Examining the Career Self-Management Model Among Native American Students With STEM Career Goals

Abstract

Native Americans are highly underrepresented in science, technology, engineering, and math (STEM) careers; however, little research exists concerning how to promote Native Americans’ participation in STEM. In this study, we address this gap by examining variables hypothesized to promote participation using the career self-management (CSM) model among Native American college students with STEM career goals. Results of stepwise regressions demonstrated that academic achievement along with the problem-solving aspects of career self-management (CSM) self-efficacy and instrumental assistance from parents, peers, and others in students’ schools and communities predicts clearer, more specific, and more personally congruent goals; and that these goals along with self-efficacy and instrumental assistance predict career exploration. Contrary to hypotheses, neither STEM outcome expectations nor gender was related to goals or exploration. These findings suggest that CSM can be used to guide research regarding the STEM career development of Native American college students.

Keywords

Native American college students career self-management model STEM career goals career exploration academic achievement

There is an urgent need for young people in the United States to prepare for and pursue careers in science, technology, engineering, and math (STEM). STEM occupations currently comprise 20% of jobs in the United States. STEM jobs grew twice as fast as non-STEM jobs between 2005 and 2015 and are projected to grow faster than occupations in any other employment sector (Fayer et al., 2017; Noonan, 2017). However, although the demand for STEM workers is rising, in many STEM fields, the labor supply is not meeting this demand (Xue & Larson, 2015).

Juxtaposed to the shortage of STEM professionals is the comparatively few ethnic minorities who enter STEM occupations (Fouad & Santana, 2017). Researchers agree that the contributions of individuals from diverse groups are integral to the unique thinking and problem-solving that can move STEM fields forward and ultimately impact U.S. economic growth and competitiveness (e.g., Page, 2008). Yet, since 2000, “racial-ethnic minorities’ graduation rates have flat-lined in engineering and physical sciences, and their numbers have dropped, specifically in mathematics and statistics” (Fouad & Santana, 2017, p. 24). As the U.S. workforce continues to diversify (Colby & Ortman, 2015), there may be even fewer STEM workers prepared if this trend is not reversed. One group of individuals that is significantly underrepresented in STEM is Native Americans (National Science Foundation, 2017).

Native Americans comprise approximately 2% (6.8 million) of the U.S. population (U.S. Census Bureau, 2018a); yet they hold only 0.4% of the baccalaureate degrees in STEM (National Center for Education Statistics, 2018). Moreover, Native Americans as a group are underprepared in high school for pursuing STEM degrees, are under-enrolled in many of the majors that train students for critical STEM occupations, and have limited knowledge of occupational opportunities in STEM (Hoffmann et al., 2005; National Assessment of Educational Progress [NAEP], 2015; U.S. Department of Energy, 2016). Thus, Native Americans are not available to help meet the STEM needs of their own communities nor are they positioned to fill critical STEM jobs across the United States or internationally.

The social cognitive model of career self-management (CSM; Lent & Brown, 2013) proposes that setting goals and initiating actions designed to fulfill those goals are initial steps into attaining personally valued careers. Therefore, because of the gap between Native American young people who would otherwise pursue STEM career goals, and the small percentage of Native Americans who actually participate in STEM, we designed a project to ascertain factors that would promote clearer, more specific, and more personally congruent STEM career goals, and to determine how those goals would be related to subsequent career enhancing actions (in this case, career exploration). We used CSM to guide our research.

Theoretical Framework: Social Cognitive Model of CSM

CSM is the last in a series of overlapping social cognitive career theory (SCCT) models that also include interests, choice, and performance models (Lent & Brown, 2013; Lent et al., 1994, 2000). CSM explains those processes by which individuals prepare for, enter into, and adjust to careers and focuses on setting viable goals leading to actions. Therefore, CSM is particularly well suited to provide an investigative framework for the career development of college students who are forming vocational goals and planning for and preparing to reach those goals (Lent & Brown, 2013; Super et al., 1996).

As shown in Figure 1, the background contextual factors and person variables (e.g., gender) in CSM directly predict self-efficacy and outcome expectations. Self-efficacy and outcome expectations, as well as proximal factors (e.g., academic achievement, instrumental assistance), directly affect goal setting and subsequent actions (Lent & Brown, 2013). Outcome expectations reflect the expected consequences for performing an action (Lent & Brown, 2013). Self-efficacy, in CSM, focuses on one’s perceived abilities to manage the “normative tasks and cope with the myriad challenges involved in career preparation, entry, adjustment, and change” (Lent & Brown, 2013, p. 557). Career self-efficacy related to problem-solving, as one facet of the self-efficacy needed to successfully engage in CSM, may be particularly important for Native American college students who face unique challenges in preparing for STEM careers (ACT, 2016; NAEP, 2015).

Figure 1.

The career model of self-management. Adapted and printed with permission from Lent & Brown, 2013.

Goals are conceptualized as motivators. Greater self-efficacy to manage career processes and stronger, more positive outcome expectations can lead to goals that are clearer, more specific, and more congruent with personal values. Goals so specified will lead to actions, such as career exploration, with self-efficacy, outcome expectations, and proximal factors also predicting goal-directed actions (Lent & Brown, 2013).

Supporting Research on CSM Variables

Studies on the CSM have produced results that are generally consistent with CSM propositions (e.g., Conkel-Ziebell et al., 2019; Kim et al., 2019; Lent et al., 2016; Lent et al., 2019). However, although several studies of CSM have included samples of ethnic minorities, we found no studies that were focused on specific ethnic groups nor did any of the research we found differentiate among the ethnic groups represented in their studies in terms of evaluating their results. Likewise, the predictive validity of CSM when applied to goal setting and career exploration has not been tested among Native American college students with STEM career goals. Thus, by extending the research on CSM to this population, we are increasing its potential explanatory value to a group not often fully represented in tests of SCCT models.

