Women, Minorities, and Technology Jacquelynne Eccles (PI), Pamela Davis-Kean (co-PI), and Oksana Malanchuk University of Michigan

General Aims  To understand the psychological mediators of gender and ethnic group differences in activity and task choices (such as computer use and enrollment in IT courses or programs) related to entry into the IT workforce.  To understand how socialization processes influence the activity and choices related to computer use and IT participation in the workforce.

Methods To meet these goals, we are collecting the necessary supplementary IT- related data and are analyzing three longitudinal datasets that contain information from childhood through early adulthood.  Michigan Study of Adult Life Transitions: This sample represents white working and middle class families. We have collected 9 waves of data from approximately 1,400 of these youth (ages 12-29). This data allow us to model comprehensively the psychological mediators of gender differences in entry into and persistence in the IT labor force.  Childhood and Beyond (CAB): Data collection began in 1987 with three cohorts of white middle class children, their parents, and their teachers. We have gathered 9 waves of data on these cohorts (4 during the elementary and beginning middle school, 4during high school and 1 two years post high school graduation). This data will allow us to model the role of experiences in the home and at school on the development of gender differences in children’s and adolescents’ interest in computers and information technology.

Methods (cont.)  Prince George’s County (PGC). This sample contains approximately 1000 African-American (61%) and European-American (35%) adolescents and their families with comparable SES distribution in each group (making it possible to look independently at the impact of family income and ethnic group membership on occupational choice). We have 5 waves of data (ages 12-22) gathered from the adolescents, their care­givers (parents, guardians, or others), and their school records. We have just completed gathering a sixth wave of data (at four years post high school) specifically focused on college experiences with a major focus on the students’ perceptions of factors likely to either enhance or undermine their interest in information technology, as well as other college majors and occupations. We added a series of items directly linked to information technology in our current wave. These indicators include stereotypes about the types of individuals who go into information technology, stereotypes about jobs in information technology, confidence and interest in various types of information technology jobs, experiences in computer science, information technology, engineering and math courses at college, and exposure to either encouragement or discouragement about majoring in an information technology subject or aspiring to a job in information technology.

Findings to Date: Psychological Processes  Linver, M., Davis-Kean, P. E., & Eccles, J. S. (April, 2002). Influences of Gender on Academic Achievement. Paper presented at the Society for Research on Adolescence, New Orleans, LA. Growth curve models for adolescents’ school math grades were estimated to address the following questions: (a) What do the average math grade trajectories look like, from 6th to 12th grade, by gender and by school track? (b) What impact does interest in math have over and above the effect of mothers’ education and gender, by school track? Overall, grades in math declined across the trajectories; the young women have both higher grades than young men (within each tracking group) and lower interest in the subject throughout junior high and high school. These results suggest that for both boys and girls, math grades fall over the course of junior high and high school. Math interest explains some of this decline, over and above students’ gender and mothers’ level of education.

Math School Grades from 7 th to 12 th Grades by Gender and 9 th Grade Math Track B C

Figure 2: Math Interest from 7th to 12th grade, by Math Class Track and Gender

Findings to Date: Socialization Processes  Simpkins, S. D., Davis-Kean, P. E., & Eccles, J. S. (2002). “Parental Socialization and Children’s Engagement in Math, Science, and Computer Activities.” Submitted to Journal of Family Psychology (Parts of this paper were presented at the International Study of Behavioral Development Conference in Ottawa, CA, August, 2002). This study examined the associations between multiple indicators of parental socialization and children’s engagement in math, science, and computer activities. Children from second (n = 125), third (n = 123), and fifth grade (n = 200) participated. Mothers and fathers reported how often they encouraged their children’s activities, engaged in parent-child coactivity, and modeled activities. Both parents and children described how often children engaged in math, science, and computer activities. Results indicated that parental socialization and child activity engagement were similar for boys and girls. Of the three types of parental socialization, parental modeling evidenced the most modest associations. Overall parental socialization emerged as a strong, positive predictor of children’s computer activities.

Other Findings from CAB data  Children who have mothers with less traditional views about gender and those with more positive math self- concepts were more likely to indicate interest in math.  The interaction of father’s gender stereotype and child’s sex was a significant predictor, indicating that as fathers’ gender stereotypes increased, girls’ interests in math decreased, while boys’ interests increased.  Mothers who purchased the most math/science toys were more likely to have children who had the highest math/science GPA’s three years later

Father’s Gender Stereotype and Child’s Interest in Math

Current Research  We asked “how good would you be at a job that uses computers for…” and detailed 14 different activities ranging between developing software to word processing. The respondents clearly distinguished two factors: one with 8 items (r=.92) that centered on using computers for programming and one with 6 items (r=81) that centered on using computer as tools (e.g., word processing, calculations).  Men (both White and African American) were significantly (F= 9.80; p<.001, M= 3.8 males; M=3.0 females) more likely than females (of both races) to report being good at using computers for programming type of activities. There was no difference between race and sex for how good they would be at using computers as tools.  Men (both White and African-American) were significantly more likely than females (of both races) to say that they would be good at a job that uses computers to develop hardware (F=12.3; p<.001, M=3.5 males; M=2.6 females) and software (F=9.2; p<.001; M=3.5 males; M=2.6 females). African-American males were significantly more likely than females (of both races) and White men to say they would be good at jobs that use computers to develop websites (F= 4.9; p<.01; M=4.06 AA; M=3.4 others) or that use graphic arts (F= 3.8; p<.01; M=4.31 AA; M=3.6 others). White females consistently reported the lowest means related to how good they would be at these jobs.

Comparison of Competence in Types of Computer Careers

Future Aims  What is the impact of good math achievement on course selection in college and eventual occupational choice?  Are those participants who are high in achievement the only ones who are going into IT jobs or are there other avenues?  Do African Americans and women feel more discrimination in the IT workplace?  What kinds of stereotypes exist involving IT jobs and are they more prevalent in certain genders or race?  How does parental and peer encouragement and socialization impact on eventual IT career choices?  Are women and minorities getting into the IT workforce without a four- year college education? If yes, what types of occupations are these?

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