229
Kuder Career Search With Person Match:
Career Assessment for the 21 st Century
Donald G. Zytowski
National Career Assessment Systems, Inc.
Adel,
Iowa
A new approach to interest assessment, Kuder Career Search, is
described. The third generation of the Kuder interest inventories,
it goes beyond the conventional homogeneous and criterion group
scaling to match inventory-takers with each of the individuals in a
pool of criterion persons employed in a wide variety of occupations.
The rationale for this novel concept is reviewed as is the methodology
of person-matching. Selected validity data are reported, along with
a case
example.
Kuder Career Search, person-match, career assessment,
internet-based assessment, occupational heterogeneity, career stability
Keywords:
a recent issue of the Journal of Career Assessment, Betz and Borgen
(2000, p. 330) assert that the integration of measures of self-efficacy and
personal styles with measures of vocational interests will increase the
perfection of person-environment fit by providing further differentiation of
occupations. But Fouad and Zao (2000) argue that traditional assessment
that predicts success and satisfaction is no longer effective. &dquo;The traditional
notion of person-environment fit has shifted to identifying multiple
individual factors to fit with multiple environments over time&dquo; (p. 404).
They argue that occupational roles are changing, thus, the occupational
prediction paradigm that perfuses career counseling will become obsolete.
Similarly, Lock and Hogan (2000) state that &dquo;the focus and definition of
occupation is changing;&dquo; (p. 412) that job seekers no longer think in terms
of one career path or one job family.
The assessment of person-environment fit requires a degree of
homogeneity of interests in a given occupational group that differentiates
it from other occupations. If a group has no interests in common, there
can be nothing with which to compare.
Dolliver and Nelson (1975) called the idea of occupational homogeneity
&dquo;an assumption&dquo; of test-makers, shared by counselors and clients, saying that
differences within occupations have been widely disregarded in order to
In
Correspondence concerning this article and requests for offprints should be
Zytowski, NCASI, 601 Visions Parkway, Adel, IA 50003.
addressed
to Donald G.
Published and
copyright © 2001 by Psychological Assessment Resources,
Downloaded from jca.sagepub.com at PENNSYLVANIA STATE UNIV on May 17, 2016
Inc. All
rights
reserved.
230
conceptualize occupational groups. Kuder (1977a) was able to demonstrate
the validity of Dolliver and Nelson’s thesis by the use of a direct measure
of homogeneity of his occupational criterion groups. He found that women
in occupations tended to have more homogeneous interests than men. For
example, women pediatricians revealed the most homogenous interest
patterns, but department store salesmen lacked a distinctive pattern of
interests and could not be included among the occupations for which Kuder
DD (Kuder & Zytowski, 1991) is scored.
Heterogeneity in occupations has been revealed in other ways. Mount and
Muchinsky (1978), using data from an early version of Holland’s (1994)
Self-Directed Search, found that 24% of persons employed in several
occupations failed to score highest on the scale identified as prime for their
occupation. Zytowski and Hay (1984) used cluster analysis to reveal as
much as 20% of five selected occupational groups of women did not score
highest on their own scale of the Kuder Occupational Interest Survey
(KOIS; Kuder & Zytowski, 1991).
Holland (1996) observes that people will no longer work in a consistent
setting in a conventional occupation, but will attain a kind of career coherence
based on working at a set of activities that they value, applied in a variety of
settings. Fouad and Zao (2000) say, &dquo;Rather than training for a specific
occupation, workers will need to be occupational generalists to enter the
work force... and become occupational specialists once in a job&dquo; (p. 405).
Another variable that has considerable impact on the validity of
occupational scales is stability of the criterion. If people change occupations,
it would seem pointless to tell inventory-takers that they have interests
similar to those of any occupation.
There is sparse empirical data on the stability of occupational membership.
Gottfredson (1977) found figures between 10% and 25%, depending on age,
of a large sample changing jobs from one Holland type to another within a
period of 5 years. Arthur, Inkson, and Pringle (1999) found in their sample
of 75 adults working in a wide variety of careers that just 41% worked in the
same general occupational area throughout the 10 years their study covered.
Some writers (Howard, 1995; Rifkin, 1995) speculate that stable careers
will cease to be the norm. Day and Rounds (1997) remark &dquo;that lifelong
security in a single adult job...has passed into history. Most people can
only hope for career coherence based on a set of activities that they value,
rather than on a consistent setting of occupational title&dquo; (p. 208). Mitchell,
Levin, and Krumboltz (1999) state, &dquo;In virtually every employment sector,
job descriptions
are
changing,
some
occupations
are
becoming obsolete,
and unforeseen occupations are being created&dquo; (p. 116).
