Validation of the Vocational Identity Status Assessment (VISA) Using Chinese Technical College Students

Abstract

Developing a vocational identity is one of the most important tasks facing any adolescent, and vocational identity has become a focus of attention in career education and guidance for decades. However, few studies have been conducted on this topic in China due to a lack of relevant measures. The purpose of this study was to validate a Chinese version of the Vocational Identity Status Assessment (VISA) using 1,650 Chinese technical college students. The 30-item VISA–Chinese Version was found to have sound reliability and validity and with measurement invariance across age groups. This study contributes to the vocational identity literature by demonstrating the usefulness of VISA–Chinese Version.

Keywords

career adaptability career and talent development self-efficacy Chinese technical college students psychometric properties vocational identity

Vocational identity is believed to develop alongside other domains of personal identity during adolescence (Erikson, 1968; Skorikov & Vondracek, 1998). It is a significant construct within career theory (Porfeli, Lee, & Vondracek, 2013), and it has been found to have close relationships with psychological well-being (Hirschi, 2011b), mental health (anxiety and depression; Holland, Johnston, & Asama, 1993), academic performance (Pop, Negru-Subtirica, Crocetti, Opre, & Meeus, 2016), and career development and decision-making (Gushue, Clarke, Pantzer, & Scanlan, 2006; Li, Hou, & Jia, 2015; Vondracek & Skorikov, 1997).

In China, an extremely large number of students study in technical schools every year. In 2015, approximately 4.4 million students were taught in 2,545 technical schools (Ministry of Human Resource and Social Security [MHRSS], 2016), and the employment rate of technical college graduates has remained at a high level (>97%) for several years (Ji, 2016; Lin, 2013; MHRSS, 2016). The technical education system includes diverse programs. For example, there is a 3-year program for junior skilled workers for junior secondary school leavers, a 3- and a 5-year program for senior skilled workers for senior and junior secondary school leavers, respectively, and a 4-year program for probational technicians for senior secondary school leavers. Due to new industrialization and the upgrading of the industrial structure, the Chinese government has strongly encouraged the training of more highly qualified workers and skilled technicians (“Outline of the National Medium- and Long-Term Talent Development Plan,” 2010). However, for a combination of cultural, historical, and social reasons, people in the community tend to view technical education students as having subpar academic performance at school (Liu, 2014). Compared to college and university graduates, technical education students tend to have more restricted career paths, fewer opportunities for promotion, lower salaries, and lower social status after they graduate. They are also reported to have poorer motivation for learning (Liu, 2014) and lower self-consistency (Ye, 2014). All of these factors may hinder the development of vocational identity in technical education students, yet the development of vocational identity (and identity in general) is vital (Erikson, 1959, 1968). For this reason, it is becoming increasingly important to conduct further research on vocational identity status in this understudied population and how this might be more effectively enhanced.

Even though the development of vocational identity is known to be essential (Hirschi, 2011a), few studies on the topic have been conducted in China, partly because of limited access to appropriate methods and materials for assessing vocational identity. Therefore, the aim of this study was to validate an existing instrument with sound psychometric properties used in the West, the Vocational Identity Status Assessment (VISA; Porfeli et al., 2011), for use with Chinese technical education students.

Literature Review

Vocational Identity

In the developmental and psychological perspectives, the concept of vocational identity was derived from that of general identity (Erikson, 1959, 1968). According to Erikson, developing a strong and stable sense of identity is crucial to all adolescents. Based on Erikson’s work, Marcia (1966, 1980) and Luyckx, Goosens, Soenens, Beyers, and Vansteenkiste (2005) further defined and operationalized two core processes, exploration and commitment, that are involved in identity formation. Later, Meeus and Crocetti (Crocetti, Rubini, Luyckx, & Meeus, 2008a; Crocetti, Rubini, & Meeus, 2008b; Meeus, 2011) added a third process, identity, which refers to career reconsideration (for a review, see Beyers & Luyckx, 2016; Porfeli et al., 2011; Porfeli et al., 2013).

On the basis of the concept of general identity, Holland, Gottfredson, and Power (1980) defined vocational identity as “the possession of a clear and stable picture of one’s goals, interests, and talents” (p. 1911). They assumed that problems with vocational identity could be linked to career decision-making difficulties and low career decision-making confidence (Holland, Gottfredson, & Power, 1980; Holland et al., 1993). However, Holland’s definition of vocational identity is limited because it overlooks the essential exploration dimension (Porfeli et al., 2013).

To obtain a more comprehensive understanding of vocational identity, Porfeli and his colleagues examined the literature on identity status (Crocetti et al., 2008a, 2008b; Luyckx, Goosens, Soenens, Beyers, & Vansteenkiste, 2005; Marcia, 1966, 1980; Meeus, 2011) and suggested a three-process acquisition of vocational identity that includes career exploration (comprising both in-depth and in-breadth exploration), career commitment (comprising commitment making and identification with commitment), and career reconsideration (comprising the commitment flexibility and career self-doubt; Porfeli et al., 2011, 2013). Here, the term “career exploration” embraces “exploring the self and the world of work broadly and deeply to ascertain the general features of the self and to learn about possible career alternatives that may exhibit a good fit with those features” (Jordaan, 1963, as cited in Porfeli et al., 2013, p. 136). Career commitment involves two components: making a decision on the career path and then adhering to that choice (Porfeli et al., 2013). Career reconsideration involves “re-examining current commitments and comparing available alternatives in an effort to find a better fit of the self and the world of work” (Porfeli et al., 2013, p. 138).

Measurement of Vocational Identity

Several measures focusing on vocational identity have been developed. A subscale called Vocational Identity within the My Vocational Situation Scale (MVS; Holland et al., 1980) was developed. It contains 18 items in a true–false forced-choice format. Although this measure has been widely used in studies (e.g., Gushue et al., 2006; Li et al., 2015), it has been criticized for two reasons. First, it assesses feelings about current career situations rather than the stable awareness of vocational goals and interests, and second, the dichotomous response of the MVS provides researchers with limited information on psychometric properties (Gupta, Chong, & Leong, 2015). For these reasons, a new instrument, vocational identity measure, which has a 5-point Likert-type response format, was developed to evaluate Holland’s definition of vocational identity as a stable construction (Gupta et al., 2015). A limitation of these two scales was that they lacked a focus on career exploration as an important dimension. The 30-item VISA (Porfeli et al., 2011) was developed with three categories (career commitment, exploration, and reconsideration). There are two subdimensions in each category (career commitment making and identification with career commitment in the commitment category, in-breadth and in-depth career exploration in the exploration category, and commitment flexibility and career self-doubt in the career reconsideration category). VISA is regarded as an updated measure with sound psychometric properties that is grounded on solid identity models and provides a comprehensive framework for assessing vocational identity. The English and Italian versions of VISA have both proven to be promising tools for assessing vocational identity (Porfeli et al., 2011; Sestito et al., 2015).

