No significant main effects for sex remained after these regressions had been accounted for. Total amount of variance accounted for by the model is summarized by R~ = .09. Table 2 reveals the results of generalized linear modeling analysis for MMPI raw scores. Significant differential linear regressions were found for Salary x Sex, F(2, 2280) = 3.59, p < .05, Education x Sex, F(2, 2280) = 22.56, p -< .001, and Age x Sex, F(2, 2280) = 24.48, p < .001. The Salary x Sex differential regression was no longer significant after other effects had been accounted for, and slopes did not differ significantly from zero for either males or females. The Education x Sex differential regression remained significant after all other effects had been accounted for, F(2, 2280) = 15.38, g < ,001, wirh slope differing significantly from zero only for the males. The Age x Sex differential regression also remained significant after all other effects had been accounted for, F(2, 2280) = 24.91, P < .001, with slope differing significantlly from zero only for the males. No significant main effects for sex remained after
TABLE 2 General Linear Models Procedure on MMPI Raw Scores Source Salary x sex Education x sex Age x sex Sex Error
df
Type I rs
F
Type I11 ss
F
2 2 2 1 2280
91.07 572.12 620.67 11.06
3.59* 22.56** 24.48** .87
29.49 390.04 63 1.63 11.06
1.16 15.38** 24.91** .87
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Parameter Intercept Salary x sex:
female male female male female male
Education x sex: Age
x sex:
Estimate
T for Hypothesis Parameter = 0
18.53675079 - .0000097 - .0000093 -.I747561 - .20089256 .01783516 .05985094
28.52** - .75 - 1.33 - 1.75 -5.26** .89 7.00**
*p < .05. **p < .Ol.
TABLE 3 General Linear Models Procedure on D30 T-Scores Source Salary x sex Education x sex Age x sex Schedule
Sex Schedule x sex Error
df
Type I ss
F
Type 111 ss
F
2 2 2 8 1 8 2280
392.33 263.15 47.29 1873.46 8.95 264.81
4.31* 2.89 .52 5.14** .20 .73
347.54 150.26 50.55 1379.73 30.20 264.81
3.82* 1.65 .56 3.79** .66 .73
Parameter Intercept Salary x sex: Education x sex: Age x sex:
*p
< .05. **p < .01.
female male female male female male
Estimate
T for Hypotksis Parameter = 0
45.48216522 .00013253 .0000414 - .I7900875 - .I1295439 .03575864 - .00788748
27.33** 2.36* 1.43 - .94 - 1.56 .94 - .49
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these regressions had been accounted for. Total amount of variance accounted for by the model is summarized by R2 = .042. Table 3 reveals the results of generalized linear modeling ~roceduresfor 0 3 0 T-scores. The Salary x Sex differential regression remained significant after all other effects had been accounted for, F(2, 2280) = 3.82, p < .05. Slope differed significantly from zero only for the females. The main effects for schedule also remained significant after all other effects had been accounted for, F(8, 2280) = 3.79, p < .001. Least squares means for the nine schedules are reported in Table 4. Sheffe's test for multiple post hoc comparisons was used to test a set of eight linearly independent contrasts, with experiment-wise alpha level set at .05. Higher level managers were compared with each of the other schedule groups. The analysis indicated no significant differences between higher level managers and lower level managers, operations personnel, professional, administrative-technical, or public safety employees. Higher level managers were found to have significantly fewer symptoms of depression than trades and labor, clerical, and janitorial employees. No significant main effects for sex were found for D3O T-scores. Total amount of variance accounted for by the model is summarized by R2 = .027. Stepwise discriminant analysis on the MMPI data yielded two items that significantly differentiated between males and females, Wilks's L = .995, F(2, 2287) = 5.71, p < .01, with probability of correct classification equal to the priors. These were MMPI Item 67, "I wish I could be as happy as others seem to be," with males more likely to score true than females; and MMPI Item 130, "I have never vomited blood or coughed up blood," with females more likely to score true than males. No items were found to differentiate between males and females on the overall D30 data. Stepwise discriminant analysis on the MMPI data yilelded eight items that significantly differentiated between males and females at the upper ranges using D T-scores (T-score 1 70), Wilks's L = S54, F(8,72) = 7.26, p 24), Wilks's L = .949, F(1,93) = 4.97, p < .05, with probability of correct classification = .80. This was MMPI Item 285, "Once in a while I laugh at a dirty joke," with females more likely to score true than males. No items were found to differentiate between males and females at the upper ranges on the D30 data (no items were selected).
