Population, employment, and marital status trends: predicting the number of women in managerial positions.

Yasin, Jehad ; Helms, Marilyn M.

ABSTRACT

There is much interest in the "glass ceiling" limiting advancement of women in top management and numerous studies have attempted to explain this trend. Possible explanations include discrimination, differing qualities, attitudes, or gender-specific management styles, and even the availability of women within organizations for upward mobility. Studies have cited the dual role of women as employees and as primary family caregivers as a potential reason for women's failure to reach top management positions.

The objective of this paper is to provide an empirical analysis on women in upper management positions by examining whether number of employed women in the workforce, the educational attainment of these women, and their marital status can be used to derive an effective statistical model to predict the presence of women in upper levels of organizational administration and also to and compare a similar statistical model for men in upper management positions.

Using regression analysis, results indicate single females in the U.S. population is not a predictor of women in top management, but being widowed, separated, married and/or divorced (but not single) does affect the number of women in upper-level positions. The number of women attaining four-year degrees has more than doubled in the past twenty years and this growth would suggest a corresponding increase in female managers. The regression analysis supports this conclusion. Implications and areas for future research are also considered.

INTRODUCTION

Numerous studies have attempted to explain the greatermade representation of men in top management positions within the United States and authors have presented various reasons ranging from differing levels of commitment to discrimination. Regardless of reason, the number of women in top management positions, unfortunately, has not increased significantly. Women's share of professional jobs increased only 0.7% between 1996 and 2002. In addition, women's share of managerial positions in 60 countries range between 20% to 40% indicating women are markedly under represented in top management compared to their overall share of employment ("The Glass Ceiling ...," 2004; and the Bureau of Labor Statistics at http://www.bls.gov/cps/wlfdatabook2005. htm). Hymowitz (2006) agrees that while women hold many entry-level and middle managers, they remain scarce at the top.

The objective of this paper is to provide an empirical analysis of women in upper management positions by examining whether the total number of employed women, their education levels, and marital status can be used to derive an effective statistical model. The resulting model is compared to and also to a similar statistical model for men in upper management positions.

EMPLOYMENT OF WOMEN AND DIVERSITY

To understand why there are so few women in upper management, it is important to begin with an understanding of how the ranks of upper management are traditionally filled. These jobs are largely based on socialization into the corporate culture. Socialization is the act of training a person to fit into a particular culture or environment. In this case, the environment in question is the corporate culture. The culture of a corporation consists of the system of shared values and beliefs that guide employee behavior. The founder of a corporation traditionally establishes the corporate culture and with upper management, reward systems, rules, policies, and procedures, maintains this culture (Thomsen, 2004; Srinivasan, 2003; Harris & Ogbonna, 1999; Quick, 1992).

Men founded most of the large companies during an era when few women were in the workforce. Examples included IBM, founded by Charles Flint in 1911; DuPont Corporation, founded by E. I. DuPont in 1834; and Procter and Gamble started by William Proctor and James Gamble in 1837. The first members of upper management were men and the established reward systems, rules, policies, and procedures supporting the corporate culture were masculine. TAlsoo ensure reinforcement of the corporate culture, formal and informal training programs were developed to socialize men. Women entered the workforce en masse following World War II. This illustrates why, historically, men have dominated management positions. However, can the lack of women in upper management be explained solely by the lack of women's participation in the workplace? but Daily (1995) found a strong relationship between CEO characteristics and board of director composition. MThusale CEOs tend to hire board members who share similar characteristics--including gender, age, background, and experience.

Since the 1980's, leadership research has concentrated on personal attributes considered essential for success as chief executive officer of large corporations (Ocasio & Kim, 1999). These traits include leadership abilities, high levels of education and training, and functional expertise (usually financial, marketing, or operational). The requirements all describe a certain competency or working knowledge required for performance (Cooper, 2000; and Jordan & Schrader, 2003). Still other competencies include creativity, innovation, continual learning, flexibility, strategic thinking, vision, conflict management skills, integrity, decisiveness, problem-solving skills, technical credibility, human resource management skills, influencing, and negotiating (Jordan & Schrader, 2003). Interestingly these competencies are not personality traits related to likeability but are traits linked to specific job performance skills and activities. Competency is often cited as the critical factor determining a CEO's success. However, are these competencies objectively measured during the lifelong career of CEOs climbing the corporate ladder? Or, could it be as the CEO-to-be develops a reputation for competency, it creates a halo effect?