Gender

Gender has been shown to be a distinct contributor to STEM career development (e.g., males’ STEM self-efficacy, outcome expectations, and goals are stronger than females’; Kanny et al., 2014). According to CSM, person variables, including gender, are related both distally (via efficacy and outcome expectations) and proximally (with direct effects) to goals and actions (Lent & Brown, 2013). The distal effects of gender in the STEM career development of women can be seen in the erosion of their self-efficacy due to beliefs that they do not have abilities to succeed in math and science (Else-Quest et al., 2013). Proximally, gender effects can be seen in education and career contexts that favor men over women, so that women may be hampered in accessing opportunities to explore and pursue STEM careers (e.g., Wang & Degol, 2017). Although gender has continued to be investigated as a likely predictor of young people’s STEM career development, there has been no research regarding the proximal effects of gender on the STEM career goals and career exploration of Native American college students.

Self-efficacy and outcome expectations

Investigators have found that CSM self-efficacy, such as career exploration and decision-making self-efficacy, are related to expected career development outcomes in adolescents and adults (Conkel-Ziebell et al., 2019; Lent et al., 2019). Among Native American college students, CSM self-efficacy has not yet been studied. Career outcome expectations, however, have been shown to be related to career interests and goals among biology and engineering students, including a small sample of Native American students (Byars-Winston et al., 2010).

Achievement

In this study, academic achievement as measured by grade point average (GPA) is conceptualized as a proximal variable that directly predicts goals, actions, and the translation of goals to actions. Academic achievement can promote both goals and actions directly and can moderate actions by either strengthening or weakening the relationship between them (Lent et al., 2000; Lent & Brown, 2013). Achievement provides a pathway to graduation and to career entry and success (e.g., Hwang et al., 2014). In particular, for STEM fields that require in-depth scientific and technological knowledge and expertise, academic achievement supports young people’s career progression (e.g., Callahan, 2016). Without achieving academically, young people may not qualify for graduate school programs, internships, or initial occupational entry.

Academic achievement has been shown to relate to many aspects of career development, including the pursuit of STEM career goals (e.g., Hwang et al., 2014). In a sample of multiethnic college students, researchers found relationships among achievement (as measured by GPA), goal setting skills, and career exploration (Sung et al., 2013). Moreover, research has shown that Native American young people recognize the value of achievement, with greater academic achievement in high school related to greater college aspirations (Jackson et al., 2003; Turner et al., 2018); however, how academic achievement relates to goal setting and career exploration among Native American college students is unknown.

Instrumental assistance

The study of how instrumental assistance relates to the career development of college students is emerging in the literature. In one study, researchers examined instrumental and emotional support as a unitary construct and found they were significantly linked to occupational engagement and career agency (Jenkins & Jeske, 2017). Other research has shown that instrumental assistance (in terms of helping college students reach their educational and career goals) from parents, peers, and others in the students’ communities was linked to academic achievement and goal setting skills (Sung et al., 2013). Conceptualizing instrumental assistance as a communal activity can be especially important for Native American students who are often motivated by communal goals and come from communities where success is valued even more when it is the result of collective effort (e.g., Smith et al., 2014).

Career exploration and goals

Finally, researchers have found that Native Americans have less career awareness and more limited career information about STEM occupations than do students from other groups (Fox, 2013; Rottinghaus et al., 2018), which could be a source of their underrepresentation in STEM career fields. However, the CSM model proposes that efficacy and outcome expectations, along with person (e.g., gender) and contextual variables proximal to adaptive behavior, lead to goals, and each of these variables in tandem leads to action. Researchers have shown a relationship between goals and career exploration among Native American adolescents (Turner et al., 2006). In this study, we are extending the Turner et al.’s (2006) study in that we are examining the relationship between STEM career goals and exploration among Native American college students.

Purpose of the Study

The number of Native Americans in STEM fields is disproportionately low compared to the number of Native Americans in the population, and there is little research regarding factors that would promote their STEM career development. CSM proposes that students who are efficacious in their beliefs about managing their own careers, and who have greater expected outcomes related to their chosen careers, can set and reach viable career choice goals. CSM also proposes that self-efficacy, outcome expectations, proximal variables, and goals are predictive of actions. While research has suggested that there are specific factors that promote career development among students across populations (e.g., Lent & Brown, 2013), how these factors apply to Native American college students is unknown. Thus, the purpose of this study was to examine social cognitive and proximal support variables and the links among them among Native American college students with STEM career goals using CSM as our guiding model.

In this research, we studied the problem-solving aspect of CSM self-efficacy, outcome expectations related to obtaining a STEM career goal, and the proximal variables of academic achievement and instrumental assistance as these were hypothesized to directly influence the formation of goals and the translation of goals into action. As proposed in the CSM model, we hypothesized that (1) STEM career goals would be positively related to CSM self-efficacy, STEM outcome expectations, male gender, academic achievement, and instrumental assistance and (2) career exploration would be positively related to STEM career goals, as well as to CSM self-efficacy, STEM outcome expectations, male gender, academic achievement, and instrumental assistance.

