Validating the Engineering Fields Questionnaire for Use With African American Science,Technology,Engineering,and Mathematics Students

Abstract

Lent’s (2003) Engineering Fields Questionnaire assesses major components of Social Cognitive Career Theory (SCCT) and includes measures of self-efficacy, coping efficacy, outcome expectations, technical interests, and educational goals. These measures have demonstrated good internal consistency in previous research, but validation information is more limited for use with African American STEM students. This paper discusses a validation study of an adapted version of Lent’s Engineering Fields Questionnaire, entitled the STEM Fields Questionnaire, with a sample of African American undergraduate STEM students (n = 526). Validating the STEM Fields Questionnaire for African Americans is particularly important given the role of cultural values and certain experiences in career development among this population. Seven factors resulted from an exploratory factor analysis conducted in the present study: engineering/technology interests, outcome expectations, self-efficacy, STEM coping, goals, bio-chemical sciences interest/self-efficacy, and mathematics interest/self-efficacy, with four of six original subscales represented. Implications for research and practice were discussed.

Keywords

validation fields questionnaire social cognitive career theory African American STEM college students Historically black colleges and Universities

Validating Lent’s Fields Questionnaire for Use With African American STEM Students

Across all fields of STEM, African Americans are underrepresented. Only 7.5% of students who complete bachelor’s degree in STEM are African American, and even less (4.8%) comprise doctoral-level graduates in STEM (U.S. Department of Education, 2022). Despite the underrepresentation of African Americans across STEM fields, psychometrically valid and culturally responsive career development measures are lacking. The vocational psychology field is in need of valid and culturally responsive measures to be used with underrepresented students, particularly racial/ethnic minorities (Worthington & Whittaker, 2006). Such measures would assist with understanding the effect of interventions aimed to increase career development skills and retention in STEM among African American students. As it stands, current measures of career development have been developed, standardized, and validated using students of dominant identities (Lent & Brown, 2019; Lent et al., 2001, 2003).

Social Cognitive Career Theory

Major Components of SCCT

Social Cognitive Career Theory (SCCT) is a theory of career development that addresses the interaction of person variables, contextual factors, and sociocognitive variables in the formation of career-related interests, goals, and actions (Lent et al., 1994). It is because this theory considers a wide variety of both internal and external factors that it could be applied to individuals of various cultures and to those who experience marginalization and oppression. The three person variables outlined by SCCT are self-efficacy (i.e., one’s belief on one’s ability to succeed in specific situations or in accomplishing certain tasks), outcome expectations (i.e., the effects or consequences of certain behaviors), and personal goals (i.e., how much an individual wants to engage in behaviors that will lead to a certain outcome; Lent, 2020). SCCT also highlights the moderating influences of proximal and distal factors (i.e., opportunities for tasks, exposure to role models, cultural and gender role socialization), personal inputs (i.e., socioeconomic status, gender, ethnicity, innate abilities), and learning experiences (i.e., performance accomplishments, vicarious learning, social persuasion, and physiological arousal; Lent, 2020). As such, this theory provides models as to how an individual engages in career development as a result of culture, socialization, learning, and opportunities while also indicating opportunities for personal and/or systemic interventions.

SCCT Impact on Research

Social Cognitive Career Theory has played a crucial role in shaping career counseling research. Its impact is evidenced by the remarkable number of citations received by key articles. According to APAPsycnet, the influential Lent et al. (1994) article has been cited 2216 times, demonstrating its enduring significance in the field. Equally impressive, Google Scholar records show that the same article has garnered 10,363 citations, further highlighting its wide reach and influence. The 2000 article, another significant contribution to SCCT, has been cited 769 times according to APAPsycnet and 3,553 times according to Google Scholar. Similarly, the 2013 article where Lent uses SCCT to examine the process of self-regulation and self-management in career development has received 290 citations from APAPsycnet and 1,252 citations from Google Scholar. These statistics underscore the profound impact of SCCT on career counseling research, solidifying its status as a foundational framework for understanding the cognitive processes and social factors that shape career development.

Culturally Appropriate Measure Development

Ethnocentric errors can occur in measure development, administration, and interpretation (Reynolds & Suzuki, 2012; Sue, 1996; Suzuki, et al., 2005). These errors can stem from a Eurocentric worldview that is embedded within the assumptions used in construct definition and the design of individual items within a measure. The resulting constructs and items may not apply to persons from racial/ethnic minority groups. Conversely, Sue (1996) postulates that culturally appropriate measures assess concepts, values, and traits that are pertinent to members of a variety of groups. For example, the establishment of the Engineering Fields Questionnaire’s cultural validity will more accurately reflect the totality of African American career development, such as experiences of racism and discrimination (Henry, 2006; Rollins & Valdez, 2006; Tovar-Murray et al., 2012). Thus, culturally appropriate career development measures (i.e., Engineering Fields Questionnaire) should reflect culturally relevant concepts and be void of ethnocentric errors (Henry, 2006; Reynolds & Suzuki, 2012).

Although the Engineering Fields Questionnaire is grounded in Social Cognitive Career Theory (discussed below), which attends to both contextual influences (proximal and distal) and person inputs (e.g., race/ethnicity; Lent & Brown, 2006), research on the validity of this questionnaire for African Americans is limited by the fact that most African American samples have been comprised of African American students from Predominantly White Institutions (PWIs). Thus, we are unsure whether this measure adequately incorporates the unique experiences of all African American STEM students including those from Historically Black Colleges and Universities (HBCUs; Lent et al., 2005, 2011). More information is needed regarding the validity of the Engineering Fields Questionnaire for use with African American students in order to ensure that career development interventions for this population are culturally responsive.

Overview of the Engineering Fields Questionnaire

The Engineering Fields Questionnaire (Lent et al., 2003) is grounded in Social Cognitive Career Theory (SCCT) and comprised of scales measuring the major components of this theory as applied to engineering students. The Engineering Fields Questionnaire (Lent et al., 2003) builds on previous research investigating components of SCCT: self-efficacy, coping efficacy, outcome expectations, technical interests, and educational goals. Although these scales were developed and published separately, Lent suggested (personal communication, 2012) their use together for research purposes and has reported on the use of these measures together as the Engineering Fields Questionnaire (Lent et al., 2005, 2007, 2008, 2011, 2013, 2015, 2016; Lent & Brown, 2006, 2019). Much of the research investigating SCCT components has been conducted with samples of STEM-specific majors (e.g., mathematics, engineering) at Predominantly White Institutions (PWIs). A recent study (Cygrymus & Lent, 2023) utilized three of the eight Engineering Fields Questionnaire measures with music majors and although reliability information (α = .87–.91) was included, the sample was comprised of only 3.9% African American students. Table 1 provides a summary of the literature on the Engineering Fields Questionnaire (Lent et al., 2003) and its different measures.