Research about CEO selection in the United States indicates executives with financial backgrounds were successful in gaining control of the highest levels of corporate power as early as the 1960s (Hayes & Abernathy, 1980). This rise of finance personnel, it is argued, led to a transformation in corporate governance that reflected a strong finance focus (Ocasio & Kim, 1999). However, the decade of the 1980's saw the destruction of corporate financial institutions. Mergers, acquisitions, leveraged buy-outs, restructurings, bankruptcies, and hostile takeovers were prevalent. This chaotic period in corporate governance saw the emergence of the role of the CEO as a leader, a catalyst for change, and a strategic visionary. Leadership research grew during this time and CEO selection was frequently studied (Paul, Costley, Howell, & Dorfman, 2002; and Judge, Bono, Ilies, & Gerhardt, 2002).

Many agree the lack of diversity itself, not individual traits, perpetuates the lack of diversity in top management. Diversity is defined as differences in the most literal form of the word but the term, according to Kahn (2002) has been transformed into a strategic direction where differences are valued. Differences can be associated with age, physical appearance, culture, job function or experience, disability, ethnicity, personal style, gender, and religion. Organizations focusing on diversity expect to increase their market share, productivity, creativity, and effectiveness and they recruit with a diversity focus. Kahn (2002) reports minorities represented only 7.6 percent of the American workforce some fifty years ago and that this percentage more than doubled by 2000 to 16 percent and will probably surpass 30 percent by the year 2020. Like minorities, the presence of women in the labor force is growing. If Bureau of Labor Statistics projections are accurate, women will represent almost 62 percent of the labor force by the year 2015. The future of the American economy will be dependent on the inclusiveness of women and minorities.

Heffernan (2002) notes one in four women earn more than their spouse earns and control about 80% of household spending. Using their own resources, women make up 47% of investors. Women purchase 81% of all products and services, 75% of over-the-counter medications, make 81% of retail purchases and buy 82% of all groceries. Women sign 80% of all check written in the U.S. and account for 40% of business travelers. Women influence 85% of all automobile purchase and 51% of all travel and electronic purchases. They head 40% of U.S. households with incomes over $600,000 and own 66% of all home-based businesses. Finally, since 1964, women have constantly been the majority of voters in the U.S from 1964 to present day.

If leaders are too similar, they will not create fresh thinking ("What Directors Think," 2002). Diversity must include individuals with different backgrounds and different opinions, which are both important and necessary, within organizations. Strauss (2002) reports that more than three decades after diversity became a buzzword in the upper ranks of corporate America, top management remains full of aging white men. For example, women hold only 1,584 (14 percent) of the 11,500 Fortune 1,000 board of director seats. The make-up and mindset of leadership hinders diversity.

Yet, Probert (2005) in her study of gender and unequal outcomes in academic careers found discrimination or bias in appointments and promotions were not significant in explaining men's domination in senior levels of leadership. Controversy remains over traits or discrimination as reasons for the lack of women in top management. Yet, could the real reason be based solely on the number or percentage of women in the workplace alone? Is it possible there are few women in upper management because fewer women are available in the workplace? This line of reasoning leads us to the following hypothesis:

[H.sub.1]: The number of total females and the number of employed females influence the number of women in management.

MARITAL STATUS AND EMPLOYMENT

The gender model suggests the dearth of women in management positions is due to attitudinal variations. The gender model contends women are less committed because they are socialized to put family before career (Dodd-McCue & Wright, 1996), and the marital status of women may explain their lack of top management participation. Researchers have found evidence that this may be true even though gender roles have changed over the years. For example, even though Glass (1992) found attitudinal differences between homemakers and women who work fulltime have diverged significantly over the past twenty years, a study of gender equity in the leisure services field found that some women still feel a promotion would cause too much family stress and conflict with their home lives (Shinew & Arnold, 1998). Other studies have concluded women are less committed to organizations when compared to men (Dodd-McCue & Wright, 1996). Prrobert (2005) in her study of academic careers found demographic changes, including high rates of separation and divorce and the impact of older children's needs, were reasons for women's failure to progress in their careers.