Method

Participants

Participants were 102 Native American college students who had STEM career goals and who were either enrolled in a STEM major (51%) or who planned to enroll (49%). Of the participants who planned to enroll, 39% were Freshmen or Sophomores who had not declared their majors, and 10% were enrolled in majors other than STEM. The mean age of the participants was 24.36 years old (SD = 6.06 years); 30 were male, 71 were female, and 1 did not identify. They were residents of 33 states, with the largest concentration of participants residing in Oklahoma (n = 13), California (n = 11), and Texas (n = 10). They identified with 48 different Native American tribes. Of the participants, 44% grew up in rural areas, 39% in urban areas, and 18% in both; 43% were first-generation college students, 22% had at least one parent who worked in a STEM occupation, and 72% came from families whose household incomes were below the U.S. median (U.S. Census Bureau, 2018b). They attended 82 different colleges (70% attended 4-year colleges, 30% attended 2-year colleges). STEM majors that students were enrolled in corresponded to STEM occupational domains established by the Standard Occupation Classification Policy Committee (life, physical, and social sciences, technology, engineering, and math; Bureau of Labor Statistics, 2018; Fayer et al., 2017).

Procedures

All data were gathered using Qualtrics Panels. Qualtrics is a project management organization that specializes in recruiting and surveying hard-to-reach populations by identifying a “pre-qualified and willing group of respondents to participate in surveys on an as-needed basis” (Qualtrics, 2018, para. 3). Qualtrics Panels use standardized recruitment methods (e.g., email, social media) to identify individuals who are interested in participating in research. Researchers then contract with Qualtrics who assembles a panel of participants who match research criteria. Data are then gathered via a web-based platform.

To ensure reliability and validity, Qualtrics works with researchers during the design phase of the survey to assess survey materials for accessibility, readability, nonambiguity, scoring accuracy, and survey look and feel. Including attention checks in surveys is discouraged by Qualtrics Panels as potentially introducing bias and social influence responding. During the piloting phase, however, Qualtrics personnel monitor hit rates, response patterns, and 24-hr and overall completion rates and notify researchers if they believe the survey should be revised. Once the survey is deployed, branch logic is used so that potential participants who do not meet researchers’ criteria are exited from the survey without completing survey items. Once the survey is completed by participants, the data must pass a series of quality control checks administered by Qualtrics consultants and via the Qualtrics ExpertReview feature before they are released to the researcher. These checks include conformity to eligibility criteria, time to completion, possible bot, duplication of respondents, and random responding (Qualtrics, 2014). Researchers are then allowed to preview the data to verify that criteria were met for participation in the research, and to check for missing data, prior to final release of the data for analysis and publication. For this study, all participants met criteria; 106 participants completed the survey, but four cases were deleted because of missing data.

Permission for recruitment and data gathering methods used by Qualtrics Panels was obtained from authors’ IRBs. Informed consent for all participants was obtained prior to participation.

Measures

As shown in Table 1, means and standard deviations were computed for all variables in order to conduct analyses of the data. Correlations, α reliabilities, skewness, and kurtosis for the current sample are also shown in Table 1.

Table 1.

Means, SD, Cronbach’s α, Skewness, Kurtosis, and Correlations for All Variables.

Variables	1	2	3	4	5	6	7
1. Self-management efficacy	—	.043	.630***	.110	.657***	.588***	.259*
2. STEM outcome expectations		—	.176	.028	.121	.154	−.097
3. Academic achievement			—	.222*	.678***	.592***	.244*
4. Instrumental assistance				—	.358***	.390***	.102
5. STEM goals					—	.671***	.252*
6. Career exploration						—	.209*
7. Gender							—
Mean	3.77	2.18	3.22	2.93	3.68	4.02	N/A
SD	0.68	0.44	0.53	0.63	0.61	0.65	N/A
Cronbach’s α	0.91	0.91	N/A	0.80	0.78	0.88	N/A
Skewness	−0.25	−0.33	−0.77	−0.50	−0.29	−0.45	N/A
Kurtosis	0.19	−0.31	0.32	0.45	−0.15	0.09	N/A

Note. N = 102. STEM = science, technology, engineering, and math.

*p < .05. **p < .01. ***p < .001.

Demographic variables

For this study, participants self-identified their gender. Gender was coded female = 2, male = 1, or another gender = 0 (please specify). Because no participants identified as another gender, the variable became dichotomous with two levels.

Academic achievement was measured via students’ self-reported GPAs. GPAs were measured on a 5-point Likert-type scale (0 = F, 1 = D, 2 = C, 3 = B, and 4 = A). Students’ mean GPA was 3.21 (SD = .53). A meta-analysis of 12 research studies representing 12,089 college students has shown a correlation between self-reported and observed grades of .90 (Kuncel et al., 2005). In a single sample of undergraduate students (N = 89), Cassady (2001) found that reported and observed GPAs correlated at .97.

CSM self-efficacy, instrumental assistance, goals, and career exploration were each measured using scales from the Structured Career Development Inventory (SCDI; Lapan, 2004; Turner et al., 2006). Career self-management self-efficacy was measured using the 5-item Self-Efficacy Scale. This scale measures individuals’ self-efficacy to solve educational/career development problems. Items were scored on a 5-point Likert-type scale (5 = strongly agree to 1 = strongly disagree). Sample items are “I am confident I can overcome problems to reach my career goals” and “I will be able to solve most of my problems in school.”

Instrumental assistance was measured using the 7-item Instrumental Assistance Scale, which measures students’ perceptions of instrumental help in reaching their STEM career goals. Items were scored on a 5-point Likert-type scale (5 = strongly agree to 1 = strongly disagree). For this scale, students were asked to rate the level of instrumental assistance they received from parents, peers, boyfriend/girlfriend/spouse, professors, counselors, and other members of their schools and communities.

STEM career goals were measured by the 4-item Goals Scale. This scale assesses the clarity, specificity, and personal congruence of participants’ STEM career goals (with each participant stating that they had a career goal that is in a STEM field). Items on this scale were also scored on a 5-point Likert-type scale (5 = strongly agree to 1 = strongly disagree). Sample items are “My education and work goals are well-defined and specific” and “Setting career goals has helped me identify steps to reach those goals.”