DISCUSSION Our findings are consistent with those of studies reporting no sex differences in depression or depressive symptoms in homogeneous populations and do not support the general stereotype that disproportionately more women than men are depressed. Although males in our study were significantly more likely to be found in the clinical range using MMPI D T-scores, this difference may be artifactual due to use of sex-based norms. No differences were found using either D raw scores or D30 scores, which do not use gender-related scoring. Although significant sex differences were found for mean MMPI D T-scores and raw scores, mean scores in each case remained quite close and within the normal range. These findings should, therefore, be considered to represent sex differences in depressive symptoms within the normal range, rather than sex differences in clinical depression or diagnosed depression. No sex differences in depressive symptoms remained among employees after effects of salary, age, education, and schedule had been accounted for. This result suggests that overall sex differences observed on MMPI D T and raw scores were moderated by social conditions and parallels Greene's (1987)finding in the context of ethnicity that MMM differences for target groups are less likely
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to be observed the more rigorously moderator variables are controlled. In our study, increases in depressive symptoms are assumed to result from adverse social conditions, which represent aetiology rather than measurement distortion. Depressive symptoms resulting from adverse social conditions appear worthy of investigation in their own right and, in sufficient magnitude or in interaction with predisposing conditions, could be assumed to lead to clinical depression. Our findings contrast with those of Radloff and Rae (1979) and Amenson and Lewinsohn (1981), who found that controlling for various social variables did not eliminate sex differences in depressive symptoms (females scored more depressed).This discrepancy may be due to differences in the particular variables controlled and the dependent measure employed. The Radloff and Amenson studies used the Center for Epidemiologic Studies-Depression (CES-D) scade, which is heavily loaded with affective items to the relative neglect of other depressive symptoms (Hammen, 1982) and may artifactually produce higher scores for females. A fuller range of symptoms (including those tending to be endorsed more frequently by each sex) appear to be represented in the MkPI and the D30. Amenson and Lewinsohn did not find social variables to be related to diagnosis of depression. Social conditions were found in our study to relate differently to level of depressive symptoms by sex. Although the reasons for these diierences are not readily apparent, similar differential relationships have been observed by other investigators (Ilfeld, 1977; Radloff & Rae, 1979). For MMPI D T and raw scores, salary did not appear to contribute independently to the variance in depressive symptoms. For males, depressive symptoms increased significantly with increasing age but decreased with higher education. Education and age were not related to depressive symptoms for females. The significant association between age and depression scores for males, with older men reporting more symptoms, has been found by other investigators (see Colligan, Osborne, Swenson, & Offord, 1983; Levitt, Lubin, & Brooks, 19133; Weissman & Myers, 1978, for current point prevalence) although the reverse relationship has also been found (see Craig & Van Natta, 1979; Myers et al., 1984; Radloff, 1980; Robins et al., 1984; Weissman et al., 1988; Weissman & Myers, 1978, for lifetime prevalence). It may be that bath younger and older individuals are at greater risk for depression or for stressful circumstances that precede depression. The significant negative association between education and depressive symptoms found here for males has been reliably found in previous work (Colligan et al., 1983; Craig & Van Natta, 1979; Levitt et al., 1983; Radloff, 1980). For the D30 measure, occupational level (schedule)was significantly related to depressive symptoms for both sexes after the effects of age, education, salary, and sex had been accounted for. Higher status jobs were generally associated with lower depression. Other researchers (Levitt et al., 1983; Radloff, 1980)have
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found a negative relationship between occupational status and depressive symptoms, similar to the one obtained presently on the D30 measure. Salary contributed significantly to the variance only for females on D30, with females revealing higher depressive symptoms with increasing salary. (Although salary did not contribute significantlyto depression on the MMM measures after other effects had been accounted for, the trend was in the inverse direction for both sexes, as expected.) The positive relationship between salary and depressive symptoms obtained for women on D30 is counterintuitive and discrepant from previous research (Amenson & Lewinsohn, 1981; Frerichs et al., 1981; Radloff, 1980; Radloff & Rae, 1979). Several previous studies examined the effects of family income rather than that of personal income, as assessed here. Although low family income might contribute to depression through a mechanism of financial stress, low