Gender Differences and Similarities in Personality Characteristics for Information Technology Professionals

Women are under represented in the Information Technology (IT) workforce relative to the overall labor force, comprising about thirty-five percent of the IT workforce and forty-five percent of the overall labor force (Information Technology Association of America, 2003). A basic question to be addressed is whether this under representation is a function of barriers to employment of women in this career field, or a function of career-related choices that a majority of women make during their lives. The research reported here is part of a series of studies attempting to better understand the reasons underlying this under representation of women in this reasonably lucrative profession. Through a grant provided by the National Science Foundation (NSF 29560) and in partnership with Consulting Psychologists Press, we have been able to design and conduct an extensive survey of professional workers, information technology (IT) professionals and a comparable set of non-information technology (non-IT) professionals. The non-IT professionals included individuals who are similar to the information technology sample in terms of education level (but not specific degree fields) and who work in jobs with comparable human attribute demands, including written comprehension, oral comprehension, oral expression, written expression, and deductive reasoning. The non-IT professionals include accountants, auditors, CEOs, CFOs, presidents, consultants, engineers, managers, administrators, management analysts, scientists, technicians, nurses, teachers, etc. The IT professionals include application developers, programmers, software engineers, database administrators, systems analysts, web administrators, and web developers. The survey items include measures of Big Five personality constructs (NEOAC), occupational personality variables (RIASEC), and personal style scales. The purpose of this article is to document similarities and differences between IT and non-IT professionals and between males and females on these variables.

A discussion of the gender composition and characteristics of the IT workforce must begin by clarifying what is meant by IT. This is difficult because IT encompasses a broad array of products and activities related to computing and communications in the modern economy (Freeman and Aspray 1999, pp. 29-31). Although many workers make use of IT in their jobs most studies agree that only those workers who are responsible for creating IT hardware and software should be included in the IT workforce, while those who are primarily users of these products should be excluded (In addition to Freeman and Aspray, see National Research Council 2001, pp. 44-54; Ellis and Lowell 1999, p. 1).

Whatever conceptual definition one adopts, however, its application is limited by the classification schemes used by agencies engaged in collecting data on different elements of the workforce. In what follows we will focus on those IT occupations that are enumerated in the Bureau of Labor Statistics’ Current Population Survey (CPS). The CPS data cover Computer Systems Analysts, Computer Programmers, Operations and Systems Researchers, Computer Operators, and Computer Operators Supervisors. These occupations constitute more or less what the National Research Council (2001, p. 48) has termed "Category 1" IT occupations: those involved with the creation of new products, services and applications. CPS data do not permit us to measure or describe the characteristics of the National Research Council’s "Category 2" occupations: those involved in the application, adaptation, configuration, support or implementation of IT products or services (National Research Council 2001, p. 49). Because occupational titles do not adequately capture the IT content of the support activities of many of the technicians and other occupations included in this group it is more difficult to adequately measure its size or demographic characteristics.

Data were obtained from individuals who voluntarily responded to an online survey prepared and managed by the Policy Research Institute at the University of Kansas between December 2003 and September 2004. Participation in the survey was solicited from employees at several large organizations with offices in the central United States, and from business school and computer science alumni of a large mid-western university.

The sample consists of 703 working professionals who completed the survey. Seventy-three percent (510) are non-IT professionals; twenty-seven percent (193) are IT professionals. Fifty-eight percent (405) are male; forty-two percent (298) are female. Table 1 shows means and standard deviations on several demographic variables for the sample.

Table 1: Demographic Information for the Professional Worker Sample

The age range of the sample is 22 to 70 years. The mean age is 38.7 years, with a standard deviation of 9.8. Twenty-two percent of the sample are in their 20s, 33% in their 30s, 30% in their 40s, 13% in their 50s, and 2% are 60 years of age or older. On average the IT professionals in the sample are 1.8 years older than the non-IT employees.

The sample is highly educated. Ninety-three percent hold four-year college degrees, and 45% have graduate school degrees. Six percent have some college, and less than one percent report having only completed high school. Mean years of formal education is 16.8 with a standard deviation of 1.5. On average non-IT professionals in the sample report having 0.8 year more formal education than the IT professionals.

