Career Exploration and Decision-Making Learning Experiences (CEDLE) Scales: Validation Among Chinese Vocational College Students

Abstract

This research aimed to conduct a validation study of a Chinese version of the Career Exploration and Decision-Making Learning Experiences (CEDLE) scales among 2,372 Chinese vocational college students. In Study 1, an exploratory factor analysis was conducted to evaluate the factor structure of the CEDLE among 625 samples. We obtained both a four-factor model and a five-factor model. Study 2 examined the structure of the CEDLE among 1,747 students, and the results demonstrate the superiority of the five-factor model. A multigroup confirmatory factor analysis supported the measurement invariance for the gender groups. The Chinese version of the CEDLE had excellent reliability values from .80 to .88, and the findings demonstrated that the Chinese CEDLE was a valid and reliable measure of self-efficacy-related learning experiences. This study contributes to the literature on prior learning experiences regarding students’ career behavior by providing evidence of the applicability of the CEDLE-Chinese version.

Keywords

learning experiences self-efficacy social support personality traits Chinese vocational college students

In the world of work, career decision making is a common and challenging issue (Kulcsár et al., 2020), and career decisions are among the most important decisions people make in their lifetime (Amir et al., 2008; Bimrose & Mulvey, 2015). Most students are incapable of making decisions for their future careers when they study in college or university (Pordelan et al., 2020). Research has demonstrated that many students have difficulties in career decision making and are unconfident in deciding their first career (Jin et al., 2009). Amir et al. (2008) noted that these difficulties are the most prevalent vocational problems for young adults causing them to avoid or stop the process or make a less optimal career decision.

As today’s youth face more uncertainties in the labor market because of the COVID-19 pandemic, arguably, they need more assistance in their career development. One potentially valuable approach is to improve their career decision self-efficacy (CDSE), as it is central to mediating career-related tasks (Betz & Luzzo, 1996). CDSE is a person’s beliefs about their ability to complete tasks and achieve goals related to career decision making (Taylor & Betz, 1983). Young adults with high CDSE participate in career decision processes more actively and demonstrate perseverance in the face of difficulties, while those with low CDSE doubt their ability to accomplish a task or achieve a goal and are thus less likely to pursue it or stop the pursuit when difficulties arise (Bike, 2013). Considering the important role of CDSE, it is a core task to improve college students’ CDSE to mitigate their career decision-making difficulties (He et al., 2020).

Four sources of efficacy information engender and increase career self-efficacy (Bandura, 1986). Bandura (1997) characterized learning experiences as self-efficacy sources. Researchers have demonstrated that learning experiences facilitate CDSE in career development (e.g., Ireland & Lent, 2018; Lent et al., 2017, 1994) and may reflect decisional skills and are conducive to career decision making along with self-efficacy (Lent et al., 2017).

Learning experiences are mastery experiences, vicarious experiences, verbal persuasions, and physiological/affective states (Bandura, 1997; Lent et al., 1994). Mastery experiences include successful and unsuccessful experiences in which success usually increases self-efficacy, while repeated failures are on the contrary. The enactive mastery experience is assumed to have the most significant influence on the efficacy information (Bandura, 1997). Vicarious experiences are gained by observing the attainments of people’s models, which is effective in raising self-efficacy (Bandura, 1997). Verbal persuasion improves efficacy belief through the evaluative feedback to a person’s capabilities such as social encouragement or discouragement. Physiological/affective states include physical statuses such as pain, aches, and positive or negative emotion.

Lent et al. (1994) prioritized learning experiences by modeling the development of basic career interests over time. Adapted from this model, Lent and Brown (2013) developed the model of career self-management (CSM). However, considering the lack of measures for learning experiences, Bike (2013) developed the Career Decision Learning Experiences scale (CDLE). She devised a five-factor scale by dividing the affective source scale into positive and negative emotional arousal subscales (Bike, 2013). However, the five sources only explained a moderate portion of the variances in CDSE, as some items seemed to be more related to other aspects of career development (e.g., the job interview process) instead of career exploration (Lent et al., 2017). The emotional arousal items were described in the present tense, thus ignoring the fact that they were past learning experiences (Lent et al., 2017).

