Career Choice Barriers: Environmental Influences on Women s STEM Career Choices

1. Career Choice Barriers:Environmental Influences on Women�s STEM Career Choices Nadya Fouad Ph.D. Gail Hackett Ph.D. Susan Haag Ph.D. Phil Smith, Ph.D. Neeta Kantamneni M.S. Mary E. Fitzpatrick M.S. Paper presented at the American Psychological Association Convention 2007; San Francisco, CA

2. Background The analogy of a leaky pipeline has been used to describe the progressive developmental decrease of women�s interest and participation in STEM preparation Something happens along the way that prompts women to think and believe they cannot or do not want to continue in STEM courses, majors, or careers. It is important to understand barriers and supports that may differ at various developmental levels within the Social Cognitive Career Theory framework. Understanding environmental barriers and supports may help to predict the resilience of women in STEM career development and also aid in the development of interventions that can facilitate resilience. The analogy of a leaky pipeline has been used frequently to describe the developmental decrease of women�s interest and participation in science, technological, engineering, and math (STEM) preparation. Gender differences in math and science achievement and attitudes are first seen in 8th grade but become more pronounced in college. Something happens along the way that prompts women to think and believe that they cannot or do not want to continue in STEM courses, majors, or careers. There is no obvious reason why this would occur for women more than men since innate abilities do not differ between men and women (NSF, 2003). Past research has focused more on the internal aspects of womens� choice rather than external factors. It is important to understand these external influences within social cognitive career theory, particularly since barriers and supports may differ at various developmental levels. Understanding environmental barriers and supports may help to predict the resilience of women in STEM career development and help develop interventions that can facilitate resilience. The analogy of a leaky pipeline has been used frequently to describe the developmental decrease of women�s interest and participation in science, technological, engineering, and math (STEM) preparation. Gender differences in math and science achievement and attitudes are first seen in 8th grade but become more pronounced in college. Something happens along the way that prompts women to think and believe that they cannot or do not want to continue in STEM courses, majors, or careers. There is no obvious reason why this would occur for women more than men since innate abilities do not differ between men and women (NSF, 2003). Past research has focused more on the internal aspects of womens� choice rather than external factors. It is important to understand these external influences within social cognitive career theory, particularly since barriers and supports may differ at various developmental levels. Understanding environmental barriers and supports may help to predict the resilience of women in STEM career development and help develop interventions that can facilitate resilience.

3. Social Cognitive Career Theory(Lent, Brown & Hackett, 1994, 2000, 2002)

4. Research Questions What are the supports/barriers for girls/boys in different levels in math and subject? How different are the barriers within math and science? By gender By developmental level Are the factors the same across gender and developmental level within subject matter? What variables predict students choices to pursue math/science courses or a math/science career? How do predictor variables differ by developmental level and by gender?

