Capstones, Theses and
\nDissertations
\n2014
\nOccupational knowledge in college students:
\nExamining relations to career certainty, career
\ndecision-making self-efficacy, and interest
\ncongruence<\/p>\n
iii
\niv
\nACKNOWLEDGEMENTS v
\nABSTRACT vi
\nCHAPTER 1. INTRODUCTION 1
\nCHAPTER 2. LITERATURE REVIEW 9
\nCHAPTER 3. METHODS 50
\nCHAPTER 4. RESULTS 58
\nCHAPTER 5. DISCUSSION 70
\nREFERENCES
\nAPPENDICES
\nDemographics Questionnaire\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026
\nWork Activity Importance Profile\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026
\nOccupation Work Activity Importance Profile \u2026\u2026\u2026\u2026\u2026\u2026
\nPerceived Occupational Knowledge Scale\u2026\u2026\u2026\u2026\u2026\u2026.\u2026..
\nCareer Certainty Scale\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026..
\nCareer Decision Self-Efficacy Scale\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026
\nCareer Exploration Checklist\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026
\nO*NET Interest Profiler \u2013 Short Form\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026.
\n81
\n91
\n92
\n96
\n97
\n98
\n99
\n101
\n102
\niii
\nTABLES
\nTable 1. Descriptive Statistics and Variable Correlations\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026… 60
\nTable 2. Multiple Regression Results for Mediation Hypotheses 1 \u2013 4\u2026\u2026\u2026\u2026\u2026…… 62
\nTable 3. Standardized Path Coefficients for the Recursive Model Predicting
\nCareer Certainty\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026….. 68
\niv
\nFIGURES
\nFigure 1. The hypothesized recursive path model predicting career certainty.\u2026\u2026\u2026.\u2026 8
\nFigure 2. Hypothesis 1 mediation model with multiple regression results\u2026\u2026\u2026\u2026\u2026… 63
\nFigure 3. Hypothesis 2 mediation model with multiple regression results\u2026\u2026\u2026\u2026\u2026… 64
\nFigure 4. Hypothesis 3 mediation model with multiple regression results\u2026\u2026\u2026\u2026\u2026… 65
\nFigure 5. Hypothesis 4 mediation model with multiple regression results\u2026\u2026\u2026\u2026\u2026… 66
\nFigure 7. Recursive path model predicting career certainty\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026.. 69
\nv
\nACKNOWLEDGEMENTS
\nDr. Lisa Larson, my major professor, is the person I must thank first and foremost for
\neverything from welcoming me as her student in the beginning, to providing constant guidance
\nand support throughout my graduate school career. She has both challenged me and respected me
\nin every way, for which I am very grateful and would not have had it any other way. I also thank
\nmy committee members, Drs. Patrick Armstrong and Frederick Lorenz, for their enthusiasm and
\nwillingness to offer conceptual and statistical guidance throughout this project ( help with nursing paper writing from experts with MSN & DNP degrees) (and for always
\nwelcoming me when I\u2019ve shown up unannounced in their offices with questions). Many thanks
\ngo to Dr. Carolyn Cutrona as well, for her Helpance in funding this study.
\nI would also like to acknowledge my cohort, as my friendships with the three of them have
\nbeen crucial sources of comfort and stability in my graduate school experience over the past two
\nand a half years. In addition, I\u2019m very grateful for the statistical support so willingly provided by
\nYi Du, and the wonderful guidance, emotional support, and comic relief provided by my
\nacademic big bro, Dusty M. F. Baker.
\nFinally and most importantly, I thank my parents, Moe and Rick, my sister, Sarah, and my
\nbrother, Jared, for being the most influential sources of support and inspiration in my life.
\nEverything I do and everything I hope to become is because of them. \u201cWith you by my side, I
\ncan have anything I need.\u201d
\nvi
\nABSTRACT
\nSeveral career decision-making models describe an ongoing career decision Assessment
\nprocess in which career decisions are constantly being re-assessed as individuals gain
\noccupational information and self-awareness. However, the relation between career decision
\ncertainty and knowledge of one\u2019s chosen career has not yet been adequately examined; in part
\ndue to the difficult nature of assessing the latter construct in large samples. The present study
\nobjectively measured occupational knowledge in 316 college students; specifically regarding the
\ncareer each student had reportedly been considering. Objective occupational knowledge of
\nparticipants\u2019 chosen careers significantly related to interest congruence only, and neither variable
\nsignificantly contributed to variance in career certainty. Results indicated career decision-making
\nself-efficacy partially drives participation in career exploration activities and perceived
\noccupational knowledge, and all three variables in turn contributed to the prediction of career
\ncertainty through direct and\/or indirect effects. This study was the first to objectively assess
\ncollege students\u2019 knowledge of the careers they were actively pursuing, and the first to examine
\nthat construct along with other important career decision-making variables. Implications and
\nfuture research directions are discussed.
\n1
\nCHAPTER ONE
\nINTRODUCTION
\nThe career choice process in college students is unique since these individuals generally
\nhave the freedom to pursue whichever careers they desire. The ideal outcome for these students
\nis to have a career they find fulfilling, and that ultimately provides them with high levels of
\nvocational satisfaction. Vocational satisfaction is consistently moderately related to life
\nsatisfaction (Bowling, Eschleman, & Wang, 2010); thus, the importance of making a \u201cgood\u201d
\ncareer decision is great. While the career decision process is complex, as is its relation to
\nsatisfaction, it is an important developmental challenge and one that will likely affect a good
\nportion of students\u2019 future lives.
\nA major construct involved in the career decision-making process is occupational
\nknowledge. Occupational knowledge has been defined as the accuracy one has in understanding
\ninformation related to careers (Schmitt-Wilson & Welsh, 2012); when one has this accurate
\nunderstanding, he or she is considered to be informed. Being informed about occupations has
\nbeen emphasized as an important factor in the career decision-making process (Gati, 1986;
\nGrotevant & Durrett, 1980; Hirschi & Lage, 2007; Super, 1983). In order for one to make a
\ndecision about which career might be fulfilling, one should have knowledge regarding the
\ncareers available to him or her, and also regarding the specific activities and responsibilities
\ninvolved in those careers. When one has sufficient accurate occupational information, he or she
\nis able to make an informed decision regarding which career to pursue; he or she is
\nknowledgeable of the requirements needed to obtain his or her career-of-interest, the daily
\nactivities and responsibilities of the job, and the types of environments one can work in. With
\n2
\nthis knowledge, students can evaluate the degree to which careers-of-interest will fit with their
\npersonal preferences.
\nSeveral career decision-making models explain an ongoing career decision Assessment
\nprocess in which career decisions are constantly being re-assessed as individuals gain
\noccupational information and self-awareness (Hirschi & Lage, 2007). Following these models,
\none would expect increasing occupational knowledge to lead individuals to either persist along
\nthe same career path or choose an alternative path. In turn, greater persistence along the same
\ncareer path is reflected in one\u2019s degree of career certainty. Career certainty has been described as
\nthe commitment individuals have to their decisions to pursue specific careers (Daniels, Clifton,
\nPerry, Mandzuk, & Hall, 2006). It is generally considered to be one of the final stages in the
\ncareer decision-making process, followed only by the planning and implementation stages
\n(Hirschi & Lage, 2007).
\nDespite the centrality of career certainty in the career decision-making process, its link to
\noccupational knowledge has not yet been directly examined in terms of individuals\u2019 knowledge
\nabout their chosen careers. This oversight seems to be due in part to a lack of adequate methods
\nto assess occupational knowledge. Three general types of assessment exist in the literature: those
\nthat gauge self-estimated levels of knowledge, those that objectively test either general career
\ninformation or general aspects of common jobs, and those that objectively test specific jobs\u2019
\nactivities and responsibilities. All have inherent problems; namely, none are able to adequately
\nand efficiently measure the accuracy of job-specific knowledge in terms of activities and
\nresponsibilities in large participant samples. Further, not only has the literature overlooked the
\nrelation between occupational knowledge and career certainty, it has failed to address the
\n3
\noverarching question of whether or not college students are sufficiently knowledgeable of the
\ncareers they are choosing to pursue.
\nThe Present Study
\nThe purpose of the present study was three-fold. First, I sought to address a hole in the
\noccupational knowledge literature regarding its objective measurement and its relation to career
\ncertainty in college students. Second, since it was the first study to objectively measure
\noccupational knowledge of college students\u2019 chosen careers, I sought to explore the relations
\nbetween that knowledge and four additional vocational constructs: 1) Interest congruence,
\ndefined as the degree to which a person\u2019s interests match those of a given work environment
\n(Spokane & Cruza-Guet, 2005); 2) career decision-making self-efficacy, defined as one\u2019s belief
\nthat he or she is able to successfully engage in and complete tasks necessary to career decisionmaking (Betz, 2000); 3) career exploration activities participants reported, a behavioral indicator
\nof participants\u2019 engagement in career exploration; and 4) perceived occupational knowledge, a
\nsubjective self-assessment of occupational knowledge which has been frequently used in studies
\nas a measure of \u201coccupational knowledge.\u201d Third, the present study sought to examine these four
\nvariables alongside occupational knowledge in terms of their roles in predicting career certainty.
\nWhile vocational researchers have made great strides in understanding the choices people
\nmake in their career pursuits, little attention has been given to examining whether college
\nstudents are knowledgeable about their future occupations. Some students may be confident that
\ntheir interests are leading them in the right direction, and others may be choosing careers based
\non insufficient or inaccurate information. These individuals may be at risk of being dissatisfied
\nwith their occupations once they enter the workforce and realize the inaccuracy of their
\nexpectations. Overall, the present study sought to gain a better understanding of the factors
\n4
\ninvolved in making career decisions in college students. A path model predicting career certainty
\nwas used to test specific mediation hypotheses involving occupational knowledge and the four
\nother variables-of-interest.
\nHypotheses
\nFour mediation hypotheses involving the two primary variables-of-interest, objective
\noccupational knowledge and career certainty, as well as the additional four secondary variablesof-interest were tested using multiple regression.
\nCareer decision-making self-efficacy has been actively researched over the past thirty
\nyears in relation to the process of career decision-making, but has not yet been examined
\nalongside occupational knowledge as defined be the present study. Previous research found
\nlower task performance self-efficacy to be associated with low commitment to goals (Locke,
\nFrederick, Lee, & Bobko, 1984). The present study sought to build on this finding and examine
\nthe relation between career decision-making self-efficacy and career certainty in the context of
\noccupational knowledge. Based on Locke and colleagues\u2019 findings, lower career decisionmaking self-efficacy was expected to be associated with lower career certainty, as certainty can
\nbe thought of as a proxy for goal commitment. Bandura and Cervone (1986) have also found that
\nsome individuals with high performance self-efficacy tended to feel content with performance on
\nexperimental tasks despite receiving feedback that they only nearly succeeded. These individuals
\nlacked the motivation required to achieve success in their task performance, with their high selfefficacy causing them to settle for sub-par performance. This phenomenon was also examined by
\nSalomon (1984) who found that high performance self-efficacy individuals increased their
\nperformance when they considered the task to be difficult, but chose to decrease their
\nperformance effort when they considered the task to be easy. Translating these findings to the
\n5
\npresent study, it is possible that high career decision-making self-efficacy could compel
\nindividuals to put less effort toward obtaining accurate occupational knowledge during their
\ncareer decision processes. Some high career decision-making self-efficacy students could still
\nput forth the necessary effort; though, there may be others who feel a sense of contentment with
\nthe inadequate exploration steps they have taken thus far. These \u201cat risk\u201d students may view
\ncareer decision-making process tasks as easy and not necessary outlets for their effort, resulting
\nin occupational knowledge deficits. In these ways, career decision-making self-efficacy may
\ncontribute to both objective occupational knowledge and career certainty, while objective
\noccupational knowledge also contributes to career certainty.
\nH1. Objective occupational knowledge would partially mediate the relation between
\ncareer decision-making self-efficacy and career certainty.
\nConsistent with findings presented here regarding task performance self-efficacy, the
\npresent study predicted career decision-making self-efficacy would be involved in both the
\nseeking out occupational knowledge, as well as the process of determining one\u2019s level of
\ncertainty about a particular career path. Thus, career decision-making self-efficacy was predicted
\nto significantly relate to both objective occupational knowledge and career certainty, with
\nobjective occupational knowledge partially mediating its relation with career certainty. Career
\ndecision-making theories (Hirschi & Lage, 2007), however, suggest more variables are involved
\nin the complex prediction of career certainty in college students. The second hypothesis
\npredicted career exploration activities would play a role in the relation between career decisionmaking self-efficacy and objective occupational knowledge.
\nH2. Career exploration activities would partially mediate the relation between career
\ndecision-making self-efficacy and objective occupational knowledge.
\n6
\nThis hypothesis proposed a possible reason for the partial mediation predicted in
\nHypothesis 1, which posited that career certainty is a function of career decision-making selfefficacy, objective occupational knowledge, and an indirect effect of career decision-making
\nself-efficacy through objective occupational knowledge. The number of reported career
\nexploration activities were posited as a mechanism for which career decision-making selfefficacy drives one to seek out objective occupational knowledge.
\nThe third hypothesized mediation effect was of interest congruence on the relation
\nbetween objective occupational knowledge and career certainty. Congruence theory posits that
\nindividuals naturally seek out work environments they believe will be compatible with their own
\ninterests, leading them toward occupational satisfaction (Holland, 1997). Thus, when individuals
\nare able to find occupations with characteristics compatible with their own interests, they are
\nmore likely to be satisfied with their choices. The literature has provided moderate support for
\nthis theory (Tranberg, Slane, & Ekeberg, 1993); though, it is important to note that assessing
\none\u2019s congruence with his or her chosen occupation is an internal judgment. Individuals must
\nassess their own levels of congruence as they navigate through the career decision process.
\nConceptually, one would expect increasing occupational knowledge to Help individuals in
\nassessing which career will yield the highest congruence. Therefore, if greater knowledge leads
\nto greater likelihood of achieving interest congruence, one might expect level of career certainty
\nwould positively relate to both objective occupational knowledge and interest congruence,
\nleading to the third hypothesis.
\nH3. Interest congruence would partially mediate the relation between objective
\noccupational knowledge and career certainty.
\nSimilar to the career decision-making self-efficacy partial mediation hypothesis,
\nHypothesis 3 predicted objective occupational knowledge would relate to career certainty both
\n7
\ndirectly and indirectly through interest congruence. Additionally, interest congruence was
\npredicted to serve as its own motivating factor, leading one to continue pursing a career path or
\nopt for an alternative path. In this way, interest congruence would relate directly to both
\nobjective occupational knowledge and career certainty, adding to the model\u2019s predictive power.
\nOccupational knowledge literature often utilized perceived occupational knowledge as a
\nproxy for objective occupational knowledge. The present study included this additional variable
\nin the model, hypothesizing it would be predicted by both objective occupational knowledge and
\ncareer decision-making self-efficacy through the same partial mediation mechanism presented in
\nHypothesis 1 for career certainty.
\nH4. Objective occupational knowledge would partially mediate the relation between
\ncareer decision-making self-efficacy and perceived occupational knowledge;
\nperceived occupational knowledge would add significantly to the prediction of
\ncareer certainty.
\nAdditional Analysis
\nAfter determining whether specific mediation models were supported by the data, a path
\nanalysis was run in order to examine relations between all six variables simultaneously. The path
\nmodel (Figure 1) was recursive and sequenced based on theory. In other words, each variable
\nwas thought to have a potential effect on later variables, but not any causal reciprocal effects
\n(Cohen, Cohen, West, & Aiken, 2003). The model contained one exogenous variable, career
\ndecision-making self-efficacy, because it has been argued that efficacy beliefs influence and
\npredict behavior (e. g., Bandura, 1987; Betz, 2000). The first endogenous variable was career
\nexploration activities, as it was hypothesized to partially result from career decision-making selfefficacy beliefs and contribute to occupational knowledge. The second endogenous variable was
\nobjective occupational knowledge, followed by interest congruence, perceived occupational
\nknowledge, and career certainty.
\n8
\nFigure 1. The hypothesized recursive path model predicting career certainty.
\nImplications
\nSignificant findings would have important implications for career counselors and
\nuniversity career centers. Findings in support of the hypotheses would direct career counselors
\nand universities to areas of need among their clients and students. Counselors would be
\ncompelled pay special attention to career certainty levels of their clients, determining whether
\nthey are in line with their levels of knowledge about those careers. Specifically, counselors
\nwould want to ensure their clients are making decisions based on accurate information regarding
\nwork activities involved in their chosen careers. Further, universities may need to bolster their
\ncareer outreaches and development courses for students in order to increase career decisionmaking self-efficacy, encourage active exploration as a way of gaining occupational knowledge,
\nand Helping students in finding occupations congruent with their interests.
\nCareer decisionmaking self-efficacy
\nPerceived
\noccupational
\nknowledge
\nInterest
\ncongruence
\nCareer certainty
\nObjective
\noccupational
\nknowledge
\nCareer
\nexploration
\nactivities
\n9
\nCHAPTER TWO
\nLITERATURE REVIEW
\nThe career decision-making process of college students is saturated with many different
\nexploration and information-gathering tasks. Central to the present discussion is the process of
\nobtaining occupational information about one\u2019s chosen career, which is emphasized in many
\ncareer decision-making models. Therefore, before discussing occupational knowledge in detail,
\nthe prominent career decision-making models are presented for a better understanding of the
\nbroad career decision-making process.
\nCareer Decision-Making Models
\nThe process of career decision-making in college students has been approached from
\nmultiple perspectives, resulting in several different models. While these models are often quite
\ncomplex, simplified versions are discussed here in order to provide a brief overview of the
\ntheoretical foundations of the career decision-making process.
\nCrites (1976) identified five aspects involved in effective career decision-making. First is
\naccurate self-appraisal involving individuals\u2019 self-assessment of interests, needs, and values.
\nSecond is gathering occupational information which is necessary in order for individuals to
\ndetermine the characteristics of occupations that might fit their personal characteristics.
\nGathering information about one\u2019s self and occupations is followed by goal selection in which
\nindividuals make decisions about which occupation(s) they prefer. The final two elements are
\nmaking future plans and problem solving. After individuals select their career goals, they must
\nplan how they will obtain those goals and be prepared to face problems that arise in their career
\npursuits. These five elements comprise \u201ccareer maturity,\u201d which Crites introduced as an
\noverarching developmental characteristic that allows individuals to effectively engage in the
\n10
\ncareer decision-making process. Crites argued that by engaging in these five elements, and
\nthereby demonstrating career maturity, individuals will be able to make effective career
\ndecisions.
\nHarren (1979) identified the need for career decision-making models specific to certain
\nlife stages so that career developmental concepts could be integrated. Thus, his model was
\ncreated with traditional college students in mind. Central to his model, Harren outlined a
\nsequential four-stage process. First is Awareness, during which one engages in appraisal of one\u2019s
\npast, present, and future, and includes analyses of one\u2019s current course of action, decisionmaking confidence, and environmental pressures. Next is Planning, characterized by information
\ngathering about one\u2019s career decision and one\u2019s self. Once enough information has been
\nacquired, the individual begins narrowing alternatives down to a specific choice in preparation
\nfor the next stage. This stage is followed by Commitment, in which one integrates the choice into
\nhis or her self-concept and begins planning to implement the choice. Finally, Implementation
\noccurs when the individual carries out the choice and evaluates it, returning to the beginning of
\nthe process if he or she is not satisfied.
\nAlternatively, Gati (1986) has presented a model characterized by systematic elimination
\nin order to identify small subsets of career alternatives, effectively simplifying the career
\ndecision-making process. In Gati\u2019s Sequential Elimination Model, occupations are considered to
\nbe made up of various aspects which determine whether or not a given occupation will be
\nincluded in or excluded from the career decision-making process. For example, if an individual
\nconsiders prestige important, occupations may be excluded if they are considered low in prestige.
\nThus, the first stage of Gati\u2019s model involves the identification of occupational aspects one
\nconsiders relevant to his or her career decision. Aspects are then ranked by importance and
\n11
\noccupation alternatives are eliminated according to the guidelines set by aspect rankings. This
\nprocess continues until the list of potential careers is of acceptable length. A strength of Gati\u2019s
\nmodel is that it inherently forces individuals to engage in career exploration, providing them with
\ninformation about the aspects of occupations they find important.
\nAn additional model, the Cognitive Information-Processing Approach for career
\ndecision-making (Sampson, Reardon, Peterson, & Lenz, 2004), presents a series of phases that
\nindividuals cycle through until a career decision has been made. Individuals first identify the
\nproblem (need to choose a career; Communication), identify all components of the problem
\n(Analysis), formulate possible solutions for the problem (career alternatives; Synthesis), rank
\nalternative solutions (Valuing), and choose alternative to implement (Execution). Individuals
\nthen return to the Communication stage to evaluate their decisions, continuing on with the
\nprocess again if problems are identified.
\nMore recently, Hirschi and Lage (2007) noted the similarities existing among the
\ndifferent career decision-making models. They sought to combine the themes identified in
\nprevious models, boiling them down to six basic phases. In Phase 1, individuals become aware of
\nthe need to make a career decision. Hirschi and Lage note the importance of this stage, since
\nsome individuals do not become concerned with career decision-making in the first place. During
\nPhase 2, career alternatives are generated based on individuals\u2019 interests, skills, and values. In
\nPhase 3, Alternatives are then eliminated in order to reduce possibilities to a reasonable number
\nin preparation for the exploration phase. Phase 4 constitutes exploration and further reduction of
\ncareer alternatives. In Phase 5, individuals make a choice and begin committing to it. Finally, in
\nPhase 6 individuals are committed to their choices and implementing action plans. This phase
\nwas intentionally included in order to distinguish between individuals still engaged in the career
\n12
\ndecision-making process (who would cycle back through the phases) from those who have firmly
\ncommitted to careers.
\nAs Hirschi and Lage (2007) identified, the different career decision-making models have
\nsubstantial overlap. Of most interest to the present discussion are the middle phases, Phases 2-5,
\nin which individuals are engaged in the actual career decision-making process rather than in the
\npre- and post-decision-making phases. During these phases, individuals are gathering
\ninformation about themselves and potential occupations, identifying which occupations might be
\nappropriate, and then narrowing down those occupations until ultimately one remains.