Such considerations led Kuder (1977b, 1980) to speculate that there
could be merit in matching a person not to the common characteristics of an
occupational group, but rather to all persons in a population of individuals,
each with their unique pattern of interests and particular ways of doing their
jobs-to &dquo;person-match.&dquo; After all, he says (1980), &dquo;If a person goes into law,
their career will be more like that of one particular lawyer than of any
other. No two people [in the same occupation] ever do exactly the same
thing... and no two careers are ever exactly alike&dquo; (p. 5). In support, he cites
Ghiselli’s (1966) observation that &dquo;there are as many jobs as there are
people&dquo; (p. 10).
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231
If a person’s similarity to another person in an occupation is the outcome
of an assessment, the information given to the interest survey-taker would
be necessarily more detailed than a single score. Kuder (1977b) suggested
that it should be a job sketch-a description of the individual’s present job
duties, sources of satisfaction and dissatisfaction, and other information.
Development
An unpublished investigation of the feasibility of the concept of personmatch was conducted in the early 1980s by the present author. One hundred
six experimental participants were given the results of their Kuder
Occupational Interest Surveys (KOIS; Kuder & Zytowski, 1991) scored
conventionally and &dquo;person-matched&dquo; within a &dquo;criterion pool&dquo; of junior
and senior students in a wide range of college majors. A little more than half
preferred the conventional KOIS, but approximately one fourth of the
participants reported that the person-match information-half-page written
descriptions of what the criterion pool majors entailed and what they
planned to do with them-was more valuable than criterion group scores on
the KOIS.
Development of a prototype version with a criterion pool of employed
men and women and of scoring software was undertaken by one of Kuder’s
associates in the early 1990s. A compilation of job sketches of the criterion
pool of 1,500 persons was published as The Kuder Book of People Who Like
Their Work (Hornaday & Gibson, 1995)
The new instrument, called the Kuder Career Search with Person-Match
(KCS; Zytowski, 2001; as well as all other Kuder inventories) was published
by National Career Assessment Services, Inc. of Adel, Iowa and is continuing
development.
Features
KCS may be thought of as three distinct assessments: (a) the preference
record, (b) Kuder career clusters, (c) person-match with job sketches, plus
various auxiliary materials. Different forms are available that incorporate
one or more of these components in pencil and paper, computer-based, or
online administration.
Most versatile is the online administration. This format affords error-free
administration and virtually instantaneous scoring around the clock from
home, school, or office. It yields a two-page summary or a full 12 to 15
page report, including five job sketches. The results can be reviewed by
parents or school personnel who have an appropriate access code and
password. Online administration also makes available an electronic portfolio
for the use of the survey-taker and an administrative management system.
Links are available to the Occupational Outlook Handbook (OOH; U.S.
Department of Labor, 2000) and newsgroups organized by cluster titles. KCS
has been adapted experimentally as a portal to several state guidance
information systems.
Auxiliary materials, including a user’s manual, overhead masters for
group administrations, aids for group and individual interpretations, lesson
plans for classroom use, and a quarterly user newsletter are available. A
counselor tutorial with a continuing education unit (CEU) quiz is located at
the www.Kuder.com website.
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232
The Preference Record
The preference record portion of KCS is composed of 180 activities,
written at a 6th-grade reading level, presented in the form of 60 triads.
Instead of Kuder’s Most-Least response format, survey-takers mark all
items in a triad, selecting the most, next most, and least preferred, in
effect, rank-ordering them.
Forty-eight of the triads were extracted from the KOIS; 12 new triads were
incorporated. Minor changes in the language of eight items were made
such as changing &dquo;listen to a radio program&dquo; to &dquo;watch a videotape.&dquo; Research
by Diamond (1965) suggests that such changes do not materially affect
response proportions. Twelve new triads were written to augment several
shorter scales. All scales are now composed of 10 items, scored 0 to 20,
except for the 9-item Nature scale.