Recent studies on prior learning experience have been designed to test the predictions of the CSM model (Ireland & Lent, 2018; Lent et al., 2017), and in this vein, Lent et al. (2017) developed the CEDLE scales. Their instrument also assesses five factors including personal mastery, verbal persuasion, vicarious learning, positive emotion, and negative emotion. Lent et al. (2017) evaluated psychometric properties of the CEDLE scales among 324 undergraduates in America. Ireland and Lent (2018) reevaluated the CEDLE scales among 450 undergraduates at the same university. After a twice validation, the English version of the CEDLE proved to have sound psychometric properties and represent a promising measure for the antecedent factors of the CDSE (Ireland & Lent, 2018; Lent et al., 2017).

Career-related processes in collectivistic cultures are different from the processes in individualist cultures (Vuyk et al., 2020). While validation was conducted twice for mainstream Western cultures (Ireland & Lent, 2018; Lent et al., 2017), Chinese cultures are more collectivistic. Chinese culture emphasizes the family as the center of life, and individuals tend to be more dependent, less autonomous, and more conforming and obedient to authority (Yang, 2006). Mau (2000) found that compared with American students, Taiwanese students are more dependent on family in career decision making, and these collective decisions are socially conforming and in line with their family’s expectations. Career development in China also seems to be related to “Guanxi,” person-to-person, and firm-to-firm social networks and relationships that are quite intricate and pervasive (Li & Wright, 2000). The meaning of self refers mainly to those social relationships (e.g., family, workplace, and classroom) in which the self is the participating part (Ames et al., 1994). These cultural discrepancies can lead to different CDSE-related learning experiences. Therefore, new insights can be found regarding learning experience by studying the reliability and validity of the CEDLE within a collectivist culture.

In China, vocational colleges are an important part of the higher education systems, providing specialized instructions to train skilled personnel. In 2019, there were more than 12.8 million registered students in 1,423 vocational colleges, accounting for 42.2% of all students in higher education (Ministry of Education, 2020). However, problems in career decision making and the falling employment rate of graduates in higher education have been a concern, and graduates face increased difficulty in finding a job because of increased enrollment in higher education institutions (Ye, 2014). Especially for vocational college students, due to social, historical, and cultural reasons, there is a view that vocational college students have poorer academic performance (Liu, 2014). They are more likely to struggle with career development and have more restricted career paths, fewer promotion opportunities, and lower salaries and social status (Zhang et al., 2019). Under the significant impact of the COVID-19 pandemic, their employment became more difficult (Wen et al., 2020).

To assist their career exploration and decision making, many studies have focused on the CDSE and the various interventions to foster CDSE among vocational college students (Dong, 2015; e.g., A. F. Wang, 2018). However, there is no existing measure for the formative factors that foster CDSE for vocational college students. Liu (2017) developed a career learning experiences questionnaire for Chinese high school students based on Social Cognitive Career Theory, but the results cannot be generalized to college students. In another study, M. Wang (2019) surveyed 720 Chinese undergraduate students to investigate the influence of career learning experiences on their career decision-making difficulties. This study also used the CEDLE scales (Lent et al., 2017). However, the reliability and validity of this CEDLE-Chinese scale were questionable. First, the translation of some items was inaccurate due to misunderstandings of the context of specific sentences. For example, the Chinese item “somebody breaks down” cannot express the original meaning of the English item “overwhelmed,” which conveys the sense that attempted goals or outcomes have not been achieved. Second, the research only carried out an item discrimination analysis and lacked sufficient explanation of why they proceeded directly to using a Confirmatory Factor Analysis (CFA) that adhered to the original scale’s five-factor model. Therefore, it is difficult to verify the applicability of the CEDLE-Chinese version among vocational college students. Validation of the CEDLE scales is needed in this area to advance knowledge and to enrich practice.

The main objective of this study was to add to previous findings by verifying the factor structure and convergent validity of the CEDLE with a sample of Chinese vocational college students. Although Lent et al. (2017) demonstrated the superiority of the five-factor model, the factor structure of the CEDLE has not been tested in Chinese samples. We examined its factor structure using the Exploratory Factor Analysis (EFA) and obtained two kinds of factor structures. We then compared them using the CFA.

