A Short Form of the Career Futures Inventory

Abstract

The purpose of this study was to test the international transferability and structural validity of the Career Futures Inventory (CFI) in a sample of Australian university students (N = 1,566). Exploratory factor analysis of the data from a random half-split of the sample supported a three-factor solution equivalent to the original CFI subscales, Career Optimism, Career Adaptability, and Perceived Knowledge. Confirmatory factor analysis of the data from the remaining random half-split supported the structural validity of a short form, the CFI-9. The subscales of the CFI-9 had acceptable internal consistencies and correlations with measures of academic major satisfaction, career choice satisfaction, and generalized self-efficacy. It was concluded that the properties of the CFI and the CFI-9 were sufficient to explore their application as measures of perceptions of employability. It was suggested that the CFI-9 has potential as a diagnostic screening tool for counseling and educational interventions.

Keywords

Career futures inventory short form CFI-9 employability optimism adaptability

The research reported in this article occurred within the context of increasing pressure from government and industry for Australian universities to address graduate employability as a latent indicator of the performance of graduates entering the workforce and the educational institutions which produce graduates for the workforce (e.g., Precision Consultancy, 2007). Employability is a complex, multidimensional construct (Clarke, 2008; Fugate, Kinicki, & Ashforth, 2004). Within the higher education sector, graduate employability may be conceived of as “a set of achievements—skills, understandings, and personal attributes—that makes graduates more likely to gain employment and be successful in their chosen occupations, which benefits themselves, the workforce, the community and the economy” (Yorke, 2006, p. 8). Thus, employability is the potential for employment; it is not employment per se (Clarke, 2008). In other words, completing a degree in a particular discipline presents a graduate with the potential to enter a particular professional field related to that discipline; however, it does not guarantee entry in the form of employment in a specific occupation typical of that discipline. The objective indicators of graduate employment outcomes (e.g., employment rates, salaries) are annually measured in Australia through the Australian Graduate Survey (Graduate Careers Australia, 2011); however, the scales used for the survey do not address the subjective, psychological dimensions that may constitute employability. Accordingly, in this article, we aim to address the psychometric measurement of a limited range of psychological qualities that may hypothetically contribute to perceptions of employability, by testing the properties of the Career Futures Inventory (CFI; Rottinghaus, Day, & Borgen, 2005) in our context. What follows is a brief overview of the literature that pertains to the context of this study.

Employability and Graduate Attributes

Fugate, Kinicki, and Ashforth’s (2004) conceptual model of employability comprises three hypothesized factors: career identity; personal adaptability; and social capital/human capital. This model of employability has been empirically supported in the Australian context with a sample of unemployed individuals (McArdle, Waters, Briscoe, & Hall, 2007); thus, it has been used to conceptually frame this research project, albeit within a different demographic segment (i.e., university students). Career identity represents the long-term meaning-making that underpins a sense of personal identity—personal construction of past and present experiences, and future sense of becoming. It entails an individual’s self-referent meaning-making that contributes to key life-effecting decisions (e.g., wanting to complete a degree in order to enter a particular profession). Personal adaptability comprises optimism that enables individuals to confront and engage with the need for change; a propensity to engage in learning; openness to experience, flexibility in moments of change; an internal locus of control, that centers the individual on their capacity to make decisions for themselves; and a general sense of self-efficacy as a global attitude of being able to take on challenges and succeed. Social capital refers to an individual’s interpersonal networks and access to information and resources through those networks. Human capital refers to education, training, and professional experiences that are demanded in the workforce generally and in workplaces specifically.

Within the Australian higher education sector, a notion related to graduate employability is graduate attributes. These attributes may be developed through the curricula of degree programs (Bath, Smith, Stein, & Swann, 2004) in any number of academic disciplines (Higher Education Academy, 2006) and entail learning outcomes distinguishable from disciplinary content per se (e.g., problem-solving, communication, interpersonal relations, team work, technology, self-management, planning and organizing, lifelong learning). Among these attributes, career self-management may be conceived of as an overarching notion under which all other graduate attributes can be directed toward the purpose of fulfilling an individual’s career aspirations, learning, and employability (cf. Bridgstock, 2009; King, 2004). Competencies of career self-management can include personal management (e.g., positive self-concept), learning and exploring work (e.g., using career information), and career building (e.g., making effective career decisions; Ministerial Council on Education Employment Training and Youth Affairs, 2009).