Career exploration was measured using the 11-item Career Exploration Scale. This scale measures participants’ engagement in exploring congruence between an individual’s preferred occupations and their skills, values, talents, and interests. Items on this scale were scored on a 5-item Likert-type scale (4 = strongly agree to 0 = strongly disagree). Sample items are “I have explored the skills and talents I want to use in my career” and “I have sought information regarding the kind of education I need to get the job I want.”

Validity and reliability have been assessed for each of the constructs measured by the SCDI. Career exploration, self-efficacy, academic achievement, and instrumental assistance have been shown to be interrelated and also related to person–environment fit, vocational interests, and hope among multiethnic college and high school students and Native American adolescents (Conkel-Ziebell et al., 2018; Sung et al., 2013; Turner et al., 2006). Goals have been associated with career decision self-efficacy and vocational outcome expectations (Conkel et al., 2019). Among multiethnic high school students, Cronbach’s α for goals = .71 and for self-efficacy = .76 (Conkel et al., 2019). Among Native American adolescents, α for self-efficacy, goals, and exploration = .79–.95 (Turner et al., 2006). Cronbach’s α for the SCDI measures in this current sample ranged from .78 to .88, skewness = −.49 to −.30, and kurtosis = −.16 to .38.

STEM outcome expectations were measured using the Mapping Vocational Challenges STEM Scale (MVC-STEM; Turner & Lapan, 2014), a revised version of the Mapping Vocational Challenges Investigative Scale (MVC-I; Lapan & Turner, 1999). The MVC-I is comprised of 15 occupational titles representing Holland’s Investigative theme. This scale has been successfully used to measure samples containing Native American and other underrepresented students’ investigative interests, self-efficacy, parent support, peer support, career choice goals, and actions (Alliman-Brissett & Turner, 2010; Turner & Lapan, 2003).

For the revised MVC-STEM Scale, job titles were selected from 10-year Bureau of Labor Statistics projected growth tables (2014–2024) that reflect the fastest growing occupations in STEM (Fayer et al., 2017) and from O*NET’s (2019) list of STEM occupations that are projected to have faster and much faster than average growth over the next 10 years. This scale was also expanded from the original 15 items to 28 items to ensure that a broader range of STEM occupations were represented; seven items each represented STEM occupations. Sample occupational titles were astronomers (science), wind turbine service technicians (technology), civil engineers (engineering), and mathematicians (math). To obtain outcome expectations scale scores, participants were presented with each job title in sequence and were asked to rate whether or not they believed they would experience positive life outcomes related to working in the STEM career. Their response choices were 3 = yes, 2 = maybe, and 1 = no, with higher scores indicating greater outcome expectations.

The construct validity for the STEM Outcome Expectations MVC-STEM Scale was established by the authors in a separate sample of Native American (n = 52) and Caucasian (n = 50) STEM college students via confirmatory factor analyses. For the STEM Outcome Expectations Scale, the β for all items loading on the latent variables ranged from .36 to .65 (p < .001). CMIN/df = 1.86 (with values closer to 2 or to 3 indicating an adequate fit of the data to the model; McIver & Carmines, 1981). The RMSEA = .09, with values < .10 indicating an adequate fit (e.g., Browne & Cudeck, 1992). In this Native American/Caucasian sample, Cronbach’s α = .90. In the current Native American sample, Cronbach’s α = .91, skewness = −.39, and kurtosis = −.30.

Preliminary Analysis

To detect univariate outliers, all mean scores were translated into z-scores, and the z-score data were examined for absolute values of greater than 3.29 (meaning 3.29 SD away from the mean; Tabachnick & Fidell, 2013). Using these criteria, no univariate outliers were found.

An a priori power analysis was conducted using G*Power Version 3 data analysis software (Faul et al., 2007) to determine adequate sample size for linear multiple regression equations with up to six predictors. A two-tailed test of a linear fixed model, single regression coefficient, with a medium effect size of f² = .15 (Cohen, 1988), an α probability level of .05, and a desired statistical power of .95, yielded a minimum required sample size of 74. Thus, we ascertained that our sample size of N = 102 was adequate to conduct the analyses.

Results

To test the hypotheses, we used stepwise multiple regression. We chose this method of analysis because it identifies the most important predictors from among all possible predictors tested and because it is an appropriate method for highly exploratory research among populations where a model has not been tested nor a theory applied (e.g., Tabachnick & Fidell, 2013). The order of entry for the IVs depends upon statistical probabilities rather than researchers’ overt choice.

Hypothesis 1 stated that STEM career goals would be positively related to CSM self-efficacy, STEM outcome expectations, male gender, academic achievement, and instrumental assistance. As shown in Table 2, Hypothesis 1 was partially supported, F(3,98) = 48.38, p = .000, R² = .60. In this model, academic achievement entered the equation first (t = 4.50, p = .000, ΔR² = .46), followed by CSM self-efficacy (t = 4.75, p = .000, ΔR² = .09), and then by instrumental assistance (t = 3.50, p = .001, ΔR² = .05).

Table 2.

Results of Stepwise Regression Analyses Predicting STEM Goals and Career Exploration.

Variables	R	R ²	ΔR²	β	t
STEM goals	.773	.597
Academic achievement			.459	.441	4.498***
Career management self-efficacy			.088	.354	4.751***
Instrumental assistance			.050	.223	3.497**
STEM outcome expectations			N/A	.495	0.590
Gender			N/A	.038	0.557
Career exploration	.727	.528
Goals			.451	.418	3.948***
Career management self-efficacy			.038	.294	3.278***
Instrumental assistance			.039	.223	2.864***
Outcome expectations			N/A	.091	1.297
Academic achievement			N/A	.171	1.716
Gender			N/A	.100	0.135

Note. N = 102. For ΔR², variables that did not enter into the regression equations are identified by N/A. STEM = science, technology, engineering, and math.