In her ten-year retrospective of women and work, MacRae (2005) cited the glass ceiling along with childcare and family concerns, as the key issues surrounding women and work. Williams (2004) found many women never get near the top management glass ceiling because of the "maternal wall," which is a gender bias, well documented demographically by the dearth of mothers in upper-level jobs. In her study of the academic profession, she found employers misuse demographics and engage in "statistical discrimination" or "descriptive stereotyping" when they assume women will confirm to a typical stereotype of a mother reducing their working hours after they marry and have children. She argues these employers are using "cognitive bias" by allowing such stereotypes to shape their perceptions as well as hiring and promotion decisions. Hewlett (2002) argued the widespread business belief that a woman who is accommodating to her family is no longer choosing to be a serious managerial contender is wrong. He further stated top management must work to change the corporate culture's erroneous belief. The issue of the influence of marital or family status leads to the second hypothesis:

[H.sub.2]: Whether females are married, single, or separated influences the number of women in management.

EDUCATION LEVELS AND EMPLOYMENT

Another explanation offered for the lack of women in top management is workplace discrimination. Numerous discrimination studies have publicized the earnings gap between females and their male counterparts. A 1986 Wall Street Journal article first used the phrase "glass ceiling" to describe the invisible barrier that prevents women from reaching top management positions (Hymowitz & Schellhardt, 1986). Arfken, Bellar, and Helms (1998 & 2004) cited glass ceilings as the reason for limited participation by women on boards of directors. The limited numbers of women CEOs running companies and/or serving on boards may be due in part, to "glass walls" in organizations that restrict women to certain fields and positions, like human resources and other staff duties that are often "dead-end" paths lacking upward mobility. Goodman, Fields, and Blum (2003) suggest that removing the glass ceiling may make good business sense and could eliminate the higher turnover and disillusionment among capable women.

Since widespread acknowledgement that workplace glass ceilings exist, businesses have made efforts to eliminate discrimination through affirmative action and diversity programs. Despite the efforts made by government and private industry, many argue the glass ceiling still exists due to discrimination. Meyerson and Fletcher (2000) argue that discrimination is so deeply embedded in organizational life as to be virtually indiscernible. Noonan and Corcoran (2004) too found multiple glass ceilings exist in the workplace.

In addition, opinions are mixed on whether work socialization, such as training and mentoring, actually has an affect on the number of women in upper management positions. Organizations have worked diligently over the past decades to restructure their formal training programs and a number of new businesses have been established to support this need for change.

Katz (2001) suggests a third level of glass ceiling exists, known as "Alice in Wonderland." She argues that due to the lack of women in top management ranks, women are more isolated and find they are judged by different standards then men in senior-level positions. Why do these glass ceilings still exist? Is it related to women's educational background? In current settings, the number of females seeking college degrees is equal to and often exceeds males enrolled in similar programs (Hymowitz, 2006). Yet in the past, men were often more highly educated. This leads to the third hypothesis:

[H.sub.3]: The education level of females influences the number of women in management.

METHODOLOGY

This study uses a cross section of national economic time series data complied from http://www.economagic.com. This methodology, using longitudinal data, was used successfully by Reid, Miller, and Kerr (2004) in their 10-year study of sex-based glass ceilings in U.S. state level bureaucracies. Maume (2004) also suggests issues like the glass ceiling must be investigated with longitudinal data.

The sample size of the study is 252 months based on the most recent data available from the period January 1982 to December 2002. In some cases, data was not available for the entire period for each variable. Separate multiple regression analyses were performed for male and female executives and correlation matrices were computed using the SPSS-X statistical package for the social sciences.

Females

The regression model correlation and analysis of variance for females are shown in Tables 1, 2, and 3. The number of women in managerial positions (EXEC-F) is the dependent variable. The independent variables are: (1) female population (POP-F), (2) females employed full-time (FT-F), (3) female high school graduates, no college, 25 years and over (HSDNC-F), (4) female college graduates, 25 years and over (CD-F) , (5) single females in the population (SINGLE-F), (6) married females in the population (MARRIED-F), and (7) separated, divorced or widowed females in the population (SEP-F).