Beyond the demand for specific disciplinary expertise, it is these graduate attributes that employers seek in graduates as potential employees, evidently to the extent that they have been deemed employability skills by employers (ACNielson Research Services, 2000; Australian Chamber of Commerce and Industry & Business Council of Australia, 2002; Precision Consultancy, 2007). Employers in other nations demand similar generic skills profiles in graduates as potential employees (e.g., Bennett, 2002; Quek, 2005). In addition to these employability skills, employers have identified personal attitudinal and behavioral qualities inherent to employability (e.g., loyalty, commitment, honesty and integrity, enthusiasm, reliability, personal presentation, commonsense, positive self-esteem, sense of humor, balanced attitude to work and home life, ability to deal with pressure, motivation, and adaptability; Australian Chamber of Commerce and Industry & Business Council of Australia).

It should be noted that employability is a critically contested construct because there is lack of shared understanding of its meaning among academic communities (Harvey, 2001; Yorke, 2006). Although the graduate attributes of all Australian universities have been comprehensively documented (Barrie, Hughes, & Smith, 2009), along with government policy and research to formulate a single national outcome measure (Hambur, Rowe, & Luc, 2002), graduate attributes (just like employability) is a complex notion, and not one that is uniformly agreed in academic communities and articulated in curricula (Green, Hammer, & Star, 2009). Furthermore, how effectively they can be developed through curricula has not gone without critical question (Cranmer, 2006).

Career Adaptability

In recent years, career development practitioners in Australian universities have promulgated career development theory and models as vehicles to enhance curricula that aim to embed graduate attributes and employability as learning outcomes (McIlveen et al., 2011). Accordingly, career self-management, has been treated as a higher-order graduate attribute that makes skill-based attributes (e.g., communication, technology) personally meaningful, and a metacognitive organizer of the other attitudinal and behavioral qualities sought by employers (e.g., motivation). The attitudinal and behavioral dimensions of employability, graduate attributes, and career self-management can be understood from the perspective of the theory of career construction (Savickas, 2005). For the current study, we drew upon the construct of career adaptability in particular, as “an individual’s readiness and resources for coping with current and imminent anticipated of vocational development tasks, occupational transitions, and personal traumas” (Savickas, 2005, p. 51). Career adaptability subsumes notions pertaining to future-perspective and optimism, openness to exploring, a sense of control, and confidence in the future. In formulating the CFI (Rottinghaus et al., 2005), the authors stated that “optimistic and adaptable people appear to strive higher academically, report greater comfort with their educational and career-related plans, and engage in activities that advance their level of career insight” (Rottinghaus et al., 2005, p. 20). Such a proactive stance is acknowledged as an attitudinal and behavioral dimension of employability (Clarke, 2008) and it can be subsumed under the conceptual frame of career adaptability in the theory of career construction. Thus, we selected the CFI as a potential tool to measure individuals’ perceptions of attitudinal and behavioral factors that may contribute to perceptions of employability.

The CFI is a 25-item measure of three factors of career self-management: Career Adaptability (CA, 11 items), Career Optimism (CO, 11 items), and Perceived Knowledge of the employment market (PK, 3 items). Rottinghaus, Day, and Borgen, (2005) defined CA as “the way an individual views his or her capacity to cope with and capitalize on change in the future, level of comfort with new work responsibilities, and ability to recover when unforeseen events alter career plans” (p. 11); CO as “a disposition to expect the best possible outcome or to emphasize the most positive aspects of one’s future career development, and comfort in performing career planning tasks” (p. 11); and PK as “perceptions of how well and individual understands job market and employment trends” (p. 11).

There is broad conceptual alignment of the three factors of career self-management (CA, CO, and PK; Rottinghaus et al., 2005) with the three factors of employability (career identity, personal adaptability, and social and human capital; Fugate et al., 2004). It is conceivable that items from the CA will load with measures of personal adaptability (e.g., I can adapt to change in the world-of-work), items from CO will load with measures of career identity (e.g., I am eager to pursue my career dreams), and items from PK will load with measures of social and human capital, not directly in terms of actual networks and skills per se, rather in terms of reflective self-awareness and the requirements in the labor market (e.g., I am good at understanding the job market trends). Just as the employability model depicts overlap between its three factors, there is correspondence between CA, CO, and PK evidenced in their statistical correlations with one another. Also, we do not posit pure, orthogonal relationships between each pair of constructions, CA and personal adaptability, CO and career identity, and PK and capital. There must be some degree of shared loading. For example, CA may have conceptual relationships with personal adaptability; however, CA is also likely to correlate with career identity. The same principles apply for the other factors in terms of conceptual correspondence.