Vocational Identity Research in China

Few studies on the topic of vocational identity have been conducted in China even though the development of vocational identity is known to be essential to adolescents and young adults (Hirschi, 2011a). Among the limited number of Chinese studies, Ouyang, Jin, and Tien (2016) explored Chinese college students’ collective vocational identity in Macau using a qualitative research method (in-depth interviews with 19 college students who had lived in Macau for over 15 years). Their findings are difficult to generalize to other Chinese cultures because of the unique nature of Macau as a former Portuguese colony until 1999. Furthermore, as a qualitative study, it failed to provide objective statistical evidence that could be compared in other settings.

Another study of vocational identity investigated 112 Taiwanese graduate and undergraduate students who studied in the United States (Shih & Brown, 2000). Using the MVS instrument, the study revealed that age and acculturation level positively predicted vocational identity (Shih & Brown, 2000). Although this study contributed to the literature by exploring the relationship between acculturation and vocational identity in the Chinese population, its results cannot be generalized to the overall Chinese population because of the special sample used. The MVS also provided fairly limited vocational identity information.

The other notable Chinese study was conducted by Jin, Watkins, and Yuen (2009), who surveyed 785 Chinese graduate students in Beijing, China, and found that personality traits such as neuroticism and conscientiousness had significant direct effects on vocational exploration and commitment and that these relationships were also mediated by career decision self-efficacy (CDSE). Furthermore, contrary to expectations, a positive association was found between CDSE and the tendency to foreclose (Jin, Watkins, & Yuen, 2009). However, this study failed to provide more information on relationships among personalities, CDSE, and other types of vocational identity such as career exploration and reconsideration (Porfeli et al., 2011).

In sum, there have been relatively few studies on vocational identity in Chinese student populations, and they revealed limited information regarding vocational identity and its relationship with other psychological and career variables. This is partly due to the qualitative methodology used and a lack of measurements that provide sufficient information on vocational identity. In order to facilitate better research in this field in China, it is therefore necessary to develop a new measure of vocational identity or to adapt existing measures from the West. The existing instrument VISA (Porfeli et al., 2011) was chosen because it includes information on career commitment, exploration, and reconsideration and because it has shown sound psychometric properties.

The Study

Given that development of vocational identity is significant to adolescents and young adults in China, the objective of this study was to validate a Chinese version of the VISA (Porfeli et al., 2011). Specifically, the study aimed to (a) evaluate the factor structure of VISA–Chinese Version, (b) provide evidence of reliability through reporting the Cronbach’s αs of each subscale, (c) assess the measurement invariance across age groups, and (d) provide evidence of convergent validity of VISA–Chinese Version by assessing the relationships among vocational identity and the three variables, career adaptability, career exploration, and talent development self-efficacy.

The present study aimed to extend researchers’ understanding of vocational identity in two important ways. First, VISA had not previously been validated in an Eastern country with a collectivistic society, specifically in a population of Chinese technical college students. Second, while research has shown vocational identity to be associated with domain-specific self-efficacy (Gushue et al., 2006; Nauta, 2007; Nauta & Kahn, 2007), this study evaluated the relationship between vocational identity and new variables, namely career exploration and talent development self-efficacy.

Rationale

The construct validity of the Chinese version of VISA was assessed in this study in two ways. First, studies have documented that career exploration and career commitment were positively associated with career adaptability, while career self-doubt was negatively associated, and commitment flexibility had no significant link (Negru-Subtirica, Pop, & Crocetti, 2015; Porfeli & Savickas, 2012). This study therefore examined the convergent validity of VISA through hypothesizing that the six types of vocational identity would have similar relationships with career adaptability. Second, as previous research has suggested positive relationships between general/vocational identity and career-related self-efficacy (Gushue et al., 2006; Jo, Ra, Lee, & Kim, 2016; Nauta, 2007), it is hypothesized in this study that there will be significant relationships between vocational identity and career exploration and talent development self-efficacy.

As an essential part of establishing the validity and psychometric properties of VISA with a Chinese population, it is necessary to evaluate its measurement invariance across age groups. The evaluation of the measurement invariance of any scale is important because it determines whether each item used in the instrument means the same thing to participants of different groups (e.g., males vs. females; children vs. adults; Cheung & Rensvold, 2002). If there is no measurement invariance across different groups, then between-group differences in scores cannot be unambiguously interpreted (Cheung & Rensvold, 2002). With respect to VISA, scalar invariance across ages in American high school and university samples was not established, making it difficult to determine later whether age-group differences in vocational identity were due to the content of items in the scale or to real age-factor influences (Porfeli et al., 2011).

General Methods

Two studies were conducted, and the statistical methods used were consistent with those used by Porfeli, Lee, Vondracek, and Weigold (2011). This enabled comparable psychometrical results between the American and Chinese versions of VISA.

The analysis in Study 1 (N = 448) included (a) an exploratory factor analysis (EFA) to evaluate the factor structure of the scale and selection of items and (b) an examination of internal consistency reliability. Based on the results, 3 items (Items 5, 7, and 8) were revised before the second study was conducted.

The analysis in Study 2 (N = 1,202) consisted of (a) an evaluation of the correlational model of the VISA–Chinese Version via confirmatory factor analysis (CFA), (b) an evaluation of the measurement invariance for age groups, and (c) an assessment of convergent validity between the VISA subscales and validated indicators for career adaptability and career exploration self-efficacy.

Item Translation and Back-Translation

A VISA–Macau Chinese Version (Ouyang, Wang, & Jin, 2014) had already been developed, but because of slight differences between Macau and Mainland China in Chinese idioms and expressions (people in Macau used Cantonese while those in mainland China speak Mandarin), the VISA–English Version was translated afresh into Chinese for this study. A professional translator majoring in English linguistics was invited to translate the VISA into Chinese, and a second bilingual and bicultural independent translator was invited to back-translate the preliminary Chinese draft. The original English version of the VISA and the back-translated version were compared by a third native English speaker. Finally, the translators discussed all of the items together, and any that appeared to have differences between the original VISA and the translated Chinese versions were modified.

At the next stage, the Chinese version was shown to 23 technical college students (age = 19–21 years) who were not part of the main study. The purpose was to check the clarity of each item. Feedback from these students led to some minor adjustments in wording to any items that were ambiguous.

Study 1

The aim of this study was to evaluate the factor structure of the VISA–Chinese Version and to determine its main psychometric features.

