Choice of Major: An Annotated Bibliography

Darcie L. Callahan

University of Kansas

Preface

I recently completed compiling this annotated bibliography of articles concerning the factors involved in college students choosing their majors. I chose this topic in part because of my job at the University Advising Center, where I am an Academic Advisor. The central purpose of my job is to assist students in choosing majors, and to help them choose courses that will lead to a timely completion of their degree. I learned a number of things about the factors that go into students choosing majors from creating the annotated bibliography, many of which will help me in my line of work.

The first element regarding choosing a major that is featured in my annotated bibliography is a section on the different types of decidedness that students experience. What I mean by “types of decidedness” is how close to the decision a student is, and how much or little anxiety the student is experiencing concerning choosing a major. A student may proceed though the decision-making process in a manner recommended by KU advising staff, using the Freshman year to explore by taking different classes and assessments, narrowing down the decision in the sophomore year while completing general education requirements, and actually declaring the major sometime in the Junior year, finishing up the degree in the Senior year, and then graduating. Other students may be overcome with anxiety and even paralysis at any point (even the first Freshman semester), thinking that they are in some way inadequate compared to students who have quickly and confidently declared majors. Other students may spend a lot of time and money taking exploratory paths to experience classes that interest them, while still others may be struggling in frustration while pursuing a major that in some way is not right for them. Some students have even used the assertion “I don’t know what I want to major in” as a reason for dropping out of school entirely, despite the fact that there are many courses they could take that would count as general education requirements for most degrees. So when we’re talking about deciding students, we’re actually talking about a wide range of experiences. Add in factors like decision making style. Some students have a compensatory, or “good enough” style when choosing their major, and make the decision quickly and easily. Others may have a non-compensatory, or perfectionist style which may lead them through endless doubting and stress. So students in the process of choosing a major are not all cut from one cloth.

The second section of my annotated bibliography looks at different theories regarding choosing a major, particularly theories that can be helpful to use as a framework while working with students. Time and again, Holland’s RIASEC codes are tested and found to be good. Holland divides people into basic groups, Realistic (works with machinery or animals), Investigative (like a scientist), Artistic, Social (like a counselor), Enterprising (business) and Conventional (data, clerical skills). Different occupations can relate to up to 3 of the codes, and it is thought that an understanding of one’s basic Holland 3-type code can help a person find a career in an area with which will be a good fit. Holland codes are widely used in career counseling. The University Career Center at KU uses the Strong Interest Inventory to determine a student’s Holland code. Of course, I also included other theories in this section, such as a developmental theory, chaos theory, and brain hemisphericity as they all have something to contribute. My favorite theory regarding choosing a major is Anti-introspectivist theory, the idea that we may think we are making completely rational decisions, but actually the factors affecting our decision are deeply held thoughts and beliefs coming from our subconscious. Some of these may be so deep we will never know what they are exactly. My boyfriend’s desire to work for Jeep Chrysler may have more to do with the fun he had playing with toy cars as a child than with any sort of stats, facts, or figures regarding the auto business as a good place to start a career.

After getting a good understanding of decidedness and the theories that surround choosing a major, I then looked at interventions, tools people came up with to help students choose majors. I mentioned the University Career Center uses the Strong Interest Inventory to determine students’ Holland codes. They also use the Do What You Are, an assessment based on the Myers-Briggs personality classifications that relates Myers-Briggs codes to career fields. One thing that surprised me, however, was that they did not look at a student’s ARTS form (informal transcript). When I advise in the University Advising Center, I don’t have assessments to give to students, so I look at their ACT and SAT scores to see if they’re better at reading/writing or science/math, and how much higher they’ve scored in one area rather than another. I also look at their grades to see which classes they did well in, if these courses cluster in any way, and I ask them questions, such as which classes do they enjoy most, which they find most interesting, etc. Because of this, I was glad to find an article about using students’ ACT scores to help them choose a major. Other interventions include such things as workshops and courses geared at helping students choose majors.

The second part of the annotated bibliography shifts from factors that apply to students in general to focus on special populations, such as the effect of gender on choosing a major (I also included here the one article I was able to find on major choice and sexual orientation). I looked at race/ethnicity (including international students), socio-economic status (including the influence of parents’ occupations), and ability, ranging from students with learning disabilities to gifted students. There are a lot of groups that have been understudied in these special populations, but researchers are working on identifying and including them. Two articles on males’ choice of careers non-traditional in terms of gender cited the need to balance out literature on females’ choice of non-traditional careers. While the factors of special populations are important, they don’t necessarily negate the effects of personality, Holland code and the like. One thing common to all populations were perceived barriers. Even gifted students might believe they would be soundly criticized for picking a major that others believed to be unsuitable for one capable of high achievement.

The cognitive factor is very interesting here. One student might perceive a barrier in terms of race or gender while another student of that race or gender does not. I remember meeting a female Vietnamese student when I was in college. She told me her whole family wanted her to be a teacher as she had been in Vietnam, but she told them, “No! This is America, and women in America can be anything they want! I’m going to be an Engineer!” So over their protests, she was successfully pursuing her Engineering degree at Purdue, one of the top engineering schools in the nation. Personality, beliefs, values, and perceptions all work together in this example to explain why she departed from what others of her culture thought appropriate for her.

While the articles range widely in topic, there is one thing almost all of the articles in my annotated bibliography have in common: they all call for career counselors to learn and employ knowledge of cultural and other differences to enhance their ability to successfully assist students who are deciding on their majors. Career counselors (and their academically advising cousins) need to be aware to diffuse career myths, and to consider the biases, preferences and needs of special populations without profiling or stereotyping individuals in those populations.

Introduction

This Master’s Project is an annotated bibliography of academic journal articles related to students’ choice of major. As institutions of higher learning drift closer in alignment with a cost-effective business model, a greater emphasis on graduating in four years is becoming a priority at universities such as the University of Kansas. One element crucial to the timely completion of a four-year degree is the timely choice of a major. At the same time, scholarly research supports emphasis being put on the value of students spending some time (generally the freshman year) deciding and exploring their options.

An understanding of the factors that affect students’ choice of major is likely to be helpful to the academic advisors and career counseling personnel who assist them. It is my hope this annotated bibliography will prove to be a resource for such university personnel.

This annotated bibliography is divided into two parts each containing several different topics. In some instances, if an article covers two or more topics, it was put in both sections.

The first part, Deciding Students, describes the characteristics of deciding students in detail. The first section of the first part, Types, contains articles outlining different categories of deciding students, based on where the student stands in the decision-making process.

The second section, Theory, covers the main theories governing academic major choice.

The third group of articles, Interventions, examines different interventions put forth intending to assist students in choosing their majors.

The second part of the paper, Special Populations, contains articles that illuminate the particular needs and concerns of different groups of students. The first section of the second part, Race/ethnicity, contains articles relating major choice to issues of race, ethnicity, and cultural background.

The second section, Gender, covers male/female differences and also contains an article on sexual orientation and major choice.

The third section, Socio-economic Status (SES), looks at socio-economic status, including parental job prestige, as a factor in choosing a major.

The fourth and final section, Ability, covers issues of both students with disabilities as well as high-ability, gifted students and their particular needs regarding major choice.

A list of all the articles contained in the annotated bibliography follows, arranged alphabetically by first author’s last name.

Contents

Deciding Students

- Types

Gordon (1981)

Kelly & Pulver (2003)

Larson (1988)

Lucas & Epperson (1990)

Martin & Dixon (1991)

Shiloh & Zakay (2001)

- Theory

Beck (1999)

Colozzi (2003)

Galotti (1999)

Gelatt (1992)

Krieshok (2001)

Krieshok (1998)

Leuwerke, Robbins, Sawyer & Hovland (2004)

McCollum (1998)

Miller & Woycheck (2003)

Mitchell, Levin & Krumboltz (1999)

Paa & McWhirter (2000)

Porter & Umbach (2006)

Reardon & Bullock (2004)

Saleh (2001)

Srsic & Walsh (2001)

- Interventions

Candrl & Heinzen (1994)

Childress (1998)

Hansen (2003)

Harris, Golden & Olson (1985)

McDaniels, Carter, Heinzen, Candrl, & Wieberg (1994)

Rayman, Bernard, Holland & Barnett (1983)

Robinson & Betz (2004)

Steele (2003)

Strasser, Ozgur & Schroeder (2002)

Special populations

- Race/ethnicity

Flores, Navarro, Smith & Ploszaj (2006)

Lent, Brown, Sheu, Schmidt, Brenner, Lyons & Treistman (2005)

Simpson (2001)

Trusty & Plata (2000)

Yi, Lin & Kishimoto (2003)

- Gender

Brown, Garavalio, Fritts & Olson (2006)

Chung (2003)

Dawson-Threat & Huba (1996)

Dodson & Borders (2006)

Flores, Navarro, Smith & Ploszaj (2006)

Grant, Battle & Heggoy (2000)

Lent, Brown, Sheu, Schmidt, Brenner, Lyons & Treistman (2005)

Madill, Montgomerie, Stewin, Fitzsimmons, Tovell, Armour &

Ciccocioppo (2000)

Trusty & Plata (2000)

Turner & Bowen (1999)

- Socio-economic Status (SES)

Leppel, Williams & Waldauer (2001)

Simpson (2003)

Trusty & Plata (2000)

- Ability

Grant, Battle & Heggoy (2000)

Hagstrom, Scovholt & River (1997)

Layton & Lock (2003)

Leung, Williams & Waldauer (1998)

Deciding Students

This section reviews the types of deciding students, theories that pertain to deciding on a major, and interventions that have been developed to help students with the process of choosing a major.

- Types

The first grouping of articles pertains to types of deciding students. Larson’s (1988) and Lucas & Epperson’s (1990) studies attempt to determine if deciding students can be divided into categories. Gordon’s (1981) article takes a developmental perspective within the context of Super’s Vocational Life Stages. Kelly & Pulver (2003) explore levels of decidedness and confidence. Martin & Dixon’s (1991) study explored the effect of internal vs. external locus of control. Shiloh & Zakay’s (2001) article examines the effects of decision-making style on choosing a major.

Gordon, V. N. (1981). The undecided student: A developmental perspective. The Personnel and Guidance Journal, 59, 433-439.

Hypotheses/Theories: This article presents two theories that the author believes need to be incorporated into career counseling strategies. The first one is called A Student Developmental Model, which includes four stages that a student undergoes chronologically when making career decisions. Most deciding students are in the first two stages, labeled Dualism and Multiplicity. The second theory presented is A Career Decision-Making Theory, consisting of seven stages. Deciding students are typically in one of the first four stages: exploration, crystallization, choice, and clarification.

Findings: Gordon believes that the two theories should be viewed within the context of Super’s Vocational Life Stages and that all three should be integrated. Counselors should strive to challenge their clients to move on to the next stage in each model.

Kelly, K.R. & Pulver, C.A. (2003). Refining measurement of career indecision types: A validity study. Journal of Counseling & Development, 81, 445-454.

Hypotheses/Theories: 10% of all college-bound students have not fully decided upon a major. There are three distinct types of career indecision (confident decided/ready to decide, confident but uninformed, and anxious undecided), and different counseling interventions are appropriate for each type. However, no studies have documented the academic, career or development outcomes associated with each type. Research on career indecision types has been plagued by the dearth of predictive validity evidence, failure to consider academic aptitude, no research conducted exclusively with deciding students, cluster analysis has been used primarily to interpret data, and too much variation in personality variables included in cluster analyses.

Methodology:

Population: male and female deciding first-semester freshmen.

Sample: Participants were 566 deciding first-semester freshmen enrolled in a career exploration class in a large Midwestern university. 292 female, 274 male. 515 Caucasian, 18 African-American, 24 Asian American, 8 Latino, 1 Native American.

Instruments: Assessments used were the Career Factors Inventory (CFI) to measure indecision, the NEO Five Factor Inventory to measure personality, and the SAT verbal and math assessments.

