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Research guide and technical manual

Korn FerryAssessment of Leadership Potential

Korn Ferry Assessment of Leadership PotentialResearch guide and technical manual

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Korn Ferry Assessment of Leadership Potential Research guide and technical manual

Version 15.2a—05/2016

Research guide and technical manual

Korn FerryAssessment of Leadership Potential

Table of contents

Section IIntroduction ................................................................................................................................................................................................. 1

Section IIIntroduction to high potential identification .............................................................................................................................3

Business need............................................................................................................................................................................................3

What is leadership potential? ...........................................................................................................................................................3

Potential and Korn Ferry’s Four Dimensions of Leadership and Talent ......................................................................6

Korn Ferry’s model of leadership potential ...............................................................................................................................8

Section IIIThe Korn Ferry Assessment of Leadership Potential..........................................................................................................10

Overview ....................................................................................................................................................................................................10

Section IVThe Korn Ferry Assessment of Leadership Potential: Measurement approach ......................................................11

Background ................................................................................................................................................................................................11

Section VThe Korn Ferry Assessment of Leadership Potential: What it measures ................................................................. 16

Drivers ......................................................................................................................................................................................................... 16

Experience ................................................................................................................................................................................................ 21

Traits............................................................................................................................................................................................................ 23

Awareness ................................................................................................................................................................................................ 24

Learning agility ...................................................................................................................................................................................... 26

Leadership traits ................................................................................................................................................................................... 27

Capacity ..................................................................................................................................................................................................... 31

Derailment risks ..................................................................................................................................................................................... 32

Section VIThe Korn Ferry Assessment of Leadership Potential: Uses, administration, scoring, and reporting overview ..............................................................................................................................................................................34

Intended uses .........................................................................................................................................................................................34

KFALP technical characteristics and validity .........................................................................................................................36

Norms .........................................................................................................................................................................................................48

Appendix A. Frequently asked questions ....................................................................................................................................50

Appendix B. Inter-method correlations ........................................................................................................................................ 55

Appendix C. Norm descriptions .......................................................................................................................................................56

Appendix D. Global Personality Inventory (GPI) definitions ..............................................................................................68

Appendix E. GPI and KFALP correlations ....................................................................................................................................70

References .................................................................................................................................................................................................... 71

© Korn Ferry 2015–2016. All rights reserved. 1

Section I Introduction

The Korn Ferry Assessment of Leadership Potential (KFALP) is a world-class, science-driven, and comprehensive

self-assessment for measuring leadership potential. The assessment measures an individual’s Drivers, Experience,

Awareness, Learning agility, Leadership traits, Capacity, and Derailment risks. Norms are applied to provide

information related to advancement to more senior leadership levels.

Leadership potential is about what could be at some point in the future, not what is currently. By focusing on

measures related to what could be, the tool has been carefully conceived and empirically designed to provide

critical data about people—data proven to differentiate those who have successfully advanced from those who

have not advanced.

The KFALP is designed to provide data important for individuals and organizations to consider as they think

about leadership potential. It is not designed for selection of individuals into specific jobs today, however, it can

be used as a data point for selection into roles where most hires are expected to further advance into leadership

within a relatively short time.

This manual is designed as a technical reference to help deepen your understanding of the research behind the

KFALP.

You can refer to this manual for a variety of purposes:

• Build your knowledge regarding the research studies on high potential identification.

• Enhance your understanding of the research foundation of the KFALP.

• Review some key findings from psychometric analyses of KFALP data, including sub-group differences.

• Find answers to some frequently asked questions.

Korn Ferry Assessment of Leadership Potential • Research guide and technical manual


Section II Introduction to high potential identification

Business needToday, organizations face a unique and unprecedented set of challenges and potential opportunities. The pace of

market change, speed of innovation, global dynamics, and changing demographics generate many opportunities

to both create and extract value, but it is often more difficult to locate those opportunities and act upon them.

Thus, how do companies compete in this increasingly complex and volatile environment? One of the central

differentiators for companies is a strong human capital foundation: the right leaders in the right places.

To succeed in driving business strategy, it is imperative for companies to have a future-focused talent strategy.

Organizations need to develop and sustain a pipeline of the right leaders, with the right abilities, in the right

roles, and at the right times to ensure a sustainable competitive advantage. The idea of identifying and

managing high potential talent has become increasingly essential for organizations.

Most organizations have recognized the need for and have implemented a formal process to identify and assess

high potential talent (Church & Rotolo, 2013; Silzer & Church, 2009). The construct of leadership potential, as

used by many organizations, refers to the possibility that individuals have the qualities (e.g., motivation, skills,

abilities, experiences, and characteristics) to advance in their careers and perform effectively in future roles. It

implies further growth and development to reach some desired end state.

According to several studies, only about one-half of companies report having a high potential identification

program (Howard, 2009; Slan-Jerusalim & Hausdorf, 2007; Wells, 2003). Companies which do have programs

frequently select individuals based on factors not necessarily related to potential, such as personal experience

with the person, performance review ratings, and past performance results (Slan-Jerusalim & Hausdorf, 2007;

Pepermans, Vloeberghs, & Perkisas, 2003). In addition, Martin and Schmidt (2010) indicated that based on

their research on leadership transitions, nearly 40% of internal job moves made by people identified by their

companies as “high potentials” end in failure.

What is leadership potential?When comparing the way in which 13 major organizations defined potential, Karaevli and Hall (2003) found

that every organization had a differing definition of the construct. Silzer and Church (2009) formed several

common potential definitions based on the results of a survey of organizations, finding that 35% defined

potential in terms of moving into a top leadership role, 25% defined it as having the potential to take on broader

responsibilities and leadership duties, 10% viewed potential as having a history of being a high performer, and

25% of organizations surveyed defined potential as successfully moving up two levels from the current level.

With so many different perspectives and definitions of potential in use, it may be most useful to start by defining

what potential is not.

Potential is not the same as current job performance. Current performance is directly visible, but potential is

a prediction about the future. Not all high performers are high potentials. Research suggests that only about

30% of high performers should be classified as high potentials (Corporate Leadership Council, 2005). Although

previous performance can be a useful piece of information in the identification of high potential individuals, it

should not be (though, often is) confused with potential (Corporate Leadership Council, 2005; Slan-Jerusalim &

Hausdorf, 2007; Pepermans, Vloeberghs, & Perkisas, 2003).


© Korn Ferry 2015–2016. All rights reserved.4

The distinction between performance and potential is highlighted by the fact that the factors required for

successful performance changes from one organizational level to the next. The Charan, Drotter, and Noel (2011)

six-passage model is often used to describe the leadership requirements throughout the various organizational

levels within a company. This “Pipeline Model of Leadership Development” defines the crucial skills for successful

management transitions from the very bottom of an organization (managing oneself) to the very top (managing

the enterprise). Each of the six management transitions in this model, illustrated in Figure 1, involves a major

change in job requirements, demanding new skills, time applications, and work values.

Figure 1. The changing requirements of leadership.

INDIVIDUALCONTRIBUTOR

FRONT-LINEMANAGER

MANAGER OFMANAGERS

BUSINESSUNIT LEADER

SENIOREXECUTIVE

CHIEFEXECUTIVE

Short–term Long–term

Limited stakeholders Multiple stakeholders

Manage tasks Manage portfolio

Get the job done Maximize shareholder value

Transactional Transformational

TECHNICAL SKILLS LEADERSHIP AND MANAGEMENT SKILLS STRATEGIC BUSINESS ACUMEN

When advancing to leadership positions of greater responsibility, leadership roles increase in their challenge,

breadth, and complexity. As leaders advance, they must reallocate their focus so that they can help others to

perform effectively. They must learn to value the work of leadership and believe that making time for others,

planning, coordinating, and coaching are imperative in their new responsibility. Strong performance at one level

does not necessarily indicate the capability to successfully progress to more complex future leadership roles.

Potential is also not a person’s readiness at present to move into an immediate next-level position or promotion.

Potential is a more long-term concept, and individuals being considered for an immediate next level should be

assessed using Korn Ferry’s readiness assessments instead.

Another important question frequently asked when discussing the identification of high potential individuals is

potential for what? Everyone has potential, but to what end (Silzer & Church, 2009). High potential employees

are typically selected earlier on for a certain level or type of role in the future. Some organizations have sub-

pools of their high potentials for future marketing executives or engineering executives (Dowell, 2010), but over

time both the individual and the role itself is likely to evolve into something a little different, and thus more

general positions or leadership levels tend to be better targets for high potentials rather than very specific

roles. Some of the common job band levels used by organizations include global leaders/senior executives,

mid-management/technical-functional, and high-value individual contributors or HiPros (Silzer & Church, 2009).

The construct of leadership potential, as used by many organizations, refers to the possibility that individuals

have the qualities (e.g., motivation, skills, abilities, experiences, and characteristics) to effectively perform and

advance in their careers. It implies further growth and development to reach some desired end state.



We define potential as “the capacity and interest to develop the qualities required for effective performance in significantly more challenging leadership roles.”

As organizations identify high potential employees and invest copious amounts of time and money to develop these

individuals, including providing them with the most challenging assignments, accurate identification is critical.

Several researchers have worked to identify individual attributes that are related to long-term potential

(Corporate Leadership Council, 2005; Lombardo & Eichinger, 2000; McCall, Lombardo, & Morrison, 1988;

Peterson & Erdahl, 2007; Silzer & Church, 2009). Based on an extensive literature review of nine external high

potential models from consulting firms and two corporate surveys, Silzer and Church (2009) identified seven

characteristics that are commonly viewed as indicators of high potential employees:

• Cognitive skills include conceptual or strategic thinking, breadth of thinking, cognitive ability, and dealing with ambiguity.

• Personality variables include interpersonal skills, dominance, stability, resilience, and maturity.

• Learning ability includes adaptability, learning orientation, learning agility, and openness to feedback.

• Leadership skills include developing others, leading and managing others, and influencing and inspiring.

• Motivation variables include energy, engagement, drive for advancement, career drive, interests, career aspirations, results orientation, and risk taking.

• Performance record includes leadership experiences and performance track record.

• Knowledge and values include cultural fit and technical/functional skills and knowledge.

They further boiled these down into three types of dimensions of potential, which could each be measured at

different stages during an individual’s career (Silzer & Church, 2009).

• Foundational dimensions include more stable attributes such as cognitive skills and personality, which could be measured even at very early career stages.

• Growth dimensions involve abilities to learn, as well as motivational variables, which may appear more clearly in certain job contexts over others.

• Career dimensions include the more level/function-specific predictors, such as performance records, leadership skills, and experiences, which should be measured and normed against the different stages during career progression.

Korn Ferry’s model of leadership potential was formed on the basis of previous findings and science and

includes predictors from each of these three broad types of dimensions found in the literature. In Section V

of this technical manual, we define and describe each predictor, summarizing key literature relevant to each

construct. Before turning to this review, we outline how the capabilities measured by the KFALP relate to

broader approaches to describing and understanding leaders’ capabilities.



Potential and Korn Ferry’s Four Dimensions of Leadership and TalentKorn Ferry organizes the qualities that lead to success into four distinct categories to describe a whole-person

view of individuals: Drivers and Traits—which describe “who you are,” and Experiences and Competencies—

which describe “what you do” (see Figure 2). The four dimensions influence one another and interact within

each person.

Figure 2. Four Dimensions of Leadership and Talent.

ExperiencesCompetencies

Traits Drivers

Skills and behaviors required for success that can be observed.

Inclinations, aptitudes and natural tendencies a person leans toward, including personality traits and intellectual capacity.

Assignments or roles that prepare a person for future roles.

Values and interests that influence a person’s career path, motivation and engagement.

What you do

Who you are

Each dimension plays a distinct role in performance, engagement, potential, and personal career development.

As a measure of potential, the KFALP focuses heavily on Traits and Drivers—who the person is and may become

in general—rather than what the person has done or can do today. The focus is potential to develop for the

future, rather than readiness or fit for a specific job today.



Feedback from the KFALP is organized into seven categories, or signposts. The seven signposts are a rational

organizing framework and not an empirical one. They are designed to assist clients in discussion and considering

critical components of potential. How the signposts relate to Korn Ferry’s Four Dimensions of Leadership and

Talent is shown in Figure 3.

Figure 3. Four Dimensions of Leadership and Talent.

ExperiencesCompetencies

Traits Drivers

• Track record of formative experiences.

• Engaged by leadership.• Learns from experience.• Self–awarness.• Leadership dispositions.• Minimal derailment risks.• Aptitude for logic and

reason.

What you do

Who you are



Korn Ferry’s model of leadership potentialDefinitions of the attributes assessed by the KFALP are shown in Table 1. These drivers, experiences, and traits

reflect leadership potential and are related to advancement in the leadership pipeline.

Table 1. Model of the Korn Ferry Assessment of Leadership Potential.

DriversMotivation to be a leader – successful leaders are driven by and prefer the challenges and work of leaders.

The Drivers signpost includes three sub-dimensions:Advancement drive: drive to advance through collaboration, ambition, challenge.

Career planning: how narrowly or broadly focused are the participant’s career goals and how specific is his/her career plan.

Role preferences: preference for the work of roles requiring versatility and achievement through others vs. professional mastery and expertise.

ExperienceTrack record of formative experiences – successful leaders have a foundation of experiences that enable the

acquisition of new skills.

The Experience signpost includes three sub-dimensions:Core experience: what a leader has learned in the course of his/her day-to-day leadership career.

Perspective: the diversity of a leader’s experience across organizations, industries, functional areas, and countries.

Key challenges: a leader’s experience with a number of seminal developmental challenges.

AwarenessSelf-knowledge and insight – leaders exhibit awareness of self in the context of their work.

The Awareness signpost includes two sub-dimensions:Self-awareness: the extent to which the leader is aware of his/her strengths and development needs.

Situational self-awareness: the extent to which the leader monitors and is aware of how events impact his/her performance.

Learning agilityAbility to learn from experience – leaders extract lessons from experience and leverage them in novel situations.

The Learning agility signpost includes four sub-dimensions:Mental agility: a leader’s tendency to be inquisitive and approach problems in novel ways.

People agility: a leader’s skill in reading others and applying the insights gained in people-related matters.

Change agility: a leader’s tendency to promote new possibilities and to take ideas from vision to reality.

Results agility: a leader’s propensity to deliver outstanding results in new and tough situations.



Leadership traitsDispositions held by leaders – leaders possess traits that make them naturally inclined to lead.