Participants

The 474 participants were recruited from two technical colleges in Shenzhen (N = 320, 68%) and Zhuhai (N = 154, 32%). From this group, 448 complete and usable sets of data were obtained (responding rate: 94.5%; age range = 15–24 years; mean age = 18.3 years; SD = 1.7 years). The sample comprised 63.4% male students, and 91.3% of students came from the program of senior skilled workers. The students’ majors were diverse including marketing, business secretarial work, biology, gardening, electrical engineering, automatic control, electronic technology, optoelectronic technology, vehicle maintenance technology, communication network application, and animation design.

Measure

Vocational identity

The VISA (Porfeli et al., 2011), which consists of 30 items, was used to assess individuals’ vocational identity in three dimensions: career commitment, career exploration, and career reconsideration. Each dimension has two subdimensions. The commitment dimension includes career commitment making and identification with career commitment, the exploration dimension includes in-breadth career exploration and in-depth career exploration, and the reconsideration dimension includes commitment flexibility and career self-doubt. Each subdimension includes 5 items. VISA uses a 5-point Likert-type scale with responses ranging from 5 (strongly disagree) to 1 (strongly agree). The Cronbach’s αs for the VISA subscales in this study ranged from .74 to .83.

Procedure

Ethical approval was obtained from the university before the survey. Data were collected in pencil-and-paper format by teachers during class periods. All of the participants were informed that (a) it was entirely voluntary to participate in this study of adolescents’ career development, (b) they could withdraw at any time without any negative consequences, (c) their answers would be confidential and there were no right or wrong answers, and (d) their participation would be highly appreciated. Their written consent to take part was obtained before the survey. The assessment was completed within 10 min.

Results

The EFA was conducted following the recommendations of Worthington and Whittaker (2006). Missing values were addressed using listwise deletion. The Kaiser–Meyer–Olkin value of .88 and the Bartlett’s test of sphericity approximate, χ²(435) = 5,300.8; p < .001, indicated that the data were good for factor analysis (Tabachnick & Fidell, 2001).

Extraction was conducted using principal axis factoring with Promax rotation. Both the eigenvalues (larger than 1) and the scree spot indicated six factors that explained 59% of the total variance. Items that met the following criteria were included (a) factor loadings larger than .40 and (b) cross-factor loadings lower than .30.

The EFA results show that the factor structure of the VISA–Chinese Version differed slightly from that of the original English version. First, Item 15 (“I need to learn a lot more before I can make a career choice.”), which was expected to load on the dimension of commitment flexibility, loaded on the dimension of in-depth career exploration instead, with an acceptable factor loading of .506. Similarly, Item 5 (“I have invested a lot of energy into preparing for my chosen career.”), which was expected to load on the dimension of career commitment making, loaded on identification with career commitment instead, with a low factor loading of .305. Moreover, Items 7 and 8 had factor loadings lower than .40. Item 7 also had a cross-factor loading higher than .30 (Table 1). The rest of the items had moderate to high factor loadings ranging from .42 to .89, with cross-factor loadings lower than .30. The translations of Items 5, 7, and 8 were thus modified based on the original English version.

Table 1.

Vocational Identity Status Assessment—Chinese Version: Factor Loadings From the Exploratory Factor Analysis in Study 1 (N = 443) and the Confirmatory Factor Analysis in Study 2 (N = 1,202).

Variables	Study 1^a						Study 2
Variables	F1	F2	F3	F4	F5	F6	F1	F2	F3	F4	F5	F6
我知道哪种工作最适合我 1. I know what kind of work is best for me	.766	.019	.058	−.059	.044	−.057	.723	0	0	0	0	0
我想从事的职业比其它职业更吸引我 2. No other career is as appealing to me as the one I expect to enter	.519	.1	−.091	.05	.008	.095	.563	0	0	0	0	0
我一直深知哪种职业最适合我 3. I have known for a long time what career is best for me	.847	−.113	.020	−.026	−.003	−.031	.761	0	0	0	0	0
没有人能够说服我改行 4. No one will change my mind about the career I have chosen	.586	.115	−.07	.082	−.07	−.074	.443	0	0	0	0	0
我投入大量精力去准备我所心仪的职业 5. I have invested a lot of energy into preparing for my chosen career	.253	.305	−.101	.018	−.040	.176	0	.558	0	0	0	0
我的职业有助于我全面实现我的个人目标 6. My career will help me satisfy deeply personal goals	.290	.491	−.059	.009	−.031	.045	0	.688	0	0	0	0
我的家人坚信我会从事我所心仪的职业 7. My family feels confident that I will enter my chosen career	.069	.377	−.053	.068	−.111	.302	0	.515	0	0	0	0
投身我所心仪的职业能实现我的理想 8. Becoming a worker in my chosen career will allow me to become the person I dream to be	−.032	.398	−.022	−.014	.069	.001	0	.795	0	0	0	0
我选择的职业让我能坚守自己的价值观 9. I chose a career that will allow me to remain true to my values	−.026	.729	.015	−.019	.047	.176	0	.781	0	0	0	0
我选择的职业能让我过上我想要的家庭生活 10. My career choice will permit me to have the kind of family life I wish to have	.116	.611	.127	.014	.048	−.030	0	.667	0	0	0	0
我的工作兴趣将来可能会改变 11. My work interests are likely to change in the future	−.085	.056	.715	.026	.017	.033	0	0	.748	0	0	0
我对工作的期望将来会改变 12. What I look for in a job will change in the future	−.038	.060	.886	−.022	.005	−.040	0	0	.757	0	0	0
我可能会改变职业生涯目标 13. I will probably change my career goals	.019	−.002	.830	−.06	−.028	.014	0	0	.721	0	0	0
我选择的职业未必尽如我所料 14. My career choice might turn out to be different than I expect	.064	−.198	.519	.118	−.004	.236	0	0	.546	0	0	0
我选择职业前必须多方了解 15. I need to learn a lot more before I can make a career choice	.170	.046	.196	.008	−.131	.506	0	0	0	0	0	.422
想到要择业就让我不安 16. Thinking about choosing a career makes me feel uneasy	−.085	.107	−.086	.626	−.052	.127	0	0	0	.582	0	0
当我跟别人谈起我的职业生涯规划时，我感到自己不够诚实 17. When I tell other people about my career plans, I feel like I am being a little dishonest	.075	.059	−.024	.614	.096	−.115	0	0	0	.563	0	0
当我畅谈职业生涯规划时，了解我的人都半信半疑 18. People who really know me seem doubtful when I share my career plans with them	.103	.137	.119	.586	.059	−.250	0	0	0	.500	0	0
我怀疑自己能否找到合适的职业 19. I doubt I will find a career that suits me	−.083	−.103	−.016	.827	−.029	.101	0	0	0	.842	0	0
我可能找不到真正想要的工作 20. I may not be able to get the job I really want	.049	−.151	.035	.768	.003	.035	0	0	0	.782	0	0
随意了解不熟悉的职业，以便找到几份值得我进一步探索的职业 21. Casually learning about careers that are unfamiliar to me in order to find a few to explore further	−.021	.152	.146	.064	.555	−.221	0	0	0	0	.423	0
尝试积累许多不同的工作经验，以便找到几份可能适合我的工作 22. Trying to have many different experiences so that I can find several jobs that might suit me	−.144	.135	−.012	.009	.757	−.045	0	0	0	0	.579	0
思考我如何才能适应许多不同的职业 23. Thinking about how I could fit into many different careers	.063	−.074	−.106	.036	.668	.136	0	0	0	0	.671	0
去了解我可能会喜欢的不同工作 24. Learning about various jobs that I might like	−.060	−.057	−.019	.019	.524	.285	0	0	0	0	.650	0
由于我了解许多不同的职业，所以我保持我的职业选择的开放性 25. Keeping my options open as I learn about many different careers	.149	−.076	.012	−.049	.513	.125	0	0	0	0	.616	0
考虑职业的时候，确认自己最强的多项才能 26. Identifying my strongest talents as I think about careers	.186	−.054	.029	−.068	.239	.418	0	0	0	0	0	.636
对于我最感兴趣的职业，尽量多了解其对教育程度方面的具体要求 27. Learning as much as I can about the particular educational requirements of the career that interests me the most	.029	.037	−.005	−.092	.076	.585	0	0	0	0	0	.709
了解如何增加机会以进入我所选职业 28. Learning what I can do to improve my chances of getting into my chosen career	−.055	.047	−.023	.074	.011	.741	0	0	0	0	0	.756
寻找职业同道中人 29. Trying to find people that share my career interests	−.105	.09	.032	.000	−.042	.707	0	0	0	0	0	.613
思考我认为重要的工作的方方面面 30. Thinking about all the aspects of working that are important to me	−.060	.042	.017	−.024	.077	.728	0	0	0	0	0	.618

