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Case Study
Typing Guidelines:
1. The critique must be typed using .12 font in Times New Roman or Arial style font. Margins should be one-inch for the entire document. The entire document should be 3 pages in length.
2. The entire critique portion of the document (sections B – F) should be double-spaced.
Include the Following:
A. APA Reference Citation (20 points): A complete APA reference citation for each article.
B. Article Overview (20 points): Provide an overview of the purpose, research methods, major findings, and conclusions of the article. This should not be taken from the published abstract for the article but should consist of assessment of the article.
C. Analysis Implications (20 points): An analysis of the major implications of the article as they relate to the management approach, business proceedings, or business issue addressed in the article.
D. Research Limitations (20 points): What were some of the weaknesses of the research for the article? Why does it not apply to every situation, every business, or every person? Why would the research not be universally accepted? What aspects of the process used to conduct the research are weak or flawed?
E. Personal Perspective (10 points): This is the only portion of the critique where you can specifically state your feelings, views, or position from a personal perspective regarding the article.
F. Critique Summary (10 points): Provide a conclusion of what you have said so far. Simply restate and reiterate sections B – E in a succinct manner. You should also make a recommendation about the type of reader likely to enjoy or benefit from the article and what types of business leader, manger, or business entity might benefit from the content of the article.
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Revised Rubric for Assignment 6 CAR 100 & 600D – Career Planning and Development Case Study Typing Guidelines: 1. The critique must be typed using .12 font in Times New Roman or Arial style font. Margins should be one-inch for the entire document. The entire document should be 3 pages in length. 2. The entire critique portion of the document (sections B – F) should be double-spaced. Include the Following: A. APA Reference Citation (20 points): A complete APA reference citation for each article. B. Article Overview (20 points): Provide an overview of the purpose, research methods, major findings, and conclusions of the article. This should not be taken from the published abstract for the article but should consist of assessment of the article. C. Analysis Implications (20 points): An analysis of the major implications of the article as they relate to the management approach, business proceedings, or business issue addressed in the article. D. Research Limitations (20 points): What were some of the weaknesses of the research for the article? Why does it not apply to every situation, every business, or every person? Why would the research not be universally accepted? What aspects of the process used to conduct the research are weak or flawed? E. Personal Perspective (10 points): This is the only portion of the critique where you can specifically state your feelings, views, or position from a personal perspective regarding the article. F. Critique Summary (10 points): Provide a conclusion of what you have said so far. Simply restate and reiterate sections B – E in a succinct manner. You should also make a recommendation about the type of reader likely to enjoy or benefit from the article and what types of business leader, manger, or business entity might benefit from the content of the article. REMEMBER DO NOT USE FIRST PERSON (I, ME, MY) Received 01/29/14 Revised 05/22/14 Accepted 06/26/14 DOI: 10.1002/cdq.12029 Articles Resilience and Decision-Making Strategies as Predictors of Career Decision Difficulties Yun-Jeong Shin and Kevin R. Kelly The purpose of this study was to examine resilience and decision-making strategies as predictors of difficulties experienced during the career decision-making process. College students (N = 364) responded to measures of resilience, career decisionmaking strategies, and career decision difficulties. Results indicated that resilience and decision-making strategies accounted for 46% of the variance in career decision difficulties. Resilience had a greater influence on problems encountered during decision making than on problems encountered at the outset of the process. Different decision-making strategies appeared to be related to difficulties encountered at different stages of the decision-making process. For example, aspiration for an ideal occupation was positively associated only with lack of readiness. Procrastination was the only strategy related to all three decision difficulties: lack of readiness, lack of information, and inconsistent information. The results indicated the importance of decreasing procrastination at all stages of decision making and the need to promote resilience to deal with decision difficulties. Keywords: career decision difficulties, resilience, decision-making strategies, career indecision Large-scale forces such as globalization and rapid technological progress have decreased work stability and security, with new jobs being rapidly created and long-established lines of