Current Study

The broader purpose of this study was to explore the potential for the CFI to act as a partial measure for dimensions of the employability model of Fugate et al. (2004), as it is understood and applied to university students and graduates. Specifically, we sought to (a) determine the CFI’s international transferability by testing its three-factor structure in an Australian sample of university students and (b) explore its validity by comparing it with measures that may be taken as indicators of career self-management as a graduate attribute: career decidedness, academic major satisfaction, and generalized self-efficacy. Furthermore, as there is an increased focus on brief or single session career counseling, there is a need to find and develop affordable and accessible brief or short form tests that can be applied as screening tools. Short forms have the advantage of the counselor or researcher assessing multiple constructs in a single session (Patton & McIlveen, 2009) and may not have the redundancy of longer instruments yet maintain adequate reliability and validity (Dreer et al., 2009).

Method

Participants

This study involved 1,566 students enrolled at the University of Southern Queensland. The university is a multi-campus institution with campus sites in metropolitan and regional Australia. It also has a significant proportion of students who are from a rural/regional background, low socio-economic status, or taking their degrees by distance education. The average age of the sample in this study was M = 33.25 years (SD = 11.47). The original validation study of the CFI (Rottinghaus et al., 2005) did not report the average age of its sample. The two-thirds/one-third female to male gender split in this study was similar to the original validation study: 1,041 (66.5%) were female and 525 (33.5%) male. Thirty-four (2.2%) identified as Indigenous Australians, and 90 (5.7%) identified English as a second language. The proportion of disciplines by academic department was arts 196 (12.5%); business 414 (26.4%); education 367 (23.4%); engineering and surveying 244 (15.6%); and sciences 331 (21.1%). A total of 14 (0.90%) did not identify with an academic department or were part of a non-award program (e.g., single course professional development studies). The relative proportions of disciplines in this sample aligned with the overall institutional proportions. Participants provided information on their employment: hours of work currently per week to determine present status; and years of employment if they had been in employment since leaving high school. On average, the participants worked 31.2 (SD = 12.96) hr per week. On average, the participants had been working for 8.50 (SD = 7.30) years. These summary statistics are consistent with the student profile of the university (i.e., the majority taking part-time studies while working). First-year students comprised almost one third (29.7%) of the sample.

The overall sample was then randomly divided into two equal subsamples (n = 783). Exploratory factor analysis (Principal components analysis) was performed on the data set from the first subsample to reduce the overall number of items in the CFI. Confirmatory factor analysis was performed on the second subsample to test the construct and criterion validity. There were no differences between the two subsamples for age (Group 1: M = 33.06, SD = 11.62; Group 2: M = 33.45, SD = 11.32) and gender. Visual inspection of frequencies also demonstrated the two subsamples were similar for proportion of disciplinary courses completed.

Measures

CFI

The initial validation of the CFI (Rottinghaus et al., 2005) on a sample of North American undergraduate students reported an exploratory factor analysis in which the three hypothesized factors accounted for 40% of the variance. Confirmatory factor analysis found a good fit to the three-factor model. Respondents indicate their agreement with each item using a 5-point Likert-type scale (1 = strongly disagree; 5 = strongly agree). Mean scores and internal consistencies reported in the original validation study were: M = 41.63, SD = 5.41, α = .85 for CA; M = 37.62, SD = 7.35, α = .87 for CO; and M = 9.20, SD = 2.13, α = .73 for PK. The study also found relationships between the CFI scales and psychometric measures of positive and negative affect, problem-solving, Big-five personality dimensions (i.e., neuroticism, extraversion, openness to experience, agreeableness, and conscientiousness), and measures of skills and confidence for occupational interests. The patterns of correlations among the subscales and these other measured variables were taken as evidence for the CFI’s validity. With regard to the previously stated assumption of overlap between the CFI subscales and their corresponding employability factor, we inspected the results drawn from the personality scale reported by Rottinghaus et al., because personality traits are stable or enduring. Rottinghaus et al. found that CA and CO had equivalently moderate-to-large positive correlations with the personality traits of conscientiousness (.41 and .51, respectively) and small-to-moderate correlations with openness to experience (.26 and .23, respectively). Also, there were equivalently small-to-moderate negative correlations between neuroticism and CA (−.30), CO (−.29), and PK (−.22), respectively.