Table 1.

Lent’s (2003) engineering fields questionnaire measures.

Name of Measure	Authors	# of Items	How measured	Different adaptations	Cronbach’s α	Sample demographics
Self-efficacy for technical/Scientific fields measure	Lent et al., 1984	15	10-Point likert	Lent et al., 1986; Lent et al., 2003	.76–.89	Not provided for original study and first adaptation; 63% White for Lent et al., 2003
Coping efficacy scale	Lent et al., 2001	18	10-Point likert	Lent et al., 2003	.89–.94	51% White, 22% African American; 63% White in adaptation
Outcome expectations measure	Lent et al., 1991	10	10-Point likert	Lent et al., 2003; Lent et al., 2008, Lent et al., 2011	.87–.93	94% White; 63% White in first adaptation; 42–45% African American, 37–39% White in last two adaptations
Math/Science interests	Lent et al., 1986	15	10-Point likert	Lent et al., 2001	.84	Not provided for original study; 51% White, 22% African American for adaptation
Technical interests measure	Lent et al., 2003	5	5-Point likert	Lent et al., 2008, Lent et al., 2013	.78–.87	63% White; 42% African American, 39% White for first adaptation; 58–66% White for second adaptation
Educational goals measure	Lent et al., 2003	4	5-Point likert	Lent et al., 2005, 2010; Lent et al., 2007, 2013, 2015; Lent et al., 2008	.79–.95	63% White; 40–95% African American in Lent et al., 2005; 2010; 58–68% White in Lent et al., 2007, 2013, and 2015; 42% African American, 6% Asian, and 5% Hispanic for Lent et al., 2008

Self-Efficacy for Technical/Scientific Fields Measure

The Self-Efficacy for Technical/Scientific Fields Measure (SETSFM; Lent et al., 1984) of the Engineering Fields Questionnaire (Lent et al., 2003) has been adapted in various studies (Lent et al., 1986, 2003). The original measure (Lent et al., 1984) was constructed based on Betz and Hackett’s (1981) procedures and assessed level of self-efficacy by asking undergraduate students considering majoring in science or engineering (n = 42; demographic information not reported) if they believed they were capable of completing educational requirements and occupational duties associated with 15 different science and engineering fields. The original measure (Lent et al., 1984) also assessed strength of self-efficacy by asking participants to rate their degree of confidence in their ability to complete the educational requirements and occupational duties pertaining to different science and engineering fields on a 10-point Likert-type scale from 1 (completely unsure) to 10 (completely sure). The resulting scale yielded four scores of self-efficacy: level of self-efficacy for educational requirements, strength of self-efficacy for educational requirements, level of self-efficacy for job duties, and strength of self-efficacy for job duties. Acceptable coefficients were found for test-retest reliability (α = .76 for Level of Self-Efficacy and α = .89 for Strength of Self-Efficacy), as well as internal consistency (α = .79 for Level of Self-Efficacy and α = .89 Strength of Self-Efficacy) in the original study using the SETSFM (Lent et al., 1984).

Items assessing level of self-efficacy were dropped from the SETSFM in Lent et al.’s (1986) study due to conceptual relevance and redundancy. The sample in the 1986 study was similar to the 1984 sample, but larger (n = 105 undergraduates in a career/educational-planning course for students considering majoring in science or engineering; demographic information not reported). Lent et al. (1986) also included a second measure of self-efficacy referred to as the Strength of Self-Efficacy for Academic Milestones. This scale included 11 items on a 10-point Likert-type scale (identical to scale used for the Strength of Self-Efficacy for Academic Milestones items), which assessed participants’ confidence in their ability to accomplish specific tasks critical to science- and engineering-related academic success. Lent et al. (1986) reported test-retest reliability (α = .89) and internal consistency (α = .89) coefficients determined from the original sample (Lent et al., 1984). However, good internal consistency (α = .89) was reported for the Strength of Self-Efficacy for Academic Milestones items in Lent et al.’s (1986) sample.

The modified version of the SETSFM used in Lent et al.’s (2003) study was adapted for engineering-specific majors and assessed strength of self-efficacy. The adapted SETSFM (Lent et al., 2003) asked undergraduate students enrolled in an introductory engineering course (n = 328; 63% White) to rate their confidence in their ability to complete 10 different engineering majors with an overall GPA of a “B” or better. Participants provided their responses using a 10-point Likert-type scale ranging from 0 (no confidence) to 9 (complete confidence). The SETSFM utilized by Lent et al. (2003) demonstrated excellent internal consistency (α = .94).

Coping Efficacy Scale

In addition to the SETSFM, the Engineering Fields Questionnaire (Lent et al., 2003) includes a second measure of self-efficacy, the Coping Efficacy Scale (CES; Lent et al., 2001, 2003). The CES was designed by Lent et al. (2001) to assess college students’ ability to cope with potential barriers in pursuing science and math-related coursework. The original CES (Lent et al., 2001) included 18 items on a 10-point Likert-type scale ranging from 0 (no confidence) to 9 (complete confidence). Items on this measure were based on responses provided in a qualitative study (Lent et al., 2002) regarding math-related barriers and coping strategies. Items also reflected categories of barriers (i.e., financial constraints, social and familial influences) included in a contextual supports and barriers measure used in Lent et al.’s (2001) study. Participants in the CES development article (Lent et al., 2001) included undergraduate students (n = 111; 51% White, 22% African American) in an introductory psychology class. The original version of the CES demonstrated excellent internal consistency (α = .94) in this sample.

Lent et al. (2003) adapted the CES for undergraduate engineering majors. This adapted version of the CES was patterned after the measure used by Lent et al. (2001). The CES used by Lent et al. (2003) included seven items asking participants (n = 328 college students in an introductory engineering class; 63% White) to rate their confidence in their ability to cope with potential barriers faced among students in their major. Lent et al.’s (2003) version of the CES was also designed to reflect barriers assessed in a contextual supports and barriers measure used in the same study. Similar to the CES used by Lent et al. (2001), responses on Lent et al.’s (2003) version of the CES were given on a 10-point Likert-type scale ranging from 0 (no confidence) to 9 (complete confidence). Although slightly lower than the original CES, Lent et al.’s (2003) adapted measure still demonstrated good internal consistency (α = .89).

Utilization of the SETSFM and CES Together

There is inconsistency in the literature regarding reporting on use of the SETSFM and CES. Early research using these scales delineated them as separate measures of self-efficacy and coping efficacy (Lent et al., 2001, 2003). However, some previous studies have added to the lack of clarity regarding these measures by either combining them together to comprise one instrument of self-efficacy (Lent et al., 2005, 2007) or using them as two separate scales of self-efficacy (Lent et al., 2008, 2010, 2011, 2013, 2015, 2016).

