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The role of thinking styles in career development among Chinese college students.

Thinking styles define individuals' marked preferences in how they learn about or process information. This study considered the function of collecting and processing information in career exploration and decision making, and examined thinking styles as a predictor of career decision-making difficulties (CDMD) and career exploration as a mediator of this relationship. Chinese college students (N = 463) responded to measures of thinking styles, career exploration, and CDMD. Results partially supported the contribution of thinking styles to career exploration and CDMD. Type I styles, characterized as more creativity-generating, positively predicted career exploration and negatively predicted CDMD. Type II styles, characterized as more norm-favoring, positively predicted CDMD. Partial mediation was supported in the link between Type I styles and lack of information through career environment exploration. The benefits of type styles should be highlighted for career guidance and counseling among C Chinese college students and should be validated in different cultural contexts.

Keywords: thinking styles, career exploration, career decision-making difficulties, career development, Chinese college students


Intellectual style--a collective term for such constructs as learning style, cognitive style, decision-making style, and thinking style--refers to one's preferred way of processing information (Zhang & Sternberg, 2005). Some authors have argued that various intellectual styles do not always effectively explain or predict human performance (Tiedemann, 1989). Yet, much empirical evidence still largely supports the idea that intellectual style could significantly contribute to individual differences in human performance in academic and nonacademic contexts (Arm strong, van der Heijden, & Sadler-Smith, 2012; Fan & He, 2012). One of the original intentions for the construct of intellectual style was to increase researchers' ability to predict or explain human behaviors in different fields, including learning and vocation. As argued by Pope (2009), career development involves the creation of a career pattern, a decision-making style, expression of values, and a vocational identity, which are influenced by individual features and contextual factors. Forty years earlier, Witkin, Moore, Goodenough, and Cox (1977) observed that "evidence is beginning to emerge that cognitive styles enter into the process of making career choices" (p. 48).

Collecting and processing career information about self and environment (mostly related to career exploration and decision making) rank among the most important processes in career development and career construction (Parsons, 1909; Stumpf, Colarelli, & Hartman, 1983; Super, 1990), especially for youth (Hartung, 2015). In particular, from the perspective of social cognitive career theory (Lent & Brown, 2013), learning experiences play an important role in an individual's career development. Considering their function in learning (Fan &: He, 2012), intellectual styles might significantly associate with such cardinal constructs as career exploration and career decision making in the process of career development and construction.

Since the 1970s, researchers have empirically examined the contributions of some key intellectual style dimensions--such as Witkin's (1962) field-dependence/independence style, Kolb's (1984) learning style, and Sternberg's (1997) thinking styles--to career development, but these limited works are mainly related to vocational interest (Alvi, Khan, Hussain, & Baig, 1988; Zhang & Fan, 2007). As Holland, Fritzsche, and Powell (1994) pointed out in their study, evidence for relationships between vocational interest and intellectual style is still far from complete in the literature, as are other important units in the career development process.

In fact, except for the relevant research on the relationship between intellectual styles and vocational interests, limited research on intellectual styles exists in the field of career development. Only a few works examined relationships between intellectual styles and other important career constructs, such as career exploration and decision making (Blustein & Phillips, 1988; Cools, Van den Broeck, & Bouckenooghe, 2009). Therefore, this study examined if and how different thinking styles, as one of the broader constructs of intellectual style, might contribute to career exploration and career decision-making processes.

Intellectual Styles, Career Exploration, and Career Decision Making

In a few studies among U.S. college student samples, researchers found that variables related to reflective thinking style might be beneficial in increasing career exploration and decreasing career indecision (Mau, 1995). For example, Blustein and Phillips (1988) reported that individuals who use a thinking-oriented style are likely to engage in exploration of the environment and, to a somewhat lesser extent, the self. In their study, Osipow and Reed (1985) also found that college students with an external style are inclined to have lower levels of career indecision than are individuals with an internal style. In a study from Belgium, Cools et al. (2009) found that people with a higher creating style scored higher on intention for career exploration behavior than did people with a lower creating style.