Career Choice Status Inventory (CCSI)

The CCSI (Savickas, 1993) is a 6-item measure of satisfaction with choice pertaining to career field, academic major, and occupational choices (e.g., I have chosen the occupation that I want to enter; I have a specific occupation in mind). Respondents indicate their satisfaction on a Likert-type scale of 1 (very dissatisfied and intend to change) to 5 (well satisfied with choice). The theoretical range of scores is 6–30. This scale has been used in other validation studies with internal consistency reported as α = .91 (Lewis & Savickas, 1995).

Academic Major Satisfaction scale (AMSS)

The AMSS (Nauta, 2007) is a 6-item measure of career satisfaction with regard to studies (e.g., I often wish I hadn’t gotten into this major; I wish I was happier with my choice of an academic major). Respondents indicate their satisfaction on a Likert-type scale of 1 (strongly disagree) to 5 (strongly agree). The theoretical range of scores is 6–30. Nauta reported internal consistency coefficients of α = .94 and α = .90 in two studies.

Generalized Self-Efficacy scale (GSES)

The GSES (Schwarzer & Jerusalem, 1995) was a 10-item measure of sense of optimistic mastery for a variety of situations (e.g., Thanks to my resourcefulness, I can handle unforeseen situations; I am certain I can accomplish my goals). Respondents indicate their confidence on a Likert-type scale of 1 (strongly disagree) to 5 (strongly agree). The theoretical range of scores for this scale is 10–50. Internal consistency coefficients ranging between α = .76 and α = .90 have been reported (Schwarzer & Jerusalem, 2000). While we accept the proposition that self-efficacy is specific to certain behavioral domains and that it is not a general construct (Lent & Brown, 2006), we retained the term generalized self-efficacy to be consistent with the title of the scale and interpreted the scale as a measure of general confidence.

Results

Exploratory Factor Analysis

The data were factorable with Kaiser–Meyer–Olkin measure of sampling at .92 and Bartlett’s Test of Sphericity; χ² = 10079, df = 300, p < .000. An exploratory factor analysis with oblique (Oblimin) rotation was used because it was assumed that the three hypothesized factors are interrelated. A forced three-factor solution converged at six rotations and accounted for 52.92% of the variance. The pattern matrix is shown in Table 1. A four-factor solution accounted for 58.61%; however, we retained the original three-factor solution, as this study was a test of the transferability of the original scale and model, and the additional variance of a four-factor solution was not considered sufficient reason to amend the model. In the validation study by Rottinghaus et al. (2005), the CFI accounted for 40% of the variance, whereas in this study it accounted for approximately 53%. Rottinghaus et al. found that CA was the predominant factor, accounting for 24.89% of the variance, followed by CO accounting for 10.09%, then PK at 4.62%. Loadings found in the current study, as shown in Table 1, indicated a reversal of CO and CA, with the first factor CO accounting for a larger proportion of the variance (one third as distinct from one quarter). PK was slightly higher too. Values under .32 were suppressed in order to aid in the ease of interpretation (Tabachnick & Fidell, 2006). All items with the exception of CA08 primarily loaded where expected. Furthermore, cross-loading was minimal.

Table 1.

Three-Factor Solution for Career Adaptability, Career Optimism, and Career Knowledge.

	Component
	1	2	3
CA01		−.788
CA02		−.863
CA03		−.674
CA04		−.744
CA05		−.867
CA06		−.791
CA07		−.767
CA08	.355
CA09		−.605
CA10	.466
CA11	.447
CO01	.780		−.392
CO02	.818		−.355
CO03	.642
CO04	.674
CO05	.613		.328
CO06	.484
CO07	.714
CO08	.627
CO09	.646
CO10	.652
CO11	.591
PK01			.724
PK02			.715
PK03			.645

Note. CA = Career Adaptability; CO = Career Optimism; PK = Perceived Knowledge. Cutoff was set at .32 (Tabachnick & Fidell, 2006). Eigenvalues were 8.39 for CO; 2.75 for CA; and 1.89 for PK.

n = 1,568.