Regardless of the manner in which the SETSFM and CES have been used by researchers, these measures have consistently demonstrated good internal consistency with alphas ranging from .84 to .94 (Lent et al., 2005, 2007, 2008, 2010, 2011, 2013, 2015). Good internal consistency of the SETSFM and CES used both together and separate has been found for predominantly White samples (Lent et al., 2007, 2013, 2015), as well as samples with more adequate representations of African Americans (i.e., at least 40% of the sample was African American; Lent et al., 2005, 2008, 2010, 2011).

Outcome Expectations Measure

The Engineering Fields Questionnaire (Lent et al., 2003) includes the Outcome Expectations Measure (OEM; Lent et al., 1991). Lent et al. (1991) developed the OEM for use in their study exploring mathematics self-efficacy among undergraduate students (n = 138; 94% White) enrolled in an undergraduate psychology course. The original OEM (Lent et al., 1991) included 10-items assessing potential positive outcomes from taking courses in mathematics on a Likert-type scale ranging from 0 (strongly disagree) to 9 (strongly agree). The measure demonstrated excellent test-retest reliability (α = .91) and internal consistency (α = .90) in Lent et al.’s (1991) sample of predominantly White undergraduate students.

Lent et al. (2003) adapted the OEM for use with undergraduate students (n = 328; 63% White) in an introductory engineering course. The adapted OEM included 10 items that instructed participants to indicate how strongly they agreed that a bachelor’s degree in engineering would allow them to achieve various positive outcomes. Participants provided their responses on a Likert-type scale ranging from 0 (strongly disagree) to 9 (strongly agree). Similar to the original OEM, excellent internal consistency (α = .91) was found for Lent et al.’s (2003) adapted version of the measure.

In addition to Lent et al.’s (2003) adaptation of the OEM for engineering students, other studies have adapted the measure for use with samples of undergraduate computing majors (42–45% African American, 37–39% White; Lent et al., 2008, 2011). The adapted 11-item OEM for computing majors (Lent et al., 2008, 2011) asks respondents to indicate how strongly they agree that earning a degree in a computing discipline will allow them to obtain various positive outcomes on a 10-point Likert-type scale from 1 (strongly disagree) to 10 (strongly agree). Excellent internal consistency (α = .92–.93) has also been found for the adapted OEM for computing majors (Lent et al., 2008, 2011).

Previous studies began to highlight the OEM as a potentially valid measure for use with African American STEM students. Several researchers have reported good internal consistency estimates (α = .87–.93) for the engineering- and computing-focused versions of the OEM used with samples comprised of a substantial percentage of African American undergraduate students (i.e., at least 40% African American; Lent et al., 2005, 2008, 2010, 2011). Although previous studies reported herein had samples comprised of a greater proportion of African American students than the studies reported for the two measure described above, our understanding of the effectiveness of the use of this measure with African Americans remains limited because these samples were still comprised of mostly White students. More research is needed to determine the validity of the OEM used as a subscale of the Engineering Fields Questionnaire (Lent et al., 2003) specifically for African American undergraduate STEM students.

Math/Science Interests

The MSI (Lent et al., 1986) is another measure included in the Engineering Fields Questionnaire. Lent et al. (1986) developed this measure for undergraduates (n = 105) enrolled in career/educational planning course for students considering science and engineering majors. The authors based the MSI on Betz and Hackett’s (1981) measure that asked undergraduates (n = 235) enrolled in a psychology course to rate their interest in 20 different occupations, indicate whether they considered pursuing each occupation, and rate the seriousness of their consideration. Lent et al.’s (1986) measure asked participants to indicate their degree of interest in 15 science and engineering fields using responses of “like,” “indifferent,” and “dislike.” Similar to Betz and Hackett’s (1981) measure, participants were also asked to rate the extent to which they seriously considered each science and engineering field on a 10-point Likert-type scale from 0 (haven’t considered at all) to 9 (considered very seriously). Reliability and validity information was not reported in the Lent et al.’s (1986) study using the MSI.

Lent et al. (2001) developed a variation of the MSI as a part of a longer measure that asked undergraduate students in a psychology course (n = 111; 51% White, 22% African American) to rate their interest in studying eight topics (e.g., computer science, statistics) and participating in seven activities (e.g., reading articles or books about scientific issues, solving complicated math problems). Responses were given on a 5-point Likert-type scale from 1 (strongly dislike) to 5 (strongly like). Lent et al.’s (2001) version of the MSI demonstrated adequate internal consistency (α = .84). Finally, Lin and Deemer (2021) utilized a modified version of the Math/Science Interest scale (MSIS; Lent et al., 2001), which was adapted for STEM students specifically. This study included 325 predominantly White/European female college students (251, 77.2%), with a Cronbach’s alpha of .73.

Technical Interests Measure

The Engineering Fields Questionnaire includes another measure of interests, the Technical Interests Measure (TIM; Lent et al., 2003). The TIM was developed by Lent et al. (2003) to measure undergraduate engineering students’ (n = 328; 63% White) degree of interest in participating in seven STEM-related activities (e.g., “reading articles or books about engineering issues,” “solving complicated technical problems”). Responses to the TIM are provided on a 5-point Likert-type scale ranging from 1 (very low interest) to 5 (very high interest). Lent et al. (2003) developed the TIM similar to other social cognitive measures of science and math interests (i.e., Lent et al., 2001; Lopez et al., 1997). Lent et al.’s (2003) TIM demonstrated adequate internal consistency with the study’s predominantly White sample (α = .83). Other studies that used Lent et al.’s (2003) version of the TIM on samples with a greater representation of African Americans (i.e., at least 40%) found internal consistency values slightly lower than the original sample (α = .78–.81; Lent et al., 2005, 2010).

Lent et al. (2008) adapted the original TIM (Lent et al., 2003) for use with a sample of undergraduate computing majors (n = 1208; 42% African American, 39% White). This adapted version (Lent et al., 2008) includes six items that ask participants to rate their degree of interest in participating in activities common among computing disciplines (i.e., “learning new computer applications,” “solving computer software problems”). Similar to the original TIM (Lent et al., 2003), responses on Lent et al.’s (2008) TIM are given on a 5-point Likert-type scale from 1 (very low interest) to 5 (very high interest). The adapted TIM for computing disciplines (Lent et al., 2008) has demonstrated adequate internal consistency (α = .80–.81) when used with diverse samples (i.e., 42–45% African American; Lent et al., 2008, 2011).

The original TIM for engineering majors (Lent et al., 2003) has also been adapted to include engineering activities not included in the original measure and reduced to six items based on the results of a factor analysis (Lent et al., 2013). This variation of the original measure (Lent et al., 2003) has been used with predominantly White samples (i.e., 58–66% White) and yielded acceptable internal consistency estimates (α = .80–.87) in these studies (Lent et al., 2013, 2015, 2016).