Note. F1 = career commitment making; F2 = identification with career commitment; F3 = commitment flexibility; F4 = career self-doubt; F5 = in-breadth career exploration; F6 = in-depth career exploration.

^a Extraction method: principal axis factoring. Rotation method: Promax with Kaiser normalization. The factor loadings larger than .30 were bold in the table.

Significant correlations were found across interfactors, except for the interrelationships between the commitment dimensions (i.e., career commitment making and identification with career commitment) and reconsideration dimensions (i.e., commitment flexibility and career self-doubt; Table 2).

Table 2.

Descriptive Statistics, Reliability Coefficients, and Correlations of the Vocational Identity Status Assessment Subscales—Chinese Version in Study 1.

The Dimensions of Vocational Identity	1	2	3	4	5	6	Mean	SD	α
1. Career commitment making	—						13.65	2.67	.78
2. Identification with career commitment	.583**	—					22.40	4.00	.74
3. Commitment flexibility	.033	.075	—				14.00	2.62	.83
4. Career self-doubt	.026	.022	.357**	—			16.20	3.69	.81
5. In-breadth career exploration	.214**	.283**	.350**	.274**	—		17.97	3.03	.77
6. In-depth career exploration	.429**	.528**	.289**	.105*	.496**	—	23.42	3.35	.74
7. Total score	.597**	.695**	.522**	.490**	.684**	.760**	107.64	12.17	.87

Note. N = 443.

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

The Cronbach’s αs of the six subscales based on the factor structure explored in this study indicated acceptable internal consistency reliability, ranging from .74 to .83 (Table 2). Given these initial indications of a favorable and solid scale, the translation of Items 5, 7, and 8 was revised. A further confirmation of the scale structure was sought in Study 2.

Study 2

With the revised VISA–Chinese Version, the aim of this second study was to assess the structure of the Chinese version with CFA. The measurement invariance of VISA by age groups was also evaluated. Convergent validity was evaluated by examining the correlations between vocational identity and validated indicators (i.e., career adaptability and career exploration and talent development self-efficacy).

Participants

Approximately 1,250 students from four technical colleges in four cities participated in this survey, and 1,202 participants finished the VISA–Chinese Version (responding rate: 96%; age range = 15–25 years; mean = 18.4 years; SD = 2.4 years). Data were collected from Shenzhen (46.3%), Guangzhou (21.5%), Zhuhai (17.3%), and Zhongshan (14.9%), all in Guangdong Province, China. Of the respondents, 76.9% completed the pencil-and-paper form of the investigation while the rest finished the online version. The sample was predominantly male (56.3%). In regard to their program types, 3.9% were from the program for junior skilled workers, 86.6% were from the program for senior skilled workers, and 7.6% were from the program for probational technicians (with data missing from 1.9%). The students had diverse majors including marketing, biology, gardening, automatic control, mold design, numerical control, electrical engineering, and vehicle maintenance technology.

Among all of the participants, 495 individuals (aged 15–23; mean age = 18.7 years, SD = 1.7) also completed questions in the Career Adapt-Ability Scale (CAAS), Career Exploration Self-Efficacy subscale (CESS), and Talent Development Self-Efficacy subscale (TDSS; see the Measures section for detailed information). These participants came from three technical colleges in three representative cities: Shenzhen (37%), Guangzhou (37%), and Zhuhai (26%). This sample was predominantly male (61%), with 4.8% from the program for junior skilled workers, 87.3% from the program for senior skilled workers, and 5.1% from the program for probationary technicians (data missing for 2.8%).

In sum, data from the entire sample (N = 1,202) were used for analyses of CFA and measurement invariance. The data of the nested sample (N = 495) were used for evaluation of the convergent validity.

Measures

Vocational identity

This construct was assessed with the 30-item VISA–Chinese Version (Porfeli et al., 2011). Items 5, 7, and 8 were retranslated according to the psychometric properties from Study 1. The instrument assesses individual’s vocational identity in three dimensions: career commitment, career exploration, and career reconsideration. Each dimension has two subdimensions. The commitment dimension includes career commitment making (4 items) and identification with career commitment (6 items), the exploration dimension includes in-breadth career exploration (5 items) and in-depth career exploration (6 items), and the reconsideration dimension includes Commitment flexibility (4 items) and career self-doubt (5 items). The instrument uses a 5-point Likert-type scale (5 = strongly disagree; 1 = strongly agree), with higher total scale or subscale scores indicating greater career commitment, exploration, and reconsideration. The Cronbach’s αs in this study ranged from .70 to .82.