Given the regression analysis in Table 1 with a ninety-nine percent confidence level and an alpha of 0.005, the statistically significant variables were females employed full time (FT-F), females with a college degree (CD-F), married females in the population (MARRIED-F) and separated, divorced, or widowed females in the population (SEP-F). At a 92% confidence level, with an alpha of 0.040, female high school graduates with no college age 25 and over enters the equation (HSDNC-F) as a statistically significant variable as does single females (SINGLE-F). Since this paper addresses confirmatory analysis rather than model building, all the independent variables except females in the population (FEMPOP-F) are included in the regression equation because they appear to have an association with the dependent variable. The Standard Error of the regression analysis is 181.983. The adjusted Coefficient of Determination is 97.3%.

Males

The regression model, correlation matrix, and analysis of variance for male executives are shown in Tables 4, 5, and 6. The number of males in managerial positions (EXEC-M) is the dependent variable. The independent variables are: (1) males in the population (POP-M), (2) males employed full-time (FT-M), (3) male high school graduates, no college, 25 years and over (HSDNCM), (4) male college graduates, 25 years and over (CD-M), (5) single males in the population (SINGLE-M), (6) married males in the population (MARRIED-M), and (7) separated, divorced or widowed males in the population (SEP-M).

A ninety percent confidence level with an alpha of 0.05, the regression model contains the statistically significant variables of males with college degrees and single males in the population. Since this paper addresses confirmatory analysis rather than model building, all independent variables except males in the total population are included in the regression equation because they appear to have an association with the dependent variable. The Standard Error of the regression analysis is 911.016. The adjusted Coefficient of Determination is 41.9%.

DISCUSSION

This study examined the influence of the total number of females and the number of full-time employed females on the number of women in management. It was expected that since the number of women in the United States population and the number of women who work full-time has increased, these variables would predict the number of female top managers. However, the regression analysis found only the number of full-time employed women to be positively related to the number of women in management whereas the female population in general was found to be statistically insignificant in predicting the number of women in management. This outcome was not expected because as the number of women in the United States rises, one expects, a priori, the number of female top managers to rise. The number of women working full-time does have a positive relationship with the number of women in management. In comparison, the number of full-time employed men shows a positive relationship with the number of men in management and similarly the male population was found to be statistically insignificant as a predictor for the number of men in management.

This study also sought to examine the affects of marital status on women in management. As previously mentioned, the gender model posits that married women are less likely to be top managers than single women are. The regression analysis shows the numbers of single females does not significantly affect women in management; however, widowed, separated, or divorced females, as well as being a married female does affect the dependent variable. Based on the regression equation, the relationship between marriage and women managers is negative. This was expected because married women who have a family tend to focus more on their family than their career. This supports traditional beliefs and stereotypes that women bear more of the household and childcare responsibilities.

However, other categories of marital status were also found to be negatively correlated with top management leadership, perhaps implying that relationships in general are a hindrance to career aspirations. Even though marital status indicators were not found to be statistically significant, both married and single variables including family, number of children, or head of household may be more indicative of the family pressures causing women to shy away from increased work responsibilities, at least in some stage of their family life cycle. In comparison, the regression analysis of males shows a positive relationship between top management participation and marital status (including and also being widowed, separated, or divorced). This result was expected because society views married men as being more stable; therefore, they would also be viewed more positively in the workplace when being considered for upper-level management positions.

Lastly, this study sought to identify the effect education levels have had on the presence of women in top management Given the growth in the number of women attaining four-year degrees, researchers expected the increase in qualified women to be reflected in an increase in female top managers. The regression analysis supports this conclusion. In fact, the most statistically significant variable was educational level. The number of females with college degrees appears to be a strong predictor of the number of females in top management as it had the largest standardized beta of the coefficients. In addition, the regression analysis for males indicates males with a college degree are a good predictor of men in management. This outcome was expected because the number of women and men with college degrees has become more equal during the years; therefore, similar results should be seen for both men and women with college degrees. It also supports the importance of higher education for job progression.