work becoming obsolete (Guichard & Dumora, 2008). Consequently, career decision making has become increasingly challenging for students pursuing higher education (Guay, Senecal, Gauthier, & Ferner, 2003). Many college students experience career indecision, often manifested in one or more decision-making difficulties (Guay et al., 2003; Kelly & Lee, 2002). These difficulties increase the risk of academic attrition and failure, maladjustment, and personal distress (Feldt et al., 2010). Decision difficulties are associated with anxiety (Santos, 2001), depression (Saunders, Peterson, Sampson, & Reardon, 2000), and low self-esteem (Gati & Amir, 2010). Researchers and practitioners have focused on identifying specific career decision difficulties (Gati, Krausz, & Osipow, 1996; Gati & Tal, 2008), with the goal of helping students to manage their academic and career decisions more effectively. Researchers have also studied a variety of personal Yun-Jeong Shin, Graduate School of Education, University of Seoul, Seoul, South Korea; Kevin R. Kelly, School of Education and Health Sciences, University of Dayton. Correspondence concerning this article should be addressed to Yun-Jeong Shin, Graduate School of Education, University of Seoul, 163 Seoulsiripdae-ro, Dongdaemun-gu, Seoul, South Korea 130-743 (e-mail: yshin@uos.ac.kr). © 2015 by the National Career Development Association. All rights reserved. The Career Development Quarterly December 2015 • Volume 63 291 characteristics in relation to career decision difficulties to inform the development of more effective counseling interventions. One approach has been to study personality variables in relation to decision difficulties. An emerging alternative approach is to examine decision-making strategies in relation to decision difficulties. Following these lines of research, we examined the relative influence of resilience and decisionmaking strategies on difficulties experienced at different stages of the career decision-making process. Personality and Career Decision Difficulties Initial explorations focused on negative personality characteristics in relation to career indecision. Leong and Chervinko (1996) found perfectionism, self-criticism, and fear of commitment to be related to global career indecision. Kelly and Shin (2009) were the first to examine personality in relation to specific decision difficulties, and they found that neuroticism related to a lack of information problems. Extant research indicates that negative personality characteristics are associated with career decision difficulties. More recently, researchers have taken a positive approach by examining personality characteristics that may prevent or ameliorate decision difficulties. For example, Albion and Fogarty (2002) found that individuals with more emotional stability perceive fewer career decision difficulties. The study of positive personality characteristics may enable counselors to develop interventions for career decision difficulties. The challenge is to determine the positive characteristics that have the greatest potential to inform career counselors and to contribute to a broader knowledge of personality functioning. Recent work regarding positive personality has focused on psychological capital, which includes hope, self-efficacy, optimism, and resiliency. Of these characteristics, there is a precedent for studying resilience in relation to decision problems. Resilient individuals tend to be self-disciplined, take responsibility for their successes and failures, and adapt new strategies for dealing with career decision-making tasks (McMahon, 2007). Therefore, we examined resilience as a positive personality characteristic in relation to career decision difficulties. Resilience Resilience denotes the constellation of personality qualities that enables positive adaptation to adversity (Luthar, 2006). Bonanno (2004) described resilience as the ability to maintain equilibrium in the face of unfavorable circumstances. Wagnild and Young (1993) defined resilience as moderating the negative effects of stress and promoting adjustment to challenging circumstances. Resilience promotes initiative and purposeful action (London & Stumpf, 1986). The essence of resilience is the ability to rebound from stress and resume adaptive functioning in the face of challenges (Luthar, 2006). We believe that resilience may be related to the ability to resolve career decision problems for two reasons. First, career decision making is a normative task of late adolescence, and resilience has been found to be related to adaptive functioning for this population. For example, Smo292 The Career Development Quarterly December 2015 • Volume 63 kowski, Reynolds, and Bezruczko (2000) found that resilient adolescents develop strategies and coping skills to adapt to stressors, including career indecision, and attain positive outcomes. Second, resilience is related to adaptive functioning in work environments. For example, Kotze and Lamb (2012) found that resilient employees in a call center relied more on their personal abilities and less on external support in responding to challenges. Carvalho, Calvo, Martin, Campos, and Castillo (2006) demonstrated that resilience is inversely related to emotional exhaustion from work. For these reasons, we hypothesized