Recently, several empirical studies were conducted based on the multidimensional profile characterization of the career decision-making process proposed by Gati, Landman, Davidovitch, Asulin-Peretz, and Gadassi (2010), which includes several important intellectual styles, such as analytic versus holistic (Riding & Cheema, 1991) and internal versus external (Sternberg, 1997) styles. In line with research by Gadassi, Gati, and Dayan (2012) in Israel and by Ginerva, Nota, Soresi, and Gati (2012) in Italy, Tian et al. (2014) reported from a study in China that more analytical and less dependent styles predicted a lower overall level of career decision making difficulties (CDMD). These findings indicate that the adaptability of those intellectual styles highlighted in the career decision-making process study by Gati et al. (2010) could be generalized to different cultural groups (Willner, Gati, & Guan, 2015).

In addition, researchers have recently investigated the influence of career exploration on career decision making. Xu, Hou, and Tracey (2014) found that both self- and environment explorations were equally predictive of information-deficit problems among Chinese college students. In another study among U.S. college students, Xu and Tracey (2014) reported that only environment exploration predicted dysfunctional beliefs and lack of information. Nevertheless, these inconsistent findings across cultures need to be clarified.

Purpose of the Study

With college entrance examination reforms, an increasing rate of college enrollment, and the influences of globalization on mainland Chinese society since the late 1990s, the construct of career development has been introduced to adolescents and young adults. This has resulted in both more choices for career paths and increased decision making (Fan & Leong, 2016; Leung et al., 2014; Willner et al., 2015). Given the lack of theoretical frameworks and indigenous career assessment tools, particularly for Chinese society, an exploration of the relationships between intellectual styles and career exploration and decision-making difficulties should be beneficial to developing effective career interventions and counseling for Chinese college students. We believed that such a study could also enrich knowledge about the nature of career development in Chinese contexts. This study examined the contributions of thinking styles (Sternberg, 1997), as one specific form of intellectual style, to CDMD and career exploration as a mediator of this relationship among mainland Chinese college students.

Thinking style. Zhang and Sternberg (2005) used intellectual style as a general term that encompasses the meanings of all major style constructs postulated in the past few decades, such as cognitive, conceptual tempo, decision making, perceptual, and thinking. Zhang and Sternberg classified all intellectual style constructs into three types: (a) Type I, characterized by creativity and a higher level of cognitive complexity; (b) Type II, characterized by normal conformity and a lower level of cognitive complexity; and (c) Type III, which includes aspects of both Type I and Type II styles depending on the stylistic demands of the task at hand.

Sternberg's (1997) theory of mental self-government defined 13 thinking styles in terms of the threefold model by Zhang and Sternberg (2005). These styles were empirically validated across cultures (Zhang, Sternberg, & Rayner, 2012). Type I styles include the legislative (being creative), judicial (evaluative of other people or products), hierarchical (prioritizing one's tasks), global (focusing on the holistic picture), and liberal (taking new approaches to tasks) styles. Type II styles include the executive (implementing tasks with prescribed procedures), local (focusing on concrete and discrete details), monarchic (working on one task at a time), and conservative (using traditional approaches to tasks) styles. In this study, because of Type III styles' situation-dependent property, they were not considered, and only Type I and II styles were selectively used. On the basis of previous studies about specific intellectual styles (Blustein & Phillips, 1988; Cools et al., 2009; Osipow & Reed, 1985), thinking styles may contribute to individuals' career exploration and career decision making.

CDMD. Gati, Krausz, and Osipow's (1996) hierarchical taxonomy of CDMD has been largely supported by empirical evidence in both Western and Eastern cultural settings (Fan, Cheung, Leong, & Cheung, 2014; Gati et al., 2010). Gati et al.'s (1996) model defines three clusters of CDMD: lack of readiness (LR), lack of information (LI) and inconsistent information (II). LR reflects the preparing work in career motivation and beliefs, which might be associated with individuals' intellectual style disposition and career exploration activities (Blustein & Phillips, 1988; Gati et al., 2010; Mau, 1995; Osipow & Reed, 1985). LI relates to the state of lacking career information and the ways of obtaining additional career information (Gati & Saka, 2001); accordingly, LI might connect with individuals' style that reflects preferences for collecting and processing information. The II cluster refers to unreliable information, internal conflicts, and external conflicts, which relate to outcomes after processing career information. Therefore, only the clusters of LR and LI were considered in this study.