IT professionals in the sample report having worked for pay 20.1 years on average, almost two years more than the non-IT employees. This is consistent with the average age of the IT professionals being almost two years older than that of the non-IT employees. The IT professionals report having worked in their current career field 13.8 years on average, three years more than the non-IT professionals. Professionals in the sample have worked for their current employer for an average of seven years (standard deviation = 6.5 years), and have held their current positions for an average of 4.3 years (standard deviation = 4.4 years). They report having held an average of 3.3 jobs in their current career field (standard deviation = 2.2).

Males in the sample report being exposed to computers an average of 1.6 years earlier than females (at 15.4 versus 17.0 years of age). The majority of the sample (55%) took no computer science courses in high school, 21% took one computer science course, 15% took two, and less than 10% report taking three or more computer science courses in high school.

Differences show up in terms of the number of computer science courses taken in college. The total sample mean is 5.3 courses with a standard deviation of 7.1, indicating substantial variability. IT professionals took an average of 10.1 computer science courses, while non-IT professionals took 3.5 computer science courses in college. Males took 6.2 computer science courses in college, while females took an average of 4.0 computer science courses.

During the 1980s, after some four to five decades of research, development and elaboration, the Five Factor Model (FFM) of personality – also called the "Big Five" model – was recognized as representing the five most basic dimensions underlying the traits identified in both natural languages and in psychological questionnaires (Digman, 1990). Essentially five synonym clusters appear to account for the majority of differences between individual personalities. These five personality traits reflect the physiological activities of different underlying arousal systems, and represent predispositions to behave in certain ways when in the presence of particular stimuli (Howard & Howard, 2001). The five traits of this model are explained briefly in the following paragraphs. These descriptions are paraphrased largely from Howard & Howard (2001) because their descriptions use less psychological terminology and are more accessible to the broader spectrum of working professionals.

Factor N or Neuroticism, refers to one’s need for stability. A person high in N is very reactive and prefers a stress-free work environment. A person low in N is typically very calm and relatively unaffected by stress that might result in ineffective behavior in others. In general, women score higher than men on measures of N.

Factor E or Extraversion, refers to one’s positive emotionality or sociability. A person high in E likes to be in the thick of the action, typically interacting with other people, while a person low in E likes to be away from the noise and hubbub, crowds, etc. In general, there are no systematic differences between women and men on measures of E.

Factor O or Openness to Experience, refers to one’s originality or imagination. A person scoring high in O has a voracious appetite for new ideas and activities, and is easily bored routine or highly familiar situations. A person low in O prefers familiar territory and tends to be more practical, conventional, and conservative. In general, there are no systematic differences between women and men on measures of O.

Factor A or Agreeableness, refers to one’s accommodation or adaptability. A person high in A tends to accommodate or adapt to the wishes and needs of others, and is often viewed as cooperative. A person low in A tends to focus on his or her own personal needs and priorities, and is often described as competitive or critical. In general, women score higher than men on measures of A.

Factor C or Conscientiousness, refers to one’s will to achieve, or consolidation. A person high in C tends to focus or consolidate his or her energy and resources on accomplishing one or more goals, and typically appears to be well-organized, ambitious, and strong-willed. A person low in C prefers a more spontaneous work style, is more comfortable switching from one task to another, is typically lackadaisical in working toward his/her goals, and often appears to be less organized, less punctual, etc. In general, there are no systematic differences between women and men on measures of C.

Core Self-Evaluations (CSE) is a broad personality trait that has been shown to be a significant predictor of job satisfaction and job performance (Judge, Erez, Bono, & Thoresen, 2003). It is a combination of four primary personality traits that have been featured prominently in psychological research for decades. These include self-esteem, the overall value one places on oneself as a person; generalized self-efficacy, an evaluation of how well one can perform across a variety of situations; neuroticism (Factor N of the Big Five), the tendency to have a negativistic cognitive/explanatory style and to focus on negative aspects of the self; and locus of control, beliefs about the causes of events in one’s life – locus is internal when individuals see events as being contingent upon their own behavior, and external when they see events as caused largely by forces and events outside themselves and not under their control. CSE is a basic, fundamental appraisal of one’s worthiness, effectiveness, and capability as a person. Individuals high in CSE are generally more satisfied with their jobs, their work, and their lives than are individuals low in CSE. Individuals high in CSE also tend to perform their work and their jobs better than those low in CSE. Judge and his colleagues (2003) have suggested that existing measures of Neuroticism are too narrow to capture self-evaluations, perhaps due to the origin of Neuroticism measures in psychopathology, and hence appear to be less valid predictors of work-related outcomes as compared to CSE. Judge and his colleagues have developed and convincingly demonstrated both the reliability and multi-faceted construct validity of a 12-item direct measure of CSE – the Core Self-Evaluations Scale (CSES). There are no systematic differences between women and men on this measure.