The preference record portion of KCS is scored for 10 Activity Preference
scales. &dquo;Activity preference&dquo; is used in place of the former &dquo;vocational
preference&dquo; as several have content that relate to leisure as well as work
activities. The 10 scales, some renamed, and in an order approximating the
well known hexagonal arrangement, are Nature (formerly Outdoors),
Mechanical, Science, Art, Communications (formerly Literary), Human
Services (formerly Social Service), Sales (formerly Persuasive), Management
(a new scale), Computations, and Office Detail (formerly Clerical). The
Music scale, although a popular activity, was dropped as having too little
relevance to careers, as might be the case for an Athletics scale.
Results are reported in percentiles based on several norm groups: middle
school-boy and girl; high school-boy and girl; adult-combined gender.
Consistent with Kuder’s tradition, survey-takers are advised that the rank
order of the scales is the important information.
Kuder Clusters
Survey-takers’ ranks on six &dquo;Kuder clusters&dquo; are reported next. Although
their content would appear to be similar, these scales are not homogeneous
scales like those of the Self-Directed Search, but are criterion group scales,
like the occupational scales of the KOIS or the Strong Interest Inventory
(Harmon, Hansen, Borgen, & Hammer, 1994). They are named Outdoor/
Mechanical, Science/Technical, Arts/Communication, Social/Personal
Services, Sales/Management, and Business Detail.
The clusters are reported without numerical scores, in rank order, obtained
by averaging an inventory-taker’s scores on individuals from the criterion
pool who are employed in occupations keyed to each of the six clusters.
Their rankings generally echo the ranks of the activity preferences, despite
their being derived quite differently. For instance, criterion pool members
from the Science/Technical cluster rank first and second on the Science
and Mechanical activity preference scales, whereas Business Detail cluster
members rank highest on Computational and Office Detail scales.
Cluster ranks comprise an intermediate level of information between
activity preferences and person-matches and recognize the utility of
occupational data. Clusters customized to other schema, such as in some
career information systems, may be constructed using this method, provided
that the clusters contain sufficient homogeneity.
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233
Person-Matches
The next information delivered to inventory-takers is their personmatches. That is, they learn which 25 persons from the criterion pool have
activity preference patterns most similar to their own. The job titles are
presented in rank order, without numerical scores. They typically reflect
several apparently diverse prospects. Table 1 presents a list of personmatches for a high school junior, Ronnie, who is undecided on a career
direction, much less a vocational or occupational choice. They are distributed
among several educational levels and types.
Counselors who are schooled in the Parsonian (1909) model of career
development and lifelong occupation may find the diversity of these
possibilities unsettling. &dquo;What one should Ronnie choose?&dquo; they might ask.
The answer, drawing inspiration from some of the arguments of Mitchell
et al. (1999) might be, &dquo;These people, not all in the same occupation, have
something in common-they all have interest patterns that closely resemble
that of the inventory-taker. What might s/he learn from them that represent
opportunities that s/he could explore for herself?&dquo;
The method for deriving person-matches bears a conceptual kinship with
the newly conceived &dquo;recommender systems&dquo; employed by dot-com (Internet)
booksellers, movie rentals, or news browsers. It is founded on the heuristic
that a person might find satisfaction in the job or occupation of another
person who is similar to him or her. Kuder (1980) originally proposed an
item-by-item scoring method aggregating similarity of the triad rankings of
the survey-taker to each of the triad rankings of the individuals that
comprise the pool of criterion persons. It can be said that in this method
inventory-takers are not able to discover in what ways they are similar to
a criterion group or person. Accordingly, in KCS, the assessment of similarity
is made by comparing the activity preference profile of the inventory-taker
with each of the profiles of the persons in the pool of criterion persons.
There are essentially two methods of assessing profile similarity. The D2
statistic (employed by Dawis & Lofquist, 1984, in the Work Adjustment
Ronnie’s
Table 1
Person-Matches
Top
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234
Project) captures similarity in score levels, but not in shape. Correlational
methods capture similarity in shape, but not score levels. A variant of the
correlational approach is comparison of rank order, either as a Spearman
correlation or the arithmetic formulation used to match Holland codes with
the codes assigned to different occupations. The bulk of the research on
recommender system methodology tends to favor correlational methods,
so rank order (Pearson) correlation was chosen to represent the similarity
between the activity preference profiles of a survey-taker and each member
of the criterion pool database. Inventory-takers are matched against the
entire pool of criterion persons. Scores range in a normal distribution from
positive to negative low .90s or high .80s. The research on which of the
various Holland coding formulas is most valid (Brown & Gore, 1994) suggests
the advantage of weighted rank-order correlation. The use of such a
formulation in Person-Match is to be investigated.