Career exploration. Career exploration refers to individuals' activities of collecting and analyzing information about their personal characteristics and about occupations and organizations (Parsons, 1909; Stumpf et al., 1983). Career exploration behaviors are pivotal for developing learning and practical experiences in individual career decision-making processes (Blustein & Phillips, 1988). Accordingly, from the perspective of social cognitive career theory (Lent & Brown, 2013), career exploration can be viewed as a proximal factor contributing to career decision-making processes.

Hypotheses. We proposed three specific hypotheses based on the theoretical meaning of and empirical evidence for particular thinking styles and those of career exploration and CDMD.

Hypothesis 1: People with Type I styles may be inclined to actively act on their environments and perform creative behaviors. Therefore, individuals with Type I styles will positively explore self and environmental career features, and their CDMD in the LR and LI domains may be decreased.

Hypothesis 2: People with Type 11 styles may prefer conservative thinking and avoid risk-taking behaviors (Fan & Zhang, 2009); thus, they may not like to explore self and environmental career features and may have more CDMD in the LR and LI domains.

Hypothesis 3: Both self- and environment career exploration will significantly mediate the influences of thinking styles on the I.R and LI domains.

Considering the inconsistent findings about the relationship between career exploration and CDMD in different cultural settings, as reported by Xu et al. (2014) and Xu and Tracey (2014), we hoped to further clarify the relationships between these variables and thinking styles.

The hypothesized model is presentee! in Figure 1. As noted earlier, Type I and Type II styles directly predict both the taxonomies of CDMD (LR and LI) and self- and environment explorations (see the direct paths of a, b, c, d, e, f, g, and h). The contributions of both self- and environment explorations to CDMD are expressed with the paths of i, j, k, and 1.



The volunteer participants were junior-level students (N = 463; 346 women, 117 men) from two comprehensive universities in Shanghai, China. The participants ranged in age from 19 to 24 years (M= 20.79, SD = 0.76); 97% were between 20 and 22 years old. The students represented nine majors: math (n = 42), physics (n = 45), chemistry (n = 77), education (n = 59), psychology (n = 41), law and politics (n = 79), public and administration management (n = 15), social work (n = 7), and foreign language (n = 96). Two participants did not report their major.


Thinking styles. The Thinking Styles Inventory-Revised (TSI-R; Sternberg, Wagner, & Zhang, 2003) was used to assess participants' thinking styles. The TSI-R is a self-report measure consisting of 65 items in 13 subscales corresponding to the 13 thinking styles in Sternberg's (1997) mental self-government theory. It has been demonstrated as reasonably reliable and valid for identifying thinking styles of students in both Western and Eastern cultural contexts (Zhang & Sternberg, 2005). Respondents rate each item on a 7-point Likert-type scale ranging from 1 (does not describe me at all) to 7 (describes me extremely well). A sample item is "When faced with a problem, I use my own ideas and strategies to solve it" (Legislative Style). In the current study, the alpha coefficients of the TSI-R subscales related to the Type I and II styles ranged from .65 (Monarchic Style) to .85 (Liberal Style).

Career exploration. The Career Exploration Survey (CES; Stumpf et al., 1983) was developed to assess career search behaviors, reactions to exploration, and beliefs about exploration with four subscales: Environment Exploration, Self-Exploration, Intended-Systematic Exploration, and Focus. In this study, we used the five-item Self-Exploration subscale, which measures behavior related to self-assessment and introspection in career exploration, and the six-item Environment Exploration subscale which examines the extent of career exploration in the work environment. Respondents rate the degree to which they have engaged in career exploration behaviors on a 5-point Likert-type scale ranging from 1 (very little) to 5 (very high). A sample item is "Initiated conversations with knowledgeable individuals in my career area." As in other studies (Fan, Cheung, Leong, & Cheung, 2012; Stumpf et al., 1983), the alpha coefficients (Self-Exploration, .85; Environment Exploration, .87) in the current study were acceptable.