Table 2 contains the means and standard deviations for 273 non-IT males, 237 non-IT females, 132 IT males, and 61 non-IT females on measures of N, E, O, A, C, and CSE. The results are expressed in standardized score (T-score) format where the norm group mean = 50 and the norm group standard deviation = 10. The measures of N, E, O, A, and C are derived from the 12-item scales of the NEO Five Factor Inventory (NEO-FFI), and standardized using combined gender norms derived from a sample of 500 men and 500 women selected in a stratified manner designed to match U.S. census projections for 1995 in the distribution of age and race groups (Costa & McCrae, 1992). The measure of CSE is the 12-item Core Self-Evaluations Scale (CSES) mentioned above, standardized using norms derived from four different samples yielding CSES results on 841 individuals (Judge, Erez, Bono and Thoresen, 2003).

Table 2 also contains the results of a two factor (gender X career field) analysis of variance used to test for significant differences in the personality variables as a function of gender (Male – Female), career field (IT – non-IT), and the interaction of gender and career field. This analysis reveals whether there are differences on the respective personality variables between males and females in total sample (gender effect), between IT and non-IT professional workers (career field effect), and whether or not there is an interaction effect (gender by career field interaction) indicating that males or females within either IT or non-IT are different from male and female professional workers in general.

Table 2: Big Five and Core Self-Evaluations Personality Scale Results by Gender (M / F) and Career Field (IT / Non-IT)

ab Means in each row with common superscripts are not reliably different from each other.

There is an overall gender effect for N. Females score higher on Neuroticism than males, a common finding for this personality construct. There is neither a career field effect nor an interaction effect for N. IT professionals are similar to non-IT professionals in need for stability.

For E there is a gender effect, a marginal career field effect, and a gender-by-career field interaction effect. The Extraversion mean for IT males is about one-half standard deviation lower than those for IT females, non-IT females, and non-IT males. While IT females are significantly more extraverted than IT males, IT females are similar in Extraversion to non-IT male and female professionals. IT males are different – lower – in Extraversion compared to other professional workers, including IT-females.

For O there is a career field effect. On average IT professionals score higher on Openness to Experience than do non-IT professionals, indicating that they are somewhat more original and imaginative, and probably more easily bored. There are no gender differences on O, and there is no interaction effect.

There is an overall gender effect for A. Females score higher on Agreeableness than males, a common finding for this personality construct, indicating that female professionals are more accommodating, helpful, and cooperative than male professionals. There is neither a career field effect nor an interaction effect for A. IT professionals are similar to non-IT professionals in accommodation and adaptability.

For C there is a gender effect and a career field effect, but no interaction effect. On average female professionals score higher on Conscientiousness than males, and non-IT professional score higher than IT professionals. Detailed analysis (post hoc comparisons of individual group means) shows that mean Conscientiousness for non-IT females is significantly higher as compared to the means for both non-IT and IT males.

For Core Self-Evaluations (CSE) there are no differences between males and females or IT and non-IT professionals, and no interaction effect, either.

In 1927, E.K. Strong introduced the Strong Vocational Interest Blank (SVIB). This measure was used to determine the degree of similarity between a person’s interests and those of workers in an occupation. Strong realized in the late 1930s that a systematic clustering of the scales was necessary but was unable to find a system that had reliable psychometric qualities. In 1959, Holland introduced six basic categories of occupational interest categories that closely resembled the dimensions found in research on vocational interests using the SVIB. Holland’s classification system was an extension of the trait and factor theory from the 1920s and implied that the main goal of vocational counseling is to match people and jobs. In 1974, Strong’s empiricism and Holland’s theory were combined to develop the General Occupational Themes. (Harmon, 1994). The six vocational types of the General Occupational Theme model are described below. The descriptions are paraphrased from Harmon, et al (1994) and Holland (1997).