Findings: Neuroticism was significantly correlated with all CFI scales, but not the SATs. Well-adjusted and extravert information seekers had the lowest posttest indecision. Low-ability information seekers and neurotic-indecisive information seekers had the highest levels of indecision. Current models for understanding career indecision may not be completely adequate. The study also establishes a template for further research to validate decision types. The Career Decision Scale (CDS) was used as an outcome measure.

Confounds: The study was limited by lack of diversity (racial/ethnic, culture, socioeconomic status), only a single criterion measure was used to study the career intervention outcome, the CDS posttest was not administered to all students during the final week of the semester, and the intervention process was not studied in relation to outcome.

Larson, L.M. (1988). Investigating multiple subtypes of career indecision through cluster analysis. Journal of Counseling Psychology, 35, 439-446.

Hypotheses/Theories: While most studies have separated students choosing a major into a dichotomy of decided vs. undecided, little attention has been paid to whether or not there are differences among deciding students. The purpose of this study was to divide deciding students into groups to classify them.

Methodology:

Type of Study: Descriptive.

Population: male and female college students

Sample: 87 deciding and 26 decided students, all but 9 were sophomores. Decided students included 8 males and 18 females. Deciding students included 33 men and 54 women.

Instruments: The Career Planning Interview (developed for this study) followed by an Interview Rating Questionnaire, 3 career planning instruments (The Career Decision Scale, The Problem Solving Inventory, The Vocational Preference Inventory), cluster and factor analysis. Cluster analysis sorts together subjects with the most similar or closest sets of scores.

Independent Variable: personality traits

Dependent Variable: type of decidedness

Findings: There are 4 distinct types of deciding students: planless avoiders, informed indecisives, confident but uninformed, and uninformed.

Confounds: Stability of the clusters is unknown across different samples.

Lucas, M.S. & Epperson, Douglas, L. (1990). Types of vocational undecidedness: A replication and refinement. Journal of Counseling Psychology, 37, 382-388.

Hypotheses/Theories: The state of not yet having decided upon one’s major(s) can be understood better if viewed as a complex multivariate phenomenon.

Methodology:

Type of Study: Descriptive.

Population: male and female college students

Sample: 510 undergraduate students (257 females, 253 males) enrolled in introductory psychology courses at a large Midwestern university. The final sample included 196 deciding students (108 females, 88 males) scoring 9 or lower on The Vocational Identity Scale on My Vocational Situation. Six instruments assessing personality features related to undecidedness were also used to help clarify patterns or subtypes of deciding students.

Instruments: The Vocational Identity Scale on My Vocational Situation.

Independent Variable: identification with personality traits

Dependent Variable: type of career indecision

Findings: Means and standard deviations were reported for 14 variables, split by gender. T tests were used to find significant variables on 3 values: Relationships (p < .01), Work (p < .05) and State Anxiety (p < .05). The study concluded that there are different types of deciding students.

Confounds: The choice of variables is always somewhat subjective.

Martin, N. K. & Dixon, P.N. (1991). Factors influencing students' college choice. Journal of College Student Development, 32, 253-257.

Hypotheses/Theories: Research question: Why do high school students choose to attend one college and not another? Purpose of study was two-fold: to provide limited construct validation for specific interpretations of scores on the CCIS (College Choice Influence Scale) and to study the effects of various demographic variables as well as locus of control. Social learning theory suggests locus of control is internal or external. Hypothesis: Externals would be more influenced by others than internals, and deciding would be more unclear about college goals.

Methodology:

Type of Study: Descriptive

Population: First year male and female college students.

Sample: 104 female and 84 male students with a mean age of 18.9 at a major southwestern university. Instruments: Internal-External Locus of Control Scale of 23 forced-choice items, and the College Choice Influence Scale (CCIS) using 5 factor-derived subscales.

Independent Variable: demographic variables and locus of control

Dependent Variable: decidedness

Findings: A t test revealed externals to be significantly more influenced by others than internals. A significant difference was found between deciding students and those who had declared a major. Education majors were significantly more influenced by family tradition than deciding students or business administration majors.

Shiloh, S., Koren, S., & Zakay, D. (2001). Individual differences in compensatory decision-making style and need for closure as correlates of subjective decision complexity and difficulty. Personality and Individual Differences, 30, 699-710.

Hypotheses/Theories: Individual differences in compensatory decision-making style and need for closure were hypothesized to associate with the subjective complexity of a natural decision-making structure and with its perceived difficulty. The aim of the study was to show possible links between stable individual differences and structural decision factors. This may add another perspective on how individual variations affect decision making. The decision in question in the study was choice of major.

Two decision making styles were defined: compensatory (characterized by a "vigilant" coping pattern) and non-compensatory (characterized by a "good enough" coping pattern). People lean towards either compensatory or non-compensatory styles based on personality traits, education, and past experiences. Compensatory instruction stimulates more complex thinking about choices, leading to more suggestions regarding choosing a major and more reasons attributed to the choice of major. Decision makers with a strong tendency toward compensatory strategies and "vigilant" coping will construct a more complex decision representation, and consider more options and dimensions than non-compensatory decision makers. Individuals with a strong need for closure process less information before committing to a major, and generate fewer competing hypotheses to account for available data. Thus they are likely to utilize more non-compensatory decision making strategies.

Methodology:

Type of Study: Descriptive

Population: Tenth-grade male and female Israeli students.

Sample: 120 tenth-grade male and female students in a Tel Aviv high school choosing between 5 majors.

Instruments: The Compensatory Style Questionnaire of 40 statements of attitudes and beliefs, measured on a Likert scale. Need for closure was measured with a Hebrew version of the Need for Closure Scale (NFCS), a 42-item self-report instrument based on agreement statements. Subjective, rather than objective, decision making was studied. Subjective complexity of choosing a major was defined as the number of alternatives the decision-maker was considering, and the number of dimensions she perceived as important to the decision. Perceived difficulty was measured on five 7-point dimensional scales. The decision was natural (characterized by ill-structured problems and ill-defined or competing goals) rather than artificial.

Independent Variable: attitudes and beliefs

Dependent Variable: decision making style

Findings: Perceived decision difficulty was significantly related to subjective decision complexity, particularly to the number of alternatives considered. Compensatory style was unrelated to number of alternative, and its relationship with subjective decision complexity was mediated through its correlation with a number of dimensions. Individual differences in decision making style and specific facets of need for closure affect perceived decision difficulty through their effects on the complexity of subjective representations of the decision. A positive correlation was found between the tendency to use a compensatory decision making style and subjective decision complexity. However, the tendency to utilize compensatory strategies in a specific decision situation is decreased when the number of alternatives is larger. The association between individual differences in need for closure and decision representations is complex. The decision making situation is molded by how people perceive and construct it, lessening the separation between person and situation factors.

Confounds: The subjects were 15-year-olds. Do adolescents make decisions in roughly the same ways as adults?

- Theory

The second section covers theories of career counseling that pertain to major choice. The best-known and most used of these theories is Holland’s code classification of personality types (Reardon & Bullock, 2004). Three articles (Miller & Woycheck, 2003; Porter & Umbach, 2006; Reardon & Bullock, 2004; Srsic & Walsh, 2001) deal with applying Holland to academic advising/career counseling to assist students with choice of major. Other useful theories include chaos theory, at which Beck takes a stab. Colozzi (2003) examines the roles of values in major choice. Gelatt (1992) and Krieshok (2001, 1998) favor the postmodern Positive Uncertainty and Anti-introspectivist theories, respectively. Mitchell, Levin, & Krumboltz (1999) highlight the role of chance and preparedness to seize opportunities in their article on Planned Happenstance. McCollum (1998) takes a developmental approach with Super’s theory of career counseling. Saleh (2001) explores brain hemisphericity and its effects on choosing a major.

Beck, A. (1999). Advising undecided students: Lessons from chaos theory. NACADA Journal: The Journal of the National Academic Advising Association 19, 45-49.

Hypotheses/Theories: Chaos theory is applied to understanding deciding students. It is suggested that deciding students may have their own unique individual patterns of career development, different from expected, linear models.

Findings: The article concludes that chaos theory may benefit academic advisors by reminding them that it is acceptable to be deciding, trust with academic advisors is paramount to a good advising relationship, risk-taking behavior can lead to healthy change, and looking at the whole student can help advisors apply developmental principles and maintain a positive attitude to change in order to reassure apprehensive students. Both must strive to “be comfortable with uncertainty.”

Colozzi, E. A. (2003). Depth-oriented values education. The Career Development Quarterly, 52, 180-189.

Hypotheses/Theories: The role of values in career decision making is examined. Interests are manifestations of values and may help the individual guide their actions, evaluate Self and others, and serve adjustive, knowledge, and self-actualization purposes. Values may be either expressed or implied. Expressed values may overshadow implied values. Implied values are similar to Rogers’ operative values, which are set in place by others. There are also life values and work values.

Methodology: A case study Depth-Oriented Values Extraction (DOVE) is examined. DOVE translates various types of psychological data (such as Holland codes) into values-based terms and language to facilitate career decision making. Dove has a 5 step process of discovery, integration and crystallization, extraction, prioritization and achievement of cognitive clarity, and congruence. The case study was of a 30-year-old Caucasian woman, employed full-time as a human resources representative with a job satisfaction of 3 on a 10 point scale.

Findings: Values clarification is of paramount importance in terms of improving career decision making. While values can be measured, we still have not demonstrated their relation or predictive validity to career choice, career satisfaction, and career success. More research is needed to evaluate the efficacy of DOVE.

Galotti, K.M. (1999). Making a “major” real-life decision: College students choosing an academic major. Journal of Educational Psychology 91, 379-387.

Hypotheses/Theories: This is a study of the way freshman and sophomore college students structure important decision-making, using major choice as the example.

Hypotheses:

1. Linear models will correlate significantly and substantially with students’ holistic impressions of different candidate majors.

2. Effective decision making (as indicated by the correlation of students’ holistic impressions with the predictions of linear models) will correlate significantly with students’ confidence and comfort with the process.

3. Students’ memories of their decision-making processes will correlate more strongly with their current views of how the process should work than with their performance as it actually initially occurred.

Methodology:

Type of Study: College students were given a written survey first in the second semester of their Freshman year, then again in their Sophomore year.

Population:: Second semester Freshmen college students at two schools in southeastern Minnesota.

Sample: 111 second semester Freshmen college students, 33 Male, 74 Female, and 4 who did not designate a gender. Students were recruited from a randomly selected sample.

Instruments:: Untimed written surveys administered in a single session, collecting demographic data, 2 open-ended questions, and “listing” questions (i.e., questions that ask students to make lists of their responses).

Findings: Students view major choice as reflecting their Selves as well as leading to future consequences. Students’ overall impressions correlated with the predictions of linear models, however students seemed unaware of using a linear model. Students should be reassured that a persisting deciding state is reflective rather than “waffling.” Students described listing major possibilities then whittling their lists down, much like a card sort. Confidence and comfort with the decision-making process were two factors that did not correlate with linear models, indicating that students may have maladaptive expectations regarding the nature of effective real life decision-making.

Confounds: The study does not mention data issues related to age or race/ethnicity.

Gelatt, H. B. (1992). Positive uncertainty: A paradoxical philosophy of counseling whose time has come (Report No. EDOCG-92-20). Ann Arbor, MI: School of Education, University of Michigan. (ERIC Document Reproduction Service No. ED347486)

Hypotheses/Theories: The author believes that a theory called Positive Uncertainty needs to be incorporated into career counseling. This theory states that two attitudes are required of the client: to be accepting of the past, present, and future as uncertain and to be positive about that uncertainty. It teaches clients to be focused and flexible, aware and wary, objective and optimistic, and practical and magical.

Findings: The author begins his article by explaining in general how the world of work has been changing: the future is no longer predictable and everything continues to change at a rapid pace. The article basically discusses the above-described theory of Positive Uncertainty.