Educational Goals Measure

The EGM (Lent et al., 2003) of the Engineering Fields Questionnaire (Lent et al., 2003) was developed based on concepts of other measures of behavioral intentions (Ajzen & Fishbein, 1980) in order to assess intended academic persistence among undergraduate students in an introductory Engineering course (n = 328; 63% White). This measure includes four items asking participants to rate their level of agreement with statements regarding academic intentions (i.e., “I intend to major in an engineering field”) on a 5-point Likert-type scale ranging from 1 (strongly disagree) to 5 (strongly agree). The original EGM demonstrated excellent internal consistency (α = .95; Lent et al., 2003). Other studies using the EGM have found adequate to excellent internal consistency estimates (α = .89–.95) in undergraduate engineering student samples with a substantial proportion (40–95%) of African Americans (Lent et al., 2005, 2010). However, the highest internal consistency estimates (α = .94–.98) seem to be found in samples that are predominantly (i.e., 58–68%) White (Lent et al., 2007, 2013, 2015).

Lent et al. (2008) adapted the original EGM (Lent et al., 2003) for use with undergraduate computing majors (n = 1208; 42% African American, 39% White). The adapted version includes four items measuring intentions to persist in a computing major on the same 5-point Likert-type scale (“1” strongly disagree to “5” strongly agree) used in the original study (Lent et al., 2003). This variation of the EGM demonstrated lower internal consistency (α = .79) in Lent et al.’s (2008) predominantly racial/ethnic minority sample (42% African American, 6% Asian, 5% Hispanic). A slightly higher internal consistency estimate (α = .85) was found in another study that used the adapted EGM for computing majors in a sample that was 45% African American and 37% White.

Several studies have found that the engineering-focused EGM (Lent et al., 2003) produces highly skewed and kurtotic distributions of scores in samples of undergraduate engineering students (Lent et al., 2010, 2013, 2015, 2016). The authors of these studies recommend utilizing a rank order transformation in order to create a more normal distribution of scores. The authors also recommend converting ranks to z-scores for further analysis (Lent et al., 2013, 2015, 2016).

Summary of the Patterns Across Studies

The aforementioned history of the Engineering Fields Questionnaire (Lent et al., 2003) highlights the varied use of the individual SCCT measures comprising the questionnaire, the multiple adaptations that have occurred over the years for each, and the differences that exist regarding the reliability of the scales when used with diverse populations. A review of studies using all or part of Lent’s Engineering Fields Questionnaire (2003) resulted in a total of 12 studies. Of those 12, 10 included African American STEM students in their sample. Three major patterns were identified across the studies. First, in almost every instance, the measure was adapted by modifying items or removing items. Second, the samples used to develop the measure differed from the intended use (i.e., measure developed on psychology majors when the intended use was with engineering majors). Third, two studies included no demographic information with which to support the use of the Engineering Fields Questionnaire with a diverse population. With the exception of at least one study using the EGM and those studies that did not provide demographic information, no sample included more than 42% of the sample as African American students. These patterns suggest the need for a validation of the Engineering Fields Questionnaire for use with African Americans and across STEM majors. Additionally, Lent and Brown (2006, 2019) emphasize the need to collect reliability and validity information for SCCT measures, including the factorial structure of scales. Validation of the Engineering Fields Questionnaire for use with African American undergraduate students is necessary given the variability of its measures in past research and lack of information known about its underlying factor structure.

Career Development of African Americans

A significant body of the literature has provided evidence for the validity of the Engineering Fields Questionnaire measures with White college students (Lent et al., 1986, 2001, 2003, 2005), but fewer studies have demonstrated its feasibility with African Americans (Lent et al., 2005, 2011). Additionally, Lent and Brown (2006) assert the need to contextualize measures grounded in SCCT to take into account factors that may influence career development. These influential factors may include race/ethnicity, gender, and socioeconomic status, amongst others. Validating the Engineering Fields Questionnaire for African Americans is particularly important based on the role of cultural values on aspects of career development, such as the decision-making process (Brown, 2002).

Since career development may be different for African American students than their White counterparts, it is prudent to consider factors involved in their career decision-making. Research suggests that career and academic self-efficacy and interests predict career choice among non-White students (Church et al., 1992; Hackett et al., 1992). Brown (1995) notes that outcome expectations might be more important for African American career development due to experiences of racism and discrimination compared to White individuals. One study found that when African American students with a strong ethnic identity experience higher levels of racism, their career aspirations increase (Tovar-Murray et al., 2012). On the contrary, when African American students do not have a strong ethnic identity, their career aspirations decrease as their racism-related stress increases (Tovar-Murray et al., 2012). Moreover, research indicates that African American students perceive earning high incomes and contributing to society as significant in their vocational choice (Daire et al., 2007). Additionally, job security, autonomy, a decent starting salary, and an important occupational role has been found to be more critical among African American college students compared to Whites in the past (Teng et al., 2001). Some researchers have identified that African Americans tend to approach career decision-making in a collaborative way with their family-of-origin compared to the independent manner that is done by Whites (Dillard & Campbell, 1981; Parham & Austin, 1994). Though the research on African American career development is limited, Hackett and Byars (1996) hypothesized and Pearson & Bieschke (2001) found that African American women glean self-efficacy and outcome expectation beliefs via the manner in which their families coped with racism. Therefore, more research is needed to determine the psychometric properties of this questionnaire for African American college students.

HBCUs Compared to Other Institutions

Historically Black Colleges and Universities (HBCUs) were created primarily in the late 1800s to provide African American men and women the opportunity for a college education (Albritton, 2012). HBCUs have a particular importance to the African American community as they serve to uplift this cultural group and empower the community (Albritton, 2012) particularly those with a Land Grant mission. Since the 1980s, HBCUs have grown in popularity as many African American students view these institutions as much more academically and interpersonally supportive compared to Predominantly White Institutions (PWIs; Tobolowsky et al., 2005).

Earlier research suggests that graduates from HBCUs have significantly higher retention rates, self-ratings, and academic goals than graduates from PWIs (Allen, 1992). Current research finds that HBCUs (most of whom enroll low-income, first-generation, Pell grant-eligible students in greater numbers) have graduation rates comparable to PWIs with similar student characteristics (Kim & Conrad, 2006) and reported (Gallup, 2015) stronger senses of well-being (purpose, social, financial, community, and physical). These findings suggest a unique academic and cultural climate of HBCUs, which may lend itself to a more supportive environment (Allen et al., 2007; Gallup, 2015) and increased thriving after college (Gallup, 2015). On the other hand, Predominantly White Institutions (PWIs) tend to be viewed as less supportive and more hostile as they typically offer fewer positive faculty-student interactions and foster less social and extracurricular engagements (Allen, 1992; Allen et al., 2007; Gallup, 2015) resulting in lower levels of thriving post-college (Gallup, 2015). Research suggests that these discrepancies may be due to a cultural disconnect and sense of isolation for many African Americans who attend PWIs, which may interfere with academic and career success (Albritton, 2012).