The Leadership traits signpost includes five sub-dimensions:Focus: the balance between attending to details and keeping an eye on the big picture.

Persistence: the passionate pursuit of personally valued long-term goals.

Tolerance of ambiguity: a leader’s capacity to deal effectively with uncertainty or confusing situations.

Assertiveness: the willingness to assume a leader role and comfort with leadership.

Optimism: a leader’s tendency to have a positive outlook.

CapacityAptitude for logic and reasoning.

The Capacity signpost includes one sub-dimension:Problem solving: the ability to spot trends and patterns and draw correct conclusions from confusing or ambiguous data.

Derailment risksManaged derailment risks – leaders manage those configurations of traits which might lead them to derail.

The Derailment risks signpost includes three sub-dimensions:Volatile: a risk toward being mercurial, erratic, or unpredictable.

Micromanaging: a risk toward controlling the work of direct reports.

Closed: a risk toward being closed to alternative perspectives and opportunities.



Section III The Korn Ferry Assessment of Leadership Potential

OverviewThe Korn Ferry Assessment of Leadership Potential (KFALP) helps organizations assess and identify talent who

have the characteristics needed to develop the competencies and gain the experience to succeed in leadership

roles.

• It gives a complete view of a person’s leadership potential, no matter where they are in the organization.

• It accurately identifies high potentials with seven key signposts of leadership potential proven by research.

• It helps organizations invest in the right talent and target the right areas for development.

• It provides organizations with the ability to objectively and accurately identify people with high leadership potential.


• It accurately identifies high potentials in seven key facets, or signposts, proven by research to be related to advancement in leadership roles.

The KFALP was built by leveraging expertise from the combined decades of knowledge and the hundreds of

thousands of leadership assessments Korn Ferry has amassed. The framework was developed based on rigorous

analysis using a combination of quantitative, qualitative, and market-based data, sourcing from both Korn Ferry’s

own extensive data stores and external literature review:

• Research analyses based on a variety of Korn Ferry intellectual property, such as Leadership Experience Inventory (LEI) and Korn Ferry Four Dimensional Executive Assessments, TalentView® of Leadership Transitions, and viaEDGE®.

• Extensive review of the scientific literature on high potential identification and leadership pipeline.

• Expert input representing decades of work in leadership research and development.

• Customer input.



Section IV The Korn Ferry Assessment of Leadership Potential: Measurement approach

The KFALP uses state-of-the-art measurement technology in this assessment: Forced-Choice Item Response

Theory (Brown, 2016; Brown & Maydeu-Olivares, 2011). Forced-Choice Item Response Theory (FC-IRT) offers a

number of advantages over more traditional approaches to measurement, such as Likert-type response scales or

Forced-Choice measures grounded in Classical Test Theory (CTT).

Background

Response distortion and fakingOne of the challenges in the measurement of the characteristics of potential by self-assessment is controlling

intentional response distortion—faking. To the extent possible, it is very important to capture data that reflects

a participant’s true standing on characteristics related to advancement. Psychometricians have become

increasingly concerned with the response distortions associated with the Likert-style response formats

commonly used in psychological measurement (Stark, Chernyshenko, Chan, Lee, & Drasgow, 2001). As use of

self-assessments of personality has increased, respondents’ desire to present themselves in a positive light

to gain advantage has become of greater concern. The growing availability of self-coaching materials and

use of unproctored Internet-based tests has further contributed to the potential for faking to be increasingly

problematic (Sliter & Christiansen, 2012). Past approaches to handling response distortion have not been highly

satisfactory.

Social desirability and/or “faking scales” have long been deployed in order to detect and address faking.

However, this approach suffers from limitations. When faking is indicated, it is difficult to know how to proceed.

In research settings, a completed assessment may simply be thrown out. In applied settings, coaches and

decision makers may simply be warned that the results are perhaps untrustworthy and to proceed with caution.

Yet others have attempted to use results from faking detection or social desirability scales to adjust observed

scores in diverse ways (Goffin & Christiansen, 2003). Such methods, however, have been repeatedly criticized

as being arbitrary and difficult to validate (McCrae & Costa, 1983; Goffin & Christiansen, 2003). Sacket (2011),

summarizing the observations contained in an edited book on faking in CTT, concludes, “In sum, we are not yet

at the point at which methods of identifying individuals engaging in faking are available for use in operational

settings,” and “So, without a reliable and valid detection method, scores cannot be adjusted to remove the

effects of faking.”

Forced-choice response formats also have been developed and employed to combat faking. These formats

force respondents to endorse or choose one item over another. When scored traditionally, these formats make

extreme high or extreme low endorsement of every item impossible. In addition to combating faking (Sackett

& Lievins, 2008), forced-choice measurement can markedly reduce response bias, “halo” or leniency effects,

and response variance attributable to individual response styles not immediately associated with item content

(Bartram, 2007; Cheung & Chan, 2002). However, when used in combination with classical test theory scoring

methods, traditional forced choice response formats always produce scale scores which are known as ipsative,

that are problematically auto-correlated and interdependent (Brown & Maydeu-Olivares, 2011). That is, an

individual’s scores on one scale is, in very large part, a direct and artificial reflection of person-scores on the

other scales included in the measure (Heggestad, Morrison, Reeve, & McCloy, 2006).

This dependency makes normative comparisons across individuals improper and violates the assumptions of

many common psychometric statistics (Blinkhorn, Johnson, & Wood, 1988; Hammond & Barrett, 1996; Hough &

Ones, 2001; Meade, 2004).



Forced-choice IRT modelsResearchers in psychological measurement have sought to tackle the many problems associated with forced-

choice measures. Stark, Chernyshenko, and Drasgow (2005) developed a pairwise preference ideal point model

that addresses most related problems by pairing and presenting items with similar levels of social desirability

and by employing scoring and parameter estimation methods that are shown to perform well under certain

conditions vis-à-vis eliminating ipsative auto-correlation. Stark and Chernyshenko (2007) point out that number

of pairwise preference ratings needed to obtain reasonable person-score standard errors using this approach

may be particularly high in non-adaptive testing situations. Hence, the Stark et al. (2005) pairwise preference

model works and is markedly more efficient with computer assisted adaptive testing administration, wherein

item presentation is customized according to real-time response patterns. Where fixed form administration

is optimal or necessary, test administration and reliability using the Stark et al. (2005) model may require

many more items than desirable, limiting its feasibility in applied organizational settings. Additional limitations

associated with this model’s reliance on an ideal point measurement framework include the relative difficulty of

writing items, the lack of invariance in parameter estimates and model fit when reversing item coding, and the

apparent reduced accuracy of item parameter estimation (Brown & Maydeu-Olivares, 2011; Maydeu-Olivares,

Hernandez, & McDonald, 2006).

As an alternative, Brown and Maydeu-Olivares (2011) developed a structured multidimensional forced-choice IRT

model that addresses problems associated with faking, response bias, and ipsativity while also addressing some

of the limitations of the paired preference Stark et al. (2005) model. The authors describe a model that is linear

in differences between latent traits.

Briefly, and in very straightforward terms, we can now simultaneously overcome the limitations and maintain the

benefits of forced-choice measurement by employing recent advances in Item Response Theory (IRT) modeling

that takes advantage of the item format and produces scale scores that are truly normative. This model is the

FC-IRT model (see Brown & Maydeu-Olivares, 2011).

The method uses large samples of people and statistical models to identify 1) item loadings, which indicate

the degree to which each item is related to the underlying trait it is intended to measure, and 2) item difficulty,

which relates to the likelihood of a person with a given level of a trait ranking the item as more like them vs.

other items. These two pieces of information are used, along with the rankings a person provides, to generate

scores. A more detailed and statistical explication of the methodology can be found in subsequent paragraphs

and sections.

The latent traits are manifest by binary comparisons of items that are presented in the forced-choice blocks.

The model rearranges forced-choice responses into a series of exhaustive binary comparisons, thereby allowing

for components of non-ipsative trait measures to drive parameter estimation, scoring, and interpretation of

person-scores. The model is novel in that it creates a relative independence among otherwise predictably auto-

correlated forced-choice based construct scores. It is flexible in terms of forced-choice block sizes and is feasible

in that parameters and scores can be estimated using existing popular statistical software packages, including

Mplus (Muthen & Muthen, 2010). We also have developed a related R package (Zes, 2015; Zes, Lewis, & Landis,

2014) that similarly estimates the Brown and Maydeu-Olivares (2011) model and related extensions of it.

FC-IRT uses a two-stage approach. If a person prefers or ranks item 1 higher than item 2, then that person’s

preference for item 1 is higher than item 2. For instance, in a block of four items, there are six possible

comparisons to make between the items. After a series of blocks are administered, we can use the information

provided by these comparisons to fit an IRT model. We first use the IRT model to obtain information about



how difficult the items are to endorse (called item difficulty), and how well the items relate to the traits being

measured (called item loadings). Then, using item difficulty and item loading data, we can accurately estimate

a person’s normative scale score for each of the underlying traits measured by the assessment. By abstracting

the ranking information into two separate yet linked statistical models, this two-stage approach allows us to take

forced-choice item responses and produce normative scale scores free of the ipsative limitations of traditional

forced-choice measurement.

Using Forced-Choice Item Response Theory, we can take advantage of the bias and faking resistance of the

forced-choice item response format while eliminating the psychometric limitations of the classical test theory

scoring methods.

FC-IRT in KFALPIn brief, the use of FC-IRT (Brown, 2016; Brown & Maydeu-Olivares, 2011) in KFALP provides a methodology with

the following advantages:

• Removes response styles and scale anchor issues by eliminating the Likert rating scale.

• Thwarts faking by eliminating the transparency of the Likert rating scale.

• Frees interpretation from specific scale content by estimating true trait level.

• Provides superior true score estimates of error.

• Takes item content into consideration when estimating true score, thus improving fidelity of the score to the trait construct.

Many participants report the forced-choice response format to be more engaging and challenging than typical

single-item rating assessments—some even describe it as difficult. This is as intended. Most understand that this

is a characteristic of an assessment designed to be more incisive. It takes unlimited control of responses away

from the participant and requires hard choices. It is this feature that inhibits faking and improves the quality of

the data.

FC-IRT measurement technology is applied to the Awareness, Learning agility, Leadership traits, and Derailment

risks signposts and to part of the Drivers signpost. This state-of-the-art technology allows true normative

comparison of individuals while effectively controlling error resulting from idiosyncratic use of a response scale

or from intentional faking (Brown & Maydeu-Olivares, 2011; Drasgow, Chernyshenko, & Stark, 2010).

Item administrationThe KFALP traits and drivers scored with FC-IRT are administered in forced-choice response format. Each

construct type is grouped together in its own test form. Traits are measured with traits in blocks of four items,

and drivers with drivers in blocks of six items. Construct scores are estimated using a modification (Zes, 2015;

Zes et al., 2014) of the Brown and Maydeu-Olivares (2011) Forced-Choice Item Response Theory (FC-IRT) model

to arrive at construct estimates whose correlations are based on the nature of the constructs and not according

to forced-choice item response format artifacts.1 Eight items were designed to tap each trait, and trait response

blocks contain four items each. Each driver is measured using ten items, with items presented in response blocks

of six items.

1 In early developmental efforts, we administered and scored forced-choice based trait scales and Likert-based trait scales of the same items and constructs to the same individuals and found, much as Brown and Maydeu-Olivares (2011) did, that alternate-form correlations between the same constructs typically had magnitudes consistent with most conceptualizations of alternate test form construct convergence (e.g., r > .70 in every case). The full correlations are shown in Table 17 in Appendix B.



An example of a forced-choice multi-item block from the Drivers test form is shown in Table 2. This example

illustrates that each response block is comprised of items measuring multiple scales within the domain. That is,

for each trait or driver response block, there is no more than one item from each scale.

Table 2. Example six-item block.

Item 1 Well-defined work objectives.

Item 2 Situations without a winner and a loser.

Item 3 Having high status within the organization.

Item 4 Avoiding meetings so I can focus on my work.

Item 5 Developing myself beyond work.

Item 6 Consistent direction in my career.

Upon seeing a block of items, candidates are tasked with ranking the items from “Most” to “Least” on some

continuum. Specifically, in this example, candidates would be asked to rank the items from “Most preferred” to

“Least preferred.”2

ScoringTo set the stage for the FC-IRT model, assume that we have a test composed of several six-item blocks (as

with the Drivers test form, which has 10 six-item blocks) where each item in a given block measures a unique

construct or dimension (much like the example in Table 2). Further, assume that candidates are asked to rank the

items from “Most preferred” to “Least preferred.” To model this setup using FC-IRT, we first employ a Thurstonian

Comparative Model (Brown & Maydeu-Olivares, 2011; for the origin of this model, see Thurstone, 1927). Using this

model, for a given block of six items there are six latent utilities/thresholds, ti. If a candidate prefers or ranks item

i larger than item j, then the utility for item i, ti is larger than the utility for item j, tj. This information can be coded

in a comparative task as

Forced Choice Item Response Theory

To assess a candidate’s standing on scales from each of the three dimensions (Drivers, Competencies,

Traits), items are presented in multi-item blocks as shown below.

Item 1 Well defined work objectivesItem 2 Situations without a winner and a loser.Item 3 Having high status within the organization.Item 4 Avoiding meetings so I can focus on my work.Item 5 Developing myself beyond work.Item 6 Consistent direction in my career.

Figure 1: Block of Six Items

Upon seeing a block of items, candidates are tasked with ranking the items from “Most” to “Least” on some

continuum. Specifically, in this example, candidates would be asked to rank the items from “Most Preferred”

to “Least Preferred”1. Traditionally—that is, in Classical Test Theory (CTT)—information gathered in this

way induces a dependence among the items and scales known as ipsativity (Brown & Maydeu-Olivares, 2011).

One attractive feature of ipsative measurement is that response biases and “halo” effects are diminished

(Bartram, 2007; Cheung & Chan, 2002). However, due to the inherent item dependence, ipsative scales

are also known to produce problems for “score interpretation and for almost every conventional type of

psychometric analysis” (Brown & Maydeu-Olivares, 2011, p. 461; see also Baron, 1996). We can overcome

this limitation of ipsative measurement and still reap the benefits by employing a Forced Choice Item

Response Theory (FCIRT) method of modeling the items and scale relationships (Brown, 2010; Brown &

Maydeu-Olivares, 2011, 2012, 2013; Maydeu-Olivares & Brown, 2010).

