Facilitating Career Decision-Making

Itamar Gati The Hebrew University of Jerusalem

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In this presentation, I will

Discuss the decision-theory viewpoint Present the PIC 3-stage cdm model Introduce the CDDQ Describe the CDSQ – cdm style Demonstrate MBCD - Making Better Career Decisions

Review research and demonstrate applications Highlight the unique features of our approach

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Unique features of career decisions

Quantity of Information: Often large N of alternatives and factors, within-occupation variance information is practically unlimited

Quality of Information:soft, subjective, fuzzy, inaccurate, biased

Uncertainty about:the individual’s future preferences, future career options, unpredictable changes and opportunities, probability of implementing choice

Non-cognitive Factors:emotional and personality-related factors, necessity for compromise, actual or perceived social barriers and biases

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From decision theory to career counseling practice

Many factors contribute to the complexity and difficulties involved in career decision-making

The basic claim:

Career counseling may be viewed as decision counseling, which aims at facilitating the clients' decision-making process, and promotes better career decisions

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If so evident, why was decision-theory not adopted until recently?

Because Normative decision theory (how individuals should

make decisions) is – too rational too arbitrary too quantitative exceeds human’s information-processing capability

Descriptive decision theory (how individuals actually make decisions) is not helpful either – it mainly documents human weakness heuristics, biases, and fallacies limited information-processing capabilities

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The Proposed Approach –

By adopting decision theory and adapting it to the unique features of career decisions, theoretical knowledge can be translated into practical interventions to facilitate individuals’ career choices

Specifically, we suggest focusing on a prescriptive approach, and designing systematic procedures that can help individuals make better career decisions (not necessarily rational ones!)

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The first stage in helping clients is needs assessment:

The 3 components of needs assessment are:

the individual’s stage in the cdm process(“where”)

the focuses of the individual’s cdm difficulties (“what”)

the individual’s cdm style (“who”)

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The PIC model (Gati & Asher, 2001)separates the career decision-making process into 3 distinct stages:

- Prescreening

- In-depth exploration

- Choice

I - Stages in the career decision-making process

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Prescreening

Goal: Locating a small set (about 7) of promising alternatives that deserve further, in-depth exploration

Method: Sequential Elimination Locate and prioritize relevant aspects or factors Explicate within-aspect preferences Eliminate incompatible alternatives Check list of promising alternatives

Outcome: A list of verified promising alternatives worth further, in-depth exploration

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A Schematic Presentation of theSequential Elimination Process (within-aspects, across-alternatives)

Potential Alternatives

1 2 3 4 . . . . N

Aspects

a (most important)

b (second in

importance)

c

.

n

Promising Alternatives

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Final step - Sensitivity Analysis

The Goal:

Verifying the adequacy of the promising list

The Method: An alternative (compensatory-model-based)

search “why not” “almost compatible” “what if” “similar alternatives”

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In-depth exploration

Goal: Locating alternatives that are not only promising but indeed suitable for the individual

Method: collecting additional information, focusing on one promising alternative at a time: Is the occupation INDEED suitable for me?

verifying compatibility with one’s preferences in the most important aspects

considering compatibility within the less important aspects Am I suitable for the occupation?

probability of actualization: previous studies, grades, achievements

fit with the core aspects of the occupation

Outcome: A few most suitable alternatives (about 3-4)

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A Schematic Presentation of the In-depth Exploration Stage(within-alternative, across aspects)

Promising Alternatives 1 2 3 4 5 6

Suitable Alternatives

2 4 5

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Choice

Goal: Choosing the most suitable alternative, and rank-ordering additional, second-best alternatives

Method: comparing and evaluating the suitable alternatives pinpointing the most suitable one

Am I likely to activate it? if not - selecting second-best alternative(s) if yes - Am I confident in my choice?

if not: Return to In-depth exploration stage if yes: Done!

Outcome: The best alternative or a rank-order of the best alternatives

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II - Career Decision-Making Difficulties

One of the first steps in helping individuals make a career decision is locating the focuses of the difficulties they face in the decision-making process

Relying on decision theory, Gati, Krausz, and Osipow (1996) proposed a taxonomy for describing career decision-making difficulties

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Prior to Engaging in the Process

Lack of Readiness due

to

Lack of motivatio

n

Indeci-sivene

ss

Dysfunc-tional beliefs

During the Process

Lack of Information

about

Cdm proce

ss

Self Occu-patio

ns

Ways of obtaining info.

