Christopher T. Belser, M. Ann Shillingford, Andrew P. Daire, Diandra J. Prescod, Melissa A. Dagley
The United States is facing a crisis with respect to filling job vacancies within science, technology, engineering, and math (STEM) industries and with students completing STEM undergraduate degrees. In addition, disparities exist for females and ethnic minorities within STEM fields. Whereas prior research has centered on disparities in STEM fields, retention rates, and some intervention programs, researchers have not given much attention to the role of career development initiatives within STEM recruitment and retention programming. The purpose of the present study was to incorporate demographic variables, math performance, and career development–related factors into predictive models of STEM retention with a sample of undergraduate students within a STEM recruitment and retention program. The resulting two models accurately predicted first-year to second-year retention with 73.4% of the cases and accurately predicted first-year to third-year retention with 70.0% of the cases. Based on the results, the researchers provide a rationale for STEM career programming in K–12 and higher education settings and for the inclusion of career development and career counseling in STEM education programming.
Keywords: STEM, retention, career development, career counseling, undergraduate student
The United States lacks an adequate number of workers to keep up with the demand for trained workers in science, technology, engineering, and mathematics (STEM) fields (National Center for Science and Engineering Statistics [NCSES], 2017; National Science Board, 2018; Sithole et al., 2017). Researchers have pointed to the overall stagnancy of undergraduate students declaring and completing STEM degrees (Carnevale, Smith, & Melton, 2011; Doerschuk et al., 2016; Sithole et al., 2017). Additionally, underrepresentation is a problem for racial and ethnic minorities and females in STEM fields (NCSES, 2017). Because of these disparities, universities have developed programs centered on recruitment and retention of STEM undergraduates (Bouwma-Gearhart, Perry, & Presley, 2014; Dagley et al., 2016; Schneider, Bickel, & Morrison-Shetlar, 2015) and both government and private entities invest billions of dollars annually toward STEM initiatives at the K–12 and higher education levels (Carnevale et al., 2011). However, many of these endeavors have failed to incorporate components centered on career development or career planning.
The National Career Development Association (2015) defined career development as “the sequence of career-related choices and transitions made over the life span” (p. 4) and career planning as a structured process through which a person makes decisions and plans for a future career. Career development activities, such as structured career planning courses, have shown efficacy with general undergraduate populations (Osborn, Howard, & Leierer, 2007; Reardon, Melvin, McClain, Peterson, & Bowman, 2015) but have been studied less commonly with STEM-specific undergraduate populations (Belser, Prescod, Daire, Dagley, & Young, 2017, 2018; Prescod, Daire, Young, Dagley, & Georgiopoulos, in press). In the present study, researchers examined a STEM recruitment and retention program that did include a career planning course. More specifically, the research team sought to investigate relationships between demographics (e.g., gender, ethnicity), math scores, and various aspects of the undergraduate STEM program and student retention in the first 2 years of college.
Gender, Ethnicity, and STEM
Gender disparities are a common sight within STEM degree programs and the larger STEM workforce (NCSES, 2017). Females who are interested in math and science are more likely to be tracked into non-diagnosing health practitioner fields, such as nursing (ACT, 2018; NCSES, 2017). Some researchers have pointed to the K–12 arena as the root of these gender disparities that permeate undergraduate programs and STEM professions (Mansfield, Welton, & Grogan, 2014), whereas others have identified specific problems, such as differences in math and science course completion over time (Chen & Soldner, 2013; Riegle-Crumb, King, Grodsky, & Muller, 2012), stereotype threat (Beasley & Fischer, 2012), and STEM confidence (Litzler, Samuelson, & Lorah, 2014). As a result, existing predictive models typically indicate a lower likelihood of females completing a STEM degree compared to male students (Cundiff, Vescio, Loken, & Lo, 2013; Gayles & Ampaw, 2014).
Similarly, disparities in STEM degree completion and STEM job attainment exist between ethnic groups (NCSES, 2017; Palmer, Maramba, & Dancy, 2011). Although progress has been made in degree attainment in certain STEM areas, other areas have stagnated or are declining in participation by ethnic minority students (Chen & Soldner, 2013; NCSES, 2017). Foltz, Gannon, and Kirschmann (2014) identified protective factors for minority students in STEM, such as receiving college-going expectations from home, establishing connections with STEM faculty members (particularly those of color), and developing connections with other minority students in STEM majors; however, the disparities in STEM programs help perpetuate a cycle of many students not being exposed to these protective factors. The intersectionality of ethnicity and gender in STEM fields has become a topic producing interesting findings (Riegle-Crumb & King, 2010). In addition to observing disparities across ethnic groups, researchers have observed disparities within ethnic groups based on gender (Beasley & Fischer, 2012; Cundiff et al., 2013; Riegle-Crumb & King, 2010). Specifically with males of color, predictive models have been inconclusive, with some showing a higher likelihood of completing a STEM degree (Riegle-Crumb & King, 2010) and others showing a lower likelihood (Cundiff et al., 2013; Gayles & Ampaw, 2014).
Mathematics and STEM
The SAT is one of the most widely used college admissions tests (CollegeBoard, 2018). Researchers have correlated the math sub-score with undergraduate math and science classes within the first year, indicating that higher SAT math scores indicate a higher probability of higher course grades in math and science courses (Wyatt, Remigio, & Camara, 2012). Additionally, researchers have identified SAT scores as predictors of academic success and university retention (Crisp, Nora, & Taggart, 2009; Le, Robbins, & Westrick, 2014; Mattern & Patterson, 2013; Rohr, 2012). Despite its wide use in higher education admissions, the SAT may not be free from bias. Numerous scholars have highlighted potential test bias, particularly against ethnic minorities (Dixon-Román, Everson, & McArdle, 2013; Lawlor, Richman, & Richman, 1997; Toldson & McGee, 2014). Nevertheless, its wide use makes it a prime instrument for research.
In addition to the SAT scores, researchers also have demonstrated that taking higher-level math courses and having higher math self-efficacy translate to better outcomes within STEM majors (Carnevale et al., 2011; Chen & Soldner, 2013; Nosek & Smyth, 2011). Specifically, taking calculus-based courses in high school correlated with retention in STEM majors (Chen & Soldner, 2013). Nosek and Smyth (2011) found connections between gender and internalized math variables, such as warmth for math, identification with math, and self-efficacy; females across the life span showed lower levels of each of these variables, but the authors did not test these against retention outcomes in STEM majors. However, one could hypothesize that having lower levels of warmth toward math and not being able to identify with math would likely impact one’s career decisions, particularly related to math and science fields.
Career Interventions and STEM
Career theory can provide for understanding one’s interest in STEM fields (Holland, 1973), one’s exposure to STEM fields (Gottfredson, 1981), and one’s beliefs or expectations about the process of choosing a STEM field (Lent, Brown, & Hackett, 2002; Peterson, Sampson, Lenz, & Reardon, 2002). However, career interventions, such as a career planning class, are more likely to make a direct impact on career outcomes with undergraduates. In one review of research on undergraduate career planning courses, more than 90% of the courses produced some measurable positive result for students, such as increased likelihood of completing a major, decreased negative career thinking, and increased career self-efficacy (Reardon & Fiore, 2014). Other researchers have reported similar results with generic undergraduate career planning courses (Osborn et al., 2007; Saunders, Peterson, Sampson, & Reardon, 2000).
Researchers have studied structured career planning courses specific to STEM majors with much less frequency. In one such study, Prescod and colleagues (in press) found that students who took a STEM-focused career planning course scored lower on a measure of negative career thinking at the end of the semester. In a similar study, STEM-interested students in a STEM-focused career planning course had lower posttest scores on a measure of negative career thinking than declared STEM majors at the end of the same semester (Belser et al., 2018). Additionally, in a pilot study, Belser and colleagues (2017) found that greater reductions in negative career thinking predicted higher odds of being retained in a STEM major from the first to second year of college; in this same study, the authors found that students who participated in a STEM-focused career planning course were more likely to be retained in a STEM major than students in an alternative STEM course. Researchers have not given ample attention to determining how career planning and other career variables fit into predictive models of retention in STEM majors.
Statement of the Problem and Hypotheses
As previously noted, prior researchers have paid limited attention to developing predictive models that incorporate career development variables along with demographics and math performance. Developing effective predictive models has implications for researchers, career practitioners, higher education professionals, and the STEM workforce. To this end, the researchers intend to test two such models related to retention in STEM majors using the following hypotheses:
Hypothesis 1: First-year to second-year undergraduate retention in STEM majors can be predicted by ethnicity, gender, initial major, math placement–algebra scores, SAT math scores, STEM course participation, and Career Thoughts Inventory (CTI) change scores.
Hypothesis 2: First-year to third-year undergraduate retention in STEM majors can be predicted by ethnicity, gender, initial major, math placement–algebra scores, SAT math scores, STEM course participation, and CTI change scores.
In this study, researchers examined multi-year retention data for students in a STEM recruitment and retention program at a large research university in the Southeastern United States and utilized a quasi-experimental design with non-equivalent comparison groups (Campbell & Stanley, 1963; Gall, Gall, & Borg, 2007). Because this study was part of a larger research project, Institutional Review Board approval was already in place.
The COMPASS Program
The COMPASS Program (Convincing Outstanding Math-Potential Admits to Succeed in STEM; Dagley et al., 2016) is a National Science Foundation–funded project that seeks to recruit and retain undergraduate students in STEM majors. To enter the program, students must have a minimum SAT math score of 550, an undeclared major at the time of applying to the university and program, and an expressed interest in potentially pursuing a STEM degree. However, some students accepted to the COMPASS Program declare a STEM major between the time that they are accepted into the COMPASS Program and the first day of class, creating a second track of students who were initially uncommitted to a major at the time of application. Students in both tracks have access to math and science tutoring in a program-specific center on campus, are matched with undergraduate mentors from STEM majors, have access to cohort math classes for students within the program, and can choose to live in a residence hall area designated for COMPASS participants. Depending on which COMPASS track students are in, they either take a STEM-focused career planning course or a STEM seminar course during their first semester.
COMPASS participants who started college without a declared major take a STEM-focused career planning class in their first semester. The activities of this course include a battery of career assessments and opportunities to hear career presentations from STEM professionals, visit STEM research labs, and attend structured career planning activities (e.g., developing a career action plan, résumé and cover letter writing, small group discussions). The first author and fourth author served as instructors for this course, and both were counselor education doctoral students at the time.
Participants who had declared a STEM major between the time they were accepted into the COMPASS Program and the first day of class took a STEM seminar course instead of the career planning class. The structure of this course included activities designed to help students engage with and be successful in their selected STEM majors, including presentations on learning styles and strategies, time management, study skills, professional experiences appropriate for STEM majors, and strategies for engaging in undergraduate research. Guest speakers for the class focused more on providing students with information about how to be successful as a STEM student. The course did not include career planning or career decision-making activities specifically geared toward helping students decide on a major or career field. A science education doctoral student served as the instructor of record for the course, with graduate students from various STEM fields serving as teaching assistants.
The university’s Institutional Knowledge Management Office provided demographic data on program participants. Table 1 displays descriptive data for participants, organized by second-year retention data (i.e., retention from the first year of college to the second year of college, for Hypothesis 1) and third-year retention data (i.e., retention from the first year of college to the third year of college, for Hypothesis 2). The frequencies for the subcategories were smaller for the third-year retention data (Hypothesis 2) because fewer participants had matriculated this far during the life of the project. Table 1 also breaks down each subset of the data based on which students were retained in a STEM major and which were not retained.
Descriptive Statistics for Categorical Variables
Second-Year Retention Descriptives
Third-Year Retention Descriptives
Note. a = percentage of the Retained group. b = percentage of the Not Retained group. c = percentage of the Total group.
Gender representation within the two samples was split relatively evenly, with female participants represented at a higher rate in the sample than in the larger population of STEM undergraduates and at a higher rate than STEM professionals in the workforce. Both samples were predominantly Caucasian/White, with no other ethnic group making up more than one-fourth of either sample individually; these ethnicity breakdowns were reflective of the university’s undergraduate population and somewhat reflective of STEM disciplines. The students who took the STEM-focused career planning course accounted for a larger percentage of both total samples and also of the not-retained groups. Regarding initial major, the largest percentage of students fell within the initially undeclared category, with the next largest group being the initially STEM-declared group (these students officially declared a STEM major but were uncommitted with their decision).
The researchers conducted an a priori power analysis using G*Power 3 (Cohen, 1992; Faul, Erdfelder, Lang, & Buchner, 2007), and the overall samples of 429 and 271 were sufficient for the binary logistic regression. With logistic regression, the ratio of cases in each of the dependent outcomes (retained or not retained) to the number of independent variable predictors must be sufficient (Agresti, 2013; Hosmer, Lemeshow, & Sturdivant, 2013; Tabachnick & Fidell, 2013). Following Peduzzi, Concato, Kemper, Holford, and Feinstein’s (1996) rule of 10 cases per outcome per predictor, the samples were sufficient for all independent variables except ethnicity, which had multiple categories with fewer than 10 cases. However, Field (2009) and Vittinghoff and McCulloch (2006) recommended having a minimum of five cases per outcome per predictor, which the sample achieved for all independent variables.
Variables and Instruments
The analysis included 10 independent variables within the logistic regression models. The university’s Institutional Knowledge Management Office (IKMO) provided data for the four categorical variables displayed in Table 1 (gender, ethnicity, course, and initial major). Four of the independent variables represented the participants’ total and subscale scores on the CTI, which students completed in either the career planning course or the STEM seminar course. The other two independent variables were participants’ scores on the SAT math subtest and the university’s Math Placement Test–Algebra subscale; the IKMO provided these data as well.
Career Thoughts Inventory (CTI). The CTI includes 48 Likert-type items and seeks to measure respondents’ levels of negative career thinking (Sampson, Peterson, Lenz, Reardon, & Saunders, 1996a, 1996b). To complete the CTI, respondents read the 48 statements about careers and indicate how much they agree using a 4-point scale (strongly disagree to strongly agree). The CTI provides a total score and scores for three subscales: (a) Decision Making Confusion (DMC); (b) Commitment Anxiety (CA); and (c) External Conflict (EC). Completing the instrument yields raw scores for the assessment total and each of the three subscales, and a conversion table printed on the test booklet allows respondents to convert raw scores to T scores. Higher raw scores and T scores indicate a higher level of problematic thinking in each respective area, with T scores at or above 50 indicating clinical significance. For the college student norm group, internal consistency alpha coefficients were .96 for the total score and ranged from .77 to .94 for the three subscales (Sampson et al., 1996a, 1996b). With the sample in the present study, the researchers found acceptable alpha coefficients that were comparable to the norm group. The researchers used CTI change scores as predictors, calculated as the change in CTI total and subscale scores from the beginning to the end of either the career planning class or the STEM seminar class.
SAT Math. High school students take the SAT as a college admissions test typically in their junior and/or senior years (CollegeBoard, 2018). Although the SAT has four subtests, the researchers only used the math subtest in the present study. The math subtest is comprised of 54 questions or tasks in the areas of basic mathematics knowledge, advanced mathematics knowledge, managing complexity, and modeling and insight (CollegeBoard, 2018; Ewing, Huff, Andrews, & King, 2005). In a validation study of the SAT, Ewing et al. (2005) found an internal consistency alpha coefficient of .92 for the math subtest and alpha coefficients ranging from .68 to .81 for the four math skill areas. The researchers were unable to analyze psychometric properties of the SAT math test with the study sample because the university’s IKMO only provided composite and subtest total scores, rather than individual item responses.
Math Placement Test–Algebra Subtest. The Math Placement Test is a university-made assessment designed to measure mathematic competence in algebra, trigonometry, and pre-calculus that helps the university place students in their first math course at the university. All first-time undergraduate students at the university are required to take the test; when data collection began, the mandatory completion policy was not yet in place, so some earlier participants had missing data in this area. The test is structured so that all respondents first take the algebra subtest and if they achieve 70% accuracy, they move to the trigonometry and pre-calculus subtests. Similar to the SAT, the researchers were unable to analyze psychometric properties of the test because the IKMO provided only composite and subtest total scores.
Because the dependent variables (second-year retention and third-year retention) were dichotomous (i.e., retained or not retained), the researchers used the binary logistic regression procedure within SPSS Version 24 to analyze the data (Agresti, 2013; Hosmer et al., 2013; Tabachnick & Fidell, 2013). The purpose of binary logistic regression is to test predictors of the binary outcome by comparing the observed outcomes and the predicted outcomes first without any predictors and then with the chosen predictors (Hosmer et al., 2013). The researchers used a backward stepwise Wald approach, which enters all predictors into the model and removes the least significant predictors one by one until all of the remaining predictors fall within a specific p value range (Tabachnick & Fidell, 2013). The researchers chose to set the range as p ≤ .20 based on the recommendation of Hosmer et al. (2013).
Preliminary data analysis included identifying both univariate and multivariate outliers, which were removed from the data file; conducting a missing data analysis; and testing the statistical assumptions for logistic regression. There were no missing values for categorical variables, but the assessment variables (CTI, SAT, and Math Placement Test) did have missing values. Results from Little’s (1988) MCAR test in SPSS showed that these data were not missing completely at random (Chi-square = 839.606, df = 161, p < .001). The researchers chose to impute missing values using the Expectation Maximization procedure in SPSS (Dempster, Laird, & Rubin, 1977; Little & Rubin, 2002). The data met the statistical assumptions of binary logistic regression related to multicollinearity and linearity in the logit (Tabachnick & Fidell, 2013). As previously discussed, the data also sufficiently met the assumption regarding the ratio of cases to predictor variables, with the exception of the ethnicity variable; after removing outliers, the Asian/Pacific Islander subcategory in the non-retained outcome had only four cases, violating the Peduzzi et al. (1996) and Field (2009) recommendation of having at least five cases. However, because the goal was to test the ethnicity categories separately rather than collapsing them to fit the recommendation, and because Hosmer et al. (2013) noted this was a recommendation and not a rule, the researchers chose to keep the existing categories, noting the potential limitation when interpreting this variable.
Results The sections that follow provide the results from each of the hypotheses and interpretation of the findings.
Hypothesis 1 stated that the independent variables could predict undergraduate STEM retention from Year 1 to Year 2. As stated previously, the backward stepwise Wald approach involved including all predictors initially and then removing predictors one by one based on p value until all remaining predictors fell within the p ≤ .20 range. This process took five steps, resulting in the removal of four variables with p values greater than .20: (a) CTI Commitment Anxiety Change, (b) CTI External Conflict Change, (c) Gender, and (d) CTI Decision Making Confusion Change, respectively. The model yielded a Chi-square value of 91.011 (df = 10, p < .001), a -2 Log likelihood of 453.488, a Cox and Snell R-square value of .198, and a Nagelkerke R-square value of .270. These R-square values indicate that the model can explain between approximately 20% and 27% of the variance in the outcome. The model had a good fit with the data, as evidenced by the Hosmer and Lemeshow Goodness of Fit Test (Chi-square = 6.273, df = 8, p = .617). The final model accurately predicted 73.4% of cases across groups; however, the model predicted the retained students more accurately (89.6% of cases) than the non-retained cases (45.8% of cases).
Table 2 explains how each of the six variables retained in the model contributed to the final model. The odds ratio represents an association between a particular independent variable and a particular outcome, or for this study, the extent that the independent variables predict membership in the retained outcome group. With categorical variables, this odds ratio represents the likelihood that being in a category increases the odds of being in the retained group over the reference category (i.e., African American/Black participants were 1.779 times more likely to be in the retained group than White/Caucasian students, who served as the reference category). With continuous variables, odds ratios represent the likelihood that quantifiable changes in the independent variables predict membership in the retained group (i.e., for every unit increase in SAT math score, the odds of being in the retained group increase 1.004 times). The interpretation of odds ratios allows them to be viewed as a measure of effect size, with odds ratios closer to 1.0 having a smaller effect (Tabachnick & Fidell, 2013).
Variables in the Equation for Hypothesis 1
95% C.I. for O.R.
Ethnicity (African American/Black)
Ethnicity (Asian/Pacific Islander)
Initial Major (Declared STEM)
Initial Major (Declared Non-STEM)
STEM Seminar (Non-CP)
CTI Total Change
Note: B = Coefficient for the Constant; S.E. = Standard Error; O.R. = Odds Ratio; * p < .05; ** p < .01; *** p < .001.
With logistic regression, the Wald Chi-square test allows the researcher to determine a coefficient’s significance to the model (Tabachnick & Fidell, 2013). Based on this test, Initial Major was the most significant predictor to the model (p < .001). Students in the initially Declared STEM category were 1.511 times more likely to be in the retained group than those in the initially Undeclared category (the reference category); the odds of being in the retained group decreased by a factor of .143 for students in the initially Declared Non-STEM group. The STEM course was the predictor with the second most statistical significance (p < .01), with students in the STEM seminar class being 2.340 times more likely to be in the retained outcome than those in the career planning class. The CTI Total Change score was statistically significant (p < .05), indicating that for every unit increase in CTI Total Change score (i.e., the larger the decrease in score from pretest to posttest), the odds of being in the retained group increase by a factor of 1.017. Ethnicity was a statistically significant predictor (p < .05), with each subcategory having higher odds of being in the retained group than the White/Caucasian group; however, the researchers caution the reader to read these odds ratios for ethnicity with caution because of the number of cases in some categories. SAT Math and Math Placement–Algebra were not statistically significant, but still fell within the recommended inclusion range (p < .20).