Confirmatory Factor Analysis

To develop a CFI short form, the CFI-9, the 3 items with the highest loadings for CA (CA05, I can adapt to change in the world of work; CA02, I can adapt to change in my career plans; and CA06, I will adjust easily to shifting demands at work) and CO (CO02, Thinking about my career inspires me; CO1, I get excited when I think about my career; and CO07, I am eager to pursue my career dreams) were retained. All 3 PK items were also retained (viz., PK1, I am good at understanding job market trends; PK2 I do not understand job market trends [reversed]; PK3, It is easy to see future employment trends). Data from Group 2 of the sample were used to test the structural validity of the short form through confirmatory factor analysis using IMB SPSS AMOS V18 (Arbuckle, 2009). The model given as Figure 1 was tested using maximum likelihood and robust statistics. According to the recommendations by Hu and Bentler (1999), a good fitting model has a χ²/(df) < 3, Comparative Fit Index > .95, and a root mean square error of approximation (RMSEA) = .05. The three-factor CFI short form represented a good fit for the data χ² = 50.80 (24), p = .001; Comparative Fit Index = .993; RMSEA = .038. All hypothesized paths to the latent variables were also significant (p < .01) with factor loadings ranging from .59 (CO07) to .95 (PK01).

Figure 1.

Structural model for the three subscales and one total scale using data from Group 2. Factor loadings of each item on the latent variables are represented with arrows. The numbers above the items indicate how much of the variance was explained.

Scale Properties and Relatedness to Other Measures

The mean scores and correlation coefficients for the CFI-9 subscales are shown in Table 2. Compared to the original study, the mean scores and variance for CA, CO, and PK in this study were comparable to those in the study by Rottinghaus et al. The short form CA and CO subscales had adequate correlations with the original scales with correlations or r = .88 and r = .73 for CA and CO, respectively. The CFI-9 subscales’ correlations with other the validation measures used for this study are shown in Table 2. The low to moderate correlations with academic major satisfaction, career satisfaction, and generalized self-efficacy are indicative of the CFI-9’s criterion validity. Overall, the CFI-9 has demonstrated adequate correlations with the original scale, significant yet moderate correlation with criterion measures, and very good structural validity are suggestive of the of the CFI-9’s construct validity.

Table 2.

Scale Descriptive Statistics, Correlations, and Alpha Reliability Coefficients and Intercorrelations Between CFI-9 Subscales, Academic Major Satisfaction, Career Choice Satisfaction Inventory, and Generalized Self Efficacy.

					Correlation matrix
Scale	M	SD	No items	α	CO	CA	PK	AMSS	CCSI	GSE
CO	13.12	2.26	3	.84	1
CA	12.99	2.11	3	.82	.27	1
PK	9.92	3.27	3	.86	.19	.30	1
AMSS	26.67	4.80	6	.91	.27	.21	.11	1
CCSI	27.67	3.71	6	.84	.28	.19	.18	.43	1
GSE	39.23	4.83	10	.86	.17	.40	.26	.22	.20	1

Note. CO = Career Optimism; CA = Career Adaptability; PK = Perceived Knowledge; AMS = Academic Major Satisfaction Scale; CCSI = Career Choice Satisfaction Inventory; GSE = Generalized Self-efficacy Scale.

n = 783. All r significant at p < .01.

Discussion

This study sought to examine the construct validity of the CFI-9 by assessing its factor structure in an Australian sample. Exploratory factor analysis performed in this study supported a three-factor solution for the CFI. An unexpected result was the swapping of CA and CO as the first and predominant factor. While this does not detract from the three-factor solution, we suggest that this may be related to the relatively high proportion of first-year students in the sample. Confirmatory factor analysis supported the structural validity of a three-factor solution and a 9-item short form of the CFI, the CFI-9. The subscales of the CFI-9 correlated with one another and their coefficients of internal consistency were comparable to those of the CFI found by Rottinghaus et al. The subscales also correlated significantly with measures of academic major satisfaction with studies using the AMSS, career-decidedness and satisfaction using the CCSI, generalized self-efficacy or confidence using the GSE scale. We therefore suggest that this study provides some evidence of the CFI’s international transferability as a psychometric tool.