CDMD. The CDMD Questionnaire-Revised (CDDQ-R; Gati & Saka, 2001) was developed based on a three-category taxonomy of CDMD by Gati et al. (1996). Items are rated on a 9-point Likert-type scale ranging from I (does not describe me) to 9 (describes me well), with higher scores indicating more CDMD.

The LR measure of the CDDQ-R consists of three subscales that measure (a) an individual's motivation for career decision making (three items), (b) general indecisiveness about career decision making (three items), and (c) career difficulties due to dysfunctional cognition (four items). The LI measure of the CDDQ-R consists of four subscales that measure information deficit in (a) the stage of the career decision-making process (three items), (b) the self (four items), (c) occupation (three items), and (d) approaches to obtain additional information (two items). A sample item is "I believe there is only one career that suits me: (Dysfunctional Beliefs). In this study, the alpha coefficients of all LR and LI subscales ranged from .51 to .92, which were better than previous results (Gati & Saka, 2001).


Chinese versions of the three measures were developed using back-translation and consensual revision procedures to ensure accuracy in translation by our research team; the translations were empirically supported in related studies (Fan et al., 2012, 2014). All measures were administrated to college students through traditional pencil-and-paper mode in a classroom setting and were completed within 45 minutes. Each participant received a 40 Renminbi (about $6) thank-you gift card. Informed consent was obtained from all participants. We also obtained information on the participants' backgrounds, including gender, age, and major.

Data Analyses

Preliminary analyses indicated that the distributions of all research variables did not violate normality. M plus (Version 7.4) was used to perform the structural equation modeling with the latent variables (see Figure 1). The subscales of the TSI-R under the domains of Type I and Type II styles were separately used as the indicators of the latent Type I and Type II style constructs. The items of the CES Environment Exploration and Self-Exploration subscales were used separately as indicators of the latent variables of environment exploration and self-exploration. The subscales of the CDDQ-R under the clusters of LR and LI were used as the indicators of the latent LRand LI constructs. The structural equation modeling bias-corrected bootstrapping approach (N = 5,000) was used to test mediation effects in the study.


Table 1 presents the means, standard deviations, alpha coefficients, and zero-order correlations of the variables. Most Type I styles negatively correlated with different types of CDMD, but positively correlated with the two kinds of career exploration. Weak positive correlations were found between Type II styles and CDMD and between Type II styles and career exploration. Negative correlations between career exploration and CDMD were well supported in the current sample.

For all research variables, a multivariate analysis of variance did not support significant differences with regard to gender, Wilks's [LAMBDA] = .95, F(18, 327) = 1.08, p = .43, [[eta].sup.2] = .05, or major, Wilks's [LAMBDA] = .47, F(144, 2419) = 1.80, p = .00, [[eta].sup.2] = .06. According to Cohen (1988), the minimum acceptable magnitude of an effect size in the field of psychology is .10. Therefore, only a general mediation model was examined from thinking styles (Type I and Type II styles) to CDMD (LR and LI clusters) through career exploration (self-exploration and environment exploration). The hierarchical measurement models of the three instruments used in the study were statistically acceptable as shown in Table 2. The fit indices of three competing models are also reported in Table 2. Compared with the modified model (deleted nonsignificant paths), the fit of the final model (see Figure 1) was significantly improved, [DELTA][chi square](10, N = 463) = 357.03, p < .01, and adequate with respect to all fit indices.

In line with Hypothesis 1, Type I styles positively predicted both types of career exploration and negatively predicted both clusters of CDMD (path a = .31, path b = -.99, path c = -.45, path d = .41). Furthermore, significant mediation models from Type I style to LI through environment exploration were supported, which partially supported Hypothesis 3 (path j = -.27), and was further confirmed with a bias-corrected bootstrapping analysis (N= 5,000): indirect effect: -.08, SE = .02, 95% confidence interval 3, -.04], Although Type II styles did not significantly predict both types of career exploration, positive contributions to both types of CDMD were found (path f = .93, path g = .38). The results were partially consistent with Hypothesis 2. In addition, self-exploration did not predict both clusters of CDMD.