Krieshok, T. S. (2001). How the decision-making literature might inform career center practice. Journal of Career Development, 27, 207-216.

Hypotheses/Theories: Anti-introspectivist theory, which suggests that most decisions are not made at a conscious level and that thinking about the decision-making process can actually confuse the individual further.

Findings: This article is a review of the current literature on career center practice. It supports an anti-introspectivist view. The article challenges many assumptions of career counseling, including the following three: decidedness is always good, interventions are never harmful, and occupational information is always helpful. The author concludes that counselors need to incorporate these new findings into career center practice.

Krieshok, T.S. (1998). An anti-introspectivist view of career decision making. The Career Development Quarterly, 46, 210-229.

Hypotheses/Theories: An anti-introspectivist perspective is used to review findings of the field of career counseling. Krieshok lists 10 things that are known with certainty:

1. Counselors can assess various aspects of career counseling.

2. People vary in degree of decidedness.

3. Decidedness develops over time.

4. Gender, SES, education, ethnicity all affect career decisions.

5. People who experience difficulty making career decisions often have other difficulties.

6. Indecision may persists after a decision is made.

7. Interventions increase decidedness.

8. The career decision-making process is complex.

9. People make mistakes when processing information.

10. People assume they know how they make career decisions.

and 10 things that the field may view with false confidence:

1. Decidedness is good.

2. Counselors should assist clients in reaching decisions.

3. Conscious decision making influences vocational behaviors.

4. Career interventions are never harmful.

5. Career counseling is more effective than simply thinking about career issues.

6. Barriers are always harmful.

7. Standard forms of career information are helpful.

8. Clients and counselors both have the same goals.

9. Dual career decision making is simply a special case of career decision making.

10. Self-report is valid as a means of data collection.

Findings: It is important to review assumptions that are held about career counseling, as with any field, to understand which have validation. Only with accurate data can we make sound analyses. It is important to look “underneath the skin” of career counseling. An anti-introspectivist perspective can assist us in doing so.

Leuwerke, W.C., Robbins, S., Sawyer, R., & Hovland, M. (2004). Predicting engineering major status from mathematics achievement and interest congruence. Journal of Career Assessment 12, 135-149.

Hypotheses/Theories: This study predicted a congruence between high school ACT Math scores (ACTM) and interests would predict students’ sophomore year persistence in engineering majors. (Students could have either been retained in the engineering major, changed their major, or dropped out.) The selection and placement of students could be helped by such a model.

Methodology:

Type of Study: This study used ACT score information gleaned from archival databases at a large Southern university.

Population:: Freshman college students at a large Southern university.

Sample: 844 Freshman college students declaring an engineering major upon entrance to college at a large Southern university as follows: 622 Male, 222 Female; 74% Euroamerican, 23% African-American, 2% Asian, 1% Hispanic, 1% Native American, with an age range of 18 to 28 years old, 90% were in-state residents.

Instruments:: Archival databases of ACT Math scores (ACTM) and ACT Interest Inventory scores (UNIACT), which are based on Holland RIASEC types. These scores were compared using the Hexagram Congruence Index (HCI).

Findings: The importance of looking at both achievement and interest is reinforced by this study. Students who were retained in the major or dropped out of school had significantly greater interest congruence scores than those who changed majors to something other than engineering. Future research needs to use this model to examine other programs.

Confounds: The sample size was too small to adequately estimate regression weights for the data in the area of gender.

McCollum, V. J. C.. (1998). Career advising: A developmental approach. NACADA Journal: The Journal of the National Academic Advising Association 18, 15-19.

Hypotheses/Theories: This article relates Super’s developmental theory of career counseling to academic advising, and sees career decision-making as a developmental step. Super’s theory is good because it deals with age and task-suitability but also acknowledges that people may need to repeat a developmental stage. Super’s theory suggests the following outline:

Freshman Year: 1.) trust-building and 2.) assessment

Sophomore Year: decision-making

Junior Year: support

Senior Year: confirmation

Confounds: This system would work best if students had the same advisor throughout all 4 years. Yet at KU, for example, advising is spread out among the University Advising Center, the University Career Center, faculty, and graduation advisors, and students see numerous advisors. Even if advising was all centered in one place, academic advising is a female-dominated, low-paying job that often results in much turnover of personnel.

Miller, B. & Woycheck, S. (2003). The academic advising implications of the self-directed search and holland’s theory: A study of kent state university exploratory students. NACADA Journal: The Journal of the National Academic Advising Association 23, 37-43.

Hypotheses/Theories: This article talks about using one of the best-known theory’s of career counseling (Holland codes) and applies it to academic advising. Holland’s theory classifies personality types as Artistic, Conventional, Enterprising, Investigative, Realistic, and Social, all of which correspond to different types of careers. Generally, the 3 highest scoring areas are combined into a Holland code. Students who score high in one area are considered “well-defined” while others who score similarly across the board are called “flatliners.”

Methodology:

Type of Study: Descriptive

Population: male and female freshmen college students.

Sample: 495 male and female students entering the Kent State University Exploratory program.

Instruments: Holland's Self-Directed Search (SDS) was used to determine career type (defined by the high point code of each student’s summary code of the Holland code).

Findings: Career decision seems to be easier for the well-defined than for the flatliners. The article also suggests that students who fall in the Social Holland code may also be distracted by the social goings-on of campus, as the deciding freshmen in the Exploratory program all had dominant Social codes. Investigative coded students seemed to have the best graduation rate. It is surmised that the ability to investigate career options and majors serves as an advantage for them. It was noted that students in the Exploratory program did not always graduate with careers that fit their Holland codes. Overall, the Holland Self-Directed Study seemed to be an effective tool for academic advisors to use with deciding students.

Mitchell, K.E., Levin, A.S., & Krumboltz, J.D. (1999). Planned happenstance: Constructing unexpected career opportunities. Journal of Counseling & Development, 77, 115-124.

Hypotheses/Theories: Planned Happenstance, Planful Serendipity, Positive Uncertainty -- all of these labels refer to the idea that the role of luck or chance in career development needs to be examined, understood, and accepted. Sometimes people feel guilty when they realize chance has played a part in their career success – yet chance affects us all, starting with the parents & neighborhoods of our birth. Anecdotes are recounted to track the effect of chance in various careers. The “planned” part refers to the need to seek ways that chance can effectively combine with talents, education, and skills to bring about positive outcomes as a person takes action to generate career opportunities. Exploration finds opportunities; skills enable people to grab those opportunities.

Five skills are important to planned happenstance: curiosity, persistence, flexibility, optimism, risk-taking.

Findings: Instead of being seen as a way to eliminate chance, career counseling should seek to make the most of it. Career counselors should see themselves as educators rather than matchmakers.

Paa, H.K., & McWhirter, E.H. (2000). Perceived influenced on high school students’ current career expectations. Career Development Quarterly, 49, 29-44.

Hypotheses/Theories: A lot has been asserted regarding influences on adolescent career choice. Paa and McWhirter wanted to see if the adolescents agreed with the construction of these influences. This qualitative piece presents the results of descriptive data regarding high school students perceived career choice influences. Perceived influences and ability are likely a greater factor in major choice than actual ability.

Hypotheses: 1.) Bandura’s view of importance of role model similarity, and 2.) Farmer’s assertion that personal variables rank higher than background or environmental factors in students’ career choice.

Methodology:

Type of Study: content analysis of qualitative survey data.

Population:: Midwestern male and female high school students in their first and second years of school.

Sample: 464 male and female high school students from two schools in a small Midwestern city, as follows: 238 Male, 226 Female; 88% Euroamerican, 3% Asian, 3% Hispanic, 2% African-American, 2% Other; median age 14.7.

Instruments:: A demographic survey, and two instruments developed for this study: a ranking instrument of perceived influences on career choice, and a survey of type of influences on career choice. Both instruments were tested for content validity.

Findings: Top 3 background influences for both genders: ability, role models, media. Bottom 3 background influences: ethnicity, gender, SES.

Top 3 personal influences for both genders: interests, personality, values. Bottom influences: luck, perception of gender roles.

Top 3 environmental influences: same gender parent, other parent, same gender friends. Bottom factors: teachers, counselors and friends of the opposite gender.

Overall, high school students are aware of a variety of influences on their career choices, and they are in alignment with the constructs put forth by adults. It is suggested that counselors might collaborate with role model influences to assist students with career choice, particularly females who may suffer from the effects of a “null environment” if role models are lacking.

Porter, S.R. & Umbach, P.D. (2006). College major choice: An analysis of Person-Environment Fit. Research in Higher Education, 47, 429-449.

Hypotheses/Theories: Holland’s RIASEC career theory underlies this study.

Methodology:

Type of Study: College major choice analyzed using a multinomial logit model. Research questions: What are the factors that predict major choice? Do race and gender affect major choice? Controlling for these factors, what role does personality play in major choice?

Population:: Freshmen students at liberal arts colleges.

Sample: Freshmen students at a liberal arts college who graduated within 6 years, mean age 18.15. Female 54%, Male 46%. Euro-American 72%, Asian 9%, African-American 8%, Hispanic 7%, Other 4%.

Instruments:: The Cooperative Institutional Research Program Student Information Form (CIRP Freshman Survey) and demographic data.

Independent Variables: demographics, parental influence, academic preparation, academic career expectations, political views, personality/goals based on Holland RIASEC typology.

Dependent Variable: 4 categories of academic major at graduation: arts & humanities, interdisciplinary, social science, life & natural sciences.

Findings: Two factors stood out as consistent predictors of major choice: political views and Holland RIASEC personality scales. Racial differences were significant, with African-Americans more likely to chose interdisciplinary and social science majors and Hispanics more likely to choose arts & humanities, interdisciplinary or social science majors. Gender was less important than personality in choosing a major.

Confounds: While doing the study all at one school controlled for institutional effects, the ability to generalize these findings to other schools is limited, particularly without further studies.

Reardon, R. & Bullock, E. (2004). Holland’s theory and implications for academic advising and career counseling. NACADA Journal: The Journal of the National Academic Advising Association 24, 111-123.

Hypotheses/Theories: This article states that while Holland’s theory is extremely influential in the field of career counseling, it’s use has not been carried over into academic advising. I doubt that this is true, although the author cites research on this point. It may be that faculty advisors know about and apply the theory less than professional advisors.

Findings: Students majors switch around during college, with students with certain codes moving towards majors that are not in alignment with the code (such as an Enterprising student moving into an Investigative career). This is due much to the social climates created by faculty members. Faculty members tend to socialize their students towards careers in the faculty member’s areas of expertise. Jobs that require Holland codes of Realistic and Conventional are least likely to need a college degree. Investigative and Artistic jobs are most likely to need one. An awareness of these patterns of movement can improve career counseling in the area of major choice.

Saleh, A. (2001). Brain hemisphericity and academic majors: A correlation study. College Student Journal, 35, 193-200.

Hypotheses/Theories: This study explored the correlation between choice of major and brain hemisphericity (right brain/left brain dominance).

Methodology:

Type of Study: 2 surveys were administered, and analyzed using SPSS and SAS.

Population:: Undergraduate and graduate students at a large Southern university.

Sample: 429 undergraduate and graduate students at a large Southern university as follows: 402 undergraduate, 27 graduate; 66.43% Female, 33.57% Male. Students were randomly selected with large draws coming from the school of education and the foreign language departments.

Instruments:: A demographic survey, and the 33 item McCarthy’s Hemispheric Mode Indicator (HMI), analyzed using descriptive statistics and ANOVA.

Findings: Brain hemisphericity significantly affects choice of major. Right brain dominance is associated with majors such as fine arts, literature, education, nursing, communication, and law. Left brain dominance is associated with business, engineering, and science majors. This study supports other studies on brain hemisphericity and choice of major and suggests that survey instruments determining brain hemisphericity be employed in academic advising to guide students into majors that suit them.

Srsic, C.S. & Walsh, W.B. (2001). Person-environment congruence and career self-efficacy. Journal of Career Assessment, 9, 203-213.