Rottinghaus et al. acknowledged that it was designed for college students and suggested that a version for working adults would be desirable. Participants in the current study included those students who had recently completed high school and those who were mature adults: the mean age of the sample was 33.21 years, thus indicating a large proportion of undergraduate students in the current sample were mature-aged. Therefore, it is reasonable to suggest that the CFI-9 is appropriate for use with mature-aged students too.

Limitations

As in the original validation study (Rottinghaus et al., 2005), the sample in the current study was predominantly female. However, there is no reason to suspect any substantive differences across gender, as the differences in mean scores were not meaningfully appreciable, and the statistical significance of differences should be carefully considered with respect to the large sample size. This study was conducted using a sample with a relatively high proportion of students from a rural/regional and lower socioeconomic backgrounds, and the majority of whom are mature-aged and do not study on campus in a full-time mode. While this is concomitantly suggestive of its relevance for such a demographic sampling, there should be some caution in assuming that the CFI-9 is appropriate for all types of universities and subpopulations within the Australian higher education sector. For example, it is unknown whether the CFI-9 would produce similar results in a university that has a considerably younger population of undergraduate students who are studying full-time on campus, and who are neither from a rural/regional nor from a low socioeconomic background. While the current data set did not reveal a statistical relationship between the CFI-9 sub-scales and age and the number of years in the workforce, some caution is warranted nonetheless because the career status of younger regional and rural students studying on campus may vary from their metropolitan counterparts due to the latter group having greater access to graduate employment opportunities and, presumably, greater opportunity to experientially explore the world of work due to its proximal location. Only by comparing a sample of younger students across the demographic ranges (i.e., rural, regional, metropolitan) and institutions would there be some evidence to determine whether this is an important moderating influence to be accounted.

Research Implications

We suggest that the CFI-9 is a useful measure that partially operationalizes the model of employability by Fugate et al. (2005) who argued that career identity, personal adaptability, and social and human capital should predict employability. However, it is inappropriate to suggest that the CFI-9 be taken as the only measure of the model. For example, the CFI or CFI-9 does not directly assess personal networks that comprise the social capital variable; nor does it assess perceptions of competence with skills that comprise the human capital variable. To further explore the validity of the CFI, there should be comparisons with other emerging measures of graduate employability that address these dimensions. For example, the measure of perception of graduate employability developed by Rothwell, Herbert, and Rothwell (2008) emphasizes perceptions of the human/social capital dimension. Their 16-item scale, validated using data drawn from undergraduate business students in the United Kingdom, measures four interrelated components regarding employment-related perceptions of (a) the university’s brand, (b) field of study, (c) state of the external labor market, and (d) self-belief. The scale was also divided into an external–internal structure; with externally oriented perceptions subsuming the university’s reputation and the discipline’s demand in the employment market; and with internally oriented perceptions subsuming sense of confidence, satisfaction with studies, and aspirations. It was found to have an acceptable factor structure and internal consistencies (Rothwell et al., 2008). Unlike the CFI, however, it was not tested against other previously published psychometric measures that tap into psychological variables to which it was hypothetically related (e.g., generalized self-efficacy and career decidedness), and Rothwell et al. recommended further research. The results of the current study of the CFI and CFI-9 address those psychological dimensions specifically and indicate a relationship with academic satisfaction and generalized self-efficacy. Other research into the CFI’s validity might compare it with tools that assess graduate attributes and that have been validated within the same context and that measure the skills dimension of employability, such as the Graduate Skills Assessment (Hambur et al., 2002).

The employability model used here (Fugate et al., 2004) includes more than graduate skills as human capital, and graduate skills alone do not equate to a conceptualization of employability in higher education. As the model dictates, other psychosocial and educational factors contribute to employability (e.g., labor market, skills). This study has demonstrated the conceptual and empirical potential for the CFI-9 to act as a partial measure of the perceptions of those psychosocial factors. Thus, it would be worthy to partner the CFI-9 with the aforementioned measures (Hambur et al., 2002; Rothwell et al., 2008) along with concurrent academic measures (e.g., grade-point average) and then proceed to advanced analytic procedures, such as structural equation modeling, that would enable testing of the employability model. In this way, the model of employability could be explored through longitudinal research that tracks students’ scores on measures over their years of study and then upon entry into their chosen field of employment.