AREAS FOR FUTURE RESEARCH

Future research is needed to define even stronger relationships using more extensive data sets for covering longer periods of time. Future research may involve testing, to see the effects of the independent variables on different levels of women in middle and upper-level management. Future research is also needed to address structural characteristics and organizational practices that may create opportunities for women to attain and remain in top management positions.

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Jehad Yasin, Fort Valley State University Marilyn M. Helms, Dalton State College

Table 1--Regression Model (Female)

 Coefficients(a)
 Standardized
 Unstandardized Coefficient Coefficients

Model B Std. Error Beta

1 (Constant) 5,151.55 1,978.183
 FT-F 0.108 0.021 0.287
 HSDNC-F -0.152 0.062 -0.037
 CD-F 0.561 0.047 0.932
 SINGLE-F -0.116 0.056 -0.162
 MARRIED-F -0.172 0.041 -0.245
 SEP-F 0.190 0.060 0.180

 Coefficients(a)

 t Sig.

(Constant) 2.604 0.010
FT-F 5.092 0.000
HSDNC-F -2.439 0.016
CD-F 11.995 0.000
SINGLE-F -2.076 0.040
MARRIED-F -4.250 0.000
SEP-F 3.161 0.002

a. Dependent Variable: EXEC-F

 Excluded Variables (b)

Model Beta In t

 1 POP-F 31.757 0.798

 Collinearity
 Statistics
 Partial
 Sig. Correlation Tolerance

POP-F 0.426 0.071 0.000

a. Predictors in the Model: (Constant), SEP-F, HSDNC-F, FT-F,
MARRIED-F, CD-F, SINGLE-F

b. Dependent Variable: EXEC-F

EXEC-F women in managerial positions
POP-F female population
FT-F females employed full-time
HSDNC-F female high school graduates, no college, 25 years
and over
CD-F single females in the population
MARRIED-F married females in the population
SEP-F separated, divorced or widowed females in the population
SINGLE-F single females in the population

Table 2--Correlation Matrix (Female)

 EXEC-F POP-F FT-F

EXEC-F Pearson 1 .988(**) .983(**)
 Sig. (2-tailed) .000 .000
 N 252 252 252

POP-F Pearson .988(**) 1 .975(**)
 Sig. (2-tailed) .000 .000
 N 252 252 252

FT-F Pearson .983(**) .975(**) 1
 Sig. (2-tailed) .000 .000
 N 252 252 252

HSDNC-F Pearson .074 .046 .098
 Sig. (2-tailed) .402 .599 .263
 N 132 132 132

CD-F Pearson .983(**) .975(**) .954(**)
 Sig. (2-tailed) .000 .000 .000
 N 132 132 132

SINGLE-F Pearson .982(**) .995(**) .966(**)
 Sig. (2-tailed) .000 .000 .000
 N 252 252 252

MARRIED-F Pearson .968(**) .988(**) .960(**)
 Sig. (2-tailed) .000 .000 .000
 N 252 252 252

SEP-F Pearson .985(**) .989(**) .973(**)
 Sig. (2-tailed) .000 .000 .000
 N 252 252 252

 HSDNC-F CD-F SINGLE-F

EXEC-F Pearson .074 .983(**) .982(**)
 Sig. (2-tailed) .402 .000 .000
 N 132 132 252

POP-F Pearson .046 .975(**) .995(**)
 Sig. (2-tailed) .599 .000 .000
 N 132 132 252

FT-F Pearson .098 .954(**) .966(**)
 Sig. (2-tailed) .263 .000 .000
 N 132 132 252

HSDNC-F Pearson 1 .106 .057
 Sig. (2-tailed) .228 .513
 N 132 132 132

CD-F Pearson .106 1 .976(**)
 Sig. (2-tailed) .228 .000
 N 132 132 132

SINGLE-F Pearson .057 .976(**) 1
 Sig. (2-tailed) .513 .000
 N 132 132 252

MARRIED-F Pearson .046 .948(**) .976(**)
 Sig. (2-tailed) .600 .000 .000
 N 132 132 252

