35
Part 4 The PIC Model: Supporting Evidence or: Does it really work? Itamar Gati The Hebrew University of Jerusalem

Part 4 The PIC Model: Supporting Evidence

  • Upload
    tuwa

  • View
    25

  • Download
    0

Embed Size (px)

DESCRIPTION

Part 4 The PIC Model: Supporting Evidence. or: Does it really work?. Itamar Gati The Hebrew University of Jerusalem. Evaluating Prescriptive Decision Models. Descriptive models are evaluated by their empirical validity Normative models by their theoretical adequacy - PowerPoint PPT Presentation

Citation preview

Page 1: Part 4 The  PIC  Model: Supporting Evidence

Part 4The PIC Model: Supporting Evidence

or: Does it really work?

Itamar Gati

The Hebrew University of Jerusalem

Page 2: Part 4 The  PIC  Model: Supporting Evidence

2

Evaluating Prescriptive Decision Models

Descriptive models are evaluated by their empirical validity

Normative models by their theoretical adequacy

Prescriptive models are evaluated by their pragmatic value – their ability to facilitate individuals' decision-making

Page 3: Part 4 The  PIC  Model: Supporting Evidence

3

Evaluating Prescriptive Decision Models

The basic assumption: the right process increases the probability of choosing the best option

The evaluation of the model should examine: Does the model improve individuals' decision-making

processes? Does it lead to greater occupational satisfaction in the

future? Do individuals generalize the model and apply it to

future career decisions?

Page 4: Part 4 The  PIC  Model: Supporting Evidence

4

Prescreening Based on Elimination: Descriptive Validity (Gati & Tikotzki,1989)

The monitored dialogues of 384 career counselees with a computer-assisted career information system were analyzed.

Results: most users (96%) employed a non-compensatory strategy during all or at least a part of the dialogue: many options considered at a previous stage of the dialogue were not considered at the following stage, showing that individuals tend to use a prescreening strategy based on eliminating alternatives

Page 5: Part 4 The  PIC  Model: Supporting Evidence

5

Examine users' perceptions of MBCD

Examine changes in user’s degree of decidedness

Examine perceived benefits

Locate factors that contribute to these variables

Criteria for Testing the Benefits of Making Better Career Decisions

Page 6: Part 4 The  PIC  Model: Supporting Evidence

6

Study 1 –Gati, Kleiman, Saka, & Zakai (2003)

Method - Participants 247 males and 465 females who filled out both a

pre-dialogue and a post-dialogue questionnaire Mean age 22.8; mean years of education 12.6

10% high-school students and graduates 58% young adults (recently discharged) 9% considering an alternative to their current major 3% college graduates deliberating a job choice 20% considering a career transition and other

Page 7: Part 4 The  PIC  Model: Supporting Evidence

7

Method - Instruments

"Future Directions"- Israeli website (in Hebrew)

Pre-dialogue questionnaire (prerequisite to accessing the system)

MBCD - Making Better Career Decisions (mean dialogue time = 40 minutes, SD=25)

Post-dialogue questionnaire

Page 8: Part 4 The  PIC  Model: Supporting Evidence

8

Mean Perceived Benefit (MPB) and Willingness to Recommend (WR) the Use of MBCD to a Friend (%) as a Function of the Difference in Decidedness after the Dialogue of MBCD (N=712)

Decidedness

  Increased No change Decreased

Frequency 355 (50%)

266 (37%)

91 (13%)

MPB 3.12 2.57 2.52

WR% 93.5 74.8 72.5

Measure

Page 9: Part 4 The  PIC  Model: Supporting Evidence

9

Frequencies of Degree of Decidedness Before and after the Dialogue with MBCD

Decidedness After the Dialogue

Decidedness 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 - Client is 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 

Page 10: Part 4 The  PIC  Model: Supporting Evidence

10

Willingness to Recommend (WR) the Use of MBCD to a friend as a Function of the Degree of Decidedness Before and After the Dialogue with MBCD (N=712)