that resilience relates positively with the ability to resolve various career decision problems. Advancing the knowledge of the relation of resilience to career decision problems may also enable career counselors to design interventions that can build psychological capital. Decision Strategies in Relation to Decision Difficulties Aside from the study of personality in relation to career decision making, there is a significant body of research examining decisional styles, processes, and strategies related to career decision problems. Harren (1979) identified three decision-making styles: rational, avoidant, and dependent. The rational style is an active and planful approach to decision making. The avoidant style is characterized by failure to attain and process career information and postponement of decisions. The dependent style involves ceding responsibility for decisions to external sources, such as significant others. The rational style is viewed favorably because it is a systematic approach that yields information relevant to decisions. The rational decision-making style has been found to be associated with career maturity (Blustein, 1987), career choice certainty (Mau & Jepsen, 1992), career decidedness (Mau, 1995), and problem-solving efficacy (Phillips, Pazienza, & Ferrin, 1984). However, there is no conclusive evidence that the rational style is associated with superior decision-making outcomes (Krieshok, Black, & McKay, 2009; Mau, 1995). Gati, Landman, Davidovitch, Asulin-Peretz, and Gadassi (2010) suggested that individuals may be described more accurately as using a combination of approaches in career decision making and that it may be more informative to consider a broad set of decision dimensions than to focus on broad styles (e.g., avoidant, dependent, rational). Gati et al. (2010) suggested using the profiles of 11 different decision dimensions. Information gathering reflects the degree of involvement in the collection and organization of information. Information processing refers to the extent of analysis of career information. Locus of control is the degree of one’s perceived control over career opportunities. Effort invested in the process reflects the time and effort devoted to career decision making. Procrastination is the delay in involvement in decision-making tasks. Speed of making the final decision reflects the time needed to make a final career decision. Consulting with others refers to the extent of consultation with others during the decision-making process. Dependence on others is the extent of reliance on others for making the career decision. Desire to please others reflects attempts to satisfy the expectations of others. Aspiration of an ideal occupation is the desire to find a perfect occupation. Finally, willingness to compromise refers to flexibility in one’s career aspirations. The Career Development Quarterly December 2015 • Volume 63 293 Gati et al. (2010) defined these 11 dimensions as “behavioral patterns for dealing with the challenge of making a decision” (p. 286). For the purposes of our investigation, we chose to use the term strategy to refer to these 11 dimensions because they are behavioral plans to achieve a goal. The term strategy seems to more aptly describe a behavioral plan for solving or deferring a career decision problem. Gati et al. (2010) suggested that sets of strategies can be viewed in relation to their adaptive or maladaptive influences on career decision making. They proposed that adaptive decision making is associated with higher scores on seven strategies: (a) information gathering, (b) information processing, (c) locus of control, (d) effort invested, (e) speed of final decision, (f) consultation with others, and (g) willingness to compromise. Gati et al. (2010) described higher procrastination, dependence on others, desire to please others, and aspiration for an ideal occupation scores as maladaptive strategies. Subsequent research by Gati, Gadassi, and Mashiah-Cohen (2012) generally supported these expectations. However, contrary to expectations, higher willingness to compromise and lower aspiration for an ideal occupation were associated with maladaptive outcomes. These unanticipated findings suggested the need to further explore the adaptive and maladaptive influences of the career decision-making strategies. Purpose of the Study Our goals were to better understand (a) the nature of decision-making difficulties and (b) how to help students cope with decision difficulties. The literature suggests that a counseling focus on resilience can help students work through decisional challenges. We also chose to examine career decision strategies as predictors of decision difficulties. We expected that findings related to these strategies would directly apply to career interventions for decision difficulties. We developed four hypotheses. First, we hypothesized that resilience would be negatively related to career decision difficulties (Hypothesis 1). Second, we hypothesized that resilience would explain significant variance in career decision difficulties after controlling for demographic variables (Hypothesis 2). Third, we hypothesized that the set of career decision strategies would explain significant variance in career decision difficulties after controlling for demographic