5. Methods Developed a Barriers and Support Instrument Items were developed based on a taxonomy of barriers/supports created through qualitative interviews and comprehensive literature review Questions were randomly worded as a barrier or support Depending on response, follow-up questions assessed for salience of barrier or support Scaling: Items were scaled from -3 (barrier) to +3 (support) Participants College N= 628, �Boys 28.5%� Girls 71.5% White� 86.4% African American 1.8%, Latino/Hispanic 3.4%, Asian American 4.5% Native American 1.5%� Middle Eastern .6%, Biracial=1.8% High School N=600�� Boys=44.5% Girls=52.9% White 75.2% African American 5.4% Latino/Hispanic 6.9% Asian American 3.6% Native American 1.3% Middle Eastern .8% Biracial 2.3% Other/Did not answer 4.6% Middle School N=323�� Boy=49.8%�� Girls=50.2% White 61.9%� African American 3.2%� Latino/Hispanic 17.9%� Asian American 4.1% Native American 1.2% Middle Eastern 1.2%� Biracial=5.0% Other/Did not answer: 5.6% In order to address these research questions, we developed an instrument to assess for barriers and supports related to math and science. As we reported last year, our developed instrument separately assessed for math and science barriers. Items on this instrument were randomly worded as a barrier or a support. Depending on participants� initial responses, follow-up questions assessed for the salience of each barrier or support in influencing math/science career choice and course decisions. Items were scaled from -3 to +3, with -3 corresponding to a high barrier salience and positive 3 corresponding to a high support salience. Thus, it is important to note that each item could act as either a barrier or support. Our instrument also was slightly adapted for each developmental level, such that we assessed for the influence on taking additional math/science courses for middle school and high school participants but assessed for the influence on choosing a math/science career for college students. Participants were recruited in middle schools, high schools, and colleges in large Midwestern and Southwestern cities. Demographic information can be seen on this slide. As you can see, a total sample of 1151 students were included in this study, with a higher representation of females than males. For all three samples, the majority of students self- identified as White. In order to address these research questions, we developed an instrument to assess for barriers and supports related to math and science. As we reported last year, our developed instrument separately assessed for math and science barriers. Items on this instrument were randomly worded as a barrier or a support. Depending on participants� initial responses, follow-up questions assessed for the salience of each barrier or support in influencing math/science career choice and course decisions. Items were scaled from -3 to +3, with -3 corresponding to a high barrier salience and positive 3 corresponding to a high support salience. Thus, it is important to note that each item could act as either a barrier or support. Our instrument also was slightly adapted for each developmental level, such that we assessed for the influence on taking additional math/science courses for middle school and high school participants but assessed for the influence on choosing a math/science career for college students. Participants were recruited in middle schools, high schools, and colleges in large Midwestern and Southwestern cities. Demographic information can be seen on this slide. As you can see, a total sample of 1151 students were included in this study, with a higher representation of females than males. For all three samples, the majority of students self- identified as White.

6. What are the perceived barriers and supports for males/females across developmental levels? For both subjects, both genders, and all developmental levels, top barriers include: Presence/absence of subject test anxiety Subject difficulty For both subjects, both genders, and all developmental levels, top supports include: Parental expectation to take more classes/choose career Teachers expectations of success and teacher support to do well emerged as top supports in middle and high school but did not emerge as a top support in college The first research question in this study assessed for the perceived barriers and supports for males/females across developmental levels. Through our analyses, a few barriers and supports emerged as particularly salient across all developmental levels. For example, the presence or absence of subject test anxiety and subject difficulty emerged as a salient barrier for both males and females in middle school, high school, and college. Participants reported that perceived anxiety in taking both math and science tests and perceived difficulty of math and science subjects strongly influenced their decisions to not take more math/science courses or choose a math/science careers. In addition, parental expectations to take more math/science classes emerged as a salient support for both males and females across all developmental levels. For middle and high school students, teacher expectations of success in math and science and teacher support to do well also emerged as salient supports for both males and females. Clearly, there are some similar barriers and supports that students perceive regardless of gender or developmental level. Yet, there are also several barriers and supports that differ across groups. Our second question examined these differences. The first research question in this study assessed for the perceived barriers and supports for males/females across developmental levels. Through our analyses, a few barriers and supports emerged as particularly salient across all developmental levels. For example, the presence or absence of subject test anxiety and subject difficulty emerged as a salient barrier for both males and females in middle school, high school, and college. Participants reported that perceived anxiety in taking both math and science tests and perceived difficulty of math and science subjects strongly influenced their decisions to not take more math/science courses or choose a math/science careers. In addition, parental expectations to take more math/science classes emerged as a salient support for both males and females across all developmental levels. For middle and high school students, teacher expectations of success in math and science and teacher support to do well also emerged as salient supports for both males and females. Clearly, there are some similar barriers and supports that students perceive regardless of gender or developmental level. Yet, there are also several barriers and supports that differ across groups. Our second question examined these differences.