SEP-F Pearson .026 .953(**) .978(**)
 Sig. (2-tailed) .763 .000 .000
 N 132 132 252

 MARRIED-F SEP-F

EXEC-F Pearson .968(**) .985(**)
 Sig. (2-tailed) .000 .000
 N 252 252

POP-F Pearson .988(**) .989(**)
 Sig. (2-tailed) .000 .000
 N 252 252

FT-F Pearson .960(**) .973(**)
 Sig. (2-tailed) .000 .000
 N 252 252

HSDNC-F Pearson .046 .026
 Sig. (2-tailed) .600 .763
 N 132 132

CD-F Pearson .948(**) .953(**)
 Sig. (2-tailed) .000 .000
 N 132 132

SINGLE-F Pearson .976(**) .978(**)
 Sig. (2-tailed) .000 .000
 N 252 252

MARRIED-F Pearson 1 .961(**)
 Sig. (2-tailed) .000
 N 252 252

SEP-F Pearson .961(**) 1
 Sig. (2-tailed) .000
 N 252 252

** Correlation is significant at the 0.01 level (2-tailed).

EXEC-F women in managerial positions
POP-F female population
FT-F females employed full-time
HSDNC-F female high school graduates, no college, 25 years
 and over
CD-F single females in the population
MARRIED-F married females in the population
SEP-F separated, divorced or widowed females in the
 population
SINGLE-F single females in the population

Table 3--Analysis of Variance (Female)

 ANOVA(b) ANOVA(b)

Model Sum of Squares df Mean Square

 1 Regression 153,799,447.071 6 25,633,241.179
 Residual 4,139,728.808 125 33,117.830
 Total 157,939,175.879 131

 ANOVA(b)

 F Sig.

Regression 774.001 0.000
Residual
Total

a. Predictors: (Constant), SEP-F, HSDNC-F, FT-F, MARRIED-F, CD-F,
SINGLE-F

EXEC-F women in managerial positions

POP-F female population

FT-F females employed full-time

HSDNC-F female high school graduates, no college,
25 years and over

CD-F single females in the population

MARRIED-F married females in the population

SEP-F separated, divorced or widowed females in
the population

SINGLE-F single females in the population

Table 4--Regression Model (Male)

 Coefficients(a)
 Standardized
 Unstandardized Coefficients Coefficients
Model B Std. Error Beta

 1 (Constant) -7,598.38 10,677.37
 FT-M 0.055 0.080 0.147
 HSDNC-M 0.400 0.332 0.154
 CD-M 0.722 0.329 0.936
 SINGLE-M -0.504 0.222 -0.680
 MARRIED-M 0.158 0.235 0.183
 SEP-M -0.073 0.350 -0.064

 Coefficients(a)

 t Sig.

(Constant) -0.712 0.478
FT-M 0.688 0.493
HSDNC-M 1.205 0.230
CD-M 2.190 0.030
SINGLE-M -2.277 0.025
MARRIED-M 0.675 0.501
SEP-M -0.209 0.835

a. Dependent Variable: EXEC-M

 Excluded Variables (b)

Model Beta In t Sig.

 1 POP-M -116.738 -0.253 0.800

 Excluded Variables (b)

 Collinearity
 Statistics
 Partial
 Correlation Tolerance

POP-M -0.023 0.000

(a.) Predictors in the Model: (Constant), SEP-M, HSDNC-M,
MARRIED-M, FT-M, SINGLE-M, CD-M

(b.) Dependent Variable: EXEC-M

EXEC-M women in managerial positions
POP-M female population
FT-M females employed full-time
HSDNC-M female high school graduates, no college, 25 years
and over
CD-M single females in the population
MARRIED-M married females in the population
SEP-M separated, divorced or widowed females in the population

Table 5--Correlation Matrix (Male)

 EXEC-M POP-M FT-M

EXEC-M Pearson Correlation 1 .866(**) .857(**)
 Sig. (2-tailed) .000 .000
 N 252 252 252

POP-M Pearson Correlation .866(**) 1 .948(**)
 Sig. (2-tailed) .000 .000
 N 252 252 252