variables and resilience (Hypothesis 3). Finally, consistent with Gati et al. (2010), we hypothesized that the following decision strategies would be negatively related to career decision difficulties: information gathering, information processing, locus of control, effort invested in the process, speed of making the final decision, consulting with others, and willingness to compromise (Hypothesis 4A). Conversely, we anticipated a positive relation of the following strategies to career decision difficulties: procrastination, dependence on others, desire to please others, and aspiration for an ideal occupation (Hypothesis 4B). Method Participants The participants were 364 college students (61% women, 39% men) at a large public university in the southeastern United States. Most participants indicated their ethnicity as European American (74%); 17% were 294 The Career Development Quarterly December 2015 • Volume 63 African American, and the remaining 9% of participants did not identify their ethnicity. There were 133 (37%) first-year, 103 (28%) second-year, and 52 (14%) third-year students; 76 (21%) students did not identify year of study. The mean age of participants was 21.17 years (SD = 6.05); the mean grade point average was 3.27 (SD = 0.52). Measures Resilience. We assessed resilience with the 14-item Resilience Scale (RS-14; Wagnild, 2009). Item response options ranged from 1 (strongly disagree) to 7 (strongly agree). Resilience was measured by the total score, with a range from 14 to 98. Scores greater than 90 indicate high resilience; scores of 60 and below indicate low resilience. Wagnild (2009) reported an internal consistency reliability coefficient of .91 for the RS-14. The internal consistency reliability coefficient for the RS-14 scores for the current study was .90. Wagnild (2009) conducted a principal component analysis and confirmed a one-factor solution. The RS-14 is correlated positively with scores from the Life Satisfaction Index–A (Neugarten, Havighurst, & Tobin, 1961) scores and negatively with Beck Depression Inventory (Beck & Beck, 1972) scores, which provides evidence of concurrent and discriminant validity (Wagnild & Young, 1993). Career decision-making strategies. We assessed career decision-making strategies with the 33-item Career Decision-Making Profile Questionnaire (CDMP; Gati et al., 2010). Participants rate level of agreement with items on a 7-point Likert-type scale (1 = don’t agree at all, 7 = highly agree). The CDMP measures 11 career decision strategies: (a) information gathering, (b) information processing, (c) locus of control, (d) effort invested in the process, (e) speed of making the final decision, (f) procrastination, (g) consultation with others, (h) dependence on others, (i) desire to please others, (j) aspiration for an ideal occupation, and (k) willingness to compromise. Each dimension is measured by the mean score of three items; higher scores represent a higher tendency to use the decision strategy. For example, higher information gathering scores reflect a greater tendency to collect and organize information comprehensively. Gati et al. (2010) reported a median internal consistency reliability of .81 for the 11 strategies, with a range of .72 to .92; the median 2-week test–retest reliability was .82, with a range of .76 to .86. Gati et al. found greater support for an 11 first-factor model than for a single-factor model in a confirmatory factor analysis. These findings indicate that there are 11 distinct decision strategies rather than a single overall decision-making style. Gati et al. (2012) found that the CDMP was correlated in expected ways with the General Decision-Making Style questionnaire (GDMS; Scott & Bruce, 1995), which provides evidence supporting convergent validity. Career decision-making difficulties. We used the 34-item version of the Career Decision-Making Difficulties Questionnaire (CDDQ; Gati & Saka, 2001). Participants respond to each CDDQ statement of decision difficulties on a 9-point Likert-type scale (1 = does not describe me, 9 = describes me well). Participants also rate their overall severity of career decision difficulty (1 = not severe at all, 9 = very severe). There are three CDDQ categories: readiness (10 items), lack of information (12 items), and inconsistent information (10 items). We did not use two validity The Career Development Quarterly December 2015 • Volume 63 295 items in the scoring. Higher categorical CDDQ scores reflect greater decision-making difficulties. Gati et al. (1996) reported median Cronbach’s alpha internal consistency coefficients of .78 and .77 for the full scale for Israeli and American samples, respectively. Osipow and Gati (1998) reported a median Cronbach’s alpha of .76. Gati et al. (1996) reported retest reliabilities of .67, .74, .72, and .80 for the three categorical and total scores, respectively. In the present study, the median scale internal consistency reliability coefficient was .70 for the three categorical scores and .91 for the total CDDQ score. Several researchers have found support for the three-factor solution identified by Gati et al. (Gati & Saka, 2001; Vahedi, Farrokhi, Mahdavi, & Moradi, 2012). Also, there is ample evidence supporting the construct, concurrent, and predictive validity of the CDDQ (Gati, Saka, & Krausz, 