7. What are the largest differences in perceptions of math and science barriers/supports by developmental level? We used separate analysis of variance tests to examine the differences in perceptions of math and science barriers between developmental levels. Due to the large number of analyses we ran, we used a p-level of less than 0.005 as a cut-off for our analyses. We found a number of significant differences and listed the six greatest differences on this slide. As you can see, whereas these differences between developmental levels are significant, many of these differences have small effect sizes. Also, in general, most of these items for both math and science became more influential as a barrier or less influential as a support as developmental level increases. These items are listed in white. For example, whereas for middle school participants, parental knowledge was an influential support in taking more math classes, for college students, parental knowledge was a much less influential support in choosing a math career. The items listed in green generally became more of a support and less of a barrier as developmental level increases. For example, participants reported that perceiving science as useful in the future was more of a support for college students than for middle and high school students. We used separate analysis of variance tests to examine the differences in perceptions of math and science barriers between developmental levels. Due to the large number of analyses we ran, we used a p-level of less than 0.005 as a cut-off for our analyses. We found a number of significant differences and listed the six greatest differences on this slide. As you can see, whereas these differences between developmental levels are significant, many of these differences have small effect sizes. Also, in general, most of these items for both math and science became more influential as a barrier or less influential as a support as developmental level increases. These items are listed in white. For example, whereas for middle school participants, parental knowledge was an influential support in taking more math classes, for college students, parental knowledge was a much less influential support in choosing a math career. The items listed in green generally became more of a support and less of a barrier as developmental level increases. For example, participants reported that perceiving science as useful in the future was more of a support for college students than for middle and high school students.

8. What are the largest differences in perceptions of math and science barriers/supports by gender? We also assessed for differences between males and females in perceptions of math and science barriers and supports. We found fewer differences here than we found across developmental levels. Again, the six largest differences are listed on this slide. The items listed in white were perceived to be more of a barrier and less of a support for females than for males. Conversely, the items listed in green were perceived as more of a support for females when compared to their male counterparts. For example, teachers� gender stereotypes for performance, or beliefs that one gender was more skilled, emerged as the largest difference between boys and girls across all developmental levels for both math and science. In other words, our male participants perceived that teachers thought boys were better than girls in both math and science, and this perception acted as a support in influencing their math course and career selection. While, our female participants agreed that teachers thought boys were better than girls, for them this perception acted as a barrier. These findings indicate there are differences in perceptions of barriers between males and females and these perceptions also differ between math and science. We also assessed for differences between males and females in perceptions of math and science barriers and supports. We found fewer differences here than we found across developmental levels. Again, the six largest differences are listed on this slide. The items listed in white were perceived to be more of a barrier and less of a support for females than for males. Conversely, the items listed in green were perceived as more of a support for females when compared to their male counterparts. For example, teachers� gender stereotypes for performance, or beliefs that one gender was more skilled, emerged as the largest difference between boys and girls across all developmental levels for both math and science. In other words, our male participants perceived that teachers thought boys were better than girls in both math and science, and this perception acted as a support in influencing their math course and career selection. While, our female participants agreed that teachers thought boys were better than girls, for them this perception acted as a barrier. These findings indicate there are differences in perceptions of barriers between males and females and these perceptions also differ between math and science.

9. Are the factors the same across gender and developmental level within subject matter? No�the factor structures are different for males and females across developmental levels for both math and science Preliminary results suggest that boys and girls at different levels put barriers and supports together in a different manner The third research question of this study assessed whether the factor structures were the same across gender and developmental level for both math and science. We don�t have time to present all the different factor structures, yet preliminary results suggest that the answer to this question is no�we were unable to find the same factor structure across all groups. In fact, the factor structures for males and females at each developmental level are quite different from one another. These preliminary results suggest that the boys and girls at different levels put barriers and supports together differently. The third research question of this study assessed whether the factor structures were the same across gender and developmental level for both math and science. We don�t have time to present all the different factor structures, yet preliminary results suggest that the answer to this question is no�we were unable to find the same factor structure across all groups. In fact, the factor structures for males and females at each developmental level are quite different from one another. These preliminary results suggest that the boys and girls at different levels put barriers and supports together differently.