Hypotheses/Theories: Theory used is Holland's theory of personality dispositions and work environments. The study's purpose was to explore the relationship between person-environment congruence and career search and decision-making self-efficacy in congruent, incongruent, and deciding women college students The primary hypothesis tested was that there are significant differences in the mean scores for the three different groups on two measuring scales (CDMSES and CSES). The researchers hope to remedy what are considered to be shortcomings of Holland's theory by wedding it to other theories to combat unaddressed areas. In this case, multiple regression analyses revealed that self-efficacy beliefs and outcome expectations are more important predictors of grades and persistence in technical/scientific majors than interest congruence.

Methodology:

Type of Study: Predictive

Population: Female college students.

Sample: 200 female college students at a large Midwestern university enrolled in introductory psychology classes.

Instruments: Holland's Self-Directed Search (SDS) was used to determine congruence (defined by the First Letter of the Holland code), as was the "C" index, which takes into account a code's position on the Holland hexagon. Self-efficacy was defined by the Career Decision-Making Self-Efficacy Scale (CDMSES) and the Career Search Efficacy Scale (CSES).

Independent Variables: person-environment congruence, outcome expectations

Dependent Variables: career search and decision-making self-efficacy

Findings: There were significant differences between the three groups (congruent, incongruent, deciding). Deciding participants scored significantly lower than the other two groups. A follow-up analysis showed that the deciding group scored significantly lower than the very incongruent group. Deciding students reported lower levels of career decision-making self-efficacy and career search self-efficacy than decided students, even if decided students had a major incongruent with their personality type.

Confounds: The sample size was on the small side.

- Interventions

This third section of the first part deals with interventions that have been put forth to assist students with choice of major. These include Career Quest (2 articles – Candrl & Heinzen, 1994; McDaniels et al, 1994), ACT materials (Childress, 1998), the National Career Development Association (Hansen, 2003), use of different decision-making models (Robinson & Betz, 2004; Steele, 2003; Strasser, Ozgur & Schroeder, 2002), a career workshop (Harris, Golden & Olsen, 1985), and a career course (Rayman et al, 1983).

Candrl, K. I. & Heinzen, C. J. (1994). Career Quest: An innovative student organization designed to meet the needs of “deciding” students. Journal of Career Development, 21, 141-148.

Hypotheses/Theories: This group for “deciding” students is based on a model of career development which has three main parts: know thyself, knowledge of the world of work, and know the process.

Findings: This article is a report on Career Quest, a student organization the purpose of which is to remove the stigma that has been associated with being undecided. The intention of Career Quest is to create an environment in which students can meet with one another to discuss their experiences. The organizers intend for the organization to eventually be run entirely by the students themselves.

Childress, B. B. (1998). Using american college testing program materials to facilitate career exploration by undecided advisees. NACADA Journal: The Journal of the National Academic Advising Association 18, 42-49.

Hypotheses/Theories: This article describes combining American College Testing (ACT) test scores with other materials such as the Occupational Outlook Handbook to help deciding students choose a major. ACT scores show students’ abilities in different academic areas, such as English, Math, Reading, Science, and a composite overall score, with breakdowns into subscore areas (such as Social Studies/Science and Arts/Literature under the Reading score).

Findings: These scores can help academic advisors determine if students are educated or gifted in certain areas, and whether some areas need remediation. For instance, a high Math score would be desirable for an Engineering major. A low Math score would indicate the student would likely have difficulty pursuing a career in Engineering, and would likely need some introductory math courses to help the student improve his/her math ability. In addition to the familiar ACT test, ACT also offers additional testing, such as the UNIACT (Unisex Edition of the ACT Interest Inventory) which combines test scores with Holland codes. These materials can be obtained at reasonable cost and can form part of the career deciding process.

Hansen, S.S. (2003). Career counselors as advocates and change agents for equality. The Career Development Quarterly, 52, 43-53.

The National Career Development Association (NCDA) is evaluated in terms of strengths, weaknesses, opportunities and threats. The NCDA is characterized by strong organizational structure and leadership for professional career counselors. Technological developments and international collaboration exist but could be stronger. The field of career counseling itself is broadening, and this is a strength. Weaknesses include the inability to agree on common definitions. Another is the focus on career counselors as agents of change for individuals, but not for systems. There is still cultural encapsulation and ethnocentrism. There is covert sexism as women’s ideas and contributions are ignored or belittled. More counselor educators need to become enthused about career counseling. New theories such as constructivism and new topics such as spirituality and work need to be expanded. Career counseling as also drifted away from the advocacy and activism of Frank Parsons. Threats include deprofessionalization of career counseling. Opportunities for improvement include multicultural career counseling, technology, and transition counseling for older adults.

Harris, S. A., Golden, B., & Olson, S. K. (1985). A workshop for undecided students. Journal of College Student Personnel, 26, 468-469.

Methodology: The authors designed a workshop to increase students’ self-awareness, self-knowledge, and self-efficacy; the main objective was to identify a major area of study. The workshop consisted of two components: the first facilitated group cohesion and exploration of values, preferences, skills, and abilities, while the second examined majors and the decision-making process and it also allowed time to present the participants with information available at their career center. A Self-Efficacy Scale was administered both immediately before and after the workshop.

Findings: They found that there was an increase in the students’ perceived ability to locate career information and to choose a major after attending the workshop. In addition, the students felt more prepared to choose a major as a result of increased self-awareness. The leaders suggest that in future workshops, groups that are more homogeneous should be used and that the sessions need to be longer with more time in between each one.

McDaniels, R. M., Carter, J. K., Heinzen, C. J., Candrl, K. I., & Wieberg, A. M. (1994). Undecided/undeclared: Working with “deciding” students. Journal of Career Development, 21, 135-139.

Hypotheses/Theories: The Career Quest group for “deciding” students is based on a model of career development which has three main parts: know thyself, knowledge of the world of work, and know the process.

Methodology: The assessments were followed up with advising by Career Specialists, who were trained undergraduates, to help them choose appropriate assessments and interpret the results.

Findings: This article was a presentation of the activities of the Career Center (CPPC) at the University of Missouri – Columbia (MU). The CPPC personnel had realized that the majority of freshman and sophomores were making decisions on majors due to the stigma attached to being undecided. To combat this stigma, they conducted research to determine the needs of the student population and then developed several programs at MU to address these issues. One of these programs was Career Quest, a support system where students would be able to share their experiences with similar students. Another program, the Explore Program, was designed to provide assessments to the students to help them gain a better understanding of their interests, abilities, skills, and congruent majors and careers. The Job Development Programs include cooperative education, internships, part-time jobs, work-study assignments, service-learning positions, and volunteer opportunities.

Rayman, J.R., Bernard, C.B., Holland, J.L., & Barnett, D.C. (1983). The effects of a career course on undecided college students. Journal of Vocational Behavior, 23, 346-355.

Hypotheses/Theories: Vocational Identity, Occupational Information, and Barriers scores would significantly change after subjects attended of a career course. There would be greater change over the first half of the course. Both student and instructor characteristics would have an effect on the scores.

Methodology:

Type of Study: Cause and effect.

Population: male and female college students

Sample: 255 male and female college students enrolled in one of 22 career courses taught by 11 instructors at a large Midwestern university.

Instruments: Pre-test and post-test using the Vocational Identity, Occupational Information and Barrier scales of the My Vocational Situation..

Independent Variable: career class and instructor

Dependent Variable: Factors that indicate level of decidedness

Findings: Males and females showed different patterns of score changes. Males’ scores increased during the first half of treatment (6.93 to 8.59, p < .025) while females’ increase was not significant (6.22 to 7.17). Females showed a substantial increase from midterm to post-test (7.17 to 9.4, p < .005). Males had no significant gains for the second half (8.59 to 9.67). Two types of ANOVA revealed no instructor differences.

Robinson, C.H., Betz, N.E. (2004). Test-retest reliability and concurrent validity of the expanded skills confidence inventory. Journal of Career Assessment 12, 407-422.

Hypotheses/Theories: Bandura’s self-efficacy theory, bolstered by hundreds of studies.

Methodology:

Type of Study: An examination of test-retest reliability and the concurrent validity of the Expanded Skills Confidence Inventory (ESCI).

Population:: Midwestern male and female undergraduate college students.

Sample: 2 samples used, 324 and 160 undergraduate male and female college students a large Midwestern university, as follows:

Sample #1: 227 Female, 97 Male; 83.2% Euroamerican, 7.1% African-American, 3.1% Asian, 2.5% Hispanic, 0.3% Native American, 2.8% Other; median age 18.2; 93% Freshman, 6% Sophomore, >1% Junior and Senior.

Sample #2: 88 Female, 87 Male; 85.5% Euroamerican, 5.4% Asian, 4.8% African-American, 0.6% Native American, 0.6% Multiracial, 2.4% Other; median age 19, with a range of 18-28; 75% Freshman, 14% Sophomore, 8% Junior, 2% Senior.

Instruments:: Expanded Skills Confidence Inventory (ESCI) and Skills Confidence Inventory (SCI). The ESCI measures self-efficacy on 17 dimensions of vocational activity analogous to the basic interest dimensions of the Strong Interest Inventory (SII). Precursor to the ESCI, the SCI is a 60 item scale of 10 items for each of the six General Confidence Themes, analogous to the six Holland Themes (RIASEC) on the SII.

Findings: Internal consistency reliability was determined for all 17 scales. Evidence for concurrent validity was found, specifically relating to the 3 letter Holland code, particularly among students in Enterprising, and then Investigative majors. Both interests and confidence (self-efficacy) must be present for career choice in a particular area to be made. Gender differences were consistent with previous studies.

Steele, G. (2003). A research-based approach to working with undecided students: A case study illustration. NACADA Journal: The Journal of the National Academic Advising Association 23, 10-20.

Hypotheses/Theories: This article attempts to outline effective interview procedures for academic advisors to use with deciding or major-changing college students. The author reviews and suggest using 4 models: Gordon’s Decision-Making Model (integrated, student-centered, heuristic), Schein and Laff’s Decision-Making Model (uses students’ self-descriptions as the starting point for exploration), Beck’s Decision-Making Model (based on chaos theory), and Bertram’s Decision-Making Model (less rational challenge to established models).

Methodology:

Type of Study: Case Study

Population: male and female deciding and major-changing college students

Sample: 1 female undergraduate student (case stud)

Findings: The writer uses the case study to explore the 4 decision-making models, and to outline a sample interview for academic advisors working with deciding and major-changing students.

Strasser, S.E, Ozgur, C., & Schroeder, D.L. (2002). Selecting a business college major: An analysis of criteria and choice using the analytical hierarchy process. Mid-American Journal of Business, 17, 47-57.

Hypotheses/Theories: This article is an exploration of the decision making process used by students to select a business major.

Methodology:

Type of Study: An exploratory study using a ranking questionnaire.

Population:: business school students.

Sample: 112 sophomore and senior business school students (63 sophomores, 49 seniors).

Instruments:: A questionnaire developed for the study, analyzed with Analytic Hierarchy Process (AHP), a multicriteria decision-making approach.

Findings: The AHP predicted students’ first choice of major with 88% accuracy. Given a choice of factors of interest, influence of others, and job factors (compensation including image, reputation and prestige; job availability; growth; job requirements), students ranked interest as the most important factor, defying the stereotype that business students are only motivated by money. While this held true overall, seniors ranked career factors such as personal abilities and interpersonal skills a bit higher than did sophomores, perhaps because their skills are more developed at the senior level and they are more focused on imminently entering the world of work. The authors remind us that major choice is multifactorial and no one criterion is solely responsible.

Confounds: No data is presented regarding gender, ethnicity, SES, age, etc. The authors also caution that the AHP predictions may not work well for freshmen who are generally at a less developed level than sophomores in their career decisions.