Career adaptability

The CAAS–Short Chinese Version (CAAS–Short Version; Maggiori, Rossier, & Savickas, 2017; Savickas & Porfeli, 2012; Yuen & Yau, 2015) was used to measure individuals’ career adaptability in four dimensions (career concern, career control, career curiosity, and career confidence) with 3 items in each dimension. This short version has already been proven to produce reliability and validity similar to the original longer version (Maggiori et al., 2017). It uses a 5-point Likert-type scale (5 = strongest; 1 = not strong), where higher scores indicate greater psychosocial resources. The Cronbach’s αs for the total scale were .94 for the German version and .92 for the French version (Maggiori et al., 2017). The α of the total scale was .92 in this study.

As this short version was being used for the first time in a Chinese context, the factor construct was assessed with the CFA in a sample of 939 technical college students. The result of four-factor model indicated an acceptable model fit, with the comparative fit index (CFI) =.927, the standardized root mean square residual (SRMR) = .040, and the root mean square error of approximation (RMSEA) =.097 (Marsh, Hau, & Wen, 2004).

Career and talent development self-efficacy

“Career exploration self-efficacy” and “talent development self-efficacy” were measured using the CESS (6 items) and TDSS (6 items) of the Career and Talent Development Self-Efficacy Scale (Fan,Hao, & Yuen, 2013; Yuen, Gysbers, Chan, Lau, & Shea, 2010). Specifically, CESS was used to measure student competencies related to the exploration of career paths, goals, and relationships between career path and study life, and TDSS was used to measure student capabilities related to academic subjects and extracurricular activities. It used a 6-point Likert-type subscale (1 = extremely lacking in confidence to 6 = extremely confident) that was first developed in Hong Kong, where higher subscale scores represented greater career exploration and talent development self-efficacy. The internal consistencies for these two subscales were .84 and .84 in this study.

Procedure

The procedure was the same as that in Study 1, except that there was an additional type of survey method used. Students participated in either the pencil-and-paper format or the online survey. For the online survey, students were asked to open a specified web page on their smartphones. The authors attempted to minimize any potential confounding effects of the two survey methods through controlling the survey environment: Both surveys were collected during class periods by their teachers. The assessments were completed within 20 min.

Results

Preliminary analysis

The robust maximum likelihood (ML) estimation method was used to assess the goodness of fit of the VISA–Chinese Version because of the normality of the data (Finney & DiStefano, 2006): The skewness of all of the items ranged from .079 to .873, and the kurtosis ranged from 0.016 to 1.774, indicating that the data were normally distributed because the absolute values of skewness were less than 1, and those of kurtosis were less than 7 (Byrne, 1998). The full information ML method was used to address any absences. The missing rates ranged from .1% to .7%.

Several different models were compared to select the optimal one. The following structural equation modeling fit indices (including their cutoff values) for each model are reported here: (a) CFI and Tucker–Lewis index (TLI; >.90), (b) RMSEA (<.10), and (c) the SRMR (<.08; Hu & Bentler, 1999; Marsh et al., 2004; Quintana & Maxwell, 1999). The Akaike information criterion (AIC) and Bayesian information criterion (BIC) values were also reported to compare nonnested models.

CFA

Two CFA models were evaluated. Model 1 followed the original structure of the VISA (Porfeli et al., 2011), including 5 items in each factor: career commitment making (items 1–5), identification with career commitment (items 6–10), commitment flexibility (items 11–15), career self-doubt (items 16–20), in-breadth career exploration (items 21–25), and in-depth career exploration (items 26–30). The result for Model 1 indicated a poor model fit, χ²(390) = 1,644; p < .001; CFI = .890; TLI = .877; RMSEA = .052; SRMS = .057; AIC = 82,102.374; BIC = 82,637.007.

Model 2 followed the structure explored in Study 1, in which Items 5 and 15 loaded instead on dimensions of identification with career commitment and in-depth career exploration, respectively. This model fit was adequate, χ²(390) = 1,473; p < .001; CFI = .905; TLI = .894; RMSEA = .048; SRMS = .045; AIC = 81,930.997; BIC = 82,465.630. Regarding the criteria of goodness of fit for the nonnested models, increases of more than 10 units in the AIC and BIC values suggest a lack of empirical support (Burnham & Anderson, 2004). Here, the ΔAIC and ΔBIC (both equal to 171.377) significantly decreased, indicating that Model 2 is a better model (see Table 1 for the factor loading of CFA), so the measurement invariance based on it was examined.

Measurement invariance

The measurement invariance of the VISA–Chinese Version across age groups was assessed by comparing four nested models step-by-step (i.e., the correlational model and models examining the configural invariance, metric invariance, and scalar invariance). The participants were separated into two groups: those 18 years of age or under and those older than 18 years. Two criteria for measurement invariance are used to assess the fit indexes of two nested models: (a) The overall model fit should be acceptable (Little, 1997) and (b) a value of ΔCFI between the two nested models smaller than or equal to −.01 indicates an existing invariance (i.e., the null hypothesis of invariance should not be rejected; Cheung & Rensvold, 2002). The results in Table 3 showed that the ΔCFIs (ranging from .000 to −.003) between the two nested models were smaller than −.01, indicating that configural, metric, scalar, and error invariances existed in the VISA–Chinese Version model across ages. These results suggest that all of the items in this scale were interpreted, conceptualized, and/or responded to in the same way by technical education students of both age groups.

Table 3.

Fit Indices for the Age Multigroup Confirmatory Factor Model of VISA.

Model	χ²	df	CFI	TLI	RMSEA	SRMR	ΔCFI	ΔRMSEA
Configural model^a	1,966	780	.896	.884	.050	.051
Metric model	1,993	804	.896	.887	.050	.053	.000^b	.000^b
Scale model	2,015	828	.896	.891	.049	.053	.000^b	.001^b
Error model	2,084	858	.893	.891	.049	.057	.003^b	.000^b

Note. N = 1,148. CFI = comparative fit index; RMSEA = root mean square error of approximation; SRMR = standardized root mean square residual; TLI = Tucker–Lewis index; VISA = Vocational Identity Status Assessment.

^a N of combined age-group = 1,202, with N of students aging ≤18 sample = 604 (50.2%) and N of students aging > 18 = 598 (49.8%). ^bΔCFI and ΔRMSEA less than the cutoffs (≥−.01 and .05, respectively) suggested by Cheung and Rensvold (2002).

Convergent validity

To offer initial evidence for the convergent validity of the VISA–Chinese Version, we provided associations across these subscale scores with the validation scale/subscale scores, namely, the career adaptability score, and talent development and career exploration self-efficacy scores. The correlation coefficients indicated a consistent pattern of links across VISA subscales and the validation scales; four vocational identity subscale scores (i.e., career commitment making, identification with career commitment, and in-breadth and in-depth career exploration) showed positive and statistical significance with all of the validated scale scores/subscale scores (p < .01), while career self-doubt had significantly negative correlations. Commitment flexibility however had no significant associations with all the scores of the career development indicators (Table 4).