Purpose of the Present Study

The purpose of this validation study is to determine the applicability of the Engineering Fields Questionnaire for use with African American undergraduate STEM students at an HBCU, including engineering, biology, psychology, and agricultural sciences majors. The study will examine differences between the normative samples and the sample herein. This study’s findings will also have implications for the vocational psychology field, particularly concerning evidence-based, culturally responsive practice with African Americans in STEM. The following research questions will be addressed:

Method

Participants

Participants included African American undergraduate STEM students (n = 531, 61.2% female, 38.8% male, 3.0% decline to report) with an average age of 21.10 (SD = 4.11; range = 18–53) involved in a larger study of African American STEM career development. The sample was relatively evenly divided between freshmen (30.4%), sophomores (18.5%), juniors (22.5%), and seniors (26.2%). Less than 5% declined to report their education level or reported it as post-bachelor’s degree. Participants came from Engineering (94, 17.9%), Agricultural Sciences (76, 14.1%), Biology (136, 25.9%), Chemistry (27, 5.1%), and Psychology (150, 28.6%); Computer Science, Mathematics, Physics, and participants who declined to respond comprised less than 5% together.

Participants were predominantly first-generation college students, with 39.7% of fathers completing high school or less and another 32.9% completing more than high school and less than a bachelor’s degree. Maternal education levels were slightly higher, with 21.4% completing high school or less, and another 34.4% completing more than high school and less than a bachelor’s degree. Participant reports on socioeconomic status indicated that 52.8% reported being from a middle-class home, with 29.8% reporting lower or lower middle class, 17.4% upper or upper middle class, and 8.2% declining to report.

Measures

This study utilized six scales from the STEM Fields Questionnaire, which was adapted with permission from the Engineering Fields Questionnaire (Lent et al., 2003) for applicability with other STEM students in addition to engineering majors. The six scales included in this study comprised adapted measures of self-efficacy, coping efficacy, outcome expectations, math/science interests, technical interests, and educational goals. The individual scales are described in the proceeding sections. A complete listing of items from these scales is provided in Table 2. Adaptations to the measures include, firstly, the replacement of the term “engineering” in all instructions with the term “STEM”; second, with the exception of references specifically to engineering majors, the term “engineering” in items was replaced with “STEM”; and lastly, items were added to the self-efficacy and math/science interests scales to represent non-engineering STEM majors.

Table 2.

STEM fields questionnaire items.

Self-Efficacy for Technical/Scientific Fields Measure (SETSFM)

How much confidence do you have in your ability to complete the following majors with a GPA of B or better

1. Aeronautical & Industrial technology

2. Agricultural sciences

3. Architectural engineering

4. Biological sciences

5. Chemistry

6. Civil engineering

7. Computer Science

8. Electrical & computer engineering

9. Mathematics

10. Mechanical/Manufacturing engineering

11. Physics

12. Psychology

Coping efficacy scale (CES)

How confident are you that you could

1. Cope with a lack of support from professors or your advisor

2. Complete a degree in STEM despite financial pressures

3. Continue on in STEM even if you did not feel well-liked by your classmates or professors

4. Find ways to overcome communication problems with professors or teaching assistants in STEM courses

5. Balance the pressures of studying for STEM courses with the desire to have free time for fun and other activities

6. Continue on in STEM even if you felt that, socially, the environment in these classes was not very welcoming to you

7. Find ways to study effectively for STEM courses despite having competing demands for your time

Outcome expectations measure (OEM)

Graduating with a BS degree in STEM will likely allow me to

1. Receive a good job offer

2. Earn an attractive salary

3. Get respect from other people

4. Do work that I would find satisfying

5. Increase my sense of self-worth

6. Have a career that is valued by my family

7. Do work that can “make a difference” in people’s lives

8. Go into a field with high employment demand

9. Do exciting work

10. Have the right type and amount of contact with other people (i.e., “right” for me)

Math/Science interests (MSI)

How much interest do you have in...

1. Aeronautical & Industrial technology

2. Agricultural sciences

3. Architectural engineering

4. Biological sciences

5. Chemistry

6. Civil engineering

7. Computer Science

8. Electrical & computer engineering

9. Mathematics

10. Mechanical/Manufacturing engineering

11. Physics

12. Psychology

Technical interests measure (TIM)

How much interest do you have in...

1. Solving practical math problems

2. Reading articles or books about engineering issues

3. Solving computer software problems

4. Working on a project involving STEM principles

5. Solving complicated technical problems

6. Learning new computer applications

7. Working on a project involving scientific concepts

Educational goals measure (EGM)

How much do you agree or disagree with the following statements about your educational goals

1. I Intend to major in a STEM field

2. I Plan to remain enrolled in my STEM major over the next semester

3. I Think that earning a BS in STEM is a realistic goal for me

4. I Am fully committed to getting my college degree in STEM

Self-Efficacy for Technical/Scientific Fields Measure

The SETSFM (Lent et al., 2003) was adapted to include STEM majors at the institution where the present study was conducted: Aeronautical and Industrial Technology, Agricultural Sciences, Architectural Engineering, Biological Sciences, Chemistry, Civil Engineering, Computer Science, Electrical and Computer Engineering, Mathematics, Mechanical/Manufacturing Engineering, Physics, and Psychology. The resulting scale included 12 items asking undergraduate students to rate their confidence in their ability to successfully complete the different STEM majors with an overall grade point average of “B” or better. Responses were obtained using a 10-point Likert-type scale ranging from 0 (no confidence) to 9 (complete confidence). The adapted SETSFM used in the present study demonstrated excellent internal consistency (α = .94), which was the same as the estimate found in the study using the original version of the measure (Lent et al., 2003).

Coping Efficacy Scale

The CES used in the present study was adapted from the original engineering-focused measure (Lent et al., 2003) by changing “engineering” to “STEM”. It included all seven items that assessed self-efficacy for coping with barriers likely experienced by undergraduate STEM students (e.g., “cope with a lack of support from professors or your advisor,” “complete a degree in STEM despite financial pressures”). Responses were obtained using a 10-point Likert-type scale ranging from 0 (no confidence) to 9 (complete confidence). The adapted version of the CES used in the present study yielded a higher alpha (α = .94) than the estimate found in Lent et al.’s (2003) study (α = .89).

