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
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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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