The study obtained the convergent validity of the CEDLE by assessing the relationships among learning experiences and CDSE, Big Five personality traits, and social support. First, studies have demonstrated that CDSE is positively associated with four of the five learning experiences, while it is negatively associated with negative emotions (Ireland & Lent, 2018; Lent et al., 2017). Therefore, this study hypothesized that the five types of learning experiences would have similar relationships with CDSE. Second, in agreement with Ireland and Lent (2018), it is hypothesized in this study that there will be significant relationships between CEDLE and conscientiousness, extraversion, and social support. Third, studies have indicated that neuroticism was negatively associated with four of the five learning experiences while being positively associated with negative emotions (Ireland & Lent, 2018). Thus, we hypothesized that the five sources would have similar relationships with neuroticism.

Additionally, the measurement invariance of the CEDLE was conducted across gender groups. No studies have been found regarding gender differences. To better understand CDSE-related learning experiences, it is important to determine whether items used in CEDLE scales have the same meaning for respondents from different groups. Another aim of this study was to report the Cronbach’s α of each subscale to provide evidence of reliability for the Chinese version of the CEDLE.

Method

Study 1

Participants

There were 799 participants who were invited from several vocational colleges in Shanghai (N = 32.5%), Jiangsu Province (N = 20.4%), and Zhejiang Province (N = 47.1%). The main colleges were Jiaxing Vocational and Technical College (N = 35%), Shanghai Jiguang Vocational College (N = 16%), and Zhejiang Vocational College of Mechanical and Electrical Engineering (N = 10%).

From this group, 625 completed surveys were received, and the response rate was approximately 78% (age range = 18–29 years; M = 19.7 years; SD = 1.2 years). The sample included 51.5% male students (n = 322) and 48.5% female students (n = 303). There were 204 first-year students (32.6%), 282 sophomores (45.1%), and 139 juniors (22.2%). Their majors are construction, preschool education, and international business, among others.

Measure

The CEDLE scale (Lent et al., 2017), which consists of 20 items, was used to assess five dimensions: personal mastery, verbal persuasion, vicarious learning, positive emotion, and negative emotion. Each subdimension includes four items. A sample item is “I have been good at putting my career-related decisions into action.” CEDLE uses a 5-point Likert-type scale with responses ranging from 5 (strongly agree) to 1 (strongly disagree). The Cronbach’s α for the CEDLE subscales in this study ranged from .83 to .92.

The CEDLE-English version was translated into Chinese for this study. An American professor, a native Chinese speaker fluent in English with research experience in career education, helped translate the English scales into Chinese. A Taiwanese professor, a Chinese expert in vocational education, possessing fluency in English, and a doctoral degree from a leading American university, helped modify the Chinese version again, making each item equally relevant to the Chinese context. We invited a professional translator majoring in English to assist us with the back-translation. Following this, a native English speaker compared the English and the back-translated version. There was only one item that did not retain the original meaning of the English scales. Hence, we had further discussions with the author who developed the instrument. The author suggested that the meaning of “overwhelmed” was close to “defeated” instead of “break down.” We revised this item and then used the Chinese version of the CEDLE with 140 vocational college students who were not part of the main study. A sample item is “The way I have approached important career-related decisions has worked well for me in the past.” From the feedback, we added more explanations to the item “overwhelmed/defeated,” which was more understandable for the vocational college students according to our interviews.

Procedure

Ethics approval was obtained. For the study, we selected those colleges that have been cooperating with our institute. We mainly chose first-year students and sophomores as our samples because juniors look for internships in their final year. We did not offer any reward to these participants. All students were verbally assured that their participation would be voluntary, and they were free to withdraw at any time. College teachers sent the questionnaire to students, and they completed it online during their class period. Each assessment was completed within 7 min.

Results

The EFA was conducted according to Worthington and Whittaker (2006). If the set had missing values, it was deleted. The Kaiser–Meyer–Olkin value of .93 and Bartlett’s test of sphericity approximate, χ² (190) = 7,642.3; p < .001, demonstrated that the data were good for the factor analysis (Tabachnick & Fidell, 2001).