*p < .05. **p < .01. ***p < .001.

Hypothesis 2 stated that career exploration would be positively related to STEM career goals, as well as to CSM self-efficacy, STEM outcome expectations, male gender, academic achievement, and instrumental assistance. Hypothesis 2 was also partially supported, F(3, 98) = 36.59, p = .000, R² = .53. In this model, goals entered the equation first (t = 3.95, p = .000, ΔR² = .45), followed by CSM efficacy (t = 3.28, p = .001, ΔR² = .04), and instrumental assistance (t = 2.86, p = .005, ΔR² = .04). Results showed that 46% of the variance in goals was predicted by academic achievement, with 9% predicted by CSM self-efficacy, and 5% predicted by instrumental assistance. In addition, 45% of the variance in career exploration was predicted by career goals, with an additional 4% predicted by CSM self-efficacy, and 4% predicted by instrumental assistance. In total, 60% of the variance in goals and 53% of the variance in career exploration were predicted.

Post hoc analyses of variance were used to assess for differences in the study variables between students already enrolled in STEM majors and those not yet enrolled. Results showed that students already enrolled in STEM majors had significantly greater GPAs (M = 3.37, SD = .40 compared to M = 3.07, SD = .59; mean difference = .30; F = 8.71, p = .004), significantly clearer, more specific, and more personally congruent STEM career goals (M = 3.87, SD = .53 compared to M = 3.51, SD = .63; mean difference = .36; F = 9.78, p = .002), significantly more engagement in career exploration (M = 4.17, SD = .62 compared to M = 3.88, SD = .66; mean difference = .29; F = 5.37, p = .023), and significantly greater CSM self-efficacy (M = 3.98, SD = .65 compared to M = 3.58, SD = .65; mean difference = .40; F = 9.48, p = .003) than students not yet enrolled. There were no differences in the expectations of positive outcomes, nor of perceptions of instrumental assistance.

Discussion

This study was the first to examine the CSM model among Native American college students with STEM career goals. This study yields some important findings. First, a substantial amount of variance in goals and career exploration (60% and 53%) were predicted by hypothesized variables in the model. This finding supports the effectiveness of CSM to describe Native American students’ STEM career development.

Next, the strength of the association between the proximal variable, academic achievement, and the development of clear, specific, personally congruent STEM career goals among these students is noteworthy. Our findings continue to support prior research (Turner et al., 2006, 2018) that demonstrate that Native American young people recognize the value of achievement and recognize that achievement can either help or hinder them in setting and reaching goals. Students who achieve academically also appear to have goals that come into focus for them and that can serve to guide them in their STEM-related career pursuits.

Achievement, did not, however, predict career exploration. This may be an area in which counselors could assist Native American students in linking what they have achieved to educational and occupational possibilities, so that their awareness of attaining a more robust GPA could also help them solidify the direction of their exploration.

The strength of the association between STEM career goals and career exploration also provides support for CSM. Actions are in part motivated by goals (Bandura, 1986). The clarity and specificity of goals not only motivates actions but also can provide a road map to exploring careers related to those goals. While CSM theory proposes a one-to-one correspondence between a goal and an action (e.g., a goal to explore is accompanied by career exploration), in this study, we have demonstrated that the clarity, specificity, and personal congruence of a career goal can also drive exploration. Thus, these results suggest that CSM could be modified to include this proposition when applied to Native American students with STEM career goals.

While academic achievement and setting clear, specific career goals were shown to be central to the STEM career development of Native American college students, students’ self-efficacy to solve educational and career-related problems was also shown in this current research to be a small but significant source of self-efficacy information. Self-efficacy to solve problems as they arise in young people’s career progression is essential to their career attainment. However, prior research has shown that Native American students face challenges related to lack of academic preparation, choice of schools, and career information about STEM careers (ACT, 2016; Fox, 2013; Rottinghaus et al., 2018). Thus, students who have the confidence in their skills to solve problems in order to overcome these challenges also envision and set goals that are clearer, more specific, and more congruent with their personal values, and they increase their engagement in career exploration.

Our findings also suggest that instrumental assistance from parents, peers, boyfriend/girlfriend/spouse, professors, counselors, advisors, and other members of Native American students’ schools and communities is a contextual variable proximal to adaptive behavior and has an important part to play in the development of clearer, more specific and more congruent career goals and in students’ efforts to explore options to reach those goals. The value of multiple sources of assistance may be culturally indicated (Smith et al., 2014) and provides an important roadmap for psychologists and counselors who aim to help Native American students develop career paths in STEM fields.

The lack of association between gender and goals or gender and exploration suggests that Native American college students have a more gender-equitable view of the outcomes of working in a STEM career than is found among some other groups (e.g., Fouad & Santana, 2017). Nevertheless, Native American women are significantly underrepresented in engineering and hard science careers to an even greater degree than men (U.S. Department of Energy, 2016), which may mean that there are additional moderating variables for women that have not yet been identified. Future research should determine the nature and extent of these variables in order to promote greater participation of Native American women in STEM. Finally, the lack of significant relationships between STEM outcome expectations and goals, and STEM outcome expectations and exploration is not surprising given the less than consistent support for outcome expectations’ predictive validity in SCCT models (Sheu & Bordon, 2017).