\nImportantly, the role of information is salient throughout these phases. Not only do individuals
\nneed to gather sufficient, accurate information about all the occupations available to them, they
\nmust continually process that information in tandem with the information they have about
\nthemselves. The information processing continues until a career choice is made, at which point
\nindividuals will likely have to gather more information for planning and implementation
\npurposes. This process, common among the various career decision-making models, makes
\nsalient the important role of information in one\u2019s ability to make a career decision.
\nThe Role of Information in Career Decision-Making
\nThe career decision-making models presented here have been developed with the purpose
\nof outlining how one goes about reaching a career decision when faced with alternatives (Gati,
\n1986). The importance of making a good career decision is great, as \u201cthere are few other
\ndecisions that exert as profound an influence on people\u2019s lives as the choice of a field of work or
\ncareer\u201d (Hackett & Betz, 1995; p. 249). Gati (1986) identified four sources of problems that can
\nprevent one from reaching an optimal career decision: a) lack of information about one\u2019s self or
\nof career alternatives, b) lack of resources to acquire the necessary information, c) cognitive
\n13
\nlimitations in the decision-maker preventing information processing, and d) lack of ability in the
\ndecision-maker to integrate and process information about one\u2019s self and one\u2019s career
\nalternatives. Central to these problem sources is information, both about the person\u2019s interests
\nand the career alternatives. By gaining information about one\u2019s self and one\u2019s occupation of
\ninterest, one can be confident that he or she has the information needed to make a good career
\ndecision. While the role of self-information is beyond the scope of the present discussion, the
\nrole of occupational information is central: When one has sufficient accurate information about
\nhis or her occupation alternatives, he or she is able to make an informed decision regarding
\nwhich of them to pursue.
\nOccupational Knowledge
\nIn light of the emphasis placed on occupational information by the career decisionmaking models, the question arises of whether or not college students are approaching career
\ndecisions in the most efficient way. That is, whether or not they are fully engaging in the stages
\nlaid out by the various models and acquiring the information necessary to reach an optimal
\ncareer decision. Without any constraints, efficiency may not matter. However, reality compels
\none to consider the potential disadvantages to approaching career decision in an inefficient way \u2013
\ni.e., making uninformed decisions to pursue occupations that may lead one to desire a change in
\ncareer paths. The purpose of measuring college students\u2019 levels of occupational knowledge then,
\nis to determine whether there is a subset of students making uninformed decisions that may result
\nin future dissatisfaction or distress; to alert professionals of a possible \u201cat risk\u201d group of students.
\nTo begin addressing the question of whether or not college students are making informed
\ndecisions while they are engaged in the career decision-making process, a discussion of the
\nliterature around occupational knowledge is necessary.
\n14
\nAssessment of Occupational Knowledge
\nBefore a review of the occupational knowledge literature findings, it is important to have
\nan understanding of the ways in which this knowledge is assessed. The overview that follows
\ndescribes the measurement approaches that have been found in the literature. This discussion is
\nnecessary since the generalizability of each study\u2019s findings are limited by the assessment
\nmethod used.
\nThe Career Maturity Inventory (CMI). The CMI (Crites, 1973, 1978) has been used to
\ntest individuals on their knowledge of aspects of common occupations. For example, participants
\nwould be asked if they agree or disagree with the statement, \u201cAnita wants to be a lawyer. She
\nmust first pass the bar exams and then go to law school.\u201d This scale was found to be the most
\ncommonly used measure of occupational knowledge, with seven studies incorporating it
\n(Achebe, 1982; Holland & Gottfredson, 1975; Johnson, Johnson, Johnson, & Yates, 1982;
\nJohnson, Johnson, & Yates, 1981; Jordan & Pope, 2001; Omvig & Tulloch, 1975; Pumfrey &
\nSchofield, 1982).
\nOverall, the CMI is quite useful in assessing broad occupational knowledge for the
\npurposes of measuring career maturity levels as defined by Crites\u2019 (1976) model of career
\ndecision-making. However, it does not satisfy the requirements of the present discussion in that
\nspecific information of individuals\u2019 chosen careers is not addressed. It seems that, while career
\nmaturity is useful for broadly investigating individuals\u2019 potential for making effective career
\ndecisions, the construct is not operationally defined in way that provides a solution to the
\nmeasurement issues at hand. Specifically, the extent to which people are informed about the
\ncareers they are choosing to pursue.
\n15
\nThe Career Development Inventory (CDI). Developed by Super, Thompson,
\nLindeman, Jordaan, and Myers (1981), the CDI is considered a well-established vocational
\nmaturity assessment and has been used in eight studies for the purpose of job knowledge
\nmeasurement. Of those, only two included the subscale of interest (Hirschi, 2011; Healy &
\nMourton, 1984); namely, the Planning Scale. The CDI \u2013 Planning Scale asks individuals to
\nidentify the career they aspire to pursue and are presented with an eight-item questionnaire in
\nwhich they rated their knowledge of the job\u2019s required abilities and education, work context,
\nexpected pay, etc., on a five-point scale from (1) poorly to (5) very well. While this scale of the
\ninstrument does address specific aspects of individuals\u2019 chosen careers, the issue of job
\nknowledge accuracy remains. It is important to note that all the statistical conclusions made
\nusing this scale are based on self-estimated levels of knowledge rather than on objective
\nassessment.
\nThe CDI – Knowledge of Preferred Occupation scale asks individuals to choose one of a
\nnumber of occupational groups in which their career choice best fits. Individuals are then
\npresented with 40 multiple choice items inquiring about facets of the job related to required
\nabilities, interests and values fulfilled, training requirements, and the environments in which
\npeople work. Notably, work activities involved in one\u2019s chosen occupation were not assessed.
\nThe CDI \u2013 Knowledge of Preferred Occupation scale certainly has potential. However, it
\nexamines participants\u2019 knowledge of occupational categories rather than titles, which are
\nrelatively broad and thus the scale does not solve the assessment problem at hand. Further, it
\ndoes not inquire about work activities, which give important information about what a given job
\nis actually like on a daily basis. The bulk of the recent research using the CDI has been almost
\nexclusive to the Australian version of the CDI (Creed, Patton, & Hood, 2010; Creed, Patton, &
\n16
\nPrideaux, 2007; Patton & Creed, 2007; Patton, Spooner-Lane, & Creed, 2005; Creed & Patton,
\n2003), which, unfortunately, omitted the Knowledge of Preferred Occupational Group scale in its
\ndevelopment (Lokan, 1984; Sadeghi, Baghban, Bahrami, Ahmadi, & Creed, 2011). Therefore,
\nmeasurement of aspects of one\u2019s preferred occupational group knowledge has not been
\nextensively investigated with the CDI.
\nAn earlier version of the CDI which included a scale labeled \u201cPart V\u201d (Super et al., 1981)
\nwas used by one study (Crowley, 1981). The scale consisted of 30 multiple choice items
\ndesigned to gauge participants\u2019 knowledge of aspects of common occupations, such as the tools
\nor equipment used, training needed, duties, and employment trends.
\nThe Jobs-Career Key (JCK). The JCK (Blank, 1978) was used in one study (Taylor,
\n1985) and is comprised of 147 multiple choice items across four occupational domains
\n(economic factors, education and training, job parts, and worker relationships) for 18
\ntraditionally feminine and 18 traditionally masculine occupations. This measure is strong in its
\nability to assess knowledge of various aspects of a variety of occupations, but it does not inquire
\nabout specific activities or responsibilities within those jobs and is limited to 36 occupations.
\nThe Cognitive Vocational Maturity Test (CVMT). The CVMT (Westbrook & ParryHill, 1973) is a 120 multiple choice-item measure with six subscales used in three studies
\n(Fouad, 1995; Westbrook, 1976; Westbrook, Elrod, & Wynne, 1996). The Duties subscale
\nobjectively measures individuals\u2019 knowledge of the typical duties performed in various wellknow occupations. For example, one item asks \u201cWhich one fills prescriptions for drugs and
\nmedicine? a) Chemist, b) Physicist, c) Pharmacist, d) Pharmacologist, or e) I don\u2019t know.\u201d
\nAdditional scales assess Fields of Work (e.g., \u201cWhich one of the following is not in the field of
\nconstruction?), Work Conditions (e.g., \u201cWhich one of the following does not have to work with
\n17
\ntools?\u201d), Education Required (e.g., \u201cA college education is not required to be a \u2026\u201d), Attributes
\nRequired (e.g., \u201cImagination is most important in which occupation?\u201d), and Job Selection which
\npresents short narratives describing a person\u2019s skills and personality, asking participants to
\nindicate which occupation would be most fitting. This method of assessment is relatively strong
\nas it inquires about details specific to a occupations. However, it does not assess students\u2019 levels
\nof knowledge of the careers they are interested in pursuing, limiting the knowledge assessment to
\na small sample of common occupations. Furthermore, the studies that utilized this measure
\nincluded relatively young samples. Eighth grade (Fouad, 1995) and ninth grade (Westbrook,
\n1976) students were assessed in two studies, with only one study sampling eleventh and twelfth
\ngrade students (Westbrook et al., 1996).
\nThe Career Planning Inventory (CPI). Developed by Westbrook (1997), this 10-item
\nscale was designed to measure individuals\u2019 breadth of career knowledge. It was found to be used
\nin only one study (Westbrook & Sloan, 2006) and is comprised of multiple choice questions in
\nwhich participants choose the occupation that fits the job task description (e.g., \u201cWhich of the
\nfollowing designs buildings?\u201d Mechanic, architect, surveyor, engineer, or \u201cI don\u2019t know\u201d). While
\na good objective measure of broad occupational knowledge, this scale suffers from the same
\nissues as the CVMT.
\nThe Job Knowledge Survey (JKS). Developed by Loesch (1977), the JKS was found to
\nbe used in two studies (Loesch & Sampson, 1978; Sampson & Loesch, 1981) and consists of 48
\noccupational titles sampled randomly from each of the RIASEC (Realistic, Investigative,
\nArtistic, Social, Enterprising, and Conventional; Holland, 1997) interest domains which
\nparticipants are asked to rate the occupational titles\u2019 levels of emphasis on people, data, and
\nthings. This measure is strong in that it asks individuals to separate occupational titles based on
\n18
\nthe general domains of typical job activities. However, the measure does little to assess
\nknowledge of the occupations\u2019 activities and responsibilities beyond broad categorization.
\nThe Career Decision Profile (CDP). The CDP (Jones, 1989), has been used by two
\nstudies to measure several career development variables, including occupational knowledge
\n(Duffy & Kingaman, 2009; Multon, Heppner, & Lapan, 1995). It includes three items gauging
\n\u201cKnowledge about Occupations and Training\u201d on a 5-point scale of agreement (e.g., \u201cI do not
\nfeel I know enough about the occupations I am considering\u201d). While the items in this measure
\nsuccessfully inquire about participants\u2019 levels of information about their chosen careers, the
\nsubjective nature makes it difficult to interpret responses.
\nThe Occupational Knowledge Scale (OKS). Adapted from Tracey\u2019s Personal Globe
\nInventory (2002) for use in one study (Rohlfing, Nota, Ferrari, Soresi, & Tracey, 2012), this
\nmeasure is comprised of 18 occupational titles of which individuals are to rate their knowledge
\nfrom (1) I don\u2019t know at all to (5) I know a lot. This method suffers from the same validity issue
\nas that of other self-estimate measures; any conclusions made from its use must be
\ncontextualized so as not to confuse objective occupational knowledge with self-estimations.
\nMiscellaneous knowledge assessments. Two studies were found that utilized personnel
\nselection knowledge tests (Ones & Viswesvaran, 2007; Van Iddekinge, Putka, & Campbell,
\n2011) developed by subject matter experts, human resources specialists, or industrialorganizational psychologists. Using a multiple choice format, they tested specific tasks and
\nactivities common in the occupations of interest. While this approach is an improvement upon
\nthe methods used by other researchers since it assesses specific job activities and tasks, creating
\nsuch comprehensive job knowledge measures for any career-diverse sample would be quite time-
\n19
\nconsuming. For this reason, this method has only been used with very specific populations and
\nlacks generalizability.
\nTwo studies used a measurement method in which individuals record their knowledge of
\nsix dimensions of 20 occupational titles (Schmitt-Wilson & Welsh, 2012; Walls, 2000). The six
\ndimensions, time (training, preparation), availability (positions, jobs), earnings, physical
\n(strength, stamina), mental (thinking, knowledge), and status (respect, prestige), have unique
\nresponse scale or scales (e.g., the mental dimension was measured by three subscales assessing
\nreasoning development, mathematical development, and language development). Responses are
\nstandardized based on information from the Dictionary of Occupational Titles. This measure taps
\ninto various important aspects of occupational knowledge; however, it does not specifically
\naddress individuals\u2019 careers of interest, nor does it assess knowledge of specific occupational
\nactivities or responsibilities which hold important information for career decision-making.
\nFinally, one study measured occupational knowledge through individual interviews
\n(DeFleur & Menke, 1975). Ten occupations were assessed in terms of knowledge of job
\nactivities, nature of the work, specializations within the occupation, education and training
\nrequirements, salary, prestige, and job availability outlook. Accuracy scores were assigned based
\non information from the Occupational Outlook Handbook. This methodology is good compared
\nto other approaches in the literature, but it is also largely impractical and inefficient for large
\nsample sizes. Assessment methods are needed that are able to incorporate the level of specificity
\nthat these interviews provided, while remaining usable in research with large samples.
\nSummary of Assessment Methods. It seems that the current psychological measurement
\nof job knowledge is largely flawed. Three general types of assessment exist in the literature:
\nthose that gauge self-estimated levels of knowledge, those that objectively test either general
\n20
\ncareer information or relatively common-knowledge aspects of specific jobs, and those that
\nobjectively test specific jobs\u2019 activities and responsibilities. All have inherent problems; namely,
\nnone are able to adequately and efficiently measure accuracy of job-specific knowledge in terms
\nof activities and responsibilities in large participant samples. Those used in personnel selection
\nmost closely resemble ideal occupational knowledge assessments, but cannot realistically be
\nused in studies involving diverse career choices. Individual interviews have been conducted in
\npast studies, but are far too inefficient and unrealistic to employ in most empirical studies.
\nNotably, many aspects of occupational knowledge have been assessed in some way; though none
\nof the available literature has investigated the extent to which college students are
\nknowledgeable about the specific job activities they will encounter after entering their
\noccupation of choice.
\nReview of the Occupational Knowledge Literature
\nA literature search using PsycInfo and the term knowledge with career, job, or
\noccupation and college, undergraduate, high school, or middle school yielded 190 peer-reviewed
\narticles written in English. Of those 190, 159 articles were excluded because they were deemed
\nirrelevant to the present discussion. Many studies were excluded because they did not report
\nresults related to occupational knowledge (e.g., Greenhaus, Hawkins, & Brenner, 1983;
\nPatterson, Curtis, & Reid, 2008; Reardon, Senier, & Lewis, 1972; Theophilides, Terenzini, &
\nLorang, 1984), were based on operational definitions of occupational knowledge that deviated
\nfrom the definition used by the present discussion (e.g., by measuring knowledge of general job
\nmarket patterns or career development tasks, having participants list occupational titles as a
\nmeasure of knowledge; e.g., Gunkel, Schlaegel, Langella, & Peluchette, 2010; Rottinghaus,
\nBuelow, Matyja, & Schneider, 2012; Rottinghaus, Day, & Borgen, 2005; Watson, Foxcroft, &
\n21
\nHorn, 1997), or if the target population was not assessed (e.g., middle school teachers\u2019
\nknowledge was measured; e.g., Arzi & White, 2008; Beswick, Callingham, & Watson, 2012;
\nGable, Tonelson, Sheth, Wilson, & Park, 2012). Empirical findings presented in the remaining
\n31 articles are reviewed below, organized by finding categories.
\nCareer intervention effects. Ten studies were found that investigated the effectiveness
\nof career interventions, measured at least in part by changes in occupational knowledge. These
\ninterventions have been found to be effective in increasing self-estimations of occupational
\nknowledge in college students on academic probation (Williams, 2011), undergraduate
\npsychology students (Atchley, Hooker, Kroska, & Gilmour, 2012), primarily Caucasian middle
\nschool students (Crowley, 1981; Johnson, Johnson, & Yates, 1981; Omvig, Tulloch, & Thomas,
\n1975), Black economically disadvantaged middle school students (Johnson, Johnson, Johnson, &
\nYates, 1982), ethnically diverse eighth grade students (Fouad, 1995), and middle school students
\nin England (Pumfrey & Schofield, 1982). Holland\u2019s Self-Directed Search (1971) was used as a
\ncareer intervention in two studies, though neither found significant intervention effects (Healy &
\nMourton, 1984; Holland & Gottfredson, 1975).
\nAge\/grade and gender. Six studies analyzed the effects of age and grade on occupational
\nknowledge levels. Cross-sectional studies have found that occupational knowledge increases by
\nage or grade in samples of 7th
\n-10th grade Nigerian students (Achebe, 1982), 6th and 8th grade
\nstudents (Omvig et al., 1975), 3
\nrd, 6th, 9th, and 12th grade students (Walls, 2000), 4th
\n-7
\nth grade
\nrural students (Schmitt-Wilson & Welsh, 2012), and high school males (DeFleur & Menke,
\n1975). However, Fouad\u2019s (1995) one-year longitudinal study using the CVMT did not produce
\nsignificant effects related to age in a sample of ethnically diverse eighth grade students. Fouad
\nalso reported no gender differences in her sample. Conversely, Miller and Hayward (2006) found
\n22
\nthat high school females\u2019 self-estimations of occupational knowledge were higher than those of
\nmales, and two studies reported higher levels of objectively measured occupational knowledge in
\nfemales (Rohlfing, et al., 2012; Westbrook & Sloan, 2006). Notably, none of these studies
\nlooked at age or gender in relation to knowledge of individuals\u2019 chosen occupations.
\nCognitive ability\/academic achievement. Six studies examined occupational knowledge
\nscores in relation to participants\u2019 cognitive abilities or academic achievement. Jordan and Pope
\n(2001) found longitudinal evidence that broad, objectively-measured occupational knowledge in
\ntheir sample of 17-year-olds was significantly predicted by mothers\u2019 vocabulary when
\nparticipants were at age 4 and participants\u2019 vocabulary at age 14. Other studies have found
\nbroad, objectively-measured occupational knowledge to be moderately and positively related to
\nachievement measured by self-reported high school achievement (Westbrook, Elrod, & Wynne,
\n1996), middle school grade point average (GPA; Schmitt-Wilson & Welsh, 2012), and high
\nschool GPA (DeFleur & Menke, 1975; Westbrook & Sloan, 2006). Additionally, Van Iddekinge
\net al. (2011) reported a moderate correlation between cognitive aptitude measured by the Armed
\nForces Qualification Test and Army job activity-specific occupational knowledge (r = .52).
\nPerformance. Three studies investigated the relation between occupational knowledge
\nand performance. Ones and Viswesvaran (2007) found that the job activity-specific knowledge
\ntests created for factory worker selection related significantly to \u201cmaximal performance\u201d (r =
\n.42), operationally defined by participants\u2019 observed performance during a hands-on work
\nsample test. Similarly, Van Iddekinge et al. (2011) found a significant relation between Army job
\nactivity-specific occupational knowledge and task proficiency (r = .29), measured by multiple
\nratings of task performance. One study sought to determine the unique contributions of
\noccupational knowledge and cognitive ability to task performance (Palumbo, Miller, Shalin, &
\n23
\nSteele-Johnson, 2005). College students\u2019 scores on a cognitive ability test and a truck driving job
\nknowledge test both separately predicted performance on a truck dispatching job simulation task,
\nexplaining 12% and 26% of performance variance, respectively. Additionally, cognitive ability
\naccounted for 23% of the variance in job knowledge. However, after controlling for the effect of
\nknowledge on performance, cognitive ability no longer made a significant contribution in
\npredicting performance. The authors concluded that job knowledge fully mediated the effect of
\ncognitive ability on task performance in this context, calling into question the findings of Ones
\nand Viswesvaran, and Van Iddekinge and colleagues.
\nExposure\/experience. Two studies addressed the relation between exposure to, or
\nexperience with, occupations on occupational knowledge. Pumfrey and Schofield (1982)
\nadministered the CMI after middle school students participated in week-long real-world work
\nexperiences and compared their scores to students in a control group who did not have the work
\nexperience. The authors found a significant main effect of the intervention on broad, objectivelymeasured occupational knowledge, suggesting that even one week of real-world work experience
\ncan increase levels of general occupational knowledge. Additionally, DeFleur and Menke (1975)
\nfound that out of ten common traditionally-male occupations, high school males were most
\naccurate in their occupational knowledge of truck driving (57.51% accuracy) and least accurate
\nfor accounting (34.46% accuracy). The authors suggest that knowledge accuracy in this context
\nmay be related to the degree of exposure individuals have to a given occupation, supported by
\nthe finding that participants had greater accurate knowledge related to an occupation they likely
\nsee frequently compared to one that they do not. Importantly, DeFleur and Menke utilized
\nindividual participant interviews which included open-ended questions regarding the activities
\ninvolved in each of the ten occupations assessed.
\n24
\nVocational interests. Five studies have examined the relation between vocational
\ninterests and occupational knowledge to some degree. Rohlfing et al. (2012) assessed 3
\nrd
\n-6
\nth
\ngrade students\u2019 general self-estimated occupational knowledge and domain-specific knowledge
\nestimations separated by Personal Globe Inventory domains (People\/Things, Ideas\/Data, and
\nPrestige; Tracey, 2002). Significant correlations were found between domain knowledge scores
\nand corresponding interest ratings: People\/Things knowledge was positively and significantly
\nrelated to People\/Things interest (r = .30); though the opposite pattern was found for the
\nIdeas\/Data domain (r = -.12). These results indicate that participants tend to rate their knowledge
\nhigher in domains they are interested in. Contrary to these findings however, Loesch and
\nSampson (1978) found no relationship between RIASEC (Realistic, Investigative, Artistic,
\nSocial, Enterprising, and Conventional; Holland, 1997) interest scores and corresponding domain
\nknowledge scores. These results conflict with those of Rohlfing et al., suggesting that knowledge
\nmay not be driven by interest. Notably, Loesch and Sampson produced these findings using an
\nobjective knowledge measure while Rohlfing and colleagues employed self-estimates.