On the basis of Zhang and Sternberg's (2005) intellectual style taxonomy, we examined the role of thinking styles in Chinese college students' career exploration and CDMD. Our results further demonstrate in a Chinese context the cultural-relevant validity of intellectual styles in the domain of career development. The mediation effect of career exploration (specifically, environment exploration) on the link of Type I thinking styles and CDMD was also partially supported.

In line with Hypothesis 1, participants with Type I styles tended to engage in self- and environment exploration behaviors, and show stronger career motivation and introspection and less overall indecisiveness compared with those with Type II styles. They also tended to have abundant career-related information and better career decision-making motivation and beliefs. These findings suggest that individuals with Type I styles might obtain higher levels of decision-making preparation before they make career choices. Because a major attribute of Type I style is to actively search for and process information (indicative of approach success motivation; Fan & Zhang, 2009), an individual with action or creativity-generating tendencies is more likely to explore career development and construction and make more adaptive career decisions. This result is consistent with other authors' conclusions in studies from different cultural contexts--that people with creative style (Cools et al., 2009), analytical style (Gadassi et al., 2012; Ginerva et al., 2012; Tian et al., 2014), thinking-oriented style (Blustein & Phillips, 1988), or adaptive disposition (Willner et al., 2015) tend to enact job search behaviors and report fewer career difficulties. Similarly, because of the association between Type I styles and ambiguity tolerance, the results from this study further confirm the relationship between ambiguity tolerance and career indecision, as reported by Xu and Tracey (2014).

In contrast, and partially consistent with Hypothesis 2, Type II styles only significantly predicted CDMD in LR and LI. According to Parsons's (1909) model, career exploration refers mainly to collecting information about self and the vocational world. Other studies suggested that individuals with Type II styles are motivated to avoid failure (Fan & Zhang, 2009) and engage in simplistic cognition processes (Zhang & Sternberg, 2005). Therefore, compared with individuals with Type I styles, individuals with Type II styles might not be inclined to conduct career exploration. This may be why a nonsignificant relationship was found between Type II styles and career exploration in general. If an individual does not actively search for career information and engage in career-relevant behaviors, the outcome of career development and construction may be reduced career adaptability.

In partial support of Hypothesis 3, the bootstrap analysis revealed that environment exploration mediated the link between Type I styles and LI. The results suggest that individuals with Type I styles might tolerate more ambiguity (Xu & Tracey, 2014), and their career exploration could yield more career-relevant information. This result further confirms the mediation of environment exploration on the link between ambiguity tolerance and LI as reported by Xu and Tracey (2014).

In addition, consistent with research by Xu and Tracey (2014) with an American sample and Xu et al.'s (2014) study with a Chinese sample, we found that environment exploration significantly predicted information-deficit problems. However, as in Xu and Tracey's study, self-exploration made a nonsignificant contribution to CDMD in our study. Nonetheless, Xu et al. found significant correlations between self-exploration and information-deficit problems in another Chinese college student sample. Therefore, it was empirically supported that environment career exploration significantly contributed to decreasing college students' CDMD across cultures; that is, individuals' circumstances, such as their family background and relational networks, which mainly comprise college students' career environment, may significantly influence career trajectories and outcomes (Fan et al., 2014; Leung et al., 2014) across cultures. However, the mixed findings for the role of self-exploration should be clarified in future studies.

Limitations, Practical Implications, and Future Directions

Our findings should be considered in light of several limitations. This study provided only cross-sectional evidence on the relationships between thinking styles and career exploration and CDMD. The results, based on self-reported measures, may not be completely consistent with those from longitudinal designs or behavioral measures, and may not generalize to other populations. Despite these limitations, the study results revealed associations between Sternberg's (1997) thinking styles and career exploration and CDMD in a Chinese context. This study enriches the literature about the validity of the effect of intellectual styles on college students' career development. According to our findings, the utility of intellectual styles for young adults' career development is well highlighted.