To set the stage for the FCIRT model, assume that we have a test composed of several six item blocks

where each item in a given block measures a unique construct or dimension (much like the example in Figure

1). Further, assume that candidates are asked to rank the items from “Most Preferred” to “Least Preferred”.

To model this setup using FCIRT, we first employ a Thurstonian Comparative Model (Brown & Maydeu-

Olivares; for the origin of this model, see Thurstone, 1927). Using this model, for a give block of six items,

there are six latent utilities, ti. If a candidate prefers or ranks item i larger than item j, then the utility for

item i, ti is larger than the utility for item j, tj . This information can be coded in a comparative task as

yl =

1 if ti ≥ tj

0 if ti < tj

. (1)

1Note - for the Competency and Trait Dimensions, candidates are asked to rank blocks of items from “Most Like Me” to“Least Like Me”.

1

Using this coding, if the latent utility for item i is larger than the latent utility for item j, the observed response,

yl is represented by 1 (i.e., yl = 1 denotes that item i is ranked higher than item j). Then, for a block of six items,

there are fifteen possible comparative tasks. With this setup, we can model the comparative tasks a latent factor

model. To do so, we first note that the observed response, yl, is dependent on a difference of two item utilities.

This difference can be represented as a latent comparative response, yl* = ti - tj, such that

Using this coding, if the latent utility for item i is larger than the latent utility for item j, the observed

response, yl is represented by 1 (i.e., yl = 1 denotes that item i is ranked higher than item j). Then, for a

block of six items, there are 15 possible comparative tasks. With this setup, we can model the comparative

tasks a latent factor model. To do so, we first note that the observed response, yl, is dependent on a difference

of two item utilities. This difference can be represented as a latent comparative response, y∗l = ti − tj , such

that

yl =

1 if y∗l ≥ 0

0 if y∗l < 0. (2)

Because we are assuming that the items measure a latent construct we can model each item’s utility as a

linear function of the underlying latent construct as

ti = µi + λiηa + εi, (3)

where µidenotes the mean of the latent utility, λi denotes a factor loading, and ηa denotes a common

latent factor underlying the utility ti, and ei denotes a unique factor. Moreover, we assume that each

item measures one and only one latent trait, the common and unique latent constructs are orthogonal and

normally distributed, and that unique factors across items are orthogonal.

Notice from (??)–(??) that we have a nested latent structure. Specifically, we have modeled each observed

binary response as being dependent on a latent comparative response, which in turn is dependent on a

linear combination of an underlying latent trait. This nested latent structure is typically referred to as a

second-order factor model. As is well known (Takane & De Leeuw, 1987), many IRT models are equivalent

to factor models of dichotomous variables. As shown by Brown and Maydeu-Olivares (2012), through

reparameterization, we can recast the second-order factor model as a first-order Thurstonian IRT model.

To reparameterize the model, we rewrite each latent comparative response as

y∗l = (ui − uj) + λiηa − λjηb + ei − ej . (4)

If we assume for the moment that the two traits are known, or conditioned on, and recalling the assumption

that the traits and unique factors are normally distributed, then the item characteristic function for preferring

item i over item j for a person can be written as

P (yl = 1|ηa, ηb) = NCDF

(ui − uj) + λiηa − λjηb√

Ψ2i +Ψ2

j

, (5)

2

2 For the trait dimensions, candidates are asked to rank blocks of items from “Most like me” to “Least like me.”



Because we are assuming that the items measure a latent construct, we can model each item’s utility as a linear

function of the underlying latent construct as






that

yl =

1 if y∗l ≥ 0

0 if y∗l < 0. (2)





















Ψ2i +Ψ2

j

, (5)

2

where μi denotes the mean of the latent utility, λi denotes a factor loading/discrimination, ηa denotes a common

latent factor underlying the utility ti, and εi denotes a unique factor. Moreover, we assume that each item

measures one and only one latent trait, that the common and unique latent constructs are orthogonal and


Notice from (1), (2), and (3) that we have a nested latent structure. Specifically, we have modeled each observed

binary response as being dependent on a latent comparative response, which, in turn, is dependent on a linear

combination of an underlying latent trait. This nested latent structure is typically referred to as a second-order

factor model. As is well known (Takane & De Leeuw, 1987), many IRT models are equivalent to factor models of

dichotomous variables. As shown by Brown and Maydeu-Olivares (2012), we can recast the second-order factor

model as a first-order Thurstonian IRT model via reparameterization.







that

yl =

1 if y∗l ≥ 0

0 if y∗l < 0. (2)





















Ψ2i +Ψ2

j

, (5)

2

If we assume for the moment that the two traits are known, or conditioned on, and recalling the assumption that

the traits and unique factors are normally distributed, then the item characteristic function for preferring item i

over item j for a person can be written as






that

yl =

1 if y∗l ≥ 0

0 if y∗l < 0. (2)





















Ψ2i +Ψ2

j

, (5)

2where ψi² is the variance of item i uniqueness. Notice that the function is a standard normal ogive, which in this

case is an IRT model that is dependent on two latent traits. Using this setup, the observed ipsative measurement

model is transformed, loses its ipsative characteristics, and becomes a normative latent IRT model.



Section V The Korn Ferry Assessment of Leadership Potential: What it measures

As mentioned previously, the KFALP measures Drivers, Experiences, and Traits that reflect leadership potential.

Feedback from the KFALP is organized into seven categories. All of the characteristics assessed by the KFALP

are related to advancement in the leadership pipeline. Relevant literature is summarized in this section of the

manual to provide deeper descriptions of each of these characteristics.

DriversDrivers are the preferences, values, and motivations that influence career aspirations. They provide the “will do”

that creates engagement and energy for a task or role. To the extent that a person’s drivers are aligned with the

role, they will be energized by it.

People with leadership potential find the role of a leader interesting and the work of leading motivating, which is

crucial to being effective. Leadership becomes progressively more difficult at every level, and the demands upon

time and energy increase. If the work doesn’t align to what drives them, it is unlikely that any leader will have

the energy and resilience needed to thrive or even to just survive. According to Silzer and Church (2010), 90% of

organizations now use an individual’s career drive as one predictor to identify high potential.

High potential leaders value the nature of leadership work, the opportunity to make a difference, having a

positive impact on their coworkers and organization, and having greater responsibility. This is evident in the

greater prevalence of goals and aspirations related to leadership at each career level.

Data collected over the past decade at Korn Ferry show that those who move up in leadership are marked by

having higher career aspirations, more specific career goals, a desire to take on general management and C-suite

positions, and are engaged by getting things done through others (see Table 3).

Table 3. Signals of leadership drive across management levels.

Percent choosing in the top three motivators:

First level leader Mid-level leaderFunctional or

business unit leader Senior/top

Influence on the direction of the organization. 38% 52% 61% 72%

Belief in the mission of the organization. 41% 41% 47% 56%

Responsibility for the performance of others and the results of the unit.

30% 42% 48% 49%

Source: Over 17,000 leaders, Career History Questionnaire (Gerstner, Hazucha, & Davies, 2012).

With KFALP, Drivers are measured in three facets, Advancement drive, Career planning, and Role preferences.

We will discuss each in turn.

Advancement driveAdvancement drive involves the motivation to advance through collaboration, ambition, and challenge.

Individuals high on Advancement drive are more willing to put time and energy into achieving greater

responsibility and power. Those low on Advancement drive are less motivated by the challenges of advancing

along the leadership pipeline.



BackgroundIn general, motivation has been a central focus of organizational research for many years. The high level of

interest can be attributed to the long-held belief that work behavior is influenced by a mix of factors, including

ability, motivation, and situational constraints/facilitators (Campbell & Pritchard, 1976). In human resource

management, this is referred to as the AMO framework. In essence, the AMO framework proposes that employee

performance (P) is a function of the employee’s ability (A), motivation (M), and opportunity (O) to perform

(Boselie, Dietz, & Boon, 2005; Boxall & Purcell, 2008).

Work motivation is a set of forces that interact with the situation to initiate work-related behavior and to

determine its direction, intensity, and duration. This definition highlights the fact that motivation can be seen

in the choices individuals make among goals to pursue (i.e., direction), the amount of effort they put forth

toward attaining the goals (i.e., intensity), and persistence of action (i.e., duration). In the workplace, notable

achievements rarely are the outcome of random activities. Rather, they typically involve a combination of choices

concerning what to do, how much attentional effort to devote to specific activities, and when to shift direction

and levels of effort.

Numerous theories have been proposed to describe, explain, and investigate motivation. A particularly notable

framework that integrates different motivation theories involves Kanfer’s (1990) distinction between distal

and proximal motivations. Motivations that have immediate and direct impact on behaviors are proximal. For

instance, goal setting can focus attention toward goal-relevant activities and away from irrelevant activities

(Locke, 1978). When individuals are committed, goals will energize individuals and initiate the execution of

action plans toward attaining the goals. Goal commitment, therefore, is a proximal motivation. In contrast,

distal motivations affect action goals through proximal motivations. The impact of distal motivations tends to

span longer time frames and across situations. For example, needs represent a set of distal motivations. The

same need can be satisfied through the pursuit of different goals. Since the assessment of leadership potential

involves predicting relatively enduring behavioral patterns, we focus on measuring distal motivation.

There are different approaches to conceptualizing and measuring motivations. Needs are variable internal

states that, when activated or aroused, energize and direct behavior (Pittman & Zeigler, 2007). A need affects

behavior when there is a discrepancy between one’s current state and a desired state. The discrepancy leads

to the experience of an internal tension that energizes behavior, leading individuals to pursue things in their

environment that can help reduce the discrepancy. Although it is not always well supported, Maslow’s (1954)

need hierarchy is perhaps the most well-known needs theory. In contrast, values are standards or criteria for

selecting among alternatives. They serve as the base for making choices. Values underlie and affect attitudes,

which in turn underlie and affect behavior. To consider values in the workplace is to probe the very reasons

people work and why they behave in the ways they do in their jobs. A value is an enduring belief that a specific

mode of conduct or end state of existence is personally and socially preferable to alternative modes of conduct

or end states (Rokeach, 1973). Therefore, values entail attention to both means (how to do) and ends (what to

pursue). For instance, two individuals may both have a desire to influence others. However, one may choose

to rely on formal power, the other may take a participative or deferential approach. This implies the difference

between needs and values. Whereas needs are considered to be at least partially biologically based, values are

shaped to a larger extent by social factors such as perceived relative status and also by culture.

Needs and values, nonetheless, are closely related. Values represent the expression of needs. When an

individual has a strong need for something, the individual places high value on situations that enable them to

satisfy this need. As such, needs and values tend to be used interchangeably in the work motivation literature

(Kooij, De Lange, Jansen, Kanfer, & Dikkers, 2011). This is revealed by the fact that measures of needs and values

often contain the same test items. For this reason, we reviewed both lines of research to inform the architecture

of our framework with the purpose of establishing a taxonomy of drivers that sufficiently synthesizes existing

theories of needs and values.



Advancement drive composite detailsFour Drivers comprise the KFALP Advancement drive composite. These are Collaboration, Structure, Power, and

Challenge.

Broadly, Collaboration refers to communion striving. It describes actions directed toward obtaining acceptance

in personal relationships and getting along with others. Socioanalytic theorists (e.g., Hogan & Warrenfeltz, 2003)

have argued that people have innate biological needs for acceptance and approval. Being connected to others,

feeling a sense of relatedness, and desire for interpersonal attachment is a fundamental human motivation

(Baumeister & Leary, 1995). Collaboration is associated with conformity and conflict avoidance, which are not

generally typical of leaders. In fact, in one longitudinal study, McClelland and Boyatzis (1982) found that the need

for affiliation was negatively related to promotion and managerial level. In today’s organizations, however, the

pace of technological change, increased complexity, competitive demands, challenging economics, and risks

involved in decision making have made it difficult for individuals to act alone. Leadership research increasingly

emphasizes collaborative approaches to leadership effectiveness (Yammarino, Salas, Serban, Shirreffs, & Shuffler,

2012). Some scholars even suggest that leaders develop and adopt “collective identities,” which involve self-

definitions based on group membership (Venus, Mao, Lanaj, & Johnson, 2012). Highly collaborative leaders

are motivated by internalizing group values and norms, fulfilling social roles and obligations, and contributing

to the group’s welfare. This typically cultivates trust among team members, which in turn results in increased

team performance (Drescher, Korsgaard, Welpe, Picot, & Wigand, 2014). Collaborative leadership is increasingly

characterized as key for innovation management. In our own data (e.g., D’Mello, 2015), we have repeatedly found

Collaboration (albeit characterized as a behavior more than a motive) to be one of the most salient predictors of

innovation and related outcomes.

Structure refers to preference for work-related stability, routine, certainty, and predictability. Individuals closely

associate certainty with comfort and safety and, by nature, often prefer (fore)knowledge concerning “what

will happen next.” Certainty and predictability facilitate control and personal agency and, where rewarding,

will reinforce and stabilize behavior. Meeting and reaping rewards according to known and clear expectations

generate even physiologically measurable outcomes, including dopamine levels in the brain, which are typically

desirable (Schultz, 1999). In contrast, when patterns do not play out according to expectation, or when if-then

reinforcement schedules are erratic, people tend to sense instability and threats to well-being.

Nevertheless, contemporary organizational design emphasizes agility and adaptability, and increasingly rewards

cross-functional efforts, related synergies, and comfort with ambiguity (Worley & Lawler, 2010). In a 2009 survey,

90% of executives, spanning all regions and industry sectors, ranked organizational agility and adaptability

as crucial to business success and survival (Sull, 2009). Clearly, individuals who value routine, security, and

order are more resistant to and disconcerted by change (Oreg, Vakola, & Armenakis, 2011). For these and other

reasons, high scores on Structure-like measures are increasingly associated with decreased success, particularly

among high-level business executives. High Structure managers and leaders, however, will likely continue to

thrive and be preferable in certain roles and contexts, particularly those characterized by strict regulations,

well-defined processes, and where the effects of not being precise, correct, and thorough are negative and

relatively serious.

A drive for Power involves a strong desire to influence others. Individuals driven by power enjoy being held

responsible for other people and broader group results. They aspire to achieve higher status and even a

prestigious title or rank. They are energized by visibility and strive to gain rewards and recognition for their

efforts. Motivation for power is arguably among the most critical for leadership success. The essence of

leadership itself is embodied in the act of influencing others, and a weak drive for power means a lack of interest

in influence and impacting others (McClelland, 1965; McClelland & Burnham, 1976). In Winter’s (1987) study of

US presidents, power motivation was significantly correlated with historian ratings of “greatness.” The same

power motivation scores have also been linked to ratings of certain aspects of presidential performance, as

well as charisma (House, Spangler, & Woycke, 1991). After reviewing the literature, Zaccaro (2001) cited power

motivation as a key and incremental predictor of leadership charisma.