\nA different approach to the interest-knowledge relationship was taken by Westbrook and
\nParry-Hill (1973). They used the six CVMT subscales (Duties, Fields of Work, Work
\nConditions, Education Required, Attributes Required, and Job Selection) that objectively and
\nbroadly assess knowledge of different aspects of common occupations. The authors found
\nsignificant differences on all subscales between middle school participants who had chosen
\n\u201cappropriate\u201d career choices and those who had not. Career decisions were considered
\nappropriate when they were of the same interest domain as the individual\u2019s reported vocational
\ninterests (categorized based on the Kuder Preference Record; Kuder, 1944). This finding
\nsuggests that more knowledge of common occupations is related to one\u2019s ability to identify
\n25
\ncareer paths that are congruent with his or her vocational interests. Conversely, Holland and
\nGottfredson (1975) found no relations between objectively-measured broad occupational
\nknowledge and degree of congruence between one\u2019s interests and career choice (categorized
\nbased on the RIASEC domains). Thus this relation remains equivocal.
\nVan Iddekinge and colleagues (2011) examined vocational interests and occupational
\nknowledge specific to common activities and responsibilities of Army soldiers. Entered as a
\ncomposite in a regression predicting occupational knowledge, RIASEC interest scores measured
\nby the Work Preferences Inventory (Amabile, Hill, Hennessey, & Tighe, 1994) yielded an R
\n2
\nof
\n.46, indicating that 46% of the variance in occupational knowledge was explained by vocational
\ninterest. Individually, Realistic (prefer hands-on, practical activities) and Investigative (prefer
\nscientific, intellectual activities) interests significantly related to occupational knowledge (r = .20
\nand .29, respectively). No other RIASEC types yielded significant relations. These findings
\nsupported the authors\u2019 hypothesis in that Army-related interests significantly related to Army
\noccupational knowledge, suggesting that greater interest in a given field may lead one to seek out
\nand acquire more knowledge of activities in that field. This implication is especially important
\ngiven the associations between occupational knowledge and performance. It may be that interests
\nserve to drive knowledge acquisition, which in turn drives job performance; a model that has
\nreceived great support in the literature (e.g., Ackerman & Heggestad, 1997; Holland, 1997). Of
\nfurther importance is the similarity between this finding and that of Westbrook and Parry-Hill:
\noccupational knowledge is greater when one\u2019s interests and career choice are congruent. In
\nconjunction with previously discussed relations between occupational knowledge and
\nperformance, these studies support the notion that occupational knowledge is an important
\nvariable within the career decision-making process.
\n26
\nCareer certainty. This career decision-making construct is arguably one of the more
\nimportant, as it is generally the goal of the career decision-making process. The final stage in all
\nof the career decision-making models described previously involves commitment to a career
\ndecision and subsequent planning and implementation. Career certainty, or career decidedness in
\nsome of the literature, is the degree of certainty one has regarding his or her chosen career (Durr
\n& Tracey, 2009). This variable has been assessed alongside occupational knowledge in four
\nstudies. One study reported a small but significant correlation between career certainty and selfestimated levels of occupational knowledge (r = .24; Multon et al., 1995), but another study
\nexamining objectively-measured occupational knowledge and certainty yielded no relation (r =
\n.00; Westbrook & Sloan, 2006). Similarly, a study of high school students by Westbrook and
\ncolleagues (1996) failed to find a significant relation between the CVMT \u2013 Duties scale and
\ncareer certainty (r = .20, N = 91). However, the same authors found a strong correlation between
\ncertainty (measured by the Career Choice Questionnaire \u2013 Stability, Certainty, and Satisfaction
\nof One\u2019s Career Choice; Westbrook, 1985) and self-estimated knowledge of the attributes (e.g.,
\nabilities and education) required for participants\u2019 chosen careers (r = .65). This self-estimated
\nknowledge of attributes scale was comprised of seven Likert scale items from (1) definitely no to
\n(5) definitely yes in which participants responded to statements such as, Know the duties required
\nin career choice and Have information about career choice. Conceptually, this relatively strong
\nrelation makes sense; greater knowledge of one\u2019s career choice is associated with greater career
\ncertainty. However, given that self-estimated knowledge was used, it remains unknown whether
\nparticipants\u2019 self-estimations are accurate.
\nOne study assessed the effects of a teacher observation intervention on undergraduate
\nsecondary education students (Thomas & Bruning, 1984). Thirty-three of the 224 participants
\n27
\ndropped out of the program after the intervention, and had significantly lower career certainty
\nthan their peers who remained in the program. The dropout students were not statistically
\ndifferent regarding career certainty before the intervention; though, after observing the
\noccupation they were pursuing at the time, their certainty levels decreased significantly. This
\nfinding may suggest that some of the students did not have a good understanding of what
\nteaching career would be like and, once they obtained that information, they no longer wanted to
\npursue it.
\nOther career development constructs. Various additional career-related variables have
\nbeen examined in relation to objectively-measured broad occupational knowledge. Sampson and
\nLoesch (1981) assessed occupational knowledge\u2019s relation to work values, but found no patterns
\namong RIASEC domains of knowledge and domains of work values. Achebe (1982) found that
\nurban middle and high school students in Nigeria had more occupational knowledge than their
\nrural counterparts, possibly relating to the number or type of occupations students were exposed
\nto in their communities. Hirschi (2011) found that self-estimated knowledge of seventh grade
\nstudents\u2019 chosen careers significantly correlated with generalized self-efficacy and self-esteem,
\nalthough the magnitude of the correlations were small (r = .27 and .26, respectively). These
\nrelations make sense given the subjective self-report nature of the occupational knowledge
\nmeasure: Higher self-efficacy and self-esteem may lead participants to rate their levels of
\noccupational knowledge has higher than those with lower self-efficacy and self-esteem.
\nRegarding competence perceptions, Rohlfing and colleagues (2012) found significant
\ncorrelations between the People\/Things knowledge domain and People\/Things competence
\nperceptions (r = .25) of 3rd
\n-6
\nth grade students. However, these results were based on self-
\n28
\nestimates of knowledge for individual occupational titles, indicating that these students tend to
\nperceive higher competence in the occupations with which they are familiar.
\nKnowledge of chosen occupations. Of particular interest to the present discussion is the
\nextent to which individuals are making informed career decisions; that is, whether or not they are
\nknowledgeable of the occupations they are choosing to pursue. Much of the occupational
\nliterature concerns general knowledge of common occupations, and only three studies have
\nexamined individuals\u2019 knowledge of their chosen occupations. A significant relation was found
\nbetween female participants\u2019 self-estimates of knowledge and their preferences for occupations,
\nwhile no significant relation was found for male participants (Miller & Hayward, 2006). This
\nresult suggests that females estimate their levels of knowledge in accordance with their levels of
\npreference for those occupations; though, it is unknown whether their self-estimates of
\nknowledge are accurate or if they perceive more knowledge about occupations which they prefer.
\nWestbrook and colleagues (1996) found a significant relation between self-estimated knowledge
\nof the attributes required in participants\u2019 chosen occupations and occupational knowledge
\nmeasured by the CVMT \u2013 Duties scale (r = .28). The CVMT \u2013 Duties scale objectively measures
\ngeneral knowledge of duties required in a subset of common occupations. Thus, this result
\nindicates that individuals with higher general knowledge of common occupational duties selfestimate higher knowledge levels of the attributes (e.g., abilities and education) required for their
\nchosen careers.
\nOne study was found that objectively measured college students\u2019 knowledge of their
\nchosen careers to an extent. Healy and Mourton (1984) employed the CDI – Knowledge of
\nPreferred Occupation scale, which asks participants to choose one of a number of occupational
\ngroups in which their career choice best fits. Participants are then presented with 40 multiple
\n29
\nchoice items inquiring about facets of the job related to required abilities, interests and values
\nfulfilled, training requirements, and work environment. Notably, work activities were not
\nassessed. The Self-Directed Search (SDS; Holland, 1971) was used as an intervention and
\nhypothesized to increase career exploration, with results indicating no effect of the intervention
\non occupational knowledge. Descriptive statistics for the experimental (M = 2.06, SD = .93) and
\ncontrol (M = 1.87, SD = .89) groups were reported; however, interpretation of these statistics is
\nuncertain since, traditionally, the CDI standardizes scores to a mean of 100 (Hansen, 1985).
\nSelf-estimates and objective measures of knowledge. As Kruger and Dunning (1999)
\nestablished, oftentimes individuals who are unaware of certain information are also unaware of
\ntheir knowledge deficit. After testing this effect using an English grammar test, Kruger and
\nDunning found that bottom-quartile participants scored, on average, in the 10th percentile, while
\nthey had estimated their test score to be in the 67th percentile. Translating this concept to the
\npresent discussion of occupational knowledge, three studies were found that investigated both
\nobjective and self-estimated levels of occupational knowledge.
\nYanico and Mihlbauer (1983) examined the relationship between self-estimated and
\nobjectively-measured occupational knowledge with regards to gender traditional and
\nnontraditional occupations. After completing the Jobs-Career Key test of 36 occupations, college
\nstudent participants were asked to indicate how much they thought they knew about each
\noccupation on a scale from 1 to 100. No significant differences were found between estimated
\nand objective knowledge levels; however, all participants underestimated knowledge levels of
\ntheir respective nontraditional occupations. These results were later replicated (Yanico & Hardin,
\n1986) and suggest that college students are accurate in their estimations of knowledge regarding
\nthe traditional and nontraditional occupational domains on the Jobs-Career Key. Importantly, this
\n30
\nstudy did not assess knowledge of chosen occupations. Participants\u2019 objective and self-estimated
\nknowledge was only measured in relation to common gender-stereotyped occupations.
\nGrotevant and Durrett (1980) reported findings from their investigation of high school
\nseniors. Participants reported their first-choice career, stated how well they understood that
\ncareer on a 1 to 5 Likert scale, then indicated their educational goals. Students\u2019 vocational
\ninterests were also assessed for the purpose of measuring their occupational congruence.
\nGrovetant and Durrett found that, of the students who claimed to have at least a \u201cconsiderable\u201d
\nunderstanding of their first career choice, only half of them planned to obtain the occupation\u2019s
\nrequired level of education.
\nWhile the literature on the accuracy of self-estimated occupational knowledge is not
\nabundant, Grovetant and Durrett\u2019s study provides solid evidence of a discrepancy solely between
\nself-estimated knowledge and appropriate educational goals. If high school seniors do not have
\nenough knowledge about a career to plan to obtain the required education, one might expect an
\neven larger discrepancy between their perceived and actual knowledge of the daily job activities
\ntheir occupational choice entails. This expectation is concerning and has not been addressed thus
\nfar in the literature, largely due to the paucity of appropriate assessment tools currently in use.
\nSummary
\nThere are important findings from this literature that can be used to inform future
\nresearch on the topic (sample nursing essay examples by the best nursing assignment writing service) of occupational knowledge. The self-estimated knowledge literature has
\nshown that subjective occupational knowledge is slightly related to self-efficacy and self-esteem
\n(e.g., Hirschi, 2011) and strongly related to career certainty (e.g., Westbrook et al., 1996).
\nHowever, the studies utilizing objectively measured occupational knowledge provide somewhat
\nmore useful information going forward. Objectively measured occupational knowledge has been
\n31
\nshown to be related to academic achievement (e.g., Schmitt-Wilson & Welsh, 2012; Westbrook,
\nElrod, & Wynne, 1996), work experience (e.g., Pumfrey & Schofield, 1982), job performance
\n(e.g., Ones & Viswesvaran, 2007), and interests (e.g., Rohlfing et al., 2012; Van Iddekinge et al.,
\n2011). It has been shown to increase by age and grade (e.g., Achebe, 1982; Pumfrey &
\nSchofield, 1982) and to predict career decisions congruent with one\u2019s interests (e.g., Westbrook
\n& Parry-Hill, 1973). Further, there is evidence that occupational knowledge is related to work
\nexposure (e.g., DeFleur & Menke, 1975), cognitive aptitude, and job proficiency (Van Iddekinge
\net al., 2011).
\nThese studies have objectively investigated many aspects of occupational knowledge,
\nincluding training and education requirements, job availability, earnings and prestige, mental and
\nphysical requirements, and interests and values fulfilled. Job activities have even been assessed
\non a job-specific level by one study (DeFleur & Menke, 1975) but the authors did not report the
\nlevel of specificity used in coding participant responses nor did they assess participants\u2019
\nknowledge of their chosen occupations. Furthermore, this study utilized interview methods,
\nmaking it quite impractical for most large sample research endeavors. Overall, while many
\naspects of occupational knowledge have been assessed in some way, none of the available
\nliterature has investigated the extent to which college students are knowledgeable about the
\ncareers they are choosing to pursue.
\nMeasurement Solutions
\nIt seems that the current psychological measurement of job knowledge is largely flawed.
\nThree general types of assessment exist in the literature: those that gauge self-estimated levels of
\nknowledge, those that objectively test either general career information or relatively commonknowledge aspects of specific jobs, and those that objectively test specific jobs\u2019 activities and
\n32
\nresponsibilities. All have inherent problems; namely, none are able to adequately and efficiently
\nmeasure accuracy of job-specific knowledge in terms of activities and responsibilities across
\nlarge participant samples. Activities and responsibilities one will encounter upon entering his or
\nher chosen occupation are of interest because they are often the aspects of jobs for which people
\nmust seek information to be fully knowledgeable. Of course, the major activities of jobs tend to
\nbe obvious; accountants work with numbers, physicians treat medical patients, interior designers
\ndecorate rooms, etc. However, beyond these general activities are duties and responsibilities that
\nmay not enter the minds of some individuals as they pursue these careers. Currently, there are no
\npractical methods of measuring these important aspects of occupational knowledge in large
\nsamples. Those used in personnel selection most closely resemble ideal occupational knowledge
\nassessments, but cannot realistically be used in studies involving diverse career choices.
\nIndividual interviews have been conducted in past studies, but are far too inefficient and
\nunrealistic to employ in most empirical studies.
\nOne assessment method that could potentially address these measurement issues and
\nproduce information regarding college students\u2019 knowledge of the work activities involved in
\ntheir chosen occupations is inherent in the O*NET online career exploration system. The system
\nincludes a Detailed Work Activities database which consists of a set of 41 work activities rated
\nby importance for each of the 974 O*NET occupational titles (National Center for O*NET
\nDevelopment, 2003). Average importance ratings for each activity are scored from 0 to 100 for
\neach occupation based on data collected from occupation experts and random samples of
\nworkers (Research Triangle Institute, 2006). For each item, respondents were to respond to the
\nquestion, \u201cHow important is the activity to your current job?\u201d Respondents\u2019 answers were then
\naveraged for each item to create the importance score for each work activity within a given
\n33
\noccupation. The 41 work activities are the same across 974 occupations in the database which
\nallows for practical, efficient comparison of occupational knowledge in large research samples.
\nAs an example, the occupation activity importance profile for an accountant is presented in
\nAppendix C.
\nA strength of this measurement method is that it allows for the use of a profile correlation
\n(Q correlation; Cronbach & Gleser, 1953) in analyzing the results. The Q correlation, which
\nprovides scores ranging from -1.00 to +1.00, would provide a standardized measurement of the
\nagreement between a participant\u2019s work activity rating profile for his or her chosen occupation
\nand the corresponding O*NET rating profile for the occupation. A score of .00 would indicate no
\npattern of similarity between profiles, scores nearing +1.00 would indicate greater profile
\nmatches, and scores reaching -1.00 would indicate nearly opposite profiles (Armstrong, Fouad,
\nRounds, & Hubert, 2010). This statistic is not descriptive of elevation differences between two
\nprofiles, but of differences in shape. This point is important because, given that a scale from 0 to
\n100 is not descriptive in and of itself, participants\u2019 knowledge would not be assessed based on
\ntheir ratings of the importance of a given activity, but rather on the importance they assign a
\ngiven activity relative to all the other activities.
\nCareer Certainty
\nSelected Literature Review
\nThe present discussion has already demonstrated the importance of occupational
\nknowledge in career decision-making. However, the question of interest is regarding the relation
\nbetween college students\u2019 knowledge of their chosen occupations and their levels of certainty
\nwith those choices. As career certainty is an important career decision-making outcome variable,
\nit has a large literature foundation. For that reason and because the population of interest in the
\n34
\npresent discussion is specific, the literature related to college students will be reviewed. In
\naddition, it will be limited to studies addressing the topic (sample nursing essay examples by the best nursing assignment writing service)s of vocational interests, self-efficacy,
\nand occupational knowledge. A PsycInfo search of career with certainty or decidedness, and
\ncollege or undergraduate yielded 71 peer-reviewed articles written in English. Twenty-one of
\nthe 71 articles examined career certainty with either interests, self-efficacy, or occupational
\nknowledge. Within this search, career certainty is predominantly measured by the Certainty
\nScale of the Career Decision Scale (Osipow, Carney, & Barak, 1976), a two-item scale reflecting
\nthe extent to which participants have made definite decisions about a career. Other measures
\ncomprised of similar item content have been used as well (e.g., Career Decision Profile, Jones,
\n1989; Occupational Alternatives Question, Zener & Schnuelle, 1976).
\nInterests. Nine studies were found that looked at both career certainty and interests. Four
\nstudies examined the relation between certainty and interest differentiation. Contrary to
\nHolland\u2019s (1997) predictions regarding differentiation, none found evidence for the power of the
\ndegree of differentiation among people\u2019s interest profiles in predicting career certainty (Alvi,
\nKhan, & Kirkwood, 1990; Hirschi, 2009; Larson, Heppner, Ham, & Dugan, 1988; Sackett &
\nHansen, 1995). Notably, the Hirschi and Sackett and Hansen studies failed to find significant
\nrelations between these two constructs concurrently or longitudinally. These results suggest that
\nrelative elevation of interests are not predictive of one\u2019s certainty regarding his or her chosen
\ncareer. Two studies looked at interest consistency, which refers to degree of similarity between
\ninterest types based on Holland\u2019s hexagon model (1997). Larson and colleagues (1988) found no
\ndifferences between decided and undecided students in interest profile consistency; however,
\ndeviation from Holland\u2019s hexagon structure interests was found to be negatively related to career
\n35
\ncertainty in a more recent study (Tracey & Darcy, 2002). Thus, the evidence regarding the
\nrelation between interest consistency and career certainty is mixed.
\nAnother construct of Holland\u2019s is congruence, which refers to the degree of similarity
\nbetween one\u2019s career choice and one\u2019s vocational interests (Holland, 1997). Three studies were
\nfound that examined congruence and they produced opposing results (Slaney & Slaney, 1986;
\nTracey, 2008; Tracey, 2010). Slaney and Slaney (1986) found no differences in career certainty
\nbetween students whose interests were congruent with their career choices and students whose
\ninterests were incongruent. However, Tracey (2008, 2010) found interest-occupation congruence
\nto be significantly related to career certainty in two separate samples (r = .29 and .26,
\nrespectively). Similar to the research examining consistency and certainty, congruence has
\nyielded mixed findings. In addition to Holland\u2019s constructs, one study was found that examined
\nthe stability of interests over four to six weeks, finding that stability was unrelated to career
\ncertainty (Berk & Fekken, 1990).
\nCareer decision-making self-efficacy. Five studies were located that examined the
\nrelation between career decision-making self-efficacy (CDMSE) and career certainty. CDMSE,
\ndefined as one\u2019s belief that he or she is able to successfully engage in and complete tasks
\nnecessary to career decision-making (Betz, 2000), has been shown to relate moderately with
\ncareer certainty in four studies (r = .28 to .46; Creed & Patton, 2003; Restubog, Florentino, &
\nGarcia, 2010; Taylor & Popma, 1990; Tracey, 2008). Creed, Prideaux, and Patton (2005) found
\nthat individuals who were certain about their future careers had significantly greater CDMSE
\nthan their uncertain counterparts, F (1, 221) = 22.75, p < .001. In addition, Tracey (2008) found
\nthat career certainty moderately relates to CDMSE concurrently, but not to CDMSE measured 13
\nmonths later at Time 2. However, Time 1 certainty significantly correlated with Time 2 certainty
\n36
\n(r = .25), which in turn correlated moderately with Time 2 CDMSE (r = .44). These results are
\nconsistent with career decision-making models, as CDMSE is thought to increase one\u2019s ability to
\nnavigate the career decision-making process.
\nOccupational knowledge. Four studies were found that examined both occupational
\nknowledge and career certainty in college students. As previously reviewed in the occupational
\nknowledge section, small relations have been found between career certainty and self-estimated
\nlevels of occupational knowledge (Multon et al., 1995; Westbrook et al., 1996). However,
\nstudies examining objective occupational knowledge and career certainty have revealed null
\nrelations with career certainty (Westbrook et al., 1996; Westbrook & Sloan, 2006). While none
\nof these findings are addressing knowledge specific to one\u2019s chosen career, Westbrook and
\ncolleagues (1996) participants did find a strong correlation between certainty and self-estimated
\nknowledge of the attributes required for participants\u2019 chosen careers (r = .65, N = 91; e.g.,
\n\u201cKnow the duties required in career choice,\u201d \u201cHave information about career choice\u201d). This
\nstrong relation makes sense; greater knowledge of one\u2019s career choice is associated with greater
\ncareer certainty. However, given that self-estimated knowledge was used, it remains unknown
\nwhether participants\u2019 self-estimations are accurate. Notably, this latter finding is the only one
\naddressing individuals\u2019 knowledge of their chosen occupations rather than broad occupational
\nknowledge. This relation is of importance to the present discussion, especially since it produced
\nsuch a high correlation. The fact that participants in that study perceived their knowledge of their
\ncareer choices to be high when their certainty was high is an association one might expect based
\non conceptual knowledge. This association could be interpreted such that greater knowledge of a
\nspecific occupation leads one to be more certain that it is the correct decision, or greater certainty
\n37
\nabout pursuing a certain career leads one to seek more knowledge about it. Either way, this
\nrelation is expected based on the career decision-making models.
\nThe fourth study examined occupational knowledge and certainty in more depth by
\ninvestigating college students majoring in secondary education who participated in a 40-hour
\nteacher observation course assignment (Thomas & Bruning, 1984). They found that 33 of the
\n224 participants dropped out of the secondary education major program after spending a week
\nobserving a teacher in their subjects of interest. Before the observation, the non-dropout and
\ndropout groups had equal levels of career choice satisfaction and career certainty. At postintervention, the dropout group had decreased on all of those variables, effectively separating
\nthemselves from the non-dropout group. Furthermore, the authors found significant differences
\nin the proportions of each group that had decided to major in secondary education within the past
\nyear (52% for dropouts vs. 29% for non-dropouts) as opposed to more than one year ago.