The constructs of career exploration and decision making are Western based and, at least traditionally, not well considered in Chinese contexts. Since the late 1990s, career exploration and decision making have been more of a focus in higher education in mainland China (Fan & Leong, 2016). In line with the Western-based tradition, some classic dispositions (e.g., personality, vocational interest) have been highlighted in recent career intervention and counseling practices in mainland China. Ultimately, on the basis of our findings, some malleable or relatively nonstable features such as intellectual styles, which could be easily changed in light of specific contexts (e.g., family or peer environments; Zhang, 2013), should be carefully considered for counseling. School counselors could help cultivate students' Type I intellectual styles by providing them with opportunities to use creative thinking. This could be implemented, for example, by providing corresponding instruction and assessment that require creativity-generating thinking and higher levels of cognitive complexity (Zhang, 2013). In this way, creative/ complex-cognitive thinking may be translated into career exploration or decision-making activities.

Future studies are needed to replicate our findings in other cultural contexts and further validate the contributions of various intellectual styles to other important career variables, such as career self-efficacy (Mau, 2000) and career outcomes (e.g., narratability, career adaptability; Hartung, 2015). Because intellectual styles are theoretically and empirically malleable (Zhang, 2013), a longitudinal design might more effectively reveal the direct or indirect influences of intellectual styles on different dimensions of career development. Future studies might provide valuable empirical evidence for identifying effective ways to promote individuals' adaptive career exploration and decision-making behavior, as well as inform counselors about career interventions that use style malleability.

Mengting Li and Weiqiao Fan, Department of Psychology, Shanghai Normal University, Shanghai, China. This article was partially supported by a grant (14BS081) from the National Program of Philosophy and Social Science, China. Correspondence concerning this article should be addressed to Weiqiao Fan, Department of Psychology, Shanghai Normal University, No. 100, Guilin Road, Shanghai 200234, China (e-mail:

Received 08/08/16

Revised 11/24/16

Accepted 11/28/16

DOI: 10.1002/cdq.12095


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Caption: FIGURE 1 Full Model of the Effects of Thinking Styles and Career Exploration on Career Decision-Making Difficulties

Means, Standard Deviations, Alpha Coefficients,
and Zero-Order Correlations of the Variables

Variable    M      SD    [alpha]

1. Leg     5.06   0.89     .75
2. Jud     4.52   0.98     .73
3. Hie     4.87   1.03     .78
4. Glo     4.14   0.89     .66
5. Lib     4.19   1.12     .85
6. Exe     4.98   0.88     .67
7. Con     4.42   1.04     .79
8. Mon     4.62   0.96     .65
9. Loc     4.11   1.00     .67
10. EE     2.05   0.85     .87
11. SE     2.94   0.99     .85
12. Rm     4.44   1.86     .51
13. Ri     5.48   1.90     .71
14. Rd     4.49   1.45     .54
15. Lp     4.90   2.19     .92
16. Ls     4.37   2.07     .86
17. Lo     5.17   2.05     .84
18. La     4.95   2.11     .76

Variable      1         2         3         4

1. Leg       --
2. Jud      .42 **     --
3. Hie      .33 **    .41 **     --
4. Glo      .22 **    .17 **   -.02        --
5. Lib      .55 **    .46 **    .26 **    .12 *
6. Exe      .13 **    .08       .30 **    .18 **
7. Con     -.07      -.06       .12 *     .10 *
8. Mon      .19 **    .16 **    .32 **    .12 *
9. Loc      .29 **    .28 **    .43 **   -.37 **
10. EE      .14 **    .21 **    .28 **    .03
11. SE      .21 **    .30 **    .24 **    .05
12. Rm     -.04      -.05      -.20 **    .04
13. Ri     -.18 **   -.12 *    -.21 **    .07
14. Rd      .07       .08      -.02       .09 *
15. Lp     -.08      -.18 **   -.21 **    .10 *
16. Ls     -.08      -.18 **   -.19 **    .05
17. Lo     -.09 *    -.16 **   -.16 **    .03
18. La     -.06      -.17 **   -.11 *     .04

Variable      5         6         7         8

1. Leg
2. Jud
3. Hie
4. Glo
5. Lib       --
6. Exe     -.16 **     --
7. Con     -.35 **    .68 **     --
8. Mon      .05       .32 **    .30 **     --
9. Loc      .25 **    .26 **    .14 **    .25 **
10. EE      .21 **    .03      -.08       .04
11. SE      .25 **    .06       .01       .08
12. Rm      .04      -.02       .03      -.05
13. Ri     -.21 **    .21 **    .29 **    .06
14. Rd      .07       .08       .14 **    .14 **
15. Lp     -.18 **    .12 *     .15 **    .01
16. Ls     -.16 **    .06       .11 *    -.07
17. Lo     -.19 **    .08       .10 *    -.04
18. La      -.07      .03       .06       .00