Individuals driven by Challenge prefer new and difficult projects that stretch their abilities. They tend to thrive

on learning and pushing their limits to acquire new proficiencies. They are excited by the prospect of making

a difference and are typically willing and eager to put forth discretionary effort in pursuit of accomplishing

goals. Individuals who are high on Challenge are also typically driven by competition and the desire to win.

Meta-analytic evidence links Challenge-like ratings to a variety of outcomes including income, job performance,

community leadership, and sales success (Spangler, 1992). In their meta-analysis, Collins, Hanges, and Locke

(2004) found that Challenge was related to choosing an entrepreneurial career as well as entrepreneurial

performance. Some previous research has suggested that Challenge is more strongly linked to leadership

success at lower levels of leadership, where the contributions and accomplishments of individuals are seen as

more important than influence over others (McClelland & Boyatzis, 1982). Studies of higher-level managers have

presented mixed results. House, Spangler, and Woycke (1991) and Deluga (1998), for example, found negative

or zero relationships between Challenge and presidential performance and greatness. In contrast, Zaccaro,

White, and colleagues (1997) found that Challenge was positively linked to senior leadership-potential ratings,

career achievement, and organizational level in a sample of army civilian managers. Taken together, this body of

research suggests Challenge is an important indicator of potential, with positive relationships to leaders’ success

at most, if not all, levels of leadership.

Career planningIndividuals who are high on Career planning have strong career preferences and have specific plans on how to

achieve their career goals. Those who are low in Career planning tend to be more opportunistic in how they

approach their career.

Career planning is the only descriptive, non-normed measure in the instrument. It reflects back participant

responses in a fairly straightforward fashion. There is not a demonstrated relationship between Career planning

and leadership advancement or success. We do know, however, that some find it useful to consider future career

moves, while others prefer to be more opportunistic.

A career plan can be useful in laying out a path to gaining experiences that are demonstrated to relate to

advancement. (See Experience below).

Role preferencesIt has long been recognized that not all individuals are interested in becoming managers and leaders. While

some employees value advancement above all else, others value primarily the intrinsic excitement of work.

In a survey of engineers conducted in early 1980s (Guterl, 1984), approximately one-third of the respondents

indicated a preference for a management career, one-third for a non-management career, and one-third were

unsure. This study reported that many engineers preferred to continue doing engineering and to avoid taking

on administrative responsibilities. In another survey of close to 1,500 individuals employed in R&D, less than

one-third of the survey participants preferred the managerial career over alternative career paths (Allen & Katz,

1986). These earlier survey findings are consistent with a more recent survey which found that only about one-

third (34%) of workers aspire to leadership positions (CareerBuilder, 2014).

Schein (1987) concluded that individuals hold a wide variety of career interests, which he labeled career anchors,

or orientations. These career orientations influence career choices, employer changes, other career-related

decisions, and ultimately how the future of career is viewed. At a high level, these differences describe the

contradiction between specialists and generalists in organizations (Gouldner, 1957; Kornhauser, 1962; Schein,

1987). Specialists are characterized by strong professional identification, craftsman-like view of skill mastery,

and a singular area of expertise. Conversely, generalists are identified by a drive to ascend the managerial

hierarchy and breadth of functional-area expertise. Given these differences, organizations were recommended

to subdivide their high potential pools and provide different approaches to manage and engage specialists and

generalists (Cesare & Thornton, 1993; Dries & Pepermans, 2008).



Therefore, we systematically conducted research to obtain a more thorough understanding of the motivations,

interests, and values of different types of talented employees in organizations. We were especially interested

in understanding the difference between high potential leaders and high potential professional employees. Our

research effort consists of two phases of investigation—one qualitative and one quantitative.

The qualitative researchThe research started with interviewing 31 high-performing, specialized employees recruited from several

organizations. They were nominated by HR professionals of the participants’ respective companies. Participants

were represented by a wealth of different industries and professions, including engineers, scientists, physicians,

financial analysts, musicians, and clinical psychologists. Respondents were asked a series of questions relating

to their role preferences, events that shaped their career decisions, things they like and don’t like at work,

challenging experiences and the lessons learned, most memorable achievements, and future aspirations.

All interviews were taped and transcribed into written documents. The transcripts were then analyzed for

emerging themes. An analysis of the findings provides a clear, descriptive picture of high professionals: most

tend to have (and desire) a linear career path; they are intrinsically motivated by their job; they have a strong

desire to be recognized and respected for their expertise. They cite administrative or “busy work” and office

politics as the biggest disruptors to their effectiveness and express a desire for autonomy and independent

decision making in their roles.

The quantitative researchBased on information from the first phase of research, Korn Ferry turned to statistically measuring differences

in role preference, motivation, and engagement for high potential leaders and high potential professionals. This

phase of research involved developing 30 forced-choice items for pilot testing. The instrument was designed to

capture the dichotomous relationship between having an orientation toward either a generalist role (i.e., a role

with leadership/management responsibility) or a specialist role (i.e., a role that requires functional expertise).

The results were compelling. Three factors that define a continuum of role preference emerged from analysis on

data collected from a global sample of 10,823 participants:

Factor I: How people perceive bounds of their role responsibilities. Some people, preferring roles in narrowly defined areas, commit to their profession and enjoy opportunities that best use their expertise. Others, believing they can be versatile and taking roles in different areas, explore and try different careers. They enjoy doing things they haven’t done before.

Factor II: Different approaches to success. Individuals on one end of the continuum want to develop expertise and pursue best outcomes. They go deep and enjoy working toward greater precision and accuracy. Individuals on the other end of the continuum prefer to leverage others’ expertise and strive for practical outcomes. They make timely decisions and like to get a lot of work done, even if the work is imperfect.

Factor III: How people engage in work. Some people want to concentrate on a few priorities for high productivity, believing it most effective to get one task done before starting another. Others enjoy multitasking, preferring to get involved in many different activities and shifting easily from priority to priority.

Twelve items were retained to assess the three factors. The sum of the three factors creates a scale that can

distinguish the career orientation between high potential leaders and high potential professional employees.

Clear response patterns emerged in our global study. Data from over 10,000 assessments show that the

specialist orientation declines steadily along the organizational hierarchy. Individual contributors (ICs) have the

highest specialist orientations. Top-level executives have the lowest specialist orientations. And this pattern

emerged consistently in different international regions.



Figure 4. Preferences.

1.30

1.32

1.34

1.36

1.38

1.40

1.42

1.44

1.46

1.48

1.50

ICs

1.45

1.42

1.38

1.35

1.33

Supervisors Managers Directors Executives

People are selected to different types of jobs. We expect the alignment between employees’ career orientation

and the type of job they do. A sample of participants (N = 134) was asked to take a supplementary survey after

they completed the career orientation assessment. Part of the supplementary survey asked participants to

rate the level of volatility, ambiguity, uncertainty, and complexity of their job, as well as the level of functional

specialization required for performing their job. The results are affirming. Individuals who are more specialist

oriented tend to report their job to be less complex and volatile (r = -.32, p < .001), and require more functional

specialization (r = .22, p < .05).

ExperienceAs leaders progress through their careers, they gain a series of experiences. Through these experiences, leaders

gain motivation, attitudes, knowledge, skills, and other capabilities that enable them to perform effectively

in future leadership roles (Campbell, 2012; Tesluk & Jacobs, 1998). Experiences, therefore, are an important

foundation for moving to new, more challenging roles.

A leader who has honed skills through depth and breadth of experiences has much more bandwidth to learn

everything else they must conquer to succeed when promoted to the next level. A leader who is behind the

curve, who lacks one or more relevant experiences, will have to learn these lessons while they are also learning

the job. This extra demand, at a time of rapid change, makes the transition risky and more likely to go awry.

Work experiences are multi-faceted, with quantitative and qualitative components that interact with each

other. They vary in specificity (task, job, team, organizational, and occupational) as well as measurement mode

(amount, time, density, timing, type) (Quinones, Ford, & Teachout, 1995; Tesluk & Jacobs, 1998). Consequently,

experience is complex, unique to individuals, and far from universal in its nature and value.



To capture this complexity, in the KFALP, we use a short, structured career walk-through that contains verifiable

questions about what an individual has done to date. This is an area where we have chosen to be brief to meet

the client’s need for brevity and to allow more robust measurement in other areas. In the KFALP, Experience is

normed against the participant’s current level. Three aspects of Experience are captured:

• Core experience can be considered “vertical experience.” It is captured by counting the number of leadership levels in which an individual has spent two-plus years working, relative to where the person is today. Each level brings unique, day-to-day experience in leadership, collaboration, and leading both up and down.

• Perspective can be considered “horizontal experience.” It represents the leadership “variations” the participant has gained from working in different organizations, industries, roles, countries, etc. Perspective helps leaders move beyond “the way we do things here” thinking to encompass the lessons and experiences of alternative or diverse ways of approaching the role, gained through variety of perspective.

• Key challenges captures the participant’s experience across 10 key leadership challenges that have been found to be particularly developmental. Not every leader will face each challenge during their career. Key challenges also reflects the leader’s role in the challenge—e.g., participant, leader, or sponsor.

The importance of developmental experiences originally was surfaced in a series of studies conducted by the

Center for Creative Leadership. In these qualitative studies, executives were interviewed and asked to describe key

events in their careers that caused the most learning. The following two questions were probed: (1) what specifically

happened on the job, and (2) what did they learn from the event. Researchers interviewed 191 executives from six

major corporations. Descriptions of the 616 events and 1,547 corresponding lessons were tabulated. The analyses

and results are summarized in the book aptly titled The Lessons of Experience (McCall et al., 1988). These researchers

observed that the most developmental experiences are challenging, stretching, and difficult.

Work experiences are challenging when existing tactics and routines are inadequate for addressing work tasks,

and new ways of dealing with work situations are required (De Pater, Van Vianen, Bechtoldt, & Klehe, 2009).

By presenting ambiguous situations and exposing individuals to a great diversity of organizational stimuli,

challenging experiences provide critical opportunities for leaders to acquire new job knowledge and practice

new skills. Stimuli that are novel, uncertain, and difficult tend to create a heightened sense of arousal within

individuals. This heightened arousal will lead to a wide range of behavioral and cognitive processes, including

learning (DeRue & Wellman, 2009). Challenging experiences also shape leaders’ work attitudes and motivation

by uncovering gaps between individuals’ current capabilities and what is required for assignment success

(McCauley, Ruderman, Ohlott, & Morrow, 1994).

Korn Ferry research has identified key career experiences that differentiate leaders. The more of these key

developmental experiences a leader accumulates, the greater the possibility that the leader will be successful

after promotion to the next level. Working with research partners at well-known universities, Korn Ferry has

also found that experience helps leaders develop their strategic thinking skills (Dragoni, Oh, Van Katwyk,

& Tesluk, 2011). In general, research has found that challenging experiences contribute to the development

of a broad variety of leadership competencies including business knowledge, visioning, strategic thinking,

problem solving, decision making, change management, and interpersonal skill (DeRue & Wellman, 2009;

Dragoni, Oh, Van Katwyk, & Tesluk, 2011; Dragoni, Tesluk, Russell, & Oh, 2009). Not surprisingly, individuals who

have had more challenging experiences are more likely to be perceived as having high promotion potential

(De Pater et al., 2009).

Leadership Experience Inventory (LEI) data at Korn Ferry indicate that, on average, leaders at the highest levels

are more likely to have had a wide range of key developmental, career-building experiences (see Figure 5). Note

also that the largest jump is from Mid-level leader to Business unit leader.



Figure 5. Prevalence of key formative career experiences by management.

0

10

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40

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Critica

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allen

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First level leader

Mid-level leader

Business unit leader

Senior executive

CEO

Even within leaders at the highest levels, the impact of formative, challenging experience is observed. Among

leaders evaluated by Korn Ferry for CEO roles, those who were judged most effective reported having more

challenging work experiences. A higher proportion of the most effective CEO candidates had challenging

experiences that were heavy in strategic and people demands (Crandell, Orr, & Urs, 2015).

TraitsAwareness, Learning agility, Leadership traits, and Derailment risks are all measured using FC-IRT trait measures.

Following is background on the development of the Trait Scale Bank used to support reporting on Awareness,

Learning agility, Leadership traits, and Derailment risks.

Traits include personality characteristics that exert a strong influence on behavior. Personality influences how

people develop—what is more natural for them and what is more of an effort. Traits are the enduring aspects

of a person, changing little over time. Decades of research efforts offer insight into the application of trait

measures among upper-level management and executive leaders. Personality is related to leadership emergence

and effectiveness (Judge, Bono, Ilies, & Gerhardt, 2002) as well as the changing nature of performance across

time (e.g., Thoresen, Bradley, Bliese, & Thoresen, 2004). Meta-analyses and meta-analyses of meta-analyses

(Judge, Bono et al., 2002; Ones, Dilchert, Viswesvaran, & Judge, 2007) have demonstrated that at least three of

the factors from the widely known and established FiveFactor model show moderated but generally consistent

relationships with success among upper-level management and executive leaders. According to Church and

Rotolo (2013), 66% of organizations used personality inventories to assess high potentials.

The scales underlying the Awareness, Learning agility, Leadership traits, and Derailment risks were chosen

from among the scales of the Korn Ferry Trait Scale Bank (KFTSB). The foundation of the KFTSB is the

well-established personality science of the Big Five. The Big Five framework is an extension of the lexical

tradition, which assumes the important descriptors differentiating persons will be represented in natural



language. This framework is a hierarchical, descriptive conceptualization of personality as opposed to a psycho-

dynamic conceptualization. Modern Big Five research is exploring linkages to neural substrates and self-

regulatory processes in the brain (DeYoung, 2010; DeYoung, Hirsh, Shane, Papademetris, Rajeevan, & Gray, 2010).

The Big Five has demonstrated global applicability (Schmitt, Allik, McCrae, & Benet-Martínez, 2007). Moreover,

this model is clearly established as the premier descriptive framework for personality science (John, Naumann, &

Soto, 2008).