\nWhile the size of the dropout group is quite small, especially compared to the sample as a
\nwhole, the findings of this study are of great conceptual importance. The 33 dropout participants
\nhad previously committed to the secondary education major; yet, after observing what teaching
\nactually entails, these students realized that they no longer desired to pursue a teaching degree. In
\naddition, 17 of those 33 dropout students had made a decision to major in education within the
\npast year, suggesting that the career may still have been new to them and their knowledge about
\nit may have been lacking. This study demonstrates an important point: there may be a portion of
\ncollege students who feel they are certain about their chosen careers, but are also uninformed
\nabout the careers they are pursuing until they are forced to experience them first-hand in the real
\nworld. Luckily, the students in this study had key exposure experiences early in their college
\n38
\neducations. Other students may not get this knowledge until they begin applying for jobs after
\ngraduation.
\nAn additional study was found that examined the role of job information on one\u2019s intent
\nto remain in that job. Assessing employed sales agents at several different companies, Pitt and
\nRamaseshan (1995) examined the extent to which participants retrospectively felt they received
\nrealistic job information during their interviews now that they had actually been hired and were
\nexperiencing the job first-hand. Participants indicated the amount, personal relevance, depth, and
\naccuracy of job information they received during their interviews. They also indicated the
\nfrequency with which they currently considered quitting their recently obtained jobs. The authors
\nthen compared those who \u201cnever\/seldom\u201d considered quitting their jobs and people who
\n\u201coccasionally\/often\/all the time\u201d considered quitting. Significant differences found on all
\ninformation indices, with people never\/seldom considering quitting reporting lower amount,
\nrelevance, depth, and accuracy of information. Further, a regression model including all
\ninformation indices accounted for 44% of the variance in quitting intentions. These results
\nsuggest that those who were informed about the job they were committing to are the same
\nindividuals who intended to remain in that job.
\nTranslating these findings to college students, it may be that some students are not fully
\ninformed regarding what they will be doing in their jobs on a daily basis. Being uninformed
\ncould lead to dissatisfaction with jobs, intentions to quit, and possibly the desire to pursue a
\ndifferent occupation altogether. The available literature suggests that students perceive
\nthemselves to be certain to the extent they are knowledgeable about the careers they are
\npursuing. However, due to the paucity of valid and reliable occupational knowledge assessment
\nmethods, the relation between objectively measured occupational knowledge students have about
\n39
\ntheir chosen careers and career certainty remains unknown. Grotevant and Durrett (1980) were
\nable to provide evidence that some students\u2019 estimates of knowledge about their chosen careers
\nwas overestimated, at least with regards to one important aspect which was the level of education
\nneeded to obtain their chosen occupations. When considered along with Westbrook and
\ncolleagues\u2019 (1996) .65 correlation between self-estimated knowledge of chosen careers and
\ncertainty, this finding causes concern. If Grotevant and Durrett\u2019s results are representative of
\nstudents\u2019 judgments of their knowledge, then Westbrook and colleagues\u2019 finding may be a gross
\noverestimation. If the true relation is closer to zero, which would be contrary to many
\nconceptually-based expectations, perhaps it is more complex and involves more variables than
\npreviously thought.
\nVariables Predicting Career Certainty Alongside Occupational Knowledge
\nA mediator is a variable that explains at least part of the relation between two other
\nvariables (Frazier, Tix, & Barron, 2004). In other words, a mediator is a mechanism through
\nwhich one variable influences another variable. Frazier and colleagues describe an example in
\nwhich a counseling intervention has an effect on well-being. However, when social support is
\nintroduced to the relation, it is revealed that the intervention actually increases social support,
\nwhich, in turn, increases well-being. Thus, the relation between the intervention and well-being
\nis fully mediated by social support.
\nThe present discussion has thus far argued, conceptually, that knowledge about one\u2019s
\nchosen occupation should be positively related to one\u2019s level of career certainty. However, no
\nliterature has been found that directly supports this argument. This is partly due to measurement
\nmethodology flaws; though it could also be due to confounding variables. In addition, the career
\ndecision process may be more complex, leading to the equivocal nature of findings presented in
\n40
\nthe literature. Therefore, four additional constructs found in the career decision-making literature
\nwere examined for their potential roles in predicting career certainty alongside occupational
\nknowledge: career decision-making self-efficacy, interest congruence, career exploration
\nactivities, and perceived occupational knowledge.
\nCareer Decision-Making Self-Efficacy
\nSelf-efficacy, theorized as one\u2019s perception of his or her probable effectiveness in a given
\nsituation in the near future (Bandura, 1977), has received significant attention in the career
\ndevelopment literature due to its broad applicability to both the content and process domains of
\ncareer decision-making. Based on Bandura\u2019s (1977, 1986, 1997) work, self-efficacy is grounded
\nin social cognitive theory and is a mechanism determining one\u2019s response to new or threatening
\nsituations. Bandura and others (e.g., Betz, 2004; Betz & Hackett, 1981; Lent, Brown, & Hackett,
\n1994) posit that higher self-efficacy leads individuals to approach new or threatening situations
\nwith the belief that they will be able to cope successfully. Conversely, lower self-efficacy may
\nresult in avoidant behavior leading to preference for the familiar and safe instead of the new and
\nuncertain.
\nSpecific to the career decision process, career decision-making self-efficacy has been the
\nfocus of a substantial amount of research. This term refers to one\u2019s belief that he or she is able to
\nsuccessfully engage in and complete tasks necessary to career decision-making (Betz, 2000). The
\nCareer Decision Self-Efficacy Scale (CDSES; Taylor & Betz, 1983), previously named the
\nCareer Decision-Making Self-Efficacy Scale, is the most well-established measure of this
\nconstruct. Anchored in Crites\u2019 (1976) five career choice competencies, the short and long forms
\nof the CDSES include five subscales: self-appraisal, occupational information, goal selection,
\nplanning, and problem solving. These subscales key in to the important domains of career
\n41
\ndevelopment that require action on the part of the individual. In other words, these areas
\ncomprise the different stages of information gathering and implementation needed to facilitate
\nmature career development.
\nCareer certainty. As reviewed previously, career decision-making self-efficacy has not
\nbeen frequently examined in the literature with career certainty. The five studies that did look at
\nthis relation utilized the CDSES, with three studies finding that it correlated moderately with
\ncareer certainty (r = .38 to .46; Creed & Patton, 2003; Restubog et al., 2010; Taylor & Popma,
\n1990). The latter authors also examined this relation in terms of the CDSES subscales, finding
\nsignificant correlations between career certainty and one\u2019s perceived ability to assess his or her
\nvocational interests, values, and abilities (r = .43); obtain occupational information (r = .32);
\nselect career goals (r = .55); make plans to pursue a career (r = .46); and solve career-related
\nproblems (r = .30). Creed, Prideaux, and Patton (2005) found that individuals who were certain
\nabout their future careers had significantly greater CDMSE than their uncertain counterparts, F
\n(1, 221) = 22.75, p < .001. In addition, Tracey (2008) found career certainty moderately related
\nto the CDSES concurrently (r = .41), but not to the CDSES measured 13 months later at Time 2
\n(r = .13). However, Time 1 certainty had a small to moderate relation to Time 2 certainty (r =
\n.25), which in turn correlated moderately with Time 2 CDSES (r = .44). These results are
\nconsistent with career decision-making theories, as career decision-making self-efficacy is
\nconceptually thought to increase one\u2019s ability to navigate the career decision-making process.
\nOccupational knowledge. Career decision-making self-efficacy has thus far not been
\nexamined in relation to occupational knowledge. Two studies have measured \u201ccareer
\nknowledge\u201d and career decision-making self-efficacy measured by the CDSES, but career
\nknowledge in both cases was operationally defined as knowledge of decision-making skills and
\n42
\nworld of work information rather than as knowledge of specific aspects of occupations (Creed &
\nPatton, 2003; Creed, Patton, & Prideaux, 2007). However, given that the CDSES \u2013 Occupational
\nInformation scale measures one\u2019s belief in his or her ability to obtain information about
\noccupations, it is possible that a relation between the CDSES and occupational knowledge could
\nexist. For example, as reviewed previously, occupational knowledge has been shown to
\nmoderately relate to academic achievement and cognitive ability in several studies. Additionally,
\nacademic performance measured by GPA has been shown to be related to scores on the CDSES
\n(r = .20; Restubog et al., 2010). It is possible that a spurious relationship between the CDSES
\nand occupational knowledge could account for some of the variance in GPA\u2019s relationship with
\nthe two variables separately.
\nCareer decision-making self-efficacy, occupational knowledge, and career certainty.
\nCareer decision-making self-efficacy has been actively researched over the past thirty years,
\npredominantly due to the development of the Career Decision Self-Efficacy Scale by Taylor and
\nBetz in 1983. However, it has not yet been examined alongside both objective occupational
\nknowledge and career certainty. Therefore, the present study sought to investigate whether career
\ndecision-making self-efficacy, measured by the CDSES, has a significant relation with
\nparticipants\u2019 objective occupational knowledge of their chosen careers. In addition, the interplay
\nbetween career decision-making self-efficacy and objective occupational knowledge will be
\nexamined in terms of their roles in predicting career certainty. A mediation effect was predicted,
\nsuch that career decision-making self-efficacy will relate to career certainty both directly and
\nindirectly, through objective occupational knowledge.
\nThe relations between these three variables is important to understand because college
\nstudents\u2019 career certainty, according to career decision-making theories (Hirschi & Lage, 2007),
\n43
\nshould be influenced largely by knowledge of the occupations they are considering. However,
\ncareer decision-making self-efficacy may be involved by providing some students with greater
\nsenses of contentment with their perceived abilities to make career decision, or greater selfefficacy regarding their abilities to seek out further occupational knowledge. Conversely, career
\ndecision-making self-efficacy may impede students\u2019 abilities to make career decisions with
\nwhich they are comfortable, or leave them paralyzed in the career decision process by preventing
\nthem from seeking occupational knowledge necessary for making decisions. In these ways,
\nobjective occupational knowledge may partially mediate the relation between career decisionmaking self-efficacy and career certainty.
\nVocational Interest
\nVocational interests describe individuals\u2019 preferences regarding work activities,
\nsituations, and environments (Su, Rounds, & Armstrong, 2009), and have been established as
\npredictors of college students\u2019 academic majors (e.g., Gasser, Larson, & Borgen, 2007). This
\nconstruct has received substantial attention in the literature, with a large part dedicated to John
\nHolland\u2019s (1997) work on one of the most well-known theories of vocational interests. He
\nidentified six factors that largely encompass the types of interests people have regarding work,
\nreferred to as the RIASEC model (Realistic: constructing, mechanical, outdoor interests;
\nInvestigative: inquiry and discovery; Artistic: creating, designing; Social: serving, helping
\nothers; Enterprising: persuading others; Conventional: organizing and structuring things\/data).
\nThis six-domain model has emerged as the dominant model vocational psychology employs to
\norganize vocational interests. Part of the utility of Holland\u2019s model is its application to both
\npeople\u2019s interests and their work environments. For both people and environments, interest
\nprofiles can be created based on the degree to which each factor fits with the individual or
\n44
\nenvironment. The order of the three highest scores when the six domains are measured comprises
\none\u2019s \u201cHolland Code\u201d (e.g., \u201cIAS\u201d for investigative, artistic, and social interests), with the first
\nletter representing the interest type with the highest score. These codes are computed for people
\nbased on their responses to interest inventories, while work environments\u2019 codes are computed
\nbased on the types of activities involved in occupations and are similarly organized around the
\nRIASEC domains (Spokane & Cruza-Guet, 2005).
\nPerson-environment fit. Holland\u2019s RIASEC model fits within a larger vocational
\noutcome variable, person-environment fit (PE fit). PE fit is the extent to which a person\u2019s
\ninterests and his or her current or prospective work environment are compatible (Dawis, 2005).
\nThe general understanding is that no person\u2019s interests will fit perfectly with the activities in his
\nor her work environment; however, the goal is for individuals to decide which career path will
\nlikely be most compatible with their vocational interests. Career indecision can arise when
\npeople have difficulty finding an acceptable fit, which is a realization that can occur anywhere
\nfrom the initial stages of the decision process to years after one has committed to a career and
\nentered the workforce.
\nInterest Congruence. Person-Environment fit can be assessed in terms of a number of
\nvocational variables. Regarding interests however, Holland\u2019s RIASEC model is commonly used
\nto measure PE fit. Holland (1997) described \u201ccongruence\u201d as an indicator of PE fit, typically
\nreferring to congruence between and individual\u2019s interest and his or her occupation. Specifically,
\ncongruence is the degree to which a person\u2019s Holland code matches that of a given work
\nenvironment (Spokane & Cruza-Guet, 2005).
\nCongruence was hypothesized by Holland to predict several positive vocational
\noutcomes, including one\u2019s job satisfaction. The importance of assessing the ability of congruence
\n45
\nto predict job satisfaction comes from the fact that, for many individuals, a career constitutes a
\nsubstantial portion of life; people tend to desire careers with which they will be satisfied.
\nHowever, the literature in support of Holland\u2019s congruence-job satisfaction hypothesis is
\nsomewhat mixed. Between congruence and job satisfaction, an overall mean correlation of .20
\nwas found across 17 studies meta-analyzed by Tranberg and colleagues (1993). Mean
\ncorrelations ranged from .12 and .45 depending on the type of congruence index used. Overall,
\nTranberg and colleagues found no significant effect of congruence on job satisfaction,
\nconcluding that it cannot predict satisfaction alone. A more recent meta-analysis again
\nexamining the congruence-job satisfaction relation produced similar findings (Tsabari, Tziner, &
\nMier, 2005). They found mean congruence-job satisfaction correlations across 36 samples to
\nrange from .07 to .35 depending on the congruence index used.
\nThe authors of both meta-analyses were compelled to conclude that the congruence-job
\nsatisfaction relation was not well-supported in the literature. However, this finding was due in
\npart to the heterogenous nature of congruence indices utilized in research. While various
\ncongruence indices exist and most have both advantages and disadvantages, the profile
\ncorrelation (Q correlation; Cronbach & Gleser, 1953) has been identified as one of the
\nstatistically stronger methods of measuring congruence (Armstrong et al., 2010). It utilizes
\nparticipants\u2019 and occupations\u2019 full interest profiles (i.e., all 6 RIASEC scores) in order to get the
\nmost comprehensive estimate of congruence. Measuring the extent to which two interest profiles
\nhave similar shapes, the Q correlation is also consistent with Holland\u2019s (1997) original
\nconceptualization of congruence (Armstrong et al., 2010).
\nThe Euclidean distance index, another method of measuring congruence, has been argued
\nas the best measure of interest profile similarity as it describes elevation differences in addition
\n46
\nto shape differences (Armstrong et al., 2010; Cronbach & Gleser, 1953). However, the present
\nstudy is concerned only about the degree to which participants\u2019 highest interests match those of
\ntheir chosen occupations. In other words, relative interest elevation is important rather than
\nabsolute elevation. Thus, in the context of the present study the Q correlation is supported as an
\nappropriate measurement of congruence.
\nCareer certainty. While the discussion of congruence and job satisfaction is more
\napplicable to currently employed adults, this relation can be prospectively assessed in college
\nstudents by examining more proximal, related constructs. Congruence has been found to relate to
\ncareer certainty (e.g., Tracey, 2010), which in turn has been related to college students\u2019 academic
\nmajor satisfaction (e.g., Ware & Pogge, 1980). Career certainty has also been associated with
\npersistence in academic major programs 18 months later (Restubog et al., 2010), a construct
\nconsidered to be conceptually related to major satisfaction (Nauta, 2007). Persistence measured
\nin a sample of working adults also revealed that individuals who were persisting in their current
\njobs, compared to those who were changing careers, had higher levels of congruence after
\ncontrolling for age and career tenure (Donohue, 2006). Overall, the available literature does
\nprovide support for the congruence-career certainty relation, both directly and indirectly.
\nOccupational knowledge. Few studies have examined occupational knowledge in
\nrelation to congruence. Significant differences between individuals with and without congruent
\ncareer choices (measured based on the KPR interest scales; Roe, 1956) were found on
\nobjectively measured broad occupational knowledge and self-estimated knowledge of chosen
\ncareers\u2019 required education and attributes (Westbrook & Parry-Hill, 1973). These findings
\nsuggest that greater knowledge of common occupations is related to one\u2019s identification of
\ncongruent career paths, and that congruence may contribute to one\u2019s self-estimated knowledge of
\n47
\naspects of his or her chosen career. In a more specific study, Van Iddekinge et al. (2011) found
\nthat levels of Realistic interests, a domain congruent with the Army work environment, are
\nassociated with Army-related job task knowledge. Knowledge in this study was objectively
\nmeasured, indicating that greater interest in a given field may lead one to seek out and acquire
\nmore knowledge of activities in that field. Notably, however, this study examined a relatively
\nunique sample of individuals and the results may not generalize to general college student
\nsamples.
\nInterest congruence as a mediator. The theory behind interest congruence suggests
\nindividuals naturally seek out work environments they believe will be compatible with their own
\ninterests, leading them toward occupational satisfaction (Holland, 1997). The literature has
\nprovided moderate support for this theory (Tranberg, Slane, & Ekeberg, 1993); though, it is
\nimportant to note that assessing one\u2019s congruence with his or her chosen occupation is an
\ninternal judgment. Individuals must assess their own levels of congruence as they navigate
\nthrough the career decision process. Conceptually, one would expect increasing occupational
\nknowledge to Help individuals in assessing which career will yield the highest congruence.
\nTherefore, if greater knowledge leads to greater likelihood of achieving interest congruence, one
\nmight expect level of career certainty to positively relate to both objective occupational
\nknowledge and interest congruence. In other words, interest congruence may act as a partial
\nmediator between objective occupational knowledge and career certainty, so objective
\noccupational knowledge would have both a direct effect on career certainty, and an indirect
\neffect through interest congruence.
\n48
\nCareer Exploration Activities
\nCareer exploration activities are specific ways of gaining occupational knowledge, both
\nbroadly and in-depth, which comprise an important career exploration phase of most career
\ndecision-making models (Hirschi & Lage, 2007). The present study operationalized this
\nconstruct by creating an checklist-style inventory to gauge the types of career exploration
\nactivities in which participants have already engaged (Appendix G). The ability to engage in
\nthese activities has been deemed a \u201cfundamental competence\u201d for individuals immersed in the
\ncareer decision-making process (Blustein, 1997).
\nThe present study examined this construct in relation to career decision-making selfefficacy and objective occupational knowledge. The Career Decision Self-Efficacy Scale (Taylor
\n& Betz, 1983) includes ten items gauging participants\u2019 self-efficacy related to actively seeking
\noccupational knowledge through various common career exploration activities. These activities,
\nin turn, are central to gaining occupational knowledge. Therefore, number of reported career
\nexploration activities was examined as a potential mediator between career decision-making selfefficacy and objective occupational knowledge. Specifically, the relation between career
\ndecision-making self-efficacy and objective occupational knowledge was hypothesized to be
\nfully mediated by career exploration activities. In other words, career decision-making selfefficacy was thought to influence engagement in career exploration activities, which would then
\ninfluence amount of occupational knowledge gained.
\nPerceived Occupational Knowledge
\nThis construct was added to the model predicting career certainty in part because of its
\nprominent role in previous studies measuring \u201coccupational knowledge.\u201d Perceived, or selfestimated, occupational knowledge has been frequently employed as a proxy to examining
\n49
\nobjectively measured knowledge of participants\u2019 chosen occupations. While its use is
\nunderstandable given the inherent difficulty in measuring this construct across large participant
\nsamples, it remains unknown whether college students\u2019 perceptions of their occupational
\nknowledge are accurate. Thus, the present study included perceived occupational knowledge for
\nthe primary purpose of determining whether it is a sufficient substitution for the difficult
\nobjective measurement of occupational knowledge. In addition, perceived occupational
\nknowledge was thought to mediate the relation between career decision-making self-efficacy and
\ncareer certainty. While career decision-making self-efficacy measures students\u2019 beliefs about
\ntheir future effectiveness in navigating career decision-making tasks, perceived occupational
\nknowledge forces students to specifically gauge the amount of occupational knowledge they
\nhave already obtained. Therefore, career decision-making self-efficacy may influence students\u2019
\nperceptions of their occupational knowledge, which in turn may influence whether students
\nbelieve they are certain about the careers they have chosen. In this way, perceived occupational
\nknowledge may act as a mediator in the relation between career decision-making self-efficacy
\nand career certainty.
\n50
\nCHAPTER THREE
\nMETHODS
\nDesign
\nThe present study utilized a descriptive correlational design as it allowed for assessment
\nof statistical relations between variables within participants. The primary variables were
\nobjective occupational knowledge and career certainty. Career decision-making self-efficacy,
\ninterest congruence, reported career exploration activities, and perceived occupational
\nknowledge were also examined alongside objective occupational knowledge in predicting career
\ncertainty.
\nParticipants
\nThe target population of the present study was undergraduate college students. Data were
\ncollected in the fall semester of 2013 from undergraduate students in introductory psychology
\ncourses at Iowa State University. Data from 357 participants were originally collected. Forty-one
\nparticipants\u2019 data were excluded prior to analysis for various reasons. As a single career-of-focus
\nwas needed for analysis of the Work Activities Profile, 21 were excluded because they identified
\na) no careers of focus, b) more than one career of focus (e.g., \u201ca sales representative or a coach\u201d),
\nc) ambiguous or invalid careers (e.g., \u201csomething in the business field\u201d), d) careers for which
\nO*NET does not yet have data (e.g., \u201cmilitary officer\u201d), or e) if the career they indicated at the
\nbeginning of the survey did not match the career they used to fill out the Work Activity
\nImportance Profile items. Additional exclusions included two participants who reported being
\nunder 18 years of age, and 18 participants due to duplicate or incomplete survey responses.
\nThe final sample consisted of 316 participants. Two hundred twenty-seven participants
\nidentified as female (71.8%) and 89 identified as male (28.2%). Participant ages ranged from 18
\n51
\nto 67 years (M = 19.36, SD = 3.71); one participant did not indicate age. One hundred seventysix participants were first-year students (55.7%), 75 were second-year students (23.7%), 39 were
\nthird-year students (12.3%), 13 were fourth-year students (4.1%), 10 were fifth-year students
\n(3.2%), 2 were sixth-year students (0.6%), and 1 was a graduate students (0.3%). Two hundred
\nthirty-seven participants had declared their majors at the time of the study (75%) and 79 had not
\n(25%). Regarding the ethnic diversity of the sample, 254 identified as European American
\n(80.4%), 15 as Pacific Islander\/Asian American (4.7%), 4 as African American (1.3%), 8 as
\nLatino\/a American (2.5%), 7 as multiracial American (2.2%), and 28 as international students
\n(8.9%).