Hypothesis 2 stated that the independent variables could predict undergraduate STEM retention from Year 1 to Year 3. As stated previously, the backward stepwise Wald approach involved including all predictors initially and then removing predictors one by one based on p value until all remaining predictors fell within the p ≤ .20 range. This process took six steps, resulting in the removal of five variables with p values greater than .20: (a) CTI Commitment Anxiety Change, (b) CTI Decision Making Confusion Change, (c) Gender, (d) CTI External Conflict Change, and (e) CTI Total Change, respectively. The model yielded a Chi-square value of 55.835 (df = 9, p < .001), a -2 Log likelihood of 307.904, a Cox and Snell R-square value of .191, and a Nagelkerke R-square value of .255. These R-square values indicate that the model can explain between approximately 19% and 26% of the variance in the outcome. The model had a good fit with the data, as evidenced by the Hosmer and Lemeshow Goodness of Fit Test (Chi-square = 9.187, df = 8, p = .327). The model accurately predicted 70.0% of cases across groups. In this analysis, the model predicted the non-retained students more accurately (72.7% of cases) than the retained cases (66.9% of cases).
Table 3 explains how the variables within the model contributed to the final model. Based on the Wald test, Initial Major was the most significant predictor to the model (p < .001). Students in the initially Declared STEM category were 1.25 times more likely to be in the retained group than those in the initially Undeclared category (the reference category); the odds of being in the retained group decreased by a factor of .167 for students in the initially Declared Non-STEM group. The Math Placement–Algebra variable was statistically significant (p < .05), and the odds ratios indicated that for every unit increase in Math Placement–Algebra test score, the odds of being in the retained group are 1.005 higher. The STEM course variable was slightly outside the statistically significant range but fell within the inclusion range, with students in the STEM seminar class being 2.340 times more likely to be in the retained outcome than students in the career planning class. SAT Math was not statistically significant but still fell within the recommended inclusion range (p < .20). Ethnicity also was not a statistically significant predictor but fell within the inclusion range, with each subcategory having higher odds of being in the retained group than the White/Caucasian group; however, the researchers caution the reader to read these odds ratios for ethnicity with caution because of the number of cases in some categories.
Variables in the Equation for Hypothesis 2
95% C.I. for O.R.
Ethnicity (African American/Black)
Ethnicity (Asian/Pacific Islander)
Initial Major (Declared STEM)
Initial Major (Declared non-STEM)
STEM Seminar (Non-CP)
Note: B = Coefficient for the Constant; S.E. = Standard Error; O.R. = Odds Ratio; * p < .05; *** p < .001.
The researchers sought to determine the degree to which a set of demographic variables, math scores, and career-related factors could predict undergraduate retention in STEM majors. Based on descriptive statistics, the participants are remaining in STEM majors at a higher rate than other nationwide samples (Chen & Soldner, 2013; Koenig, Schen, Edwards, & Bao, 2012). The sample
in this study was quite different based on gender than what is commonly cited in the literature; approximately 46% of the study’s sample was female, whereas the NCSES (2017) reported that white females made up approximately 31% of those in STEM fields, with minority females lagging significantly behind. The present study’s sample was more in line with national statistics with regard to ethnicity (NCSES, 2017; Palmer et al., 2011).
With Hypothesis 1, the researchers sought to improve on a pilot study (Belser et al., 2017) that did not include demographics or math-related variables. Adding these additional variables did improve the overall model fit and the accuracy of predicting non-retained students, but slightly decreased the accuracy of predicting retained students, as compared to the Belser et al. (2017) model. In addition to improving the model fit, adding in additional variables reversed the claim by Belser et al. (2017) that students in the STEM-focused career planning class were more likely to be retained than the STEM seminar students. In the present study, the STEM seminar students, who declared STEM majors prior to the first day of college, were more likely to be retained in STEM majors, which is in line with prior research connecting intended persistence in a STEM major to observed retention (Le et al., 2014; Lent et al., 2016).
With Hypothesis 2, the researchers sought to expand on the Belser et al. (2017) study by also predicting retention one year farther, into the third year of college. In this endeavor, the analysis yielded a model that still fit the data well. However, this model was much more accurate in predicting the non-retained students and was slightly less accurate in predicting the retained students, with the overall percentage of correct predictions similar to Hypothesis 1. This finding indicates that the included predictors may provide a more balanced ability to predict long-term retention in STEM majors than in just the first year. The initial major and STEM course variables performed similarly as in Hypothesis 1, and as such, similarly to prior research (Le et al., 2014; Lent et al., 2016).
Although sampling issues warrant the reader to read ethnicity results with caution, ethnicity did show to be a good predictor of retention in STEM majors with both Hypotheses 1 and 2. More noteworthy, the African American/Black and Hispanic students had higher odds of being retained. This is inconsistent with most research that shows underrepresented minorities as less likely to be retained in STEM majors (Chen & Soldner, 2013; Cundiff et al., 2013; Gayles & Ampaw, 2014); however, at least one study has previously found results in which ethnic minority students were more likely to be retained in STEM majors (Riegle-Crumb & King, 2010).
Gender was removed as a predictor from both models because of its statistical non-significance. Prior research has shown that females are less likely to be retained in STEM majors (Cundiff et al., 2013; Gayles & Ampaw, 2014; Riegle-Crumb et al., 2012), which separates this sample from prior studies. However, the COMPASS sample did have a larger representation of females than typically observed. Moreover, the COMPASS Program has been mindful of prior research related to gender and took steps to address gender concerns in program development (Dagley et al., 2016).
The continuous variables retained in the models showed only a mild effect on predicting STEM retention. The SAT Math and Math Placement–Algebra scores did perform consistently with prior research, in which higher math scores related to higher odds of retention (CollegeBoard, 2012; Crisp et al., 2009; Le et al., 2014; Mattern & Patterson, 2013; Rohr, 2012). The CTI variables that were retained in the models performed in line with the Belser et al. (2017) pilot study specific to STEM majors and with prior research examining negative career thoughts in undergraduate retention in other majors (Folsom, Peterson, Reardon, & Mann, 2005; Reardon et al., 2015).
Limitations and Implications
The present study has limitations, particularly with regard to research design, sampling, and instrumentation. First, the researchers used a comparison group design rather than a control group, and as such, there were certain observable differences between the two groups. Not having a control group limits the researchers’ ability to make causal claims regarding the predictor variables or the STEM career intervention. The researchers also only included a limited number of predictors; the inclusion of additional variables may have strengthened the models. Although the sample size was sufficient based on the a priori power analysis, the low number of participants in some of the categories may have resulted in overfitting or underfitting within the models. Finally, the researchers were not able to test psychometric properties of the SAT Math subtest or the Math Placement–Algebra subtest with this sample because of not having access to the participants’ item responses for each. The researchers attempted to mitigate limitations as much as possible and acknowledge that they can and should be improved upon in future research.
Future research in this area would benefit from the inclusion of a wider variety of predictor variables, such as math and science self-efficacy, outcome expectations, and internal processes observed with gender and ethnic minority groups (e.g., stereotype threat; Cundiff et al., 2013; Litzler et al., 2014). The researchers also recommend obtaining a larger representation of ethnic minority groups to ensure an adequate number of cases to effectively run the statistical procedure. Future researchers should consider more complex statistical procedures (e.g., structural equation modeling) and research designs (e.g., randomized control trials) to determine more causal relationships between predictors and the outcome variables.
Because the results of this study indicate that a more solidified major selection is associated with higher odds of retention in STEM majors, university career professionals and higher education professionals should strive to develop programming that helps students decide on a major earlier in their undergraduate careers. Structured career development work, often overlooked in undergraduate STEM programming, may be one such appropriate strategy. Additionally, any undergraduate STEM programming must be sensitive to demographic underrepresentation in STEM majors and the STEM workforce and should take steps to provide support for students in these underrepresented groups.
Similar to work with undergraduates, this study’s results provide a rationale for school counselors to engage students in STEM career work so that they can move toward a solidified STEM major prior to enrolling in college. The industry-specific career development work discussed within this study is just as important, if not more important, for students in K–12 settings. Moreover, school counselors, through their continued access to students, can serve as an access point for researchers to learn more about the STEM career development process at an earlier stage of the STEM pipeline. All of these endeavors point to the need for counselor educators to better prepare school counselors, college counselors, and career counselors to do work specifically with STEM and to become more involved in STEM career research.
In the present study, the researchers built upon prior research in the area of STEM retention to determine which variables can act as predictors of undergraduate STEM retention. The binary logistic regression procedure yielded two models that provide insight on how these variables operate individually and within the larger model. Finally, the researchers identified some key implications for counselors practicing in various settings and for researchers who are interested in answering some of the key questions that still exist with regard to STEM career development and retention.
Conflict of Interest and Funding Disclosure
Data collected in this study was part of a dissertation study by the first author. The dissertation was awarded the 2018 Dissertation Excellence Award by the National Board for Certified Counselors.
ACT. (2018). The condition of STEM 2017. Retrieved from www.act.org/stemcondition
Agresti, A. (2013). Categorical data analysis (3rd ed.). Hoboken, NJ: Wiley.
Beasley, M. A., & Fischer, M. J. (2012). Why they leave: The impact of stereotype threat on the attrition of women and minorities from science, math, and engineering majors. Social Psychology of Education, 15, 427–448. doi:10.1007/s11218-012-9185-3
Belser, C. T., Prescod, D. J., Daire, A. P., Dagley, M. A., & Young, C. Y. (2017). Predicting undergraduate student retention in STEM majors based on career development factors. The Career Development Quarterly, 65, 88–93. doi:10.1002/cdq.12082
Belser, C. T., Prescod, D. J., Daire, A. P., Dagley, M. A., & Young, C. Y. (2018). The influence of career planning on career thoughts in STEM-interested undergraduates. The Career Development Quarterly, 66(2), 176–181. doi:10.1002/cdq.12131
Bouwma-Gearhart, J., Perry, K. H., & Presley, J. B. (2014). Improving postsecondary STEM education: Strategies for successful interdisciplinary collaborations and brokering engagement with education research and theory. Journal of College Science Teaching, 44, 40–47.
Campbell, D. T., & Stanley, J. C. (1963). Experimental and quasi-experimental designs for research. Chicago, IL: Rand McNally.
Carnevale, A. P., Smith, N., & Melton, M. (2011). STEM: Science, technology, engineering, mathematics. Washington, DC: Georgetown University.
Chen, X., & Soldner, M. (2013). STEM attrition: College students’ paths into and out of STEM fields. Retrieved from http://nces.ed.gov/pubs2014/2014001rev.pdf
Cohen, J. (1992). A power primer. Psychological Bulletin, 112, 155–159. doi:10.1037/0033-2909.112.1.155
CollegeBoard. (2018). Math Test. Retrieved from https://collegereadiness.collegeboard.org/sat/inside-the-test/math
Crisp, G., Nora, A., & Taggart, A. (2009). Student characteristics, pre-college, college, and environmental factors as predictors of majoring in and earning a STEM degree: An analysis of students attending a Hispanic serving institution. American Educational Research Journal, 46, 924–942. doi:10.3102/0002831209349460
Cundiff, J. L., Vescio, T. K., Loken, E., & Lo, L. (2013). Do gender-science stereotypes predict science identification and science career aspirations among undergraduate science majors? Social Psychology of Education, 16, 541–554. doi:10.1007/s11218-013-9232-8
Dagley, M. A., Young, C. Y., Georgiopoulos, M., Daire, A. P., Parkinson, C., Prescod, D. J., & Belser, C. T. (2016). Recruiting undecided admits to pursue a STEM degree. Proceedings from the American Society for Engineering Education 123rd Annual Conference & Exposition. Retrieved from https://peer.asee.org/recruiting-undecided-admits-to-pursue-a-stem-degree
Dempster, A., Laird, N., & Rubin, D. (1977). Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society, Series B, 39, 1–38.
Dixon-Román, E. J., Everson, H. T., & McArdle, J. J. (2013). Race, poverty, and SAT scores: Modeling the influences of family income on black and white high school students’ SAT performance. Teachers College Record, 115, 1–33.
Doerschuk, P., Bahrim, C., Daniel, J., Kruger, J., Mann, J., & Martin, C. (2016). Closing the gaps and filling the STEM pipeline: A multidisciplinary approach. Journal of Science Education and Technology, 25, 682–695. doi:10.1007/s10956-016-9622-8
Ewing, M., Huff, K., Andrews, M., & King, K. (2005). Assessing the reliability of skills measured by the SAT (Report No. RN-24). New York, NY: CollegeBoard. Retrieved from https://files.eric.ed.gov/fulltext/ED562595.pdf
Faul, F., Erdfelder, E., Lang, A. G., & Buchner, A. (2007). G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behavior Research Methods, 39, 175–191.
Field, A. (2009). Discovering statistics using SPSS (3rd ed.). London, England: Sage Publications.
Folsom, B., Peterson, G. W., Reardon, R. C., & Mann, B. A. (2005). Impact of a career planning course on academic performance and graduation rate. Journal of College Student Retention: Research, Theory & Practice, 6, 461–473. doi:10.2190/4WJ2-CJL1-V9DP-HBMF
Foltz, L. G., Gannon, S., & Kirschmann, S. L. (2014). Factors that contribute to the persistence of minority students in STEM fields. Planning for Higher Education Journal, 42(4), 46–58.
Gall, M. D., Gall, J. P., & Borg, W. R. (2007). Educational research: An introduction (8th ed.). Boston, MA: Allyn & Bacon.
Gayles, J. G., & Ampaw, F. (2014). The impact of college experiences on degree completion in STEM fields at four-year institutions: Does gender matter? The Journal of Higher Education, 85, 439–468.
Gottfredson, L. (1981). Circumscription and compromise: A developmental theory of occupational aspirations. Journal of Counseling Psychology, 28, 545–579. doi:10.1037/0022-018.104.22.1685
Holland, J. L. (1973). Making vocational choices: A theory of vocational personalities and work environments (2nd ed.). Upper Saddle River, NJ: Prentice Hall.
Hosmer, D. W., Jr., Lemeshow, S., & Sturdivant, R. X. (2013). Applied logistic regression (3rd ed.). Hoboken, NJ: Wiley.
Koenig, K., Schen, M., Edwards, M., & Bao, L. (2012). Addressing STEM retention through a scientific thought and methods course. Journal of College Science Teaching, 41(4), 23–29.
Lawlor, S., Richman, S., & Richman, C. L. (1997). The validity of using the SAT as a criterion for black and white students’ admission to college. College Student Journal, 31, 507–515.
Le, H., Robbins, S. B., & Westrick, P. (2014). Predicting student enrollment and persistence in college STEM fields using an expanded P-E fit framework: A large-scale multilevel study. Journal of Applied Psychology, 99, 915–947. doi:10.1037/a0035998
Lent, R. W., Brown, S. D., & Hackett, G. (2002). Social cognitive career theory. In D. Brown (Ed.), Career choice and development (4th ed., pp. 255–311). San Francisco, CA: Jossey-Bass.
Lent, R. W., Miller, M. J., Smith, P. E., Watford, B. A., Lim, R. H., & Hui, K. (2016). Social cognitive predictors of academic persistence and performance in engineering: Applicability across gender and race/ethnicity. Journal of Vocational Behavior, 94, 79–88. doi:10.1016/j.jvb.2016.02.012
Little, R. J. A. (1988). A test of missing completely at random for multivariate data with missing values. Journal of the American Statistical Association, 83, 1198–1202. doi:10.2307/2290157
Little, R. J. A., & Rubin, D. B. (2002). Statistical analysis with missing data (2nd ed.). New York, NY: Wiley.
Litzler, E., Samuelson, C. C., & Lorah, J. A. (2014). Breaking it down: Engineering student STEM confidence at the intersection of race/ethnicity and gender. Research in Higher Education, 55, 810–832.
Mansfield, K. C., Welton, A. D., & Grogan, M. (2014). “Truth or consequences”: A feminist critical policy analysis of the STEM crisis. International Journal of Qualitative Studies in Education, 27, 1155–1182.
Mattern, K. D., & Patterson, B. F. (2013). The relationship between SAT scores and retention to the second year: Replication with the 2010 SAT validity sample (College Board Statistical Report No. 2013-1). New York, NY: The College Board. Retrieved from https://files.eric.ed.gov/fulltext/ED563087.pdf
National Career Development Association. (2015). Career development theory and its application. Broken Arrow, OK: Author.
National Center for Science and Engineering Statistics. (2017). Women, minorities, and persons with disabilities in science and engineering. Retrieved from https://www.nsf.gov/statistics/2017/nsf17310/
National Science Board. (2018). Science & engineering indicators 2018. Retrieved from https://www.nsf.gov/statistics/2018/nsb20181/
Nosek, B. A., & Smyth, F. L. (2011). Implicit social cognitions predict sex differences in math engagement and achievement. American Educational Research Journal, 48, 1125–1156. doi:10.3102/0002831211410683
Osborn, D. S., Howard, D. K., & Leierer, S. (2007). The effect of a career development course on the dysfunctional career thoughts of racially and ethnically diverse college freshmen. The Career Development Quarterly, 55, 365–377. doi:10.1002/j.2161-0045.2007.tb00091.x
Palmer, R. T., Maramba, D. C., & Dancy, T. E., II (2011). A qualitative investigation of factors promoting the retention and persistence of students of color in STEM. The Journal of Negro Education, 80, 491–504.
Peterson, G. W., Sampson, J. P., Jr., Lenz, J. G., & Reardon, R. C. (2002). A cognitive information processing approach to career problem solving and decision making. In D. Brown (Ed.), Career choice and development (4th ed., pp. 312–369). San Francisco, CA: Jossey-Bass.
Peduzzi, P., Concato, J., Kemper, E., Holford, T. R., & Feinstein, A. R. (1996). A simulation study of the number of events per variable in logistic regression analysis. Journal of Clinical Epidemiology, 49, 1373–1379. doi:10.1016/S0895-4356(96)00236-3
Prescod, D. J., Daire, A. P., Young, C. Y., Dagley, M. A., & Georgiopoulos, M. (in press). Exploring negative career thoughts between STEM declared and STEM interested students. Journal of Employment Counseling, 55(4), 166–176.
Reardon, R., & Fiore, E. (2014). College career courses and learner outputs and outcomes, 1976-2014 (Technical report No. 55). Tallahassee, FL: Center for the Study of Technology in Counseling & Career Development, Florida State University. Retrieved from http://career.fsu.edu/content/download/223105/1906289/TechRept_55_201406.pdf
Reardon, R. C., Melvin, B., McClain, M. C., Peterson, G. W., & Bowman, W. J. (2015). The career course as a factor in college graduation. Journal of College Student Retention: Research, Theory, & Practice, 17, 336–350. doi:10.1177/1521025115575913
Riegle-Crumb, C., & King, B. (2010). Questioning a white male advantage in STEM: Examining disparities
in college major by gender and race/ethnicity. Educational Researcher, 39, 656–664. doi:10.3102/0013189X10391657
Riegle-Crumb, C., King, B., Grodsky, E., & Muller, C. (2012). The more things change, the more they stay the same? Prior achievement fails to explain gender inequality in entry into STEM college majors over time. American Educational Research Journal, 49, 1048–1073. doi:10.3102/0002831211435229
Rohr, S. L. (2012). How well does the SAT and GPA predict the retention of science, technology, engineering, mathematics, and business students? Journal of College Student Retention: Research, Theory, & Practice, 14, 195–208. doi:10.2190/CS.14.2.c
Sampson, J. P., Jr., Peterson, G. W., Lenz, J. G., Reardon, R. C., & Saunders, D. E. (1996a). Career Thoughts Inventory: Professional manual. Odessa, FL: Psychological Assessment Resources.
Sampson, J. P., Jr., Peterson, G. W., Lenz, J. G., Reardon, R. C., & Saunders, D. E. (1996b). Improving your career thoughts: A workbook for the Career Thoughts Inventory. Odessa, FL: Psychological Assessment Resources.
Saunders, D. E., Peterson, G. W., Sampson, J. P., Jr., & Reardon, R. C. (2000). Relation of depression and dysfunctional career thinking to career indecision. Journal of Vocational Behavior, 56, 288–298. doi:10.1006/jvbe.1999.1715
Schneider, K. R., Bickel, A., & Morrison-Shetlar, A. (2015). Planning and implementing a comprehensive student-centered research program for first-year STEM undergraduates. Journal of College Science Teaching, 44(3), 37–43.
Sithole, A., Chiyaka, E. T., McCarthy, P., Mupinga, D. M., Bucklein, B. K., & Kibirige, J. (2017). Student attraction, persistence and retention in STEM programs: Successes and continuing challenges. Higher Education Studies, 7, 46–59. doi:10.5539/hes.v7n1p46
Tabachnick, B. G., & Fidell, L. S. (2013). Using multivariate statistics (6th ed.). Upper Saddle River, NJ: Pearson Education.
Toldson, I. A., & McGee, T. (2014). What the ACT and SAT mean for black students’ higher education prospects. The Journal of Negro Education, 83, 1–3.
Vittinghoff, E., & McCulloch, C. E. (2006). Relaxing the rule of ten events per variable in logistic and Cox regression. American Journal of Epidemiology, 165, 710–718.
Christopher T. Belser, NCC, is an assistant professor at the University of New Orleans. M. Ann Shillingford is an associate professor at the University of Central Florida. Andrew P. Daire is a dean at Virginia Commonwealth University. Diandra J. Prescod is an assistant professor at Pennsylvania State University. Melissa A. Dagley is an executive director at the University of Central Florida. Correspondence can be addressed to Christopher Belser, 2000 Lakeshore Drive, Bicentennial Education Center Room 174, New Orleans, LA 70148, email@example.com.
Career adaptability, resiliency and perceived obstacles to career development of adolescent mothers were examined using a proposed conceptual framework that combined resiliency and career adaptability. The goals of this study were to gauge the current state of the career development and resiliency of adolescent mothers, including areas of strength and weakness, and to better understand the interactions between the three components of career adaptability (i.e., planfulness, exploration, decision-making), resiliency and perceived obstacles. Adolescent mothers were similar to nonparenting peers on the planfulness and decision-making dimensions of career adaptability, yet lower on career exploration. While adolescent mothers’ traits of personal resiliency and emotional reactivity were comparable to those of their peers, their relational resiliency was lower. Based on the findings of the study, proposed strategies to further the three components of career adaptability and the resiliency of adolescent mothers are suggested.