The Criterion Pool Database
An initial pool of &dquo;criterion persons&dquo; obtained from volunteer adults
solicited from magazine subscriber lists comprised the initial database for
the person-match feature. It is now continuously updated to accommodate
newly emerging occupations (e.g., stem-cell researcher or a real-time TV
caption transcriber) and is maintained at a maximum of 2,500 persons. It
is intended to be representative of the full scope of occupational groups in
the OOH, accounting for 90% of the workers in the U.S. labor force.
Occupations that require choice and planning to enter, especially in the form
of education and training, are overrepresented.
It is often asked, &dquo;How do you know that the persons you select for the
criterion pool are representative of their occupations?&dquo; Kuder (1977b) would
respond that if an occupation has little or irrelevant homogeneity, no person
can be representative of it. They are simply themselves, working in their
unique way at their job at this time.
Counselors who have been schooled in Parsons’ (1909) concept of choosing
a life-long occupation may find the diversity of the person-matches
unsettling. Inventory-takers should not be advised, &dquo;You have interests
like those of (name of occupation)&dquo; but rather, &dquo;Here are several people
who have interests like yours. See what you can learn about them by
reading their descriptions of their jobs.&dquo;
An examination of the entries in the Kuder Book of People Who Like
Their Work (Hornaday & Gibson, 1995) of criterion persons from a single
occupational title, such as Accountant, reveals a wide variation in position
descriptions, job settings, education, prior employment, and aspirations. The
database also includes a number of occupations that are relatively
infrequent: ski instructor, horticultural therapist, athletic footwear engineer,
test pilot, and train attendant, among many others.
Job Sketches
A final question concerns what clients ought to learn from the results of
their interest assessment. In person-to-group match, as in the KOIS, it is
simply the title of an occupation. Many inventory-takers believe that they
know what people in various occupations do. But it has been shown (e.g.,
Beardslee & O’Dowd, 1962) that their knowledge may be incomplete, if not
downright
erroneous.
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235
Each of the individuals in the database of criterion persons has written a
brief description of their job (or jobs). They touch with varying emphasis on
what they do or a typical day at work, what they like and dislike about their
job, how they got into their occupation, and what they plan to do next. An
example of a job sketch is presented in Table 2.
Although no empirical test of the utility of first-person job descriptions
compared with generic descriptions has been undertaken, the potential of the
former is suggested by the advocacy of &dquo;informational interviewing,&dquo; and the
recent proliferation of collections of worker interviews, such as Real People,
Real Jobs (Montrose, Liebowitz, & Shinkman, 1995), or One Hundred Jobs:
A Panorama of Work in the American City (Howell, 2000). This approach is
strongly analogous to Wanous’ (1992) concept of realistic job previews that have
been found to reduce voluntary attrition among new hires.
Report Form
KCS results are provided in a combined graphic and narrative selfinterpreting multipage report, printed and returned to the inventory-taker
if scored from a pencil and paper administration. Online administration
results in a similar on-screen report that can be printed out. Results are
presented in rank order throughout, accompanied by descriptive material
to aid survey-takers’ understanding of what is being reported.
The first information presented is a profile of the 10 activity preference
scales rank ordered by percentiles, normed appropriately to their age and
gender. Brief descriptions of each scale are given, along with an explanation
of the percentile score, with an admonition to attend chiefly to the rank order.
A
Sample
Table 2
Person-Match Sketch
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236
Kuder cluster ranks are given next. Sample occupational titles are given
to illustrate the contents of each cluster at three levels of education; high
school diploma, 2-year degree, and 4-year degree or higher are included.
The report then gives the titles of the top-ranking person matches: 1%25 in all-of the criterion pool. In the mail-back scored versions, the job
sketches of the top five are included. In the online administration, links are
provided to access job sketches, corresponding OOH entries, other
occupational information sources on the Internet, and recently published
newspaper articles grouped by cluster titles. A final page offers inventorytakers guidance for continuing their career exploration and planning.
Reliability
and
Validity
Fouad (1999, p. 194) has recapitulated the interest measurement Messick’s
(1995) six aspects of validity: content, or how the test items sample the
domain; substantive, or the quality of responses to items; structural, the
factor structure and reliability of scales; generalizability, that results apply
across time, populations, and settings; external, or what is called concurrent
and predictive validity; and consequential, what happens as a result of the
assessment process.