FT-M Pearson Correlation .857(**) .948(**) 1
 Sig. (2-tailed) .000 .000
 N 252 252 252

HSDNC-M Pearson Correlation .528(**) .727(**) .831(**)
 Sig. (2-tailed) .000 .000 .000
 N 132 132 132

CD-M Pearson Correlation .638(**) .985(**) .919(**)
 Sig. (2-tailed) .000 .000 .000
 N 132 132 132

SINGLE-M Pearson Correlation .853(**) .995(**) .938(**)
 Sig. (2-tailed) .000 .000 .000
 N 252 252 252

MARRIED-M Pearson Correlation .865(**) .993(**) .942(**)
 Sig. (2-tailed) .000 .000 .000
 N 252 252 252

SEP-M Pearson Correlation .338(**) .371(**) .394(**)
 Sig. (2-tailed) .000 .000 .000
 N 252 252 252

 HSDNC-M CD-M SINGLE-M

EXEC-M Pearson Correlation .528(**) .638(**) .853(**)
 Sig. (2-tailed) .000 .000 .000
 N 132 132 252

POP-M Pearson Correlation .727(**) .985(**) .995(**)
 Sig. (2-tailed) .000 .000 .000
 N 132 132 252

FT-M Pearson Correlation .831(**) .919(**) .938(**)
 Sig. (2-tailed) .000 .000 .000
 N 132 132 252

HSDNC-M Pearson Correlation 1 .722(**) .729(**)
 Sig. (2-tailed) .000 .000
 N 132 132 132

CD-M Pearson Correlation .722(**) 1 .969(**)
 Sig. (2-tailed) .000 .000
 N 132 132 132

SINGLE-M Pearson Correlation .729(**) .969(**) 1
 Sig. (2-tailed) .000 .000
 N 132 132 252

MARRIED-M Pearson Correlation .658(**) .964(**) .980(**)
 Sig. (2-tailed) .000 .000 .000
 N 132 132 252

SEP-M Pearson Correlation .764(**) .968(**) .364(**)
 Sig. (2-tailed) .000 .000 .000
 N 132 132 252

 MARRIED-M SEP-M

EXEC-M Pearson Correlation .865(**) .338(**)
 Sig. (2-tailed) .000 .000
 N 252 252

POP-M Pearson Correlation .993(**) .371(**)
 Sig. (2-tailed) .000 .000
 N 252 252

FT-M Pearson Correlation .942(**) .394(**)
 Sig. (2-tailed) .000 .000
 N 252 252

HSDNC-M Pearson Correlation .658(**) .764(**)
 Sig. (2-tailed) .000 .000
 N 132 132

CD-M Pearson Correlation .964(**) .968(**)
 Sig. (2-tailed) .000 .000
 N 132 132

SINGLE-M Pearson Correlation .980(**) .364(**)
 Sig. (2-tailed) .000 .000
 N 252 252

MARRIED-M Pearson Correlation 1 .370(**)
 Sig. (2-tailed) .000
 N 252 252

SEP-M Pearson Correlation .370(**) 1
 Sig. (2-tailed) .000
 N 252 252

** Correlation is significant at the 0.01 level (2-tailed).

EXEC-M women in managerial positions

POP-M female population

FT-M females employed full-time

HSDNC-M female high school graduates, no college, 25 years and
over

CD-M single females in the population

MARRIED-M married females in the population

SEP-M separated, divorced or widowed females in the population

Table 6--Analysis of Variance (Male)

 ANOVA(b)

Model Sum of Squares df Mean Square

 1 Regression 83,548,016.168 6 13,924,669.361
 Residual 103,743,677.559 125 829,949.420
 Total 187,291,693.727 131

 ANOVA(b)

 F Sig.

Regression 16.778 0.000
Residual
Total

(a.) Predictors: (Constant), SEP-M, HSDNC-M, MARRIED-M, FT-M,
SINGLE-M, CD-M

(b.) Dependent Variable: EXEC-M

EXEC-M women in managerial positions

POP-M female population

FT-M females employed full-time

HSDNC-M female high school graduates, no college, 25 years
 and over

CD-M single females in the population

MARRIED-M married females in the population

SEP-M separated, divorced or widowed females in the population