Special populations

Individuals from certain populations are affected by different factors when choosing a major. This second part of the annotated bibliography explores race/ethnicity, gender, sexual orientation, socioeconomic status, and ability (both giftedness and learning disabilities).

- Race/ethnicity

Flores, et al (2006) tests a model of career choice developed by Lent, Brown & Hackett (1994) to see if it can explain nontraditional career choice goals of Mexican American adolescent males. The Lent, et al (2005) article refers to African-Americans. Simpson (2001) covers a wider range. Trusty & Plata (2000) examines the interactions of gender, socio-economic status, and race/ethnicity, and the article by Yi, Lin & Kishimoto (2003) looks at how international students utilize university counseling services for career counseling.

Flores, L.Y., Navarro, R.L., Smith, J.L. & Ploszaj, A.M. (2006). Testing a model of nontraditional career choice goals with Mexican American adolescent men. Journal of Career Assessment, 14, 214-234.

Hypotheses/Theories: This study seeks to test the Lent, Brown & Hackett (1994) model of social cognitive career theory (SCCT) by testing the hypothesis that nontraditional career self-efficacy plus nontraditional career interests would predict the choice of a nontraditional career path. The study focused on Mexican Americans because Euro Americans have been much more vastly studied. The study focused on men because nontraditional career choices of women have been more greatly studied.

Methodology:

Type of Study: Survey packets were given to assess variables of background contextual affordances (acculturation level, mother’s and father’s career choice nontraditionality, parental support, perceived occupational barriers). Demographic data was also collected.

Population:: Mexican American adolescent males. Mexican American adolescent females were also surveyed for an additional study not addressed in this article.

Sample: 302 Mexican American adolescent males from 2 large public high schools in a Texas border town with close to 90% of residents having Mexican heritage, age range 15 to 21 years (average age 17.42), in the 11^th or 12^th grade, 55% planning on pursuing post-secondary education, split into 2 groups of 151 students each.

Instruments:: The 30-item Likert-style Acculturation Rating Scale for Mexican Americans (ARSMA-II) along with the Anglo Orientation subscale (AOS) and Mexican Orientation subscale (MOS). The 24- item Perceptions of Barriers scale (POB) and POB-Future Gender Discrimination subscale. The 30-item Career Support Scale (CCS) measuring support from mothers and fathers separately. Both nontraditional career self-efficacy and nontraditional career interests were measured using a modified version of the 31-item Church, et al (1992) occupational questionnaire. Demographic questions, including both mother’s and father’s occupations, and post-secondary plans.

Independent Variables: acculturation level, mother’s and father’s career choice nontraditionality, parental support, perceived occupational barriers.

Dependent Variables: traditional and nontraditional career choice

Findings: The results support a modified model of nontraditional career self-efficacy, predicted nontraditional career interests. The results suggest that if a student is nontraditional in one way, he is more likely to be nontraditional in additional ways.

Lent, R.W., Brown, S.D., Sheu, H., Schmidt, J., Brenner, B.R., Lyons, H., & Treistman, D. (2005). Social cognitive predictors of academic interests and goals in engineering: Utility for women and students at historically black universities. Journal of Counseling Psychology, 52, 84-92.

Hypotheses/Theories: Social cognitive career theory (SCCT) which is based on Bandura’s social cognitive theory as well as additional theory and research on career and academic self-efficacy. SCCT suggests that people’s major choice is affected by self-efficacy and outcome expectations. Environmental supports and stressors also play important roles. SCCT research has predominantly been done among STEM (science, technology, engineering, math) domains.

Methodology:

Type of Study: Descriptive

Population: male and female college students

Sample: 487 (365 male, s122 female, 1 non-response) students enrolled in introductory engineering courses at 3 universities.

Instruments: measures of cognitive-person, contextual, and outcome variables (names of measures not specified). A 3 (university site) x 2 (gender) MANOVA was performed, followed by a covariance matrix with 15 measured variables, and a multiple-groups analysis to study the effect of gender.

Independent Variables: self-efficacy, outcome expectations, environmental supports and stressors

Dependent Variable: major choice

Findings: The SCCT model proved reliable across gender and university type. Gender differences were small, with women perceiving a bit more social support and fewer social barriers than did men, probably due to university efforts to support women who choose majors that are not traditional for their gender. The study suggests that social cognitive variables may be helpful in understanding major choice of engineering students, regardless of gender or university type, although in general all respondents reported strong environmental support and weak environmental stressors.

Confounds: One individual did not specify gender.

Simpson, J.C. (2001). Segregated by subject: racial differences in the factors influencing academic major between european americans, asian americans, and african, hispanic, and native americans. The Journal of Higher Education, 72, 63-100.

Hypotheses/Theories: Supposedly, the more education, the more likely an individual will attain occupational prestige and higher income. Focus on years of education has ignored content or subject matter. This study explores whether choice of major is responsible for Asian American socioeconomic success compared to other racial groups. The factors that influence major choice by race are explored. Socioeconomic explanations, particularly neo-Marxism, are considered.

Factors such as early tracking, test scores, cultural and social capital, institutional factors (including institutional racism) and racial group values affect choice of major. Members of racial groups, particularly involuntary immigrants (African-Americans and Native Americans) may develop an oppositional identity that includes a fatalistic attitude towards education.

Methodology:

Type of Study: Predictive

Population: male and female high school students with follow-up throughout college

Sample: 2,359 male and female high school students with follow-up throughout college

Instruments: High School and Beyond (HS&B), a national longitudinal demographic sample of high school students involving numerous follow-up studies, was utilized. 2,359 students were sampled for this project. Multinomial logit modeling (MNL) was used to distinguish which factors were important in choice of major.

Independent Variables: gender, factors affecting choice of major such as early tracking, test scores, cultural and social capital, institutional factors (including institutional racism) and racial group values

Dependent Variable: choice of major

Findings: Three factors impacted choice of major: sex, academic preparation, and private control of the high school of college. Sex: African-American, Native American, and Hispanic females are all more likely than males to choose non-technical degrees. Academic preparation: students' choice of technical major was increased by a high number of high school math courses, high math test score, and high number of science courses. Private control: Private high school attendance decreases the likelihood of choosing a technical major, while attending a private college or university increases its likelihood. Differences were also found within racial groups. Asian-American students were the only group to have a large number of high school English courses increase, rather than decrease, their likelihood of choosing a technical degree. Asian-Americans with mothers who took an interest in their high school course work were more likely to choose public service rather than technical degree programs. White students with more cultural capital are more likely to major in health rather than technical degree programs.

The significant differences in choice of degree program occur between Asian-Americans and non-Asians. Euroamericans earn more than African-Americans and Hispanics holding the same degree, suggesting employment discrimination to be a greater factor in success than choice of major. Students at traditionally African-American universities viewed their race more positively in terms of their careers than African-American students at other universities (85% compared to 50-60%).

Confounds: Different types of math (geometry, calculus, etc.) distinguished by difficulty were differentiated, but different types of English courses (literature, creative writing) were not. Thus British Literature counted the same as Remedial Writing. Other possible influential factors, such as generational status or financial aid were not studied.

Trusty, J., Ng, K., & Plata, M. (2000). Interaction effects of gender, SES, and race-ethnicity on postsecondary educational choices of U.S. students. Career Development Quarterly, 49, 45-59.

Hypotheses/Theories: This study seeks to understand the influence of gender, SES, and race/ethnicity on major choice, as well as their interactions. Holland’s RIASEC model is employed.

Methodology:

Type of Study: An analysis of a subsample of the National Education Longitudinal Study of 1988 (NELS:88).

Population:: students who entered college within 2 years of high school graduation and have chosen a major.

Sample: N not given. 53% Female, 47% Male; 73% Euroamerican, 12% African-American, 7% Hispanic, 3% Asian, 1% Native American.

Instruments:: Chi Square Automatic Interaction Detector (CHAID). CHAID is similar to both regression and cluster analysis, yet also actively analyzes missing data by creating a “missing” category, rather than ignoring it.

Independent Variables: gender, SES, race/ethnicity. SES was a composite variable based on parents’ income, parent’s education, and parents’ job prestige. The SES variable reflected the general population, not the SES of the sample or subsample.

Dependent Variable: Holland code

Findings: The clearest result was that race/ethnicity effects are strongest for males at lower SES levels and weakest for females at high SES levels. Counselors and clients need to be aware of the effects of the three independent variables, and the assumptions of counselors should be challenged (such as encouraging females in general towards the “S” careers, and males of all races towards the “E” careers.)

Confounds: The sample size is not given.

Yi, J.K., Lin, J.G. & Kishimoto, Y. (2003). Utilization of counseling services by international students. Journal of Instructional Psychology, 30, 333-342.

Hypotheses/Theories: International students represent a unique group of college students with unique needs. It is also a population that exhibits continual growth, increasing by 6.4% over two academic years (1999-2000 and 2000-2001). One way of finding out their needs is to study the reasons why they visit a university counseling center.

Methodology:

Type of Study: Descriptive

Population: male and female international college students.

Sample: 516 male (N = 306) and female (N = 210) international graduate and undergraduate college students who visited the counseling center. These students were 59% Asian, 24.1% Latin American, 10.2% European, 3.8% African, 2.9% Canadian. 56.6% of the students were undergraduates, 43.4% graduate students.

Instruments: The data was gathered over 6 years. SPSS was used to group and sort the data into simple group statistics, using Chi square, t test and analysis of variance.

Findings: Of the many types of international students, the ones who came to the counseling center for career counseling were younger, female undergraduates. Choice of major is influenced by concerns and needs related to the geographic location of their future employment, whether they plan to go back to their home countries or remain in the U.S. Nearly 70% reported being worried about their futures. It is speculated that personality traits also affect choice of major as well as help-seeking behavior.

- Gender

These articles cover issues specific to gender regarding choosing a major. The Brown et al (2006) article explores sex role orientation among computer science majors. Chung’s article (2003) addresses the special populations based on sexual orientation: bisexual, gay, lesbian and transgendered. Dawson-Threat & Huba (1996) look at clarity of purpose and sex role identification. Dodson & Borders (2006) compare men in mechanical engineering and elementary school counseling to understand gender role attitudes, conflict, and job satisfaction. Flores, et al (2006) tests a model of career choice developed by Lent, Brown & Hackett (1994) to see if it can explain nontraditional career choice goals of Mexican American adolescent males. Grant, Battle & Heggoy (2000) examines gifted females and major choice. The Lent, et al (2005) article refers to African-American women. Madill et al (2000) offer a longitudinal study of value change and role salience in women over a period of three years starting in their junior year of high school. Trusty & Plata (2000) examines the interactions of gender, socio-economic status, and race/ethnicity, and Turner & Bowen (1999) explore the “gender gap” between male- and female-identified majors.

Brown, C., Garavalia, L.S., Hines-Fritts, M.L. & Olson, E.A. (2006). Computer science majors: Sex role orientation, academic achievement, and social cognitive factors. Career Development Quarterly, 54, 331-345.

Hypotheses/Theories: Sandra Bem’s (1975) conception of sex role orientation as being masculine, feminine, androgynous or undifferentiated (as opposed to just masculine or feminine) is foundational to this study.

Methodology:

Type of Study: Research questions: Do male and female computer science students possess different sex role orientations? Do computer science students representing masculine, feminine, androgynous, and undifferentiated orientations differ on self-efficacy for career decision tasks, career and general locus of control, and academic achievement?

Population:: Midwestern male and female computer science majors.

Sample: 188 male and female computer science majors in a male-dominated degree program at a Midwestern university, enrolled in entry-level computer science courses. 100 Euroamerican, 41 Asian, 16 Hispanic, 12 African American, 9 Middle Eastern, 4 Other African descent, 5 Other, 1 Unreported. Age range 18 to 50 with an average of 24.4 years.

Instruments:: The 25-item Career Decision Self-Efficacy Scale-Short Form (CDSE-SF), the 23-item Rotter Internal-External Control Scale (I-E), the Career Locus of Control Scale (CLCS), the 24-item Personal Attributes Questionnaire (PAQ) for sex role orientation, and academic achievement was measured by course grades on a 4-point GPA scale.