In general, the development of KCS has emphasized utility in preference
to elegant psychometrics. For example, higher scale homogeneities can be
achieved, as they are in the Kuder General Interest Survey (Kuder & Zytowski,
1988), but at the expense of substantially longer administration time.
Content validity of the KCS is well established in Kuder’s (1977a)
discussion of item selection. Substantive validity, as Messick (1995) describes
it, would appear to encompass the forced-choice method of responding to the
item triads. Kuder (1975) explains his approach, saying &dquo;[It] is not enough
to know that an individual strongly likes both ’attending a baseball game’
and ’attending an opera’. What is important is which of these he or she
prefers when faced with a choice&dquo; (p. 6). Jackson (1977) finds that a liking
or disliking response set accounts for as much as 35% of the response
variance and elects the forced-choice methodology for his assessment.
Part of the structural validity of the KCS resides in scale reliabilities and
intercorrelations. The median homogeneity of the activity preference scales
has been found to be .72, in a range from .64 to .90 (n 146 adults, online
administration). The median whole-profile test-retest reliability has been
found to be .81 (n
90 college students, 3-week interval). This figure is
to
that
found
for the vocational scales of the KOIS and to those
comparable
Jackson
and Hansen and Swanson (1983).
(1977)
reported by
Fouad (1999, p. 200) concludes that the generalizability of interest
inventories across racial and ethnic groups is high, but suggests that
interpretations of results (What is a high score?) may not be so robust.
Studies of the previous Kuder (1983, 1988, 1991) inventories draw similar
conclusions, but a separate study of KCS is merited.
Establishing the validity of the novel application of the preference scale
results in person-match is problematic. The procedure would appear to
possess face validity: if a survey-taker has a profile of activity preferences
identical to or very much like those (r .90) of certain other persons, there
must be real similarities. There are no instruments like the KCS, precluding
concurrent validation.
=
=
=
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237
The KCS is intended
more to stimulate or facilitate career exploration than
occupation, thus, the usual form of predictive validity cannot
apply. The new consequential validity would appear to be the more
appropriate test. Several investigations of the effects of taking the KCS on
career maturity and information seeking are currently in progress.
to select
an
Applications
The activity preference profile and cluster scoring of KCS are usable in
conventional career counseling ways, informing inventory-takers of their
preference patterns and similarity with several clusters of occupations. The
person-match portion seems best applied as &dquo;a structure for processing
possibilities&dquo; (Tyler, 1978) or the consideration of &dquo;possible selves&dquo; (Markus
& Nurius, 1986), an approach that has recently been suggested by Meara, Day,
Chalk, and Phelps (1995) and Krieshok, Hastings, Ebberwein, Wetterstein,
and Owen (1999). There are many individuals who would seem well served
by such a view: high school students, college students who have not yet
declared a major field, and displaced homemakers and workers, all of whom
may benefit by entertaining a wide range of possible occupational roles.
Counselors who adhere to the &dquo;constructivist&dquo; perspective of career
counseling (Peavy, 1997) might use person-matches and job sketches to
promote &dquo;as if’ thinking, the use of counterfactual scenarios, and generation
of alternate possibilities.
KCS lends itself to both individual and group applications. It is adaptable
to individual or small group counseling in response to stated requests for
help in developing career and educational plans. It may be used in schools
as a component of an ongoing career education program or as a basis for
instructional units in the regular classroom. Individual or small group
interpretation with a skilled counselor may help the survey-taker identify
themes that reflect preferences for various aspects of work such as are not
found in the popular conceptualizations of careers.
Counselor Use
Interpretation of the Activity Preference scales, whether individually or
a group, proceeds in the usual manner, although emphasis is given not
as much to percentile scores, but to rank order. It is not a matter of whether
the survey-taker &dquo;likes&dquo; or &dquo;dislikes&dquo; a particular activity, but rather one’s
relative preference for it among the 10 possibilities. Survey-takers should
be helped to understand that they most prefer (for instance) Human Services,
then Sales, then Art activities, and least prefer Computations.
Kuder career clusters, which may not be familiar to survey-takers, may
be explained either by highlighting some of the definitions that are given
in the report, or by naming some of the occupations they contain from the
in
charts that follow them in the report.