Table 4.

Descriptive Statistics, Reliability Coefficients, and Correlations of the Vocational Identity Status Assessment (VISA) and Validated Indicators in Study 2.

The Dimensions of Vocational Identity	1	2	3	4	5	6	7	8	9	10	11	12	13	Mean	SD	α
1. CCM	—													12.67	2.31	.71
2. ICC	.387**	—												17.58	3.28	.82
3. CF	.010	.010	—											13.58	2.46	.79
4. CSD	−.213**	−.134**	.221**	—										15.47	3.84	.80
5. IBCE	.146**	.212**	.138**	.082	—									17.58	2.85	.72
6. IDCE	.256**	.424**	.163**	−.024	.447**	—								22.33	3.34	.80
7. Total VISA	.421**	.592**	.447**	.374**	.626**	.717**	—							99.22	9.63	.81
8. Concern	.355**	.348**	.029	−.206**	.210**	.305**	.297**	—						9.36	2.76	.75
9. Control	.277**	.273**	.036	−.173**	.193**	.271**	.250**	.671**	—					10.39	2.47	.73
10. Curiosity	.277**	.304**	.035	−.173**	.218**	.322**	.286**	.714**	.727**	—				10.02	2.49	.72
11. Confidence	.263**	.336**	.014	−.159**	.237**	.342**	.307**	.680**	.715**	.776**	—			10.26	2.56	.78
12. Career adaptability	.332**	.357**	.032	−.201**	.242**	.350**	.322**	.871**	.875**	.905**	.894**	—		40.02	9.11	.92
13. TD-SE	.296**	.368**	.019	−.235**	.250**	.428**	.330**	.469**	.434**	.457**	.486**	.521**	—	26.76	4.76	.84
14. CE-SE	.287**	.406**	−.040	−.294**	.270**	.444**	.313**	.496**	.443**	.464**	.472**	.530**	.879**	26.86	5.10	.84

Note. N = 495. CCM = career commitment making; CE-SE = career exploration self-efficacy; CF = commitment flexibility; CSD = career self-doubt; IBCE = in-breadth career exploration; IDCE = in-depth career exploration; ICC = identification with career commitment; TD-SE = talent development self-efficacy.

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

Among the scores of the six identity dimensions, identification with career commitment and in-depth exploration had the strongest and positive correlations with all of the total scores of each career-related indicator (i.e., career adaptability, career exploration, and talent development self-efficacy, with correlation coefficients ranging from .350 to .444). In addition, the identification with career commitment had stronger correlations with career and talent development self-efficacy and weaker associations with career adaptability than career commitment making did (Table 4).

Discussion and Implications

This research assessed the psychometric properties and factor structure of the VISA–Chinese Version. This version had undergone rigorous translation and back-translation processes and is concise and understandable to Chinese adolescents and young adults. The results reported here support the view that the revised 30-item VISA–Chinese Version has sound internal consistency (reliability) and convergent validity. The six dimensions from the original (Porfeli et al., 2011) were confirmed in this population and the three types of vocational identity status (career commitment, exploration, and reconsideration) from each dimension. There are three subtypes: The career commitment dimension consists of making commitments (4 items) and career commitment identification (6 items), the career exploration dimension consists of in-depth (6 items) and in-breadth (5 items) career exploration, and the career reconsideration dimension consists of commitment flexibility (4 items) and career self-doubt (5 items). The correlation between the two subfactors within a single dimension is higher than the correlations across the subfactors outside the dimensions, which is consistent with findings from the American sample (Porfeli et al., 2011).

Slight differences in the construct of VISA were found between the English and the Chinese versions. Unlike the original, in which each of the six dimensions was interpreted by 5 items, 2 items here are loaded on dimensions that are different from the original. First, Item 5 (“I have invested a lot of energy into preparing for my chosen career”) loaded on the dimension of identification with career commitment in the Chinese sample instead of on career commitment making. This suggests that Chinese technical education students viewed their investment of energy into the preparation for their chosen career as not only making a career decision but also as a connection of the self and career choice (Porfeli et al., 2013). Second, Item 15 (“I need to learn a lot more before I can make a career choice”) loaded on the dimension of in-depth career exploration in the Chinese version rather than on career flexibility in the English version. It was likely that the contents of the 2 items were interpreted differently by students from Western and Eastern cultures. For example, unlike the other 4 items in career commitment making (which measured career decidedness from the cognitive perspective), Item 5 included involvement with the career chosen, which may be perceived as a kind of career attachment of the self and career choice by Chinese participants. If so, how the cultural factors cause the different interpretations may be worthy of further research. Furthermore, the construct of the VISA–Chinese Version is worthy of further confirmation in other Chinese samples.

Measurement invariance for age was identified in this study, which was inconsistent with the results found for the English version. It is likely that there were totally different samples selected in the two studies. In Porfeli et al.’s (2011) study, the two age groups were high school and university students. Hence, it was to some extent difficult to identify whether the lack of scalar invariance in the U.S. sample was influenced by age or by other contextual factors. In this study, even though all of the participants were technical education students, the sample was quite heterogeneous, consisting of students from the program for junior skilled workers, the program for senior skilled workers, and the program for probational technicians. These students were of diverse ages at different stages of enrollment and graduation. Therefore, students who are of the same age but at different learning stages (e.g., freshmen and graduates) may develop different levels of vocational identity because they integrate their idiosyncratic and meaningful contexts into their identities (Syed & McLean, 2016). The contextual interference may be the reason for the scalar invariance across ages in this study. In sum, the measurement invariance of ages in technical education students indicates that the VISA–Chinese Version can be used and interpreted with students across ages (Cheung & Rensvold, 2002).

Evidence also indicates that the VISA–Chinese Version possesses sound convergent validity: Significant correlations were shown across the career commitment and exploration dimension and career adaptability and the career exploration and talent development self-efficacy. In contrast, commitment flexibility had no significant links to these validation indicators. These results were consistent with those of Porfeli and Savickas (2012) and Negru-Subtirica, Pop, and Crocetti (2015), who found no significant (or only slight) associations among commitment flexibility and career adaptability and its subscales.

Limitations and Future Directions

This study had several limitations. First, the validity and reliability of the VISA–Chinese Version were assessed in this study with technical college students only; thus, the psychometric properties of the instrument should be replicated with other Chinese samples such as high school students and vocational and university students using cross-sectional and longitudinal designs to obtain further evidence of reliability and construct validity. The instrument could also be evaluated in Hong Kong, Macau, and Taiwan, where the Chinese populations have somewhat different characteristics from the population of Mainland China. Second, there is a lack of evidence on the predictive value of the VISA–Chinese Version regarding students’ personal well-being, which is an issue worthy of future exploration. Third, for practical reasons, this study was not able to assess the test–retest reliability of the VISA–Chinese Version. This form of reliability could be assessed in a future study.