personally earned income could contribute to depression through low self-esteem. In two of these studies, income was treated as a categorical (blocking) variable, with differentiation neither specified above $12,000 in the Amenson study nor above $4,000 in the Radloff study. Radloff, Amenson and Lewinsohn, and Frerichs et al. found lower income to be associated with higher depression. In our study, income was treated as a continuous variable extending into the $70,000 range. Females at upper salary levels may be under heightened pressure to produce due to high visibility. (Males, n = 161, outnumber females, n = 49, by more than 3 to 1 among employees in our study earning more than $40,000.) Heightened visibility may lead to increased levels of depressive symptoms as a stress response. The two items found to differentiatebetween males and females on the overall MMPI data, reflecting comparative unhappiness for males and denial of somatic symptomatologyfor females, could be considered reflective of gender-stereotypic responding in general. Items differentiating between males and females only in the clinical range are more likely to reflect differential expression of depression between the sexes. For the clinical range defined by MMPI T-score 2 70, males were more likely than females to endorse items reflecting obsessive worry, denial of excitement, poor health, difficulty with concentration, and lack of motivation and energy. Females more frequently endorsed an item reflective of embarrassment. Three of these items-'? have difficulty in starting to do things," "I have had periods of days, weeks, months when I couldn't get going," and "I find it hard to keep my mind on a task or job"-werd also found by Padesky and Hammen (1981) to differentiatebetween males and females zit the upper ranges of the D30 scale. Replication of these items thus strengthens their conduaion that cognitive and motivational deficits are more typical of depressed males than females. The tendency of females in the clinical range, defined by M W I raw score >24, to endorse more frequently laughing at a dirty joke appears to be a more singular finding. Both Dempsey (1964) and Padesky and Hammen (1981) found endorsement
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of the item "Icry easily" to be strongly associated with gender in both depres~~ed and overall samples. The failure of this item to differentiate between males and females in any of these analyses (the item was endorsed very infrequently in our study by both males and females) may be due to the context of testing. Both males and females may interpret endorsement of the item1 as reflecting adversely on their suitability for a nuclear clearance. Another possibility is that women in our study cry less than women in the general population. There is a possibility that defensiveness due to testing context could account for the overall lack of sex differences, because both sexes might be motivated to minimize psychopathology. However, as noted, protocols with 30 or more unanswered items were omitted from the analyses, thus eliminating cases of obvious invalidity. Defensive styles reflected by L and K do not appear to contribute to D30 scores. MMPI D T and raw scores tend to be positively associated with L scale scores but negatively associated with K scale scores. Because males tend to score higher on both L and K than do females, the scores of males would appear to be both elevated to a greater extent by L and lowered to a greater extent by K than would the scores of females. It seems reasonable: to assume that these processes might cancel each other out. D scores are not K-corrected during standard scoring of the MMPI. K lhas actually been found to reduce the discrimination between normative and criterion groups for the D scale (McKinley, Hathaway, & Meehl, 1948). Because L and K may reflect personality styles that contribute in substantive ways to level of depression, no attempt was made in our study to exclude or control on the basis of these variables. In any event, high scorers on L and K represent very small percentages of the total sample. It is important to note in general that, due to the large sample size, relationships reported here as significant accounted for a very small proportion of variance. Most differences, although statistically significant, do not appear to be clinically significant. The 4.8 point MMPI D T-score difference between males and females approaches the effect size considered by Greene (1987) to be clinically relevant, but it appears that this difference is created artifactually by the use of sex-based norms. Use of MMPI T-score norms produces higher mean scores for males and a disproportion of males in the clinical range, although no differences were observed with either MMPI raw scores or D30 T-scores, which do not use gender-based transformations. Our study, however, provides no evidence as to whether MMPI T-scores, MMPI raw scores, or D30 T-scores represent the better measure of depression, because no independent criterion variable was available. Dempsey (1964) maintained that the D30 is a purer measure which eliminates items not related to the depression construct. We were able to find little research on this scale bearing upon its construct validity. The different pattern of relationships observed with covariates for the MMM and the D30 underscores the lack of consistency between the two measures.
