Correlates of Assessment Center Performance Across Contexts

Transcription

Correlates of Assessment Center Performance Across Contexts
Correlates of AC Performance
across Contexts:
An Investigation of Aggregated
Measures of Executive Performance
Jing Jin & Doug Reynolds
Development Dimensions International (DDI)
1
© Development Dimensions Int’l, Inc., MMXIV. All rights reserved.
Overview
•
Common findings regarding personality and
executive performance findings
•
Sample and methods, machine learning
focus
•
Results across several methods
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© Development Dimensions Int’l, Inc., MMXIV. All rights reserved.
Personality Correlates of Executive
Performance
Approach
Representative Finding
Single trait
studies
Narcissism correlates with exec performance
(e.g., Chaterjee & Hambrick, 2011)
Extraversion and Openness to Experience relate to
leadership performance (e.g., Judge et al., 2002)
Big-5 metaanalyses
Exec
performance
studies
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Ambition (E), Learning orientation (OE), adjustment (N),
show stronger correlations than other traits for exec
performance (Hogan, 2008)
Extraversion, Openness, correlate with exec experience
and strategic thinking competency (Dragoni et al., 2011)
© Development Dimensions Int’l, Inc., MMXIV. All rights reserved.
Sample and Methods
•
Executive assessment results aggregated
across industries and regions
•
Assessments included behavioral measures
of executive competencies and personality
tests
•
Machine learning techniques used to explore
resulting database
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Data source: tech-facilitated assessment center
Key characteristics:
• Day-in-the-life format
• Paired personality testing
• Live interactions
• Deployed globally
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© Development Dimensions Int’l, Inc., MMXIV. All rights reserved.
Karen Gates
Vice President , Operations
Interpersonal Skills
P
Compelling Communication
P
Cultivating Networks
S
Navigating Politics
D
Influence
Business Management
D
Building Organizational Talent
S
Driving Execution
P
Financial Acumen
P
Operational Decision Making
P
Entrepreneurship
P
Establishing Strategic Direction
Leadership
PERSONALITY PATTERNS
COMPETENCIES
Started: 02.04.2000
Previous Position: Director, Operations
Education: MBA, Wharton Business School
Known Aspirations: VP of Eastern Region
Sample Output
Leadership Effectiveness Inventory
Adjustment
72
Ambition
87
Sociability
25
Interpersonal…
100
Prudence
98
Inquisitiveness
88
Learning Orientation
95
Leadership Challenge Inventory
Volatile
Argumentative
33
10
Risk Averse
S
Leading Change
P
Coaching and Developing Others
Avoidant
S
Selling the Vision
Arrogant
P
Empowerment/Delegation
55
Imperceptive
Impulsive
24
15
13
11
Attention Seeking
Personal Competencies
P
Executive Disposition
S
Passion for Results
53
Eccentric
Perfectionistic
Approval Dependent
69
24
72
Multi-organization
analyses
Aggregated
database
Strengths
22,212
Executives
Strategic
Leaders
Establishing Strategic
HRIS
Direction
Financial/Business Acumen
Entrepreneurship Company
Profiles
Strengths
Strong
Managers
Driving for Results
Vary by:
Prior
Assessments
ExecutiveLevel?
Disposition
Communicating
with
Industry?
Impact Experiences?
Decision Making
Personality profile?
Customer Focus
Company
Performance
Machine Learning/Data Mining:
When to Use?
• Noisy predictor and criterion data
• Minimal theoretical grounding
• Limited experimental control
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© Development Dimensions Int’l, Inc., MMXIV. All rights reserved.
Analysis design:
• Executive assessment database
• Predictors: 18 personality scales
• Outcome:
– Strategic vs. Non-Strategic profiles
– Interpersonal vs. Non-Interpersonal profiles
• Predictive Accuracy: Cross-Validation
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© Development Dimensions Int’l, Inc., MMXIV. All rights reserved.
Variable Importance:
Comparison of Three Methods
Traditional
Regression
Relative
Importance
Random
Forests
Multicollinearity
X
√
√
Non-normality
X
X
√
Non-linearity
X
X
√
No A-Priori-Model
X
X
√
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© Development Dimensions Int’l, Inc., MMXIV. All rights reserved.
Example: Classification Tree
Buy BMW (Y/N)?
Income (>100K)
Income (<100K)
Attend sport event (0)
Buy other lux. item (<2)
Attend sport event (<4)
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Attend sport event (>0)
Buy lux. item (>2)
Attend sport event (>=4)
© Development Dimensions Int’l, Inc., MMXIV. All rights reserved.
Study 1: Predict Strategic Profiles
•