Finally, post hoc analyses revealed that while the model was predictive both for all Native American students with STEM career goals, students already enrolled in STEM majors exceeded those not yet enrolled in academic achievement, exploration, career management self-efficacy, and setting clearer, more specific, and more personally congruent career goals. Both subgroups valued STEM careers the same in that they both had positive outcome expectations for developing a STEM career, and both reported no differences in instrumental assistance. These findings suggest the important role that enrolling in a STEM major plays in the development of a STEM career over and above gaining academic knowledge.

Taken together, this research has provided valuable evidence regarding important links between achievement and clearer, more specific, and more personally congruent STEM career goals, and between STEM career goals and action, although the hypothesized model was only partially predictive for this population. The findings from this study can guide the development of effective interventions so that counselors and psychologists can assist Native American students to develop and reach their career goals in STEM.

Implications for Practice

Psychologists and counselors should continue to help Native American students achieve academically, both via their course grades and via course completion and time to graduation. Counselors can help students strategize about how to focus on curricular areas with which they need the most assistance and where to garner academic help. Peer tutoring and math lab help students who are already enrolled in STEM majors and those planning to enroll can increase their achievement and assist them to reach their educational and career goals. Helping students find instrumental assistance resources in their own communities can also be culturally indicated and can help them locate the support they need to be successful in their STEM endeavors.

Native American students can be supported in their CSM efforts, including their efforts to overcome career-related challenges, by helping them identify on- and off-campus resources, and providing them with information and instrumental assistance so that they can access these resources. Native American students should be apprised of how to access counseling and career development centers, online counseling and support, cultural and student support groups, as well as resources related to student housing, financial aid, and academic life. The confidence with which Native American students approach their STEM career goal setting and exploration can help them be successful in their STEM career development.

Psychologists and counselors can also support the efforts of those in the students’ families and communities to provide assistance to the student. In particular, providing career information regarding STEM careers can help parents and community members guide students’ career exploration efforts. Additionally, providing professors, counselors, and advisors with information regarding Native American cultures as well as the barriers that Native American students encounter in school and career pursuits could enable them to provide nuanced, detailed, and meaningful assistance.

Finally, helping students learn how to better engage in career exploration related to STEM career goals can also help them reach their desired careers. Exploration for college students consists of formulating goals and plans, elaborating on these plans and how to reach their goals, searching for and obtaining employment, and stabilizing their vocational goals and plans (Lent & Brown, 2013; Super et al., 1996). Finding creative ways to include the community could help increase the relevance of career exploration for Native American students across each of these career exploration tasks. For example, elders in the community could help students by discussing with them the American Indian Science and Engineering Society’s (2018) Top 50 STEM Workplaces for students seeking inclusive, diverse, and supportive work environments.

Limitations and Suggestions for Future Research

A limitation to this study is the sample size. Although the sample size was sufficient to carry out the analyses of this exploratory study with a highly specific, small population, a larger sample may provide greater statistical stability (Cohen, 1988). Thus, this study should be replicated with additional samples to validate the results.

There is some evidence of a ceiling effect for academic achievement for both STEM majors and students not yet enrolled in STEM (M = 3.21, SD = .53 on a 0 to 4-, 5-point Likert-type scale). Additionally, Native American college students in this sample may be representative of Native American students who have STEM career goals but not necessarily representative of the broader population of Native American college students. These issues may mean that our sample was biased toward higher achieving, career-decided students, which may limit generalizability.

Given the exploratory nature of this study, the uniqueness of the investigation and the use of CSM theory to guide the research, results should be replicated and extended to examine other potential contributors to Native American students’ STEM career goals and actions. Longitudinal and cross-sectional studies that examine additional independent variables could provide greater clarity and directionality to the development of their CSM.

Conclusion

In conclusion, our findings provide partial support for the effectiveness of using CSM model predictors to describe career development processes among Native American students with STEM career goals. Our findings highlight the importance of proximal variables to the career development of these students, thus suggesting that meaningful counseling and career development assistance can be provided to them within the college setting. Further, our study emphasized the central role of academic achievement and goal setting in the STEM career development of Native American students. Moreover, this study provides a foundation for future research within this population, which can extend the impact of these findings by including cultural variables and contextual factors (e.g., school types, mentoring opportunities, socioeconomic status). Thus, this study provides a unique contribution to the STEM career development literature and to our understanding of the STEM career development of Native American college students.

Supplemental Material

Supplemental Material, sj-docx-1-jcd-10.1177_0894845320959464 - Examining the Career Self-Management Model Among Native American Students With STEM Career Goals

Supplemental Material, sj-docx-1-jcd-10.1177_0894845320959464 for Examining the Career Self-Management Model Among Native American Students With STEM Career Goals by Sherri L. Turner, Hangshim Lee, Aaron P. Jackson, Steve Smith, Gale Mason-Chagil and Sue C. Jacobs in Journal of Career Development

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Sherri L. Turner

Hangshim Lee

Supplemental Material

The supplemental material for this article is available online.

References

ACT. (2016). The condition of college & career readiness 2015: American Indian students. https://equityinlearning.act.org/wp-content/uploads/2016/06/2015-american-indian.pdf

Alliman-Brissett

A. E.

Turner

S. L.

(2010). Racism, parent support, and math-based career interests, efficacy, and outcome expectations among African American adolescents. Journal of Black Psychology, 36(2), 197–225. https://doi.org/10.1177/0095798409351830

American Indian Science and Engineering Society. (2018, Spring). The Top 50 STEM workplaces. Winds of Change. https://woc.aises.org/content/top-50-stem-workplaces

Bandura

(1986). Social foundations of thoughts and action: A social cognitive theory. Prentice Hall.