The KFTSB is a state-of-the-art personality instrument developed as a new measure of the Five-Factor

personality model. There are 21 total scales designed to capture individual differences on those aspects of

personality most related to on-the-job performance and organizational fit.

In the development of the trait bank, work proceeded using a Classical Test Theory (CTT) approach and was

followed by a Forced-Choice Item Response Theory (FC-IRT) approach. Items were written by a global team to

represent each trait. Items were refined using traditional item analysis through multiple waves of data collection

focusing on factor structure and internal consistency of scales. Retained items were then tested using FC-IRT.

Factor structure was tested using exploratory factor analysis and confirmatory factor analysis (CFA) for both the

CTT and IRT versions of the scales. The final item set resulted in scales with primary loadings on the intended

factor for all scales. To test whether the structure would hold at the latent trait level—that is, on the IRT trait

scores—we conducted a CFA. Using the Goodness of Fit (GFI) and Standardized Root Mean Square Residual

(SRMSR) fit indices, we found that the intended a priori structure adequately described the underlying structure

of the IRT trait scores. Specifically, we found that the GFI = .904 and the SRMSR = .064, both within the range of

acceptable model fit as discussed in the psychometric literature (see Hu & Bentler, 1999).

From among these trait scales, appropriate scales and composites were chosen to form the core of the KFALP,

with a focus on scales that differentiate across levels of leadership. The Awareness, Learning agility, Leadership

traits, and Derailment risks signposts include trait-based measures using FC-IRT measurement technology.

Persons further along the leadership pipeline tend to score higher (or lower on derailers) than persons earlier

in their leadership career. Each scale is reported relative to normative data appropriate to the target role of the

assessment.

AwarenessTo achieve high performance, leaders must begin with a clear-eyed view of their existing strengths and their

development needs. They need to know where they excel, when they can trust their instincts and abilities, and

when they need to rely on the insights and abilities of others. They must also have keen awareness of their

thoughts, feelings, and behaviors on a moment-to-moment basis, what is sometimes called mindfulness. This

allows them to manage themselves and others more effectively.

• Self-awareness is awareness of strengths and freedom from blind spots. It is known to be related to leadership success and inoculate, to some degree, against derailment. Self-awareness is a process of continuous reassessment of self-knowledge and refinement of true self-awareness.

• Situational self-awareness is the capability of being aware of present experience in a non-judgmental way, paying attention to the importance of a variety of demands, being more aware of one’s expert intuitions, and more able to improvise in a dynamic environment.

Self-awarenessResearch suggests that derailed managers and executives share a common attribute—a lack of self-awareness

(Quast, Wohkittel, Chung, Vue, Center, & Phillips, 2013; Sala, 2003). Leaders who lack self-awareness perceive

themselves differently than other people perceive them. They tend to perceive themselves more positively than

others, which in turn makes them less aware of the weaknesses that put them at odds with the demands of the

organization (Atwater, Waldman, Ostroff, Robie, & Johnson, 2005). This lack of self-awareness can result in many

destructive behaviors, which may lead to derailment (Lombardo & McCauley, 1988; McCall et al., 1988).



Evidence suggests that highly self-aware leaders have a positive impact on company performance; prevalence of

high self-awareness correlates with high rate of return. Korn Ferry’s Zes and Landis (2013) analyzed 6,977 self-

assessments from professionals at 486 publicly traded companies and found that those with high self-awareness

tend to be concentrated in companies with a robust rate of return, suggesting that they might contribute to

greater business outcomes (see Figure 6).

Figure 6. Stock performance and self-awareness.

Low self–awareness

High self–awareness

0% 5% 10% 15% 20% 25%

Percent chance that a person works for a high-performing company as a factor of their self-awareness level.

Situational self-awarenessSituational self-awareness is an emerging construct in the industrial/organizational psychology literature. It is sometimes referred to as mindfulness, and has been called a “western adaptation to an eastern way of thought” (Haigh, Moore, Kashdan, & Fresco, 2011). Situational self-awareness involves one’s ability to regulate emotions, anticipate and be proactive for change, accept circumstances, live in the moment, reserve judgment, and be aware of even subtle internal and external information. Low scorers on Situational self-awareness are more likely to be focused on past or future events, are less aware of their impact on situations as they occur, and are more likely to use strict and well-defined heuristics when making decisions or characterizing a situation. Across studies and measurement instruments, Situational self-awareness has repeatedly shown compelling evidence of construct validity and has displayed key correlations with many other psychological constructs and outcomes (Haigh et al., 2011; Feldman, Hayes, Kumar, Geeson, & Laurenceau, 2007). Together with its theoretical foundations, correlational patterns help to elucidate the nature of Situational self-awareness and its potential utility. It has shown considerable positive relationships with positive affect, curiosity and exploration, emotional regulation, mood repair, and cognitive flexibility. Conversely, it has shown substantial negative relationships with a variety of maladaptive and problematic emotional and affective states. Specifically, increases in Situational self-awareness are associated with decreased anxiety, distress, depression, worry, rumination, thought suppression, avoiding experiences, and brooding (Kumar, Feldman, & Hayes, 2008; Johnson, 2007).

For much of its history, Situational self-awareness has been used as part of developmental plans for designing psycho-social interventions in diverse clinical and non-clinical settings. These include acceptance and commitment, relational frame theory, and a host of other cognitive-behavioral interventions (Baer, 2003). Related interventions designed to boost scores on Situational self-awareness-like constructs are emerging rapidly in organizational contexts as well (Hayes, Bond, & Barnes-Holmes, 2006) and have been explicated specifically for high-level executives (Passmore, 2007; Passmore & Marianetti, 2007). The potential utility of Situational self-awareness measures in organizations extends beyond its promising application for predicting who will be successful in the executive ranks. Situational self-awareness also can provide a framework or otherwise assist in coaching and development activities that show indications of substantially helping organizational personnel to manage stress, take advantage of stress, produce results while learning on the job, and mitigate derailment (Lee, 2012).



Emerging conceptualizations of emotional regulation increasingly embed Situational self-awareness in a larger

framework as a component and an antecedent to pro-social behavior. It has otherwise been associated with

effective strategic decision making, novelty seeking, adaptive risk taking, and awareness of key resources

among key players in organizations (Langer, 2009; Nadkarni & Barr, 2008; Weick & Roberts, 1993). Currently,

the consensus in the extant literature is that Situational self-awareness has unilaterally positive effects in

organizational contexts and beyond (Lee, 2012; Dane, 2011). Although no empirical work has shown otherwise,

this notion is not without critique (Dane, 2011). The paucity of skepticism on Situational self-awareness as

a strictly positive characteristic focuses mostly on its “wide attentional breadth” and how it might distract

skilled professionals whose charge is to focus on limited information and limited scope issues in considerable

depth (Dane, 2011). Ultimately, the criticism is that Situational self-awareness scores in executives may reach a

debilitating critical mass associated with indecision and failure to react, more especially among management

personnel whose roles involve more narrowly defined task expertise and relatively static task environments

(Chajut & Algom, 2003)—job characteristics that are notably more common at lower levels of management,

although not exclusively so. Still, given the clear and substantial positive relationships between Situational self-

awareness and many general measures of positive adaptive behavior, strategic coping, and emotional states, it

is likely that job roles and organizational context will only moderate the magnitude of its otherwise generally

positive effect in occupational contexts (Goleman, 1998). Interestingly, Situational self-awareness may also

moderate the link between other psychological constructs and ratings of job performance, such that higher

Situational self-awareness strengthens positive associations where applicable (Barrick, Parks, & Mount, 2005).

Learning agilityLearning and skill development play an important role in an individual’s long-term effectiveness and career

success (Silzer & Church, 2009; Tannenbaum, 1997). The most effective way to assess a person’s potential to

learn from experience is by measuring learning agility. Learning agility is defined as the willingness and ability

to learn from experience, and subsequently apply that learning to perform successfully under new or first-time

conditions (Lombardo & Eichinger, 2000). Learning agile individuals are nimble and adaptable in changing

environments; they are key players who fill the leadership bench. Their ability to learn from experiences and take

on novel challenges sets them apart as high potentials, as evidenced by their speedy career ascent (Dai, Tang, &

Feil, 2014; Dai, De Meuse, & Tang, 2013). Nearly 25% of the Fortune 100 assess learning agility as one component

of potential.

Learning agility is especially crucial during job transitions—such as a promotion—when an individual invariably

faces new and unfamiliar situations. Instead of automatically defaulting to favorite past solutions or problem-

solving tactics, learning agile leaders apply fresh and varied approaches, ideas, solutions, and techniques to

solve those new, tough problems. In short, learning agile leaders find new ways to successfully navigate unknown

and unforeseen challenges.

The KFALP uses four scales to assess learning agility:

• Mental agility is a broad curiosity about the complex issues, challenges, and novel situations that leaders face daily, and sets the stage for effective problem solving. This curiosity about issues and problems helps spot patterns, trends, and relationships.

• People agility is understanding the value of getting work done with and through people, being attuned to individuals’ needs and motivations, and typically skilled at reading people with an effective influencing style.

• Change agility is embracing change and taking well-reasoned risks even in the face of that change. It includes openness and acceptance of change and willingness to balance the risks and trade-offs vs. waiting.

• Results agility is being energized by new, tough assignments and overcoming obstacles to achieve stretch work objectives. It includes the enjoyment of being judged against external standards of achievement.



These four aspects of learning agility were selected based on prior factor analysis results with multi-rater

measures. Each aspect is associated with others’ (e.g., bosses’, human resource professionals’) ratings of

potential and propensity to stay out of trouble (i.e., unlikely to derail) (Lombardo & Eichinger, 2000).

The ROI for organizations and leaders is clear. Research shows that learning agile leaders are rated more

competent, recognized as having the most potential for advancement, get promoted faster and more often than

their peers, and outperform their peers after a promotion (Dai et al., 2013; Dragoni, Tesluk, & Oh, 2009; Dries,

Vantilborgh, & Pepermans, 2012; Lombardo & Eichinger, 2000). Learning agility also accounts for incremental

validity in performance above and beyond intelligence and personality (De Meuse, Dai, & Hallenbeck, 2010).

Korn Ferry has the most extensive research for describing and measuring learning agility. This includes

observable competencies and a set of related traits. Korn Ferry research found that highly learning agile

people earn promotion much more quickly (Dai et al., 2014; Dai et al., 2013). After grouping individuals by low,

moderate, and high learning agility scores, our analysis found that managers with high learning agility received

twice as many promotions over the 10-year period as those with low learning agility (see Figure 7).

Figure 7. Number of promotions managers were likely to receive over 10 years.

Low learning agility

Moderate learning agility

High learning agility

0 0.5 1

Number of promotions

1.5 2 2.5

Leadership traitsThe more an individual’s traits align with the traits that are characteristic of successful leaders, the greater the

potential for future success at higher organizational levels. Traits factor heavily into questions of leadership

potential because personality profiles look substantially different at each progressive level of management

(Crandell, Hazucha, & Orr, 2014).

The KFALP reports leaders’ scores on five traits. Each is defined briefly below, and then discussed in more detail.

• Focus is the willingness to let go of personal attention to all details and seek a bigger-picture perspective rather than the pursuit of minutiae at the expense of the big picture.

• Persistence is having and pursuing closely held and personally valued long-term goals despite obstacles and distraction. This focus helps sustain leaders through difficulties and detours. This is different from focusing on near-term goal attainment or on goals set by others.

• Tolerance of ambiguity is the capability to thrive in a volatile, uncertain, complex, and ambiguous (VUCA) world. Some leaders find energy in these situations and can work productively despite a lack of a clear view of the future, while others are more comfortable with conditions providing clarity and certainty.



• Assertiveness is the willingness to take the lead if a clear leader is not apparent and to do so with little hesitation. Successful leaders are comfortable taking charge, and they find leading the way to feel natural and expect good outcomes when they do.

• Optimism is the degree to which people tend to disregard disappointment, are satisfied with who they are, and expect the future to be bright. Successful leadership requires a steady healthy optimism and good expectations for the future.

FocusFocus taps the extent to which individuals are detail oriented, thorough, and careful in decision making and

work processes. Very high scorers may even be described as dogmatic and/or problematically perfectionist.

Anecdotally, a problematically high Focus score can be described by the aphorism “the perfect can be the

enemy of good.” Attention to detail may contribute to early career success, but inhibit or even derail a top

executive. That is, Focus is positively associated with performance for lower-level managers and individual

contributors whose roles involve a notable degree of task orientation and applicability of expertise, perhaps

as well as deference to protocol and well-defined process standards. But, Focus and Focus-like scores tend

to decrease at higher levels of management (Brousseau, Driver, Hourihan, & Larsson, 2006; Lewis & Ream,

2012). Among executive leaders, Focus and Focus-like measures typically correlate negatively with executive

performance and other management outcomes including career success (Lewis, 2012). Conceptually

convergent or otherwise markedly correlated measures have even been characterized as derailers for executive

managers (viz., “dutiful” in Hogan & Hogan, 2009) and are negatively correlated with defining components of

transformational leadership behaviors and traits. This shift across management level accounts, in part, for the

paradox of a merely satisfactory new manager who simultaneously has the potential to be a superior-performing

executive. And it explains, in part, why some leaders plateau despite early success. Having the right level—not

too much, not too little—of these traits is one indicator of future high performance as a leader.

PersistencePersistence refers to a tendency toward passionate and steadfast pursuit of personally valued long-term or

lifetime goals or values, in spite of obstacles, discouragement, or distraction. High scorers tend to push through

adversity and tend not to give up on difficult tasks and pursuits. They are typically characterized as resilient and

as having stamina and long-term or stable focus. Low scorers are more likely to change course when faced with

adversity, while putting emphasis on emergent opportunities and short-term pursuits and accomplishments.

Persistence has reference to long-term goal or value perseverance, resilience to adversity, and is not primarily

maintained by short-term periodic and ongoing work-related feedback from others or from comparison with

easily defined standards of excellence.