Keywords: adolescent mothers, career development, career adaptability, resiliency, decision-making
In the United States, becoming a parent during adolescence has been described as a premature and nonnormative life event that can present lifelong challenges and growth opportunities in the career development of adolescent mothers (Gruber, 2012; Zachry, 2005). Taylor (2009) reported the most prevalent negative outcomes associated with adolescent parenthood as lowered high school graduation rates, limited educational opportunities after high school, and difficulty achieving stable work and financial independence. These are important career development considerations for this population given the national statistics on adolescent motherhood, previous research findings on the impact of parenting programs on the long-term career outcomes for adolescent mothers, and the viability of the proposed theoretical framework of the integration of career adaptability and resiliency (Barto, Lambert, & Brott, in press).
The national statistics on adolescent mothers indicate a disparity between racial groups with 8.3% of Latina, 6.5% of African American and 2.7% of Caucasian (non-Hispanic) adolescent females becoming mothers (Guttmacher Institute, 2010). Race and ethnicity may influence how an adolescent pregnancy is perceived by the adolescent mother and those around her, further contributing to the mother’s obstacles to and opportunities for career development (McAdoo, 2007; Santiago-Rivera, Arredondo, & Gallardo-Cooper, 2002). Support from families has been shown to be a positive factor in furthering the career development of adolescent mothers (Brosh, Weigel, & Evans, 2009). Although both African American and Latino families may be disappointed by adolescent pregnancies, these families tend to discourage pregnancy termination or adoption, instead offering assistance to adolescent mothers (McAdoo, 2007; Santiago-Rivera et al., 2002). Conversely, Caucasian adolescent mothers have the highest rates of formal adoptions outside the family; thus, family support for attempting to combine motherhood and career development may be lower for Caucasian adolescent mothers than for adolescent mothers in other racial or ethnic groups (Low, Moely, & Willis, 1989).
Adolescent mothers typically report more challenges with life planning when compared to nonparenting peers (Spear, 2004). Related issues can be viewed through the lens of obstacles to and opportunities for career development for adolescent mothers. These obstacles may include completing an education, finding employment and experiencing increased financial strain. Conversely, becoming a mother during adolescence may stimulate resiliency and growth opportunities in the working role (Zachry, 2005). These opportunities could foster the desire to provide financially for self and child, positive attitudes toward the future after becoming a mother (Brubaker & Wright, 2006), and a greater sense of maturity and purpose about the future (Rosengard, Pollock, Weitzen, Meers, & Phipps, 2006). Therefore, adolescent parenting can be simultaneously stressful and meaningful (Perrin & Dorman, 2003) while impacting all areas of life, particularly the working role.
Career development can be viewed as a holistic, dynamic and lifelong process, whereby individuals construct meaning and determine the most appropriate expression of their life roles (Savickas et al., 2009). Life roles are conceptualized as a constellation of interacting enactments that have relative importance to the individual within the context that these roles occur (Brown & Associates, 2002). For adolescent mothers, the addition of the parenting role can influence the dynamics between life roles and affect the perceived importance of the working role (Savickas, 1997).
In both school (Kaplan, Blinn-Pike, Wittstruck, Berger, & Leigh, 2002) and community settings (Gruber, 2012; Sarri & Phillips, 2004), programs and services are designed to meet the unique needs of adolescent mothers. Adolescent mothers have reported that parenting programs are moderately helpful in providing information relevant to their parenting role, such as medically related advice to improve the health of child and mother (Sarri & Phillips, 2004). However, these programs typically do not address finding employment and educational training opportunities (Kaplan et al., 2002, Sarri & Phillips, 2004).
Longitudinal studies investigating the career outcomes (i.e., being employed and self-supporting adults) for adolescent mothers participating in parenting programs have produced mixed results. Horwitz, Klerman, Kuo, and Jekel (1991) reported that 82% of the mothers who participated in an adolescent parenting program were financially self-supporting 20 years later. However, Taylor (2009) reported that when compared with nonparenting peers, adolescent parents had lower incomes and less prestigious occupations 20 years later. Neither Horwitz et al. (1991) nor Taylor (2009) indicated which program components helped or hindered participants’ career outcomes. Research is needed to derive evidenced-based intervention strategies and programs for improving career development outcomes of adolescent mothers (Brindis & Philliber, 2003). In the current study, career adaptability and resiliency were used to better understand career development of adolescent mothers as they adjust to their new role as a parent in relation to other life roles, especially the role of worker. Career adaptability includes the dimensions of planning, exploring and decision-making about one’s future (Savickas, 1997). Resiliency includes the attributes to develop personal and relational strengths in the process of overcoming adversity (Prince-Embury, 2006). In the current study, attention was given to the unique obstacles in the adolescent mother’s career development, as she constructs meaningful expression of her working role (Klaw, 2008; Savickas et al., 2009). The goals of this study were to gauge the current state of the career development and resiliency of adolescent mothers, including areas of strength and weakness; and to better understand the interactions between the components of career adaptability, resiliency and perceived obstacles.
Limited research has focused specifically on the career development and adaptability of adolescent mothers (e.g., Brosh et al., 2009). From a review of the literature, the current authors (in press) found the following impediments to career development of adolescent mothers: pressing immediate needs (e.g., housing, transportation, childcare), limited career development skills (e.g., decision-making skills) and lack of career-related knowledge (e.g., occupational information). Based on the existing literature, a career resiliency model has been suggested to promote career adaptability among high-risk individuals who are experiencing a dramatic life event, such as adolescent mothers (Rickwood, 2002; Rickwood, Roberts, Batten, Marshall, & Massie, 2004). The proposed conceptual framework for the career development of adolescent mothers combines resiliency and career adaptability and (a) addresses challenges (e.g., obstacles), (b) capitalizes on opportunities and strengths (e.g., increased sense of maturity/responsibility), and (c) develops positive intervention strategies and programs to better the long-term outcomes of adolescent mothers. Constructs that support this framework are career adaptability and resiliency, as previously combined by Rickwood (2002) and Rickwood et al. (2004).
Career adaptability is a central construct in adolescent career development (Hirschi, 2009) and is defined as the ability to adjust oneself to fit new and changed circumstances in one’s career by planning, exploring and making decisions about one’s future (Brown & Associates, 2002; Savickas, 1997). Planfulness is a learned skill that allows individuals to develop a future orientation to increase adaptability (Savickas, 1997). Exploration encompasses the understanding of relationships between individual differences and contextual factors that influence career development (Blustein, 1997). In the current conceptual framework, decision-making is expanded beyond the traditional models of career development to consider the multiple alternatives and objectives that are present in the career decision-making process (Phillips, 1997).
Career adaptability is currently used as a theoretical basis for both (a) the assessment of career-related skills and knowledge, and (b) the development and implementation of intervention strategies for adolescents (Creed, Fallon, & Hood, 2009; Hirschi, 2009). The concept of career adaptability is applicable to adolescent mothers, as it focuses on developing skills to address the individual and contextual factors associated with career development (Savickas et al., 2009). These career adaptability skills (i.e., planning, exploring, decision-making) are most relevant to the working role, but can be generalized and utilized easily in considering other life roles (e.g., parenting).
Resiliency has been defined as one’s ability to overcome adversity and be successful (Greene, Galambos, & Lee, 2004). This concept represents a paradigm shift from looking at risk factors associated with problematic situations to searching for more strengths-based personal attributes that help individuals overcome adverse or stressful situations (Richardson, 2002). Some researchers believe that resiliency is a combination of protective factors (i.e., personal characteristics and relationships) and areas of vulnerability (i.e., ability to self-regulate through adversity; Prince-Embury, 2006; Richardson, 2002; Zachry, 2005). In the current study, mastery (i.e., internalized personal characteristics of optimism, self-efficacy and adaptability) is referred to as personal resiliency. Relatedness (i.e., social and relational experience concerning trust, support, comfort and tolerance) is referred to as relational resiliency, and emotional reactivity (i.e., level of sensitivity, recovery and impairment to self-regulation in response to adverse events or circumstances) is referred to as emotional vulnerability (Prince-Embury, 2006; Richardson, 2002). These three resiliency constructs are helpful in understanding the attributes that are displayed by resilient individuals who are able to adapt to difficult or stressful situations (Prince-Embury, 2006; Richardson, 2002).
Researchers have measured the resiliency of adolescent mothers in various ways. For example, resiliency has been paired with the assessment of risks to better understand both the risks and protective factors that promote resiliency, thus moderating the negative effect of adolescent motherhood (Kennedy, 2005). Black and Ford-Gilboe (2004) used resiliency to validate and predict theoretical relationships among variables associated with creating a healthy family environment for adolescent mothers. Furstenberg, Brooks-Gunn, and Morgan (1987) found that a substantial portion of adolescent mothers demonstrated resiliency by overcoming the challenges of adolescent parenthood through maintaining regular employment and establishing financial stability without the need for public assistance (as cited in Kennedy, 2005). In summary, resiliency is thought to be one of the factors influencing the degree of success that adolescent mothers experience as adults (e.g., Schilling, 2008).
Career Adaptability and Resiliency
Linking career adaptability to resiliency may be more favorable to adolescent mothers than approaches that focus on risk factors, problems associated with adolescent motherhood, and career-related skill deficiencies (Perrin & Dorman, 2003). However, even resilient mothers can find the day-to-day demands of motherhood overwhelming. Without attention to the obstacles they may encounter, adolescent mothers may be unable to attend to career adaptability skill development (Klaw, 2008). Recognizing and addressing these pressing immediate needs helps adolescent mothers gain the ability to focus attention and effort on developing their personal career adaptability (Klaw, 2008).
Furthermore, adolescent mothers need to cultivate their own personal and relational attributes in order to foster and encourage resiliency (Zippay, 1995). Personal characteristics (i.e., optimism, self-efficacy, adaptability) can influence levels of resiliency (Prince-Embury, 2006). Socially supportive relationships based on trust, support, comfort and tolerance with family members and mentors have been effective in helping further the career adaptability of adolescent mothers by providing them with career-related information and aiding them in developing career-related skills (Klaw, Rhodes, & Fitzgerald, 2003; Prince-Embury, 2006). Both career adaptability skills and higher levels of personal and relational resiliency may be helpful in overcoming the obstacles experienced by adolescent mothers.
The Current Study
In the present study, the current state of career adaptability, resiliency and potential obstacles to career development among adolescent mothers from one state in the mid-Atlantic region of the United States was examined. Data were gathered using the career planning (CP) scale from the Career Development Inventory-School Form (CDI-S; Super, Thompson, Lindeman, Jordaan, & Myers, 1979), the self-exploration and environmental exploration scales from the Career Exploration Survey (CES; Stumpf, Colarelli, & Hartman, 1983), the Career Decision-Making Self-Efficacy Scale-Short Form (CDSE-SF; Betz, Klein, & Taylor, 1996), the Resiliency Scales for Children and Adolescents (RSCA; Prince-Embury, 2006), and the Obstacle Survey (Klaw, 2008). The participants also received a demographic questionnaire. The research questions that guided the study included the following: (1) What are the relationships between the dimensions of career adaptability (i.e., planfulness, exploration, decision-making) and resiliency? (2) What are the reported obstacles to the career development of adolescent mothers? (3) Can measures of resiliency predict career adaptability in adolescent mothers?
Participants in community- and school-based parenting programs were solicited for the study. The community-based parenting program is a support and self-help organization for assisting members in becoming more self-sufficient, but no specific career development component exists. The school-based parenting program addresses the unique academic, career and personal issues of parenting students, allowing attainment of a high school diploma in an alternative school setting. Study participants (N = 101) ranged in age from 15–18 years old (65%) and 19–21 years old (35%). Participants’ racial backgrounds included Hispanic, Latino or Spanish origin (74%); African American (22%); Caucasian (2%); Asian American (1%); and bi-racial (1%). Roughly half (52%) indicated that English was not the primary language used in their home. All participants had at least one child; some participants had multiple children (one mother had three children, 12 mothers had two children and 14 were currently pregnant with their second child).
Their current living situations included residing with parent or grandparent (57%), with their child(ren)’s father (20%), in foster care with their child(ren) (9%), with family of their child(ren)’s father (8%) or on their own with their child(ren) (6%). Their primary source of income support was from parents and family (38%), their child(ren)’s father or his family (32%), self (20%) or assistance programs (10%). While most participants (63%) reported not being currently employed, 53 participants indicated that they were actively looking for a job; 31 participants worked part-time and six worked full-time. Only seven participants had graduated high school and were not currently enrolled in school. The remaining participants included ninth graders (11%), tenth graders (16%), eleventh graders (26%), twelfth graders (31%) and college students (9%). Participants indicated that their educational plans included pursuing a college degree (65%), only graduating from high school (23%), unsure (7%) and at risk for not graduating from high school (4%).
Career Development Inventory-School Form. The CDI-S has been utilized to assess the career development and adaptability of adolescents (Super et al., 1979; Thompson & Lindeman, 1981). For this study, the CDI-S’s CP scale was used, with 12 items for career-planning engagement and eight items for career knowledge. Items are rated on a five-point Likert-type scale: career-planning engagement ranges from 1 (I have not yet given thought to this) to 5 (I have made definite plans and know what to do to carry them out); career knowledge ranges from 1 (hardly any knowledge) to 5 (a great deal of information). For female students in grades 9–12 for the CP scale, CDI-S reliability alphas range from .87–.90 (Betz, 1988; Thompson & Lindeman, 1981). The reliabilities for the current study were .89 for both CP subscales and .90 for the total scale. The content validity has been demonstrated on all scales and subgroups; the factor structure was validated as the scale items appropriately loaded on the subscales (Thompson & Lindeman, 1981). Both content and construct validity have been supported (Savickas & Hartung, 1996).
Career Exploration Survey. The CES (Stumpf et al., 1983) was developed to measure aspects of the career exploration process, including reactions and beliefs (Stumpf et al., 1983). The following two subscales were used in the current study to measure career exploration behaviors: the six-item subscale on environmental exploration (e.g., learning about specific jobs and careers) and the five-item subscale on self-exploration (e.g., reflecting on future career choice based on past experiences). Frequency of career exploration behaviors are self-rated on a five-point Likert scale. The reliabilities reported for the self-exploration and environmental exploration subscales are .87 and .88, respectively (Stumpf et al., 1983). Acceptable content and construct validity have been established (Creed et al., 2009; Stumpf et al., 1983).
Career Decision-Making Self-Efficacy Scale-Short Form. The CDSE-SF (Betz et al., 1996) measures one’s confidence in making career-related decisions. The 25-item instrument measures self-reported career decision-making behaviors on five subscales: self-appraisal, occupational information, goal selection, planning and problem solving. Reported reliabilities for the subscales range from .73–.83, and reliability for the total scale is .94 (Taylor & Betz, 1983). Content, concurrent and construct validity of the CDSE-SF have been established (Betz, Klein, & Taylor, 1996; Taylor & Betz, 1983).
Resiliency Scales for Children and Adolescents. The RSCA identifies resiliency attributes in children and adolescents (Prince-Embury, 2006) using three scales: Sense of Mastery (MAS), Sense of Relatedness (REL) and Emotional Reactivity (REA). The MAS, which assesses personal resiliency, includes 20 items in three subscales (optimism, self-efficacy and adaptability). The REL assesses relational resiliency and has 24 items in four subscales (sense of trust, support, comfort and tolerance). Emotional vulnerability is measured by the REA, which includes 20 items in three subscales (sensitivity, recovery and impairment). The sum of the subscale scores became the raw score for the respective scale (MAS, REL, REA), which converts to a T score. Higher T scores on the MAS and REL scales and lower scores on the REA indicate more resiliency resources.
The RSCA reliability alphas range from .79–.90 for 15- to 18-year-old females and are considered acceptable (Prince-Embury, 2006). Convergent and divergent validity have been correlated with those of conceptually similar instruments that measure resiliency (e.g., Reynolds Bully Victimization Scale); the criterion validity was established by comparing groups of clinical samples to matched groups of nonclinical samples of children and adolescents (Prince-Embury, 2006).
Obstacle Survey. The OS (Klaw, 2008) was designed to determine the specific obstacles that adolescent mothers encounter in daily life that could potentially impede their career adaptability, such as needing childcare and facing discrimination because of race. The survey consists of 26 items that could potentially impact participants’ career adaptability. The OS is a relatively new instrument designed for use with adolescent mothers; therefore, there is little information available about psychometric properties. However, the information provided by the OS was expected to be helpful in developing a better understanding of the perceived obstacles to the career adaptability of adolescent mothers.
Demographic Questions. The demographic items were 12 questions designed to gather the following information about the participants: age, racial/ethnic identity, language used in the home, number and age(s) of children, living situation, socioeconomic status, current school status, and employment status.
After obtaining approval from the Institutional Review Board, the first author developed relationships with the directors of one community-based and one school-based parenting program in order to recruit study participants. All adolescent mothers in both programs who met the study criteria received the opportunity to participate in the study. Given the unstructured nature of both programs, it is unclear what exact percentage of study-eligible adolescent mothers elected not to participate in the study, but informal observations from the first author suggest that almost all the study-eligible adolescent mothers completed the survey. Attendance was voluntary in the community-based program, so the number of adolescent mothers present varied from week to week, but the first author was present at a total of four meetings. For the school-based program, the first author made two scheduled visits to the school, during which she invited adolescent mothers who were present in classes specifically provided for them (e.g., life skills, support group) to participate in the study. Participants under age 18 received parental permission forms and older participants received informed consent forms. Participants completed all instruments via the computer using an online questionnaire created in Survey Monkey, with the exception of the RSCA (Prince-Embury, 2006), which they completed using a paper-and-pencil version as the publisher required. Survey completion was untimed. Participants who completed all aspects of the study received $10.00 in compensation to encourage completion. Three incomplete surveys were excluded from the statistical analysis.
Career adaptability, resiliency and perceived obstacles were measured using a number of established instruments in order to generate descriptive statistics to better understand the current state of adolescent mothers’ career development. Career adaptability and resiliency were correlated to look for relationships between the two and entered into a multiple regression to determine the predictive power. Career adaptability was defined as and measured by the participants’ process of planfulness, exploration and decision-making. In the area of career planfulness, participants’ scores were slightly higher than the average score for the norm sample of female adolescents (Thompson & Lindeman, 1981): CP (M = 3.34, SD = 0.78), career-planning engagement (M = 3.15, SD = 0.93) and career knowledge (M = 3.61, SD = 0.88). This finding suggests that adolescent mothers in this study were similar to their peers in terms of career planfulness. For career exploration (M = 2.73, SD = 0.99), participants reported a moderate amount of career exploration behaviors with slightly higher self-exploration (M = 3.16, SD = 1.12) involving reflection on one’s future career and past experiences, than environmental exploration (M = 2.34, SD = 1.08) that involves investigating career possibilities. The reliabilities for the current study were .89 for both CP subscales and .90 for the total scale. In terms of career decision-making, there was little variation between the total score (M = 3.26, SD = 0.95) and each of the subscale scores, which ranged from 3.12–3.37. The subscale reliabilities ranged from .87–.90, and reliability for the total scale was .90. Thus, participants were neither strong nor weak in terms of decision-making skills related to selecting a college major, determining one’s ideal job, deciding on values related to occupations and preparing for a job search.
Regarding resiliency, participant T scores for the three scales and scaled scores for the subscales were compared to those of the female adolescent norm group (Prince-Embury, 2006). T scores over 60 are considered high, 50–59 are above average, 46–49 are average, 41–45 are below average, and below 40 are low. The reported T scores for participants were average for both the MAS (M = 48.29, SD = 7.93) and the REA (M = 49.44, SD =10.58) and below average for the REL (M = 44.47, SD = 10.11). The manual reports that scaled scores for the subscales over 16 are considered high, 13–15 are above average, 8–12 are average, 5–7 are below average, and below 5 are low. The related subscale scores for the MAS were average (M = 9.45–9.75); subscales for the REL were average (M = 8.12–8.75); and subscales for the REA were average (M = 9.80–10.39). The subscale reliabilities ranged from .57–.87 and the scale reliabilities ranged from .84–.93.
The participants rated 25 perceived obstacles using the OS (Klaw, 2008). The obstacles were organized into seven categories plus other to capture themes that have been reflected in the literature (e.g., pressing immediate needs, work-related concerns, education-related concerns). Ratings of 2 (somewhat of a concern) and 3 (a large concern) were combined and categorized for descriptive and contextual purposes. The most frequent obstacles for adolescent mothers were related to pressing immediate needs (childcare [73%] and transportation [72%]), work-related concerns (need for more job training [72%] and not many jobs available in my area [72%]), and education-related concerns (need more preparation to continue my education [71%] and need money to continue my education [68%]). Another identified obstacle was health-related concerns for mother or child (68%). Of lesser concern for these adolescent mothers was discrimination (facing discrimination because I am a woman [26%] and facing discrimination because of where I live [20%]) and relationship concerns (parents wanting me to work full-time [27%] and my baby’s father doesn’t want me to work [19%]). Deviant behaviors do not appear to be obstacles for most adolescent mothers surveyed; these behaviors include education-related concerns such as suspended/expelled from school (14%) and community concerns such as fear of community violence (21%), being in jail or in trouble with the police (14%), and being part of a gang (5%).
Relationships Between Career Adaptability and Resiliency
The mean scores for the three dimensions of career adaptability were correlated with the three resiliency scales scores (see Table 1). Within the resiliency measures, personal resiliency (as measured by the MAS scale) and relational resiliency (as measured by the REL scale) demonstrated a moderately strong positive correlation (r = 0.65), while emotional vulnerability (as measured by the REA scale) was weakly and negatively related to the other two measures (r = -0.22; r = -0.26). The relationships among career adaptability measures suggest that, while each dimension of career adaptability is a separate aspect of career adaptability, they are related. The strongest correlation was between exploration and decision-making (r = 0.70). The interrelationships among career adaptability dimensions and the three resiliency attributes were found to moderately correlate with personal (r = 0.29; r = 0.39; r = 0.49) and relational resiliency (r = 0.27; r = 0.26; r = 0.35); emotional vulnerability was not related to any of the scales for career adaptability. Decision-making demonstrated the strongest positive relationship with personal and relational resiliency (r = 0.49; r = 0.35).