Inconsistent Information due

to

Unreliable Info.

Internal conflict

s

Externalconflic

ts

Possible Focuses of Career Decision-Making Difficulties

(Gati, Krausz, & Osipow, 1996)

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The Career Decision-making Difficulties Questionnaire (CDDQ)

The Career Decision-making Difficulties Questionnaire (CDDQ) was developed to test this taxonomy and serve as a means for assessing individuals’ career decision-making difficulties

Cronbach Alpha internal consistency estimate of the total CDDQ score is high (above .90)

The proposed structure was empirically supported (N=10,000)

For additional information – see www.cddq.org--- the CDDQ is offered free of charge ---

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www.cddq.org

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1. Ascertaining Credibility, using validity items and the time required to fill out the questionnaire

2. Estimating Differentiation based on the standard deviation of the 10 difficulty-scale scores

3. Locating the salient, moderate, or negligible difficulties, based on the individual's absolute and relative scale scores

4. Determining the confidence in the feedback and the need to add reservations to it (based on doubtful credibility, partial differentiation, or low informativeness)

The Four Stages of Interpretation

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The 4 Stages of Interpretation

CredibleDoubtful

HighQuestionable

Locate Salient Difficulties

Add Reservationto Feedback

Low

No Feedback

Compute Informativeness

(B /W )

Receives Feedback

B/W > 1

B/W < 1

Estimating Differentiation

EvaluatingCredibility

Not Credible

AggregateReasons to Add

Reservation (RAR)

RAR ≤ 2RAR = 3

1

2

3

4

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Four Studies -for validating the proposed interpretation

Method Participants: 15-30 career counselors and 25-80

graduate counseling students Questionnaires – including CDDQ responses:

- in Study 1 and 4 – all possible responses;

- in Studies 2 and 3 – responses of 16 actual clients Results:

High similarity within-groups as well as between counselors’ and students’ judgments High similarity between the experts’ judgments and the proposed algorithm at each stage

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The distribution of types of feedback in the four groups

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

P & P Internet P & P Internet

feedback

add reservation

no feedback

Hebrew English

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Conclusions

The incorporation of an intermediate level of discrimination increases the usefulness of the feedback and decreases the chances and implications of potential errors

Adding reservations when appropriate is

essential for providing a meaningful feedback and decreasing the chances of misleading conclusions

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III – Career Decision-Making Styles

Diagnosing the client’s career decision-making style is important in order to “tailor” the career-counseling intervention to his or her unique characteristics

Previous research often did not take into consideration the complexity and variety of aspects related to the decision process, and classified decision-styles based only on a single, most dominant characteristic (e.g., rational vs. intuitive)

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Goals

Developing a multidimensional model for describing career decision-making styles

Developing the Career Decision-making Styles Questionnaire (CDSQ) for testing the model and enabling a more accurate assessment of individuals’ career decision-making styles

Empirically deriving a typology of the CDSQ profiles from a large sample of individuals

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Derivation of the 11 Dimensions

Comparing the most common 12 prototypes deduced from previous research to uncover the various characteristics differentiating among them

From this list we derived 11 basic dimensions relevant for characterizing individuals' cdm styles.

On each dimension, individuals can be characterized along a continuum of a bipolar scale: e.g., on the dimension pattern of information processing individuals can be characterized from "analytical" to "holistic"; desire to please others – "high" to "low"

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The 11 Proposed Dimensions

Information processing (analytic vs. holistic) Information gathering (much vs. little) Amount of effort invested in the process (much vs. little) Consultation with others (frequent vs. rare) Aspiration for an "ideal occupation" (high vs. low) Willingness to compromise (high vs. low) Locus of control (internal vs. external) Procrastination in entering the process (high vs. low) Speed of making the final decision (fast vs. slow) Dependence on others (high vs. low) Desire to please others (high vs. low)

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The Career-Decision-making Style Questionnaire (CDSQ)

44 statements (4 items x 11 dimensions)

Response scale: 1 – Strongly disagree to

7 – Strongly agree

The CDSQ is embedded in career-related self-help Internet sites Future Directions (Hebrew), CDDQ.ORG (English)

3 Development samples (N=230, 404, 411)

Fourth sample - 479 subjects

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Results – (Items)