10. What variables predict students choices to pursue math courses or a math career?  Finally, our last research question assessed for variables that predicted students� choices to pursue math courses or choose a math career. We found several significant predictors across the groups and highlighted the strongest three predictor for each group in this chart. In general, for both college boys and girls, the strongest predictor for pursuing a math career was math interest. For high school boys, the strongest predictor was parental support in making math decision and for high school girls, the strongest predictor was possessing math-related career goals. For middle school boys, sel-evaluation of math ability emerged as the strongest predictor whereas for middle school girls, again math interest emerged as the strongest predictor. Finally, our last research question assessed for variables that predicted students� choices to pursue math courses or choose a math career. We found several significant predictors across the groups and highlighted the strongest three predictor for each group in this chart. In general, for both college boys and girls, the strongest predictor for pursuing a math career was math interest. For high school boys, the strongest predictor was parental support in making math decision and for high school girls, the strongest predictor was possessing math-related career goals. For middle school boys, sel-evaluation of math ability emerged as the strongest predictor whereas for middle school girls, again math interest emerged as the strongest predictor.

11. What variables predict students choices to pursue science courses or a science career? We also examined the variables that predicted students� choices to pursue science courses or choose a science career. Again, we found several significant predictors and the highlighted the strongest three predictor for each group in this chart. For college and high school boys, and college girls, the strongest predictor that emerged was science interest. For high school girls, parentla expectation to choose a science career emerged as the strongest predictor. Finally for middle school boys, self-evaluation of science ability emerged as the strongest predictor whereas for girls, science career goals emerged as the strongest predictor. We also examined the variables that predicted students� choices to pursue science courses or choose a science career. Again, we found several significant predictors and the highlighted the strongest three predictor for each group in this chart. For college and high school boys, and college girls, the strongest predictor that emerged was science interest. For high school girls, parentla expectation to choose a science career emerged as the strongest predictor. Finally for middle school boys, self-evaluation of science ability emerged as the strongest predictor whereas for girls, science career goals emerged as the strongest predictor.

12. Conclusion/Future Direction Barriers and supports for future careers and course selection are different for: Males and Females Middle School, High School, and College students Math and Science Future Direction Examine relationship between barriers/supports and expressed occupational choice Further examine factor structures and implications for interventions in math and science Examine barriers and supports relationship within other hypothesized related constructs within SCCT (i.e. self-efficacy, outcome expectations, etc.) Also examine whether barriers/supports are proximal or distal So, in conclusion, this study found that barriers and supports for future career and course selection differ between males and females, between middle school, high school, and college students, and between math and science. This was found in examining the top three barriers and supports for each of these groups, by examining differences in barriers and supports across gender and developmental level in math and science, in examining the factor structures for all of these groups, and in examining the predictors for future math and science course and career selection. Whereas our analyses on the differences between groups found small effect sizes, our analyses examining predictors found our items to account for a large amount of variance in choosing math/science courses or choosing math/science careers. These fiindings clearly highlight that barriers and supports do indeed act as a contextual factor in predicting math and science related decisions. Future directions for our research will focus on examining the relationship between barriers and supports and expressed occupational choice, further examining factor structures and implications for interventions in math and science and examining the relationship of these barriers and support within the context of the social cognitive career model. Another future direction is to develop a website with specific intervention ideas for studnets at differnet developmental levels. So, in conclusion, this study found that barriers and supports for future career and course selection differ between males and females, between middle school, high school, and college students, and between math and science. This was found in examining the top three barriers and supports for each of these groups, by examining differences in barriers and supports across gender and developmental level in math and science, in examining the factor structures for all of these groups, and in examining the predictors for future math and science course and career selection. Whereas our analyses on the differences between groups found small effect sizes, our analyses examining predictors found our items to account for a large amount of variance in choosing math/science courses or choosing math/science careers. These fiindings clearly highlight that barriers and supports do indeed act as a contextual factor in predicting math and science related decisions. Future directions for our research will focus on examining the relationship between barriers and supports and expressed occupational choice, further examining factor structures and implications for interventions in math and science and examining the relationship of these barriers and support within the context of the social cognitive career model. Another future direction is to develop a website with specific intervention ideas for studnets at differnet developmental levels.