The factors were extracted based on eigenvalues; the eigenvalues greater than 1, and the scree plot indicated four factors that accounted for 68.7% 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 result demonstrated a four-factor solution, in which the items designed to reflect vicarious learning and personal mastery were loaded on a single dimension.

We followed the method developed by Lent et al. (2017) in which they also obtained an eight-item factor that they separated into two types (four mastery and four persuasion items) by retaining only the top four loading items of each factor. They trimmed these factors because they thought it would be useful to retain the conceptual essence of Bandura’s (1997) primary sources while developing reasonably short scales for future research purposes (Lent et al., 2017). Therefore, we decided to trim the eight-item factor into two types (four vicarious learning and personal mastery items; Table 1) by fixing the number of factors through the extraction. We obtained a five-factor solution, which was more plausible and explained 72.7% of the total variance.

Table 1.

Career Exploration and Decision-Making Learning Experiences—Chinese Version: Factor Loadings From the EFA in Study 1 and the CFA in Study 2.

	Study 1					Study 2
Variables	F1	F2	F3	F4	F5	F1	F2	F3	F4	F5
1. Important others have let me know that I have been good at evaluating the choice options that would best meet my needs in making career-related decisions. 我的家人、朋友或老师曾经肯定过我选择最能满足我需求的生涯决策的能力.。	.835	−.115	.228	.198	.213	.869	0	0	0	0
2. Important others have let me know I do a good job of considering the positives and negatives of different choice options when making career-related decisions. 我的家人、朋友或老师曾经肯定过我考虑不同生涯选择优缺点的能力.。	.817	−.069	.263	.150	.228	.826	0	0	0	0
3. Important others have let me know that I am good at managing challenges that arise when making career-related decisions. 我的家人、朋友或老师曾经肯定过我应对生涯决策中各种困难的能力.。	.773	−.110	.267	.190	.256	.821	0	0	0	0
4. Important others (e.g., family, friends, teachers, mentors) have let me know that I am resourceful when it comes to gathering information needed to make career-related decisions. 我的家人、朋友或老师曾经肯定过我收集生涯决策所需信息的能力.。	.756	−.091	.335	.162	.232	.784	0	0	0	0
5. Nervous 紧张不安	−.069	.881	.012	−.061	−.057	0	.874	0	0	0
6. Afraid 害怕	−.106	.854	−.052	−.013	−.094	0	.841	0	0	0
7. Upset 心烦意乱	.027	.824	−.019	−.097	.016	0	.731	0	0	0
8. Overwhelmed/defeated (because your career options or decisions cannot meet your expected goals or outcomes) 挫败(由于您的职业生涯选择或决策没有达到您的预期目标而产生的)	−.138	.764	−.060	−.062	−.139	0	.735	0	0	0
9. I have role models who are knowledgeable about how their interests and abilities fit different career options. 我有一些学习榜样，他们知道如何将自己的兴趣和能力与生涯选择相结合.。	.272	−.004	.786	.172	.252	0	0	.790	0	0
10. I have observed people I admire who are resourceful at gathering the information they need to make career-related decisions. 我观察过一些我钦佩的人，他们善于收集生涯决策所需的信息.。	.277	.017	.786	.178	.247	0	0	.769	0	0
11. I have role models who have explained to me how they chose an academic major or career path. 我的一些学习榜样向我介绍了他们是如何选择专业或职业道路.。	.436	−.059	.700	.155	.223	0	0	.735	0	0
12. I have role models who are good at making important career decisions. 我有一些善于做生涯决策的学习榜样.。	.263	−.104	.602	.144	.435	0	0	.719	0	0
13. Active 积极主动	.144	−.107	.103	.831	.171	0	0	0	.837	0
14. Determined 意志坚定	.136	−.091	.116	.810	.196	0	0	0	.785	0
15. Excited 激动兴奋	.147	−.014	.064	.795	.083	0	0	0	.717	0
16. Inspired 备受鼓舞	.107	−.045	.186	.699	.036	0	0	0	.674	0
17. The way I have approached important career-related decisions has worked well for me in the past. 我曾经使用过的做生涯决策的方法很有用.。	.184	−.061	.169	.152	.790	0	0	0	0	.675
18. I have done a good job of weighing the positives and negatives of different options when I have had to make career-related decisions. 必须做生涯决策时，我能很好地比较不同选择的优缺点.。	.260	−.060	.214	.159	.768	0	0	0	0	.774
19. I have been good at putting my career-related decisions into action. 我会将职业生涯的决策付诸行动.。	.275	−.140	.348	.157	.667	0	0	0	0	.722
20. I have been resourceful at gathering the information I need to make career-related decisions. 我能收集职业生涯决策所需的信息.。	.225	−.135	.454	.111	.598	0	0	0	0	.689