Independent Variables: masculine, feminine, androgynous, and undifferentiated sex role orientations.

Dependent Variables: Career decision-making self-efficacy, academic achievement, general and career locus of control.

Findings: Surprisingly, most students scored as feminine (36%), androgynous (19%), or undifferentiated (44%) – only 1 of the 188 students scored as masculine. Feminine and androgynous students scored higher on career decision-making self-efficacy, implying that undifferentiated students feel less able to make career decisions. No significant differences were found for academic achievement and career locus of control.

Chung, Y.B. (2003). Career counseling with lesbian, gay, bisexual, and transgendered persons: The next decade. The Career Development Quarterly, 52, 78-86.

Hypotheses/Theories: This article speculates on the future of career counseling for Lesbian, Gay, Bisexual and Transgendered (LGBT) people. Transgendered is a relatively recent addition. Theories that have been applied to LGBT people include Holland, Super, sociocognitive, and women’s vocational psychology. Identity development has also been explored, and special theoretical frameworks have been proposed to describe LGBT vocational behavior and address the area of discrimination. Additional research is required to test and expand upon current theoretical models.

Findings: Researchers need to be careful with assessments. They may not have the same reliability and validity. Counseling students often feel unprepared to work with LGBT clients. The Human Rights Movement should benefit LGBT people. More research should be done, and better training of counselors. Theory development, empirical research, career assessment, counseling practice and counselor education are all areas ripe for improvement.

Confounds: Lesbians continue to be underrepresented in empirical research. Bisexual people’s vocational issues are almost totally ignored, and transgendered people get even less attention. and Trusty covers the interactions of gender, socio-economic status, and race/ethnicity.

Dawson-Threat, J. & Huba, M.E. (1996). Choice of major and clarity of purpose among college seniors as a function of gender, type of major, and sex-role identification. Journal of College Student Development, 37, 297-308.

Hypotheses/Theories: This study sought to examine clarifying purpose, an aspect of identity development typically addressed by young adults. Purpose is clarified by articulating the direction and goals that define their future, formulating plans of action and priorities. One major element of this is pursuit of vocation. Colleges may assist in the development of purpose by requiring students to choose majors. The relationship between choosing a major and developing purpose differ based on gender, partly due to the increase in opportunities for women over the past few decades. Sex-role identification (masculine, feminine, androgynous, undifferentiated) rather than gender may throw light on development of purpose because sex-role identification takes into account the person's own view of self in relation to gender roles.

The researchers had 2 purposes: 1.) to study the relationship between sex-role identification and choice of major, and 2.) to examine the clarity of purpose of seniors in relation to gender and sex-role identification.

Methodology:

Type of Study: Descriptive

Population: male and female college students.

Sample: 396 male and female college seniors at a large Midwestern land-grant university, 43% in male-dominated majors, 57.1% in female-dominated majors.

Instruments: Student Developmental Task and Lifestyle Inventory Revised (SDTLI) to measure the resolution of the developmental tasks associated with young adult college students and the Bem Sex Role Inventory (BSRI), a measurement of 60 personality characteristics to determine sex-role identification.

Independent Variables 1: gender, clarification of purpose

Dependent Variable 1: choice of major

Independent Variables 2: gender, sex role identification

Dependent Variable 2: clarity of purpose

Findings: Some evidence for traditional sex-role identification was found, notably the majority of both men and women choosing majors traditionally associated with their gender. Females in female-dominated majors described themselves as more feminine than females in male-dominated majors , but gender domination of majors did not seem to affect males. For androgynous students, almost half of males (45.2%) chose a female-dominated major, while only 23.2% of females chose a male-dominated major.

As for clarifying purpose, the average scores of females were comparable to the average scores of seniors, while the average scores of males were closer to the average scores of juniors. All individuals in female-dominated majors had higher average purpose scores. The act of choosing a major may play a role in clarification of purpose, with males and females affected by different issues. Sex-role identification may be a factor common to both males and females. Male-dominated professions in particular seem difficult for feminine persons who must reduce their level of femininity in order to perform well, whereas males in male-dominated professions may sense society's approval and assume inevitable success.

Confounds: The land-grant university had a strong vocational orientation, particularly in respect to women-dominated majors. Findings at a liberal arts institution may be different.

Dodson, T.A., & Borders, L. D. (2006). Men in traditional and nontraditional careers: Gender role attitudes, gender role conflict, and job satisfaction. Career Development Quarterly, 54, 283-296.

Hypotheses/Theories: This study seeks to compare men in traditional (mechanical engineering) and nontraditional (elementary school counseling) to understand gender role attitudes, conflict, and job satisfaction. Men were the focus of this study to balance previous studies largely focused on women. L. Gottfredson’s (1981) career choice theory was foundational to this study. Research questions: When making career compromise choices, do men working in traditional occupations sacrifice prestige over sex type, and do men working in nontraditional careers sacrifice sex type over prestige? Do men established in traditional careers, as compared with men established in nontraditional careers, have more traditional gender role attitudes and greater gender role conflict? To what extent do gender role attitudes and gender role conflict predict the prestige choice when making a career compromise decision in traditional and nontraditional career men? Do men established in traditional versus nontraditional careers report different levels of job satisfaction? To what extent to gender role attitudes and gender role conflict predict job satisfaction of traditional and nontraditional career men?

Methodology:

Type of Study: Surveys to explore job satisfaction, occupational choice, masculine ideology and demographic data.

Population:: Male mechanical engineers and elementary school counselors from North Carolina and Virginia.

Sample: 100 male mechanical engineers and 100 male elementary school counselors randomly selected from mailing lists from the North Carolina and Virginia branches of the American Society of Mechanical Engineers and education departments in each state. The mean ages of both groups were similar (44.61 overall), with the counselors having more education and the engineers having higher salaries. Over 90% of both groups were Euroamerican.

Instruments:: The 18-item Job in General scale (JIG), the 30 for-men items of the 60-item Occupational Choice Dilemma Inventory (OCDI), the Male Role Norms Scale (MRNS), and the Likert-style Gender Role Conflict Scale (GRCS) along with the Conflict Between Work and Family Relations subscale.

Independent Variables: sex type, prestige

Dependent Variable: choice of mechanical engineering or elementary school counseling major

Findings: The engineers preferred traditional sex type occupations while the school counselors preferred nontraditional occupations that rewarded them with prestige. The engineers also held more traditional gender role attitudes. An understanding of gender role attitudes and beliefs are important for career counselors.

Confounds: The study only examined 2 occupations that were very different in educational level. Most participants (over 90%) were Euroamerican. All were from 1 of 2 states.

Flores, L.Y., Navarro, R.L., Smith, J.L. & Ploszaj, A.M. (2006). Testing a model of nontraditional career choice goals with mexican american adolescent men. Journal of Career Assessment, 14, 214-234.

Hypotheses/Theories: This study seeks to test the Lent, Brown & Hackett (1994) model of social cognitive career theory (SCCT) by testing the hypothesis that nontraditional career self-efficacy plus nontraditional career interests would predict the choice of a nontraditional career path. The study focused on men because nontraditional career choices of women have been more greatly studied. The study focused on Mexican Americans because Euroamericans have been much more vastly studied.

Methodology:

Type of Study: Survey packets were given to assess variables of background contextual affordances (acculturation level, mother’s and father’s career choice nontraditionality, parental support, perceived occupational barriers). Demographic data was also collected.

Population:: Mexican American adolescent males. Mexican American adolescent females were also surveyed for an additional study not addressed in this article.

Sample: 302 Mexican American adolescent males from 2 large public high schools in a Texas border town with close to 90% of residents having Mexican heritage, age range 15 to 21 years (average age 17.42), in the 11^th or 12^th grade, 55% planning on pursuing post-secondary education, split into 2 groups of 151 students each.

Instruments:: The 30-item Likert-style Acculturation Rating Scale for Mexican Americans (ARSMA-II) along with the Anglo Orientation subscale (AOS) and Mexican Orientation subscale (MOS). The 24- item Perceptions of Barriers scale (POB) and POB-Future Gender Discrimination subscale. The 30-item Career Support Scale (CCS) measuring support from mothers and fathers separately. Both nontraditional career self-efficacy and nontraditional career interests were measured using a modified version of the 31-item Church, et al (1992) occupational questionnaire. Demographic questions, including both mother’s and father’s occupations, and post-secondary plans.

Independent Variables: acculturation level, mother’s and father’s career choice nontraditionality, parental support, perceived occupational barriers.

Dependent Variables: traditional and nontraditional career choice

Findings: The results support a modified model of nontraditional career self-efficacy, predicted nontraditional career interests. The results suggest that if a student is nontraditional in one way, he is more likely to be nontraditional in additional ways.

Grant, D.F., Battle, D.A., & Heggoy, S.J. (2000). The journey through college of seven gifted females: influences on their career related decisions. Roeper Review, 22, 251-260.

Hypotheses/Theories: Research question: What were the background and educational factors that might have, over time, influenced the career related decisions of gifted females whose precollege education occurred primarily in rural schools? Research bases include developmental career theories (Super), gender role expectations (in relation to career related decisions), and giftedness.

Methodology:

Type of Study: Descriptive, using case studies

Population: gifted female college students.

Sample: 7 gifted female students in rural areas of southeastern Georgia from the end of high school through college over a 5 year period. Giftedness was determined by participation in a public school program for the gifted, and/or admission to a freshman honors program at a mid-size university.

Instruments: Data gathering included two questionnaires; a structured face-to-face interview with 22 questions to explore school achievement, role identity beliefs, and future plans; and telephone interviews to clarify written responses. Multiple researchers and data sources provided reliability; time-series analysis and peer researcher debriefing provided validity.

Independent Variable: gender (held constant), school achievement, gender role identification beliefs, future plans

Dependent Variable: choice of major

Findings: Findings were consistent with those influences identified by Vermuelen and Minor for rural women: parents, natal family, gender role beliefs, work expectations, others' expectations, sense of empowerment, work conditions, personal values. These influences make it easy for gifted females to "slip through the cracks" and not actualize their full potential. Additionally, indecision of major was seen as related to less clear educational goals. Overall the women were not adequately prepared to make career related decisions.

Confounds: The sample size of 7 is extremely small, even for case study research.

Lent, R.W., Brown, S.D., Sheu, H., Schmidt, J., Brenner, B.R., Lyons, H.,, & Treistman, D. (2005). Social cognitive predictors of academic interests and goals in engineering: Utility for women and students at historically black universities. Journal of Counseling Psychology, 52, 84-92.

Hypotheses/Theories: Social cognitive career theory (SCCT) which is based on Bandura’s social cognitive theory as well as additional theory and research on career and academic self-efficacy. SCCT suggests that people’s major choice is affected by self-efficacy and outcome expectations. Environmental supports and stressors also play important roles. SCCT research has predominantly been done among STEM (science, technology, engineering, math) domains.

Methodology:

Type of Study: Descriptive

Population: male and female college students

Sample: 487 (365 male, s122 female, 1 non-response) students enrolled in introductory engineering courses at 3 universities.

Instruments: measures of cognitive-person, contextual, and outcome variables (names of measures not specified). A 3 (university site) x 2 (gender) MANOVA was performed, followed by a covariance matrix with 15 measured variables, and a multiple-groups analysis to study the effect of gender.

Independent Variables: self-efficacy, outcome expectations, environmental supports and stressors

Dependent Variable: major choice

Findings: The SCCT model proved reliable across gender and university type. Gender differences were small, with women perceiving a bit more social support and fewer social barriers than did men, probably due to university efforts to support women who choose majors that are not traditional for their gender. The study suggests that social cognitive variables may be helpful in understanding major choice of engineering students, regardless of gender or university type, although in general all respondents reported strong environmental support and weak environmental stressors.

Confounds: One individual did not specify gender.