Intercorrelations between Resiliency, Dimensions of Career Adaptability, and Obstacles
1. Sense of Mastery (MAS)
2. Sense of Relatedness (REL)
3. Emotional Reactivity (REA)
4. Career Planfulnessb
5. Career Explorationc
6. Career Decision Makingd
Note. Reliability values for this study are shown diagonally (Cronbach alphas). N = 101
* p < 0.05
a RSCA (Prince-Embury, 2006)
b CDI-S (Super et al., 1979)
c CES (Stumpf, Colarelli, & Hartman, 1983)
d CDSE-SF ( Betz, Hammond, & Multon, 2005)
Predictive Power of Resiliency for Career Adaptability
Multiple regression was used to examine the predictive power in the three constructs of resiliency to the three dimensions of career adaptability (see Table 2). The three resiliency measures explained a statistically significant 25% of variance in career decision-making (F = 10.96), 15% of variance in career exploration (F = 5.84) and 9% of variance in career planfulness (F = 3.37). Personal resiliency (MAS) was the only resiliency scale that produced statistically significant results in two of the three career adaptability measures (see Table 2). The lack of statistical significance for relational resiliency is due to its high correlation with personal resiliency. Therefore, adolescent mothers who possess higher personal resiliency appear to possess higher levels of career adaptability.
Predicting Career Adaptability by Resiliency Scores
Career Decision Making
Note. N= 101. MAS = Sense of Mastery; REL = Sense of Relatedness; REA = Emotional Reactivity.*p< 0.05** p<0.001
The results of this study should inform researchers and practitioners who are interested in assessing and advancing the career adaptability and resiliency of adolescent mothers while concurrently being mindful of perceived obstacles. In terms of career adaptability skills, the adolescent mother participants endorsed similar skills to their peers in both career planfulness and career decision-making, but lower scores in career exploration. Overall, participants appear to be average in their career planfulness skills, including engagement in career planning and career knowledge. This finding suggests that adolescent mothers are just as competent with respect to career planfulness as nonparenting peers in the normative sample of the CP of the CDI-S (Thompson & Lindeman, 1981).
The career exploration scores indicate that environmental exploration (e.g., gathering information about careers of interest, jobs/careers in a local geographical region, jobs/careers with specific companies, career training opportunities; making contact with professionals in career areas of interest) is the most pressing of exploration needs. The results suggest that the participants show a need for increased career exploration skills, especially regarding environmental exploration. However, Porfeli and Skorikov (2010) stressed the importance of both aspects of career exploration. Thus, developing self-exploration skills (i.e., reflecting and connecting past experiences to future career choices and plans) would be beneficial for the participants. Consistent with the findings of Creed et al. (2009), targeted exploration initiatives are recommended to develop effective environmental and self-exploration skills to help adolescent mothers improve their overall career exploration skills.
For career decision-making, participants indicated feeling the most confident in assessing their own interests and abilities, conducting career-related research on the Internet, and planning and goal setting. They indicated feeling the least confident in navigating issues related to college, preparing a résumé, clarifying values, knowing about salary and wages for specific jobs and careers, and identifying potential employers. Several of the skills about which participants felt the least confident are reflected in the lower environmental exploration scores (e.g., knowledge of specific career information, such as salary and being able to identify potential employers). Interventions with adolescent mothers surrounding career decision-making skills should be targeted at areas of reported need (Fouad, Cotter, & Kantamneni, 2009).
In terms of resiliency, the participant profiles offer some consistent information about areas of strength and concern. Participants possess similar levels of personal resiliency and emotional vulnerability as same-age and same-gender peers within the normative sample of the RSCA (Prince-Embury, 2006). However, some differences are apparent between the study sample and the norm group on relational resiliency. The adolescent mothers indicated that they had more trouble communicating with others, less effective support systems, less favorable views of interpersonal relationships, and difficulty initiating and maintaining socially supportive and healthy relationships with family and friends, which is consistent with previous research findings (Gee & Rhodes, 2007; Klaw et al., 2003). It is unclear whether the inability to develop and maintain healthy interpersonal relationships is a result of contextual factors related to adolescent pregnancy/parenthood, inadequate social skills present before the pregnancy/parenthood or a combination of factors.
The multiple regression showed that all of the resiliency measures had statistically significant power in predicting the career adaptability dimensions. Personal resiliency, a relative strength for this sample of adolescent mothers, showed the most predictive power. The relational resiliency scores demonstrated less predictive power and were lower than those of the participants’ same-age peers. Participants have difficulty initiating and maintaining interpersonal relationships that are comforting, supportive, tolerant and trusting, which is consistent with previous findings (Gee & Rhodes, 2007). This finding raises questions about the relationship between below-average relational resiliency scores and average career adaptability scores. If the relatedness scores were higher, indicating that the adolescent mothers had strong interpersonal relationships, would the career adaptability scores also be higher? Looking at the relationship between career adaptability and resiliency in larger groups of adolescents, both parenting and nonparenting, might provide more information about correlation or predictive relationships between the two variables (e.g., supportive relationships may provide adolescent mothers with more career-related skills and knowledge).
Data collected from adolescent mothers on their reported obstacles are helpful in understanding the challenges of motherhood. Consistent with Klaw’s (2008) findings, the most frequently cited challenges were pressing immediate needs (e.g., transportation, childcare, caring for the baby, healthcare). The next most mentioned obstacles were career and education-related concerns (e.g., job training and difficulty in school), also similar to Klaw’s (2008) findings. Although the obstacles were not statistically related to career adaptability and resiliency, understanding the obstacles encountered by adolescent mothers may be helpful in designing and implementing strategies to further develop career adaptability and foster resiliency.
The results indicate that the dimensions of career adaptability (i.e., planfulness, exploration, decision-making) can be quantitatively measured and used for assessment purposes to inform future intervention strategies. Additionally, the nature of career adaptability is expanding to include such attributes of resiliency (Savickas, 1997; Savickas et al., 2009). Theorists are moving away from the linear definition of career adaptability as planfulness, exploration and decision-making skills in order to create a more holistic, contextual and developmental conceptualization of career adaptability (Savickas et al., 2009).
Proposed Intervention Strategies
The following are proposed strategies to further the three components of career adaptability (i.e., planfulness, exploration, decision-making) and resiliency among adolescent mothers. Interventions to increase career-planning skills include fostering a future orientation and optimism, reinforcing positive attitudes toward planning, and teaching and providing practice in planning and goal-setting skills (Muskin, 2004; Savickas, 2005). While Muskin (2004) advocated for more generalized interventions designed to teach adolescents long- and short-term goal setting, Savickas (2005) recommended specific interventions to develop career-planning skills, like the Real Game (Jarvis & Richardt, 2001).
Interventions to help foster exploration include activities designed to help adolescents learn more about themselves (e.g., clarifying values, reflecting on past exploration experiences, assessing personal interests and abilities) and the world of work (e.g., job shadowing, volunteering, reading about various careers) with exercises designed to encourage both types of exploration (Porfeli & Skorikov, 2010). Interventions to foster decision-making must consider how differing perspectives on decision-making (e.g., collectivist or individualistic) can impact the decision-making process (Cardoso & Moreira, 2009; Shea et al., 2009). Other interventions such as assertiveness and decisional training, time and self-management skills training, and discussion groups can be used to foster career decision-making skills (Muskin, 2004; Savickas, 2005). Interventions to foster resiliency focus on building self-efficacy in order for adolescents to feel that they are strong enough to handle current and future situations and typically include role modeling, encouragement, anxiety reduction and developing problem-solving skills (Savickas, 2005).
Suggestions for Future Research and Limitations
The information gathered in this study highlights the need for assessment to accurately measure and enhance the career adaptability and resiliency of adolescent mothers. Adolescent mothers face additional obstacles that necessitate intervention strategies carefully be constructed based on both theoretical and contextual considerations. The combination of resiliency and career adaptability may provide the positive, strengths-based assessment and intervention strategies framework necessary to assist adolescent mothers in overcoming obstacles and becoming self-supporting adults.
Based on observations during data collection, two recommendations were generated for researchers and practitioners working directly with adolescent mothers in further research, assessment or intervention endeavors. First, adolescent mothers indicated that they would have preferred an interview rather than a written survey. The desire for verbal communication over written communication may provide insight into the most effective means of implementing assessment and intervention strategies. Second, many of the participants expressed immediate interest in the results of the study, both personal and overall results. These inquiries suggest that adolescent mothers are interested in and committed to developing career adaptability skills. Capitalizing on this initial enthusiasm may be a key factor in structuring assessment and intervention strategies. Delays in providing results and subsequent interventions to participants may diminish their interest in further developing career adaptability skills. Prescod and Daire (2013) noted the critical need for adolescent mothers to be involved in career development counseling services in both a time-sensitive and culturally sensitive manner for optimal results.
Given the challenges of studying this population, one limitation of the current study is that the assessment data were gathered from a purposeful sample of three programs in a limited geographical region. Yet, this sample was incredibly diverse, adding much information to the literature. Another important limitation was observed during data collection—not all of the scheduled participants attended data collection sessions. As the results of the OS (Klaw, 2008) also demonstrated, the lack of childcare and reliable transportation was evident during the data collection. Many participants brought their children to the data collection sites or were unable to get transportation to the sites.
Furthermore, research has indicated that childcare may lead to socioeconomic advancements of adolescent mothers, as they have increased available time to focus on school and work (Mollborn & Blalock, 2012). Thus, exploring childcare resources and possibly providing childcare resources while adolescent mothers partake in career development programs may be essential in their ability to focus on such efforts. The childcare challenge likely far exceeds the typical time management struggles of today’s nonparenting adolescents who are in the process of exploring careers as described by Strom, Strom, Whitten, and Kraska (2014). In the current study, program staff at community and school program sites indicated that attendance was a challenge for adolescent mothers because of these obstacles (i.e., childcare and transportation), highlighting the need for researchers and practitioners to address obstacles that more than 70% of adolescent mothers face in order to work effectively with these clients.
Conflict of Interest and Funding Disclosure
The authors reported no conflict of
interest or funding contributions for
the development of this manuscript.
Barto, H. H., Lambert, S. F., & Brott, P. E. (in press). The career development of adolescent mothers: A review of 25 years of professional literature. Journal of Employment Counseling.
Betz, N. E. (1988). The assessment of career development and maturity. In W. B. Walsh & S. H. Osipow (Eds.), Career decision making (pp. 77–136). Hillsdale, NJ: Erlbaum.
Betz, N. E., Hammond, M. S., & Multon, K. D. (2005). Reliability and validity of five-level response continua for the career decision self-efficacy scale. Journal of Career Assessment, 13, 131–149. doi:10.1177/1069072704273123
Betz, N. E., & Klein, K. L. (1996). Relationships among measures of career self-efficacy, generalized self-efficacy, and global self-esteem. Journal of Career Assessment, 4, 285–298. doi:10.1177/106907279600400304
Betz, N. E., Klein, K. L., & Taylor, K. M. (1996). Evaluation of a short form of the career decision-making self-efficacy scale. Journal of Career Assessment, 4, 47–57. doi:10.1177/106907279600400103
Black, C., & Ford-Gilboe, M. (2004). Adolescent mothers: Resilience, family health work and health-promoting practices. Issues and Innovations in Nursing Practice, 11, 351–360. doi:10.1111/j.1365-2648.2004.03204.x
Blustein, D. L. (1997). A context-rich perspective of career exploration across the life roles. The Career Development Quarterly, 45, 260–274.
Brindis, C., & Philliber, S. (2003). Improving services for pregnant and parenting teens. Prevention Researcher, 10(3), 9–13.
Brosh, J., Weigel, D., & Evans, W. (2009). Assessing the supports needed to help pregnant and parenting teens reach their educational and career goals. Journal of Extension, 47. Retrieved from http://www.joe.org/joe/2009february/pdf/JOE_v47_1rb8.pdf
Brown, D., & Associates. (2002). Career choice and development (4th ed.). San Francisco, CA: Jossey-Bass.
Brubaker, S. J., & Wright, C. (2006). Identity transformation and family caregiving: Narratives of African American teen mothers. Journal of Marriage and Family, 68, 1214–1228. doi:10.1111/j.1741-3737.2006.00324.x
Cardoso, P., & Moreira, J. M. (2009). Self-efficacy beliefs and the relation between career planning and perception of barriers. International Journal for Educational and Vocational Guidance, 9, 177–188. doi:10.1007/s10775-009-9163-2
Creed, P. A., Fallon, T., & Hood, M. (2009). The relationship between career adaptability, person and situation variables, and career concerns in young adults. Journal of Vocational Behavior, 74, 219–229. doi:10.1016/j.jvb.2008.12.004
Fouad, N., Cotter, E. W., & Kantamneni, N. (2009). The effectiveness of a career decision-making course. Journal of Career Assessment, 17, 338–347. doi:10.1177/1069072708330678
Furstenberg, F. F., Jr., Brooks-Gunn, J., & Morgan, S. P. (1987). Adolescent mothers in later life. New York, NY: Cambridge University Press.
Gee, C. B., & Rhodes, J. E. (2007). A social support and social strain measure for minority adolescent mothers: A confirmatory factor analytic study. Child: Care, Health and Development, 34, 87–97. doi:10.1111/j.1365-2214.2007.00754.x
Greene, R. R., Galambos, C., & Lee, Y. (2004). Resilience theory: Theoretical and professional conceptualizations. Journal of Human Behavior in the Social Environment, 8(4), 75–91.
Gruber, K. J. (2012). A comparative assessment of early adult life status of graduates of the North Carolina adolescent parenting program. Journal of Child and Adolescent Psychiatric Nursing, 25, 75–83. doi:10.1111/j.1744-6171.2012.00324.x
Guttmacher Institute. (2010). U.S. teenage pregnancies, births and abortions: National and state trends and trends by race and ethnicity. Retrieved from http://www.guttmacher.org/pubs/USTPtrends.pdf
Hirschi, A. (2009). Career adaptability development in adolescence: Multiple predictors and effect on sense of power and life satisfaction. Journal of Vocational Behavior, 74, 145–155. doi:10.1016/j.jvb.2009.01.002
Horwitz, S. M., Klerman, L. V., Kuo, H. S., & Jekel, J. F. (1991). School-age mothers: Predictors of long-term educational and economic outcomes. Pediatrics, 87, 862–868.
Jarvis, P., & Richardt, J. (2001). The real game series: Bringing real life to career development. Retrieved from ERIC database. (ED446297)
Kaplan, M. B., Blinn-Pike, L., Wittstruck, G., Berger, T. J., & Leigh, S. (2002). Journaling for pregnant and parenting adolescents. Reclaiming Children and Youth, 11, 42–46.
Kennedy, A. C. (2005). Resilience among urban adolescent mothers living with violence. Violence Against Women, 11, 1490–1514. doi:10.1177/1077801205280274
Klaw, E. (2008). Understanding urban adolescent mothers’ visions of the future in terms of possible selves. Journal of Human Behavior in the Social Environment, 18, 441–462. doi:10.1080/10911350802486767
Klaw, E. L., Rhodes, J. E., & Fitzgerald, L. F. (2003). Natural mentors in the lives of African American adolescent mothers: Tracking relationships over time. Journal of Youth and Adolescence, 32, 223–232. doi:10.1023/A:1022551721565
Low, J. M., Moely, B. E., & Willis, A. S. (1989). The effects of perceived parental preferences and vocational goals on adoption decisions: Unmarried pregnant adolescents. Youth & Society, 20, 342–354. doi:10.1177/0044118X89020003007
McAdoo, H. P. (2007). Black families. Thousand Oaks, CA: Sage.
Mollborn, S., & Blalock, C. (2012). Consequences of teen parents’ child-care arrangements for mothers and children. Journal of Marriage and Family, 74, 846–865. doi:10.1111/j.1741-3737.2012.00988.x
Muskin, M.-B. (2004). The need for a comprehensive competency-based career guidance curriculum for teen mothers. Educational Considerations, 31(2), 41–45.
Perrin, K. M., & Dorman, K. A. (2003). Teen parents and academic success. The Journal of School Nursing, 19, 288–293. doi:10.1177/10598405030190050701
Phillips, S. D. (1997). Toward an expanded definition of adaptive decision-making. The Career Development Quarterly, 4, 275–287.
Porfeli, E. J., & Skorikov, V. B. (2010). Specific and diversive career exploration during late adolescence. Journal of Career Assessment, 18, 46–58. doi:10.1177/1069072709340528
Prescod, D. J., & Daire, A. P. (2013). Career intervention considerations for unwed young black mothers in the United States. Adultspan Journal, 12, 91–99.
Prince-Embury, S. (2006). Resiliency scales for children & adolescents: A profile of personal strengths (RSCA). San Antonio, TX: Pearson Education.
Richardson, G. E. (2002). The metatheory of resilience and resiliency. Journal of Clinical Psychology, 58, 307–321. doi:10.1002/jclp.10020
Rickwood, R. R. (2002). Enabling high-risk clients: Exploring a career resiliency model. Retrieved from http://contactpoint.ca/wp-content/uploads/2013/01/pdf-02-10.pdf
Rickwood, R. R., Roberts, J., Batten, S., Marshall, A., & Massie, K. (2004). Empowering high-risk clients to attain a better quality of life: A career resiliency framework. Journal of Employment Counseling, 41, 98–104. doi:10.1002/j.2161-1920.2004.tb00883.x
Rosengard, C., Pollock, L., Weitzen, S., Meers, A., & Phipps, M. G. (2006). Concepts of the advantages and disadvantages of teenage childbearing among pregnant adolescents: A qualitative analysis. Pediatrics, 118, 503–510. doi:10.1542/peds.2005-3058
Santiago-Rivera, A. L., Arredondo, P., & Gallardo-Cooper, M. (2002). Counseling Latinos and la familia: A practical guide. Thousand Oaks, CA: Sage.
Sarri, R., & Phillips, A. (2004). Health and social services for pregnant and parenting high risk teens. Children and Youth Service Review, 26, 537–560. doi:10.1016/j.childyouth.2004.02.010
Savickas, M. L. (1997). Career adaptability: An integrative construct for life-span, life-space theory. The Career Development Quarterly, 45, 247–259.
Savickas, M. L. (2005). The theory and practice of career construction. In S. D. Brown & R. W. Lent (Eds.), Career development and counseling: Putting theory and research to work (pp. 42–70). Hoboken, NJ: Wiley & Sons.
Savickas, M. L., & Hartung, P. J. (1996). The career development inventory in review: Psychometric and research findings. Journal of Career Assessment, 4, 171–188. doi:10.1177/106907279600400204
Savickas, M. L., Nota, L., Rossier, J., Dauwalder, J.-P., Duarte, M. E., Guichard, J., . . . van Vianen, A. E. M. (2009). Life designing: A paradigm for career construction in the 21st century. Journal of Vocational Behavior, 75, 239–250. doi:10.1016/j.jvb.2009.04.004
Schilling, T. A. (2008). An examination of resilience processes in context: The case of Tasha. TheUrban Review, 40, 296–316. doi:10.1007/s11256-007-0080-8
Shea, M., Ma, P.-W. W., Yeh, C. J., Lee, S. J., & Pituc, S. T. (2009). Exploratory studies on the effects of a career exploration group for urban Chinese immigrant youth. Journal of Career Assessment, 17, 457–477. doi:10.1177/1069072709334246
Spear, H. J. (2004). A follow-up case study on teenage pregnancy: “Havin’ a baby isn’t a nightmare, but it’s really hard.” Pediatric Nursing, 30, 120–125.
Strom, P. S., Strom, R. D., Whitten, L. S., & Kraska, M. F. (2014). Adolescent identity and career exploration. NASSP Bulletin, 98, 163–179. doi:10.1177/0192636514528749
Stumpf, S. A., Colarelli, S. M., & Hartman, K. (1983). Development of the career exploration survey (CES). Journal of Vocational Behavior, 22, 191–226. doi:10.1016/0001-8791(83)90028-3
Super, D. E., Thompson, A. S., Lindeman, R. H., Jordaan, J. P., & Myers, R. A. (1979). Career development inventory-school form. Palo Alto, CA: Consulting Psychologists Press.
Taylor, J. L. (2009). Midlife impacts of adolescent parenthood. Journal of Family Issues, 30, 484–510. doi:10.1177/0192513X08329601
Taylor, K. M., & Betz, N. E. (1983). Applications of self-efficacy theory to the understanding and treatment of career indecision. Journal of Vocational Behavior, 22, 63–81. doi:10.1016/0001-8791(83)90006-4
Thompson, A. S., & Lindeman, R. H. (1981). Career Development Inventory: Volume 1: User’s Manual. Retrieved from http://www.vocopher.org/ncda2008/data/resources/CDI%20User%20Manual.pdf
Zachry, E. M. (2005). Getting my education: Teen mothers’ experiences in school before and after motherhood. Teachers College Record, 107, 2566–2598. doi:10.1111/j.1467-9620.2005.00629.x
Zippay, A. (1995). Expanding employment skills and social networks among teen mothers: Case study of a mentor program. Child and Adolescent Social Work Journal, 12, 51–69. doi:10.1007/BF01876139
Heather Barto is an Assistant Professor at Messiah University. Simone Lambert, NCC, is an Assistant Professor at Argosy University. Pamelia Brott, NCC, is an Associate Professor at Virginia Polytechnic Institute and State University. Correspondence can be addressed to Heather Barto, Suite 3052, Messiah College, One College Avenue, Mechanicsburg, PA 17055, firstname.lastname@example.org.