Interpretation of the person-matches is another matter. For the KOIS,
counselors typically say to the survey-taker, &dquo;Your preferences rank highest
on Human Services, Sales, and Art. They are most similar to those of (say)
Nurses.&dquo; In person-match, the explanation should be on the order of the
following: &dquo;Your preferences rank highest on Human Services, Sales, and Art.
People from the database who have a profile of interests most like yours are
employed as an obstetrics nurse, a school librarian, a nurse administrator,
a nurse manager, and a manager of a theatrical company, among others. You
can learn about yourself by learning how they describe the ways they make
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238
their
living.&dquo; The sequence is from activity preferences to possibilities to
gathering information about the persons most similar in interests to the
survey-taker.
It is important that survey-takers read and digest the job sketches of their
top person-matches. They should be encouraged to notice what activities
(verbs) they denote, the objects of those activities (such as the data/people/
things trichotomy), the skills they require, the educational levels that are
reflected, what the criterion persons find to be the rewards of their jobs, and
so on. They may continue with the relevant generic descriptions from a
publication like the OOH, or use their results to formulate their personal
program of informational interviewing.
Case
Study:
Lea
pseudonym) presented herself at the college counseling center
assistance
in clarifying her career goals. She is a 20-year-old
seeking
woman; her father is an engineer, her mother is active in the arts, and she
was reared in an English-speaking country abroad. The family immigrated
to the United States the year before; one older brother continues engineering
study at a college in her former home. Lea has developed a community of
friends and says she does not regret having made the move with her family.
Lea had completed 2 years of university study in psychology in her former
country of residence. She thought it was interesting, but decided to
discontinue it because it seemed too concerned with &dquo;illness.&dquo; In the past
year, she had a temporary job with a company that offered private beginner
instruction to adults in the use of computers. She said that she enjoyed this
work and was considering enrolling in a vocational-technical school to
further develop her computer skills. Although she thought this could prove
to be a good opportunity for her, she felt it would be prudent to seek the
expert help of the counseling service.
Lea took the KCS online at home, and was able to review her results before
returning to see the counselor. It revealed four activity preference scales
between the 95th and 87th percentiles, in order, Science/Technical, Human
Services, Communications, and Art. In view of their eight percentile spread,
they could be considered as virtually tied for first rank. Office Detail,
Computations, and Mechanical comprised the bottom quartile of her profile.
Her first remark was, &dquo;Science? I don’t like science.&dquo; In talking about what
she did enjoy, the counselor was able to draw an inference for her: &dquo;It appears
to me that you get a lot of pleasure out of learning things. Do you think
that’s what your high rank on science is saying?&dquo; She accepted this description
as more fitting than &dquo;liking science.&dquo; Often the work of the counselor is to help
the client articulate unrecognized self-concepts that the survey results offer
to the client in general terms such as &dquo;science.&dquo; (Zytowski, 1999, elaborates
on techniques of interpreting interest inventories.)
Lea was more willing to affirm her high rank on Human Services,
mentioning her admiration for a woman who had achieved a high position
from modest beginnings in the education field. She dismissed her high
rank in Art as true, but nothing she wanted to pursue in her worklife.
Nevertheless, the counselor suggested that hers was a well founded patternthat her interests fit together (based on their similarity to the IAS quadrant
of the Holland, 1997, hexagon)-and were probably unlikely to change
radically. And, incidentally, they seemed to echo her parents’ interests.
Lea (a
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239
But her intention in visiting the counselor was to see if there were
possibilities that might have as much or more potential satisfaction as her
computer classes might provide. Her top ranking person-match unsettled her,
for it was Catholic Priest’. She was &dquo;certainly not Roman Catholic, and
not really religious at all.&dquo; This was aggravated by the presence of Missionary
farther down the list of her top matches. The counselor did not dispute
her response, but called her attention to the number of teachers (nine, in a
variety of settings) in her top 25 ranks, generalizing that they represented
a theme that could include the religious workers as well.
In rather typical sophomore haste, Lea had not read any of the job
sketches associated with her high-ranking person-matches. The counselor
suggested that Lea scan the job sketches to see if she could synthesize
some common themes in them. Other helping occupations based in science,
such as Nutritional Scientist, Physician Assistant, and nurses in a number
of different specialties were present in her list of top rankers, all apparently
consistent with her activity preferences. As the discussion progressed, Lea
especially focused her attention on several titles from the ranking matches:
Director of Adult Education, Director of Employment Training;
Administrator, Training Department; and Senior Personnel Assistant. The
counselor urged Lea to survey all of the job sketches from her top matches,
available online, and return to talk about what she found.