Note. Study 1: N = 625; Study 2: N = 1,747; F1 = verbal persuasion; F2 = negative emotion; F3 = vicarious learning; F4 = positive emotion; F5 = personal mastery. The factor loadings larger than .30 were bold in the table

Significant correlations were found across inter-factors, except for the interrelationship between positive and negative emotions (Table 2). The Cronbach’s α of five subscales, based on the factor structure explored in this study, demonstrating acceptable internal consistency reliability, ranging from .83 to .92 (Table 2). A further confirmation of the scale structure was sought in Study 2.

Table 2.

Descriptive Statistics, Reliability Coefficients, and Correlations of the Career Exploration and Decision-Making Learning Experiences-Chinese Version in Study 1.

The Dimensions of the CEDLE	1	2	3	4	M	SD	α
Personal mastery					3.65	.14	.84
Vicarious learning	.256***				3.78	.08	.88
Verbal persuasion	.260***	.278***			3.69	.04	.92
Negative emotion	−.104***	−.056**	−.109***		2.23	.12	.86
1. Positive emotion	.161***	.155***	.183***	−.084***	3.05	.19	.83

Note. N = 625.

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

Study 2

Participants

Approximately 1,756 students from three vocational colleges in Zhejiang province participated in the survey, and 1,747 participants completed the CEDLE-Chinese version (response rate: 99.5%, age range = 18–25 years, mean = 19.9 years, SD = 1.0 year). Data were collected from Jinhua Vocational and Technical College (JVTC, 68.6%), Wenzhou Vocational & Technical College (WVTC, 19.3%), and Zhejiang Vocational College of Economics and Trade (ZVCET, 12.1%). There were 1,008 first-year students (57.7%), 681 sophomores (39.0%), and 58 juniors (3.3%). Most respondents were female (63.7%). Their majors were diverse, including preschool education, hospitality management, and e-commerce, among others.

From the participants, 695 individuals (aged 18–25; mean age = 20.1 years, SD = 1.1 years) completed questions using the CDSE Scale, Personality subscale, and Perceived Social Support Scale; 21.2% of them came from JVTC, 48.5% from WVTC, and 30.3% from ZVCET. Their majors were business management, international business, and marketing, among others. A total of 47.7% (n = 332) were first-year students, and 44.5% (n = 309) were sophomores, while only 7.8% (n = 54) were juniors. There were 40.6% males (n = 282).

Measures

The 20-item CEDLE-Chinese version (Lent et al., 2017) was used to assess career exploration and decision-making learning experiences across five dimensions: personal mastery, verbal persuasion, vicarious learning, positive emotion, and negative emotion. Each dimension had four items. A sample item is “I have role models who are good at making important career decisions.” The instrument used a 5-point Likert-type scale (5 = strongly agree, 1 = strongly disagree), with higher subscale scores indicating greater personal mastery, verbal persuasion, vicarious learning, positive emotion, and negative emotion. The Cronbach’s α, in this study, ranged from .81 to .89.