Implications for Practice

As higher education practitioners, we acknowledge the salience of relatively stable traits and general mental abilities; however, we are interested in the psychological—cognitive, behavioral, and emotional—aspects of students’ perceptions of their employability that are amenable to the curriculum and within the scope of extracurricular interventions supplied by universities to their students (e.g., career counseling, career education, and career information). The three hypothesized factors of employability—career identity (e.g., career decidedness), personal adaptability (e.g., generalized self-efficacy, personality traits, general mental abilities), and social and human capital—are subject to influence and change. Indeed, Fugate et al. (2004) emphasized the personal malleability necessary for employability (a) that an individual has little or no control over the criteria used by employers to make employment decisions and (b) that individuals have more control over their personal qualities that contribute to employability. Thus, it may well be more productive to focus upon developing students’ and graduate’s attributes within their realm of control and within curricula.

The CFI or CFI-9 may be used as a formative diagnostic measure to determine whether students are engaged with their career and studies. Presumably, students who score low on career optimism and career adaptability may be at risk of feeling rather disinterested in their studies and not seeing the purpose of their being at university. This reason may be enough for the student to seek the support of a career development practitioner located at the university before the situation spirals into a state of disengagement or despair and a heightened chance of withdrawal from studies. This assertion is supported by the finding that CA and CO have positive correlations with positive affect and negative correlations with negative affect (Rottinghaus et al., 2005). Students who present with a low perceived career knowledge of the world of work may benefit from career counseling or learning experiences that expose them to work-integrated learning programs that are taught in a career development learning framework (Smith et al., 2009). Yet, we urge caution: the CFI may very well serve useful in these given examples, but until there has been research into its clinical utility on a wider scale it might be prudent to embed its application with other educational assessment and intervention methods (e.g., reflective essays or journaling used in formative and summative assessment; psychometrics used for career counseling).

Conclusion

This study has provided initial evidence of the CFI-9 having psychometric properties equivalent to the original version that was validated in North America by Rottinghaus et al. (2005). Pending further testing of the CFI-9 in other Australian population samples, it is suggested that this study presents evidence of the construct validity of the CFI-9 in an Australian context. This offers some evidence to reason that its properties may be similar in other nations with similar cultural and educational systems. Furthermore, the CFI-9 subscales of career optimism, career adaptability, and knowledge of the world-of-work are presented as potential measures of students’ perceptions of the attitudinal and behavioral qualities that contribute to graduate employability.

Footnotes

Declaration of Conflicting Interests

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

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

References

ACNielson Research Services. (2000). Employer satisfaction with graduate skills. Research report. Canberra, Australia: Commonwealth of Australia. Department of Education, Training and Youth Affairs.

Australian Chamber of Commerce and Industry & Business Council of Australia. (2002). Employability Skills for the Future. Canberra, Australia: Department of Education, Science & Training.

Barrie

Hughes

Smith

. (2009). The national graduate attributes project: Integration and assessment of graduate attributes in curriculum. Retrieved from http://www.itl.usyd.edu.au/projects/nationalgap/resources/GAPpdfs/National%20Graduate%20Attributes%20Project%20Final%20Report%202009.pdf

Bath

Smith

Stein

Swann

(2004). Beyond mapping and embedding graduate attributes: Bringing together quality assurance and action learning to create a validated and living curriculum. Higher Education Research & Development, 23, 313–328.

Bennett

(2002). Employers' demand for personal transferable skills in graduates: A content analysis of 1000 jobs advertisements and an associated empirical study. Journal of Vocational Education and Training, 54, 457–475.

Bridgstock

(2009). The graduate attributes we've overlooked: Enhancing graduate employability through career management skills. Higher Education Research & Development, 28, 31–44.

Clarke

(2008). Understanding and managing employability in changing career contexts. Journal of European Industrial Training, 32, 258–284.

Cranmer

(2006). Enhancing graduate employability: Best intentions and mixed outcomes. Studies in Higher Education, 31, 169–184.

Dreer

L. E.

Berry

Rivera

Snow

Elliott

T. R.

Miller

Little

T. D

. (2009). Efficient assessment of social problem-solving abilities in medical and rehabilitation settings: A Rasch analysis of the social problem-solving inventory-revised. Journal of Clinical Psychology, 65, 653–669.

10.

Fugate

Kinicki

A. J.

Ashforth

B. E.

(2004). Employability: A psycho-social construct, its dimensions, and applications. Journal of Vocational Behavior, 65, 14–38.

11.