Conclusion

This study provides evidence that the VISA–Chinese Version is a promising tool that has shown evidence of sound internal consistency, convergent validity, and measurement invariance across age groups. Six vocational identity dimensions (e.g., career commitment making, identification of career commitment, in-depth and in-breadth career exploration, career commitment flexibility, and career self-doubt) were identified. This study contributes to the vocational identity literature by showing a sound fit of the VISA–Chinese Version in the Chinese context.

Footnotes

Authors’ Note

This article is based on the PhD research of the first author under the supervision of the second and third authors at Faculty of Education, University of Hong Kong.

Acknowledgments

The authors are grateful to Dr. Baixue Ouyang from Macau for her sharing of the Vocational Identity Status Assessment (VISA)—Macau Chinese Version and to Ms. Jiashu Zhang for her contribution to the translations of the VISA—Chinese version. Gratitude is also due to Yimei Chen, Jiehua Li, Huiyu Lou, Huijuan Li, Zhimei Li, and Yuqing Liu for their kind assistance with the data collection.

Declaration of Conflicting Interests

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

Funding

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

References

Beyers

Luyckx

(2016). Ruminative exploration and reconsideration of commitment as risk factors for suboptimal identity development in adolescence and emerging adulthood. Journal of Adolescence, 47, 169–178. doi:10.1016/j.adolescence.2015.10.018

Burnham

K. P.

Anderson

D. R.

(2004). Multimodel inference understanding AIC and BIC in model selection. Sociological Methods & Research, 33, 261–304. doi:10.1177/0049124104268644

Byrne

B. M.

(1998). Structural equation modeling with LISREL, PRELIS, and SIMPLIS: Basic concepts, applications, and programming. Mahwah, NJ: Lawrence Erlbaum Associates.

Cheung

G. W.

Rensvold

R. B.

(2002). Evaluating goodness-of-fit indexes for testing measurement invariance. Structural Equation Modeling, 9, 233–255. doi:eproxy2.lib.hku.hk/10.1207/S15328007SEM0902_5

Crocetti

Rubini

Luyckx

Meeus

(2008a). Identity formation in early and middle adolescents from various ethnic groups: From three dimensions to five statuses. Journal of Youth and Adolescence, 37, 983–996. doi:10.1007/s10964-007-9222-2

Crocetti

Rubini

Meeus

(2008b). Capturing the dynamics of identity formation in various ethnic groups: Development and validation of a three-dimensional model. Journal of Adolescence, 31, 207–222. doi:10.1016/j.adolescence.2007.09.002

Erikson

E. H.

(1959). Identity and the life cycle: Selected papers. Psychological Issues, 1, 1–171.

Erikson

E. H.

(1968). Identity: Youth and crisis. New York, NY: Norton.

Fan

Hao

Yuen

(2013). Psychometric properties of the career and talent development self-efficacy scale when used with Chinese university students. Asian Journal of Counselling, 20, 59–84.

10.

Finney

S. J.

DiStefano

(2006). Nonnormal and categorical data in structural equation modeling. In Hancock

G. R.

Mueller

R. O.

(Eds.), Structural equation modeling: A second course (pp. 269–314). Greenwich, CT: IAP.

11.

Gupta

Chong

S.-H.

Leong

F. T. L.

(2015). Development and validation of the vocational identity measure. Journal of Career Assessment, 23, 79–90. doi:10.1177/1069072714523088

12.

Gushue

G. V.

Clarke

C. P.

Pantzer

K. M.

Scanlan

K. R.

(2006). Self-efficacy, perceptions of barriers, vocational identity, and the career exploration behavior of Latino high school students. The Career Development Quarterly, 54, 307–317.

13.

Hirschi

(2011a). Relation of vocational identity statuses to interest structure among Swiss adolescents. Journal of Career Development, 38, 390–407. doi:10.1177/0894845310378665

14.

Hirschi

(2011b). Vocational identity as a mediator of the relationship between core self-evaluations and life and job satisfaction. Applied Psychology: An International Review, 60, 622–644. doi:10.1111/j.1464-0597.2011.00450.x

15.

Holland

J. J.

Gottfredson

D. C.

Power

P. G.

(1980). Some diagnostic scales for research in decision making and personality: Identity, information, and barriers. Journal of Personality and Social Psychology, 39, 1191–1200.

16.

Holland

J. L.

Johnston

J. A.

Asama

N. F.

(1993). The vocational identity scale: A diagnostic and treatment tool. Journal of Career Assessment, 1, 1–12. doi:10.1177/106907279300100102

17.

Bentler

P. M.

(1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6, 1–31. doi:10.1080/10705519909540118

18.

(2016, December 23). Let more technical graduates have good jobs—The first 5-year plan for technical education in Ministry of Human Resources and Social Security. Chinese Labor and Social Security News. Retrieved from http://www.cettic.gov.cn/zxzx/zhzx/2016-12/23/content_449442.htm

19.

Jin

Watkins

Yuen

(2009). Personality, career decision self-efficacy and commitment to the career choices process among Chinese graduate students. Journal of Vocational Behavior, 74, 47–52. doi:10.1016/j.jvb.2008.10.002

20.

Y. A.

Lee

Kim

W. H.

(2016). Impact of dysfunctional career thoughts on career decision self-efficacy and vocational identity. The Career Development Quarterly, 64, 333–344. doi:eproxy1.lib.hku.hk/10.1002/cdq.12069

21.

Jordaan

J. P.

(1963). Exploratory behavior: The formation of self and occupational concepts. Career development: Self-concept theory (pp. 42–78). Princeton, NJ: College Entrance Examination Board.

22.

Hou

Z. J.

Jia

(2015). The influence of social comparison on career decision-making: Vocational identity as a moderator and regret as a mediator. Journal of Vocational Behavior, 86, 10–19. doi:10.1016/j.jvb.2014.10.003

23.

Lin

(2013). Technical college students were in high demand. Employment and Security, 12, 10–12.

24.

Little

T. D.

(1997). Mean and covariance structures (MACS) analyses of cross-cultural data: Practical and theoretical issues. Multivariate Behavioral Research, 32, 53–76. doi:eproxy2.lib.hku.hk/10.1207/s15327906mbr3201_3

25.

Liu

(2014). Research on Technical School Students’ Professional Accomplishment (Master’s thesis). Lanzhou University, Lanzhou, China.

26.