Duckworth, Peterson, Matthews, and Kelly (2007) explain that Persistence as a construct has arguably one of

the longest histories in all of psychology and particularly in the “psychology of achievement.” Several early

researchers, going back as far as the late 19th century, were interested in variables that separated similar and

even similarly gifted individuals into levels of achievement. Many found that persistence, perseverance, and

resilience were often key differentiating traits among individuals who otherwise had similar ability levels or

similar IQ (Terman & Oden, 1947; Howe, 1999; as noted in Duckworth et al., 2007). Simonton (1994) concludes

that Persistence, or “grit,” is among the more certain and consistent variables that high-impact and notable

historical figures most often have in common. Persistence is typically found to be uncorrelated or slightly

negatively correlated with IQ levels, and its incremental utility (over IQ and aptitude) for predicting life and

occupational outcomes seems well established (Duckworth et al., 2007; Ackerman & Heggestad, 1997; Moutafi,

Furnham, & Paltiel, 2005; Eskreis-Winkler, Shulman, Beal, & Duckworth, 2014). In fact, its utility in predicting

success is sometimes seen as the cornerstone for understanding the differential and additive utility of natural

ability vs. disposition-related variables in understanding life’s outcomes—including work-related outcomes

(Ericsson & Charness, 1994). High Persistence scores are associated with increased emotional stability,



increased standardized test scores, achievement motivation, educational attainment, educational performance,

employment retention, and retention in challenging educational programs—including highly selective military

training programs (Duckworth et al., 2007; Eskreis-Winkler et al., 2014).

Persistence-like constructs are also associated with increased levels of EQ, learning agility, strategic vision,

adaptability, motivation to lead, and stakeholder sensitivity among leaders or potential leaders in organizations

(Dries & Pepermans, 2012). Persistence has also been positively associated with CEO and entrepreneurial

success (Baum & Locke, 2004). CEOs having higher levels of Persistence-like traits tend to be more resourceful

and confident. They are more effective at communicating, setting, and reaching goals, as well as growing

businesses (Baum & Locke, 2004). Persistence may be characterized as a component or expression of work-

related “passion” (Houlfort, Philippe, Vallerand, & Menard, 2014) which, when associated with other socio-

emotional adaptive states, is positively predictive of increased enthusiasm, discretionary effort, positive work-

related relationships, positive organizational outcomes, work satisfaction, and resilience to burnout (Cardon,

Wincent, Singh, & Drnovsek, 2009; Cardon, Zietsma, Saparito, Matherne, & Davis, 2005; Liu, Chen, & Yao, 2011;

Philippe, Vallerand, Houlfort, Lavigne, & Donahue, 2010).

Tolerance of ambiguityA comfort with uncertainty and a willingness to make decisions and plans in the face of incomplete information

are hallmarks of high scorers on measures of Tolerance of ambiguity. Tolerance of ambiguity is a common

and critical component of measures used in executive selection, development, and succession contexts

(Lewis & Ream, 2012). Although the strength of association may be moderated by the nature of job roles and

contexts, high Tolerance of ambiguity among executives has been almost unilaterally associated with positive

individual- and company-level outcomes (Yukl & Mashud, 2010). Business climate and organizational functioning

characterized by ambiguity and uncertainty has repeatedly been characterized as “the new normal” (Cone,

2013), and management professionals and managerial scientists include Tolerance of ambiguity among the top

characteristics of successful executive leaders into the foreseeable future (Gratton & Erickson, 2007; Gratton,

2010). High Tolerance of ambiguity is markedly associated with innovation and an entrepreneurial orientation to

vocational pursuits, whether within or without organizational contexts. High scorers on measures of Tolerance of

ambiguity are more likely to seek and value diverse feedback, experiment, seek opportunities for innovation, and

avoid micromanaging (Kirschkamp, 2007). For medical organizations, Tolerance of ambiguity has been called a

key indicator differentiating between physicians who can and cannot successfully make the difficult transition

from clinical to executive management functions (Sherrill, 2001). Interestingly, high scorers on measures of

Tolerance of ambiguity do not eschew data or avoid seeking information by which planning and executing

decisions can be guided. Rather, an effective executive with an ambiguity tolerant disposition typically has a

more adaptive and nimble sense of when a critical mass of key information has been gathered, and they proceed

without problematic anxiety in cases where others may not when faced with information that seems inadequate

or incomplete. Brainstorming to fill in data gaps, pragmatism, and contingency plans are usually key accessories

for effective and highly ambiguity-tolerant executives (Strosaker, 2010).

AssertivenessEmpirical findings show Assertiveness to be a key component of leadership emergence and potential as well

as results-drive and achievement orientation (Dries & Pepermans, 2012).3 Assertiveness measures whether

people are inclined to proactively assume wide responsibility, take charge, and lead others. A notably assertive

individual is convinced that she/he should be in charge, and that both individual and group outcomes will

be optimized when she/he is granted group-level decision-making discretion, leadership status, authority

to delegate, and authority to set or heavily influence organizational objectives. As such, high Assertiveness

might be characterized, at least in part, as self-efficacy for leadership in general (Amos & Klimoski, 2014). High

3 Dries & Pepermans (2012) separate components of Assertiveness into multiple constructs for which they argue conceptual divergence. Taking initiative, they assert, is a component of “drive,” assertiveness in decision making is a component of “analytical skill,” and actively looking for opportunities to lead, delegating, and objective setting are components of “emergent leadership.” In their study, these higher-order constructs, however, show markedly and arguably statistically convergent correlational patterns (all having r > .75).



Assertiveness scorers may also tend to be seen as confident, aggressive, and decisive, while low scorers are

likely perceived as tentative, passive, reserved, or indecisive and more comfortable deferring to and following

the lead of other individuals or groups. Low scorers may also have and attain leadership roles, but this is far

more likely when leadership status has been formally assigned and is associated with known and explicated

relative managerial rank and job title. High scorers on Assertiveness-like measures, on the other hand, will take

charge because they feel like it will benefit organizational members and collective pursuits whether or not they

were told or were granted clearance to assume responsibility as such. In short, high Assertiveness individuals are,

to some extent, in charge because they have decided they are in charge, and not necessarily because somebody

else, with or without authority, has told them that they are in charge. Their leadership status and effective

leadership status often is or at least begins as a de facto more than a de jure leadership status.

In the extant Big Five personality literature, a construct similar to Assertiveness is sometimes conceptualized as

a component of the factor of Extraversion, and is often called Dominance (e.g., Costa & McCrae, 1992; Depue &

Collins, 1999). Ones et al. (2007), however, in a comprehensive meta-analytic review, show marked differential

predictive utility for these two components of Extraversion—Sociability and Dominance, particularly for

managerial professionals. They find the impact of Dominance on managerial performance is positive and notably

different and larger than the impact of Sociability. Judge, Bono et al. (2002) similarly found Sociability and

Dominance having separate effects on leadership. Others have conceptualized and supported Assertiveness-

like constructs as belonging to higher-order factors removed from Sociability or other social-behavior-related

measures (e.g., Dries & Pepermans, 2012; Northouse, 1997; Mann, 1959; Stogdill, 1948; Hogan, 1983; Wiggins,

1996). Hogan (1983) and others (e.g., King & Figueredo, 1997) in empirically-based higher-order personality

structures separate Dominance from Extraversion or Sociability, concluding that the latter is better dubbed

“Surgency”—having reference to general positive mood and sociability, whereas, Dominance emerges as its own

factor with primary reference to confidence, independence, and aversion to submissiveness or deference. Others

argue Assertiveness and social variables are clearly associated, but not necessarily conceptualized as sub-

components of a single common latent factor (Dries & Pepermans, 2012). Yet others (e.g., McCrae & Costa, 1987)

assert that Sociability is not best combined with Assertiveness in an Extraversion factor, but that Sociability

belongs with emotional and affective variables.

Assertiveness predicts both self and other rating of Sociability, as well as “competency” domains like creativity,

analytical thinking, and problem solving (e.g., Anderson & Kilduff, 2009). Interestingly, Assertiveness seems

to affect others’ perceptions of competence in various leadership domains incrementally in models also

containing scores of actual competence. As such, Anderson and Kilduff (2009), among others, show that high

Assertiveness leaders typically instill trust and confidence in others in ways that are not always directly linked

to rationality, truth, or more objective measures of actual leadership status or skill. Increased and even very high

marks on Assertiveness-like measures among CEOs are positively associated with company innovation and

company patent counts, and the effect is notably stronger for CEOs operating in highly competitive markets

(Galasso & Simcoe, 2011). Assertiveness to lead, however, can sometimes be associated with lack of receptivity,

micromanaging, and/or need for control in ways that create challenges for team performance, particularly when

high Assertiveness marks are present in individuals having low marks on affiliation-type measures or measures of

EQ and/or positive affect (Driskell & Salas, 1992).

OptimismLeaders who are high in Optimism expect the future to be bright, are likely to disregard disappointment, and

are satisfied with themselves. Optimists are likely to be warm and slightly dominant interpersonally, and tend to

handle relationships successfully (Carver & Scheier, 2014).

Optimism is a compound trait, related to multiple factors in the Five Factor model of personality (Hough & Ones,

2001). Originally thought to be a blend of Extraversion and Emotional Stability (Hough & Ones, 2001), Optimism

has consistently been found to have moderate to strong relationships with each (Carver & Scheier, 2014; Kam &



Meyer, 2012). More recently, Optimism has been shown to be related to four of the Big Five, having somewhat

smaller associations with Agreeableness and Conscientiousness (Sharpe, Martin, & Roth, 2011). Optimism also has

been conceptualized as a key component of mixed models of Emotional Intelligence (Livingstone & Day, 2005).

Although Optimism is a facet of personality, it also is cognitive in nature because it involves expectations for

the future. Through these expectancies, Optimism is also connected to motivation and self-regulation (Carver

& Scheier, 2014). Positive expectancies are associated with more persistent and effective pursuit of goals

(Segerstrom, 2007). Consequently, individuals high in Optimism appear to be highly effective at investing effort

in complex situations, effectively increasing their engagement for higher-priority goals and under favorable

circumstances (Carver & Scheier, 2014). Leaders who are confident about the future will continue to invest effort;

those who are doubtful about eventual success are likely to disengage (Carver & Scheier, 2014).

Optimism has been associated with future success in a variety of contexts. For example, one study found

Optimism measured during first semester in law school predicted higher salaries a decade later after controlling

for hours worked (Segerstrom, 2007). Personality traits that contribute to Optimism have been associated with

leadership emergence and effectiveness. For example, in one meta-analysis, Extraversion and Emotional Stability

were robustly related to leadership in business settings; Emotional Stability and Conscientiousness were robustly

related to leadership in military and government settings (Judge, Bono et al., 2002). Composites of Emotional

Intelligence that include Optimism are positively related to job and life satisfaction, accounting for incremental

validity in these outcomes beyond the Big Five personality traits (Livingstone & Day, 2005).

CapacityCapacity refers to logic and reasoning, or cognitive ability. Research has shown that cognitive ability influences

virtually every aspect of job performance and potential (Ones, Dilchert, Viswesvaran, & Salgado, 2010). It is

positively related to leadership emergence and effectiveness (Judge, Colbert, & Ilies, 2004). High-performing

leaders are effective analytical and conceptual thinkers. They are astute at spotting patterns or trends in data

that others miss. And they solve problems with aplomb—at first individually, and then as leaders—by marshaling

and focusing resources on the right challenges.

It is an individual skill and there is a subtle trap when thinking about Capacity as one moves up in leadership: a

person’s role changes from being the primary problem solver to ensuring that the problem gets solved. Leaders

who cannot shift out of individual problem-solving mode and into the job of coaching and mentoring others to

analyze problems will struggle beyond mid-level leadership roles.

Likewise, organizations that rely on individual problem solving as their sole or even primary indicator of high

leadership potential risk flooding their pipeline with people who will peak in mid-level roles because they revert

to solving complex problems themselves. For this reason, it’s risky to assess pure cognitive ability without

simultaneously considering how this cognitive ability is imparted in a leadership role. Cognitive ability is most

strongly related to leader effectiveness when stress is low and when leaders are directive (Judge, Colbert et al.,

2004).

There is a great deal of confusion and misinformation in the popular press about terms such as cognitive ability,

intelligence, reasoning, IQ, and the like. All tests of cognitive ability are related, that is, correlated with each other

to some greater or lesser degree. There are many tests of cognitive ability, each with strengths and limitations,

and each with a somewhat unique interpretation and meaning. Perhaps the best available survey of cognitive

ability tests, their relationship to each other, and how they fit in the overall domain of cognitive ability is Carroll

(1993).

Our approach is pragmatic. We define Capacity very specifically as skill at detecting patterns and trends even in

ambiguous, contradictory, or otherwise “noisy” environments, and we use a test that is independent of language

or tacit knowledge of a domain in order to provide global comparability of score. To measure Capacity, we rely

on the Raven’s APM Version 2 (NCS Pearson, 2007) because it is the only effectively language-free test available,



a feature important to global clients who wish to apply the same test anywhere in the world. The Raven’s APM

is the most widely-used, high-level, language-independent cognitive ability option available. It is a non-verbal

measure of gf, fluid intelligence (Horn, 1980). Gf is the capacity to think logically and solve problems in novel

situations, independent of language or tacit knowledge of a domain.

Any strong measure of fluid intelligence like the Raven’s APM is likely to have a quirk to consider. It will be

negatively correlated with age. This is because when younger, we rely more on gf for performance in novel

learning situations and for adaptive behavior. As we get older, we rely more on crystalized intelligence, gc

(experience, knowledge, and deductive reasoning). As a result, we tend to lose gf reasoning skills represented

due to disuse—or at least we rely on them less. This is not always the case, if we keep practicing, but many

people increasingly rely on heuristics, tacit knowledge, and experience for adaptive problem solving and less of

gf over time.

Derailment risksDerailment is the failure to achieve one’s potential. The outcomes associated with leadership derailment can

be very costly on many dimensions. In addition to the millions of dollars of direct and indirect financial costs,

derailed managers can engender a negative impact at the individual, team, and organizational levels. Such

leaders don’t build cohesive teams, dwindle the morale of coworkers, damage customer relationships, and fail

to meet business objectives (Bunker, Kram, & Ting, 2002; Hughes, Ginnett, & Curphy, 2008). Some estimate that

30% to 50% of high potential managers and executives derail (Lombardo & Eichinger, 1989).

The risks related to derailment go up at higher job levels: expectations are higher and consequences of failure

are higher (Hogan & Hogan, 2001; Tang, Dai, & De Meuse, 2013). As shown in Table 4, the potential for derailment

is rated significantly higher for upper management than lower and middle management (Tang & Dai, 2013). The

reasons for this increase in rated likelihood of derailment by others by level include: (1) the strengths that propel

leaders to the top often have corollary weaknesses; and (2) increased demands and higher expectations yield

more focused scrutiny.

Table 4. Derailment risk ratings across management.