Browne

M. W.

Cudeck

(1992). Alternative ways of assessing model fit. Sociological Methods and Research, 21(2), 230–258. https://doi.org/10.1177/0049124192021002005

Bureau of Labor Statistics. (2018). The 2018 standard occupational classification system. https://www.bls.gov/soc/2018/home.htm

Byars-Winston

Estrada

Howard

Davis

Zalapa

(2010). Influence of social cognitive and ethnic variables on academic goals of underrepresented students in science and engineering: A multiple-groups analysis. Journal of Counseling Psychology, 57(2), 205–218. https://doi.org/10.1037/a0018608

Callahan

J. T.

(2016). Assessing online homework in first-semester calculus. PRIMUS: Problems, Resources, and Issues in Mathematics Undergraduate Studies, 26(6), 545–556. https://doi.org/10.1080/10511970.2015.1128501

Cassady

J. C.

(2001). Self-reported GPA and SAT: A methodological note. Practical Assessment, Research & Evaluation, 7, Article 12. https://scholarworks.umass.edu/pare/vol7/iss1/12/

10.

Cohen

(1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum.

11.

Colby

S. L.

Ortman

J. M.

(2015). Projections of the size and composition of the U.S. population: 2014 to 2060. https://census.gov/content/dam/Census/library/publications/2015/demo/p25-1143.pdf

12.

Conkel-Ziebell

J. L.

Gushue

G. V.

Turner

S. L.

(2019). Anticipation of racism and sexism: Factors related to setting career goals for urban youth of color. Journal of Counseling Psychology, 66(5), 588–599. https://doi.org/10.1037/cou0000357

13.

Conkel-Ziebell

J. L.

Turner

S. L.

Gushue

G. V.

(2018). Testing an integrative contextual career development model with adolescents from high-poverty urban areas. Career Development Quarterly, 66(3), 220–232. https://doi.org/10.1002/cdq.12144

14.

Else-Quest

N. M.

Mineo

C. C.

Higgins

(2013). Math and science attitudes and achievement at the intersection of gender and ethnicity. Psychology of Women Quarterly, 37(3), 293–309. https://doi.org/10.1177/0361684313480694

15.

Faul

Erdfelder

Lang

A. G.

Buchner

(2007). G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behavior Research Methods, 39, 175–191. https://doi.org/10.3758/BF03193146

16.

Fayer

Lacey

Watson

(2017). STEM occupations: Past, present, and future. U.S. Bureau of Labor Statistics. https://www.bls.gov/spotlight/2017/science-technology-engineering-and-mathematics-stem-occupations-past-present-and-future/pdf/science-technology-engineering-and-mathematics-stem-occupations-past-present-and-future.pdf

17.

Fouad

N. A.

Santana

M. C.

(2017). SCCT and underrepresented populations in STEM fields: Moving the needle. Journal of Career Assessment, 25(1), 24–39. https://doi.org/10.1177/1069072716658324

18.

Fox

L. J.

(2013). Influence of family on Native American students. Dissertation Abstracts International Section A: Humanities and Social Sciences, 74(6-A(E)).

19.

Hoffmann

L. L.

Jackson

A. P.

Smith

S. A.

(2005). Career barriers among Native American students living on reservations. Journal of Career Development, 32, 31–45. https://doi-org.ezp2.lib.umn.edu/10.1177/0894845305277038

20.

Hwang

M. H.

Lee

Lim

H. J.

Seon

H. Y.

Hutchison

Pope

(2014). Academic underachievement and recovery: Student perspectives on effective career interventions. Career Development Quarterly, 62(1), 81–94. https://doi.org/10.1002/j.2161-0045.2014.00072.x

21.

Jackson

A. P.

Smith

S. A.

Hill

C. L.

(2003). Academic persistence among Native American college students. Journal of College Student Development, 44(4), 548–565. https://doi.org/10.1353/csd.2003.0039

22.

Jenkins

Jeske

(2017). Interactive support effects on career agency and occupational engagement among young adults. Journal of Career Assessment, 25(4), 616–631. https://doi.org/10.1177/1069072716652891

23.

Kanny

M. A.

Sax

L. J.

Riggers-Piehl

T. A.

(2014). Investigating forty years of STEM research: How explanations for the gender gap have evolved over time. Journal of Women and Minorities in Science and Engineering, 20(2), 127–148. https://doi.org/10.1615/JWomenMinorScienEng.2014007246

24.

Kim

J. G.

Kim

H. J.

Lee

K. H.

(2019). Understanding behavioral job search self-efficacy through the social cognitive lens: A meta-analytic review. Journal of Vocational Behavior, 112, 17–34. https://doi.org/10.1016/j.jvb.2019.01.004

25.

Kuncel

N. R.

Credé

Thomas

L. L.

(2005). The validity of self-reported grade point averages, class ranks, and test scores: A meta-analysis and review of the literature. Review of Educational Research, 75(1), 63–82. https://doi.org/10.3102/00346543075001063

26.

Lapan

R. T.

(2004). Career development across the K–16 years: Bridging the present to satisfying and successful futures. American Counseling Association.

27.

Lapan

R. T.

Turner

S. L.

28.

Lent

R. W.

Brown

S. D.

(2013). Social cognitive model of career self-management: Toward a unifying view of adaptive career behavior across the life span. Journal of Counseling Psychology, 60(4), 557–568. https://doi.org/10.1037/a0033446

29.

Lent

R. W.

Brown

S. D.

Hackett

(1994). Toward a unifying social cognitive theory of career and academic interest, choice, and performance. Journal of Vocational Behavior, 45(1), 79–122. https://doi.org/10.1006/jvbe.1994.1027

30.

Lent

R. W.

Brown

S. D.

Hackett

(2000). Contextual supports and barriers to career choice: A social cognitive analysis. Journal of Counseling Psychology, 47(1), 36–49. https://doi.org/10.1037/0022-0167.47.1.366

31.