Variable      9        10        11        12

1. Leg
2. Jud
3. Hie
4. Glo
5. Lib
6. Exe
7. Con
8. Mon
9. Loc        --
10. EE       .15 **     --
11. SE       .20 **    .52 **     --
12. Rm      -.03      -.27 **   -.12 **    --
13. Ri      -.05      -.13 **   -.11 *    .12 **
14. Rd       .04       .05       .01      .00
15. Lp      -.08      -.33 **   -.27 **   .31 **
16. Ls      -.07      -.30 **   -.24 **   .33 **
17. Lo      -.08      -.28 **   -.20 **   .22 **
18. La      -.02      -.26 **   -.17 **   .23 **

Variable     13        14        15        16        17     18

1. Leg
2. Jud
3. Hie
4. Glo
5. Lib
6. Exe
7. Con
8. Mon
9. Loc
10. EE
11. SE
12. Rm
13. Ri       --
14. Rd     .24 **      --
15. Lp     .47 **     .11 *      --
16. Ls     .42 **     .10 *    .69 **      --
17. Lo     .40 **     .06      .67 **    .66 **      --
18. La     .39 **     .14 **   .62 **    .63 **    .73 **   --

Note. N = 463. Leg = legislative style; Jud = judicial
style; Hie = hierarchical style; Glo = global style;
Lib = liberal style; Exe = executive style; Con = conservative
style; Mon = monarchic style; Loc = local style; EE = environment
exploration; SE = self-exploration; Rm = lack of motivation;
Ri = general indecisiveness; Rd = dysfunctional beliefs;
Lp = the stages of the career decision-making process; Ls = self;
Lo = occupations; La = ways of obtaining additional information.

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


Summary of Chi-Square Values and Fit Indices
for Measurement Models and Full Models

Model                                 [chi square]   df
Measurement models
  Type I and II thinking styles (a)         67.22    18
    Career exploration                     155.29    43
  CDMD (b)                                  41.01    12
Full models
  Original model                         1,120.38    309
  Modified model                         1,126.61    317
  Final model (c)                          769.58    307

Model                                 [chi square]/df   CFI
Measurement models
  Type I and II thinking styles (a)        3.73         .96
    Career exploration                     3.61         .95
  CDMD (b)                                 3.42         .98
Full models
  Original model                           3.63         .84
  Modified model                           3.55         .84
  Final model (c)                          2.51         .91


Model                                 Estimate     90% CI     SRMR
Measurement models
  Type I and II thinking styles (a)     .08      [.06, .10]   .06
    Career exploration                  .08      [.06, .09]   .05
  CDMD (b)                              .07      [.05, .10]   .04
Full models
  Original model                        .08      [.07, .08]   .08
  Modified model                        .08      [.07, .08]   .08
  Final model (c)                       .06      [.05, .06]   .07

Note. N = 463. RMSEA = root-mean-square error of approximation;
CFI = comparative fit indices; CI = confidence interval;
SRMR = standardized root mean square residual;
CDMD = career decision-making difficulties.

(a) Eight correlation paths were set in light of modification
indices: local versus global, conservative versus liberal,
executive versus liberal, conservative versus executive,
legislative versus liberal, global versus hirarchical,
liberal versus judicial, and judicial versus legislative.
(b) For the measurement model of CDMD-R, the association
between ways of obtaining additional information and
occupations was set. (c) In the final model, five correlation
paths were set: local versus global, conservative versus liberal,
executive versus liberal, conservative versus executive, and ways
of obtaining additional information versus occupations. These added
correlation paths were largely consistent with the zero-order
correlations (see Table 1) and could be explained in theory.
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Article Details
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Author:Li, Mengting; Fan, Weiqiao
Publication:Career Development Quarterly
Article Type:Report
Geographic Code:9CHIN
Date:Sep 1, 2017
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