Derailment factor Individual contributorN = 1,256

First level leaderN = 3,957

Mid-level leaderN = 3,307

Senior executiveN = 1,005

Insensitive to others 1.34 1.51 1.60 1.62

Overmanaging 1.54 1.67 1.73 1.77

Unable to adapt to differences

1.42 1.54 1.54 1.55

The KFALP Derailment risks measures use profiles or configurations of scores on traits to highlight a propensity

to behave in a way that may be problematic in future situations. For example, in stressful, ambiguous, or complex

situations, leaders may have a propensity to perform in derailing ways. That is, to handle extremely challenging

tasks, leaders may act in a way that could have a negative impact on the people they work with and their teams.

It cannot be overemphasized that risks are not destiny. Feedback on these risks is provided to equip leaders with

the awareness needed to expand their repertoire of behaviors and avoid defaulting to potentially problematic

patterns when faced with challenging circumstances.



The KFALP provides feedback on three derailment risks, chosen because scores differentiate those who have

advanced from those who have not:

• Volatile is behaving in unexpected or detrimental ways. Effective leaders tend to be steady, even-tempered, and composed. Leaders who behave in a volatile way find it more difficult to build trust and confidence among their people.

• Micromanaging is staying involved in too many decisions rather than passing on responsibility, doing detailed work rather than delegating it, and staying too involved with direct reports. Effective leaders allow their people to succeed through their own efforts and skills.

• Closed is being dismissive of differing perspectives. Being closed makes it more difficult to respond to the need for change or to cultivate new ideas that can improve performance of the leader or team. Effective leaders are open to the perspectives and ideas of others.



Section VI The Korn Ferry Assessment of Leadership Potential: Uses, administration, scoring, and reporting overview

Intended usesAt Korn Ferry, we are committed to offering science-based and experience-tested assessments that support the

success of our clients. The Korn Ferry Assessment of Leadership Potential (KFALP) is designed to provide data

important for individuals and organizations to consider as they think broadly about the leadership potential of

their internal talent. It is intended to evaluate long-term potential of individuals and leadership talent pools.

• It provides organizations with the ability to objectively and accurately identify people with high leadership potential.


• It accurately identifies high potentials in seven key facets, or signposts, proven by research to be related to advancement in leadership roles.

The KFALP does not measure readiness to perform in a specific new job today nor does it measure fit with a

particular role. KFALP is designed to answer the question, “Who has the potential to take on higher-level, bigger

leadership roles in the future?” It was not developed or intended for use as a stand-alone potential screening

tool, but rather as a supplement to all available indicators of potential to assist with careful consideration and

identification of high potentials using the full range of information available to clients and as part of a complete

client process.

When used as a supplement to other measures for selection of persons into specific jobs, target roles are

assumed to be feeder roles for a leadership career progression where persons selected are expected within

a reasonable time to advance to higher leadership roles of greater responsibility. Use of the KFALP assumes

that this selection is for Professional/Individual contributor, First level leader, or Mid-level leader roles. Separate

Best Practices and Job Characteristics Questionnaires are available to assist in gaining maximum value from

the KFALP in selection contexts. The KFALP is best used in conjunction with all other data and information that

clients may have relevant to the potential of talent pools (education, specific domain knowledge, etc.).

The KFALP is not designed to supplement executive level leader selection, except for post-selection

development insight and onboarding support. It is not useful for evaluating potential at the top officer levels.

This is because the goal is to evaluate potential for developing, over time, the skills and experiences required in

roles of significantly greater breadth, complexity, and responsibility. Once a leader has attained an executive role,

evaluating their potential for advancement to the C-suite requires a more individualized level of granularity than

is provided by the KFALP. In addition, at this level, talent reviews seek to include a view of both potential and

readiness for the target role.

Administration and timingThe KFALP is available exclusively online and in many major business languages. The KFALP is extremely

efficient, taking 35–40 minutes to complete, exclusive of Raven’s Progressive Matrices, which adds up to 40

minutes. There are a number of branching items, and several items require multiple responses. The total number

of responses will be approximately 240–280 for most participants.

There are a variety of item formats included. The majority is the FC-IRT item administration which requires

choices to be made in a ranking format. It is more demanding than traditional rating formats. Many candidates

welcome this level of structure and the engagement required, correctly inferring that more accurate insights

are possible.



Scale scoringWith the exceptions of Career planning and Experience, raw scores are normed against the target role level and

reported as percentiles on a scale such as the one in Figure 8. As noted above, Career planning is not normed;

however, higher scores indicate increasing focus of career planning and greater specificity of career plans.

Experience is normed against current level.

Persons reaching or exceeding the 50th percentile are scored as “green” for that sub-dimension. Individual

Reports contain specific feedback for scores at the very top of the range (yellow at the top of the range in

Figure 8), suggesting cautions regarding possible overuse of or dependence on a strong characteristic. These

are still positive scores.

Figure 8. Individual Report scale.

Tolerance of ambiguity

More likely to be disoriented or even immobilized by uncertainty or ambiguity in information or situations.

Comfortable with uncertain, vague, or contradictory information. Open to alternate solutions.

1 11 50 91 99

85

The meaning of yellow and green is simply whether or not the individual scored above or below the average of

persons who have already advanced to the target level. One should keep in mind that the scoring is against a

high target.

Signpost markersSignposts are marked “green” based on the number of sub-dimensions on which an individual has reached a

green threshold:

• Drivers – any two of three sub-dimensions.

• Experience – any two of three sub-dimensions.

• Awareness – both sub-dimensions.

• Learning agility – any three of four sub-dimensions.

• Leadership traits – any four of five sub-dimensions.

• Capacity – one (of one) sub-dimension.

• Derailment risks – none of the three Derailment risks factors.

The green and yellow signpost markers are provided as an “at a glance” view of the participants’ results. They

are also used to assist in sorting the Talent Grid Report.

No signpost or scale appearing yellow should be considered on its own as a “knock out” factor for consideration of potential. The KFALP data must be considered as a whole-person view and in conjunction with

everything job-relevant that is known about the person.



ReportingMultiple report and data formats are available:

• Individual Report – shows individual participants their results in each sub-dimension of each of the seven signposts against the chosen target level. The report provides insights to help map individual development strategies based on the individual’s pattern of responses. Based on the results, three specific development priorities with suggestions are provided.

• Talent Grid Report – provides an “at a glance” view of groups of individuals and how they compare with others within the organization, arming leadership with the ability to segment talent and make talent plans based on the pattern of results.

• Data Extract – contains exactly the same data as the Individual Report, extracted into a manipulable Excel format.

• Group Summary Report – a rich report comparing client participants against relevant global benchmarks.

KFALP technical characteristics and validity

ReliabilityReliability is the consistency or dependability of a score. It provides an estimate of the amount of error, or

noise, in the test’s measurement. In principle, if a test was completely reliable, all of the variation in people’s

scores would be due to their true scores—all signal. In practice, there is always some noise in test scores, similar

to there being some static on a phone call. So reliability can be thought of as the signal portion of a signal to

noise ratio for a test.

There is no exact determination of reliability, all estimates are—estimates. There are a number of ways

to estimate reliability, such as test-retest (multiple administrations), or various internal methods (single

administrations). The primary technical requirement is for a quality estimate of the proportion of true score

(non-error, non-noise) included in a measure. Test-retest is a relatively poor estimate of the proportion of true

score in a measure. This is because 1) it is not clear what retest time period is correct for this purpose, 2) it is not

possible to preclude true change on a measured characteristic during the interim period, and 3) it is not possible

to remove the effects of any process learning, assessment memory, retest boredom, or other effects from the

retest results. Crocker and Algina (1986) conclude, “In view of these issues, it is probably sensible to assume

that the test-retest coefficients probably present a somewhat inaccurate estimate of the theoretical reliability

coefficient.” We focus, then, on the superior single measurement event procedures.

In Classical Test Theory (CTT), a single measurement event indicator of reliability is used to characterize the

reliability of a test, typically, Cronbach’s a (Cronbach, 1951). It has been demonstrated that Cronbach’s a tends

to be a conservative, lower-bound estimate of reliability (Osburn, 2000), which is one of the reasons it is so

commonly used. A sufficiently high estimate of Cronbach’s a implies other types of reliability estimates will be

even higher. Typically, in CTT terms, a value of .70 or higher is considered good in personality testing (Nunnally &

Bernstein, 1994).

In Item Response Theory (IRT), there is no direct analog to Cronbach’s a, however, there is a similarly accurate

approach. In IRT, an estimate of error in a test can be calculated for each and every possible score along the

full range of scores. For a conservative estimate comparable to Cronbach’s a, we calculate the proportion of

true score included in the measure in scores across the full range of scores. We have computed from this an

“average” reliability for each trait scale. These averages were computed by estimating the IRT score and error

variances. The IRT score variance was estimated by computing the variance of computed IRT scores; the error

variance was estimated by averaging the squared Standard Error of Measurement across the trait range. With

an estimate of the IRT score and error variance in hand, the reliability, r’tt, is estimated as the ratio of true score

variance (IRT score variance minus error variance) to total score variance (IRT score variance). Nearly all of the

scales in the KFALP are quite high and most are in the .8s or even .9s.



Reliability estimates are presented for each scale in Table 5 below. The FC-IRT scales are estimated as described

above. Role preferences is a CTT scale and is estimated as Cronbach’s alpha. Raven’s APM Version 2 is also a

CTT scale and estimates are taken from Raven’s Advanced Progressive Matrices (APM): Evidence of Reliability

and Validity (NCS Pearson, 2007). Note that the Experience scales and Career planning are not included. This is

because these measures do not involve estimates of a latent trait, but directly reflect back in summary form the

participant’s experiences and planning reported by the participant in the career walk-through.

Table 5. Reliability estimates.

r’tt, All N > 500

Drivers

Advancement drive

Role preferences

.92

.67

Awareness

Self-awareness

Situational self-awareness

.67

.72

Learning agility

Mental agility

People agility

Change agility

Results agility

.81

.89

.91

.81

Leadership traits

Focus

Persistence


Assertiveness

Optimism

.80

.84

.85

.88

.84

Capacity

Raven’s APM Version 2 .87

Derailment risks

Volatile

Micromanaging

Closed

.89

.91

.91

Relationship of KFALP FC-IRT measures with other measuresCorrelation of the scales of one instrument with similarly conceptualized and defined scales of another

instrument is considered evidence of construct validity (AERA, 2014). We have a sample of N = 230 persons

who have taken KFALP and the Global Personality Inventory (GPI) scales (Corporate Executive Board, 2001).

The Global Personality Inventory is a 37-scale assessment of adult personality. The Global Personality Inventory

scales are not necessarily similarly conceived or defined as KFALP scales and tend to be somewhat more

narrowly defined than scales included in the KFALP. Scale definitions are found in Appendix D.

CTT and FC-IRT self-assessment methods are quite different in response format and scoring procedures. One

would not necessarily expect high correlations between administrations of even the same scales and items using

the two methods due to 1) item ranking vs. Likert rating data collection, 2) simple arithmetic scoring of CTT vs.

two-stage linear model scoring in IRT, 3) practical caps on correlations due to imperfect reliability of scales, and

4) the fake resistance of FC-IRT methodology.



In our case, we would expect moderate correlations between conceptually related scales using different

instruments, items, and scoring methodologies and a pattern of correlations which generally support the

construct validity of KFALP scales. The full correlation matrix is 37 x 16. Due to its size, it is presented in

Appendix E, along with GPI scale definitions, found in Appendix D. The results, however, are recapped in Table 6

below with notable positive and negative correlations from the full matrix of correlations.

Table 6. Notable correlations between KFALP FC-IRT trait-based and GPI scales.

Global Personality Inventory scale

KFALP scale Most notable positive r Most notable negative r

Drivers

Advancement drive AdaptabilityDesire for AchievementEnergy LevelSociabilityCompetitivenessTaking ChargeRisk-taking

.37

.36

.35

.33

.32

.31

.30

Attention to DetailPassive-Aggressive

-.23-.28

Role preferences AdaptabilityEnergy LevelDesire for AchievementRisk-taking

.36

.25

.22

.22

Attention to DetailPassive-Aggressive

-.25-.25

Awareness

Self-awarenessSituational self-awareness

Stress ToleranceEmotional ControlSelf-AwarenessWork Focus

.26

.23

.22

.21

Learning agility

Mental agility InnovationAdaptabilityDesire for AchievementThought FocusVisionRisk-taking

.40

.36

.30

.29

.27

.26

Passive-Aggressive -.20

People agility InfluenceSociabilityEmpathyConsiderationSocial Astuteness

.37

.34

.32

.25

.22

Independence -.20

Change agility AdaptabilityRisk-takingDesire for AchievementEnergy LevelOpennessInitiative

.49

.40

.36

.32

.30

.28

Attention to DetailMicro-ManagingPassive-Aggressive

-.21-.24-.29

Results agility Energy LevelDesire for AchievementSelf-ConfidenceResponsibilityInitiative

.38

.36

.24

.23

.23



Global Personality Inventory scale

KFALP scale Most notable positive r Most notable negative r

Leadership traits

Focus Adaptability .24 Micro-ManagingWork FocusImpressingAttention to Detail

-.34-.35-.35-.61

Persistence Work FocusSelf-AwarenessResponsibilityEnergy LevelOptimism

.31

.29

.23

.22

.22

Tolerance of ambiguity AdaptabilityDesire for AchievementRisk-takingEnergy LevelInnovation

.49

.35

.32

.28

.26

Attention to DetailPassive-AggressiveMicro-Managing

-.26-.27-.27

Assertiveness Taking ChargeInfluenceSociabilityCompetitivenessInitiativeDesire for Achievement

.53

.34

.33

.29

.29

.27

Optimism Stress ToleranceOpennessSociabilityOptimism

.30

.25

.22

.20

Micro-ManagingNegative Affectivity

-.24-.26

Derailment risks

Volatile Emotional ControlStress ToleranceWork FocusSelf-Awareness

-.42-.39-.29-.23

Micromanaging Attention to DetailMicro-ManagingPassive-AggressiveWork FocusIndependenceEgo Centered

.51

.38

.26

.25

.21

.20

ConsiderationEmpathyInterdependenceSociabilityOpennessTrustAdaptability

-.20-.20-.20-.21-.21-.22-.34

Closed Passive-AggressiveMicro-ManagingAttention to Detail

.31

.29

.21

InitiativeEnergy LevelRisk-takingDesire for AchievementOpennessAdaptability

-.25-.31-.31-.32-.32-.52

(continued)



Relationship with advancementCriterion-related validity is evidence for the relationship of a measure to desired outcomes. Table 7 below

displays effect sizes for the differences in score for Individual contributors against each higher organizational

level. This is the foundation of differentiation of those who have advanced from those who have not.