Madill, H.M., Montgomerie, T.C., Stewin, L.L., Fitzsimmons, G.W., Tovell, D.R., Armour, M-A., & Ciccocioppo, A-L. (2000). Young women’s work values and role salience in grade 11: Are there changes three years later? Career Development Quarterly, 49, 16-28.

Hypotheses/Theories: Super’s Theory of Career Development – Super’s Career Pattern Study (CPS) was one of the first longitudinal career studies. Current career theories may fail to adequately describe the experience of women in the areas of values, motivations, and choices. Are grade 11 measurements for females long lasting into their post-secondary years?

Methodology:

Type of Study: Longitudinal study – a series of career inventory surveys followed up with a series of structured telephone interviews over the next 2 years.

Population:: Female high school students in western Canada.

Sample: 154 female high school students in western Canada starting at 11^th grade, continuing two years into college or the world of work. These students had a declared interest in science, and applied to the 1994 Summer Residence Program of the Women In School, Engineering, Science and Technology (WISEST). Most were in urban high schools. The mean age was 16.48 years.

Instruments:: Value Scale (VS), a list of 20 values with Likert scale ratings; and Salience Inventory (SI), behavioral and affective components of 5 major life roles.

Findings: Little change was found in work values throughout the period of the study, but great change was found in role salience. Home, family and work tension likely affect females strongly. The VS can be used with confidence, but counselors need to discuss the factors of home, work, and family with females early on.

Trusty, J., Ng, K., & Plata, M. (2000). Interaction effects of gender, SES, and race-ethnicity on postsecondary educational choices of U.S. students. Career Development Quarterly, 49, 45-59.

Hypotheses/Theories: This study seeks to understand the influence of gender, SES, and race/ethnicity on major choice, as well as their interactions. Holland’s RIASEC model is employed.

Methodology:

Type of Study: An analysis of a subsample of the National Education Longitudinal Study of 1988 (NELS:88).

Population:: students who entered college within 2 years of high school graduation and have chosen a major.

Sample: N not given. 53% Female, 47% Male; 73% Euroamerican, 12% African-American, 7% Hispanic, 3% Asian, 1% Native American.

Instruments:: Chi Square Automatic Interaction Detector (CHAID). CHAID is similar to both regression and cluster analysis, yet also actively analyzes missing data by creating a “missing” category, rather than ignoring it.

Independent Variables: gender, SES, race/ethnicity. SES was a composite variable based on parents’ income, parent’s education, and parents’ job prestige. The SES variable reflected the general population, not the SES of the sample or subsample.

Dependent Variable: Holland code

Findings: The clearest result was that race/ethnicity effects are strongest for males at lower SES levels and weakest for females at high SES levels. Counselors and clients need to be aware of the effects of the three independent variables, and the assumptions of counselors should be challenged (such as encouraging females in general towards the “S” careers, and males of all races towards the “E” careers.)

Confounds: The sample size is not given.

Turner, S.E. & Bowen, W.G. (1999). Choice of major: the changing (unchanging) gender gap. Industrial and Labor Relations Review, 52, 289-313.

Hypotheses/Theories: This study examines the extent to which differences in choice of major between men and women reflect the effects of pre-collegiate preparation as opposed to other forces. Major choice is both an immediate outcome of the educational process as well as a determinant of later outcomes of many kinds.

Methodology:

Type of Study: Descriptive

Population: male and female college students

Sample: 3,373 male and 4,290 female college students from the College and Beyond database

Instruments: The Scholastic Aptitude Test (SAT) was employed. The Dissimilarity Index showed the percentage of students who would need to change majors in order for parity to be achieved in the distributions. The category of "Social Sciences" includes the female-dominated field of psychology and the male-dominated field of economics, so these were differentiated for this study.

Independent Variable: SAT score

Dependent Variable: choice of major

Findings: Women with high SAT scores are drawn more to the life sciences than the math/physical science fields that draw men with equally high SAT scores. Other factors in major choice are preferences (encouraged by parental and societal expectations), labor market prospects (which may relate to gender), and gender-specific effects of the college experience. Women have moved from the field of education and toward business programs, accounting for much of the reduction in the dissimilarity index. However, the dissimilarity index did not change for the arts-science-engineering fields. Women attending traditionally women's colleges may be more inclined to major in scientific fields than female students at coed institutions.

The fact that women's math SAT scores tend to be lower than men's does not translate into a proportionate difference in representation in each of the sciences (including quantitative social sciences). Thus, other factors must pull women away from math and science majors to the point that gender differences in choice of major appear to have become entrenched despite slow convergence in SAT scores. Additionally, more men with low math SAT scores attempt majoring in engineering than do women with low math SAT scores. The fact that there is a widening divide between women's attraction to the life sciences versus math/physical science/engineering indicates that a science/non-science dichotomy is too simplistic to aid understanding of gender differences in major choice.

- Socioeconomic Status (SES)

These articles address how socioeconomic status affects choice of major. Leppel, Williams & Waldauer (2001) pay special attention to parents’ occupations. Simpson (2003) looks specifically at the influence mothers have on choice of major, and Trusty & Plata (2000) covers the interactions of gender, socio-economic status, and race/ethnicity.

Leppel, K., Williams, M.L., & Waldauer, C. (2001). The impact of parental occupation and socioeconomic status on choice of college major. Journal of Family and Economic Issues 22, 373-394.

Hypotheses/Theories: This study looks at the effects of SES and parental occupations on choice of major and in particular, male and female differences.

Hypotheses:

1. Having a parent in a professional or executive occupation affects choice of major.

2. The influence of the mother having a professional or executive occupation is greater than the influence of the father having a professional or executive occupation.

3. The impact of the mother’s occupation on the selection of field of study is greater for females than for males.

4. Whether a student feels it is very important to be very well off financially will have an impact on the probability that the student selects a particular major.

5. SES also influences the probability that the student selects a particular major.

6. The effect of SES on females will be greater than on males regarding selecting a particular major.

Methodology:

Type of Study: Multinomial logit analysis applied to survey data from the National Center for Educational Statistics (NCES) 1990 survey of Beginning Postsecondary Students (BPS).

Population:: college students

Sample: 4161 college students

Instruments:: the National Center for Educational Statistics (NCES) 1990 survey of Beginning Postsecondary Students (BPS).

Variables: gender, age (>25, 25+), race/ethnicity (Euroamerican, African-American, Asian, Hispanic), self-reported SES, self-reported ability, mother’s occupation, father’s occupation. An additional SES variable was a composite variable created by the NCES.

Findings: Having an opposite sex parent in a professional or executive occupation has a larger effect on students than having a same-sex parent in a professional or executive occupation. In families with high SES, males are more likely to major in business, females less likely. Students placing high importance on personal finance are more likely to major in business.

Confounds: The article does not break down the sample for us in regards to age, gender, race/ethnicity, etc.

Simpson, J.C. (2003). Mom matters: Maternal influence on the choice of academic major. Sex Roles 48, 447-460.

Hypotheses/Theories: This study looks at parental impact on choice focusing on the mother. The goal of the study is to understand how mothers affect students’ choice of major.

Methodology:

Type of Study: Multinomial logit analysis applied to survey data from the High School and Beyond database, a national longitudinal sample of high school students that involved initial survey plus four follow-up surveys.

Population:: high school students

Sample: 2359 high school sophomore and senior students, as follows: 52% Female, 48% Male; 57% Euroamerican, 20% Hispanic/Native American, 18% African-American, 5% Asian.

Instruments:: High School and Beyond, a national longitudinal questionnaire study. Measures for both mothers and fathers.

Dependent Variable: choice of major from among these 5 categories: business, health & life sciences, liberal arts, public service, technical.

Control Variables: gender; race/ethnicity (Euroamerican, African-American, Asian, Hispanic, Native American); single parent households.

Independent Variables: SES, Mother’s influence, Father’s influence, academic preparation, missing cases.

Findings: Mothers influence through emotional and normative channels. Mothers overwhelmingly encourage pursuit of non-technical majors while fathers encourage the pursuit of technical majors. If either parent cares about the student’s education, the likelihood of choosing a health & life science major increases. Mother’s occupational prestige is important, and increased prestige led to an increased likelihood of choosing a technical major. Mothers should be included more systematically in career models regarding major choice.

Confounds: The article assumes that single parent household is the same as a female-headed household, and vice-versa. This is not always the case.

Trusty, J., Ng, K., & Plata, M. (2000). Interaction effects of gender, SES, and race-ethnicity on postsecondary educational choices of U.S. students. Career Development Quarterly, 49, 45-59.

Hypotheses/Theories: This study seeks to understand the influence of gender, SES, and race/ethnicity on major choice, as well as their interactions. Holland’s RIASEC model is employed.

Methodology:

Type of Study: An analysis of a subsample of the National Education Longitudinal Study of 1988 (NELS:88).

Population:: students who entered college within 2 years of high school graduation and have chosen a major.

Sample: N not given. 53% Female, 47% Male; 73% Euroamerican, 12% African-American, 7% Hispanic, 3% Asian, 1% Native American.

Instruments:: Chi Square Automatic Interaction Detector (CHAID). CHAID is similar to both regression and cluster analysis, yet also actively analyzes missing data by creating a “missing” category, rather than ignoring it.

Independent Variables: gender, SES, race/ethnicity. SES was a composite variable based on parents’ income, parent’s education, and parents’ job prestige. The SES variable reflected the general population, not the SES of the sample or subsample.

Dependent Variable: Holland code

Findings: The clearest result was that race/ethnicity effects are strongest for males at lower SES levels and weakest for females at high SES levels. Counselors and clients need to be aware of the effects of the three independent variables, and the assumptions of counselors should be challenged (such as encouraging females in general towards the “S” careers, and males of all races towards the “E” careers.)

Confounds: The sample size is not given.

- Ability

This final section covers the effects of ability on choice of major, from learning disabilities (Layton & Lock, 2003) to giftedness (Grant, 2000; Hagstrom, Scovholt & River, 1997; Leung, Williams & Waldauer, 1998).

Grant, D.F., Battle, D.A., & Heggoy, S.J. (2000). The journey through college of seven gifted females: influences on their career related decisions. Roeper Review, 22, 251-260.

Hypotheses/Theories: Research question: What were the background and educational factors that might have, over time, influenced the career related decisions of gifted females whose precollege education occurred primarily in rural schools? Research bases include developmental career theories (Super), gender role expectations (in relation to career related decisions), and giftedness.

Methodology:

Type of Study: Descriptive, using case studies

Population: gifted female college students.

Sample: 7 gifted female students in rural areas of southeastern Georgia from the end of high school through college over a 5 year period. Giftedness was determined by participation in a public school program for the gifted, and/or admission to a freshman honors program at a mid-size university.

Instruments: Data gathering included two questionnaires; a structured face-to-face interview with 22 questions to explore school achievement, role identity beliefs, and future plans; and telephone interviews to clarify written responses. Multiple researchers and data sources provided reliability; time-series analysis and peer researcher debriefing provided validity.

Independent Variable: gender (held constant), school achievement, gender role identification beliefs, future plans

Dependent Variable: choice of major

Findings: Findings were consistent with those influences identified by Vermuelen and Minor for rural women: parents, natal family, gender role beliefs, work expectations, others' expectations, sense of empowerment, work conditions, personal values. These influences make it easy for gifted females to "slip through the cracks" and not actualize their full potential. Additionally, indecision of major was seen as related to less clear educational goals. Overall the women were not adequately prepared to make career related decisions.

Confounds: The sample size of 7 is extremely small, even for case study research.

Hagstrom, S. J., Scovholt, T. M., & River, D. A. (1997). The advanced undecided college student: A qualitative study. NACADA Journal: The Journal of the National Academic Advising Association 17, 23-30.

Hypotheses/Theories: The article suggests that advanced (Junior status) students find being deciding an extremely undesirable state fraught with stress and unhappiness. 8 “themes” in deciding were determined: frustration, anxiety, and hopelessness; fear of commitment; fear of judgment; self-doubt and low self-esteem; difficulty setting goals; family issues; reluctance to seek help; and the desire for a personal, caring advising relationship.