Women’s experiences in academia are laden with a fundamental set of issues pertaining to gender inequalities. A model reflecting women’s career development and experiences around their academic pipeline (or career in academia) is presented. This model further conveys a new perspective on the experiences of women academicians before, during and after their faculty appointments and can help in career counseling. Specifically, this model provides career counselors with a framework to conceptualize the concerns of women clients who work in academic environments. Other implications for career counseling as well as limitations and future directions also are discussed.
Keywords: women, academia, career development, pipeline, career counseling
There is a documented trend of women prematurely leaving higher education and academia. In a groundbreaking contribution spearheaded by women academicians, the Massachusetts Institute of Technology (MIT) Special Edition Newsletter reported on the experiences of women faculty, stating that “the pipeline leaks at every stage of career” (MIT, 1999, p. 8). Pipeline refers to careers in academia, which often require many years of education and training prior to entry to the pipeline. More recent work has supported and deepened this assertion with empirical investigation (e.g., Goulden, Mason, & Frasch, 2011; Wang & Degol, 2013). Researchers have approached the question of why this is the case from a myriad of research perspectives, including sociological, psychological and cultural. The existing body of literature investigating women’s experiences as academicians addresses the issue of women’s struggle for equality in the institution, but does not comprehensively address how female faculty develop their career aspirations and expectations, how the essential component of career development influences their experiences within the pipeline, and how counselors and institutions might address women’s career outcomes.
In this article, the authors first discuss the process of scholarly questioning, which guided the authors’ choice to examine certain bodies of literature that seemed relevant to women in academia. Second, a brief literature review identifies different variables that influence how women choose careers as academicians, how they decide whether to stay in those careers and how institutions have been called to respond to women’s experiences. Next, the authors present a model combining issues relevant to women in academia from the perspectives of several bodies of scholarly literature (i.e., sociology, women’s studies, psychology). The authors also make predictions based on the model, and address limitations and implications for counselors.
The idea for this article originated from a limited review of literature that addressed women as a cultural minority in a career field. Upon reviewing articles that centered on women in academia, the present authors observed that the vocational, cultural, social and psychological variables investigated in these studies focused substantially on women’s present experiences in academia—a realm often referred to as the pipeline. The present authors wondered how women’s life experiences before and after their faculty appointments influence their pipeline experiences.
The idea for the proposed model grew out of the literature review process itself. Through examining the available research on the subject of women in academia, it became clear that there were a multitude of perspectives on how and why women’s experiences exist as they do in the academic world. However, it also was apparent that these perspectives were not linked systematically to the overall literature. The primary goal of creating this model was to better understand and organize constructs that explain how women’s experiences before their career in academia, as well as how women experience that career. By organizing and linking these ideas into a model, the authors offer professional counselors a working model to refer to when helping academicians with career issues.
The authors utilized a qualitative research methodology in which they combined largely quantitative data with a qualitative analysis called grounded theory. According to Tesch (1990), grounded theory involves the “identifying and categorizing of elements and explanation of their connections” (p. 63), wherein one sorts the data into categories, compares their content, “defines properties of the categories” and then “relates categories to each other” (p. 64). The present authors modified their grounded theory approach by using published literature comprised mostly of quantitative studies as their data. As stated in the rationale for this paper in the previous section, the authors wanted to understand how women’s experiences leading up to and resulting in a career in academia, as well as how women experience that academic career. As is typical in qualitative research, these general questions served as their guide, and led to a generative process by which they surveyed the relevant literature of career development and gender as well as women’s academic careers. More precisely, the authors conducted the initial explorations of the literature using the key search terms women, academia or academe, faculty or professors, career development, and pipeline in various combinations to yield the largest body of results. The review process consisted of eliminating all articles concerning the academic experiences of women outside the United States, as this paper focuses exclusively on women within U.S. institutions. Throughout this process, the authors met weekly for at least 4 months and, beyond that, met 1–2 times a month for a minimum of 1 year. Also, two graduate student researchers made the initial classifications and the faculty subject matter expert reviewed those classifications, checking for consistency and accuracy.
The authors began by engaging in the strategy of inquiry called grounded theory. When reading through the collected literature, they noticed patterns in which variables (and later, themes) tended to appear again and again. Thus, the first major critical themes emerged through an inductive process, reflecting the grounded theory methods first championed by Glaser (Kelle, 2005). Glaser’s work focused on identifying similar codes whose content is gathered and organized into larger groups or concepts, and these groups or concepts form themes or categories (Kelle, 2005). Utilizing this approach in their exploration, the authors separated the articles into three groups based on their relevance to women across the career life span: early career development (preacademic appointment, which included experiences up to graduate school when some graduate students start participating in faculty and faculty-like roles), the pipeline (graduate school through academic job/career) and postpipeline (e.g., transitioning to a different career, retirement). It seemed important that these ideas present throughout the literature become more connected, and thus the present authors decided to create a model to show how person and environment interact to mold women’s expectations and experiences regarding education and career in academia. From this point forward, they carefully recorded the theoretical constructs and variables investigated by each research article and entered them into a spreadsheet. Once this process was complete, they critically reviewed the list of variables and constructs and collapsed some categories within each section together in order to capture both the broadest and the most succinct picture of the variables within the literature. Through this process, the authors were able to isolate the variables that were addressed by multiple articles (generally four or more), and these variables became the basis for the model.
Finally, the authors found that the variables tended to cluster together logically in each section. Through dialogue, critical thinking and specific knowledge within the field of vocational psychology, the authors categorized the variables into groups based on their similarity to and difference from one another, and created themes for the groups of variables within each section. These labels served to organize the variables into manageable concepts and tie the model together. In addition, these themes separated the larger social, psychological and systemic processes in ways that reflect how these concepts function for women in the world.
This literature review of over 120 articles revealed that, to the authors’ knowledge, no existing model binds career development and outcomes to the concepts of women’s career development and the leaky pipeline. Given the magnitude of such a project, the authors felt that it was best to create the model based on the research and resources that already exist in each area of scholarly inquiry. The variables and themes that exist in their model reflect their interpretation of the literature as well as their conceptualization of how these constructs interact with one another.
Variables Underlying Women Academicians’ Career Processes
Previous researchers have identified many variables related to women academicians’ career processes before, during and after their decision to pursue an academic job. The current authors reviewed and organized these variables by superordinate labels into the following three categories: career development, pipeline influences and pipeline outcomes.
Part I: Career Development
Excellent reviews of the literature on women’s general career development have been published (e.g., Betz, 2005; Fitzgerald, Fassinger, & Betz, 1995; Phillips & Imhoff, 1997). The current authors described variables important to women’s career development while they avoided recreating what others have already explored. Continuing with their modified grounded theory approach detailed above, for organizational purposes, the authors created five categories of variables and gave each category a superordinate label. The categories are cognitive, coping, environmental, personality and relational.
Cognitive theme. These variables were considered to be cognitive in nature: career aspirations, career choice, career expectations, intellectual abilities and liberal gender role attitudes.
Career aspirations. Career aspirations, or one’s dreams for one’s career, are important in career development and choice (Astin, 1984; Farmer, 1985; Gottfredson, 1981). Women’s career aspirations are affected by verbal ability, support from teachers, race, age and social class (Farmer, 1985); a desire for work–family balance and an intrinsic valuing of occupations (Frome, Alfeld, Eccles, & Barber, 2006); and parental influence (Li & Kerpelman, 2007).
Career choice. Fitzgerald et al. (1995) addressed career choice by considering how it can be limited as a result of being female, pointing out how stereotyping of occupations and women’s compromised career aspirations work to limit women’s career choices.
Career expectations. Brooks and Betz (1990) demonstrated that college student expectations for success in pursuing a job path, obtaining a job and advancing in that work, as well as preferences for a given type of work, explained 12%–41% of the variance in choosing a job. Men tended to have higher levels of expectations for more traditionally male occupations, whereas women tended to exhibit higher levels for more traditionally female occupations.
Intellectual abilities. Women’s career development can be promoted with higher verbal and math abilities (Fassinger, 1990; O’Brien & Fassinger, 1993). Ceci, Williams, and Barnett (2009) found that women with high math abilities were more likely than men to also have high verbal abilities, resulting in a greater range of career choices.
Liberal gender role attitudes. Fassinger (1990) and O’Brien and Fassinger (1993) found that having more liberal attitudes toward one’s gender role regarding one’s roles in the family and in the workforce was related to and predictive of career choice. Flores and O’Brien (2002) found that liberal gender role attitudes were predictive of Mexican American adolescent women’s self-efficacy for nontraditional careers. Liberal gender role attitudes can increase women’s perceived career options, leading them to consider both traditional and nontraditional gender career choices.
Coping theme. The following variables involve coping: career decision-making coping, career maturity and adaptability, career self-efficacy, and self-esteem.
Career decision-making coping. Career decision-making coping can be defined as one’s perceived confidence (self-efficacy) and/or coping skills when making career decisions. O’Hare and Beutell (1987) examined gender differences in career decision-making coping with undergraduate college students. Men had significantly higher scores than women on career decision-making self-efficacy behavior, or “a constructive, positive sense of control over the decision” (O’Hare & Beutell, 1987, p. 177). However, women scored significantly higher on reactive behavior, wanting “to be superorganized and do all that is expected,” as well as support-seeking behavior (p. 177). Men tended to be more confident, likely because they are socialized to appear strong and confident to others. On the other hand, women tended to place importance on maintaining a relational style and reacting to situations as opposed to being proactive. Also, Betz, Hammond, and Multon (2005) found that career decision-making self-efficacy was negatively related to career indecision and positively related to career identity.
Career maturity and adaptability. Career maturity means making good career decisions during adolescence (Super, 1977). King (1989) showed that career maturity determinants can differ by gender: for girls, family cohesion, locus of control, age and cultural participation were most important; however, for boys, age, locus of control, family cohesion and parental aspirations mattered more. Career adaptability is a postadolescence extension of career maturity, and has been linked with career self-efficacy, career interests and problem-solving ability (Rottinghaus, Day, & Borgen, 2005).
Career self-efficacy. Believing in one’s ability to perform career behaviors has been found to predict the number of career options considered (Betz & Hackett, 1981; Hackett, 1985), and is related to (r = .59) and predictive of career interests (Rottinghaus, Larson, & Borgen, 2003). Lower career self-efficacy beliefs predict women’s more traditional career choices (Hackett & Betz, 1981), while higher career self-efficacy beliefs predict career achievement (Betz, 2005).
Self-esteem. Self-esteem affects career development and achievement, and “increases occupational prestige . . . and income” (Kammeyer-Mueller, Judge, & Piccolo, 2008, p. 204). Self-esteem aids in persistence when one is confronted with career barriers (Richie et al., 1997).
Environmental theme. This group impacts one’s environment and includes the following: availability of resources and opportunities, low status of traditionally female jobs, previous work experience, social class and socioeconomic status, and socialization influences.
Availability of resources and opportunities. Astin’s (1984) career choice model describes major concepts affecting women’s careers: work motivation, with the driving needs of survival, pleasure and contribution; gender role socialization; and structure of opportunity, which includes elements such as job availability, barriers to work opportunities and economic considerations. Astin suggested that differences in gender socialization produce different work expectations, ultimately limiting women’s career opportunities by what is seen as appropriate women’s work. However, some opportunities provide women with a greater range of work–family alternatives (e.g., reproductive technologies).
Low status of traditionally female jobs. So-called women’s work has been devalued in terms of status and equitable pay. In paid work, there is a well-documented gap between women’s and men’s wages (e.g., Bielby & Bielby, 1992; Corbett & Hill, 2012). A number of authors have formed postulations about the low status of traditionally female jobs and career processes (e.g., England, 2010; Fassinger, 1990; O’Brien & Fassinger, 1993). For example, in order for women to advance socioculturally (e.g., economically), they must consider work in male-dominated fields, such as academia; higher status jobs in U.S. culture are jobs traditionally held by men (England, 2010).
Previous work experience. Previous work experience during adolescence is predictive of career aspirations and career choice (Betz & Fitzgerald, 1987).
Social class and socioeconomic status.Social class can shape career aspirations (Farmer, 1985). For example, social class privilege for European American adolescent women served to increase their perceptions of having many career options, as well as narrow the range of options they considered (Lapour & Heppner, 2009).
Socialization influences. Exposure to environmental learning, or socialization, can shape an individual’s career processes. For instance, Gottfredson’s (1981) model of circumscription and compromise in career development describes how one’s environment and heredity impact his or her career. Leung, Ivey, and Suzuki (1994) found Asian American women more likely than European American women to consider nontraditional gender role careers in order to pursue higher prestige occupations—that is, prestige was most important to these women, as opposed to compromising based on gender role fit or perceived gender typing of certain jobs. For example, an Asian American woman might choose a career in engineering over a career in teaching, as the engineering career would have greater prestige but would be a less traditional career for women than teaching.
Personality theme. Personality variables include achievement motivation, career interests, instrumentality and other personality variables, and valuing graduate education.
Achievement motivation.Achievement motivation refers to the impetus toward seeking career attainments and accomplishments. Two major models of women’s career development address achievement. In explaining gender differences in achievement by focusing on women’s decision making, Eccles (1987) proposed that the decisions women make regarding the work–family balance may be based on the subjective valuing of tasks as per socialization and stereotypes. Eccles suggested that some women may choose to focus more on family than work because other work is less satisfying to them than nurturing a family. In a different, empirically supported model, Farmer (1985) considered achievement motivation in career development to be influenced by cultural, personality and environmental factors. Achievement motivation culminates in the creation of career aspirations, motivation to pursue mastery experiences, and commitment to a career (Farmer, 1985).
Career interests. Women are more likely to have higher career interest scores for artistic and social domains and lower scores for realistic and investigative domains, when compared with men (Betz, 2005; Fitzgerald et al., 1995). Additionally, Evans and Diekman (2009) investigated how the presence of gendered beliefs about careers predicted differences in career goals and career interests along traditional gender lines. Women and men who thought about careers in a gender-stereotypical manner were less likely to endorse career interests in gender-atypical fields (Evans & Diekman, 2009).
Instrumentality and other personality variables.Instrumentality, which is defined as the ability to make decisions with confidence, was examined by O’Brien and Fassinger (1993) in their test of the Fassinger (1990) career model. The authors concluded that “young women who possess liberal gender role attitudes, are instrumental and efficacious with regard to math and careers, and exhibit moderate degrees of attachment and independence from their mothers tend to value their career pursuits” (O’Brien & Fassinger, 1993, p. 466).
Valuing graduate education. Battle and Wigfield (2002) found that college women with a strong career orientation had more positive views of graduate education, evidencing that the perceived usefulness of attending graduate school, a sense of attainment, and intrinsic motivation to pursue graduate studies were major reasons behind women’s graduate school plans.
Relational theme. The following variables have a central relationship component: dual roles of marital and parental status, perceived encouragement, psychosocial needs, relationships with parents and presence of role models, and rewards and costs of career and parenthood.
Dual roles of marital and parental status. As Fassinger (1990) pointed out, past research has supported a negative relationship between being both a wife and mother and developing one’s career. However, having liberal gender role attitudes helps women engage more fully in their own career development as opposed to more traditional attitudes (Betz & Fitzgerald, 1987; Fassinger, 1990; Flores & O’Brien, 2002). Morrison, Rudd, and Nerad (2011) found that parenting young children was a barrier at all levels of the pipeline for women, and that married men advanced faster through the tenure process than married women.
Perceived encouragement. Parents, role models, teachers and supportive others may offer women perceived encouragement regarding their career options (e.g., Fassinger, 1990; Leslie, 1986), ultimately facilitating women’s choice and attainment of both traditional and nontraditional careers (e.g., Hackett, Esposito, & O’Halloran, 1989). Perceived encouragement is especially important for the educational expectations and work identity of African American and Mexican American college students (Fisher & Padmawidjaja, 1999).
Psychosocial needs. Although psychosocial needs may be individually defined, women share needs for survival, satisfaction and pleasure (see Eccles, 1987; Farmer, 1985). Work can provide important sources of satisfaction and pleasure as well as meet survival needs, and underutilization of abilities has been associated with lower levels of mental health (Betz, 2005).
Relationships with parents and presence of role models. For college women, the positive influence of female teachers and high performance self-esteem (i.e., agency, or a feeling of being able to be autonomous) was most predictive of career salience (i.e., the importance of one’s career relative to one’s other roles) and educational aspirations (i.e., aspirations to pursue different levels of education). Also, having the positive influences of fathers and male teachers, as well as high performance self-esteem, predicted women wanting to pursue less traditional careers (Hackett et al., 1989).
Rewards and costs of career and parenthood.Leslie (1986) found that the daughters of homemakers had more positive feelings toward employment when mothers were not satisfied with homemaking and the children helped more with housework. Daughters of employed mothers viewed employment more positively when they perceived their mothers as happy and busy with their work. Daughters of homemakers indicated most concern with the costs of work and the costs of having children in the future, whereas the daughters of employed mothers also were concerned with the rewards of work. Also, Campione (2008) found that depression stemmed from family issues (e.g., caring for a disabled family member) and work issues (e.g., working irregular hours at a job), and working shifts during odd hours was associated with marital stress and family difficulties.
Conclusion of Part I: Career Development. In Part I, the current authors reviewed evidence on variables pertinent to a woman developing her career as an academician, or having access to developing a job or career as an academician. The next section focuses on the pipeline.
Part II: Pipeline Influences
The present authors conceptualize the pipeline, or the route to an academic career and the academic career itself, as beginning in graduate school and extending through all stages of a career in academia. The career development literature focuses heavily on undergraduates, whose experiences the present authors consider to be separate from graduate student experiences, which are conceptually more proximal to and overlap with the concerns of academic careers. Thus, for the authors’ purposes, once a woman decides to pursue a graduate-level degree, her experiences are characterized as part of the pipeline. Again, the authors have grouped variables using superordinate labels. The themes include academic duties, academic environment, individually centered, resources and social variables.
Academic duties theme. In this section the authors describe variables associated with women’s status within the academic institution, including administrative-level representation, institutional housekeeping and service-oriented activities, teaching and research productivity, and tenure-track versus nontenure-track status.
Administrative-level representation. Quite simply, women are not represented at the administrative level of academic institutions as frequently as men (Kimball, Watson, Canning, & Brady, 2001). Women’s underrepresentation can be associated with the amount of effort they have invested in teaching, mentoring and service, along with an inability to decline projects, which may compromise women’s career trajectory toward higher levels of authority within the institution. Kimball et al. (2001) suggested that women may not understand how to effectively negotiate the male-dominated and hierarchical structure of academia in order to fulfill broader career advancement desires.
Institutional housekeeping and service-oriented activities.Bird, Litt, and Wang (2004) defined institutional housekeeping as “the invisible and supportive labor of women to improve women’s situation within the institution” (p. 195), based on Valian’s (1998) work. Valian (2005) described these activities as “low-visibility, low-power, low-reward, and labor-intensive” (p. 205). Women may often be called upon to participate on committees or in groups that bolster the department or institution with regard to advising and teaching, or even issues pertinent to women in the academy. Providing service work may detract from time performing research, which is often the most heavily weighted criterion for tenure decisions (Misra, Lundquist, Holmes, & Agiomavritis, 2011). On the other hand, service activities are recently gaining more recognition as an important component of tenure decisions (Sampson, Driscoll, Foulk, & Carroll, 2010).
Teaching and research productivity.Data gathered for the MIT (1999) report on women faculty members revealed “inequitable distributions” regarding “teaching assignments” (p. 8). Women, by cultural standard, bear the weight of the more relational processes involved in academia (e.g., teaching, advising, mentoring), so research and administration are areas still disproportionately male dominated. A more recent study of university deans focused on what was considered important in achieving tenure, and supported the salience of research productivity above other faculty contributions such as service and, to some extent, teaching (Balogun, Sloan, & Germain, 2007). Furthermore, “heavy teaching workloads may be detrimental to the chances of obtaining tenure” (Balogun, Sloan, & Germain, 2006, p. 532).
Tenure track versus nontenure track.Harper, Baldwin, Gansneder, and Chronister (2001) found stark differences between men and women faculty members in both the tenure-track and nontenure-track categories. Generally, they found that men spent the fewest number of hours teaching, with more time spent on administrative, research and other activities, while women in all categories spent a slightly larger percentage of their time teaching. Differences also were found between the tenure-track categories and the relative amounts of time spent teaching undergraduate courses, with nontenure-track faculty spending a majority of their time teaching undergraduate courses versus tenure-track faculty who are teaching graduate courses more (Harper et al., 2001). Generally speaking, women make up a much larger percentage of nontenure-track faculty (e.g., August & Waltman, 2004; Equal Rights Advocates [ERA], 2003). Often the issue of tenure is complicated for women due to role conflict related to childcare and its incompatibility with the demands of the tenure process (Comer & Stites-Doe, 2006; O’Laughlin & Bischoff, 2005; Stinchfield & Trepal, 2010). In addition, there are other complex processes that influence women’s ability to gain tenure, an overview of which is outside the scope of this article (see American Association of University Women [AAUW], 2004; Marchant, Bhattacharya, & Carnes, 2007; Park, 2007; Rudd, Morrison, Sadrozinski, Nerad, & Cerny, 2008).
Academic environment theme. This theme focuses on variables that pertain to the college or university environment, and the literature is reviewed regarding departmental climate, isolation and invisibility, and transparency of departmental decision making (including tenure).
Departmental climate. Various authors have described departmental climates within institutions as “hostile” (ERA, 2003, p. 3), “challenging and chilly” (August & Waltman, 2004, p. 179), and “toxic” (Hill, Leinbaugh, Bradley, & Hazler, 2005, p. 377). These authors also pointed out how the lack of a supportive departmental climate contributes to other issues women face as academicians, such as having less access to resources or feeling isolated.
Isolation and invisibility.Winkler (2000) asserted that women faculty themselves define the limits of their productivity (which tends to be the largest factor in salary increase and tenure decisions) based on “feelings of exclusion, disconnectedness, marginalization, intellectual and social isolation, and limited access to resources” (p. 740). She also argued that women more than men tend to have more rigid and higher standards for quality over quantity in research, and that women may be more perfectionistic in research activities, which leads to a lower overall rate of publication.