Luyckx

Goosens

Soenens

Beyers

Vansteenkiste

(2005). Identity statuses based on 4 rather than 2 identity dimensions: Extending and refining Marcia’s paradigm. Journal of Youth and Adolescence, 34, 605–618. doi:10.1007/s10964-005-8949-x

27.

Maggiori

Rossier

Savickas

M. L.

(2017). Career adapt-abilities scale–short form (CAAS-SF) construction and validation. Journal of Career Assessment, 25, 312–325. doi:10.1177/1069072714565856

28.

Marcia

J. E.

(1966). Development and validation of ego-identity status. Journal of Personality and Social Psychology, 3, 551–558.

29.

Marcia

J. E.

(1980). Identity in adolescence. In Adelson

(Ed.), Handbook of adolescent psychology (pp. 159–186). New York, NY: Wiley.

30.

Marsh

H. W.

Hau

K. T.

Wen

(2004). In search of golden rules: Comment on hypothesis-testing approaches to setting cutoff values for fit indexes and dangers in overgeneralizing Hu and Bentler’s (1999) findings. Structural Equation Modeling, 11, 320–341. doi:10.1207/s15328007sem1103_2

31.

Meeus

(2011). The study of adolescent identity formation 2000–2010. A review of longitudinal and narrative research. Journal of Research on Adolescence, 21, 75–94. doi:10.1111/j.1532-7795.2010.00716.x

32.

Ministry of Human Resource and Social Security. (2016). The “13th Five-Year Plan for Technical Education” from the Ministry of Human Resources and Social Security of China. Beijing, China: China Labour and Social Security Publishing House.

33.

Nauta

M. M.

(2007). Career interests, self-efficacy, and personality as antecedents of career exploration. Journal of Career Assessment, 15, 162–180. doi:10.1177/1069072706298018

34.

Nauta

M. M.

Kahn

J. H.

(2007). Identity status, consistency and differentiation of interests, and career decision self-efficacy. Journal of Career Assessment, 15, 55–65. doi:10.1177/1069072705283786

35.

Negru-Subtirica

Pop

E. I.

Crocetti

(2015). Developmental trajectories and reciprocal associations between career adaptability and vocational identity: A three-wave longitudinal study with adolescents. Journal of Vocational Behavior, 88, 131–142. doi:10.1016/j.jvb.2015.03.004

36.

Outline of the National Medium- and Long-Term Talent Development Plan (2010–2020). (2010, June 6). Xinhua News Agency. Retrieved from http://www.gov.cn/jrzg/2010-06/06/content_1621777.htm

37.

Ouyang

Jin

S. R.

Tien

H. L. S.

(2016). Vocational identity formation of college students in Macau. The Career Development Quarterly, 64, 244–258. doi:10.1002/cdq.12058

38.

Ouyang

B. X.

Wang

Jin

S. R.

(2014, May 19–21). Structure of multi-dimensional vocational identity status assessments on mainland China and Macau college students. Paper presented at the 2014 Asian Pacific Career Development Association Conference, Waikiki, Hawaii. Retrieved from http://www.asiapacificcda.org/2014-Conference-Presentations

39.

Pop

E. I.

Negru-Subtirica

Crocetti

Opre

Meeus

(2016). On the interplay between academic achievement and educational identity: A longitudinal study. Journal of Adolescence, 47, 135–144. doi:10.1016/j.adolescence.2015.11.004

40.

Porfeli

E. J.

Lee

Vondracek

F. W.

(2013). Identity development and careers in adolescents and emerging adults: Content, process, and structure. In Savickas

M. L.

Walsh

W. B.

Hartung

P. J.

(Eds.), Handbook of vocational psychology (4th ed., pp. 133–154). New York, NY: Routledge.

41.

Porfeli

E. J.

Lee

Vondracek

F. W.

Weigold

I. K.

(2011). A multi-dimensional measure of vocational identity status. Journal of Adolescence, 34, 853–871. doi:10.1016/j.adolescence.2011.02.001

42.

Porfeli

E. J.

Savickas

M. L.

(2012). Career adapt-abilities scale-USA form: Psychometric properties and relation to vocational identity. Journal of Vocational Behavior, 80, 748–753. doi:10.1016/j.jvb.2012.01.009

43.

Quintana

S. M.

Maxwell

S. E.

(1999). Implications of recent developments in structural equation modeling for counseling psychology. Counseling Psychologist, 27, 485–527.

44.

Savickas

M. L.

Porfeli

E. J.

(2012). Career adapt-abilities scale: Construction, reliability, and measurement equivalence across 13 countries. Journal of Vocational Behavior, 80, 661–673. doi:10.1016/j.jvb.2012.01.011

45.

Sestito

L. A.

Sica

L. S.

Ragozini

Porfeli

Weisblat

Palma

T. D.

(2015). Vocational and overall identity: A person-centered approach in Italian university students. Journal of Vocational Behavior, 91, 157–169. doi:10.1016/j.jvb.2015.10.001

46.

Shih

S. F.

Brown

(2000). Taiwanese international students: Acculturation level and vocational identity. Journal of Career Development, 27, 35–47.

47.

Skorikov

Vondracek

F. W.

(1998). Vocational identity development: Its relationship to other identity domains and to overall identity development. Journal of Career Assessment, 6, 13–35. doi:10.1177/106907279800600102

48.

Syed

McLean

K. C.

(2016). Understanding identity integration: Theoretical, methodological, and applied issues. Journal of Adolescence, 47, 109–118. doi:10.1016/j.adolescence.2015.09.005

49.

Tabachnick

B. G.

Fidell

L. S.

(2001). Using multivariate statistics (4th ed.). New York, NY: Harper & Row.

50.

Vondracek

F. W.

Skorikov

V. B.

(1997). Leisure, school, and work activity preferences and their role in vocational identity development. The Career Development Quarterly, 45, 322–340. doi:10.1002/j.2161-0045.1997.tb00537.x

51.

Worthington

R. L.

Whittaker

T. A.

(2006). Scale development research: A content analysis and recommendations for best practices. The Counseling Psychologist, 34, 806–838. doi:10.1177/0011000006288127

52.

(2014). A research on technical school students’ self-consistency. Contemporary Vocational Education, 4, 73–76.

53.

Yuen

Gysbers

N. C.

Chan

R. M.

Lau

P. S.

Shea

P. M.

(2010). Talent development, work habits, and career exploration of Chinese middle-school adolescents: Development of the career and talent development self-efficacy scale. High Ability Studies, 21, 47–62. doi:10.1080/13598139.2010.488089

54.

Yuen

Yau

(2015). Relation of career adaptability to meaning in life and connectedness among adolescents in Hong Kong. Journal of Vocational Behavior, 91, 147–156. doi:10.1016/j.jvb.2015.10.003