Career decision self-efficacy

CDSE was measured using the Chinese version of the brief decisional self-efficacy (CEDSE-BD, eight items) of the Career Exploration and Decision Self-Efficacy Scale (CEDSE; P. Wang et al., 2018). It was used to measure career exploration and decision-making activities in CSM. The short version was first developed in English and validated against the more established CDSE-SF in predictive equations (Lent et al., 2016). It uses a 5-point Likert-type scale (5 = complete confidence; 1 = no confidence at all), where higher scores indicate greater CDSE. P. Wang et al. (2018) validated the CEDSE in two Chinese universities where it exhibited adequate internal reliability. A sample item is “How much confidence do you have in your ability to identify careers that best use your skills.” The Cronbach’s α for the total scale was .88 in the Chinese version of CEDSE-BD. The α of the total scale was .96 in this sample.

Personality

The brief version of Chinese Big Five Personality traits (CBF-PI-B) with 40 items was used to assess conscientiousness, neuroticism, and extraversion (M. C. Wang et al., 2011). Each dimension had eight items, with the Cronbach’s α ranging from .74 to .85. A sample item is “I often worry about small issues.” The items are rated on a 6-point scale, ranging from 1 (disagree strongly) to 6 (agree strongly). Higher scores on the scale indicated greater conscientiousness, neuroticism, and extraversion. The Cronbach’s α in the current study ranged from .84 to .86.

Social support

The scale by Zimet et al. (1988) was used to measure the overall perceived social support for university students in their career development. It reflected three kinds of social support, namely, from family, friends, and significant others. Students responded to 12 items by using a 5-point Likert-type scale (5 = strongly agree; 1 = strongly disagree). Zhou (2017) translated this scale into Chinese and measured its relationship with career psychological states by providing the Cronbach’s α of .88. In this study, Cronbach’s α was .91. A sample item is “My family is willing to help me make decisions.”

Procedure

Study 2 followed the same procedure as Study 1. This assessment was completed within 15 min. Mplus Version 7.4 was used for CFAs.

Results

Preliminary analysis

Due to the normality of the data, this study took the robust maximum likelihood (ML) estimation method to assess the goodness of fit of the CEDLE-Chinese version (Muthén & Kaplan, 1985). The skewness of all items ranged from −.886 to .504, and the kurtosis ranged from −0.539 to 1.920. It demonstrated that the data were normally distributed because the absolute values of skewness were less than 2, and those of kurtosis were less than 7 (Finney & DiStefano, 2006; West et al., 1995). All items with missing data were deleted in this study.

Two models were compared in this study. Each model reported structural equation modeling fit indices that also contains their cut-off values. The indices were CFI (>.95), the Tucker–Lewis index (TLI; >.90), SRMR (<.08; Hu & Bentler, 1999), and RMSEA (<.10; Steiger, 1990). Additionally, the values of the Akaike information criterion (AIC) and Bayesian information criterion (BIC) were reported as well.

CFA

According to the results of the EFA, there were two different models. One was the four-factor model, including verbal persuasion, vicarious learning, and personal mastery (VLPM loaded on one dimension), negative emotion, and positive emotion. Each of them included four items, except for the VLPM, which had eight items. The second was the five-factor model, including verbal persuasion, vicarious learning, personal mastery, negative emotion, and positive emotion. Each factor had four items.

The results for Model 1 were χ² (164) = 1,189; p < .001; CFI = .944; TLI = .935; RMSEA = .060; SRMR = .037; AIC = 64,746.310; BIC = 65,107.043. The results for Model 2 were χ² (160) = 760; p < .001; CFI = .967; TLI = .961; RMSEA = .046; SRMR = .031; AIC = 64,324.816; BIC = 64,707.412. According to the criteria of goodness of fit for models, Model 2 indicated an acceptable model fit which was superior to the Model 1. Therefore, the measurement invariance based on model 2 was examined.

Measurement invariance

The measurement invariance was an important indicator of group comparisons (e.g., Byrne & Watkins, 2006; Byrne et al., 2009). We assessed the CEDLE-Chinese version across the gender groups. There were two criteria for assessing the fit indexes of the nested models. One was that the fit of the overall model should be acceptable (Little, 1997). Another was that the critical values of ΔCFI and ΔRMSEA were less than the cut-off values (≥ −.01 and .05, respectively; Cheung & Rensvold, 2002), demonstrating that regardless of the group membership, respondents having the same latent factors score would have the same indicator scores (Milfont & Fisher, 2010). In Table 3, the results indicate that ΔCFIs (ranging from .000 to −.005) were smaller than −.01, meaning that there were configural, metric, scalar, and error invariances in the CEDLE-Chinese Version model across the gender groups.