Outcome Expectations Measure

The present study’s adapted OEM based on Lent et al.’s (2003) version of the scale included 10 items assessing participants’ level of agreement with positive outcomes resulting from earning a bachelor’s degree in a STEM field (e.g. “receive a good job offer,” “get respect from other people,” “increase my sense of worth”). Responses were obtained using a 10-point Likert-type scale ranging from 0 (strongly disagree) to 9 (strongly agree). The Lent et al. (2003) version of the OEM yielded a coefficient alpha of .91, while the coefficient alpha for the current sample was .93.

Math/Science Interests

The current MSI was adapted from Lent et al.’s (2001) version of the measure to include interests in STEM areas reflected in majors at the institution where the present study was conducted. The MSI in the present study included 15 items that assessed undergraduate students’ interests in Aeronautical and Industrial Technology, Agricultural Sciences, Architectural Engineering, Biological Sciences, Chemistry, Civil Engineering, Computer Science, Electrical and Computer Engineering, Mathematics, Mechanical/Manufacturing Engineering, Physics, and Psychology. Participants were asked to respond using a 5-point Likert-type scale ranging from 0 (very low interest) to 4 (very high interest). The current sample yielded a slightly higher alpha coefficient (α = .86) than what was found in the Lent et al. (2001) study (α = .84).

Technical Interests Measure

The TIM (Lent et al., 2003) used in the present study included seven questions asking students to rate their interest in technical issues that may be encountered in a STEM research project (e.g., “solving complicated technical problems,” “working on a project involving scientific concepts”). Responses were obtained using a 5-point Likert scale ranging from 1 (very low interest) to 5 (very high interest). The sample in the present study obtained a slightly higher alpha coefficient (α = .86) compared to Lent et al.’s (2003) sample (α = .83).

Educational Goals Measure

The EGM utilized in the present study was adapted from the original engineering-specific measure (Lent et al., 2003). The current EGM included four items asking participants to indicate their level of agreement with statements about their academic intentions (e.g., “I think that earning a BS in STEM is a realistic goal for me,” “I am fully committed to getting my college degree in STEM”). Responses to this measure were given on a 5-point Likert-type scale ranging from 1 (strongly disagree) to 5 (strongly agree) to assess level of commitment to major-related goals. The Lent et al. (2003) version of this scale yielded a coefficient alpha of .95, but the current sample yielded a higher alpha (α = .97).

Results

With adequate ratio of participants to items, data were screened for assumptions with satisfactory results. Table 3 reports item means, standard deviations, standard errors, and communalities for the sample. As the entire structure of the questionnaire was being examined, an exploratory factor analysis was conducted using Maximum Likelihood Estimation and evaluated parallel analysis (O’Connor, 2000), scree plot, and Kaiser rule to determine the number of factors to interpret. With a determinant not equal to zero, the KMO measure of sampling adequacy greater than .6 (.917), communalities ranging from .196–.908, and a ratio of participants to items of 10.21/item, the sample appears adequate to interpret. The results of the parallel analysis suggested that no more than 8 factors be interpreted (mean eigenvalue of 1.39 versus raw data eigenvalue 1.35 at 9 factors), which agreed with the Kaiser rule (eigenvalues > 1), and the scree plot.

Table 3.

Means, standard deviations, and communalities.

	N	Mean	SE	SD	h _ii
Self-efficacy: Aeronautical & Industrial technology	529	4.256	.119	2.735	.761
Self-efficacy: Agricultural sciences	531	5.424	.114	2.629	.564
Self-efficacy: Architectural Eng	530	4.394	.119	2.748	.830
Self-efficacy: Biological Science	529	5.761	.115	2.639	.799
Self-efficacy: Chemistry	530	5.279	.116	2.679	.727
Self-efficacy: Civil engineering	529	4.422	.112	2.755	.843
Self-efficacy: Computer Science	531	5.070	.115	2.657	.773
Self-efficacy: Electrical & Comp. Engineering	527	4.619	.121	2.782	.849
Self-efficacy: Mathematics	529	5.639	.121	2.777	.781
Self-efficacy: Mechanical/Manuf. Engineering	524	4.492	.125	2.860	.838
Self-efficacy: Physics	530	4.660	.117	2.687	.631
Self-efficacy: Psychology	531	7.159	.096	2.215	.261
Coping: Cope w/Lack of support	530	5.226	.107	2.459	.303
Coping: Complete BS despite financial pressures	531	5.938	.105	2.413	.506
Coping: Continue even if didn’t feel well-liked	530	6.769	.096	2.203	.644
Coping: Overcome communication problems	529	6.662	.090	2.060	.731
Coping: Balance studying w/leisure desires	529	6.540	.089	2.049	.667
Coping: Persist in unwelcoming social environ	528	6.313	.092	2.114	.703
Coping: Balance studying w/competing demands	487	6.517	.091	2.003	.655
Outcome: Receive good job	531	7.258	.071	1.642	.621
Outcome: Earn attractive salary	531	7.235	.073	1.692	.633
Outcome: Respect from other people	531	6.964	.080	1.846	.427
Outcome: Work satisfying	531	7.326	.076	1.739	.656
Outcome: Increase self-worth	529	6.932	.090	2.080	.387
Outcome: Career valued by family	529	7.558	.074	1.708	.567
Outcome: Make a difference	531	7.831	.067	1.553	.586
Outcome: High employment demand	530	7.501	.071	1.644	.627
Outcome: Do exciting work	528	7.472	.074	1.697	.683
Outcome: Right type of contact	531	7.333	.075	1.726	.666
Interest: Aeronautical & Industrial technology	530	1.232	.052	1.191	.524
Interest: Agricultural sciences	529	1.728	.061	1.405	.371
Interest: Architectural engineering	529	1.352	.055	1.257	.672
Interest: Biological Science	531	2.260	.065	1.492	.715
Interest: Chemistry	531	1.878	.060	1.373	.649
Interest: Civil engineering	529	1.327	.053	1.225	.687
Interest: Computer Science	531	1.702	.057	1.311	.681
Interest: Electrical & computer engineering	530	1.558	.060	1.369	.769
Interest: Mathematics	530	1.968	.063	1.446	.790
Interest: Mechanical/Manuf. Engineering	528	1.506	.062	1.424	.706
Interest: Physics	527	1.480	.055	1.263	.510
Interest: Psychology	531	2.842	.061	1.394	.196
Tech. Interest: Solving practical math problems	531	3.264	.056	1.298	.703
Tech. Interest: Read STEM articles	531	2.465	.053	1.226	.590
Tech. Interest: Solving software problems	531	2.633	.055	1.256	.742
Tech. Interest: Work on STEM projects	529	3.304	.049	1.137	.530
Tech. Interest: Solving complicated problems	529	2.887	.056	1.283	.706
Tech. Interest: Learning computer applications	530	3.285	.055	1.263	.576
Tech. Interest: Scientific concepts	530	3.438	.052	1.1905	.458
Ed. Goals: Major in STEM field	531	4.105	.052	1.199	.893
Ed. Goals: Remain enrolled in STEM	529	4.123	.052	1.193	.908
Ed. Goals: Earning a BS in STEM	529	4.151	.050	1.153	.871
Ed. Goals: Fully committed	531	4.175	.051	1.185	.896