Transparency of departmental decision making (including tenure).August and Waltman (2004) investigated job satisfaction of faculty members and found that women at different levels of the tenure process were influenced by different job satisfaction criteria. All faculty women surveyed reported being impacted by the following: having a supportive relationship with the head or chair of the department, having a perceived ability to influence decisions made within their department and receiving an equitable salary as compared to others within the department. Tenured women rated the equitable salary and departmental influence variables as more significant. For nontenured women, level of influence was also significant.
Individually centered theme. These psychosociocultural variables pertain to women as individuals, and include academic self-concept, age, and race and ethnicity, as well as gender schemas and feminism, and personal power and self-promoting behavior.
Academic self-concept. Guidelines for mentorship posed by Williams-Nickelson (2009) include specific action components aimed at bolstering a woman graduate student’s academic self-concept, or an individual’s conception of herself as a student. Mentors should “facilitate independent thinking” and encourage mentees to “develop self-assurance,” “be mentored” and “be receptive to autonomy and divergence” (Williams-Nickelson, 2009, p. 289). Ülkü-Steiner, Kurtz-Costes, and Kinlaw (2000) found that women’s academic self-concept and mentor support (regardless of the mentor’s gender) in graduate programs best predicted women graduate students’ career commitment. In addition, women and men who were attending graduate school in a male-dominated department reported lower levels of academic self-concept than those in more gender-balanced programs (Ülkü-Steiner et al., 2000).
Age. For women entering the academy 20 or more years ago, being an older student (after having children or supporting a partner through his or her career) could be a barrier to entrance into graduate school; some women, however, reported positive effects of being leaders and mentors as older graduate students (Bronstein, 2001). In addition, women reported feeling marginalized, being overlooked, being seen as a mom, and being overtly discriminated against in academia (Bronstein, 2001). Junior and senior women faculty also may experience rifts with one another based on different feelings about discrimination and inclusion (MIT, 1999). Furthermore, Jacobs and Winslow (2004) compiled data on faculty ages, tenure track, tenure status and job satisfaction, and found that the high-end child-bearing years for women (late 30s through early 40s) are spent working toward tenure, which complicates the work–family balance.
Race and Ethnicity.There has been “no growth in the percentage of minority students receiving doctoral degrees since 1999” (Maton, Kohout, Wicherski, Leary, & Vinokurov, 2006, p. 126). Women of color are at a disadvantage before the pipeline even begins, a problem that persists through the academic career level, where they may experience marginalization, discrimination and microaggressions (Marbley, Wong, Santos-Hatchett, Pratt, & Jaddo, 2011). Thomas, Mack, Williams, and Perkins (1999) studied the effects of personal fulfillment (or an individual’s sense of meaning and/or satisfaction in life) on the research agendas of academicians who are women of color. Often, women of color who assume an outsider within stance (a professional orientation toward using one’s personal experiences and interests to fuel one’s research) may be disadvantaged for scholarly recognition and promotion, though researching topics of personal multicultural concern can increase one’s level of personal fulfillment (Thomas et al., 1999).
Gender schemas and feminism. Gender schemas exist that work against women in male-dominated professional environments (Valian, 2005). Lynch (2008) touched on clashing life roles for women in the early pipeline. One recurring theme for the participants was women graduate students’ feeling that they had traded off their feminist ideals and independence by getting married and/or having children, and by being financially dependent on their husbands during their time in graduate school. Krefting (2003) discussed ambivalent sexism, which essentially contrasts the concepts of having “perceived competence” (i.e., masculine) and being “likeable” (i.e., feminine; p. 269). The intersection of these two concepts for women in competitive academic environments can be a conundrum: How does a woman garner respect for her competence when likability is the trait with which students and colleagues are most concerned?
Personal power and self-promoting behavior. Kimball et al. (2001) posited that previous research has shown that women place more emphasis on “external attributions” than men (p. 136). That is, although men and women both believe that internal attributes such as intelligence and ambition contribute to one’s career success in academia, women place much greater weight on their social capital—for instance, the people they know and the prestige of their educating institution. These authors also discussed the fact that many women feel uncomfortable with the self-promoting behavior that may facilitate advancement in academia.
Resources theme. This theme includes variables related to resources within institutions that impact women’s career paths as academicians, including access to resources; financial issues; and salary, rewards, and recognition.
Access to resources. Krefting (2003) conceptualized women’s access to resources as an uphill climb. Whereas men are included in the network of those expected to succeed within academia, women are fighting for both inclusion and the resources to make them worthy of inclusion. Winkler (2000) also echoed Krefting’s (2003) notion that resources (and subsequently, productivity) flow from being included in “the networks in which ideas are generated and evaluated, in which human and material resources circulate, and in which advantages are exchanged” (2000, p. 740). MIT’s (1999) seminal report on women’s experiences as academics in its own School of Science uncovered “inequitable distributions . . . involving space, amount of 9-month salary paid from individual research grants, teaching assignments, awards and distinctions, inclusion on important committees and assignments within the department” (p. 7).
Financial issues. Students in psychology doctoral programs tend to graduate with student loan amounts that exceed $75,000 (Williams-Nickelson, 2009). Springer, Parker, & Leviten-Reid (2009) discussed a multitude of stressors for graduate student parents, including lack of financial support, a struggle to afford childcare and FMLA leave issues. Lynch (2008) reported that the most common complaint of women graduate student mothers is a lack of financial support from their academic departments.
Salary, rewards and recognition. August and Waltman’s (2004) survey uncovered that tenured women faculty’s career satisfaction was heavily influenced by their “salary comparable to similar peers” (p. 188). Harper et al. (2001) conducted a cross-discipline analysis of men’s and women’s experiences in academia and reported that “overall, men’s salaries appear to be more related to their disciplines and responsibilities while women’s salaries are more related to their tenure status and the degree they hold” (p. 248). In addition, Harper et al. (2001) noted that women tend to earn less because they are often employed in academic positions that pay less (e.g., nontenure track, assistant professor).
Social theme. This theme subsumes the influence of family, work and peer relationship variables, including peer and mentor relationships; presence of women in the field and the decision to pursue a doctorate; and work and family issues such as parenthood, marriage and the division of responsibility.
Peer and mentor relationships. Several articles review or note the positive impact of supportive peer relationships on female graduate student success (Lynch, 2008; Ülkü-Steiner et al., 2000; Williams-Nickelson, 2009). Also, mentoring and advising relationships provide essential resources to women graduate students, including elements such as emotional support and professional guidance (Williams-Nickelson, 2009). Hill et al. (2005) outlined the importance of supportive peers and social/teaching environments as a factor of satisfaction in their study of women faculty members in counselor education. Also, Pruitt, Johnson, Catlin, and Knox (2010) found that women counseling psychology associate professors who were seeking promotion to full professor indicated that having the support of a current mentor was helpful. Compared to men, women typically place a higher value on a supportive work environment and may often find these types of relationships through service-oriented work in the institution (Bird et al., 2004; Kimball et al., 2001).
Presence of women in the field and the decision to pursue a doctorate. Women are more likely to leak from the educational pipeline before doctoral completion, and they still earn less than men in the world of work (Ülkü-Steiner et al., 2000; Winkler, 2000). Ülkü-Steiner et al. (2000) found that the mere presence of women faculty in any academic department bolstered career commitment and academic self-concept for men and women doctoral students. Similarly, Winkler (2000) reported that women academicians benefit from relationships with female students and that female students tend to graduate more quickly when female faculty are present within the department. However, because women tend to be underrepresented as faculty members in general, there is an overall shortage of role models for women wishing to pursue doctoral education and become academicians themselves (August & Waltman, 2004; Harper et al., 2001).
Work and family issues: Parenthood, marriage and division of responsibility. Springer et al. (2009) and Lynch (2008) discussed the unique role conflicts that occur early in the pipeline for women graduate students who also are mothers. These women often find themselves caught between their desire to excel in graduate school and to be a mother, and also experience challenges with respect to finding peer support from their non-mother peers.
Wolfinger, Mason, and Goulden (2008) conceptualized family and marriage as having a direct effect on the leaky pipeline when women are trying to earn tenure. Generally speaking, when family issues and domestic responsibilities are at stake, women academics receive less support from their male partners than men academics do from their female partners (Bird et al., 2004). However, evidence for the effect that children and marriage have on scholarly productivity paints a different picture. Winkler (2000) reviewed the literature and found that though women on the whole publish less than men, single women are less productive in publication than married women. Krefting (2003) reported that “neither marriage nor parenthood seems to affect women’s productivity (or men’s, Valian, 1998)” (p. 264).
Conclusion of Part II: Pipeline Influences. This section discussed the themes and variables that are relevant to women’s experiences in the pipeline as graduate students and as academicians. The final section addresses key outcomes.
Part III: Pipeline Outcomes
The following section examines academic women’s career outcomes and satisfaction as well as institutional responses to women’s issues. The literature search for this section included the search terms women’s career satisfaction, women in academia, and university (or college) response.
Women’s career outcomes and satisfaction. As discussed previously, fewer women are granted tenure than their male counterparts. As one travels through the pipeline, chances of leaking out are greater for women at all stages of their career than for men (Mason & Goulden, 2004; Winkler, 2000; Wolfinger et al., 2008). In August and Waltman’s (2004) study, women’s career satisfaction was predicted by “departmental climate; the quality of student relationships and such related activities as mentoring and advising students . . . ; a supportive relationship with the unit chairperson; and the level of influence within the department or unit” (p. 187). In addition, for tenured women faculty, “comparable salary and the importance of departmental influence” rose to the forefront (p. 187). Harper et al. (2001) found that both tenured and tenure-track women were “least satisfied with their authority to make other job decisions . . . and the time they have available to advise students. . . . Non-tenure-track women as a group were the least satisfied with their authority to decide which courses they teach” (p. 251).
Institutional response. The call for institutional change to address the needs of women academicians is a direct result of research conducted on this topic in the past several decades. Although a full review of institutional initiatives on behalf of changing women’s experiences in academia is beyond the scope of this article, the current authors have highlighted some recommendations for change that exist in the literature.
Many authors have called for higher education institutions to implement initiatives to address the issues that women academics face (e.g., AAUW, 2004; ERA, 2003; MIT, 1999; Stinchfield & Trepal, 2010). Generally speaking, these initiatives include, but are not limited to the following: (a) changing hiring practices to seek out women and people of color for all faculty positions, especially tenure-track positions; (b) encouraging mentorship programs for faculty; (c) instituting policies in which the tenure clock may be stopped and restarted; (d) adjusting views on career commitment to accommodate academicians’ family and other responsibilities; (e) promoting women to higher-level administrative positions; (f) eliminating gender discrimination regarding salary and access to resources; (g) revising the tenure review process to include merits for service-oriented work; (h) making evaluation standards for tenure clear and transparent; (i) expanding understanding of the psychosociocultural variables that influence academicians differently; (j) conducting research on institutional policy and its effects on faculty members; (k) being active beyond hiring practices by encouraging women and people of color to pursue careers as academicians; and (l) being vigilant of and punitive toward gender discrimination taking place within the institution (Bird et al., 2004; Bronstein, 2001; ERA, 2003; Harper et al., 2001; Jacobs & Winslow, 2004; MIT, 1999; Thomas et al., 1999; Valian, 2005; Winkler, 2000).
Conclusion of Part III: Pipeline Outcomes. This section provided an overview of career outcomes and satisfaction among women academicians and how institutions have been called to respond to these issues. The following section reviews the authors’ model for women’s career processes in academia.
A Model for the Career Process of Women in Academia
Women’s career development is related to a variety of psychological, social and cultural influences. Researchers have studied many of these influences with girls and women, demonstrating the powerful effects shaping women’s career aspirations, choices and development. In the present authors’ model, career development influences, pipeline influences (factors affecting entry into academia), and pipeline outcomes (outcomes of a career in academia) are addressed. Here, the authors explain the structure of and rationales behind each section of the model (see Figure 1 and Table 1).
Overview of the Model
To promote parsimony of the literature and model coherence, the authors organized women’s career development influences into five major groups of variables: cognitive, coping, environmental, personality and relational. Each of these major themes is present within the top portion of Figure 1. These five domains of career development lead up to a decision to pursue a graduate degree, labeled “pursue terminal degree” in the model. The authors used the phrase “terminal degree” for the sake of simplicity, even though some employers and fields do not require a doctorate (e.g., school psychology).
While previous collegiate accomplishments certainly facilitate matriculation into a graduate program, the authors consider the pipeline as beginning in graduate school and continuing with women taking academic positions. The numerous variables affecting women’s experiences in academia are grouped into the following categories: academic duties, academic environments, individually centered, resources and social.
The pipeline is considered to be one piece, since the literature seemed to indicate this understanding and it resulted in the most parsimonious interpretation. However, future evidence may lead to consideration of the pipeline in two pieces, in which there is an early pipeline that focuses on graduate students and a midpipeline that pertains to women in academic positions. For example, some variables may not be relevant to graduate students (e.g., tenure-track versus nontenure-track), which lends support to the idea of breaking the pipeline into two groups. However, many variables have been found to be a consideration for both graduate students and academicians (e.g., age, work, family issues). Also, some variables that are currently considered part of one group may actually show evidence of salience with the other group (e.g., academic self-concept, financial issues). For now, since the themes seem interwoven with the experiences of both graduate students and academicians, the current authors have considered them together as one group.
Once a woman decides to pursue a graduate degree, a host of psychosociocultural factors begin to influence both her educational experiences and her experiences in academia. As the model shows, women may leak out of the pipeline at different points of their academic careers (i.e., early, mid- or late career), with early leaking meaning that one might never enter academe. The final section of the model indicates two major outcomes of women’s career development and the academic pipeline. First, women may report different levels of career satisfaction. Second, institutional responses to women’s issues within the academy may vary.
Figure 1. The Leaky Pipeline: Career Development of Women in Academia Before, During, and After Careers in Academia
Themes and Variables Comprising the Career Development and Leaky Pipeline Experiences of Women in Academia
Based on the literature review and the resulting model, the authors can make several predictions to describe the processes involved in women entering, traversing and exiting the pipeline.
Entry into the Pipeline. As women begin their careers as faculty members they bring their career development history with them, which in turn influences their education and career. The interaction of these factors creates a unique experience for women in faculty positions. Specifically, the career development variables are relevant to entry into the pipeline. First, the authors predict that the cognitive theme affects career trajectory in that women must have career aspirations, career choices and career expectations that are compatible with an academic career, as well as sufficient intellectual abilities and liberal gender role attitudes to endure and succeed in graduate school and beyond. Second, the coping theme also facilitates pipeline entrance, as women must have career decision-making coping, career maturity and adaptability, career self-efficacy, and self-esteem to transition effectively from graduate school into academic careers. Third, the authors predict that lower social class and socioeconomic status diminish the likelihood that a woman will enter an academic career (environmental theme), because lower social class and socioeconomic status tend to be associated with less access to opportunity structures such as those afforded by the educational attainment required for many academic careers. Fourth, the authors predict that having high achievement motivation, possessing career interests that complement an academic path, exhibiting high instrumentality and valuing graduate education facilitate an academic career (personality theme). Fifth, the authors hypothesize that the presence of perceived encouragement and supportive relationships with parents and role models facilitate these career paths (relational theme).
In addition, pipeline variables like feminism, personal power and self-promoting behavior have been evidenced as beneficial to women, and the present authors predict that these trends will likely remain consistent. For instance, academic self-concept can be a facilitative variable for women’s futures as academicians when that self-concept is consistent with an academic career and when women attend graduate programs that are more gender balanced than male dominated.
Traversing and Exiting the Pipeline. Once a woman enters graduate school, she is officially in the pipeline, and must maintain a level of teaching and research productivity commensurate with the expectations of the institution. Women academicians may leak out of the pipeline if they are denied tenure due to a lack of research productivity as a result of spending a disproportionate amount of time performing unrecognized service-oriented activities, particularly in research-intensive institutions (Misra et al., 2011). However, there is some evidence that institutions are recognizing service activities more frequently (Sampson et al., 2010). The current authors predict that experiencing a hostile departmental climate, feeling isolated and invisible, and encountering little or no transparency in departmental decision making facilitate conditions that increase the likelihood of a woman leaking from the pipeline before, during and after tenure decisions are made.
In addition, the authors predict that women leave their academic careers behind due to feeling stuck in positions with little hope for meaningful promotion, having restricted access to resources, dealing with financial issues or feeling dissatisfied with their salaries, rewards or level of recognition. Posttenure, the authors predict that a lack of administrative-level representation leads some women to leave academia because they are not able to realize administrative-level career goals, or because they may have less support (e.g., lack of available mentors) and more career challenges (e.g., greater isolation and invisibility) within institutions that lack women in these positions.
As the authors have shown through the model and its explanation, women academicians experience a unique set of personal and career challenges. Socialization and educational and career development processes stack the deck early, especially against women entering traditionally male-dominated fields. When one adds these processes to the existing structure of the academic system, it becomes clear that there are inherent systemic disadvantages for women in academic fields, which contribute to the leaks during each stage of the academic pipeline. The influences that women experience as children and young adults, and the discrepancies between women in different positions within academia, point to the necessity of a more holistic understanding of how women choose and navigate the complex path that leads them to and through academia.
It is the authors’ contention that each section of the model builds the groundwork for the next stage of the model in such a way that women in later stages of their careers have a multiplicity of additive strains that inhibit their career and personal satisfaction. To be sure, there are women who feel happy and fulfilled in their academic careers. At the same time, the present authors believe that this picture of satisfaction or dissatisfaction is supported by achievements and growth that occurs in different ways and for different reasons than it does for men. The authors hope to understand these influences and encourage responses at individual, societal and systemic levels. There exist numerous implications of this model, and here the authors highlight a few key points.
Barriers for women. Women receive opportunities in the work world in ways that constrain their choices from a young age (e.g., Gottfredson, 1981; Gottfredson & Lapan, 1997; Mello, 2008; Riegle-Crumb, Moore, & Ramos-Wada, 2011). Factors such as low self-efficacy, little perceived encouragement and few role models can create barriers for career choice. However, some women do pursue academic careers, succeeding in their efforts and finding the work enjoyable and satisfying. Identifying a combination of protective factors that help women to succeed in academia could help offset some of these barriers. Also, career and mental health counselors can help women to develop these strategies and traits for themselves.
Women seem to struggle throughout the lifespan with perfectionism that inhibits their ability to feel fulfilled by their endeavors as well as their ability to produce academic work at the same rates as their male peers. It may be that women decide to leave the pressure of the academic environment because they experience burnout, working tirelessly and too meticulously toward a goal that men may reach more easily since they may be less influenced by perfectionistic tendencies. It is the authors’ hope that graduate training programs, mentors, counselors and academic institutions will continue to work together to provide women with guidance, support and psychoeducation in order to cultivate new perspectives on achievement in academia.
Gender role socialization. How women glean messages from the dominant U.S. culture regarding what types of jobs are suitable for women and gendered expectations for behavior influence and constrain young women’s career interests, self-efficacy, view of parenthood and achievement motivation. Should a woman find herself with the resources necessary to enter graduate school with aspirations of an academic career, these socialization processes could potentially continue to restrain her because she may find herself with fewer female than male mentors and professors. If she has children, she also may find that the role strain between graduate student and mother is exhausting. If she is successful and becomes an academic, she may find herself balancing feelings of marginalization, isolation and frustration regarding her work and collegial relationships with the expectation that she be more “likeable” than “competent” (Krefting, 2003, p. 269). Often she may be called upon to perform activities in service of the institution that reinforce the gendered nature of “housework” for the institution (Valian, 2005, p. 205). Depending on the institution, performing service-oriented activities for the institution may help (Sampson et al., 2010) or hurt (Misra et al., 2011) her progress toward promotion and tenure. Hence, women may leak from the pipeline. For those women who do not leak, there are lingering discriminatory practices and beliefs that may flavor each day they spend pursuing their career goals and navigating the male-dominated terrain of the U.S. academic institution. The authors hope that this model will inspire others to consider the tangible reality of gender discrimination and combat its very specific effects on women academicians.
Role models and mentors. Women’s experiences with role models in early life affect how these women aspire to and place importance upon career success (Hackett et al., 1989). In addition, girls’ decisions about work and family are influenced in part by their perception of their mothers’ work behavior, both inside and outside the home; by their emerging gender role attitudes; and by sociocultural messages regarding the gendered nature of careers and opportunities that exist. The work–family issue does not dissipate as women age, but is consistently present throughout women’s lives in the pipeline. It seems logical to conclude that some women with doctoral degrees and families decide to leave the pipeline due to the strain that academic jobs place on them. Providing more modern and family-friendly practices within institutions, such as daycare services and paternity leave, might well encourage women to enter or remain in academia.
One limitation to the model presented here pertains to its broad overview of some of the variables relevant to women’s career development in academia and job satisfaction. The variables in this model are by no means the only contributing variables, and thus the authors welcome feedback, extensions and rearrangement of this model based on other scholarly bodies of knowledge and research findings.
Also, an important consideration for future researchers and scholars is the question of how best to represent the model itself, specifically regarding the academic pipeline. Two major issues that arose for the authors involved (a) the troublesome nature of conceptualizing women’s academic career paths as linear in the form of this pipeline, and (b) whether to conceptualize women graduate students and women academicians as representing different phases of pipeline processes. With more study, conceptualization of these variables and how they fit together may lead to shifts in the current model. Finally, the authors’ review has been limited in that a comprehensive survey of this voluminous literature was not possible given the realities of publication space limitations.