Predictors: 18 personality traits (pos. & neg.)
•
Criterion: ratings on strategic competencies
 Business
Acumen
 Establishing
Strategic Direction
 Entrepreneurship
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© Development Dimensions Int’l, Inc., MMXIV. All rights reserved.
Example
using alternative methods
Study
1: Sample
9,566
Executives
w/strategic
profile data
Weak
Strategists
n = 2219
13
Strong
Strategists
n = 1391
Vary by:
Personality profile?
© Development Dimensions Int’l, Inc., MMXIV. All rights reserved.
Study 1: Variable Importance
Random Forests:
Logistic Regression:
Relative Importance:
LearningOrientati…
LearningOrientation
Ambition
Ambition
Ambition
LearningOrientation
Argumentative
RiskAverse
RiskAverse
Volatile
AttentionSeeking
Perfectionistic
Imperceptive
Avoidant
Avoidant
RiskAverse
Inquisitiveness
Inquisitiveness
Perfectionistic
Argumentative
Argumentative
Arrogant
Perfectionistic
Sociability
Inquisitiveness
Sociability
AttentionSeeking
AttentionSeeking
ApprovalDependent
Adjustment
Avoidant
Volatile
Arrogant
Adjustment
Imperceptive
Prudence
Prudence
Arrogant
Volatile
Imperceptive
ApprovalDependent
InterpersonalSensitivity
Eccentric
Impulsive
Impulsive
Eccentric
InterpersonalSensitivity
Sociability
Prudence
InterpersonalSen…
ApprovalDepend…
Eccentric
Adjustment
Impulsive
Accuracy: 65.05%
Accuracy: 66.02%
Study 1: Feature Selection
70%
69%
Cross-Validation Accuracy
68%
67%
66%
65%
64%
63%
62%
61%
60%
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2
3
4
5
6
7
8
9
10 11 12 13 14 15 16 17 18
Number of Predictors
15
© Development Dimensions Int’l, Inc., MMXIV. All rights reserved.
Study 1: Sample Classification Tree
Ambition
(Dominance)
>= 51
< 51
Non-Strategic(79%)
Learning Orientation
< 83.5
>= 83.5
Learning Orientation
< 41
Strategic (58.8%)
>= 41
Risk Averse
Non-Strategic(70.2%)
> 22
Non-Strategic(60.4%)
<= 22
Strategic (56.0%)
ACCURACY:
CART: 71.76%
LR: 66.10%
Study 2: Predict Interpersonal Profiles
•
Predictors: 18 personality traits (pos. & neg.)
•
Criterion: ratings on interpersonal competencies
 Cultivating
Networks
 Communicating
with Impact
 Influence
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© Development Dimensions Int’l, Inc., MMXIV. All rights reserved.
Example
alternative methods
Study 2: using
Sample
9,739
Executives
w/interpersonal
profile data
Weak
Interpersonal
n = 1100
18
Strong
Interpersonal
n = 1365
Vary by:
Personality profile?
© Development Dimensions Int’l, Inc., MMXIV. All rights reserved.
Study 2: Variable Importance
Logistic Regression:
Relative Importance:
Random Forests:
Ambition
Ambition
AttentionSeeking
AttentionSeeking
AttentionSeeking
Ambition
Argumentative
Argumentative
Argumentative
LearningOrientation
Inquisitiveness
Perfectionistic
RiskAverse
Perfectionistic
Inquisitiveness
Perfectionistic
RiskAverse
Sociability
Sociability
Avoidant
Inquisitiveness
Arrogant
Adjustment
Avoidant
ApprovalDependent
Impulsive
Imperceptive
LearningOrientation
Arrogant
Adjustment
Impulsive
Prudence
Volatile
Adjustment
Volatile
Eccentric
Avoidant
RiskAverse
Arrogant
Eccentric
Imperceptive
InterpersonalSensitivity
Prudence
Impulsive
Imperceptive
Sociability
ApprovalDependent
Volatile
Eccentric
Prudence
InterpersonalSensitivity
LearningOrientati…
InterpersonalSen…
ApprovalDepend…
Accuracy: 64.27%
Accuracy: 64.18%
Study 2: Feature Selection
69%
Cross-Validation Accuracy
67%
65%
63%
61%
59%
57%
55%
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2
3
4
5
6
7
8
9
10
11
12
13
14
15
Number of Predictors
20
© Development Dimensions Int’l, Inc., MMXIV. All rights reserved.
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17
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Study 2: Sample Classification Tree
Ambition
(Dominance)
≥ 59.5
< 59.5
Non-Interpersonal
Attention Seeking
(62.2%)
< 66.5
≥ 66.5
Interpersonal
Argumentative
ACCURACY:
≥ 65.5
CART: 62.68%
LR: 61.66%
(68.2%)
< 65.6
Non-Interpersonal
Interpersonal
(56.8%)
(62.9%)
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© Development Dimensions Int’l, Inc., MMXIV. All rights reserved.
Conclusions:
•
Personality variables have consistently shown
relationships with executive performance
•
•
Strategic: ambitious, willing to learn, not afraid of risks
Interpersonal: ambitious, willing to be on the spot, not
argumentative
•
Complex relationships among variables may
be obscured by traditional analyses
•
Machine learning and “big data” analytics
provide useful tools for extending our
understanding of these complex relationships
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© Development Dimensions Int’l, Inc., MMXIV. All rights reserved.

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