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With respect to the MMI'I, overall results from t tests suggest that D scale sex-based T-scores overcorrect for observed raw score differences. It is tempting to speculate about changes in MMPI D scores relative to the original normative sample. Scores for males were found to be slightly higher, paralleling trends (more pronounced for males) observed by Colligan et al. (1983)for higher scores in general relative to the normative sample. MMM scores were slightly lower in our study than normative data for females. Although the lower scores for females in relation to male scores could be attributed to a self-selection bias, the same pattern was found by Colligan, who used a random community sample matched to the 1980 U.S. census for age and sex among Whites. These changes may, in part, represent changes over time in the meaning of MMPI items, as suggested by Colligan et al., or could be due to the sharply reduced frequency of omitted items in more recent protocols (Greene, 1987). Another possibility is that our results reflect decreased pathology in females over time. Pathology such as depression might be expected to decrease in females over time due to positive mental health benefits resulting from increased employment rates (Bebbington, Hurry, Tennant, Sturt, & Wing, 1981; Birnbaum, 1975; Brown & Harris, 1978; Gove & Geerken, 1977; Ilfeld, 1977). Other investigators have obtained data supportive of the hypothesis that women's mental health has improved in the recent past. Weissman and Myers's (1978) failure to find sex differences on a current point prevalence measure of depression, although finding increased rates for women on a lifetime prevalence measure, is consistent with the improvement hypothesis. Also consistent is Srole and Fischer's (1980)finding that the younger generation of like-age female cohorts showed improvement in mental health to the level of similar-age males, for whom rates remained stable across like-age intergenerational cohorts. However, although not looking at intergenerational cohort differences by sex, the NIMH Epidemiologic Catchment Area studies found higher rates for major depressive episode and dysthymia among younger participants (Myers et al., 1984.; Robins et al., 1984; Weissman et al., 1988), which would appear to contradict an improvement hypothesis. These authors noted that these findings are counter to their predictions and possibly artifactual. But given the pattern of finding increased rates at younger ages, Weissman et al. (1988), looking at lifetime prevalence for dysthytnia across five sites, found the diierence between the sexes to be greatest in the 45 to 64 age group, This finding is consistent with improvement of younger women possibly due to changed social conditions. (Howevw, the 65 or older age group showed the lowest rates of all.) Inferences to an improvement hypothesis in our data as well as in the just-mentioned studies remain highly speculative. The possibility exists that women in our study are a self-selected, healthy sample of a population in which women are generally more depressed than men. It may be that a more select sample of women versus men choose or are chosen to work in this somewhat high-powered and male-dominated occupational setting.
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It is clear that increased rates of depression for women continue to be found in the general population. Generali~abilit~ of our results should be considered limited by the particular characteristics of the occupational setting studiled (subjects were not scientifically selected) and by the fact that diagnosable cases of depression were not identified.
ACKNOWLEDGMENT
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We thank Paul G. Cooper for his helpful consultation and advice concerning the statistical analyses.
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Patricia A. Maffeo 4160 Towanda Trail Knoxville, TN 379 19 Received August 22, 1989 Revised January 22, 1990