Implications for Counselors and Other Future Directions
The model has many potential applications for counselors. First, counselors can utilize the model to conceptualize women academicians’ career development issues, using Figure 1 and Table 1 as quick reference tools. Also, counselors can assist women with career decision making and coping with their academic careers, which may help alleviate leaks in the pipeline. For example, expanding this model may help to guide the development of career counseling interventions for girls and young women during their career development and college or graduate school years. In addition, women academicians can benefit from interventions designed to explicate their experiences in a male-dominated career field, help them find support and challenge institutions for policy changes. In addition, the model can guide further research and interventions. Expanding, reframing or finding supportive or contradictory evidence for the model and its variables can be informative for academicians who conduct research in vocational psychology, women’s issues or other areas, as this information can guide future research, theory, and clinical practice. Finally, career counselors can act as advocates working in partnership with academic institution administrators, who may benefit from this model by looking critically at their own practices and policies and working with departments and faculty members to address critical issues that influence women’s decisions to pursue, remain in or leave academic careers.
The authors have merged and organized several bodies of literature regarding women in academia before, during and after their faculty appointments. Women’s unique career development and socialization experiences are the foundation for understanding how women navigate careers in academia. Barriers do exist for women that constrain career development, yet resources such as counseling and mentoring can counteract these barriers. In addition to highlighting the obstacles within the leaky pipeline, the authors hope to encourage the adjustment and repair of the pipeline itself.
American Association of University Women Educational Foundation and American Association of University Women Legal Advocacy Fund. (2004). Tenure denied: Cases of sex discrimination in academia. Washington, DC: Author.
Astin, H. S. (1984). The meaning of work in women’s lives: A sociopsychological model of career choice and work behavior. The Counseling Psychologist, 12, 117–126. doi:10.1177/0011000084124002
August, L., & Waltman, J. (2004). Culture, climate, and contribution: Career satisfaction among female faculty. Research in Higher Education, 45, 177–192.
Balogun, J. A., Sloan, P. E., & Germain, M. (2006). Determinants of tenure in allied health and nursing education. Journal of Advanced Nursing, 56, 532–541. doi:10.1111/j.1365-2648.2006.04045.x
Balogun, J. A., Sloan, P. E., & Germain, M. (2007). Core values and evaluation processes associated with academic tenure. Perceptual & Motor Skills, 104, 1107–1115. doi:10.2466/PMS.104.3.1107-1115
Battle, A., & Wigfield, A. (2002). College women’s value orientations toward family, career, and graduate school. Journal of Vocational Behavior, 62, 56–75. doi:10.1016/S0001-8791(02)00037-4
Betz, N. E. (2005). Women’s career development. In S. D. Brown & R. W. Lent (Eds.), Career development and counseling: Putting theory and research to work (pp. 253–280). Hoboken, NJ: Wiley & Sons.
Betz, N. E., & Fitzgerald, L. F. (1987). The career psychology of women. Orlando, FL: Academic Press.
Betz, N. E., & Hackett, G. (1981). The relationship of career-related self-efficacy expectations to perceived career options in college women and men. Journal of Counseling Psychology, 28, 399–410.
Betz, N. E., Hammond, M. S., & Multon, K. D. (2005). Reliability and validity of five-level response continua for the career decision self-efficacy scale. Journal of Career Assessment, 13, 131–149. doi:10.1177/1069072704273123
Bielby, W. T., & Bielby, D. D. (1992). I will follow him: Family ties, gender-role beliefs, and reluctance to relocate for a better job. American Journal of Sociology, 97, 1241–1267.
Bird, S., Litt, J., & Wang, Y. (2004). Creating status of women reports: Institutional housekeeping as “women’s work.” NWSA Journal, 16, 194–206.
Bronstein, P. (2001). Older women in academia: Contemporary history and issues. Journal of Women’s History, 12, 184–201. doi:10.1353/jowh.2001.0004
Brooks, L., & Betz, N. E. (1990). Utility of expectancy theory in predicting occupational choices in college students. Journal of Counseling Psychology, 37, 57–64.
Campione, W. (2008). Employed women’s well-being: The global and daily impact of work. Journal of Family and Economic Issues, 29, 346–361. doi:10.1007/s10834-008-9107-x
Ceci, S. J., Williams, W. M., & Barnett, S. M. (2009). Women’s underrepresentation in science: Sociocultural and biological considerations. Psychological Bulletin, 135, 218–261. doi:10.1037/a0014412
Comer, D. R., & Stites-Doe, S. (2006). Antecedents and consequences of faculty women’s academic-parental role balancing. Journal of Family and Economic Issues, 27, 495–512. doi:10.1007/s10834-006-9021-z
Corbett, C., & Hill, C. (2012). Graduating to a pay gap: The earnings of women and men one year after college graduation. Retrieved from http://www.aauw.org/files/2013/02/graduating-to-a-pay-gap-the-earnings-of-women-and-men-one-year-after-college-graduation.pdf
Eccles, J. S. (1987). Gender roles and women’s achievement-related decisions. Psychology of Women Quarterly, 11, 135–172. doi:10.1111/j.1471-6402.1987.tb00781.x
England, P. (2010). The gender revolution: Uneven and stalled. Gender & Society, 24, 149–166. doi:10.1177/0891243210361475
Equal Rights Advocates. (2003). Creating gender equity in academia: Equal rights advocates’ higher education legal advocacy project roundtable report. Retrieved from http://universitywomen.stanford.edu/reports/Creating_gender_equity_academia.pdf
Evans, C. D., & Diekman, A. B. (2009). On motivated role selection: Gender beliefs, distant goals, and career interest. Psychology of Women Quarterly, 33, 235–249. doi:10.1111/j.1471-6402.2009.01493.x
Farmer, H. S. (1985). Model of career and achievement motivation for women and men. Journal of Counseling Psychology, 32, 363–390. doi:10.1037/0022-022.214.171.1243
Fassinger, R. E. (1990). Causal models of career choice in two samples of college women. Journal of Vocational Behavior, 36, 225–248. doi:10.1016/0001-8791(90)90029-2
Fisher, T. A., & Padmawidjaja, I. (1999). Parental influences on career development perceived by African American and Mexican American college students. Journal of Multicultural Counseling and Development, 27, 136–152. doi:10.1002/j.2161-1912.1999.tb00220.x
Fitzgerald, L. F., Fassinger, R. E., & Betz, N. E. (1995). Theoretical advances in the study of women’s career development. In W. B. Walsh & S. H. Osipow (Eds.), Handbook of vocational psychology: Theory, research, and practice (2nd ed., pp. 67–109). Hillsdale, NJ: Erlbaum.
Flores, L. Y., & O’Brien, K. M. (2002). The career development of Mexican American adolescent women: A test of social cognitive career theory. Journal of Counseling Psychology, 49, 14–27. doi:10.1037/0022-0126.96.36.199
Frome, P. M., Alfeld, C. J., Eccles, J. S., & Barber, B. L. (2006). Why don’t they want a male dominated job? An investigation of young women who changed their occupational aspirations. Educational Research and Evaluation: An International Journal on Theoryand Practice, 12, 359–372. doi:10.1080/13803610600765786
Gottfredson, L. S. (1981). Circumscription and compromise: A developmental theory of occupational aspirations. Journal of Counseling Psychology, 28, 545–579. doi:10.1037/0022-0188.8.131.525
Gottfredson, L. S., & Lapan, R. T. (1997). Assessing gender-based circumscription of occupational aspirations. Journal of Career Assessment, 5, 419–441. doi:10.1177/106907279700500404
Goulden, M., Mason, M. A., & Frasch, K. (2011). Keeping women in the science pipeline. The ANNALS of the American Academy of Political and Social Science, 638, 141–162. doi:10.1177/0002716211416925
Hackett, G. (1985). Role of mathematics self-efficacy in the choice of math-related majors of college women and men: A path analysis. Journal of Counseling Psychology, 32, 47–56. doi:10.1037/0022-0184.108.40.206
Hackett, G., & Betz, N. E. (1981). A self-efficacy approach to the career development of women. Journal of Vocational Behavior, 18, 326–339. doi:10.1016/0001-8791(81)90019-1
Hackett, G., Esposito, D., & O’Halloran, M. S. (1989). The relationship of role model influences to the career salience and educational and career plans of college women. Journal of Vocational Behavior, 35, 164–180. doi:10.1016/0001-8791(89)90038-9
Harper, E. P., Baldwin, R. G., Gansneder, B. G., & Chronister, J. L. (2001). Full-time women faculty off the tenure track: Profile and practice. The Review of Higher Education, 24, 237–257. doi:10.1353/rhe.2001.0003
Hill, N. R., Leinbaugh, T., Bradley, C., & Hazler, R. (2005). Female counselor educators: Encouraging and discouraging factors in academia. Journal of Counseling & Development, 83, 374–380. doi:10.1002/j.1556-6678.2005.tb00358.x
Jacobs, J. A., & Winslow, S. E. (2004). The academic life course, time pressures and gender inequality. Community, Work & Family, 7, 143–161. doi:10.1080/1366880042000245443
Kammeyer-Mueller, J. D., Judge, T. A., & Piccolo, R. F. (2008). Self-esteem and extrinsic career success: Test of a dynamic model. Applied Psychology, 57, 204–224. doi:10.1111/j.1464-0597.2007.00300.x
Kelle, U. (2005). “Emergence” vs. “forcing” of empirical data? A crucial problem of “grounded theory” reconsidered. Forum: Qualitative Social Research, 6(2), 27.
Kimball, C. N., Watson, T., Canning, S. S., & Brady, J. L. (2001). Missing voices: Professional challenges for academic women. Journal of Psychology and Christianity, 20, 132–144.
King, S. (1989). Sex differences in a causal model of career maturity. Journal of Counseling & Development, 68, 208–215. doi:10.1002/j.1556-6676.1989.tb01359.x
Krefting, L. A. (2003). Intertwined discourses of merit and gender: Evidence from academic employment in the USA. Gender, Work, & Organization, 10, 260–278. doi:10.1111/1468-0432.t01-1-00014
Lapour, A., & Heppner, M. (2009). Social class privilege and adolescent women’s perceived career options. Journal of Counseling Psychology, 56, 477–494. doi:10.1037/a0017268.
Leslie, L. A. (1986). The impact of adolescent females’ assessments of parenthood and employment on plans for the future. Journal of Youth and Adolescence, 15, 29–49.doi:10.1007/BF02140782
Leung, S. A., Ivey, D., & Suzuki, L. (1994). Factors affecting the career aspirations of Asian Americans. Journal of Counseling & Development, 72, 404–410. doi:10.1002/j.1556-6676.1994.tb00958.x
Li, C., & Kerpelman, J. (2007). Parental influences on young women’s certainty about their career aspirations. Sex Roles, 56, 105–115. doi:10.1007/s11199-006-9151-7
Lynch, K. D. (2008). Gender roles and the American academe: A case study of graduate student mothers. Gender and Education, 20, 585–605. doi:10.1080/09540250802213099
Marbley, A. F., Wong, A., Santos-Hatchett, S. L., Pratt, C., & Jaddo, L. (2011). Women faculty of color: Voices, gender, and the expression of our multiple identities within academia. Advancing Women in Leadership, 31, 166–174.
Marchant, A., Bhattacharya, A., & Carnes, M. (2007). Can the language of tenure criteria influence women’s academic advancement? Journal of Women’s Health, 16, 998–1003. doi:10.1089/jwh.2007.0348
Mason, M. A., & Goulden, M. (2004). Marriage and baby blues: Redefining gender equity in the academy. The ANNALS of the American Academy of Political and Social Science, 596, 86–103. doi:10.1177/0002716204268744
Massachusetts Institute of Technology. (1999, March 4). A study on the status of women faculty in science at MIT. The MIT Faculty Newsletter, 11(4). Retrieved from http://web.mit.edu/fnl/women/women.pdf
Maton, K. I., Kohout, J. L., Wicherski, M., Leary, G. E., & Vinokurov, A. (2006). Minority students of color and the psychology graduate pipeline: Disquieting and encouraging trends. American Psychologist, 61, 117–131. doi:10.1037/0003-066X.61.2.117
Mello, Z. R. (2008). Gender variation in developmental trajectories of educational and occupational expectations and attainment from adolescence to adulthood. Developmental Psychology, 44, 1069–1080. doi:10.1037/0012-16220.127.116.119
Misra, J., Lundquist, J. H., Holmes, E., & Agiomavritis, S. (2011, January–February). The ivory ceiling of service work. Academe, 97(1). Retrieved from http://aaup.org/article/ivory-ceiling-service-work#.UxDboF5hfuo
Morrison, E., Rudd, E., & Nerad, M. (2011). Onto, up, off the academic faculty ladder: The gendered effects of family on career transitions for a cohort of social science Ph.D.s. The Review of Higher Education, 34, 525–553.
O’Brien, K. M., & Fassinger, R. E. (1993). A causal model of the career orientation and career choice of adolescent women. Journal of Counseling Psychology, 40, 456–469.
O’Hare, M. M., & Beutell, N. J. (1987). Sex differences in coping with career decision making. Journal of Vocational Behavior, 31, 174–181. doi:10.1016/0001-8791(87)90055-8
O’Laughlin, E. M., & Bischoff, L. G. (2005). Balancing parenthood and academia: Work/family stress as influenced by gender and tenure status. Journal of Family Issues, 26, 79–106. doi:10.1177/0192513X04265942
Park, C. (2007). Gender in academic career tracks: The case of Korean biochemists. Sociological Forum, 22, 452–473. doi:10.1111/j.1573-7861.2007.00031.x
Phillips, S. D., & Imhoff, A. R. (1997). Women and career development: A decade of research. Annual Review of Psychology, 48, 31–59. doi:10.1146/annurev.psych.48.1.31
Pruitt, N. T., Johnson, A. J., Catlin, L., & Knox, S. (2010). Influences on women counseling psychology associate professors’ decisions regarding pursuit of full professorship. The Counseling Psychologist, 38, 1139–1173. doi:10.1177/0011000010377666
Richie, B. S., Fassinger, R. E., Linn, S. G., Johnson, J., Prosser, J., & Robinson, S. (1997). Persistence, connection, and passion: A qualitative study of the career development of highly achieving African American–Black and White women. Journal of Counseling Psychology, 44, 133–148. doi:10.1037/0022-018.104.22.168
Riegle-Crumb, C., Moore, C., & Ramos-Wada, A. (2011). Who wants to have a career in science or math? Exploring adolescents’ future aspirations by gender and race/ethnicity. Science Education, 95, 458–476. doi:10.1002/sce.20431
Rottinghaus, P. J., Day, S. X., & Borgen, F. H. (2005). The Career Futures Inventory: A measure of career-related adaptability and optimism. Journal of Career Assessment, 13, 3–24. doi:10.1177/1069072704270271
Rottinghaus, P. J., Larson, L. M., & Borgen, F. H. (2003). The relation of self-efficacy and interests: A meta-analysis of 60 samples. Journal of Vocational Behavior, 62, 221–236. doi:10.1016/S0001-8791(02)00039-8
Rudd, E., Morrison, E., Sadrozinski, R., Nerad, M., & Cerny, J. (2008). Equality and illusion: Gender and tenure in art history careers. Journal of Marriage and Family, 70, 228–238. doi:10.1111/j.1741-3737.2007.00474.x
Sampson, J. P., Jr., Driscoll, M. P., Foulk, D. F., & Carroll, P. S. (2010). Successful faculty performance in teaching, research and original creative work, and service. Tallahassee, FL: Florida State University, Dean of the Faculties Office. Retrieved from http://fda.fsu.edu/content/download/25448/163336/Successful-Faculty-Performance.pdf
Springer, K. W., Parker, B. K., & Leviten-Reid, C. (2009). Making space for graduate school parents: Practice and politics. Journal of Family Issues, 30, 435–457. doi:10.1177/0192513X08329293
Stinchfield, T. A., & Trepal, H. (2010). Academic motherhood for counselor educators: Navigating through the academic pipeline. International Journal for the Advancement of Counselling, 32, 91–100. doi:10.1007/s10447-009-9086-0
Super, D. E. (1977). Vocational maturity in mid-career. Vocational Guidance Quarterly, 25, 294–302. doi:10.1002/j.2164-585X.1977.tb01242.x
Tesch, R. (1990). Qualitative research: Analysis types and software tools. New York, NY: Routledge.
Thomas, K. M., Mack, D. A., Williams, K. L., & Perkins, L. A. (1999). Career development strategies and experiences of “outsiders within” in academe. Journal of Career Development, 26, 51–67. doi:10.1023/A:1022965823336
Ülkü-Steiner, B., Kurtz-Costes, B., & Kinlaw, C. R. (2000). Doctoral student experiences in gender-balanced and male-dominated graduate programs. Journal of Educational Psychology, 92, 296–307. doi:10.1037/0022-0622.214.171.1246
Valian, V. (1998). Why so slow? The advancement of women. Cambridge, MA: MIT Press.
Valian, V. (2005). Beyond gender schemas: Improving the advancement of women in academia. Hypatia: A Journal of Feminist Philosophy, 20, 198–213. doi:10.1111/j.1527-2001.2005.tb00495.x
Wang, M.-T., & Degol, J. (2013). Motivational pathways to STEM career choices: Using expectancy-value perspective to understand individual and gender differences in STEM fields. Developmental Review, 33, 304–340.
Williams-Nickelson, C. (2009). Mentoring women graduate students: A model for professional psychology. Professional Psychology: Research and Practice, 40, 284–291. doi:10.1037/a0012450
Winkler, J. A. (2000). Faculty reappointment, tenure, and promotion: Barriers for women. The Professional Geographer, 52, 737–750. doi:10.1111/0033-0124.00262
Wolfinger, N. H., Mason, M. A., & Goulden, M. (2008). Problems in the pipeline: Gender, marriage, and fertility in the ivory tower. The Journal of Higher Education, 79, 388–405. doi:10.1353/jhe.0.0015
Courtney E. Gasser, NCC, is an assistant professor at the University of Baltimore. Katharine S. Shaffer is a doctoral candidate at the University at Albany, State University of New York. Correspondence can be addressed to Courtney E. Gasser, Division of Applied Behavioral Sciences, University of Baltimore, 1420 North Charles Street, Baltimore, MD 21201, email@example.com.
The authors wish to acknowledge Dr. Deborah Kohl, Division of Applied Behavioral Sciences, University of Baltimore, for her feedback on this manuscript; Sean D. Lough, Morgan State University, for preparing and revising their model graphic; and Angela Brant, Krissa M. Jackson, Alexandra Mattern-Roggelin and Christina Pimble, University of Baltimore, for their research assistance.
Seth Hayden, Kathy Ledwith, Shengli Dong, Mary Buzzetta
Student veterans often encounter unique challenges related to career development. The significant number of student veterans entering postsecondary environments requires career-development professionals addressing the needs of this population to decide upon appropriate career intervention topics. This study utilized a career-needs assessment survey to determine the appropriate needs of student veterans in a university setting. Student veterans indicated a desire to focus on the following topics within career intervention: transitioning military experience to civilian work, developing skills in résumé-building and networking, and negotiating job offers. Results of the needs survey can be used in the development of a career-related assessment.
Keywords: student veterans, career development, needs assessment, military, career-related assessment
In 2013, there were 21.4 million male and female veterans aged 18 and older in the civilian noninstitutional population (U.S. Bureau of Labor Statistics, 2014a). The post-9/11 GI Bill, authorized by Congress in 2008, has contributed to a large number of veterans seeking postsecondary degrees (Sander, 2012). Since 2008, more than 817,000 military veterans have used the bill to attend U.S. colleges (Sander, 2013). Student veterans face many challenges on college campuses, including transition issues, relational challenges, feelings of isolation, and lingering effects of combat-related injuries (Green & Hayden, 2013).
One of the most significant concerns is that veterans typically experience unemployment at a higher rate than their civilian counterparts (U.S. Bureau of Labor Statistics, 2014b). In 2013, the unemployment rate for Gulf War II-era veterans was 10.1 %; Gulf War I-era veterans 5.5%; and World War II, Korean War, and Vietnam War veterans 5.5% (U.S. Bureau of Labor Statistics, 2014b). Younger veterans in particular struggled with unemployment. As of 2013, about 2.8 million of the nation’s veterans had served during the Gulf War II era (September 2001–present; U.S. Bureau of Labor Statistics, 2014a). The unemployment rate for the Gulf War II-era veterans (10.1%) is significantly higher than their civilian counterparts (6.8%; U.S. Bureau of Labor Statistics, 2014b). As young military personnel continue to return to college campuses, it is important to address the career-readiness needs of this population utilizing evidence-based practices.
Cognitive Information Processing
The Cognitive Information Processing (CIP) approach to career decision making (Sampson, Reardon, Peterson, & Lenz, 2004) has been suggested as a way to aid veterans as they transition into the civilian workforce (Bullock, Braud, Andrews, & Phillips, 2009; Buzzetta & Rowe, 2012; Clemens & Milsom, 2008; Hayden, Green, & Dorsett, in press; Phillips, Braud, Andrews, & Bullock, 2007; Stein-McCormick, Osborn, Hayden, & Van Hoose, 2013). The CIP approach is designed to assist individuals in making both current and future career choices (Sampson et al., 2004; Buzzetta & Rowe, 2012). This theoretical approach states that career problem solving and decision making are skills that can be learned and practiced (Sampson et al., 2004). Once clients have improved their problem-solving and decision-making skills, then they can apply these same skills to choices they make in the future. According to the CIP approach, the key aspects of career problem solving and decision making are self-knowledge, occupational knowledge, decision-making skills, and metacognitions (Sampson et al., 2004). Engels and Harris (2002) suggest that military individuals would benefit from understanding their self-knowledge, occupational information and decision-making skills.