Scale Reliabilities: median - .80, range .73 – .85Factor analysis: 10 factors Accounted-for Variance = .65 2 dimensions were included in one factor

(Speed of making the final decision; Procrastination) Two items loaded higher on a “neighbor factor” (Information-

processing; effort invested)

Cluster analysis: Accounted-for Variance = .81 Items of 7 dimension clustered perfectly (4/4)

4 dimension – 3/4 items

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Conclusions & Implications

The proposed and tested 11 dimensions can be used to characterize individuals' career decision-making styles

Using the CDSQ, homogeneous groups of clients with similar career decision-making styles can be empirically identified

The CDSQ allows a more accurate assessment of the counselees' career decision-making styles, thus better “tailoring” the intervention to the individual

The CDSQ allows individuals to learn about their career decision-making style, and thus to consider adopting more desirable strategies

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So far, I reviewed

3 components of client’s needs assessment: The individual’s stage in the cdm process (“Where”)

The focuses of the individual’s cdm difficulties (“What”)

The individual’s cdm style (“Who”)

So, what’s next? Some demonstrations of how can the decision-making

approach be implemented in order to actually facilitate clients’ cdm

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Specifically,if career decision-making requires collectinga vast amount of information, and if complex information-processing is needed,

we must then utilize the best available resource:

Career counselors’ expert knowledge, that canbe elicited and transformed into Information and Communication Technology-based systems

Indeed, - The computer-assisted career guidance systems, based on a decision-theory model, can help overcome human’s cognitive limitations

- There are several computer-assisted career guidance systems available today on the Internet

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MBCD Making Better Career Decisions

MBCD is an Internet-based career planning system that is a unique combination of a career-information system a decision-making support system an expert system

Based on the rationale of the PIC model, MBCD is designed to help deliberating individuals make better career decisions

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Making Better Career Decisions

http://mbcd.intocareers.org

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However,

Although Internet-based, career-related self-help sites are flourishing, these sites vary greatly in quality

Therefore,

it is very important to investigate the utility and validity of these self-help programs

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So,

Making Better Career Decisions

Does it really work?

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• Examine users' perceptions of MBCD • Examine changes in user’s decision status  • Examine perceived benefits • Locate factors that contribute to these variables

Criteria for Testing the Benefits of Making Better Career Decisions

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MBCD’s Effect (Cohen’s d) on Reducing Career Decision-Making Difficulties

(Gati, Saka, & Krausz, 2003)

0.31

0.72

0.11

0.65

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

Lack ofReadiness

Lack ofInformation

InconsistentInformation

Total CDDQ

d

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Decision Status Before and After the “Dialogue” with MBCD

After the dialogue

Before the dialogue

1 2 3 4 5

1- no direction 34 7 6 7 0 

2 - only a general direction

41 66 15 9 5 

3 - considering a few specific alternatives

27 58 84 30 6  

4 - would like to examine additional alternatives

23 51 35 54 6  

5 - would like to collect information about a specific occupation

9 20 21 41 28  

6 - sure which occupation to choose

3 0 1 9 16 

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Perceived Suitability of the "Promising Alternatives" List (N=693)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

26+(n=37)

16-25(n=46)

11-15(n=40)

8-10(n=45)

7(n=236)

6(n=121)

5 (n=71)

3-4(n=74)

2 (n=23)

Number of Alternatives (n - of users)

too long

suitable

too short

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Predictive Validity of MBCD (Gati, Gadassi, & Shemesh, 2006)

Design: Comparing the Occupational Choice Satisfaction (OCS) of two groups six years after using MBCD and getting a list of occupations recommended for further exploration:

those whose present occupation was included in MBCD’s recommended list (44%)

those whose present occupation was not included in MBCD’s recommended list (56%)

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Method

Participants The original sample included 123 clients who

used MBCD in 1997, as part of their counseling at the Hadassah Career-Counseling Institute

Out of the 73 that were located after six+ years, 70 agreed to participate in the follow-up: 44 women (64%) and 26 men (36%),aged 23 to 51 (mean = 28.4, SD = 5.03)

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84%

38%

16%

44%

18%

0%10%20%30%40%50%60%70%80%90%

100%

accepted

recommendations

did not accept

recommendations

low satisfaction

medium satisfaction

high satisfaction

Frequencies of Occupational Choice Satisfaction by “Acceptance” and “Rejection” of MBCD's Recommendations (Gati, Gadassi, & Shemesh, 2006)