\nMeasures
\nObjective occupational knowledge. Objective occupational knowledge was measured
\nby the Work Activity Importance Profile (WAIP), adapted for the present study based on
\nO*NET\u2019s Detailed Work Activity database (National Center for O*NET Development, 2003).
\nThe database includes a set of 41 work activities (Appendix B) rated by importance for each of
\nthe 974 O*NET occupational titles. Average importance ratings for each activity are scored on a
\nscale from (0) not important to (100) extremely important for each occupation based on data
\ncollected from occupation experts and random samples of workers (Research Triangle Institute,
\n2006). For each item, respondents answered the question, \u201cHow important is the activity to your
\ncurrent job?\u201d Respondents\u2019 answers were then averaged for each item to create the importance
\nscore for each work activity within a given occupation. The 41 work activities are the same
\nacross 974 occupations in the database which allows for practical, efficient comparison of
\noccupational knowledge in large research samples. As an example, a portion of the occupation
\nactivity importance profile for an accountant is presented in Appendix C.
\n52
\nTo measure participants\u2019 objective occupational knowledge in the present study, they
\nwere asked to indicate the degree to which each of the 41 activities of the WAIP is important
\nwith regards to the career choice each participant listed at the beginning of the survey. Eight
\nactivities were seen on a page at a time. Participants responded on a 5-point Likert scale ranging
\nfrom (1) not important to (5) extremely important. The coefficient alpha for this scale in the
\npresent study was .93. Additional reliability statistics were unavailable as this was the first study
\nto utilize these items in this manner.
\nAccuracy of occupational knowledge. A profile correlation (Q correlation; Cronbach &
\nGleser, 1953) was computed for each participant to produce occupational knowledge scores. The
\nQ correlation, with scores ranging from -1.00 to +1.00, provided a standardized measurement of
\nthe agreement between a participant\u2019s WAIP ratings for his or her chosen occupation and the
\ncorresponding O*NET work activity rating profile for the same occupation. A score of .00 would
\nindicate no pattern of similarity between profiles, scores nearing +1.00 would indicate greater
\nprofile matches, and scores reaching -1.00 would indicate nearly opposite profiles (Armstrong et
\nal., 2010). This statistic is not descriptive of elevation differences between two profiles, but of
\ndifferences in shape. Since the importance scale is not descriptive in and of itself, participants\u2019
\nknowledge was not assessed based on their ratings of the importance of a given activity, but
\nrather on the importance they assigned a given activity relative to all other activities. Computed
\nin this way, a score of +1.00 indicates the highest level of occupational knowledge, and as scores
\nmove further away from +1.00 they become associated with lower occupational knowledge.
\nPerceived occupational knowledge. A subjective measure of occupational knowledge,
\nthe Perceived Occupational Knowledge Scale developed by the author (Pesch, 2014a; Appendix
\nD), was also administered. Each participant was asked to estimate the knowledge he or she has
\n53
\nabout the activities involved in his or her chosen career. The 3-item scale created by the author
\nwas used to assess self-estimated knowledge on a 6-point scale from (1) strongly disagree to (6)
\nstrongly agree with one reverse-scored item. Mean scores were computed. This measure
\nprovided information about the relation between objectively- and subjectively-measured
\noccupational knowledge. The coefficient alpha for this scale in the present study was .73.
\nCareer certainty. Career certainty was measured using the Career Certainty Scale
\n(CCS), a 3-item scale adapted from the Career Decision Scale (Osipow et al., 1976; Appendix
\nE). This scale assessed the extent to which participants were certain about pursuing the career
\nthey indicated as their current career of choice. Response options were on a 6-point scale from
\n(1) strongly disagree to (6) strongly agree. Mean scores were computed. The coefficient alpha
\nfor this scale in the present study was .92. The 2-item original version of this scale (Osipow et
\nal., 1976) has been established with internal consistency estimates between .80 and .85
\n(Hartman, Fuqua, & Hartman, 1983; Tracey, 2008). Osipow (1980) reported a two-week testretest correlation of .82, and its validity has been well-established (Betz, 1992; Osipow, 1980).
\nCareer decision-making self-efficacy. In order to assess for career decision-making
\nself-efficacy, the present study employed the Career Decision Self-Efficacy Scale (CDSES;
\nTaylor & Betz, 1983; Appendix F). The CDSES is among the most well-established measures of
\nthis construct. It was designed to measure one\u2019s belief that he or she will be able to successfully
\nengage in and complete tasks necessary to career decision-making (Betz, 2000). Anchored in
\nCrites\u2019 (1976) five career choice competencies, the short and long forms of the CDSES include
\nfive subscales: self-appraisal, occupational information, goal selection, planning, and problem
\nsolving. Participants responded by indicating their perceived abilities to complete career
\ndecision-making tasks on a 5-point Likert scale from (1) no confidence at all to (5) complete
\n54
\nconfidence. Mean scores were used, with higher scores reflecting stronger beliefs regarding one\u2019s
\nprobable effectiveness in career decision-making tasks.
\nInternal consistency has been established for the 5-level continuum scoring method of the
\nCDSES (\u03b1 = .78 to .87) and for the total score (\u03b1 = .93 to .95; Betz, Hammond, & Multon, 2005;
\nBetz & Voyten, 1997; Betz, Klein, & Taylor, 1996). Moderate estimates of concurrent validity
\nwith vocational identity have been shown (.40 to .66; Betz et al., 1996). Meta-analytic findings
\nhave also provided evidence of concurrent validity with self-esteem (mean r = .49) and career
\nindecision (mean r = -.52; Choi et al., 2012), which refers to a general inability to make careerrelated decisions.
\nIn the present study, the CDSES subscales were found to be highly related with all
\ncorrelations above .74. Therefore, the whole CDSES mean score was used in analyses. The
\ncoefficient alpha for this scale was .96.
\nCareer exploration activities. The Career Exploration Checklist, developed by the
\nauthor (Pesch, 2014b), was used to assess the extent to which participants have engaged in career
\nexploration activities. Items were derived from career exploration activities described in the
\nCareer Decision Self-Efficacy Scale (Taylor & Betz, 1983), career decision-making models
\n(Crites, 1976; Gati, 1986; Harren, 1979, Hirschi & Lage, 2007), and Iowa State University\u2019s
\nCareer Exploration Services program. This 17-item checklist (Appendix G) asked each
\nparticipant to indicate the extent to which he or she has engaged in the various exploration
\nactivities related to his or her chosen career. Responses were recorded on a 3-point scale, (0)
\nnever, (1) once, and (2) more than once. Sum scores were computed. The present study utilized
\nthis measure to assess the level of correspondence between participants\u2019 career decision-making
\n55
\nself-efficacy beliefs specific to career exploration activities, and actual engagement in those
\nactivities. The coefficient alpha for this scale was .84.
\nVocational interests. The O*NET Interest Profiler \u2013 Short Form (IP; Rounds, Su, Lewis,
\n& Rivkin, 2010; Appendix H) is a public domain 60-item instrument designed to measure
\nHolland\u2019s (1997) six types of occupational interests (Realistic, Investigative, Artistic, Social,
\nEnterprising, and Conventional). Each of Holland\u2019s interest types comprises its own 10-item
\nsubscale. Each item presented an activity and participants responded by indicating their
\npreference for the activity on a 5-point scale from (0) strongly dislike to (4) strongly like.
\nSummed scores were calculated for each subscale with higher scores indicating more interest in a
\ngiven interest category. Rounds and colleagues reported internal consistency estimates ranging
\nfrom .78 to .90, and test-retest reliability estimates ranging from .78 to .86. Furthermore,
\nconvergent and discriminant validity evidence was found with the RIASEC scales of the InterestFinder (Wall & Baker, 1997). Correlations for same-named subscales ranged from .74 to .82, and
\nfrom .12 to .48 for differing subscales. The coefficient alpha for this scale in the present study
\nwas .93. The O*NET Interest Profiler \u2013 Short Form was used by the present study in tandem
\nwith the Occupational Interest Profiles for O*NET to construct a measure of interest congruence
\n(both described below).
\nOccupational interest profiles. Interest profiles for occupations were determined using
\nthe Occupational Interest Profiles for O*NET (OIP; Rounds, Smith, Hubert, Lewis, & Rivkin,
\n1999). As part of a larger occupational information database, occupational interest profiles were
\ncreated. The profiles are based on Holland\u2019s RIASEC model (1997), and thus comparable to the
\nperson interest profiling instruments. Each of O*NET\u2019s 1172 occupational titles is paired with a
\nRIASEC interest profile, with unique values for each interest type based on the occupation\u2019s
\n56
\ncharacteristics. Job incumbent interest profiles, discriminant function analysis, and expert
\njudgments were used in determining interest profiles for each occupation. This system, created
\nfor organizing and describing occupations, has replaced the Dictionary of Occupational Titles for
\nthe U.S. Department of Labor and is currently the only comprehensive system available.
\nEvidence for external and structural validity is presented in O*NET\u2019s developmental report
\n(Rounds et al., 1999).
\nInterest congruence. Interest congruence scores were computed using the O*NET
\nInterest Profiler \u2013 Short Form for participants\u2019 interest scores and the Occupational Interest
\nProfiles for O*NET (both described above). Congruence is the degree to which a person\u2019s
\nHolland code matches that of a given work environment (Spokane & Cruza-Guet, 2005). In the
\npresent study, congruence was illustrated using the Q correlation (Cronbach & Gleser, 1953)
\nwhich is a correlation of two response profiles. Each participant\u2019s interest profile across the
\nRIASEC, measured by the IP, was correlated with the O*NET occupation interest profile of his
\nor her chosen career. In the context of the present study, the Q correlation was supported as an
\nappropriate measurement of congruence (Armstrong et al., 2010). It is consistent with Holland\u2019s
\n(1997) original conceptualization of congruence and measures the extent to which two interest
\nprofiles have similar shape. Validity for less stringent, but more frequently used congruence
\ncalculations (e.g., 1st letter agreement, 2-letter agreement) are presented in the meta-analysis by
\nTranberg and colleagues (1993). The specific congruence calculation method used in the present
\nstudy has not yet been used in the literature, but is more comprehensive; comprising scores on all
\nsix interest categories for both participants and careers.
\n57
\nProcedure
\nApproval by the Institutional Review Board at Iowa State University was obtained prior
\nto data collection in order to ensure the present study is in compliance with the ethical standards
\ndefined by the American Psychological Association.
\nParticipants were recruited using the Department of Psychology\u2019s online research
\nparticipation system that moderates undergraduate students\u2019 participation in departmentassociated research project ( help with nursing paper writing from experts with MSN & DNP degrees)s. The present study was one of many web-based survey options in
\nwhich students were able to participate. Qualtrics was used to create the survey and collect
\nresponses. Undergraduate students who opted to partake in the survey received one credit for
\nparticipating. Prior to their participation in the survey, students were presented with an informed
\nconsent statement containing a brief description of the study\u2019s purpose and procedures
\n(Appendix I). They indicated their informed consent in order to begin the survey, though they
\nwere informed of their right to discontinue study participation at any time without forfeiting
\nparticipation credit.
\nStudents were first presented with demographic questions that included items asking each
\nparticipant to provide the career he or she is pursuing or seriously considering pursuing.
\nSubsequent pages of the survey presented participants with the Perceived Occupational
\nKnowledge Scale, Career Certainty Scale, Career Decision Self-Efficacy Scale, Career
\nExploration Checklist, O*NET Interest Profiler \u2013 Short Form, and the Work Activity Importance
\nProfile. Upon reaching the end of the survey, participants were debriefed regarding the purpose
\nof the study (Appendix J). The researcher\u2019s contact information was provided if participants had
\nquestions or concerns regarding the study.
\n58
\nCHAPTER FOUR
\nRESULTS
\nDescriptive Statistics and Preliminary Analyses
\nSPSS 20 and Mplus Version 6.0 (Muth\u00e9n & Muth\u00e9n, 2010) were used for data analysis.
\nFisher z prime (z`) transformations were conducted on two variables before running descriptive
\nstatistics and preliminary analyses (Cohen et al., 2003). This transformation was statistically
\nappropriate for the occupational knowledge and interest congruence Q score distributions, given
\nthey ranged from -1.00 to +1.00. The resulting distributions for those two variables are presented
\nas z-scores; they were used throughout data analysis in place of the original Q scores. To
\ncalculate z-scores, the following equation was used: z` = \u00bd (ln(1+r)-ln(1-r)), where r represents
\neach individual\u2019s Q score for the respective variable. This transformation adjusts the
\ndistributions to make them symmetrical and normalize the standard error, allowing for more
\naccurate confidence intervals when estimating standardized beta values in path analysis (Cohen
\net al., 2003). The ranges for objective occupational knowledge and interest congruence remain
\nbetween -1.00 and +1.00, with scores near zero indicating no relation between participant and
\noccupation profiles, and scores near +1.00 indicating greater similarity between the two profiles.
\nMeans, standard deviations, coefficient alphas, and zero-order Pearson product moment
\ncorrelations among the six variables are presented in Table 1. A comparison between the present
\nstudy\u2019s sample statistics for the Career Decision Self-Efficacy Scale was compared to that of
\nTracey\u2019s (2008) sample, comprised of a similar distribution of first- through fourth-year college
\nstudents. The present study\u2019s sample (N = 316, M = 3.72, SD = 0.54) was statistically different
\nfrom Tracey\u2019s Time 1 data using the Short Form of the scale (N = 283, M = 3.40, SD = 0.96; t =
\n5.09, p < .001), but not statistically different from Tracey\u2019s Time 2 data collected three months
\n59
\nlater (N = 283, M = 3.82, SD = 1.07; t = 1.47, p = .143). This was the only comparable sample
\nsince all other studies utilized high school samples or the 10-point response scale of the Career
\nDecision Self-Efficacy Scale.
\nCareer decision-making self-efficacy positively correlated with career certainty (r = .47,
\np < .001), reported number of career exploration behaviors (r = .42, p < .001), and perceived
\noccupational knowledge (r = .39, p < .001). All of these relations were medium effect sizes
\n(Cohen, 1992). Career certainty also positively correlated with career exploration behaviors (r =
\n.28, p < .001) , but did not significantly correlate with interest congruence (p > .05). Objective
\noccupational knowledge correlated significantly with interest congruence (r = .20, p < .001).
\nAlthough the effect was small, this correlation provides some support for the validity of the
\nWork Activity Importance Profile as a measure of occupational knowledge in this sample.
\nObjective occupational knowledge did not significantly correlate with career certainty, perceived
\noccupational knowledge, reported number of career exploration activities, or career decisionmaking self-efficacy (ps > .05). Correlations and descriptive statistics are presented in Table 1.
\n60
\nTable 1.
\nDescriptive Statistics and Variable Correlations (N = 316)
\n*Indicates significance at p \u2264 .001
\na
\nFor all constructs, higher values indicate higher levels of the construct.
\nNote. Career decision-making self-efficacy as measured by the Career Decision-Making Self-Efficacy Scale (Taylor
\n& Betz, 1983); career exploration activities as measured by the Career Exploration Checklist (Pesch, 2014b);
\nobjective occupational knowledge as measured by the Work Activity Importance Profile (adapted from National
\nCenter for O*NET Development, 2003); interest congruence as measured by the O*NET Interest Profiler (Rounds et
\nal., 2010); perceived occupational knowledge as measured by the Perceived Occupational Knowledge Scale (Pesch,
\n2014a); career certainty, as measured by the Career Certainty Scale (adapted from Osipow et al., 1976).
\nVariablea
\n1 2 3 4 5 6
\n1 Career Decision-Making Self-Efficacy 1.00 .42* .06 .04 .39* .47*
\n2 Career Exploration Activities 1.00 .00 .00 .41* .28*
\n3 Objective Occupational Knowledge 1.00 .20* .06 -.02
\n4 Interest Congruence 1.00 .08 .10
\n5 Perceived Occupational Knowledge 1.00 .39*
\n6 Career Certainty 1.00
\nMean 3.72 15.55 0.65 0.63 4.48 4.72
\nStandard Deviation 0.54 6.97 0.32 0.58 1.06 1.06
\nCoefficient Alpha .96 .84 .93 .93 .73 .92
\n61
\nHypotheses
\nThe four hypotheses were tested through multiple regression based on the mediation
\ntesting model presented by Baron and Kenny (1986). They identified three conditions that must
\nbe met in order for a variable to function as a mediator: 1) the independent variable must
\nsignificantly relate to the hypothesized mediator (Path A), 2) the hypothesize mediator must
\nsignificantly relate to the dependent variable (Path B), and 3) the previously significant relation
\nbetween the independent and dependent variables significantly decreases (or becomes null) when
\nPaths A and B are controlled. Three-step multiple regression models were run to test these
\nmediation hypotheses. Results are described below and presented in Table 2.
\n62
\nTable 2.
\nMultiple Regression Results for Mediation Hypotheses 1 \u2013 4
\nNote. CDMSE = Career decision-making self-efficacy as measured by the Career Decision-Making Self-Efficacy
\nScale (Taylor & Betz, 1983); CEA = Career exploration activities as measured by the Career Exploration Checklist
\n(Pesch, 2014b); OOK = Objective occupational knowledge as measured by the Work Activity Importance Profile
\n(adapted from National Center for O*NET Development, 2003); IC = Interest congruence as measured by the
\nO*NET Interest Profiler (Rounds et al., 2010); POK = Perceived occupational knowledge as measured by the
\nPerceived Occupational Knowledge Scale (Pesch, 2014a); CC = Career certainty, as measured by the Career
\nCertainty Scale (adapted from Osipow et al., 1976).
\nStep Predictor(s) Criterion \u03b2 t p
\nHypothesis 1
\n1 CDMSE OOK .064 1.14 .256
\n2 CDMSE CC .466 9.35 < .001
\n3
\nCDMSE
\nCC
\n.470 9.39 < .001
\nOOK -.049 -0.97 .331
\nHypothesis 2
\n1 CDMSE CEA .419 8.18 < .001
\n2 CDMSE OOK .064 1.14 .256
\n3
\nCDMSE
\nOOK
\n.080 1.29 .199
\nCEA -.038 -0.61 .544
\nHypothesis 3
\n1 OOK IC .202 3.65 < .001
\n2 OOK CC -.019 -0.33 .742
\n3
\nOOK
\nCC
\n-.040 -0.70 .482
\nIC .108 1.88 .061
\nHypothesis 4
\n1 CDMSE POK .405 7.86 < .001
\n2 CDMSE CC .466 9.35 < .001
\n3
\nCDMSE
\nCC
\n.371 7.00 < .001
\nPOK .234 4.42 < .001
\n63
\n.47*
\n.06 -.05
\nHypothesis 1 predicted objective occupational knowledge would partially mediate the
\nrelation between career decision-making self-efficacy and career certainty (Figure 2). In Step 1,
\nmultiple regression results revealed a non-significant effect of career decision-making selfefficacy on objective occupational knowledge, and Step 2 revealed a significant effect of career
\ndecision-making self-efficacy on career certainty (\u03b2 = .466, p < .001). Step 3 included both
\ncareer decision-making self-efficacy and objective occupational knowledge in the prediction of
\ncareer certainty. No significant changes occurred in this step; career decision-making selfefficacy had a significant effect on career certainty (\u03b2 = .470, p < .001), while objective
\noccupational knowledge did not (see Table 2). This hypothesis was not supported by the data.
\nInstead, a significant relation existed between career decision-making self-efficacy and career
\ncertainty after all other variables have been accounted for in the model; objective occupational
\nknowledge does not significantly relate to either variable. Like the figure
\nFigure 2. Hypothesis 1 mediation model with multiple regression results.
\n*Indicates significance at p < .001
\nNote: Solid lines indicate significant paths; dashed lines indicate non-significant paths. Career decision-making selfefficacy as measured by the Career Decision-Making Self-Efficacy Scale (Taylor & Betz, 1983); Objective
\noccupational knowledge as measured by the Work Activity Importance Profile (adapted from National Center for
\nO*NET Development, 2003); Career certainty, as measured by the Career Certainty Scale (adapted from Osipow et
\nal., 1976).
\nHypothesis 2 predicted career exploration activities would partially mediate the relation
\nbetween career decision-making self-efficacy and objective occupational knowledge (Figure 3).
\nCareer decisionmaking self-efficacy Career certainty
\nObjective
\noccupational
\nknowledge
\n64
\nIn Step 1, multiple regression results revealed a significant effect of career decision-making selfefficacy on career exploration activities (\u03b2 = .419, p < .001), and Step 2 revealed a nonsignificant effect of career decision-making self-efficacy on objective occupational knowledge.
\nStep 3 included both career decision-making self-efficacy and career exploration activities in the
\nprediction of objective occupational knowledge. No significant changes occurred in this step;
\nneither career decision-making self-efficacy nor career exploration activities had a significant
\neffect on objective occupational knowledge (see Table 2).
\nFigure 3. Hypothesis 2 mediation model with multiple regression results.
\n*Indicates significance at p < .001
\nNote: Solid lines indicate significant paths; dashed lines indicate non-significant paths. Career decision-making selfefficacy as measured by the Career Decision-Making Self-Efficacy Scale (Taylor & Betz, 1983); Objective
\noccupational knowledge as measured by the Work Activity Importance Profile (adapted from National Center for
\nO*NET Development, 2003); Career exploration activities as measured by the Career Exploration Checklist (Pesch,
\n2014b).
\nHypotheses 2 was not supported by the data. Results indicated career exploration
\nactivities did not mediate the relation between career decision-making self-efficacy and objective
\noccupational knowledge as predicted. Objective occupational knowledge did not significantly
\nrelate to either variable; though career decision-making self-efficacy did have a significant direct
\neffect on career exploration activities. Neither mediation hypothesis was supported.