Pyramid of Information Processing
The CIP approach consists of two key components: the pyramid of information processing, or the knowing, and the CASVE cycle, or the doing. The interactive elements are analogous to a recipe used in cooking. The pyramid is like the ingredients for the dish, while the CASVE cycle reflects the necessary steps to make the dish. Both are critical for effective career decision making and problem solving (Sampson et al., 2004). The pyramid of information processing includes three domains involved in career decision making: knowledge, decision-making skills, and executive processing (Sampson et al., 2004). Sampson et al. (2004) theorized that all components of the pyramid are affected by dysfunctional thinking and negative self-talk. The knowledge domain consists of two main areas: self-knowledge and occupational knowledge. Self-knowledge is the cornerstone of a client’s career-planning process, and is comprised of an individual’s knowledge of his or her values, interests, skills, and employment preferences (Reardon, Lenz, Peterson, & Sampson, 2012; Sampson et al., 2004). Occupational knowledge is the second cornerstone of a client’s career-planning process; it encompasses knowledge of options, including educational, leisure, and occupational alternatives, as well as how occupations can be organized.
The decision-making skills domain consists of a systematic process to help clients improve their problem-solving and decision-making skills, and includes the CASVE cycle, which is a multi-phase decision-making process, intended to increase client awareness and improve a client’s decision-making skills. The executive processing domain includes metacognitions, which include an individual’s thoughts about the decision-making process. There are three cognitive strategies included in the executive processing domain: self-talk, self-awareness, and monitoring and controlling an individual’s progress in the problem-solving process. Metacognitions can include dysfunctional career thinking, which can present problems in career decision making, influence other domains in the pyramid, and impact individuals’ perceptions of their capabilities to perform well (Sampson et al., 2004).
The CASVE cycle is used as a means of approaching a career problem or decision, and consists of five sequential stages (communication, analysis, synthesis, valuing, and execution), with repeated circuits when the problem still exists or new problems arise (Sampson et al., 2004). An individual enters the CASVE cycle after receiving either internal or external cues that he or she must make a career decision. In the communication stage, individuals are required to examine these prompts, and identify a gap that exists between where they are currently and where they would like to be. In the analysis phase, individuals clarify their existing self-knowledge by determining their occupational preferences, abilities, interests and values. The process of clarifying existing knowledge and gaining new information about potential options also is included. In the synthesis phase, individuals narrow down and further develop the options they are considering.
In the valuing phase, individuals assess the costs and benefits of each remaining alternative. This task involves prioritizing the alternatives, as well as selecting a tentative primary and secondary choice. In the execution phase, individuals create and commit to a plan of action for accomplishing their first choice. Upon completion of the execution phase, individuals return to the communication phase to determine whether the gap has been filled. The CASVE cycle is recursive in nature. Therefore, if the gap has not been removed and problems still exist, an individual will progress through the CASVE cycle again (Sampson et al., 2004).
Several studies have found that negative thoughts are related to career decision-making difficulties (Kleiman et al., 2004; Sampson, Peterson, Lenz, Reardon, & Saunders, 1996; Sampson et al., 2004). Kleiman et al. (2004) examined the relationship between dysfunctional thoughts and an individual’s degree of career decidedness in a sample of 192 college students enrolled in an undergraduate career-planning course. The researchers found that dysfunctional thinking during the decision-making process can negatively influence rational decisions. Assessing for dysfunctional career thoughts and working with individuals to reduce negative career thinking can have a positive impact on the knowledge and decision-making skills domains of the pyramid of information processing. More importantly, utilizing a theoretical approach can provide a structure in which to address the needs of student veterans.
Needs Assessment Survey
In order to address the needs of student veterans, counselors must first assess what these needs are. Student veterans offer a unique subset of our veteran population in that they operate within an educational environment while possessing diverse life experiences, and are therefore often unique in relation to their peers (Cook & Kim, 2009). Given the aforementioned employment difficulties for younger veterans (U.S. Bureau of Labor Statistics, 2014b), a need for career-focused interventions designed to assist this population is apparent.
While various supportive services for veterans are available, determining an appropriate allocation of resources and time to address the needs of this population can enhance the quality of services. To match intervention with need, the authors created a needs survey designed to inform the development of a theoretically based career intervention, the purpose of which is assisting student veterans in developing skills in career decision making and problem solving.
The sample for this needs assessment was collected from a sample of student veterans attending a large southeastern university (n = 92). Currently, this university has approximately 317 student veterans enrolled and receiving educational benefits through either the Montgomery GI Bill or post-9/11 GI Bill. This means of identifying veterans is imperfect, as there may be student veterans attending the university who do not utilize educational benefits. However, this is a common method of identifying veterans within university settings (University of Arizona, 2007). The participants were asked to complete the needs survey by both the university veterans association and the veterans benefit officer. Both social media and e-mail were used to elicit participation.
All 317 identified members of the population receiving education benefits were provided the opportunity to respond to the survey, via both an e-mail request with the electronic survey attached and a post on the student veteran organization’s social media Web page. A total of 92 (29%) completed surveys were collected. Of the 92 respondents, a majority identified as graduate students (47; 51%). The remaining respondents indicated their classifications as undergraduate students with the classifications of junior (25; 23%), senior (18; 20%), and sophomore (2; 2%). No students classified as freshmen responded to the survey.
The research team constructed the Veterans Needs Survey after examining the common career-development needs of both veterans and nonveterans encountered in the university’s career center. The instrument was created via a Qualtrics survey management system and attached to an electronic communication addressed to the potential respondents, as well as embedded in a social media thread of the university’s student veteran organization. The measure inquired about whether respondents had heard of the university career center; whether they had previously visited the university career center; what they would like to learn more about related to the career-development process; what modalities of treatment they were most interested in attending (e.g., group counseling, workshop series); how likely they were to attend the option indicated; education status; major/field of study; additional comments related to their career development; and an opportunity to participate in an intervention (an e-mail address was requested). The authors did not collect significant demographic information, instead focusing on variables like utilization of services (e.g., contact with the career center) and students’ academic classification, as these factors appear directly connected with career-development concerns.
The survey examined utilization and perceptions of career-development needs. The majority of respondents (80; 87%) indicated that they had heard of the career center, but a smaller number indicated actually visiting the career center (66; 73%). The question pertaining to perceived career-development needs provided a multiple-option response set in which one could indicate several options. The most frequently indicated response was transferring skills gained in the military to the workplace (49; 55.06%). The second most frequently indicated response was preparing a résumé/CV (46; 51.69%), followed by negotiating a job offer (45; 50.56%). Table 1 provides a detailed description of additional responses regarding the career-development process.
A significant majority (54; 61%) indicated that they would be most interested in attending a group format, and fewer respondents selected the workshop series as their first choice (24; 27%). Respondents indicating the other category specified that they would attend career fairs, take advantage of individual counseling, and utilize online workshops. Following up on the previous question, one item inquired how likely a respondent would be to attend the option indicated. The most frequently indicated response was somewhat likely (42; 47%) followed by very likely (34; 38%) with unlikely (14; 16%) being the least frequently indicated response. The majors/fields of study with a significant number of responses were law (9), business-related (undergraduate and graduate; 9), social work (7), and criminology (8).
Participants provided diverse general comments related to their career development. One student veteran stated, “I have an associates [sic] degree in Laboratory Technology from the military and would also like assistance building a résumé trying to find employment now.” Another shared, “As a distance learner, it is possible to feel out of reach when it comes to on-campus resources. But, I know we can overcome that. I may be a combat disabled veteran. But, I won’t let disabilities stop my self-actualization quest.”
The information obtained from the needs survey can be utilized to inform an intervention designed to assist student veterans in their career development, which will provide a grounded approach in addressing these issues. The following section offers a proposal for meeting student veteran needs with a career-development intervention.
Perceived Career-Development Needs
A Proposed Theoretically Based Career Intervention
Based upon the CIP theoretical framework (Sampson et al., 2004) and the feedback received from the needs assessment, psychoeducational groups will be conducted in order to achieve the following goals: expanding student veteran self-knowledge and career options through the CIP approach, exploring transferable skills gained through military experiences, gaining knowledge of resources that can assist student veterans in the job search and application processes, and identifying and decreasing negative metacognitions and dysfunctional career thoughts.
The psychoeducational group will meet once a week for 4 weeks. The group is open to all student veteran members attending the university through a campus-wide recruitment effort. Considering the tight connections between each CIP component, the group will be conducted in a closed-group format. The group facilitators will be graduate students pursuing doctoral degrees in counseling psychology or school psychology, and/or master’s students studying career counseling.
The group activities will center on the student veterans’ needs obtained through the needs assessment survey and the CIP components that have been proposed to serve the needs of veterans (Bullock et al., 2009; Clemens & Milsom, 2008). The structure of the psychoeducational group is based on the CIP model and five stages of the CASVE cycle diagram: communication, analysis, synthesis, valuing, and execution.
During the first session (communication), the group leader(s) will help to identify gaps between where group members are currently and where they aspire to be. Group members’ baseline information will be obtained by completing the Career Thoughts Inventory (CTI; Sampson, Peterson, Lenz, Reardon, & Saunders, 1996/1998) and My Vocational Situation (MVS; Holland, Daiger, & Power, 1991). The group leader(s) will explain the CIP Pyramid, CASVE Cycle Diagram, Self-Directed Search (SDS; Holland 1985) and assessment procedures. Group members will have an opportunity to interact with each other and complete one section of the Guide to Good Decision Making (Sampson, Peterson, Lenz, & Reardon, 1992). As a part of the homework assignment listed on the Individual Learning Plan (ILP), a document designed to identify career-related goals and associated action steps, group members will complete the SDS, and bring a copy of their current résumé to the next session.
During the second session (analysis/synthesis), the group leader(s) will help the student veterans examine and identify their interests, values, and skills (including transferable skills). The group leader(s) will assist group members in interpreting their SDS results, and examine any potential dysfunctional career thoughts that may be impacting group members’ career choices and decision-making abilities. To expand their career options, group members will be exposed to career-related resources such as the Occupational Outlook Handbook (U.S. Bureau of Labor Statistics, 2014c) and the Military Crosswalk Search via O*Net Online (National Center for O*NET Development, n.d.). In addition to gaining self-knowledge and occupational information in the analysis process, group members will have opportunities to practice synthesis skills. Group members will improve their résumé-writing skills through practice and feedback from peers and the group leader(s). Exploring and highlighting transferable skills is another important component. As part of their assignment listed on the ILP, group members will enhance their career networking skills by accessing supportive professionals via an alumni network and the Student Veterans Association, among other resources. Group members will also conduct an informational interview to gain firsthand experiences for their chosen career options. They will bring updated versions of their résumés and cover letters for the next session to obtain feedback from the group.
During the third session (valuing and execution), group members will present reflections on their informational interviews and provide feedback on their peers’ résumés and cover letters. In addition, group members will be exposed to various career resources such as VetJobs (VetJobs, Inc., 2014), Feds Hire Vets (U.S. Office of Personnel Management, n.d.), Job-hunt.org (NETability, Inc., 2014), the Riley Guide (Riley Guide, 2014), and the National Resource Directory (U.S. Departments of Defense, Labor and Veterans Affairs, n.d.). The group leader(s) will explain the “elevator speech” exercise and ask group members to practice this exercise in order to maximize their interview skill development. The group will also enhance members’ ability to use social networking to optimize their job search and applications. All activities aim to help members weigh their career options and execute their career decision making through careful planning. The group leader(s) will encourage members to initiate career networking and start exploring job and career opportunities.
During the last session (communication), group members will share what they originally included in their ILPs and what they have achieved, and offer suggestions and feedback to one another. They will retake the CTI and MVS and compare their new and initial results. Group leaders will help group members examine whether the gaps identified at the communication stage have successfully been closed, and suggest further measures to close gaps if necessary.
The information gathered from the needs survey provides a thorough description of student veterans’ career-development needs. Interventions designed to support this population by determining appropriate interventions are often constructed using anecdotal information rather than objective needs. Student veteran responses to the survey indicate that veterans are concerned about transitioning their military experiences to civilian employment opportunities. In addition, student veterans appear to desire assistance with practical elements of the career-development process such as creating a résumé, negotiating a job offer, and networking. The purpose of this study is to develop a theoretically based intervention, and the study offers a framework in which to create effective career-development interventions for student veteran population.
Student veterans appear to engage in a wide array of academic programs, with a significant portion of veterans selecting majors within the realm of business, law, sociology, social work and criminology. These survey results provide a snapshot of the majors/fields of study that student veterans seem to gravitate toward. These preferences could be attributed to the hierarchical and meritocratic nature of some of these fields, which are somewhat analogous to the culture of the military.
Responses to the survey also provided a glimpse into the preferred modality of receiving career-related assistance. Oftentimes, military transition programs are designed to serve a large number of people, using seminar or workshop modalities in which to provide information. Student veterans indicated a strong preference for a smaller group counseling format that would provide more individual career-development support.
An additional important consideration for future interventions is the high number of respondents who identified themselves as distance learners in the needs assessment (some of them may have been on active service, whereas others were simply enrolled in the university from a remote location). Given the technological capabilities that allow online learning environments, it is reasonable that student veterans could utilize e-learning opportunities. Designing online interventions could be helpful in determining appropriate modalities by which to deliver services.
The student veterans’ comments and responses regarding their desired areas of focus for career development indicate a preference for a balanced approach of skill development. Ensuring that interventions focus on practical elements such as résumés and networking skill development, while also addressing broader topics such as transitioning from the military to the civilian workforce, appears to be a desired method for addressing the career-development needs of student veterans.
The needs survey is limited in generalizability, as the results were collected from one educational institution, confining interpretations to the student veterans in this institution. Despite this limitation, the career-development concerns of student veterans provide a snapshot of the needs of this unique subset of the veteran population. Given the paucity of research in this area, it seemed necessary to facilitate an in-depth examination of this population’s career-development concerns, allowing the development of an informed intervention and establishing replicable protocol for future needs surveys.
The low response rate to the online survey also limits the application of findings. Though the response rate of 29% may be considered reasonable for an online assessment, having a large portion of the sample disregard the assessment presents a gap in fully substantiated information on this topic. Developing methods for collecting more information would enhance the validity of the data.
Finally, the high rate of graduate students who responded to the survey presents a challenge in applying the results to a primarily undergraduate institution. While there may be analogous experiences between graduate and undergraduate students, specific aspects of undergraduate student veterans’ career development may need additional evaluation.
Implications for Practice and Research
In this needs assessment, collaborative efforts between career services professionals at the institution and the university veterans’ center resulted in informative data on the career concerns of student veterans. Co-sponsored initiatives targeting these expressed needs could increase the number of student veterans impacted by career services. Survey respondents, along with group or workshop participants, could be recruited to provide feedback as part of a career-development focus group, further informing research and application for student veterans’ career concerns. Survey results could also be useful for marketing career services to student veterans. In addition, career centers or university libraries could acquire career resources such as books and print materials on topics that survey respondents considered desirable, especially those specifically tailored for veterans.
At the larger university level, major data on their students’ career-development concerns would be valuable information for college and department academic advisors and other university stakeholders. Career center staff members focus on various academic units as part of their career outreach, but further research regarding the unique career concerns of student veterans in specific majors could allow career center liaisons to impact veterans more effectively in their designated areas. As previously stated, since the survey was conducted at one higher education institution, duplicating the needs survey across a larger sample of colleges and universities would provide additional data sets for analysis, as well as broader application possibilities. Survey data could also be applied outside the institution to identify the most optimal partnerships in order to meet the comprehensive needs of student veterans. For example, career counselors might collaborate with mental health professionals, school counselors, and rehabilitation professionals to identify challenges and provide resources in order to maximize development for student veterans.
The results of this survey also support future research on the efficacy and suitability of online career-development options. There are many online programs designed to provide veterans the opportunity to pursue their education while in active duty. While the convenience of remote educational options for a mobile population is understood, ensuring that universities also provide career-development resources to distance learners is an important consideration in addressing the needs of veterans. Career-development opportunities such as webinars and online workshops offer the flexibility of distance learning. For example, online formats could provide veterans an opportunity to participate in such workshops collaboratively. Possible areas of research would include effective use of distance learning for veterans and comparative benefits and costs of in-person versus distance formats.
Based on the information collected, in future needs surveys, adjusting the survey items to detail reasons for certain item selections could allow greater understanding of both the responses and student veterans’ career thinking in general. Resulting career interventions would provide additional opportunities for further research to investigate aspects of career decision making and CIP theory, including relationships between student veterans’ self-knowledge, options knowledge, decision-making skills and metacognitions.
While veterans’ needs receive significant attention, programs are often created based on anecdotal and intuitive information. Developing needs assessments to solicit veterans’ perceptions of career development can inform interventions. Specifically regarding career development, utilizing a theoretically based, researched approach offers a framework to guide practice and research. Ongoing assessment of needs and services that utilizes established approaches will ensure quality services for those who have sacrificed greatly in service of their country.
Bullock, E. E., Braud, J., Andrews, L., & Phillips, J. (2009). Career concerns of unemployed U.S. war veterans: Suggestions from a Cognitive Information Processing approach. Journal of Employment Counseling, 46, 171–181. doi:10.1002/j.2161-1920.2009.tb00080.x
Buzzetta, M., & Rowe, S. (2012, November 1). Today’s veterans: Using Cognitive Information Processing (CIP) approach to build upon their career dreams. Career Convergence Magazine. Retrieved from http://www.ncda.org/aws/NCDA/pt/sd/news_article/66290/_self/layout_details/false
Clemens, E. V., & Milsom, A. S. (2008). Enlisted service members’ transition into the civilian world of work: A cognitive information processing approach. The Career Development Quarterly, 56, 246–256. doi:10.1002/j.2161-0045.2008.tb00039.x
Cook, B. J., & Kim, Y. (2009). From soldier to student: Easing the transition of service members on campus. Retrieved from http://www.acenet.edu/news-room/Documents/From-Soldier-to-Student-Easing-the-Transition-of-Service-Members-on-Campus.pdf
Engels, D. W., & Harris, H. L. (2002). Career counseling with military personnel and their dependents. In S. G. Niles (Ed.), Adult career development: Concepts,issues, and practices (3rd ed., pp. 253–266). Tulsa, OK: National Career Development Association.
Green, L., & Hayden, S. (2013). Supporting student veterans: Current landscape and future directions. Journal of Military and Government Counseling, 1, 89–100.
Hayden, S., Green, L., & Dorsett, K. (in press). Perseverance and progress: Career counseling for military personnel with traumatic brain injury. VISTAS. Retrieved from http://www.counselingoutfitters.com/vistas/vistas_2013_TOC-QR3-section_01.htm
Holland, J. L. (1985). Making Vocational Choices: A theory of vocational personalities and work environments. Englewood Cliffs, NJ: Prentice Hall.
Holland, J. L., Daiger, D. C., & Power, P. G. (1991). My Vocational Situation. Mountain View, CA: Consulting Psychologists Press.
Kleiman, T., Gati, I., Peterson, G., Sampson, J., Reardon, R., & Lenz, J. (2004). Dysfunctional
thinking and difficulties in career decision making. Journal of Career Assessment, 12,
National Center for O*NET Development. (n.d.). Military crosswalk search [Database]. O*NET Online. Retrieved from http://www.onetonline.org/crosswalk/MOC/
NETability, Inc. (2014). How to succeed in your job search today. Job-hunt.org. Retrieved from http://www.job-hunt.org
Phillips, J., Braud, J., Andrews, L., & Bullock, E. E. (2007, November 1). Bridging the gap from job to career for U.S. veterans. Career Convergence Magazine. Retrieved from
Reardon, R. C., Lenz, J. G., Peterson, G. W., & Sampson, J. P., Jr. (2012). Career development and planning: A comprehensive approach (4th ed.). Dubuque, IA: Kendall Hunt Publishing.
Riley Guide. (2014). The Riley Guide. Retrieved from http://www.rileyguide.com/
Sampson, J. P., Jr., Peterson, G. W., Lenz, J. G., & Reardon, R. C. (1992). A cognitive approach
to career services: Translating concepts into practice. The Career Development Quarterly, 41, 67–74.
Sampson, J. P., Jr., Peterson, G. W., Lenz, J. G., Reardon, R. C., & Saunders, D. E. (1996). Career Thoughts Inventory: Professional manual. Odessa, FL: Psychological Assessment Resources.
Sampson, J. P., Jr., Peterson, G. W., Lenz, J. G., Reardon, R. C., & Saunders, D. E. (1998). The design and use of a measure of dysfunctional career thoughts among adults, college students, and high school, students: The Career Thoughts Inventory. Journal of Career Assessment, 6, 115–134. doi:10.1177/106907279800600201
Sampson, J. P., Jr., Reardon, R. C., Peterson, G. W., & Lenz, J. G. (2004). Career counseling & services: A cognitive information processing approach. Belmont, CA: Brooks/Cole.
Sander, L. (2012, July). Colleges expand services for veterans, but lag in educating faculty on veterans’ needs. The Chronicle of Higher Education. Retrieved from https://chronicle.com/
Sander, L. (2013, January). Veterans tell elite colleges: ‘We Belong’. The Chronicle of Higher Education. Retrieved from https://chronicle.com/
Stein-McCormick, C., Osborn, D. S., Hayden, S. C. W., & Van Hoose, D. (2013). Career development for transitioning veterans. Broken Arrow, OK: National Career Development Association.
U.S. Bureau of Labor Statistics. (2014b). Table A-5. Employment status of the civilian population 18 years and over by veteran status, period of service, and sex, not seasonally adjusted [Economic news release]. Retrieved from http://data.bls.gov/cgi-bin/print.pl/news.release/empsit.t05.htm
U.S. Bureau of Labor Statistics. (2014c). 2014–2015 Occupational outlook handbook. Retrieved from http://www.bls.gov/ooh/
U.S. Departments of Defense, Labor and Veterans Affairs. (n.d.). National resource directory. Retrieved from https://www.nrd.gov/
Seth Hayden, NCC, is the Program Director of Career Advising, Counseling and Programming at Florida State University. Kathy Ledwith, NCC, is the Assistant Director for Career Counseling, Advising and Programming at Florida State University. Shengli Dong is an Assistant Professor at Florida State University. Mary Buzzetta, NCC, is a doctoral student at Florida State University. Correspondence can be addressed to Seth Hayden, 100 S. Woodward Avenue, Tallahassee, FL 32308, firstname.lastname@example.org.