At her next visit, Lea exhibited more, yet cautious enthusiasm for some
of the possibilities in her list of person-matches. The pastor’s job sketch
revealed that he has been assigned as a parish and elementary school
administrator. The missionary had lived abroad and was planning to extend
her/his skills by studying nursing. Lea could see how they both could have
similarities with her preferences, but, nevertheless, rejected them as
revealing possibilities for herself.
Still harboring her expressed avoidance for &dquo;sick people,&dquo; she dismissed
all of the health-related occupations in her top ranking person-matches.
Discussion of the job sketches of the several training or personnel jobs was
more productive. Lea introduced similarities between them and her recent
temporary job, working with adults in teaching or advising. She did find it
intriguing that none were early career choices, but rather had been
opportunities that had presented themselves unforeseen, as Mitchell et al.
(1999) have described. This bit of insight she felt supported her choice to
continue with her choice of computer training, seeking it’s exact application
later. She felt that this was sufficient planning and that she could develop
a satisfying worklife without naming a specific long-range career objective.
This may not be viewed as an entirely successful case by some readers.
The counselor was apparently unable to dissuade Lea from her superficial
rejection of a number of possibilities presented in her person-matches. If the
counselor’s goal was to have Lea choose a specific occupational objective, then
that too might be regarded as failed. On the other hand, Lea did learn of a
career path-training and development in the private sector-that she had
not previously known as well as something about career planning that
relies on more near-term rather than long-term goals-all in all, perhaps not
unsatisfactory progress.
Interested readers may consult the job sketches for these titles
1
of People Who Like Their Work (Hornaday & Gibson, 1995).
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in the Kuder Book
240
References
Arthur, M. B., Inkson, K., & Pringle, J. K. (1999). The
and economic change. London: Sage.
new careers:
Individual action
Beardslee, D. C., & O’Dowd, D. D. (1962). Students and the occupational world. In
(Ed.), The American college (pp. 597-626). New York: Wiley.
Betz, N. E., & Borgen, F. H. (2000). The future of career assessment: Integrating vocational
interests with self-efficacy and personal styles. Journal of Career Assessment, 8, 329-338.
Brown, S. D., & Gore, P. A. (1994). An evaluation of interest congruence measures:
Distribution characteristics and measurement properties. Journal of Vocational Behavior, 45,
N. Sanford
310-327.
Dawis, R. V., & Lofquist, L. H. (1984). A psychological theory of work adjustment.
Minneapolis: University of Minnesota Press.
Day, S. X., & Rounds, J. (1997). "A little more than kin, and less than kind:" Basic interests
in vocational research and career counseling. Career Development Quarterly, 45, 207-220.
Diamond, E. E. (1965, April). Changes in response to a Kuder verification scale item as a
result of substitution of synonymous phrasing. Paper presented at the meeting of the National
Council on Measurement in Education, New York.
Dolliver, R. H., & Nelson, R. E. (1975). Assumptions regarding vocational counseling.
Vocational Guidance Quarterly, 24, 12-19.
Fouad, N. A. (1999). Validity evidence for interest inventories. In M. L. Savickas & A. R.
Spokane (Eds.), Vocational interests (pp. 193-209). Palo Alto, CA: Davies-Black.
Fouad, N. A., & Zao, K. E. (2000). Meeting the revolution: Future trends in vocational
assessment. Journal of Career Assessment, 8, 403-409.
Ghiselli, E. E. (1966). The validity of occupational aptitude tests. New York: Wiley.
Gottfredson, G. D. (1977). Career stability and redirection in adulthood. Journal ofApplied
Psychology, 62, 436-445.
Hansen, J. C., & Swanson, J. L. (1983). Stability of interests and the predictive and
concurrent validity of the 1981 SCII for college majors. Journal of Counseling Psychology, 30,
194-201.
Harmon, L. W., Hansen, J. C., Borgen, F. H., & Hammer, A. L. (1994). Strong Interest
Inventory: Applications and technical guide. Palo Alto, CA: Consulting Psychologists Press.
Holland, J. L. (1994). The Self-Directed Search: Professional users guide. Odessa FL:
Psychological Assessment Resources.
Holland, J. L. (1996). Exploring careers with a typology: What we have learned and some
new directions. American Psychologist, 51, 397-406.