Methodology:

Population: male and female college Juniors

Sample: 16 college Juniors, split evenly by gender, at a large Midwestern university.

Instrument: a 22 question interview that addressed several topics related to deciding upon and selecting a major.

Findings: The bulk of the article is the qualitative responses of students sorted into the 8 theme categories. The deciding experience of these students was extremely negative, considered most likely do to their advanced academic status. Advisors need to establish rapport and build solid relationships with these students, and help them to develop their decision-making skills.

Confounds: Although the study was qualitative, 16 is still a small sample size. All participants were the same race (Caucasian).

Layton, C.A. & Lock, R.H. (2003). The impact of reasoning weaknesses on the ability of post-secondary students with learning disabilities to select a college major. NACADA Journal: The Journal of the National Academic Advising Association 23, 21-29.

Hypotheses/Theories: This study seeks to examine the plausible impact of reasoning weaknesses on the choice of major selection processes of post-secondary students with learning disabilities.

Methodology:

Type of Study: Descriptive

Population:: male and female freshman college students with learning disabilities

Sample: 31 male (N = 29) and female (N = 2) freshman college students with learning disabilities enrolled in a fee-for-service support program, with identified and documented learning disabilities.

Instruments:: The Learning Disability Diagnostic Inventory (LDDI) was employed to measure reasoning ability. The LDDI contains 6 scales of 15 items each. It measures generalization, problem solving, executive functioning, and the understanding of consequences, all of which impact self-determination skills. In this study the LDDI was used as a self-report instrument.

Findings: Students with learning disabilities, who may be less mature than other students, may be closely influenced by well-meaning or effective parents and teachers in their task of selecting a major.

Confounds: 31 is a small sample size; the sample was severely unbalanced in terms of gender. Students tended to have intermittent difficulties in the areas of generalization, problem solving, executive functioning, and the understanding of consequences, all of which impact self-determination skills. Academic advisors can try to support students in those areas.

Leung, S.A. (1998). Vocational identity and career choice congruence of gifted and talented high school students. Counseling Psychology Quarterly 11, 325-335.

Hypotheses/Theories: This study explores the vocational identity and congruence of gifted high school juniors. Congruence is defined by the fit of Holland code with a student’s tentative major.

Hypotheses:

1. Gifted students with a tentative major choice should have a higher level of vocational identity.

2. There will be gender differences found regarding level of vocational identity.

3. There will be gender differences in choice congruence.

4. A higher level of vocational identity will be related to a higher level of major choice congruence.

Methodology:

Type of Study: Analysis of the data of various career assessments of gifted high school juniors.

Population:: gifted high school juniors in the Midwest.

Sample: 336 high school juniors (230 Female, 136 Male) who attended a one-day career development program by the Laboratory for the Gifted and Talented at the University of Nebraska.

Instruments:: The Vocational Identity (VI) portion of the My Vocational Situation (MVS) assessment; the Self-Directed Search (SDS) to measure interests; a demographic questionnaire; Holland’s Alphabetized Occupations Finder to code career choices; The College Majors to code majors; and 3 indices of congruence: the Hexagonal Model Index, the Zener-Schnuelle Index of Agreement, and the Iachan Agreement Index.

Findings: Students with tentative majors had higher levels of vocational identity. Males had higher levels of vocational identity than females. Gifted girls struggle to harmonize multiple roles. No gender differences were found in career choice congruence. Gifted students are often ignored by counselors with the assumption that they are self-sufficient when actually they may be undergoing more stress due to the high expectations put upon them. Gifted students need assurance that it is okay to still be deciding on a major as they enter college.

Confounds: No demographic breakdown given other than gender.

References

Beck, A. (1999). Advising undecided students: Lessons from chaos theory. NACADA Journal: The Journal of the National Academic Advising Association 19, 45-49.

Brown, C., Garavalia, L.S., Hines-Fritts, M.L. & Olson, E.A. (2006). Computer science majors: Sex role orientation, academic achievement, and social cognitive factors. Career Development Quarterly, 54, 331-345.

Candrl, K. I. & Heinzen, C. J. (1994). Career Quest: An innovative student organization designed to meet the needs of “deciding” students. Journal of Career Development, 21, 141-148.

Childress, B. B. (1998). Using american college testing program materials to facilitate career exploration by undecided advisees. NACADA Journal: The Journal of the National Academic Advising Association 18, 42-49.

Chung, Y.B. (2003). Career counseling with lesbian, gay, bisexual, and transgendered persons: The next decade. The Career Development Quarterly, 52, 78-86

Colozzi, E.A. (2003). Depth-oriented values education. The Career Development Quarterly, 52, 180-189.

Dawson-Threat, J. & Huba, M.E. (1996). Choice of major and clarity of purpose among college seniors as a function of gender, type of major, and sex-role identification. Journal of College Student Development, 37, 297-308.

Dodson, T.A., & Borders, L. D. (2006). Men in traditional and nontraditional careers: Gender role attitudes, gender role conflict, and job satisfaction. Career Development Quarterly, 54, 283-296.

Flores, L.Y., Navarro, R.L., Smith, J.L. & Ploszaj, A.M. (2006). Testing a model of nontraditional career choice goals with mexican american adolescent men. Journal of Career Assessment, 14, 214-234.

Galotti, K.M. (1999). Making a “major” real-life decision: College students choosing an academic major. Journal of Educational Psychology 91, 379-387.

Gelatt, H. B. (1992). Positive uncertainty: A paradoxical philosophy of counseling whose time has come (Report No. EDOCG-92-20). Ann Arbor, MI: School of Education, University of Michigan. (ERIC Document Reproduction Service No. ED347486).

Gordon, V. N. (1981). The undecided student: A developmental perspective. The Personnel and Guidance Journal, 59, 433-439.

Grant, D.F., Battle, D.A., & Heggoy, S.J. (2000). The journey through college of seven gifted females: influences on their career related decisions. Roeper Review, 22, 251-260.

Hagstrom, S. J., Scovholt, T. M., & River, D. A. (1997). The advanced undecided college student: A qualitative study. NACADA Journal: The Journal of the National Academic Advising Association 17, 23-30.

Hansen, S.S. (2003). Career counselors as advocates and change agents for equality. The Career Development Quarterly, 52, 43-53.

Harris, S. A., Golden, B., & Olson, S. K. (1985). A workshop for undecided students. Journal of College Student Personnel, 26, 468-469.

Kelly, K.R. & Pulver, C.A. (2003). Refining measurement of career indecision types: A validity study. Journal of Counseling & Development, 81, 445-454.

Krieshok, T.S. (1998). An anti-introspectivist view of career decision making. The Career Development Quarterly, 46, 210-229.

Krieshok, T. S. (2001). How the decision-making literature might inform career center practice. Journal of Career Development, 27, 207-216.

Larson, L.M. (1988). Investigating multiple subtypes of career indecision through cluster analysis. Journal of Counseling Psychology, 35, 439-446.

Layton, C.A. & Lock, R.H. (2003). The impact of reasoning weaknesses on the ability of post-secondary students with learning disabilities to select a college major. NACADA Journal: The Journal of the National Academic Advising Association 23, 21-29.

Lent, R.W., Brown, S.D., Sheu, H., Schmidt, J., Brenner, B.R., Lyons, H., & Treistman, D. (2005). Social cognitive predictors of academic interests and goals in engineering: Utility for women and students at historically black universities. Journal of Counseling Psychology, 52, 84-92.

Leppel, K., Williams, M.L., & Waldauer, C. (2001). The impact of parental occupation and socioeconomic status on choice of college major. Journal of Family and Economic Issues 22, 373-394.

Leung, S.A. (1998). Vocational identity and career choice congruence of gifted and talented high school students. Counseling Psychology Quarterly 11, 325-335.

Leuwerke, W.C., Robbins, S., Sawyer, R., & Hovland, M. (2004). Predicting engineering major status from mathematics achievement and interest congruence. Journal of Career Assessment 12, 135-149.

Lucas, M.S. & Epperson, Douglas, L. (1990). Types of vocational undecidedness: A replication and refinement. Journal of Counseling Psychology, 37, 382-388.

Madill, H.M., Montgomerie, T.C., Stewin, L.L., Fitzsimmons, G.W., Tovell, D.R., Armour, M-A., & Ciccocioppo, A-L. (2000). Young women’s work values and role salience in grade 11: Are there changes three years later? The Career Development Quarterly, 49, 16-28.

Martin, N. K. & Dixon, P.N. (1991). Factors influencing students' college choice. Journal of College Student Development, 32, 253-257.

McCollum, V. J. C.. (1998). Career advising: A developmental approach. NACADA Journal: The Journal of the National Academic Advising Association 18, 15-19.

McDaniels, R. M., Carter, J. K., Heinzen, C. J., Candrl, K. I., & Wieberg, A. M. (1994). Undecided/undeclared: Working with “deciding” students. Journal of Career Development, 21, 135-139.

Miller, B. & Woycheck, S. (2003). The academic advising implications of the self-directed search and holland’s theory: A study of kent state university exploratory students. NACADA Journal: The Journal of the National Academic Advising Association 23, 37-43.

Mitchell, K.E., Levin, A.S., & Krumboltz, J.D. (1999). Planned happenstance: Constructing unexpected career opportunities. Journal of Counseling & Development, 77, 115-124.

Paa, H.K., & McWhirter, E.H. (2000). Perceived influenced on high school students’ current career expectations. Career Development Quarterly, 49, 29-44.

Porter, S.R. & Umbach, P.D. (2006). College major choice: An analysis of Person-Environment Fit. Research in Higher Education, 47, 429-449.

Rayman, J.R., Bernard, C.B., Holland, J.L. & Barnett, D.C. (1983). The effects of a career course on undecided college students. Journal of Vocational Behavior, 23, 346-355.

Reardon, R. & Bullock, E. (2004). Holland’s theory and implications for academic advising and career counseling. NACADA Journal: The Journal of the National Academic Advising Association 24, 111-123.

Robinson, C.H., Betz, N.E. (2004). Test-retest reliability and concurrent validity of the expanded skills confidence inventory. Journal of Career Assessment 12, 407-422.

Saleh, A. (2001). Brain hemisphericity and academic majors: A correlation study. College Student Journal, 35, 193-200.

Shiloh, S., Koren, S., & Zakay, D. (2001). Individual differences in compensatory decision-making style and need for closure as correlates of subjective decision complexity and difficulty. Personality and Individual Differences, 30, 699-710.

Simpson, J.C. (2003). Mom matters: Maternal influence on the choice of academic major. Sex Roles 48, 447-460.

Simpson, J.C. (2001). Segregated by subject: racial differences in the factors influencing academic major between european americans, asian americans, and african, hispanic, and native americans. The Journal of Higher Education, 72, 63-100.

Srsic, C.S. & Walsh, W.B. (2001). Person-environment congruence and career self-efficacy. Journal of Career Assessment, 9, 203-213.

Steele, G. (2003). A research-based approach to working with undecided students: A case study illustration. NACADA Journal: The Journal of the National Academic Advising Association 23, 10-20.

Strasser, S.E, Ozgur, C., & Schroeder, D.L. (2002). Selecting a business college major: An analysis of criteria and choice using the analytical hierarchy process. Mid-American Journal of Business, 17, 47-57.

Trusty, J., Ng, K., & Plata, M. (2000). Interaction effects of gender, SES, and race-ethnicity on postsecondary educational choices of U.S. students. Career Development Quarterly, 49, 45-59.

Turner, S.E. & Bowen, W.G. (1999). Choice of major: the changing (unchanging) gender gap. Industrial and Labor Relations Review, 52, 289-313.

Yi, J.K., Lin, J.G. & Kishimoto, Y. (2003). Utilization of counseling services by international students. Journal of Instructional Psychology, 30, 333-342.