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Means of the Gender Dominance Ratings According to Type-of-List and Gender

3.18

2.96

3.13

2.71

2.42.52.62.72.82.933.13.23.3

Directly ElicitedIndirectly Derived

Men

Women

Gender Differences in Directly Elicited and Indirectly Derived Preferred Occupations

(279 Women + 79 Men, Mean Age=23; Gadassi & Gati, 2008)

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Summary of Major Findings

PIC is compatible with people’s intuitive ways of making decisions (Gati & Tikotzki, 1989)

Most users report progress in the career decision-making process (Gati, Kleiman, Saka, & Zakai, 2003) Satisfaction was also reported among those who did not

progress in the process Users are “goal-directed” – the closer they are to making a

decision, the more satisfied they are with MBCD

The list of “recommended” occupations are less influenced by gender stereotypes (Gadassi & Gati, 2008)

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In Conclusion – Features of our Approach

Prescreening is essential when the number of potential alternatives is large

Instead of focusing on occupations (alternatives) we suggest to focus on aspects

Instead of a “snap-shot” assessments of vocational interests (e.g., the 3-highest RIASEC Holland’s code), use for prescreening a wide range of factors elicited by a dynamic, interactive process

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In Conclusion – Features of our Approach (cont.)

From the viewpoint of the individual, this enables: - Differentiating between relative importance of factors, the optimal level, and the willingness to compromise- Assessing the individual’s preference crystallization (does s/he knows what s/he is looking for?)

With respect to occupations, this enables:- Characterizing occupations in terms of a range of levels, representing the within-occupation variance - Highlighting the essence of the occupation (using the core aspects)

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We believe that . . .

Computers can and should be used not only for scoring, but also for monitoring a dynamic interaction, and providing flexible interpretations

Experts’ knowledge can and should be elicited and transformed to design and improve interpretive feedbacks on assessments

Career choices are the outcome of decision-making processes; therefore, career counseling is, in fact, decision counseling

The goal should be promoting a systematic decision making process – not a rational one

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Finally, we also believe that . . .

Career-related assessments can be transformed into user-friendly Internet-based systems, which can also be incorporated into counseling interventions

Interpretive feedback is important but has to be “tailored” and validated

Theory-based interventions should always be tested for empirically validity as well as practical effectiveness

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www.cddq.org

itamar.gati@huji.ac.il

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end

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------sfsfsf------------

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Previous Research

1. 39 labels used for describing decision-making styles were located

2. In light of the high resemblance among some of them (e.g., logical [Arroba, 1977], rational [Harren, 1979], active-planning [Jepsen, 1974], systematic [Johnson, 1978]), these 39 types were narrowed down to 12 prototypes :

rational, perfectionist, procrastinator, searching for tools, satisfying, hesitant, impulsive, fatalist, intuitive, dependent, rebellious, and pleasing.

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Alternative Explanations [to MH] – were not supported

Differences in the lengths of the lists

No difference was found in the OCS between clients whose list included 15 or fewer occupations and clients whose list included more than 15 occupations

Clients who accepted MBCD’s recommendations are more compliant, and therefore more inclined to report a high level of satisfaction

However, following the compensatory-model-based recommendations did not contribute to the OCS

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Results - Typology

GroupG1G2G3G4G5G6G7

DIMENSIONn=38n=77n=79n=65n=47n=87n=64

Information-processing3.654.914.765.173.455.355.09

Information gathering3.945.215.345.833.435.945.79

Amount of effort invested4.826.066.055.414.626.325.72

Consultation with others5.645.586.264.025.335.645.83

"ideal occupation"4.122.013.122.253.392.562.14

Willingness to compromise3.084.814.913.335.123.603.03

Locus of control5.595.244.916.105.265.615.58

Speed of making decision3.733.242.255.423.022.474.38

Procrastination4.215.102.665.782.583.534.97

Dependence6.165.603.896.475.766.196.40

Desire to please others5.884.724.265.695.795.945.99

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Locating and prioritizing aspects or factors

Explicating within-factor preferences in the most important factor not yet considered

Eliminating incompatible alternatives

Too many promising alternatives?

This is the recommended list of occupations

worth further, in-depth exploration

yes

no

Steps in Sequential Elimination