Decidedness Before the Dialogue with MBCD

Decidedness After MBCD 

1 2 3 4 5

1- no direction 

38 

14 17 

29  

--

2 - only a general direction 85 73 67 67 100

3 - considering a few specific alternatives

100 93 82 97 100

4 - client would like to examine additional alternatives

100 92 100 82 100

5 - would like to collect information about a specific occupation

100 

85 

90 

98 

89 

6 - Client is sure which occupation to choose

100 

-- 

100 

100 

81 

itamareduchp
in addition, we measured the mean perceived benefit (MPB) of using MBCD as a function of the user's reported degree of decidedness, before and after the dialogue with MBCD, in a sample of 712 individuals. The results reflected a correlation between the improvement in the individual's degree of decidedness and the reported MPB, although the MPB's were very high also for individuals who did not reported any improvement in their DOD.
msgati1
must include marginal numbers in previous and this slide
Page 11: Part 4 The  PIC  Model: Supporting Evidence

11

Taxonomy of Career Decision-Making Difficulties (CDDQ; Gati, Krausz, & Osipow, 1996)

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

Internal conflict

s

Externalconflic

ts

Unreliable

Info.

Page 12: Part 4 The  PIC  Model: Supporting Evidence

12

Page 13: Part 4 The  PIC  Model: Supporting Evidence

13

MBCD’s Effect (d, Cohen, 1992) 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

Page 14: Part 4 The  PIC  Model: Supporting Evidence

14

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

Page 15: Part 4 The  PIC  Model: Supporting Evidence

15

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

Design: Comparing the Occupational Choice Satisfaction (OCS) of two groups:

those whose chosen occupation was

included in MBCD’s recommended list

those whose chosen occupation was not

included in MBCD’s recommended list

Page 16: Part 4 The  PIC  Model: Supporting Evidence

16

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)

Page 17: Part 4 The  PIC  Model: Supporting Evidence

17

InstrumentsMBCD Questionnaire: clients were asked to report

their field of studies, their satisfaction with their occupational choice (scale of 1 – 9): “low” (1-4), “moderate” (5-7), “high” (8-9)

Procedure the located clients were interviewed by

phone, six+ years after visiting the career-counseling center

Method

Page 18: Part 4 The  PIC  Model: Supporting Evidence

18

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

ResultsFrequencies of Occupational Choice Satisfaction by Acceptance and Rejection of MBCD's Recommendations, Based on Sequential Elimination

Page 19: Part 4 The  PIC  Model: Supporting Evidence

19

Conclusions

Accepting the recommendations of the sequential-elimination-based search of MBCD produces the best outcomes (i.e., highest levels of satisfactions with the occupation)

The data does not support the effectiveness of the compensatory-based search

The data does not support any advantage of using the conjunction list over using only the sequential-elimination-search list

Page 20: Part 4 The  PIC  Model: Supporting Evidence

20

Alternative Explanations

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.

Therefore, this explanation can be ruled out.

Page 21: Part 4 The  PIC  Model: Supporting Evidence

21

Alternative Explanations (cont.)

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.

Therefore, this explanation can be ruled out too.

Itamar1
unclear, consider rephrase
Page 22: Part 4 The  PIC  Model: Supporting Evidence

22

Conclusion

Following the recommendations of the sequential-elimination-based search of MBCD produces the best outcome

Page 23: Part 4 The  PIC  Model: Supporting Evidence

23

Gender Differences in Directly and Indirectly Elicited Career-Related Preferences (Gadassi & Gati, 2009)

Method Participants: 226 females (74.1%) and 79

males (25.9%) who entered the Future Directions Internet site

Age: 17-30, mean=22.84 (median = 22, SD = 3.34) Years of education: mean=12.67 (median 12, SD =

1.48)