Graduate Careers Australia. (2011). Australian Graduate Survey. Retrieved from http://www.graduatecareers.com.au/Research/Surveys/AustralianGraduateSurvey/index.htm

12.

Green

Hammer

Star

(2009). Facing up to the challenge: Why is it so hard to develop graduate attributes? Higher Education Research & Development, 28, 17–29.

13.

Hambur

Rowe

Luc

L. T.

(2002). Graduate Skills Assessment: Stage one validity study. Canberra, Australia: Department of Education Science and Training.

14.

Harvey

(2001). Defining and measuring employability. Quality in Higher Education, 7, 97–109.

15.

Higher Education Academy. (2006). Student employability profiles. Heslington, England: Author.

16.

L-t.

Bentler

P. M.

(1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6, 1–55.

17.

King

(2004). Career self-management: Its nature, causes and consequences. Journal of Vocational Behavior, 65, 112–133.

18.

Lent

R. W.

Brown

S. D.

(2006). On conceptualizing and assessing social cognitive constructs in career research: A measurement guide. Journal of Career Assessment, 14, 12–35.

19.

Lewis

D. M.

Savickas

M. L.

(1995). Validity of the career factors inventory. Journal of Career Assessment, 3, 44–56.

20.

McArdle

Waters

Briscoe

J. P.

Hall

D. T.

(2007). Employability during unemployment: Adaptability, career identity and human and social capital. Journal of Vocational Behavior, 71, 247–264.

21.

McIlveen

Brooks

Lichtenberg

Smith

Torjul

Tyler

(2011). Career development learning frameworks for work-integrated learning. In Billett

Henderson

(Eds.), Developing learning professionals: Integrating experiences in university and practice settings (pp. 149–165). Dordrecht, The Netherlands: Springer.

22.

Ministerial Council on Education Employment Training and Youth Affairs. (2009). The australian blueprint for career development. Retrieved October 10, 2009, from http://www.blueprint.edu.au/

23.

Nauta

M. M.

(2007). Assessing college students' satisfaction with their academic majors. Journal of Career Assessment, 15, 446–462.

24.

Patton

McIlveen

(2009). Practice and research in career counseling and development-2008. Career Development Quarterly, 58, 118–161.

25.

Precision Consultancy. (2007). Graduate employability skills: Prepared for the business, industry and higher education collaboration council. Canberra, Australia: Commonwealth of Australia.

26.

Quek

(2005). Learning for the workplace: A case study in graduate employees' generic competencies. Journal of Workplace Learning, 17, 231–242.

27.

Rothwell

Herbert

Rothwell

(2008). Self-perceived employability: Construction and initial validation of a scale for university students. Journal of Vocational Behavior, 73, 1–12.

28.

Rottinghaus

P. J.

Day

S. X.

Borgen

F. H.

(2005). The career futures inventory: A measure of career-related adaptability and optimism. Journal of Career Assessment, 13, 3–24.

29.

Savickas

M. L.

(1993). Career Choice Status Inventory. Unpublished manuscript. Rootstown, OH: Northeastern Ohio Universities College of Medicine.

30.

Savickas

M. L.

(2005). The theory and practice of career construction. In Brown

S. D.

Lent

R. W.

(Eds.), Career development and counseling: Putting theory and research to work (pp. 42–70). Hoboken, NJ: John Wiley & Sons.

31.

Schwarzer

Jerusalem

(1995). Generalised self-efficacy scale. In Weinmann

Wright

Johnston

(Eds.), Measures in health psychology: A user's portfolio. Causal and control beliefs. (pp. 35–37). Windsor, UK: NFER-Nelson.

32.

Schwarzer

Jerusalem

. (2000). General perceived self-efficacy scale. Retrieved May 23, 2011, from http://userpage.fu-berlin.de/~health/selfscal.htm

33.

Smith

Brooks

Lichtenberg

McIlveen

Torjul

Tyler

(2009). Career development learning: Maximising the contribution of work-integrated learning to the student experience. Australian Learning & Teaching Council final project report. Wollongong, Australia: University of Wollongong.

34.

Tabachnick

B. G.

Fidell

L. S.

(2006). Using multivariate statistics (5th ed.). Boston, MA: Allyn and Bacon.

35.

Yorke

(2006). Employability in higher education: What it is—What it is not. York, UK: The Higher Education Academy.