Table 3.

Verifying Measurement Invariance Across Gender Groups.

Model	χ²	df	CFI	TLI	RMSEA	SRMR	ΔCFI	ΔRMSEA
Men versus women
Configural model	665.941	320	.973	.968	.035	.034	—	—
Metric model	700.796	335	.971	.967	.035	.038	.002	.000
Scale model	785.282	355	.966	.964	.037	.043	.005	.002
Error model	810.337	375	.966	.965	.036	.044	.000	.001

Note. N = 1,747. CFI = comparative fit index; TLI = Tucker–Lewis index; RMSEA = root mean square error of approximation; SRMR = standardized root mean square residual.

Convergent validity

To offer additional evidence for the validity of the CEDLE-Chinese version, we conducted correlational analyses to provide associations between the Chinese CEDLE scales and those subscales, namely the CDSE scale, personality subscales, and social support scale. The correlation coefficients in Table 4 illustrate that four CEDLE subscales scores (i.e., personal mastery, vicarious learning, verbal persuasion, and positive emotion) were positively correlated with CDSE, conscientiousness, extraversion, and social support and demonstrated negative significance with neuroticism. Negative emotion, however, demonstrated positive significance with neuroticism and negative significance with other validation scales.

Table 4.

Descriptive Statistics, Reliability Coefficients, and Correlations of the Career Exploration and Decision Learning Experience (CEDLE) and Validated Indicators in Study 2.

The Dimensions of the CEDLE	1	2	3	4	5	6	7	8	9	10	M	SD	α
1. Personal mastery	(.209)										3.54	.14	.80
2. Vicarious learning	.191***	(.268)									3.64	.12	.82
3. Verbal persuasion	.207***	.255***	(.398)								3.50	.05	.88
4. Positive emotion	.120***	.135*	.180***	(.361)							2.97	.18	.81
5. Negative emotion	−.063***	−.066***	−.100***	−.077***	(.419)						2.14	.13	.85
6. Career decisional Self-efficacy	.168***	.181***	.223***	.311***	−.136***	(.585)					3.05	.09	.96
7. Neuroticism	−.090***	−.080***	−.168***	−.150***	.285***	−.209***	(.609)				2.99	.29	.86
8. Conscientiousness	.106***	.100***	.121***	.161***	−.084***	.205***	−.121***	(.221)			4.13	.14	.85
9. Extraversion	.148***	.151***	.215***	.244***	−.108***	.314***	−.213***	.213***	(.731)		3.76	.41	.84
Social support	.085***	.104***	.145***	.092***	−.098***	.137***	−.152***	.105***	.194***	(.330)	3.90	.14	.91

Note. N = 695.

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

Discussion

This study validated the Chinese version of the CEDLE by examining its factor structure, reliability, and convergent validity, finding that this Chinese version appears culturally appropriate to assess CDSE-related learning experience among vocational college students in China’s Jiangsu–Zhejiang–Shanghai district.

The factor structure of the CEDLE-Chinese version was examined with EFA. The EFA results demonstrated that similar to the original study (Ireland & Lent, 2018; Lent et al., 2017), the CEDLE-Chinese version also had a large covariance between the two subscales, revealing that the process of making successful career decisions seems to be somewhat ambiguous (Ireland & Lent, 2018). However, the two subscales that had large covariance were very different. In the English version, all mastery experience and verbal persuasion items had been loaded on a common factor in the preliminary four-factor model (Ireland & Lent, 2018; Lent et al., 2017). In the Chinese version, mastery experience and vicarious learning items were loaded on a single dimension.

Lent et al. (2017) thought that mastery and persuasion were loaded on one factor because they did not allow participants to distinguish between them. Although Ireland and Lent (2018) developed new items to better distinguish between the two learning experiences, they identified the same underlying factor, demonstrating that the large covariance between the two factors was not caused by the measurement artifact. It was more likely that those who had limited experience in the process of making important life decisions depended on the feedback from significant others (e.g., family, friends, and teachers) in determining their objective accomplishments in this field (Ireland & Lent, 2018).