Page 24: Part 4 The  PIC  Model: Supporting Evidence

24

Instruments

Future Directions (http://www.kivunim.com) Making Better Career Decisions (MBCD,

http://mbcd.intocareers.org) The preference questionnaire: this

questionnaire imitated the preference elicitation in MBCD Participants were presented with 31 aspects, and were asked to rank-order them according to importance, and to report their preferences in all 31 aspects

Page 25: Part 4 The  PIC  Model: Supporting Evidence

25

Preliminary analysis

Two lists of occupations were compared: We used MBCD to generate the

recommended list of occupations based on the individual’s preferences in the career aspects (the “elimination” list)

We compared the “elimination list” with the “explicit list” – individuals were asked to freely declare a list of occupations suited for them.

Page 26: Part 4 The  PIC  Model: Supporting Evidence

26

Preliminary analysis

Determining the degree of gender-ratings of occupations was based on the judgments of 10 undergraduate students. 1 – “most (that is, over 80%) of the individuals who

work in this occupation are women” 5 – “most (that is, over 80%) of the individuals who

work in this occupation are men – over 80%" The inter-judge reliability was .96

We computed the mean gender-ratings of the lists of occupations for each participants

Page 27: Part 4 The  PIC  Model: Supporting Evidence

27

Means of the Femininity-Masculinity 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

ExplicitElimination

Men

Women

Gender Differences in Directly and Indirectly Elicited Preferred Occupations (Gadassi & Gati, 2009)

Page 28: Part 4 The  PIC  Model: Supporting Evidence

28

MBCD - Summary of Major Findings

Most users reported progress in the career decision-making process

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 the MBCD

Page 29: Part 4 The  PIC  Model: Supporting Evidence

29

MBCD - Summary of Major Findings

Using MBCD contributed to a decrease in career decision-making difficulties related to a lack of information

Using MBCD can contribute to decrease in the gender-bias of career choices

Following the MBCD’s advice doubled the probability of high occupational choice satisfaction 6 years later

Page 30: Part 4 The  PIC  Model: Supporting Evidence

30

Summary of PIC

Career counseling may be viewed as decision counseling, which aims at promoting making better career decisions

The PIC model facilitates the complex process of career choice by separating it into a sequence of well-defined tasks

MBCD is a unique combination of career information system, expert system, and a decision-support system based on the PIC rationale

Page 31: Part 4 The  PIC  Model: Supporting Evidence

31

Summary of PIC (cont.)

The use of the PIC model and MBCD contributes to: progress in the decision process, reduction in decision-making difficulties, reduction of gender (and possibly other) stereotypes, and higher occupational satisfaction in the future

PIC and MBCD can be incorporated into career-counseling interventions

Page 32: Part 4 The  PIC  Model: Supporting Evidence

32

WWW.CDDQ.ORG [email protected]

Page 33: Part 4 The  PIC  Model: Supporting Evidence

33

END

• sofsof

Page 34: Part 4 The  PIC  Model: Supporting Evidence

34

MBCD’s Effect on Reducing Career Decision-Making Difficulties (d, Cohen, 1992)

Scaled

Lack of Readiness

Motivation

General indecisiveness

Dysfunctional Beliefs

.31

. 13

.29

. 16

Lack of Information About

The Process

The Self

Occupational Alternatives

Additional Sources

.72

. 48

. 45

. 78

. 20

Inconsistent Information

Unreliable Information

Internal Conflicts

External Conflicts

. 11

. 18

. 01

.-13

Total CDDQ. 65

Page 35: Part 4 The  PIC  Model: Supporting Evidence

35

Monitoring the Dialogue

•Evaluating the input–The 3 facets of preferences (relative importance of

aspect, optimal level, willingness to compromise)–Crystallization of preferences (differentiation,

consistency, coherence)•Evaluating the process

–Which options were used and in what order (almost compatible, additional search, why not? what if?

Compare occupations, similar occupations)•Evaluating the outcome (list of career

alternatives)–The number of alternatives on the list–The similarity among the alternatives on the list