2001; Osipow & Gati, 1998). Procedure Participants completed paper-and-pencil versions of the RS-14, CDMP, and CDDQ and a demographic information form. Participants completed all questionnaires in the same order; the typical administration time was 20 minutes. Results Means, standard deviations, internal consistency reliability coefficients, and the zero-order correlations of the primary variables are reported in Table 1. The bivariate correlations indicate that total career decision difficulty was negatively correlated with resilience (r = –.29, p < .01) and four of the decision strategies: information gathering (r = –.15, p < .01), locus of control (r = –.39, p < .01), speed of making the final decision (r = –.41, p < .01), and aspiration for an ideal occupation (r = –.13, p < .05). Total career decision difficulty was positively related with four strategies: procrastination (r = .57, p < .01), dependence on others (r = .50, p < .01), desire to please others (r = .34, p < .01), and willingness to compromise (r = .19, p < .01). Resilience was negatively related with lack of readiness (r = –.16, p < .01), lack of information (r = –.28, p < .01), and inconsistent information (r = –.30, p < .01). These results indicate that lack of resilience matters before and during the decision-making process. Next, we examined the relation of resilience to the decision strategies. Resilience was positively correlated with information processing (r = .39, p < .01), information gathering (r = .43, p < .01), locus of control (r = .18, p < .01), effort invested in the process (r = .36, p < .01), speed of making the final decision (r = .12, p < .05), and willingness to compromise (r = .13, p < .05). Resilience was negatively correlated with procrastination (r = –.27, p < .01), dependence on others (r = –.19, p < .01), and aspiration for an ideal occupation (r = –.32, p < .01). The results indicate that resilience is associated with an active, persistent, and focused approach to decision making. We conducted four hierarchical regression analyses to determine the influence of resilience and decision strategies on the career decision difficulties of college students. In the first step, we entered the demographic and human capital variables of sex, year of study, and age. For the second and third steps, we added the resilience and decision strategies, respectively. 296 The Career Development Quarterly December 2015 • Volume 63 297 81.14 15.72 15.71 14.75 14.88 11.16 9.28 14.02 9.26 10.96 16.96 14.00 14.62 9.64 10.04  1. Res  2. IP  3. IG  4. LC  5. EI  6. SP  7. PR  8. CO  9. DO 10. DP 11. AI 12. WC 13. Rd 14. LI 15. II 16. TL 11.60 3.43 3.43 4.02 3.64 4.33 4.43 4.48 4.17 4.48 3.51 4.32 3.76 5.85 6.05 SD .90 .75 .73 .66 .76 .79 .74 .78 .74 .79 .70 .78 .63 .96 .90 .95 α — .39** .43** .18** .36** .12* –.27** –.05 –.19** –.01 –.32** .13* –.16** –.28** –.30** –.29** 1 — .76** .12* .65** –.13** –.13* .05 –.17** –.08 .33** .20** .01 –.11* –.13* –.10 2 — .07 .71** –.14** –.18** .06 –.15** –.05 .38** .16** .01 –.17** –.18* –.15** 3 — .04 .25** –.34** .17** –.31** –.24** .12* –.13* –.36** –.33** –.36** –.39** 4 — –.32** –.11* .06 .02 .07 .30** .10 .11* –.06 –.06 –.02 5 — –.48** .01 –.41** –.25** .01 –.11* –.45** –.34** –.34** –.41** 6 — –.16* .49** .24** –.28** .18** .37** .54** .55** .57** 7 — .15** .01 .04 –.01 –.04 –.05 –.05 –.06 8 — .52** –.15** .10 .43** .46** .42** .50** 9 — .04 .13* .36** .30** .30** .34** 10 — .12* .07 –.15** –.19** –.13* 11 — .12* .21** .13** .19** 12 — .53** .50** .70** 13 15 — .84** — .95** .92** 14 — 16 Note. Res = resilience; IP = information processing; IG = information gathering; LC = locus of control; EI = effort invested in the process; SP = speed of making the final decision; PR = procrastination; CO = consultation with others; DO = dependence on others; DP = desire to please others; AI = aspiration for an ideal occupation; WC = willingness to compromise; Rd = readiness; LI = lack of information; II = inconsistent information; TL = Career Decision-Making Difficulties Questionnaire total score. *p < .05. **p < .01. M Variable Means, Standard Deviations, Internal Consistencies, and Zero-Order Correlations of Main Variables Table 1 At the first step, age was significantly related to decision difficulties, specifically the problem of readiness (r = –.18, p < .01). Younger students reported less readiness to engage in career decision-making. However, age was not related to a lack of or inconsistent information. These results indicate that students become involved in career decision making as they mature. Difficulty with a lack of or inconsistent information did not appear to vary with age within this relatively narrow age range. Examination of Table 2 indicates that resilience was negatively related to total career decision difficulty (r = –.26, p < .01), as well as readiness, lack of information, and inconsistent information. Thus, there was support for the first hypothesis that resilience would be negatively associated with career decision difficulties. In addition, resilience accounted for 7% (p < .01) of the variance in total career decision difficulties after we entered the first block of background variables. Resilience accounted for 2% of readiness, 6% of lack of information, and 7% of inconsistent information after controlling for