Following Lent & Brown’s (2006) suggestion, a Varimax rotation with Kaiser Normalization was used to rotate the data to five through eight factors (Table 4). The eight-factor solution (x²₍₉₃₈₎ = 3214.82, p < .001) explained 63.44% of the variance, with two items being removed (Psychology interest/self-efficacy) and three significant secondary loadings. The seven-factor solution (x²₍₉₈₃₎ = 3951.36, p < .001; Table 5) explained 60.86% of the variance with the same two items deleted and four significant secondary loadings. In this factoring the two interest scales combined and the interest and self-efficacy in Agricultural Sciences moved from their respective factors to join Biology and Chemistry items. At six factors (x²₍₁₀₂₉₎ = 4681.41, p < .001) 58.00% of the variance was explained, with the Mathematics interest/self-efficacy and Practical Math items moving to their expected factors. The same two items were deleted (Psychology interest/self-efficacy) and three significant secondary loadings were noted. The five-factor model (x²₍₁₀₇₆₎ = 5959.99, p < .001) did not replicate the five scales, with both interest scales combining into one factor, as well as coping and outcome expectations combining into a single factor.

Table 4.

Reliability comparisons.

# of factors	Self-efficacy	Outcomes	Coping	Goals	Tech./Interest	Interests mix	Bio/Chem	Math
8	.907	.945	.927	.970	.888	.832	.830	.797
7	.945	.927	.907	.970	.915		.810 (+Ag)	.797
6	.940	.927	.907	.970	.882		.810 (+Ag)
5	.927	.920		.970	.916		.714 (+Ag)

Table 5.

Rotated factor Matrix.

	Factor
	1	2	3	4	5	6	7
Interest - Electrical & Comp. Engineering	.820	.053	.127	.095	.059	.027	.109
Interest - Mechanical/Manuf. Engineering	.750	.027	.225	.039	.026	−.016	.231
Tech. Interests – Solving software problems	.703	.031	.129	.102	.077	.011	−.020
Tech. Interests - Read STEM articles	.697	.097	.157	.100	.062	−.043	.090
Interest - computer Science	.695	.073	.091	.044	−.004	.129	.012
Interest - Architectural engineering	.680	.012	.211	−.017	−.008	.032	.103
Tech. Interests – Solving complicated problems	.678	.066	.215	.202	.092	.065	.171
Interest - Civil engineering	.673	.106	.208	.013	−.007	.056	.141
Interest – Aeronautical & Industrial technology	.619	−.024	.159	−.019	.014	.102	.085
Tech. Interests – learning computer applications	.583	.083	.098	.159	.058	.026	−.028
Tech. Interests - work on STEM projects	.420	.213	.075	.219	.292	.225	.175
Interest -physics	.408	.103	.091	.077	−.005	.365	.369
Outcome - right type of contact	.041	.776	.048	.122	.165	.128	−.043
Outcome - earn attractive salary	.077	.770	.046	.094	.032	.035	.081
Outcome - do exciting work	.088	.768	.044	.165	.209	.095	−.018
Outcome - receive good job	.108	.758	.026	.098	.043	.045	.104
Outcome - high employment demand	.066	.756	.084	.161	.094	.062	.064
Outcome – find work satisfying	.099	.751	.044	.161	.206	.092	.014
Outcome - career valued by family	.094	.732	.058	.083	.108	.014	.022
Outcome – career can make a difference	.001	.714	.027	.197	.168	.011	.007
Outcome – respected by other people	−.016	.630	.097	.088	.014	.036	.074
Outcome - increase self-worth	.032	.608	.027	.043	.050	.032	.070
Self-efficacy - Architectural Eng	.404	.078	.780	.116	.067	−.008	.063
Self-efficacy - Aeronautical & Industrial tech	.328	.087	.779	.156	.057	.063	.034
Self-efficacy - Civil engineering	.434	.059	.775	.140	.085	−.005	.101
Self-efficacy - Mechanical/Manufacturing engineering	.458	.045	.740	.181	.067	−.060	.199
Self-efficacy - Electrical & Comp. Engineering	.482	.031	.717	.192	.085	−.039	.088
Self-efficacy - computer Science	.404	.072	.666	.167	.051	.069	.057
Self-efficacy -physics	.203	.117	.612	.227	.028	.219	.317
Self-efficacy - agricultural sciences	.138	.085	.610	.128	.099	.293	−.049
Self-efficacy - psychology	−.151	.012	.258	.188	−.070	.152	−.054
Coping: Overcome communication problems	.106	.212	.105	.797	.096	.086	.095
Coping: Persist in unwelcoming social environ	.164	.222	.143	.758	.138	.077	.064
Coping: Balance studying w/leisure desires	.051	.212	.144	.751	.124	.066	.094
Coping: Balance study w/competing demands	.044	.238	.124	.736	.121	.095	.078
Coping: Continue even if didn’t feel well-liked	.122	.200	.129	.725	.190	.067	.074
Coping: Complete BS despite financial pressures	.112	.189	.168	.626	.174	.065	.061
Coping: Lack of support from advisors	.091	−.007	.134	.526	.023	.004	.023
Ed. Goals: Remain enrolled in STEM	.050	.230	.081	.221	.885	.114	.041
Ed. Goals: Intend to major in STEM field	.049	.221	.080	.202	.881	.074	.099
Ed. Goals: Fully committed to obtaining STEM degree	.081	.256	.047	.212	.875	.102	.034
Ed. Goals: Earning a BS in STEM is realistic	.065	.272	.085	.225	.848	.121	.037
Interest - psychology	−.130	−.069	−.134	.063	−.171	.132	−.038
Interest - biological Science	−.018	.110	.043	.093	.099	.820	.045
Interest - chemistry	.063	.100	.079	.054	.050	.769	.207
Self-efficacy - biological Science	−.025	.142	.506	.225	.138	.645	.027
Self-efficacy - chemistry	.030	.166	.523	.209	.117	.563	.222
Interest - agricultural sciences	.262	.025	−.004	−.032	.024	.413	−.025
Tech. Interest - scientific concepts	.277	.162	.139	.281	.208	.367	.096
Tech. Interests - interest - mathematics	.337	.079	.009	.080	.049	.180	.794
Self-efficacy - mathematics	.166	.106	.401	.204	.103	.096	.707
Tech. Interests: Solving practical math problems	.285	.171	.119	.184	.115	.072	.690

Note: Significant primary loadings > .300 are bolded; Significant secondary loadings > .300 are italicized.