Holland, J. L. (1997). Making vocational choices: A theory of vocational personalities and
work environments (3rd ed.). Odessa, FL: Psychological Assessment Resources.
Hornaday, J. A., & Gibson, L. A. (1995). The Kuder book of people who like their work.
Amherst, NH: Motivation Press.
Howard, A. (Ed.). (1995). The changing nature of work. San Francisco: Jossey-Bass.
Howell, R. (2000). One hundred jobs: A panorama of work in the American city. New York:
New Press.
Jackson, D. N. (1977). Jackson Vocational Interest Survey. Ontario, Canada: Research
Psychologists Press.
Krieshok, T. S., Hastings, S., Ebberwein C., Wetterstein, K., & Owen, A. (1999). Telling
a good story: Using narratives in vocational rehabilitation with veterans. Career Development
Quarterly, 47, 204-214.
Kuder, F. (1975). Manual for the Kuder E General Interest Survey. Chicago: Science
Research Associates.
Kuder, F. (1977a). Activity interests and occupational choice. Chicago: Science
Research Associates.
Kuder, F. (1977b). Career matching. Personnel Psychology, 30, 1-4.
Kuder, F. (1980). Person matching. Educational and Psychological Measurement, 40, 1-8.
Downloaded from jca.sagepub.com at PENNSYLVANIA STATE UNIV on May 17, 2016
241
Kuder, F. (1983). Manual
Career Assessment Services.
for
the Kuder
Preference
Record: Form C.
Adel, IA: National
Kuder, F., & Zytowski, D. G. (1988). Kuder General Interest Survey, Form E, Preliminary
manual. Adel, IA: National Career Assessment Services.
Kuder, F., & Zytowski, D. G. (1991). Kuder Occupational Interest Survey, General manual
(3rd ed.). Adel, IA: National Career Assessment Services.
Lock, J. D., & Hogan, R. (2000). Expanding the focus of career assessment. Journal of Career
Assessment, 8, 411-417.
Markus, H., & Nurius, P. (1986). Possible selves. American Psychologist, 41, 954-969.
Meara, N. M., Day, J. D., Chalk, L. M., & Phelps, R. E. (1995). Possible selves: Applications
for career counseling. Journal of Career Assessment, 3, 259-277.
Messick, S. (1995). Validity of psychological assessment: Validation of inferences from
persons’ responses and performances on scientific inquiry into score meaning. American
Psychologist, 50, 741-749.
Mitchell, K. E., Levin, A. S., & Krumboltz, J. D. (1999). Planned happenstance: Constructing
unexpected career opportunities. Journal of Counseling and Development, 77, 115-124.
Montrose, D. H., Liebowitz, Z. B., & Shinkman, C. J. (1995). Real people, real jobs:
Reflecting your interests in the world of work. Palo Alto, CA: Davies-Black.
Mount, M. K., & Muchinsky, P. M. (1978). Concurrent validation of Holland’s hexagonal
model with occupational workers. Journal of Vocational Behavior, 39, 348-354.
. Boston: Houghton-Mifflin.
Parsons, F. (1909). Choosing a vocation
Peavy, R. V. (1997). A constructive framework for career counseling. In T. L. Sexton &
B. L. Griffin (Eds.), Constructivist thinking in counseling practice, research and training
(pp. 122-141). New York: Teachers College, Columbia University.
Rifkin, J. A. (1995). The end of work. New York: Putnam.
Tyler, L. E. (1978). Individuality: Human possibilities and personal choice in the
psychological development of men and women. San Francisco: Jossey-Bass.
U.S. Department of Labor. (2000). Occupational outlook handbook. Washington, DC: Author.
Wanous, J. P. (1992). Organizational entry: Recruitment, selection, orientation, and
socialization of newcomers. Reading, MA: Addison-Wesley.
Zytowski, D. G. (1999). How to talk to people about their interest inventory results. In
M. L. Savickas & A. R. Spokane, (Eds.), Vocational interests: Meaning, measurement, and
counseling use (pp. 277-293). Palo Alto, CA: Davies-Black.
Zytowski, D. G. (2001). Kuder Career Search: User’s manual. Adel, IA: National Career
Assessment Services.
Zytowski, D. G., & Hay, R. (1984). Do birds of a feather flock together? A test of the similarities
and differences between five occupations. Journal of Vocational Behavior, 24, 242-248.
Downloaded from jca.sagepub.com at PENNSYLVANIA STATE UNIV on May 17, 2016