The Realistic Theme or R, refers to a person’s preference for activities that entail the explicit, ordered, or systematic manipulation of objects, tools, and machines. Realistic types enjoy jobs and activities that involve mechanical manipulations or repairs and construction. They are interested in action rather than thought and prefer concrete problems to ambiguous, abstract problems. Sample Realistic occupations include auto mechanic, gardener, plumber, and engineer.

The Investigative Theme or I, refers to a person’s preference for activities that entail the systematic or creative investigation of physical, biological, and cultural phenomena. Investigative types enjoy gathering information, uncovering new facts or theories, and analyzing and interpreting data. They prefer to rely on themselves rather than on others in a group project. Sample Investigative occupations include college professor, physician, psychologist, and chemist.

The Artistic Theme or A, refers to a person’s preference for activities that are ambiguous, free, non-systematic and that entail the manipulation of materials to create art forms or products. Artistic types have a great need for self-expression. They are also comfortable in academic or intellectual environment. Sample Artistic occupations include artist, lawyer, librarian, musician, architect, reporter and English teacher.

The Social Theme or S, refers to a person’s preference to lead others or for activities that entail the manipulation of others to inform, train, develop, cure, or enlighten. The Social type enjoys working with people, sharing responsibilities, and being the center of attention. They also like to solve problems through discussions of feelings and interactions with others. Sample Social occupations include elementary school teacher, nurse, social worker, and occupational therapist.

The Enterprising Theme or E, refers to a person’s preference for activities that entail the manipulation of others to attain organizational goals or economic gain. The Enterprising type seeks positions of power, leadership, and status. They like to take financial risks and participate in competitive activities. Sample Enterprising occupations include traveling salesperson, buyer, realtor, sales manager, and marketing executive.

The Conventional Theme or C refers to a person’s preference for activities that entail the explicit, ordered, systematic manipulation of data. The Conventional Type often enjoys mathematics and data management activities. They work well in large organizations but do not show a distinct preference for or against leadership positions. Sample Conventional occupations include bookkeeper, accountant, banker, actuary, and proofreader.

Table 3 contains the means and standard deviations for 190 non-IT males, 192 non-IT females, 95 IT males, and 46 IT females on measures of the RIASEC occupational personality inventory. The results are expressed in standardized score format (T-Score) where the norm group mean and standard deviation are 50 and 10, respectively. The measures of R, I, A, S, E, and C are derived from the 20 item scales of the Strong Interest Inventory and are standardized using combined gender norms derived from a sample of 9,484 men and 9,467 women. (Harmon, 1994) Table 3 also contains the results of a two factor (gender X career field) ANOVA, similar to the one described for Table 2.

Table 3: RIASEC Occupational Personality Scale Results by Gender (M / F) and Career Field (IT / Non-IT)

abc Means in each row with common superscripts are not reliably different from each other.

There is an overall gender effect for R. Males score higher on the Realistic Theme than females, a common finding for this GOT. There is also an overall career effect for R. IT professionals scored significantly higher than non-IT professionals in the Realistic Theme. There is not an interaction effect for R.

For I, there is an overall gender effect. Males scored higher on the Investigative Theme than did females. In addition, there is an overall career effect for the Investigative Theme. IT professionals scored significantly higher than non-IT professionals in I. There is not a significant interaction effect for I.

There is an overall gender effect for A. Females scored significantly higher than males in the Artistic Theme. There is neither a career effect nor an interaction effect for A.

For S, there is an overall gender effect. Females scored significantly higher than males on the Social Theme. In addition, there is a career effect for S. Non-IT professionals scored significantly higher in S than did IT professionals. There is not a significant interaction effect for S.

There is an overall career effect for E. Non-IT professionals scored significantly higher on the Enterprising Theme than did IT professionals. There is not an overall gender effect or an interaction effect for E.

For C, there is an overall gender effect. Females scored higher than males in the Conventional Theme. There was neither an overall career effect nor an interaction effect for C.

The Personal Styles Scales (PSS) were added to the Strong Interest Inventory (SII) in 1994. The PSS measure a person’s broad styles of living, learning, playing, and working. They complement the traditional vocational interest scales (i.e. RIASEC) that measure preferences for more specific aspects of the work itself. A distinguishing characteristic of the Personal Style Scales is that they are constructed as bipolar scales, with a distinctive style (or preference) associated with both the right and left pole of each scale. (Harmon, et. al, 1994) There are five Personal Style Scales attached to the SII. The PSS are work style, learning environment, leadership style, risk-taking/adventure, and team orientation. Descriptions for the first four were taken from Harmon, et al., (1994).