Since the two interest scales (Math/Science and Technical) failed to effectively separate in any of the models, a seven- factor solution was chosen. The seven-factor model retained four of the six scales intact, leaving the added items in self-efficacy and interest (as well as Practical Math and Scientific Concepts items) to the final two factors, suggesting these majors differ in significant ways from the Engineering and Technology careers. Factor 1, with 12 items and internal reliability = .915, appeared to represent Engineering and Technology interest items and so was named Engineering/Technology Interests. Factor 2, with 10 items and an internal reliability = .927, appeared to represent Outcome Expectations. Factor 3, with 8 items and an internal reliability of .945, appeared to represent Self-Efficacy for Engineering, Technology, & Agriculture, and was labeled Self-Efficacy. Factor 4, with 7 items and an internal reliability of .907 contained the STEM Coping items and was so labeled. Factor 5, with four items and an internal reliability = .970, appeared to represent the Educational Goals items and was so labeled. Factor 6, with six items and a reliability of .810, included the interest and self-efficacy items for Agricultural, Biological, and Chemistry sciences, being labeled Bio-Chemical Sciences Interest/Self-Efficacy. Finally, Factor 7, with three items and a reliability of .797 represented the mathematical interests and self-efficacy and so were labeled Mathematics Interest/Self-Efficacy. In summary, seven factors were interpreted, accounting for 60.86% of the variance. Four of the seven factors represented one specific scale each (Self-Efficacy, Outcome Expectations, STEM Coping, and Educational Goals). One factor represented both of the original interest scales, excepting those items related to Agricultural, Biological and Chemical sciences.

Discussion

This study was designed to provide information related to the utility of the STEM Fields Questionnaire, adapted from the Engineering Fields Questionnaire (Lent et al., 2003), for use with African American undergraduate STEM majors at an HBCU (i.e., students in engineering, biology, psychology, and agricultural sciences). Results of an exploratory factor analysis provided evidence for a seven-factor model underlying the STEM Fields Questionnaire when implemented with African American college students at an HBCU. The factors in this model included Engineering/Technology Interests, Outcome Expectations, Self-Efficacy, STEM Coping, Educational Goals, Bio-Chemical Sciences Interest/Self-Efficacy, and Mathematics Interest/Self-Efficacy. Four of the six original Field’s Questionnaire measures were replicated: Outcome Expectations, Self-Efficacy, STEM Coping, and Educational Goals.

The seven-factor model was retained due to the higher reliabilities of the subscales thus created. This model also retained more of the original scales, retaining four of the six original scales (outcome expectations, self-efficacy, STEM coping, and goals). This suggests that, for African American STEM students, the constructs in four of the original scales appear to measure similarly to the previous validation studies (see Table 1 for listing of studies). The items pertaining to biology and chemical sciences as well as math students’ interests and self-efficacy loaded onto two new factors. This suggests that the items in the Engineering Fields Questionnaire as they were originally published are not appropriate for use with these two groups of STEM students and that the modified set of questions created for this study is needed if the measure is to be utilized with these groups. Therefore, these results suggest that engineering/technology majors differ from biological sciences, chemistry, and mathematics majors in significant ways, which is supported by research that has found variations in the relationships between social cognitive variables among underrepresented students from different STEM disciplines (Byars-Winston et al., 2010).

A number of other differences were observed in several of the remaining scales. Firstly, the combined Interest/Technical Interest factor did not include the interests/technical interest items for Biological Sciences, Chemistry, Mathematics, and Agricultural Sciences students, which comprised a separate factor. Thus, Technical Interests may be more integrated into the decision to choose an engineering major than for other groups. Secondly, the separation of all math-focused items into a single factor suggests that, for African American STEM students, the mathematical aspects of their field are seen as a unique part of their STEM studies rather than integrated into the technical aspect of their chosen STEM field. Thirdly, the finding that the additional interest and self-efficacy items specific to biological sciences, chemistry and agricultural sciences combined into one factor is consistent with past research on the validity of the measures of the Engineering Fields Questionnaire, which has highlighted the relationship between interests and self-efficacy among underrepresented college students in STEM (Byars-Winston et al., 2010; Lent et al., 2011).

Limitations & Future Directions

There are several limitations pertinent to the present study, which provide directions for future research investigating the validity of the STEM Fields Questionnaire for underrepresented college students, including attention to gender, STEM discipline, and type of institution. The need for validation with African American male college students is particularly important for the self-efficacy and outcome expectations measures, as past research has suggested variations in the levels of self-efficacy and outcome expectations among men compared to women (Byars-Winston & Fouad, 2008), as well as differences in the relationship between these two social cognitive variables by gender (Lent et al., 2011). As the current sample was primarily comprised of students from four STEM disciplines (i.e., agricultural sciences, biology, engineering, and psychology), examination of the validity for use with other STEM majors (e.g., chemistry, computer science, mathematics, and physics) is warranted. Future validation research on the STEM Fields Questionnaire with African American samples should aim to include a more representative number of students from chemistry, computer science, math, and physics to determine if the factor model in the present study is retained.

The final limitation that is important to mention is that the present study was conducted at a mid-size, public HBCU in the southern region of the United States. This HBCU is a research institution, and it is afforded the resources needed for conducting large-scale STEM research. Given the setting in the present study, further examination of the validity of the measure in other Historically Black College/University settings is needed.

Implications

The results from this study suggest that four of the STEM Fields Questionnaire measures generalized across STEM majors. Furthermore, this research suggests that STEM interests and technical interests may be better considered as a single factor and that interests and self-efficacy remain separate factors for this population. Thus, further examination factors and potential differences between the biology-related STEM majors (biology, chemistry, and agricultural sciences) and other STEM majors is warranted.

The results of this study have implications for vocational psychology practice. The seven-factor model proposed suggests that while the Engineering Fields Questionnaire is an effective set of measures for use with engineering majors, its use with other STEM students should be limited because two of the six measures were not supported in this analysis. The insight offered by the results of this study regarding the possible differences between STEM fields suggest further exploration of the measure and incorporation of the unique experiences and needs of African American students pursuing careers across these disciplines. Career counselors working with under-represented students in these major fields should attend to such differences and aim to understand contextual factors influencing the impact of pertinent social cognitive variables on their career decision-making and retention.

Footnotes

Author Notes

Sarah Girresch-Ward is now at the St. Louis Children’s Hospital, Washington University School of Medicine, St. Louis, Missouri, Natalie Rochester is now at the Michael E. DeBakey Veterans Affairs Hospital, Houston, Texas. Carin Teeters is now at the Veterans Affairs Medical Center in Portsmouth, Virginia.

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) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was supported by a grant from NSF/HBCU-UP/BPR (NSF 1238778).

ORCID iDs

Marie S. Hammond

Jenna S. Lehmann

Taylor N. Roberts

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