However, the single dimension of mastery experience and vicarious learning items appear to indicate that in China, young adults seem to be less dependent on feedback from significant others. They are more likely to observe role models they admire to gain information and experience in making important career decisions. There are several possible explanations for this result. First, significant others play a crucial role in individuals’ career decision making in the Chinese context, and individuals tend to demonstrate subservience and obedience (H. Xu et al., 2014). It seems that significant others express more their expectations instead of their feedback to help individuals to determine their objective accomplishments in this field. Additionally, most families, teachers, or mentors in China educate children through harsh discipline that restricts their opinions on how well they perform in their domains. For example, Y. Xu et al. (2005) found that the authoritarian parenting style was highly associated with traditional Chinses values that emphasize parental strictness, in which Chinese parents will not demonstrate warmth to children using praise and affection. Another reason may be that Chinese people are taught to be modest and learn from others from a young age, leading to a preference to observe their role models. Modesty is one of the behavior characteristics of the intentional content in Chinese familism (Yang, 2006).

The structure of the CEDLE-Chinese version was determined based on the CFA. The CFA results demonstrated that the five-factor model had an acceptable model fit, which was superior to the four-factor model. This result strongly supports the original factor structure of the CEDLE (Ireland & Lent, 2018; Lent et al., 2017). Additionally, no studies have examined gender invariance in the CEDLE. The configural, metric, and scalar invariances in the measurement invariance indicated that they supported the use of the CEDLE in different gender groups.

The results demonstrate that the CEDLE-Chinese scales have sound convergent validity. They indicate that CDSE, conscientiousness, extraversion, and social support positively associated four of the five learning experiences and negative association with negative emotion. Conversely, neuroticism had a negative relationship with four of the five learning experiences and a positive relationship with the negative emotion. These results were consistent with those of Lent et al. (2017) and Ireland and Lent (2018).

Internal consistency and item-subscale total correlation values were conducted to test the reliability of the CEDLE. The results of the five-factor model demonstrated that the CEDLE had sound reliability. The Cronbach’s α values of subscales, ranging from .80 to .88, were quite similar to the original studies, ranging from .81 to .89 (Lent et al., 2017) and from .80 to .88 (Ireland & Lent, 2018).

In summary, this study is novel for several reasons. First, it is interesting that we found some cultural differences when we obtained the four-factor model. We supported the five-factor model because of the strong theoretical reasons, but we are still curious whether we would obtain the four-factor model as in the original study. The results demonstrate that we obtain the four-factor model, but it is different from the original study, which is probably caused by cultural differences. Second, this study attempts to make up the gap in the existing Chinese literature by confirming the factor structure of the CEDLE as well as testing invariance across gender.

However, this study still has some limitations. First, the samples we chose were mainly in colleges that cooperated with us and were located in developed areas of mainland China. However, China has more than 1,400 vocational colleges in different areas. We are unsure as to what extent our findings can be generalized for other vocational colleges. Further research should be done to see whether these results are replicated among other samples in China. Second, there was a lack of evidence of the predictive values of the CEDLE-Chinese version. Future studies should investigate its predictive reliability. Third, in our findings, mastery and vicarious learning experience had large covariance. Hence, further exploration of the interrelated reasons is needed.

In conclusion, the CEDLE appears to be a promising instrument with sound reliability and validity to measure career exploration and decision-making learning experiences among vocational college students in the Chinese context. The Chinese version of the CEDLE contributes to the area of career counseling by facilitating Chinese studies on CDSE-related learning experiences. For example, it could be more convenient for career counselors to investigate the relationships between CEDLE and CDSE, career outcome expectations, career exploratory goals, and career decidedness. Moreover, it adds to the existing research literature that supports the applicability of the CEDLE with diverse cultural groups. However, further research is required to examine the replicability and psychometric properties of the CEDLE-Chinese version among more diverse samples that have different levels of career development and socioeconomic status and determine its generalizability.

Footnotes

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.

ORCID iD

Yingwen Zhou

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