The Work Style Scale distinguishes individuals who prefer to work with ideas, data, or things (left pole or low scores) from those who prefer to work with people (right pole or high scores). The "works with people" pole links strongly to the Enterprising and Social Types. The "works with ideas/data/things" pole ties strongly to the Realistic and Investigative types. Occupations whose members prefer to work with ideas, data or things include biologist, chemist, and computer programmer. Occupations whose members prefer to work with people include high school counselor, flight attendant, and human resources director.

The Learning Environment Scale differentiates people who prefer more practically oriented, hands-on learning situations (left pole or low scores) from those who prefer academic learning environments (right pole or high scores). Occupations whose members prefer an academic learning environment include college professor, lawyer, psychologist, and physicist. Occupations whose members prefer a practical learning environment include auto mechanic, dental assistant, and nurse.

The Leadership Scale contrasts those who lead by example and prefer to work alone (left pole or low score) from those who enjoy meeting, directing, persuading, and leading other people (right pole or high score). Occupations whose members prefer a "leads by example" leadership style include auto mechanic, chemist, farmer, and mathematician. Occupations whose members prefer a "directs others" leadership style include elected public official, minister, broadcaster, and realtor.

The Risk Taking/Adventure Scale differentiates between those who like to "play it safe" (left pole or low scores) from those who like to take a chance or be spontaneous (right pole or high scores). Occupations whose members prefer a "play it safe" approach include librarian, mathematician, and dental hygienist. Occupations whose members prefer the "take a chance" approach include an athletic trainer, police officer, and electrician.

In 2004, a new PSS, Team Orientation, was added to the SII. This construct distinguishes between those who prefer to accomplish tasks independently (low scores or left pole) from those who prefer to accomplish tasks as part of a team a team (high score or right pole). Occupations whose members prefer to accomplish tasks independently include artist, graphic designer, medical illustrator, and musician. Occupations whose members prefer to accomplish tasks as part of a team include operations manager, school administrator, sales manager, and rehabilitation counselor. (Donnay, Thompson, Morris, & Schaubhut, 2004)

Table 4 contains the means and standard deviations for 190 non-IT males, 192 non-IT females, 95 IT males, and 46 IT females on measures of the Personal Styles Scales of the SII occupational personality inventory. The results are expressed in standardized score format (T-Score) where the norm group mean and standard deviation are 50 and 10, respectively. The measures of work style, learning environment, leadership, risk taking, and team orientation are derived from the 20 item scales of the Strong Interest Inventory and are standardized using combined gender norms derived from a sample of 9,484 men and 9,467 women. (Harmon, 1994) Table 4 also contains the results of a two factor (gender X career field) ANOVA, similar to the one described for Table 2.

Table 4: Personal Style Scale Results by Gender (M / F) and Career Field (IT / Non-IT)

abc Means in each row with common superscripts are not reliably different from each other.

There is an overall gender effect for work style. Females scored substantially higher than males meaning that in general, females prefer to work with people and men prefer with data, ideas, and things. There is also a significant career effect for work style. Non-IT professionals scored substantially higher than IT professionals, meaning that non-IT professionals prefer to work with people while IT professionals prefer to work with data, ideas, and things. There is not an interaction effect for work style.

For the learning environment scale, there are not any significant differences between males and females or between IT professionals and non-IT professionals.

There is an overall career effect for leadership. Non-IT professionals scored higher than IT professionals. In general, this means that non-IT professionals enjoy meeting, directing, persuading others to greater extent than IT professionals who tend to prefer to lead by example and work alone. There is neither an overall effect for gender nor an interaction effect for leadership.

For risk taking, there is a significant gender effect. Males scored significantly higher than females, meaning that males in general are more likely to take risks and live spontaneously as compared to females in general. There is also an overall career field effect for risk taking. Non-IT professionals scored higher than IT professionals, meaning that Non-IT professionals are somewhat more likely to take risks than are IT professionals. There is not an interaction effect for risk taking.