age, sex, and year of study. These findings support the second hypothesis that resilience would explain significant variance in career decision difficulties after controlling for demographic variables. The influence of resilience on readiness was smaller than its influence on lack of information and inconsistent information. The results indicate that resilience is more influential during career decision making rather than before the process. Hypothesis 3 stated that decision strategies would explain significant variance in career decision difficulties. In the hierarchical models presented in Table 2, the third block accounted for 39% of additional variance in total career decision difficulty after controlling for background variables and resilience. The set of decision strategies explained 34%, 33%, and 32% of unique variance in readiness, lack of information, and inconsistent information, respectively. Therefore, Hypothesis 3 was also supported. Finally, we hypothesized that specific decision strategies would be negatively associated with career decision difficulty (Hypothesis 4A). We also hypothesized that procrastination, dependence on others, desire to please others, and aspiration for an ideal occupation would be positively related to career decision difficulty (Hypothesis 4B). Six of the 11 decision strategies were significantly related to readiness. Locus of control and speed of making the final decision were negatively associated with readiness. Procrastination, dependence on others, desire to please others, and aspiration for an ideal occupation were positively associated with readiness (see Table 2). Lack of information, which is relevant during decision making, was significantly related to four decision strategies: information gathering, procrastination, dependence on others, and willingness to compromise. Information gathering was negatively related to lack of information (r = –.14, p < .05); dependence on others, procrastination, and willingness to compromise were positively correlated to lack of information (see Table 2). Last, inconsistent information, which is also relevant during the decisionmaking process, was significantly correlated with three strategies: locus of control, procrastination, and desire to please others. Inconsistent information was negatively related to locus of control (r = –.15, p
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Running head: CASE STUDY: RESILIENCE AND DECISION-MAKING STRATEGIES

Case Study: Resilience and Decision
Student
Institution

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CASE STUDY: RESILIENCE AND DECISION-MAKING STRATEGIES

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Case Study: Resilience and Decision
Article Overview
Making career decision has become a challenge amongst modern students owing to the
ever changing career environments. Inability to make a decision affects the students' well beings
in a negative way. Several studies have been conducted with the aim of assisting students
addressing the difficulties in career decision making. Some studies have focused on the
relationship between the individuals' personalities and the decision making while others focus on
the resilience. Resilience, in this case, refers to the unique ability to stay functioning amidst
challenges. Other studies have focused on the decision strategies in responding to difficulties in
making the decision. The overall aim of this paper was thus to help in gaining a clear
understanding of the difficulties in decision-making, and how to assist students in overcoming
difficulties in decision making. Based on prior literature, the author cited that counseling that
focuses on resilience can assist students in overcoming difficulties in decision making. The
research thus hypothesized a negative relationship between resilience and career decision
difficulties. The second hypothesis was that resilience would denote significant variation in
career decision difficulties. The third hypothesis was that decision strategies would make a
significance difference in career decision difficulties. Finally, the fourth hypothesis was that
sticking to the decision strategies has a negative relationship with the career decision difficulties
(Shin & Kelly, 2015).
Research Methods
The study was conducted in the form of questionnaires that were designed to measure
resilience, career decision-making strategies and, career decision-making difficulties. The

CASE STUDY: RESILIENCE AND DECISION-MAKING STRATEGIES

3

participants were asked to fill in the questionnaire forms in the same order and within 20
minutes. A total of 364 students were involved, 61% women and 39% men. The majority were
European American, 17% African American and 9% did not identify with any group. The
distribution regarding classes was 37% first years, 28% second years, 14% were third years, and
21% fourth years (Shin & Kelly, 2015, p. 294-296).
Major Findings
The total career decision difficulty was negatively correlated with resilience and the four
decision strategies. The total career decision difficulty was positively related to the four
strategies. Resilience was negatively related with lack of readiness, lack of information and
inconsistent information. Resilience had a positive co...


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