\nHypothesis 3 predicted interest congruence would partially mediate the relation between
\nobjective occupational knowledge and career certainty (Figure 4). In Step 1, multiple regression
\nresults revealed a significant effect of objective occupational knowledge on interest congruence
\n.42* -.04
\nCareer decisionmaking self-efficacy
\nObjective occupational
\nknowledge
\nCareer
\nexploration
\nactivities
\n.08
\n65
\n(\u03b2 = .202, p < .001), and Step 2 revealed a non-significant effect of objective occupational
\nknowledge on career certainty. Step 3 included both objective occupational knowledge and
\ninterest congruence in the prediction of career certainty. No significant changes occurred in this
\nstep; neither objective occupational knowledge nor interest congruence had a significant effect
\non career certainty (see Table 2). This partial mediation hypothesis was not supported by the
\ndata. Results indicated objective occupational knowledge and interest congruence were
\nsignificantly related; though neither had a significant relation with career certainty.
\nFigure 4. Hypothesis 3 mediation model with multiple regression results.
\n*Indicates significance at p < .001
\nNote: Solid lines indicate significant paths; dashed lines indicate non-significant paths. Objective occupational
\nknowledge as measured by the Work Activity Importance Profile (adapted from National Center for O*NET
\nDevelopment, 2003); Interest congruence as measured by the O*NET Interest Profiler (Rounds et al., 2010); Career
\ncertainty, as measured by the Career Certainty Scale (adapted from Osipow et al., 1976).
\nHypothesis 4 predicted perceived occupational knowledge would partially mediate the
\nrelation between career decision-making self-efficacy and career certainty (Figure 5). In Step 1,
\nmultiple regression results revealed a significant effect of career decision-making self-efficacy
\non perceived occupational knowledge (\u03b2 = .405, p < .001), and Step 2 revealed a significant
\neffect of career decision-making self-efficacy on career certainty (\u03b2 = .466, p < .001). Step 3
\nincluded both career decision-making self-efficacy and perceived occupational knowledge in the
\nprediction of career certainty. Significant effects of both career decision-making self-efficacy (\u03b2
\n= .371, p < .001) and perceived occupational knowledge (\u03b2 = .234, p < .001) on career certainty
\n.20* .12
\nInterest
\ncongruence
\nObjective occupational
\nknowledge
\nCareer certainty -.04
\n66
\nremained (see Table 2). While the effect of career decision-making self-efficacy decreased with
\nthe addition of perceived occupational knowledge, the change was not significant; results did not
\nsupport this partial mediation hypothesis.
\nFigure 5. Hypothesis 4 mediation model with multiple regression results.
\n*Indicates significance at p < .001
\nNote: Career decision-making self-efficacy as measured by the Career Decision-Making Self-Efficacy Scale (Taylor
\n& Betz, 1983); Career certainty, as measured by the Career Certainty Scale (adapted from Osipow et al., 1976);
\nPOK = Perceived occupational knowledge as measured by the Perceived Occupational Knowledge Scale (Pesch,
\n2014a).
\nAdditional Analyses
\nIn order to examine relations between all six variables simultaneously, a path analysis
\nwas run using the recursive model (i.e., direct effects of each predictor variable on all subsequent
\nvariables in the model were tested, with no reciprocal effects; Cohen et al., 2003) illustrated in
\nFigure 1. Table 3 presents the standardized path coefficients for the series of five steps involved
\nin the model predicting career certainty. The predictor variables are listed on the left side of the
\ntable in the order they were hypothesized in the model. Criterion variables for each equation are
\nlisted across the top, associated with the five steps. The direct effects of each predictor variable
\non each criterion variable are listed under the corresponding step. Indirect effects are indicated
\nwith arrows below the associated predictor variable. The standardized path coefficients listed in
\nthe table represent the unique contributions of the corresponding predictor variables on the
\n.23*
\n.37* Career decisionmaking selfefficacy
\nPerceived
\noccupational
\nknowledge
\nCareer
\ncertainty
\n.41*
\n67
\ncriterion variable, after taking into account the other predictor variables included in that
\nparticular model. Both direct and indirect effects are reported in the table; though indirect effects
\ninvolving more than two predictor variables were not reported. Summary statistics are reported
\nin the table, including the proportion of variance explained by the predictor variable(s) for each
\nmodel (R
\n2
\n), the sums of squares regression (sum of the squared differences predicted and
\nobserved values) and the mean squared errors (indicators of quality of the estimated model;
\nCohen et al., 2003).
\nThe proposed model contained one exogenous variable, career decision-making selfefficacy. The first endogenous variable was career exploration activities, followed by objective
\noccupational knowledge, interest congruence, perceived occupational knowledge, and career
\ncertainty. The previous mediation hypotheses examined specific relations among these variables,
\nbut not all of them simultaneously. Figure 7 presents the full model with the standardized path
\ncoefficients from Table 3. While objective occupational knowledge was central to this model, it
\nonly significantly related to interest congruence. Upon examination of the full model predicting
\ncareer certainty, results indicated 27% of the variance in career certainty could be explained by
\nthe interplay of three variables: career decision-making self-efficacy, career exploration
\nactivities, and perceived occupational knowledge. In general, it appears career decision-making
\nself-efficacy drives the model through its direct and indirect effects involving career exploration
\nactivities and perceived occupational knowledge, while objective occupational knowledge and
\ninterest congruence play no significant roles in predicting career certainty.
\nTable 3.
\nStandardized Path Coefficients for the Recursive Model Predicting Career Certainty (N = 316)
\nCriterion Variables
\nStep 1:
\nCareer
\nExploration
\nActivities
\nStep 2:
\nObjective
\nOccupational
\nKnowledge
\nStep 3:
\nInterest
\nCongruence
\nStep 4:
\nPerceived
\nOccupational
\nKnowledge
\nStep 5:
\nCareer
\nCertainty
\nPredictor Variables
\nCareer Decision-Making Self-Efficacy (CDMSE) .42** .08 .04 .28** .36**
\nCDMSE \u2192 Career Exploration Activities -.02 -.01 .12** .02
\nCDMSE \u2192 Objective Occupational Knowledge .02 .00 -.01
\nCDMSE \u2192 Interest Congruence .00 .00
\nCDMSE \u2192 Perceived Occupational Knowledge .06*
\nCareer Exploration Activities (CEA) -.04 -.02 .29** .04
\nCEA \u2192 Objective Occ. Knowledge -.02 .00 .00
\nCEA \u2192 Interest Congruence .00 .00
\nCEA \u2192 Perceived Occ. Knowledge .06*
\nObjective Occupational Knowledge (OOK) .20** .04 -.07
\nOOK \u2192 Interest Congruence .01 .02
\nOOK \u2192 Perceived Occ. Knowledge .01
\nInterest Congruence (IC) .06 .08
\nIC \u2192 Perceived Occ. Knowledge .01
\nPerceived Occupational Knowledge .22**
\nR
\n2
\n.18** .01 .04 .24** .27**
\nSum of Squares Regression 2690.51 .18 4.44 52.20 96.44
\nMean Squared Error 40.18 .11 .33 .54 .82
\n*Indicates significance at p < .003
\n**Indicates significance at p < .001
\nNote: All path coefficients are listed in the table. Empty cells indicate that particular path was not analyzed. Career decision-making self-efficacy as measured by
\nthe Career Decision-Making Self-Efficacy Scale (Taylor & Betz, 1983); career exploration activities as measured by the Career Exploration Checklist (Pesch,
\n2014b); objective occupational knowledge as measured by the Work Activity Importance Profile (adapted from National Center for O*NET Development,
\n2003); interest congruence as measured by the O*NET Interest Profiler (Rounds et al., 2010); perceived occupational knowledge as measured by the Perceived
\nOccupational Knowledge Scale (Pesch, 2014a); career certainty, as measured by the Career Certainty Scale (adapted from Osipow et al., 1976).
\n69
\n-.017
\n.082
\n.038
\n.199*
\n.041
\n.361*
\n.221*
\n.278* .291*
\n-.037
\n-.072
\n.058 .080
\n.035
\nFigure 7. Recursive path model predicting career certainty.
\nNote. Career decision-making self-efficacy as measured by the Career Decision-Making Self-Efficacy Scale (Taylor
\n& Betz, 1983); career exploration activities as measured by the Career Exploration Checklist (Pesch, 2014b);
\nobjective occupational knowledge as measured by the Work Activity Importance Profile (adapted from National
\nCenter for O*NET Development, 2003); interest congruence as measured by the O*NET Interest Profiler (Rounds et
\nal., 2010); perceived occupational knowledge as measured by the Perceived Occupational Knowledge Scale (Pesch,
\n2014a); career certainty, as measured by the Career Certainty Scale (adapted from Osipow et al., 1976). Solid lines
\nindicate significant paths; dashed lines indicate non-significant paths.
\n*Indicates significance at p < .003
\n**Indicates significance at p < .001
\n.419*
\nCareer decisionmaking self-efficacy
\nPerceived
\noccupational
\nknowledge
\nInterest
\ncongruence
\nCareer certainty
\nObjective
\noccupational
\nknowledge
\nCareer
\nexploration
\nactivities
\n70
\nCHAPTER FIVE
\nDISCUSSION
\nA major construct involved in the career decision-making process is occupational
\nknowledge. Occupational knowledge has been defined as the accuracy one has in understanding
\ninformation related to careers (Schmitt-Wilson & Welsh, 2012). Occupational knowledge has
\nbeen emphasized as an important factor in the prominent career decision-making models (Hirschi
\n& Lage, 2007). Following these models, one would expect increasing occupational knowledge to
\nlead individuals to either persist along the same career path or choose an alternative path. In turn,
\ngreater persistence along the same career path is reflected in one\u2019s degree of career certainty,
\nwhich is the commitment individuals have to their decisions to pursue specific careers (Daniels
\net al., 2006). It is generally considered to be one of the final stages in the career decision-making
\nprocess, followed only by the planning and implementation stages (Hirschi & Lage, 2007).
\nDespite the centrality of career certainty in the career decision-making process, its link to
\noccupational knowledge had not yet been directly examined in terms of individuals\u2019 knowledge
\nabout their chosen careers. This deficit in the literature is in large part due to the inherent
\ndifficulty in measuring levels of occupational knowledge individuals have regarding their chosen
\ncareers. Therefore purpose of the present study was three-fold. First, it sought to address a hole
\nin the occupational knowledge literature regarding its objective measurement and its relation to
\ncareer certainty in college students. Second, since it was the first study to objectively measure
\noccupational knowledge of college students\u2019 chosen careers, it sought to explore the relations
\nbetween that knowledge and four additional vocational constructs: 1) interest congruence,
\ndefined as the degree to which a person\u2019s interests match those of a given work environment
\n(Spokane & Cruza-Guet, 2005); 2) career decision-making self-efficacy, defined as one\u2019s belief
\n71
\nthat he or she is able to successfully engage in and complete tasks necessary to career decisionmaking (Betz, 2000); 3) career exploration activities participants reported, a behavioral indicator
\nof participants\u2019 engagement in career exploration; and 4) perceived occupational knowledge, a
\nsubjective self-assessment of occupational knowledge which has been frequently used in studies
\nas a measure of \u201coccupational knowledge.\u201d Third, the present study sought to examine these four
\nvariables alongside occupational knowledge in terms of their roles in predicting career certainty.
\nOverall, the present study sought to gain a better understanding of the factors involved in making
\ncareer decisions in college students, with particular focus on objective occupational knowledge.
\nA path model predicting career certainty was used to test specific mediation hypotheses
\ninvolving occupational knowledge and the four other variables-of-interest.
\nOverview of Findings
\nConsistent with previous research, career decision-making self-efficacy had moderate
\npositive correlations with career certainty (Creed & Patton, 2003; Creed et al., 2005; Restubog et
\nal., 2010; Taylor & Popma, 1990; Tracey, 2008) and reported number of career exploration
\nbehaviors. Career certainty additionally significantly correlated to reported number of career
\nexploration behaviors and perceived occupational knowledge (Westbrook, 1985), but did not
\nsignificantly correlate with interest congruence (Slaney & Slaney, 1986). Correlational findings
\nin the present study were similar to some previous occupational knowledge research. Objective
\noccupational knowledge correlated significantly, though modestly, with interest congruence
\n(Westbrook & Parry-Hill, 1973), providing some indication the Work Activity Importance
\nProfile was a valid measure of occupational knowledge. Objective occupational knowledge did
\nnot significantly correlate with career certainty (Westbrook et al., 1996; Westbrook & Sloan,
\n2006), perceived occupational knowledge (Grotevant & Durrett, 1980), reported number of
\n72
\n.36*
\ncareer exploration activities, or career decision-making self-efficacy. These results suggest
\noccupational knowledge, as measured by the Work Activity Importance Profile, does not relate
\nto college students\u2019 certainty about the careers they are pursuing, their self-estimates of
\nknowledge about those careers, the amount of career exploration they have engaged in, or their
\nbeliefs about their abilities to make career decisions.
\nHypotheses
\nHypothesis 1 predicted objective occupational knowledge would partially mediate the
\nrelation between career decision-making self-efficacy and career certainty. Path analysis results
\nindicated this hypothesis was not supported by the data. Instead, a significant relation existed
\nbetween career decision-making self-efficacy and career certainty after all other variables have
\nbeen accounted for in the model; objective occupational knowledge does not significantly relate
\nto either variable.
\nHypothesis 2 predicted career exploration activities would partially mediate the relation
\nbetween career decision-making self-efficacy and objective occupational knowledge. Path
\nanalysis results indicated this hypothesis was not supported by the data. Objective occupational
\nknowledge did not significantly relate to either variable; though career decision-making selfefficacy did have a significant direct effect on career exploration activities.
\nHypothesis 3 predicted interest congruence would partially mediate the relation between
\nobjective occupational knowledge and career certainty. Path analysis results indicated this partial
\nmediation hypothesis was not supported by the data. Results suggested objective occupational
\nknowledge and interest congruence were significantly related; though neither had a significant
\nrelation with career certainty.
\n73
\nFinally, Hypothesis 4 predicted objective occupational knowledge would partially
\nmediate the relation between career decision-making self-efficacy and perceived occupational
\nknowledge. Path analysis results indicated this hypothesis was also not supported by the data. A
\nsignificant direct effect of career decision-making self-efficacy on perceived occupational
\nknowledge was found; though the indirect effect of career decision-making self-efficacy on
\nperceived occupational knowledge through objective occupational knowledge was not
\nsignificant.
\nRelation of Occupational Knowledge to Career Certainty
\nOne flaw in the existing occupational knowledge literature is the difficulty in accurately
\nand adequately measuring occupational knowledge specific to careers individuals are actively
\npursuing. Work Activity Importance Profiles (WAIP) based on O*NET data were introduced as
\na possible solution to the pervasive measurement problem that has thus far impeded vocational
\npsychologists\u2019 abilities to understand how knowledge plays a role in career decision-making.
\nWhile it remains uncertain whether the present study\u2019s methodology validly measures true
\noccupational knowledge, a few specific pieces of evidence support its use in assessing this
\nconstruct. First, while its analysis required more preparation than most assessment methods, the
\nWAIP allowed for investigation of participants\u2019 knowledge specific to the careers they were
\nactively pursuing at the time rather than their knowledge of the \u201cworking world\u201d (e.g., Creed et
\nal., 2007), general clusters of careers (e.g., Job Knowledge Survey; Loesch, 1977), stereotypical
\ncareers (e.g., Career Maturity Inventory; Crites, 1978), or their self-estimated knowledge (e.g.,
\nCareer Development Inventory; Super et al., 1981). Second, the WAIP focused on 41 work
\nactivities that were applicable to some degree across all careers. Participants were forced to
\nconsider the daily experience of the careers they were pursuing rather than superficial,
\n74
\nstereotypical information they had gained. Third, convergent validity was achieved through its
\nrelation to interest congruence. Career decision-making models (e.g., Hirschi & Lage, 2007) and
\nvocational interest theory (e.g., Holland, 1997) both suggest increased knowledge of the career
\none is pursuing would lead one to determine whether it is a good fit. Fit, in this case, is
\ndetermined by the congruence between one\u2019s vocational interests and the interests fulfilled by
\nthe career he or she is pursuing. The present study found that objective occupational knowledge,
\nmeasured by the WAIP, correlated .20 with interest congruence, measured by Holland\u2019s (1997)
\ntheory of vocational interests and O*NET data. This finding indicated that degree of
\noccupational knowledge was modestly related to the degree to which participants were pursuing
\ncareers congruent with their vocational interests. Both of these constructs have been weakly to
\nmoderately related to job satisfaction in past research (e.g., Thomas & Bruning, 1984; Tranberg
\net al., 1993). Finally, the lack of a meaningful relation of objective occupational knowledge to
\nperceived occupational knowledge is striking, especially because perceived occupational
\nknowledge had a moderate correlation to career decision-making self-efficacy. This finding
\nprovides discriminant validity for the WAIP: participants\u2019 commitment to career decisions
\nappears to be influenced in part by perceptions of their decision-making abilities and knowledge
\nrather than being rooted in actual knowledge. This finding is consistent with the idea that
\nefficacy beliefs can influence behavior (Bandura, 1977).
\nIf one considers the WAIP to be an adequate measure of objective occupational
\nknowledge, the full model predicting career certainty can be discussed. The present study found
\ncareer certainty to significantly relate to career decision-making self-efficacy, reported career
\nexploration activities, and perceived occupational knowledge. Upon examining the direct and
\nindirect effects of the recursive path model predicting career certainty, the significant direct
\n75
\neffects of career decision-making self-efficacy and perceived occupational knowledge remain,
\nthough muted. Significant indirect effects were found as well; specifically, career decisionmaking self-efficacy through perceived occupational knowledge, career decision-making selfefficacy through both career exploration activities and perceived occupational knowledge, and
\ncareer exploration activities through perceived occupational knowledge. The full model had a
\nmedium effect on career certainty (Cohen, 1992), explaining 27.4% of the variance. Notably,
\nneither objective occupational knowledge nor interest congruence significantly contributed to the
\nprediction of career certainty, directly or indirectly. Interestingly, the zero-order relation between
\ncareer exploration activities and career certainty was almost fully mediated by perceived
\noccupational knowledge; though career exploration activities did have a small significant indirect
\neffect through perceived occupational knowledge. This result suggests actual participation in
\ncareer exploration activities partially accounts for levels of perceived occupational knowledge,
\nwhich in turn drives beliefs about career certainty. Results also suggest career decision-making
\nself-efficacy plays a large role in driving the model.
\nThese results make conceptual sense if career certainty is looked at through the lens of
\ncareer decision-making self-efficacy theory (Taylor & Betz, 1983). Higher career decisionmaking self-efficacy may lead individuals to approach new or threatening situations with the
\nbelief they will be able to cope successfully, while lower career decision-making self-efficacy
\nmay result in avoidant behavior leading to preference for the familiar and safe instead of the new
\nand uncertain (Bandura, 1997; Betz, 2004; Betz & Hackett, 1981; Lent et al., 1994; Taylor &
\nBetz, 1983). Career exploration and making career decisions are certainly new and uncertain
\nsituations, of which college students are the determinant of success. That level of responsibility
\n76
\nmay be perceived as exciting or terrifying depending on the levels of career decision-making
\nself-efficacy students have achieved thus far in their lives.
\nWhat is concerning based on results from the present study is the lack of influence
\nobjective occupational knowledge has on career certainty. It appears that, in general, college
\nstudents are making career decisions in part based on career decision-making self-efficacy and
\nperceived occupational knowledge; relying on beliefs of success and achievement and eschewing
\nany need for objective proof. Likely, this behavior will not have catastrophic consequences for
\nmost students once they finish their education and begin working; though for some overconfident
\nindividuals, entering the working world may be more difficult than anticipated. Their lack of
\nprevious knowledge may become apparent and they may or may not feel satisfied with the
\nchoices they made. This latter scenario is supported by current rates of job dissatisfaction in the
\ngeneral working population, which was estimated at a striking 52.8% in 2011 (Ray & Rizzacasa,
\n2012). Of course, this rate included individuals working in all job levels, not just college
\ngraduates. However, even if the dissatisfaction rate were to drop to 20% for college graduates
\nalone, it may signal that university programs and professionals are overlooking the need to guide
\nstudents toward informed, thoughtful career decisions.
\nAn additional group of students also deserves some attention in light of these findings:
\nstudents who appear to be taking a cautious approach to career decision-making. While these
\nstudents may be taking a more thoughtful, guarded approach to career decisions than their
\noverconfident counterparts, they likely experience some of their own unique struggles.
\nSpecifically, these cautious students may be dealing with greater current distress resulting from
\nworries or fears about their futures, and low confidence or self-esteem. The overconfident
\nstudents may be at greater risk of setting themselves up for future distress in the form of job
\n77
\ndissatisfaction, career changes, or potentially depressive symptoms due to the lack of personal
\nfulfillment provided by their jobs. In contrast, the cautious students may be experiencing similar
\nlevels of distress currently; adding to their difficulty making a career decision. Both of these
\ngroups should be the focus of attention from university professionals and academic major
\nprogram personnel in order to preemptively remedy potential effects of poor career decisionmaking.
\nConclusions & Implications
\nIn an attempt to improve assessment of objective occupational knowledge in college
\nstudents, the present study utilized the Work Activities Importance Profile in tandem with five
\nvariables to predict career certainty. Importantly, objective occupational knowledge of
\nparticipants\u2019 chosen careers significantly related to interest congruence; however, neither
\nvariable significantly contributed to variance in career certainty. Results indicated career
\ndecision-making self-efficacy largely drives participation in career exploration activities and
\nperceived occupational knowledge, and all three variables contribute to the prediction of career
\ncertainty through direct or indirect effects. This study was the first to objectively assess college
\nstudents\u2019 knowledge of the careers they were actively pursuing, and the first to examine that
\nconstruct along with career decision-making self-efficacy, participation in career exploration
\nactivities, perceived occupational knowledge, interest congruence, and career certainty in a large
\ncollege student sample (N = 316).
\nIf the results of this study are truly reflective of career decision processes being utilized
\nby college students, the implications would be widespread and compel universities to respond
\naccordingly. Results suggest college students are determining their career certainty in part based
\non perceptions of career decision-making ability and perceived knowledge of chosen careers
\n78
\ninstead of on objective evidence. These students should be encouraged to seek out real-world
\nexperiences of the careers they are pursuing as a way of confirming decisions or doubts.