Lent

R. W.

Ezeofor

Morrison

M. A.

Penn

L. T.

Ireland

G. W.

(2016). Applying the social cognitive model of career self-management to career exploration and decision-making. Journal of Vocational Behavior, 93, 47–57. https://doi.org/10.1016/j.jvb.2015.12.007

32.

Lent

R. W.

Morris

T. R.

Taylor

Penn

L. T.

Ireland

G. W.

(2019). Social-cognitive predictors of career exploration and decision-making: Longitudinal test of the career self-management model. Journal of Counseling Psychology, 66(2), 184–194. https://doi.org/10.1037/cou0000307

33.

McIver

J. P.

Carmines

E. G.

(1981). Unidimensional scaling. Sage. https://doi.org/10.4135/9781412986441

34.

National Assessment of Educational Progress. (2015). The nation’s report card: 2015; mathematics & reading at grade 12. https://www.nationsreportcard.gov/reading_math_g12_2015/#mathematics?grade=12

35.

National Center for Education Statistics. (2018). Digest of education statistics: Table 322.20. Bachelor’s degrees conferred by postsecondary institutions, by race/ethnicity and sex of student: Selected years, 1976–77 through 2016–17. https://nces.ed.gov/programs/digest/d18/tables/dt18_322.20.asp

36.

Noonan

(2017). STEM jobs: 2017 update (ESA Issue Brief # 02–17). U.S. Department of Commerce, Economics and Statistics Administration.

37.

National Science Foundation. (2017). Women, minorities, and persons with disabilities in science and engineering (NSF 17-310). https://www.nsf.gov/statistics/2017/nsf17310/static/downloads/nsf17310-digest.pdf

38.

O*NET Online. (2019, February). Browse bright outlook occupations. https://www.onetonline.org/find/bright?b=1&g=Go

39.

Page

(2008). The difference: How the power of diversity creates better groups, firms, schools, and societies (Rev ed.). Princeton University Press.

40.

Qualtrics. (2014). ESOMAR 28. https://success.qualtrics.com/rs/qualtrics/images/ESOMAR%2028%202014.pdf

41.

Qualtrics. (2018). Panels and samples: What is a research panel & should we have one? https://www.qualtrics.com/experience-management/research/research-panels-samples/

42.

Rottinghaus

P. J.

Falk

N. A.

Park

C. J.

(2018). Career assessment and counseling for STEM: A critical review. Career Development Quarterly, 66(1), 2–34. https://doi.org/10.1002/cdq.12119

43.

Sheu

H. B.

Bordon

J. J.

(2017). Research in the international context: Empirical evidence, future directions, and practical implications. Journal of Career Assessment, 25(1), 58–74. https://doi.org/10.1177/1069072716657826

44.

Smith

J. L.

Cech

Metz

Huntoon

Moyer

(2014). Giving back or giving up: Native American student experiences in science and engineering. Cultural Diversity and Ethnic Minority Psychology, 20(3), 413–429. https://doi.org/10.1037/a0036945

45.

Sung

Turner

S. L.

Kaewchinda

(2013). Career development skills, outcomes, and hope among college students. Journal of Career Development, 40(2), 127–145. https://doi.org/10.1177/0894845311431939

46.

Super

D. E.

Savickas

M. L.

Super

C. M.

(1996). The life-span, life-space approach to careers. In Brown

Brooks

, & Associate (Eds.), Career choice and development: Applying contemporary theories to practice (3rd ed., pp. 121–178). Jossey-Bass.

47.

Tabachnick

B. G.

Fidell

L. S.

(2013). Using multivariate statistics (6th ed.). Pearson Education.

48.

Turner

S. L.

Joeng

J. R.

Mason-Chagil

Conkel-Ziebell

J. L.

Dade

S. N.

Kim

(2018). College readiness counseling to promote Native American adolescents’ college aspirations: The integrated conceptual model of student success. Professional School Counseling, 22(1), 1–11. https://doi.org/10.1177/2156759X18816671

49.

Turner

S. L.

Lapan

R. T.

(2003). Native American adolescent career development. Journal of Career Development, 30(2), 159–172. https://doi.org/10.1177/089484530303000205

50.

Turner

S. L.

Lapan

R. T

51.

Turner

S. L.

Trotter

M. J.

Lapan

R. T.

Czajka

K. A.

Yang

Brissett

A. E. A.

(2006). Vocational Skills and outcomes among Native American adolescents: A test of the integrative contextual model of career development. Career Development Quarterly, 54(3), 216–226. https://doi.org/10.1002/j.2161-0045.2006.tb00153.x

52.

U.S. Census Bureau. (2018a). American FactFinder. https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=bkmk

53.

U.S. Census Bureau. (2018b). Income and poverty in the United States: 2017. https://www.census.gov/content/census/en/library/publications/2018/demo/p60-263.html

54.

U.S. Department of Energy (Sandia National Laboratories). (2016). The current state of STEM education for American Indian and Alaska Native communities (SAND2016-7525 PE). https://www.energy.gov/sites/prod/files/2018/02/f48/begay-stem-education.pdf

55.

Wang

M. T.

Degol

J. L.

(2017). Gender gap in science, technology, engineering, and mathematics (STEM): Current knowledge, implications for practice, policy, and future directions. Educational Psychology Review, 29, 119–140. https://doi.org/10.1007/s10648-015-9355-x

56.

Xue

Larson

R. C.

(2015). Monthly labor review: STEM crisis or STEM surplus? Yes and yes. https://www.bls.gov/opub/mlr/2015/article/stem-crisis-or-stem-surplus-yes-and-yes.htm doi.org/10.21916/mlr.2015.14

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