There is an overall effect by gender for team orientation. Females scored higher than males in team orientation, meaning that females have a stronger preference than males for accomplishing tasks as a team, whereas males show a stronger preference for accomplishing tasks individually. There is also a career effect for team orientation. Non-IT professionals scored higher than IT professionals, meaning that non-IT professionals have a stronger preference for accomplishing tasks as part of a team whereas IT professionals show a stronger preference for accomplishing tasks individually.

In this article we report descriptive results comparing professionals in IT and non-IT career fields by gender on six major personality constructs, six occupational personality constructs, and five personal style scales. We found significant differences between IT and non-IT professionals on 10 of these 17 variables, and significant differences between male and female professionals on 12 of these 17 variables. However, we found significant career field by gender interaction effects for only one of the 17 variables.

IT professionals are higher than non-IT professionals on the following variables:
     Openness to Experience (more original and imaginative)
     Realistic (prefer ordered and systematic manipulation of things, action)
     Investigative (prefer systematic or creative investigation)

IT professionals are lower than non-IT professionals on the following variables:
     Extraversion (prefer to be away from the noise, hubbub, crowds, etc.)
     Conscientiousness (less organized, less punctual, more spontaneous)
     Social (lower preference for work requiring interactions and discussions)
     Enterprising (lower preference for competition, working toward organization goals)
     Work Style (prefer working with ideas, data, or things as opposed to people)
     Leadership (prefer to work alone, lead by example)
     Risk Taking (prefer to play it safe)
     Team Orientation (prefer to accomplish tasks independently)

Relative to male professionals, female professionals are higher on the following variables:
     Neuroticism (more reactive, higher preference for stress-free environment)
     Extraversion (prefer to be in the thick of the action, interacting with others)
     Agreeableness (tend to accommodate or adapt to the wishes and needs of others)
     Conscientiousness (more organized, ambitious, goal-directed, focused)
     Artistic (prefer ambiguous, free, non-systematic activities)
     Social (prefer working with people, sharing responsibilities, discussions, interactions with others)
     Conventional (prefer data management activities)
     Work Style (prefer working with people as opposed to ideas, data, or things)
     Team Orientation (prefer to accomplish tasks as part of a team)

Relative to male professionals, female professionals are lower on the following variables:
     Realistic (lower preference for ordered and systematic manipulation of things, physical action)
     Investigative (lower preference for systematic or creative investigation)
     Risk Taking (prefer to play it safe)

While there are a number of notable personality differences between IT professionals and other professionals, and an even larger number of personality differences between male and female professionals, there are very few differences (only one uncovered in this study) between male and female IT professionals that do not also exist between male and female professionals in general. Males in the IT profession tend to be significantly lower in extraversion relative to females in IT and other non-IT male and female professionals. Females in IT are quite similar to other male and female professionals in terms of positive emotionality and sociability, whereas males in IT have a significantly stronger preference to be away from noise, crowds, social stimulation, etc.

Is the nature of IT work such that individuals lower in extraversion have a significantly better chance of being successful and happy in this career field? Our findings suggest that relative to other professionals, IT professionals tend to be more realistic, more investigative, less social, and less enterprising, prefer to work with ideas/data/things rather than people, prefer to work alone, prefer to accomplish tasks independently, and prefer to minimize risks. This certainly sounds like a profession dominated by individuals lower in extraversion.

Our findings also suggest that compared to males, females tend to be less realistic, less investigative, more artistic, more social, and more conventional, prefer working with people rather than ideas/data/things, prefer to accomplish tasks as part of a team, and prefer to minimize risks. This does not sound like a particularly good match to IT work.

These findings raise the possibility that a higher proportion of males relative to females are attracted to work in IT due to a better match of that work with personality differences underlying preferences for aspects of IT work. In general, women are more social and more cooperative relative to men. Of course we have examined only one aspect of the potential set of causes for the notable difference in proportions of males and females in the IT career field, and at this point the role of gender in career choice remains an open question.

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About the authors

Ronald A. Ash is a Professor, University of Kansas School of Business.

Joshua L. Rosenbloom is a Professor, University of Kansas Department of Economics and Research Association National Bureau of Economic Research.

LeAnne Coder is a Graduate Student, University of Kansas, School of Business.

Brandon Dupont is a Graduate Student, University of Kansas Department of Economics.