\nUniversity officials should also be encouraging students to assess and understand their reasons
\nfor choosing and persisting in academic major programs, and providing resources to help guide
\nthem through this process in a thoughtful and comprehensive way. Career counseling and
\nexploration services should be emphasized as important aids in making good career decisions
\n(i.e., based on accurate and comprehensive knowledge, and interest congruence), and utilized
\nmore often than not by students before they are allowed to commit to a degree program. These
\npractices would likely result in greater proportions of students graduating with majors they can
\nbe confident will lead them to satisfying careers. The alternative, it seems, is that students may
\nbe navigating through external pressures in the form of expectations from parents and society,
\nfinancial debt, and timeliness in degree completion, that are making them persist in somewhat
\nincongruent major programs or experience distress related to paralyzing indecision. Neither of
\nthese scenarios is ideal; further research should be conducted to understand the process and
\nexperience of college students in order to best prepare them for lives in the working world.
\nLimitations
\nThe limitations in the present study are largely related to the assessment of objective
\noccupational knowledge. This was the first time O*NET\u2019s work activity profiles were used in
\nresearch for the purpose of measuring participants\u2019 levels of knowledge about careers they were
\nactively pursuing. While evidence for the validity of this measure was presented earlier in this
\nsection, it remains a new method of measurement and, thus, its validity and reliability remain
\nspeculative. Furthermore, participants\u2019 free responses indicating which career each one was
\nactively pursuing had to be qualitatively matched to O*NET\u2019s career titles. While some career
\n79
\ntitle matches were obvious (e.g., \u201caccountant\u201d), others were subject to judgment by the author
\nand her major professor (e.g., \u201cFBI\u201d became \u201cCriminal Investigators and Special Agents\u201d). The
\nO*NET also did not have data for every career indicated by participants (e.g., military officer).
\nFuture research utilizing this assessment method may consider providing O*NET career titles for
\nparticipants to choose from rather than allowing free-responses. Additionally, the Career
\nCertainty Scale was adapted by the author from a previous scale (Osipow et al., 1976), and the
\nPerceived Occupational Knowledge Scale and the Career Exploration Checklist were created by
\nthe author for use in the present study. Notably, however, reliability in the form of coefficient
\nalpha was acceptable for all these scales.
\nAn additional important limitation is related to the present study\u2019s sample. Participants
\nwere recruited from a large Midwestern university, which resulted in a sample of mostly
\nEuropean American students; only 19.6% of the sample identified belonging to an ethnic group
\nother than the majority. This limitation requires that the present study\u2019s results be interpreted
\nwith caution, as they may not be applicable to more ethnically-diverse samples.
\nFuture Directions
\nGiven the present study was the first of its kind in many respects, future researchers can
\nbuild off of its findings in various ways. Initially, further research should be done using the Work
\nActivity Importance Profiles in order to determine its reliability and validity. In contrast, other
\nforms of objective occupational knowledge could be examined that may provide more proximal
\nassessments of college student knowledge (e.g., testing knowledge of one\u2019s academic major,
\ncomparing personal work values to those fulfilled by careers-of-interest), thus providing a more
\naccurate picture of their occupational knowledge. Career certainty is certainly a piece of the
\n80
\ncareer decision-making process that could use additional research. Variables in the present study
\nonly explained 27.4% of the variance in career certainty, leaving the majority unexplained.
\nTypes of careers being pursued by college students could provide additional information
\nregarding the process of career decision-making. Prestige, aspiration levels, job availability,
\nsocial exposure, and prospective financial rewards, among others, could be influencing students
\nto pursue careers rather than job details, activities, and interest congruence. Overall, it appears
\nthat the career decision process in college students is complex and in need of further research.
\nVocational researchers, with the help of universities, should be focusing on guiding college
\nstudents toward thoughtful career decisions in hopes of ultimately maximizing future job
\nsatisfaction.
\nFuture research should also examine the present study\u2019s findings in relation to more
\ndiverse groups of students in terms of ethnicity and year in school. Examining students
\nlongitudinally or an equal amount of students in their freshman through senior years would be
\nimportant in order to determine the nature of career certainty and interest congruence over time.
\nIn addition, longitudinal research could shed light on the utility of career certainty in predicting
\nmajor at graduation or behavior after graduation, and whether students gravitate toward more
\ncongruent careers over the course of their educations.
\n81
\nReferences
\nAchebe, C. (1982). Assessing the vocational maturity of students in the east central state of
\nNigeria. Journal of Vocational Behavior, 20, 153-161.
\nAlvi, S. A., Khan, S. B., & Kirkwood, K. J. (1990). A comparison of carious indices of
\ndifferentiation for Holland\u2019s model. Journal of Vocational Behavior, 36, 147-152.
\nAmabile, T. M., Hill, K. G., Hennessey, B. A., & Tighe, E. M. (1994). The work preference
\ninventory: Assessing intrinsic and extrinsic motivational orientations. Journal of
\npersonality and social psychology, 66, 950-967.
\nArmstrong, P. I., Fouad, N. A., Rounds, J., & Hubert, L. (2010). Quantifying and interpreting
\ngroup differences in interest profiles. Journal of Career Assessment, 18, 115-132.
\nArzi, H. J., & White, R. T. (2008). Change in teachers’ knowledge of subject matter: A 17-year
\nlongitudinal study. Science Education, 2, 221-251.
\nAtchley, P., Hooker, E., Kroska, E., & Gilmour, A. (2012). Validation of an online orientation
\nseminar to improve career and major preparedness. Teaching of Psychology, 39, 146-151.
\nBandura, A. (1977). Social learning theory. Englewood Cliffs, NJ: Prentice-Hall.
\nBandura, A. (1986). The explanatory and predictive scope of self-efficacy theory. Journal of
\nSocial and Clinical Psychology, 4, 359-373.
\nBandura, A. (1997). Self-efficacy: The exercise of control. New York, NY: Freeman.
\nBandura, A., & Cervone, D. (1986). Differential engagement of self-reactive influences in
\ncognitive motivation. Organizational Behavior and Human Decision Processes, 38, 92-
\n113.
\nBerk, L. A., & Fekken, G. C. (1990). Person reliability evaluated in the context of vocational
\ninterest assessment. Journal of Vocational Behavior, 37, 7-16.
\nBeswick, K., Callingham, R., & Watson, J. (2012). The nature and development of middle
\nschool mathematics teachers\u2019 knowledge. Journal of Mathematics Teacher Education,
\n15(2), 131-157.
\nBetz, N. E. (1992). Career assessment: A review of critical issues. In S. D. Brown & R. W. Lent
\n(Eds.), Handbook of counseling psychology (pp. 453\u2013484). New York: Wiley.
\nBetz, N. E. (2000). Self-efficacy theory as a basis for career assessment. Journal of Career
\nAssessment, 8, 205-222.
\n82
\nBetz, N. E. (2004). Contributions of self-efficacy theory to career counseling: A personal
\nperspective. The Career Development Quarterly, 52, 340-353.
\nBetz, N. E., & Hackett, G. (1981). The relationship of career-related self-efficacy expectation to
\nperceived career options in college women and men. Journal of Counseling Psychology,
\n28, 399-410.
\nBetz, N. E., Hammond, M., & Multon, K. (2005). Reliability and validity of response continua
\nfor the CDSE. Journal of Career Assessment, 13, 131-149.
\nBetz, N. E., Klein, K., & Taylor, K. M. (1996). Assessment of a short form of the Career
\nDecision making Self-efficacy Scale. Journal of Career Assessment, 4, 47-57.
\nBetz, N. E. & Voyten, K. K. (1997). Efficacy and outcome expectations influence career
\nexploration and decidedness. The Career Development Quarterly, 46, 179-189.
\nBlank, J. R. (1978). Jobs-Career Key: A test of occupational information. Vocational Guidance
\nQuarterly, 27, 6-17.
\nBlustein, D. L. (1997). A context-rich perspective on career exploration across life roles. Career
\nDevelopment Quarterly, 45, 260-274.
\nBowling, N. A., Eschleman, K. J., & Wang, Q. (2010). A meta-analytic examination of the
\nrelationship between job satisfaction and subjective well-being. Journal of Occupational
\nand Organizational Psychology, 83, 915-934.
\nChoi, B. Y., Park, H., Yang, E., Lee, S. K., Lee, Y., & Lee, S. M. (2012). Understanding career
\ndecision self-efficacy: A meta-analytic approach. Journal of Career Development, 39,
\n443-460.
\nCohen, J. (1992). A power primer. Psychological Bulletin, 112, 155-159.
\nCohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2003). Applied multiple regression\/correlation
\nanalysis for the behavioral sciences (3rd ed.). Mahwah, NJ: Lawrence Erlbaum
\nAssociates, Inc.
\nCreed, P. A., & Patton, W. (2003). Differences in career attitude and career knowledge for high
\nschool students with and without paid work experience. International Journal for
\nEducational and Vocational Guidance, 3, 21-33.
\nCreed, P. A., Patton, W., & Hood, M. (2010). Career development and personal functioning
\ndifferences between work-bound and non-work bound students. Journal of Vocational
\nBehavior, 76, 37-41.
\nCreed, P. A., Patton, W., & Prideaux, L. (2007). Predicting change over time in career planning
\nand career exploration for high school students. Journal of Adolescence, 30, 377-392.
\n83
\nCreed, P. A., Prideaux, L., & Patton, W. (2005). Antecedents and consequences of career
\ndecisional states in adolescence. Journal of Vocational Behavior, 67, 397-412.
\nCrites, J. O. (1973). Theory and research handbook for the Career Maturity Inventory.
\nMonterey, CA: CTB, McGraw Hill.
\nCrites, J. O. (1976). A comprehensive model of career development in early adulthood. Journal
\nof Vocational Behavior, 9, 105-118
\nCrites, J. O. (1978). Career maturity inventory. Monterey, CA: CTB\/McGraw-Hill.
\nCronbach, L. J., & Gleser, G. C. (1953). Assessing similarity between profiles. The
\nPsychological Bulletin, 50, 456-473.
\nCrowley, A. D. (1981). Evaluating the impact of a third-year careers education programme.
\nBritish Journal of Guidance & Counselling, 9, 207-213.
\nDaniels, L. M., Clifton, R. A., Perry, R. P., Mandzuk, D., & Hall, N. C. (2006). Student teachers\u2019
\ncompetence and career certainty: The effects of career anxiety and perceived control.
\nSocial Psychology of Education, 9, 405-423.
\nDawis, R. V. (2005). The Minnesota theory of work adjustment. In Brown, S.D., & Lent, R.W.
\n(Eds.), Career Development and Counseling: Putting Theory and Research to Work (pp.
\n3-23). Hoboken, NJ: John Wiley & Sons, Inc.
\nDeFleur, L. B., & Menke, B. A. (1975). Learning about the labor force: Occupational knowledge
\namong high school males. Sociology of Education, 48, 324-345.
\nDonohue, R. (2006). Person-environment congruence in relation to career change and career
\npersistence. Journal of Vocational Behavior, 68, 504-515.
\nDuffy, R. D., & Klingaman, E. A. (2009). Ethnic identity and career development among firstyear college students. Journal of Career Assessment, 17, 286-297.
\nDurr, M. R., & Tracey, T. J. G. (2009). Relation of person-environment fit to career certainty.
\nJournal of Vocational Behavior, 75, 129-138.
\nFouad, N. A. (1995). Career linking: An intervention to promote math and science career
\nawareness. Journal of Counseling & Development, 73, 527-534.
\nFrazier, P. A., Tix, A. P., & Barron, K. E. (2004). Testing moderator and mediator effects in
\ncounseling psychology. Journal of Counseling Psychology, 51, 115-134.
\nGable, R. A., Tonelson, S. W., Manasi, S., Wilson, C., & Park, K. L. (2012). Importance, usage,
\nand preparedness to implement evidence-based practices for students with emotional
\n84
\ndisabilities: A comparison of knowledge and skills of special education and general
\neducation teachers. Education and Treatment of Children, 35, 499-519.
\nGasser, C. E., Larson, L. M., & Borgen, F. H. (2007). Concurrent validity of the 2005 Strong
\nInterest Inventory: An examination of gender and major field of study. Journal of Career
\nAssessment, 15, 23-43.
\nGati, I. (1986). Making career decision \u2013 A sequential elimination approach. Journal of
\nCounseling Psychology, 33, 408-417.
\nGreenhaus, J. H., Hawkins, B. L., & Brenner, O. C. (1983). The impact of career exploration on
\nthe career decision-making process. Journal of College Student Personnel, 24, 495-502.
\nGrotevant, H. D., & Durrett, M. E. (1980). Occupational knowledge and career development in
\nadolescence. Journal of Vocational Behavior, 17, 171-182.
\nGunkel, M., Schlaegel, C., Langella, I. M., & Peluchette, J. V. (2010). Personality and career
\ndecisiveness: An international empirical comparison of business students\u2019 career
\nplanning. Personnel Review, 39, 503-524.
\nHackett, G., & Betz, N. E. (1995). Self-efficacy and career choice and development. In Hackett,
\nG., & Betz, N. E. (Eds.), Self-efficacy, Adaptation, and Adjustment: Theory, Research,
\nand Application (pp. 249-280). New York, NY: Plenum Press.
\nHansen, J. C. (1985). Career development inventory. Measurement and Assessment in
\nCounseling and Development, 17, 220-224.
\nHarren, V. A. (1979). A model of career decision making for college students. Journal of
\nVocational Behavior, 14, 119-133.
\nHartman, B., Fuqua, D., & Hartman, P. (1983). The construct validity of the Career Decision
\nScale administered to high school students. Vocational Guidance Quarterly, 31, 250\u2013258.
\nHealy, C. C., & Mourton, D. L. (1984). The effects of an abbreviated self-directed search on the
\ncareer decision competencies of community college students. Vocational Guidance
\nQuarterly, 33, 55-62.
\nHirschi, A. (2009). Development and criterion validity of differentiated and elevated vocational
\ninterests in adolescence. Journal of Career Assessment, 17, 384-401.
\nHirschi, A. (2011). Career-choice readiness in adolescence: Developmental trajectories and
\nindividual differences. Journal of Vocational Behavior, 79, 340-348.
\nHirschi, A., & Lage, D. (2007). The relation of secondary students\u2019 career-choice readiness to a
\nsix-phase model of career decision making. Journal Career Development, 34, 164-191.
\n85
\nHolland, J. L. (1971). The counselor\u2019s guide to the Self-Directed Search. Odessa, FL:
\nPsychological Assessment Resources.
\nHolland, J. L. (1997). Making Vocational Choices: A theory of vocational personalities and
\nwork environments (3rd ed.). Odessa, FL: Psychological Assessment Resources.
\nHolland, J. L., & Gottfredson, G. D. (1975). Predictive value of psychological meaning of
\nvocational aspirations. Journal of Vocational Behavior, 6, 349-363.
\nJohnson, N., Johnson, S. C., Johnson, J., & Yates, C. (1982). Use of the vocational exploration
\ngroup with economically disadvantaged black students. Journal for Specialists in Group
\nWork, 7(2), 96-101.
\nJohnson, N., Johnson, J., & Yates, C. (1981). A 6-month follow-up on the effects of the
\nvocational exploration group on career maturity. Journal of Counseling Psychology, 28,
\n70-71.
\nJones, L. K. (1989). Measuring a three-dimensional construct of career indecision among college
\nstudents: A revision of the vocational decision scale \u2013 The Career Decision Profile.
\nJournal of Counseling Psychology, 36, 477-486.
\nJordan, T. E., & Pope, M. L. (2001). Developmental antecedents to adolescents\u2019 occupational
\nknowledge: A 17-year prospective study. Journal of Vocational Behavior, 58, 279-292.
\nKruger, J., & Dunning, D. (1999). Unskilled and unaware of it: How difficulties in recognizing
\none’s own incompetence leads to inflated self-assessments. Journal of Personality and
\nSocial Psychology, 77, 1121\u20131134.
\nKuder, G. F. (1944). Intermediate manual for the Kuder Preference Record. Oxford, England:
\nScience Research Associates.
\nLarson, L. M., Heppner, P. P., Ham, T., & Dugan, K. (1988). Investigating multiple subtypes of
\ncareer indecision through cluster analysis. Journal of Counseling Psychology, 35, 439-
\n446.
\nLent, R.W., Brown, S.D., & Hackett, G. (1994). Toward a unifying social cognitive theory of
\ncareer and academic interest, choice, and performance. Journal of Vocational Behavior,
\n45, 79-123.
\nLocke, E. A., Frederick, E., Lee, C., & Bobko, P. (1984). Effect of self-efficacy, goals, and task
\nstrategies on task performance. Journal of Applied Psychology, 69, 241-251.
\nLoesch, L. C. (1977). Job knowledge survey. Unpublished mimeograph, University of Florida.
\nGainesville, FL.
\n86
\nLoesch, L. C., & Sampson, J. P. (1978). Job knowledge and vocational preferences. Vocational
\nGuidance Quarterly, 27, 55-60.
\nLokan, J. (1984). Manual of the career development inventory. Melbourne: ACER.
\nMacKinnon, D. P., Krull, J. L., & Lockwood, C. M. (2000). Equivalence of the mediation,
\nconfounding, and suppression effect. Prevention Science, 1, 173-181.
\nMiller, L., & Hayward, R. (2006). New jobs, old occupational stereotypes: Gender and jobs in
\nthe new economy. Journal of Education and Work, 19, 67-93.
\nMulton, K. D. (1995) An empirical derivation of career decision subtypes in a high school
\nsample. Journal of Vocational Behavior, 47, 76-92.
\nMuth\u00e9n, L. K., & Muth\u00e9n, B. O. (2010). Mplus user\u2019s guide (6th ed.). Los Angeles: Muth\u00e9n &
\nMuth\u00e9n.
\nNational Center for O*NET Development (2003). Summary report: Updating the Detailed Work
\nActivities. Alexandria, VA: Author.
\nNauta, M. M. (2007). Assessing college students\u2019 satisfaction with their academic majors.
\nJournal of Career Assessment, 15, 446-462.
\nOmvig, C. P., Tulloch, R. W., & Thomas, E. G. (1975). The effect of career education on career
\nmaturity. Journal of Vocational Behavior, 7, 265-273.
\nOnes, D. S., & Viswesvaran, C. (2007). A research note on the incremental validity of job
\nknowledge and integrity tests for predicting maximal performance. Human Performance,
\n20, 293-303.
\nOsipow, S. H. (1980). Manual for the Career Decision Scale. Columbus, OH: Marathon
\nConsulting & Press.
\nOsipow, S. H., Carney, C. G., & Barak, A. (1976). A scale of educational-vocational
\nundecidedness: A typological approach. Journal of Vocational Behavior, 9, 233-243.
\nPalumbo, M. V., Miller, C. E., Shalin, V. L., & Steele-Johnson, D. (2005). The impact of job
\nknowledge in the cognitive ability-performance relationship. Applied H. R. M. Research,
\n10(1), 13-20.
\nPatterson, C., Curtis, J., & Reid, A. (2008). Skills, knowledge, and attitudes expected of a newlygraduated mental health nurse in an inpatient setting. International Journal of Mental
\nHealth Nursing, 17, 410-418.
\nPatton, W., & Creed, P. (2007). Occupational aspirations and expectations of Australian
\nadolescents. Australian Journal of Career Development, 16, 46-59.
\n87
\nPatton, W., Spooner-Lane, R., & Creed, P. (2005). Validation of the Short Form of the Career
\nDevelopmental Inventory \u2013 Australian Version with a sample of university students.
\nAustralian Journal of Career Development, 14(3), 49-60.
\nPesch, K. M. (2013a). Perceived Career Knowledge Scale. Unpublished test, Iowa State
\nUniversity. Ames, Iowa.
\nPesch, K. M. (2013b). Career Exploration Checklist. Unpublished test, Iowa State University.
\nAmes, Iowa.
\nPitt, L. F., & Ramaseshan, B. (1995). Realistic job information and salesforce turnover: An
\ninvestigative study. Journal of Managerial Psychology, 10(5), 29-36.
\nPumfrey, P. D., & Schofield, A. (1982). Work experience and the career maturity of fifth-form
\npupils. British Journal of Guidance & Counselling, 10, 167-175.
\nRay, R. L., & Rizzacasa, T. (2012). Job Satisfaction: 2012 Edition. New York, NY: The
\nConference Board, Inc.
\nReardon, F., Senier, J., & Lewis, J. (1972). The development and Assessment of an occupational
\ninventory. Journal of Educational Measurement, 9, 151-153.
\nResearch Triangle Institute (2006). O*NET Data Collection Program. Retrieved from
\nhttps:\/\/onet.rti.org\/pdf\/index.cfm.
\nRestubog, S. L., Florentino, A. R., & Garcia, P. R. J. M. (2010). The mediating roles of career
\nself-efficacy and career decidedness in the relationship between contextual support and
\npersistence. Journal of Vocational Behavior, 77, 186-195.
\nRoe, A. (1956). The psychology of occupations. New York, NY: John Wiley.
\nRohlfing, J. E., Nota, L., Ferrari, L., Soresi, S., & Tracey, T. J. G. (2012). Relation of
\noccupational knowledge to career interests and competence perceptions in Italian
\nchildren. Journal of Vocational Behavior, 81, 330-337.
\nRottinghaus, P. J., Buelow, K. L., Matyja, A., & Schneider, M. R. (2012). The Career Futures
\nInventory \u2013 Revised: Measuring dimensions of career adaptability. Journal of Career
\nAssessment, 20, 123-139.
\nRottinghaus, P. J., Day, S. X., & Borgen, F. H. (2005). The Career Futures Inventory: A measure
\nof career-related adaptability and optimism. Journal of Career Assessment, 13, 3-24.
\nRounds, J., Smith, T., Hubert, L., Lewis, P., & Rivkin, D. (1999). Development of occupational
\ninterest profiles for O*NET. Raleigh, NC: O*NET Consortium.
\n88
\nRounds, J., Su, R., Lewis, P., & Rivkin, D. (2010). O*NET Interest Profiler Short Form
\npsychometric characteristics: Summary. Raleigh, NC: National Center for O*NET
\nDevelopment.
\nSackett, S. A., & Hansen, J. C. (1995). Vocational outcomes of college freshmen with flat
\nprofiles on the strong interest inventory. Measurement and Assessment in Counseling and
\nDevelopment, 28(1), 9-24.
\nSadeghi, A., Baghban, I., Bahrami, F., Ahmadi, A., & Creed, P. (2011). Validation of the short
\nform of the career development inventory with an Iranian high school sample.
\nInternational Journal for Educational and Vocational Guidance, 11, 29-38.
\nSalomon, G. (1984). Television is \u201ceasy\u201d and print is \u201ctough\u201d: The differential investment of
\nmental effort in learning as a function of perceptions and attributions. Journal of
\nEducational Psychology, 76, 647-658.
\nSampson, J. P., Jr., Reardon, R. C., Peterson, G. W., & Lenz, J. G. (2004). Career counseling
\nand services: A cognitive information processing approach. Pacific Grove, CA:
\nBrooks\/Cole.
\nSampson, J. P., & Loesch, L. C. (1981). Relationships among work values and job knowledge.
\nVocational Guidance Quarterly, 29, 229-235.
\nSchmitt-Wilson, S., & Welsh, M. C. (2012). Vocational knowledge in rural children: A study of
\nindividual differences and predictors of occupational aspirations and expectations.
\nLearning and Individual Differences, 22, 862-867.
\nSlaney, R. B., & Slaney, F. M. (1986). Relationship of expressed and inventoried vocational
\ninterests of female career counseling clients. The Career Development Quarterly, 35(1),
\n24-33.
\nSpokane, A. R., & Cruz-Guet, M. C. (2005). Holland\u2019s theory of vocational personalities in
\nwork environments. In Brown, S.D., & Lent, R.W. (Eds.), Career Development and
\nCounseling: Putting Theory and Research to Work (pp. 24-41). Hoboken, NJ: John Wiley
\n& Sons, Inc.
\nSu, R., Rounds, J., & Armstrong, P. I. (2009). Men and things, women and people: A metaanalysis of sex differences in interests. Psychological Bulletin, 135, 859-884.
\nSuper, D. E. (1983). Assessment in career guidance: Toward truly developmental counseling.
\nThe Personnel and Guidance Journal, 61, 555-562.
\nSuper, D. E., Thompson, A. S., Lindeman, R. H., Jordaan, J. P., & Myers, R. A. (1981). The
\ncareer development inventory. Palo Alto, CA: Consulting Psychologists Press.
\n89
\nTaylor, M. S. (1985). The roles of occupational knowledge and vocational self-concept
\ncrystallization in students\u2019 school-to-work transition. Journal of Counseling Psychology,
\n32, 539-550.
\nTaylor, K. M., & Betz, N. E. (1983). Applications of self-efficacy theory to the understanding
\nand treatment of career indecision. Journal of Vocational Behavior, 22, 63-81.
\nTaylor, K. M., & Popma, J. (1990). An examination of the relationships among career decisionmaking self-efficacy, career salience, locus of control, and vocational indecision. Journal
\nof Vocational Behavior, 37, 17-31.
\nTheophilides, C., Terenzini, P. T., & Lorang, W. (1984). Relation between freshman-year
\nexperience and perceived importance of four major educational goals. Research in
\nHigher Education, 20, 235-252.
\nThomas, R. G., & Bruning, C. R. (1984). Cognitive dissonance as a mechanism in vocational
\ndecision processes. Journal of Vocational Behavior, 24, 264-278.
\nTracey, T. J. G. (2002). Personal Globe Inventory: Measurement of the spherical model of
\ninterests and competence beliefs. Journal of Vocational Behavior, 60, 113\u2013172.
\nTracey, T. J. G. (2008). Adherence to RIASEC structure as a key career decision construct.
\nJournal of Counseling Psychology, 55(2), 146-157.
\nTracey, T. J. G. (2010). Relation of interest and self-efficacy occupational congruence and career
\nchoice certainty. Journal of Vocational Behavior, 76, 441-447.
\nTracey, T. J. G., & Darcy, M. (2002). An idiothetic examination of vocational interests and their
\nrelation to career decidedness. Journal of Counseling Psychology, 49(4), 420-427.
\nTranberg, M., Slane, S., & Ekeberg, S. E. (1993). The relation between interest congruence and
\nsatisfaction: A meta-analysis. Journal of Vocational Behavior, 42, 253-264.
\nTsabari, O., Tziner, A., & Meir, E. I. (2005). Updated meta-analysis on the relationship between
\ncongruence and satisfaction. Journal Career Assessment, 13, 216-232.
\nVan Iddekinge, C. H., Putka, D. J., & Campbell, J. P. (2011). Reconsidering vocational interests
\nfor personnel selection: The validity of an interest-based selection test in relation to job
\nknowledge, job performance, and continuance intentions. Journal of Applied Psychology,
\n96, 13-33.
\nWall, J. E., & Baker, H. E. (1997). The interest-finder: Evidence of validity. Journal of Career
\nAssessment, 5, 255-273.
\nWalls, R. T. (2000). Vocational cognition: Accuracy of 3rd
\n-, 6th
\n-, 9th
\n-, and 12th
\n-grade students.
\nJournal of Vocational Behavior, 56, 137-144.
\n90
\nWare, M. E., & Pogge, D. L. (1980). Concomitants of certainty in career-related choices.
\nVocational Guidance Quarterly, 28, 322-327.
\nWatson, M. B., & Foxcroft, C. D. (1997). Occupational aspirations of black south African
\nadolescents. Psychological Reports, 80, 627-635.
\nWestbrook, B. W. (1976). The relationship between vocational maturity and appropriateness of
\nvocational choices of ninth-grade pupils. Measurement and Assessment in Guidance, 9(2),
\n75-80.
\nWestbrook, B. W. (1985). Predictive and construct validity of six experimental measures of
\ncareer maturity. Journal of Vocational Behavior, 27, 338-355.
\nWestbrook, B. W. (1997). Career Planning Inventory. Unpublished test, North Carolina State
\nUniversity. Raleigh, North Carolina.
\nWestbrook, B. W., Elrod, T., & Wynne, C. (1996). Career assessment and the Cognitive
\nVocational Maturity Test. Journal of Career Assessment, 4, 139-170.
\nWestbrook, B. W., & Parry-Hill, J. W. (1973). The measurement of cognitive vocational
\nmaturity. Journal of Vocational Behavior, 3, 239-252.
\nWestbrook, B., & Sloan, S. (2006). Construct validity and predictive validity of career planning
\ninventory. Journal of Career Assessment, 14, 385-404.
\nWilliams, D. F. (2011). The impact of career workshops on freshman college students at-risk for
\ndropout: An action research study. Journal of College Student Retention: Research,
\nTheory & Practice, 13, 37-62.
\nYanico, B. J., & Hardin, S. I. (1986). College students\u2019 self-estimated and actual knowledge of
\ngender traditional and nontraditional occupations: A replication and extension. Journal of
\nVocational Behavior, 28, 229-240.
\nYanico, B. J., & Mihlbauer, T. C. (1983). Students\u2019 self-estimated and actual knowledge of
\ngender traditional and nontraditional occupations. Journal of Vocational Behavior, 22,
\n278-287.
\nZener, T. B., & Schnuelle, L. (1976). Effects of the Self-Directed Search on high school
\nstudents. Journal of Counseling Psychology, 23, 353-359.
\n91
\nAPPENDIX A
\nDemographics Questionnaire
\nAge: _______
\nGender: Male
\nFemale
\nOther
\nEthnicity: Asian American\/Pacific Islander
\nArab American
\nCaucasian\/White
\nNative American
\nBlack\/African American
\nLatino\/a American
\nBiracial ______________
\nInternational Student
\nOther: _____________
\nYear in school: First
\nSecond
\nThird
\nFourth
\nFifth
\nSixth
\nGraduate Student
\nHave you declared your major? YES \/ NO
\nIf YES, what is it? _____________
\nIf NO, what major are you considering? _____________
\nWhat career are you currently considering pursuing? _____________
\nWhat is the education level required for that career?
\nHigh school degree
\nBachelor\u2019s degree
\nMaster\u2019s degree
\nDoctoral degree (e.g., PhD, MD, JD)
\n92
\nAPPENDIX B
\nWork Activity Importance Profile: O*NET Detailed Work Activities
\n(National Center for O*NET Development, 2003)
\nNot Important Somewhat
\nImportant Impotant Very Important Extremely
\nImportant
\n1 2 3 4 5
\n1 Getting Information: Observing, receiving, and otherwise obtaining information from all
\nrelevant sources.
\n2 Identifying Objects, Actions, and Events: Identifying information by categorizing,
\nestimating, recognizing differences or similarities, and detecting changes in circumstances or
\nevents.
\n3 Monitoring Processes, Materials, or Surroundings: Monitoring and reviewing information
\nfrom materials, events, or the environment, to detect or assess problems.
\n4 Inspecting Equipment, Structures, or Materials: Inspecting equipment, structures, or
\nmaterials to identify the cause of errors or other problems or defects.
\n5 Estimating the Quantifiable Characteristics of Products, Events, or Information:
\nEstimating sizes, distances, and quantities; or determining time, costs, resources, or materials
\nneeded to perform activity.
\n6 Judging the Qualities of Objects, Services, or People: Assessing the value, importance, or
\nquality of things or people.
\n7 Evaluating Information to Determine Compliance with Standards: Using relevant
\ninformation and individual judgment to determine whether events or processes comply with
\nlaws, regulations, or standards
\n8 Processing Information: Compiling, coding, categorizing, calculating, tabulating, auditing,
\nor verifying information or data.
\n9 Analyzing Data or Information: Identifying the underlying principles, reasons, or facts of
\ninformation by breaking down information or data into separate parts.
\n10 Making Decisions and Solving Problems: Analyzing information and evaluating results to
\nchoose the best solution and solve problems.
\n11 Thinking Creatively: Developing, designing, or creating new applications, ideas,
\nrelationships, systems, or products, including artistic contributions.
\n12 Updating and Using Relevant Knowledge: Keeping up-to-date technically and applying
\nnew knowledge to your job.
\n93
\n13 Developing Objectives and Strategies: Establishing long-range objectives and specifying
\nthe strategies and actions to achieve them.
\n14 Scheduling Work and Activities: Scheduling events, programs, and activities, as well as the
\nwork of others.
\n15 Organizing, Planning, and Prioritizing Work: Developing specific goals and plans to
\nprioritize, organize, and accomplish your work.
\n16 Performing General Physical Activities: Performing physical activities that require
\nconsiderable use of your arms and legs and moving your whole body, such as climbing,
\nlifting, balancing, walking and handling of materials.
\n17 Handling and Moving Objects: Using hands and arms in handling, installing, positioning,
\nmoving materials, and manipulating things.
\n18 Controlling Machines and Processes: Using either control mechanisms or direct physical
\nactivity to operate machines or processes (not including computers or vehicles).
\n19 Working with Computers: Using computers and computer systems (including hardware and
\nsoftware) to program, write software, set up functions, enter data, or process information.
\n20 Operating Vehicles, Mechanized Devices, or Equipment: Running, maneuvering,
\nnavigating, or driving vehicles or mechanized equipment, such as forklifts, passenger
\nvehicles, aircraft, or water craft
\n21 Drafting, Laying Out, and Specifying Technical Devices, Parts, and Equipment:
\nProviding documentation, detailed instructions, drawings, or specifications to tell others
\nabout how devices, parts, e or structures are to be fabricated, constructed, assembled,
\nmodified, maintained, or used.
\n22 Repairing and Maintaining Mechanical Equipment: Servicing, repairing, adjusting, and
\ntesting machines, devices, moving parts, and equipment that operate primarily on the basis of
\nmechanical (not electronic) principles.
\n23 Repairing and Maintaining Electronic Equipment: Servicing, repairing, calibrating,
\nregulating, fine-tuning, or testing machines, devices, and equipment that operate primarily on
\nthe basis of elect electronic (not mechanical) principles.
\n24 Documenting\/Recording Information: Entering, transcribing, recording, storing, or
\nmaintaining information in written or electronic\/magnetic form.
\n25 Interpreting the Meaning of Information for Others: Translating or explaining what
\ninformation means and how it can be used.
\n94
\n26 Communicating with Supervisors, Peers, or Subordinates: Providing information to
\nsupervisors, coworkers, and subordinates by telephone, in written form, e-mail, or in person.
\n27 Communicating with People Outside the Organization: Communicating with people
\noutside the organization, representing the organization to customers, the public, government,
\nand other external source information can be exchanged in person, in writing, or by telephone
\nor e-mail.
\n28 Establishing and Maintaining Interpersonal Relationships: Developing constructive and
\ncooperative working relationships with others, and maintaining them over time.
\n29 Helping and Caring for Others: Providing personal Helpance, medical attention,
\nemotional support, or other personal care to others such as coworkers, customers, or patients.
\n30 Selling or Influencing Others: Convincing others to buy merchandise\/goods or to otherwise
\nchange their minds or actions.
\n31 Resolving Conflicts and Negotiating with Others: Handling complaints, settling disputes,
\nand resolving grievances and conflicts, or otherwise negotiating with others.
\n32 Performing for or Working Directly with the Public: Performing for people or dealing
\ndirectly with the public. This includes serving customers in restaurants and stores, and
\nreceiving clients or guests.
\n33 Coordinating the Work and Activities of Others: Getting members of a group to work
\ntogether to accomplish tasks.
\n34 Developing and Building Teams: Encouraging and building mutual trust, respect, and
\ncooperation among team members.
\n35 Training and Teaching Others: Identifying the educational needs of others, developing
\nformal educational or training programs or classes, and teaching or instructing others.
\n36 Guiding, Directing, and Motivating Subordinates: Providing guidance and direction to
\nsubordinates, including setting performance standards and monitoring performance.
\n37 Coaching and Developing Others: Identifying the developmental needs of others and
\ncoaching, mentoring, or otherwise helping others to improve their knowledge or skills.
\n38 Providing Consultation and Advice to Others: Providing guidance and expert advice to
\nmanagement or other groups on technical, systems-, or process-related topic (sample nursing essay examples by the best nursing assignment writing service)s.
\n39 Performing Administrative Activities: Performing day-to-day administrative tasks such as
\nmaintaining information files and processing paperwork.
\n95
\n40 Staffing Organizational Units: Recruiting, interviewing, selecting, hiring, and promoting
\nemployees in an organization.
\n41 Monitoring and Controlling Resources: Monitoring and controlling resources and
\noverseeing the spending of money.
\nWhat career did you have in mind while you were rating the importance of those activities?
\n__________________
\n96
\nAPPENDIX C
\nOccupation Work Activity Importance Profile Example:
\nThe top six work activities for accounting
\n97
\nAPPENDIX D
\nPerceived Occupational Knowledge Scale
\n(Pesch, 2014a)
\nPlease respond to the following statements about the career you listed earlier in the survey.
\nStrongly
\ndisagree Disagree Somewhat
\ndisagree
\nSomewhat
\nagree
\nAgree Strongly
\nAgree
\n1 2 3 4 5 6
\n1. I have a good understanding of the daily activities that will be involved in this job.
\n2. I do not know what I will be doing on a daily basis in this job.
\n3. I know the types of work activities I will be required to do in this job.
\n98
\nAPPENDIX E
\nCareer Certainty Scale
\n(Adapted from Osipow, Carney, & Barak, 1976)
\nPlease respond to the following statements about the career you listed earlier in the survey.
\nStrongly
\ndisagree Disagree Somewhat
\ndisagree
\nSomewhat
\nagree
\nAgree Strongly
\nAgree
\n1 2 3 4 5 6
\n1. I am certain that this is the career I want when I finish my education.
\n2. I definitely want to pursue this career.
\n3. I have decided this is the career I am most interested in pursuing.
\n99
\nAPPENDIX F
\nCareer Decision Self-Efficacy Scale
\n(Taylor & Betz, 1983)
\nINSTRUCTIONS: For each statement below, please read carefully and indicate how much
\nconfidence you have that you could accomplish each of these tasks by marking your answer
\naccording to the following 5-point continuum. Mark your answer by filling in the correct circle
\non the answer sheet.
\nNO CONFIDENCE
\nAT ALL
\nVERY LITTLE
\nCONFIDENCE
\nMODERATE
\nCONFIDENCE
\nMUCH
\nCONFIDENCE
\nCOMPLETE
\nCONFIDENCE
\n1 2 3 4 5
\nHOW MUCH CONFIDENCE DO YOU HAVE THAT YOU COULD:
\n1. List several majors that you are interested in.
\n2. Use the internet to find information about occupations that interest you.
\n3. Select one major from a list of potential majors you are considering.
\n4. Make a plan of your goals for the next five years.
\n5. Determine the steps to take if you are having academic trouble with an aspect of your
\nchosen major.
\n6. Accurately assess your abilities.
\n7. Find information about companies who employ people with college majors in English.
\n8. Select one occupation from a list of potential occupations you are considering.
\n9. Determine the steps you need to take to successfully complete your chosen major.
\n10. Persistently work at your major or career goal even when you get frustrated.
\n11. List several occupations that you are interested in.
\n12. Find information about educational programs in engineering.
\n13. Choose a career that will fit your preferred lifestyle.
\n14. Prepare a good resume.
\n15. Change majors if you did not like your first choice.
\n16. Determine what your ideal job would be.
\n17. Talk to a faculty member in a department you are considering for a major.
\n18. Make a career decision and then not worry about whether it was right or wrong.
\n19. Get letters of recommendation from your professors.
\n20. Change occupations if you are not satisfied with the one you enter.
\n21. Decide what you value most in an occupation.
\n22. Ask a faculty member about graduate schools and job opportunities in your major.
\n23. Choose a major or career that your parents do not approve of.
\n24. Get involved in a work experience relevant to your future goals.
\n25. Resist attempts of parents or friends to push you into a career or major you believe is
\nbeyond your abilities.
\n26. Figure out whether you have the ability to successfully take math courses.
\n27. Describe the job duties of the career\/occupation you would like to pursue.
\n100
\n28. Choose a career in which most workers are the opposite sex.
\n29. Find and use the Placement Office on campus.
\n30. Move to another city to get the kind of job you really would like.
\n31. Determine the academic subject you have the most ability in.
\n32. Find out the employment trends for an occupation in the next decade.
\n33. Choose a major or career that will fit your interests.
\n34. Decide whether or not you will need to attend graduate or professional school to achieve
\nyour career goals.
\n35. Apply again to graduate school after being rejected the first time.
\n36. Determine whether you would rather work primarily with people or with information.
\n37. Find out about the average yearly earnings of people in an occupation.
\n38. Choose a major or career that will suit your abilities.
\n39. Plan course work outside of your major that will help you in your future career.
\n40. Identify some reasonable major or career alternatives if you are unable to get your first
\nchoice.
\n41. Figure out what you are and are not ready to sacrifice to achieve your career goals.
\n42. Talk with a person already employed in the field you are interested in.
\n43. Choose the best major for you even if it took longer to finish your college degree.
\n44. Identify employers, firms, institutions relevant to your career possibilities.
\n45. Go back to school to get a graduate degree after being out of school 5-10 years.
\n46. Define the type of lifestyle you would like to live.
\n47. Find information about graduate or professional schools.
\n48. Choose the major you want even though the job market is declining with opportunities in
\nthis field.
\n49. Successfully manage the job interview process.
\n50. Come up with a strategy to deal with flunking out of college.
\n101
\nAPPENDIX G
\nCareer Exploration Checklist
\n(Pesch, 2014b)
\nPlease indicate whether or not you have used the following methods to gain information about
\nthe career you chose for this survey.
\nNever Once More than
\nonce
\n0 1 2
\n1. Searched the internet to learn about this career
\n2. Read about the career somewhere other than the internet
\n3. Talked with a family member who is employed in this career
\n4. Talked with a friend who is pursuing this career
\n5. Talked with a professor or graduate student about this career
\n6. Talked with an academic advisor about this career
\n7. Talked with someone who is employed in the career about the career
\n8. Did an informational interview with someone about this career
\n9. Job shadowed someone who is employed in the career
\n10. Did an internship or job related to the career
\n11. Did an externship with someone related to this career
\n12. Did volunteering related to the career
\n13. Attended a career or job fair
\n14. Attended a presentation about this career or this career field
\n15. Attended a meeting sponsored by a student organization related to this career
\n16. Took a career course related to this career
\n17. Talked to a career counselor about options
\n102
\nAPPENDIX H
\nO*NET Interest Profiler \u2013 Short Form
\n(Rounds, Su, Lewis, & Rivkin, 2010)
\nBelow are 60 questions about work activities that some people do on their jobs. Please read each
\nquestion carefully and decide how you would feel about doing each type of work:
\nStrongly
\ndislike
\nStrongly
\nLike
\n0 1 2 3 4
\nRealistic Items
\n1. Build kitchen cabinets
\n18. Lay brick or tile
\n19. Repair household appliances
\n20. Raise fish in a fish hatchery
\n21. Assemble electronic parts
\n22. Drive a truck to deliver packages to offices and homes
\n23. Test the quality of parts before shipment
\n24. Repair and install locks
\n25. Set up and operate machines to make products
\n26. Put out forest fires
\nInvestigative Items
\n1. Develop a new medicine
\n2. Study ways to reduce water pollution
\n3. Conduct chemical experiments
\n4. Study the movement of planets
\n5. Examine blood samples using a microscope
\n6. Investigate the cause of a fire
\n7. Develop a way to better predict the weather
\n8. Work in a biology lab
\n9. Invent a replacement for sugar
\n10. Do laboratory tests to identify diseases
\nArtistic Items
\n1. Write books or plays
\n2. Play a musical instrument
\n3. Compose or arrange music
\n4. Draw pictures
\n5. Create special effects for movies
\n6. Paint sets for plays
\n7. Write scripts for movies or television shows
\n8. Perform jazz or tap dance
\n9. Sing in a band
\n103
\n10. Edit movies
\nSocial Items
\n1. Teach an individual an exercise routine
\n2. Help people with personal or emotional problems
\n3. Give career guidance to people
\n4. Perform rehabilitation therapy
\n5. Do volunteer work at a non-profit organization
\n6. Teach children how to play sports
\n7. Teach sign language to people with hearing disabilities
\n8. Help conduct a group therapy session
\n9. Take care of children at a day-care center
\n10. Teach a high-school class
\nEnterprising Items
\n1. Buy and sell stocks and bonds
\n2. Manage a retail store
\n3. Operate a beauty salon or barber shop
\n4. Manage a department within a large company
\n5. Start your own business
\n6. Negotiate business contracts
\n7. Represent a client in a lawsuit
\n8. Market a new line of clothing
\n9. Sell merchandise at a department store
\n10. Manage a clothing store
\nConventional Items
\n1. Develop a spreadsheet using computer software
\n2. Proofread records or forms
\n3. Load computer software into a large computer network
\n4. Operate a calculator
\n5. Keep shipping and receiving records
\n6. Calculate the wages of employees
\n7. Inventory supplies using a hand-held computer
\n8. Record rent payments
\n9. Keep inventory records
\n10. Stamp, sort, and distribute mail for an organization<\/p>\n","protected":false},"excerpt":{"rendered":"
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