An effect size is a quantitative measure of the strength of a phenomenon, in this case, the standardized mean

difference between groups, Cohen’s δ. An effect size can be interpreted as small, medium, or large depending on

its context. A commonly used interpretation is as follows: an effect size of 0.2 is considered a small effect, 0.5 a

medium effect, and 0.8 and up a large effect (Cohen, 1988).

As shown in Table 7 below, bold indicates a statistically significant difference. The results show that there were

progressively higher average scores for persons who have advanced to the next level for most scales, and, as

expected, progressively lower scores for Derailment risks scales. Most measures have a medium to large effect size.

Table 7. Effect sizes for different position levels.

(Individual contributor N > 440 to 483)

Individual contributor/professional to First level leader(N = 1299 to 2001)

Individual contributor/professional to Mid-level leader(N = 1275 to 1958)

Individual contributor/professional to Functional leader(N = 1297 to 2218)

Individual contributor/professional to Business or organizational unit/division leader (N = 1110 to 1972)

Individual contributor/professional to Senior/top functional executive(N = 864 to 1593)

Individual contributor/professional to Top business or organizational group executive(N = 265 to 595)

Advancement drive .33 .38 .47 .62 .61 .70

Role preferences .15 .25 .33 .43 .42 .49

Situational self-awareness .14 .17 .21 .25 .30 .31

Self-awareness .05 .15 .19 .14 .20 .17

Mental agility .13 .21 .30 .35 .34 .35

People agility .18 .32 .35 .43 .50 .55

Change agility .28 .43 .56 .72 .75 .70

Results agility .20 .31 .36 .40 .44 .56

Focus .39 .53 .60 .60 .59 .52

Persistence .06 .15 .20 .27 .40 .40

Tolerance of ambiguity .42 .56 .73 .78 .85 .83

Assertiveness .33 .50 .52 .69 .69 .82

Optimism .12 .24 .28 .38 .37 .42

Volatile -.27 -.50 -.65 -.69 -.77 -.65

Micromanaging -.44 -.32 -.38 -.42 -.48 -.52

Closed -.12 -.61 -.73 -.75 -.79 -.73

The progression of score changes by level can be seen clearly in percentile terms in Figure 9 below. Individual

contributor is the baseline and is represented at the 50th percentile.



Figure 9. Progressive increase (decrease) in scores with level.

0%

10%

20%

30%

40%

50%

60%

70%

80%

Advanc

emen

t driv

e

Role pre

fere

nces

Situat

iona

l self

-awar

enes

s

Self-a

waren

ess

Menta

l agilit

y

People

agilit

y

Chang

e ag

ility

Resul

ts a

gility

Optimism

Persis

tenc

e

Focus

Toler

ance

of a

mbig

uity

Microm

anag

ing

Close

d

Volatile

Asser

tiven

ess

Individualcontributor/professional

First levelleader

Mid-levelleader

Functionalleader

Business or organizational unit/division leader

Senior/top functional executive

Top business or organizational group executive

A note regarding Capacity: The Raven’s APM Version 2 (NCS Pearson, 2007) was chosen as the measure of Capacity because it is the only effectively language-free test available, a feature important to global clients. As is true of all tests of cognitive ability, the Raven’s APM Version 2 carries with it an elevated possibility of adverse impact against traditionally disadvantaged ethnic groups. It also has a modest negative correlation with age. This is why Raven’s scores go down with level, rather than up, as shown in Table 8.

Table 8. Raven’s APM effect sizes for different position levels.

(Individual contributor N > 440)

Individual contributor/professional to First level leader(N = 1299)

Individual contributor/professional to Mid-level leader(N = 1275)

Individual contributor/professional to Functional leader(N = 1297)

Individual contributor/professional to Business or organizational unit/division leader (N = 1110)

Individual contributor/professional toSenior/top functional executive(N = 864)

Individual contributor/professional to Top business or organizational group executive (N = 265)

Raven’s APM Total Score

-.37 -.50 -.65 -.69 -.77 -.65



Reaching a minimum threshold for raw reasoning ability relative to other leaders is considered desirable for

leadership success and the Raven’s test can provide information in that area. However, the characteristics

mentioned above, and concern for clients’ desire to meet diversity and inclusion goals leads us to offer the

inclusion of Capacity in the KFALP as optional, and it is each client’s decision whether or not to do so.

Accuracy of classificationKFALP scale scores differentiate those who have repeatedly advanced from those who have not advanced. We

are able to use those scores and logistic regression to demonstrate the ability of KFALP scores in aggregate to

accurately classify people into their correct organization level using only those scores.

The analysis uses scores from 19 KFALP scales. Career planning is excluded because it is not a normed measure.

Core experience is based on the number of levels in which a leader has served a significant period of time and

is not included because it is so closely linked to level attainment. Perspective and Key challenges Experience

scores have been pre-processed for this analysis to statistically control for years of full-time employment. For

this analysis, they represent the range of experiences a person has gained, holding time constant. The analysis

displays classification with and without Raven’s APM.

In Table 9 below, there are 584 persons to be classified: 354 Individual contributors/ professionals and 230 Top

business or organizational group executives. In the Raven’s APM condition, among Individual contributors, 323 of

354 were correctly classified. Among Top business or organizational group executives, 182 of 230 were correctly

classified. Overall correct classification rate was 86%. In the case of KFALP without Raven’s APM, among Top

business or organizational group executives, 176 of 230 were correctly classified, and among the 354 Individual

contributors/ professionals, 322 were correctly classified. Overall correct classification was 84%. These are high

levels of correct classification.

Table 9. Accuracy of level classification using KFALP data only.

KFALP with Raven’s APM KFALP without Raven’s APM

Actual position Actual position

Individual contributor/professional

Top business or organizational

group executive


Top business or organizational

group executive

Pre

dic

ted

p

osi

tio

n


323 48 322 59


31 182 32 171

Note: A statistic is available to characterize classification accuracy while taking chance into consideration.

It is Cohen’s kappa (Cohen, 1960). Kappa is defined as K = Po – Pe

1 – Pe , where Po is defined as the observed agreement, and Pe is defined as the expected probability of chance agreement. By using this formalization of classification, chance agreement is removed by “conditioning on the marginal distribution for the two classifications” (Thompson, 1990, p. 357). In Cohen’s kappa, a value of 0 is equal to chance classification. A value of 1 is equal to perfect classification. For the classifications above, kappa = .72 with Raven’s APM, and .67 without Raven’s APM. These values are regarded very good classification, knowing only the scores on KFALP.

Prediction of Work engagementWork engagement, the amount of discretionary effort a respondent is willing to expend toward their work,

is a critical component of leadership performance. In a recent meta-analysis, Work engagement has been

demonstrated to provide substantial prediction of task and contextual work performance (Mr = .43 and .34,

respectively), as well as prediction of organization commitment (Mr = .31) (Christian, Garza, & Slaughter, 2011).



In Table 10 below, correlations of KFALP scales and Work engagement are displayed. In addition to the

association with advancement described above, many of the scales included in the KFALP provide economically

valuable prediction of Work engagement. This indicates that, in addition to differentiating by organizational

level, many scales also differentiate those who are likely to be better, committed performers because they are

more engaged in their work. For Work engagement, correlations are reported both raw and corrected for the

reliability of the criterion variable (Spearman, 1904). With the exception of Raven’s APM and Work engagement,

all correlations are statistically significant p < .05.

Multiple regression using Work engagement as the dependent variable and the 19 scales in Table 10 below as the

predictors indicates that KFALP variables account for a meaningful part of the variance in Work engagement,

multiple R = .433, (F [18, 6429] = 82.387, p <.000).

Table 10. Correlations of KFALP scales with Work engagement.

Work engagement,r’tt = .72

N raw corrected

Advancement drive 10621 .23 .28

Role preferences 10621 .04 .06

Core experience 10621 .13 .17

Perspective 10621 .09 .11

Key challenges 10621 .14 .18

Self-awareness 10621 .08 .11

Situational self-awareness 10621 .05 .08

Mental agility 10621 .19 .25

People agility 10621 .13 .17

Change agility 10621 .22 .28

Results agility 10621 .36 .47

Optimism 10621 .16 .20

Persistence 10621 .22 .27

Focus 10621 .04 .05

Tolerance of ambiguity 10621 .22 .27

Assertiveness 10621 .17 .20

Raven’s APM 6451 -.01 -.01

Micromanaging 10621 -.09 -.11

Closed 10621 -.19 -.24

Volatile 10621 -.10 -.13

FairnessAn important question to examine is how various sub-groups score on assessment tools. This helps to anticipate

the expected effect of using the tools on the demographics of the workforce. Fairness of assessments is a

markedly important objective at Korn Ferry, and assessments are designed not to disadvantage any group.

Adverse impact occurs when employee selection procedures used in making employment decisions have the

effect of selecting persons belonging to a historically disadvantaged group at a rate that is substantially lower

than that of the group with the higher selection rate. Adverse impact may occur due to the characteristics of an

assessment tool or other components included in the selection process, or, due to characteristics of the labor

pool, recruitment practices, or other process factors.



Korn Ferry has carefully evaluated the scales in KFALP for the potential of adverse impact using 50th percentile

thresholds such as included in KFALP reports. A typical way of describing the potential for adverse impact is

in terms of effect size comparing individuals from historically disadvantaged groups with the majority group.

An effect size can be interpreted as a small, medium, or large difference in average score. A commonly used

interpretation is as follows: an effect size of +/- 0.2 is considered a small effect, +/- 0.5 a medium effect, and

+/- 0.8 a large effect (Cohen’s δ; Cohen, 1988).

Our goal is to keep group differences to a minimum. To place the effort in context, a review of the literature

(Hough, Oswald, & Ployhart, 2001) describes cognitive ability test effect sizes of up to -1.0, resulting in

substantial disadvantage to some minority groups. By contrast, non-cognitive, or trait-based measures, tend to

have far smaller effect sizes, with most near 0 and some ranging up toward absolute values of .30. In general,

these are far smaller effect sizes than cognitive tests. The KFALP uses one cognitive ability test, Raven’s APM,

as a measure of Capacity. We rely on tests of non-cognitive characteristics and experience for all other scales

in KFALP. In general, with standard and reasonable uses of assessments, Cohen’s δ effect sizes having absolute

values ≤ .25 are unlikely to provide either substantial advantage or disadvantage for any group. Mean and

median (Cohen’s δ) effect sizes for KFALP scale scores are displayed in Table 11 for trait-based and experience

scales. KFALP aggregate scores typically produce small or negligible overall effect sizes across gender and

ethnic groups, and aggregated effect sizes are never > +/- .09 for any scale type and are typically considerably

lower. An effect size of -.09 is roughly equivalent to an impact ratio of .95 with mean cut scores. This means that

the lower scoring group will be selected at a rate of 95% the rate of the higher scoring group, a rate far above

the 80% 4/5ths rule threshold.

Table 11. Mean and median effect sizes for ethnicity and gender contrasts across KFALP.

Score group African American Hispanic-Latino Asian Female

Mean δ Median δ Mean δ Median δ Mean δ Median δ Mean δ Median δ

Trait-based -.07 -.09 -.02 .02 -.07 -.05 -.03 -.02

Experience -.05 -.03 -.08 -.04 -.03 -.02 -.09 -.08

Note. White/Caucasian participants served as the reference group for the ethnic contrasts. Effect sizes are Cohen’s δ.

KFALP scales, when evaluated in aggregate as intended, are unlikely to disadvantage any traditionally

disadvantaged group to a significant degree.

Our examination of adverse impact is ultimately far more detailed than the aggregate findings shown in Table 11.

In the subsections that follow, we describe our in-depth examination and explicate related findings.



Data sourcesAll KFALP data for which individuals reported Ethnicity and Gender were used in the analysis in order to

maximize sample sizes of underrepresented groups. Gender and Ethnicity were the primary variables of interest

in this analysis. In each analysis, though sample sizes vary, the maximum available cases with complete data

were used in order to maximize our ability to arrive at stable inferences. Reporting of ethnicity is required only in

the US and is optional for participants. Sample sizes are reported below in Table 12.

Table 12. Sample sizes for group differences analyses for ethnicity and gender across KFALP measures.

N

White/Caucasian 1172

Hispanic-Latino 103

African American 41

Asian 81

Male 919

Female 526

Analytic strategyData were analyzed using a repeated measures multivariate analysis of covariance (MANCOVA). KFALP

assessment scores served as dependent variables in each analysis. Management level, full-time work experience

(years), categorical education, categorical employer size, categorical industry, and categorical job function were

included as covariates. These served to isolate the effects of gender and ethnicity and avoid spurious findings

or non-findings based on related omitted variable bias and/or unequal cell sizes. A single simultaneous gender

and ethnicity analysis was conducted to isolate effects. Two post-hoc analyses were completed, one for gender

and one for ethnicity. Omnibus between-group main effects for gender and ethnicity on centroids, and omnibus

profile parallelism (gender x KFALP vector and ethnicity x KFALP vector interactions) are examined first. In

the case of significant omnibus tests, planned post-hoc pairwise contrasts of least squares adjusted means

are pursued and examine whether historically disadvantaged groups differ from gender (male) and ethnicity

(Caucasian) reference groups. Pooled two-sample z-tests (2SD) were employed to examine whether pairwise

contrasts were significant (p ≤ .05). We report standard deviation unit discrepancies (Cohen’s δ) from reference

groups and the 4/5ths “impact ratio” (IRA) consistent with EEOC guidelines.



KFALP resultsMANCOVA results can be examined in Table 13 and show effects for several control variables on the centroid.

Management level (F [1, 1386] = 97.179, p < .001), education (F [7, 1386] = 6.061, p < .001), company size

(F [4, 1386] = 5.849, p < .001), industry (F [17, 1386] = 1.683, p < .040), function (F [21, 1386] = 3.963, p < .001),

and years of experience (F [1, 1150] = 30.764, p < .001) are each significantly associated with centroid differences.

Gender showed an effect on centroids (F [1, 1386] = 7.661, p < .006), however, ethnicity did not impact centroids

significantly (p = .103). Omnibus tests of parallelism show that Management level (F [19, 1368] = 32.966, p < .001),

education (F [133, 9618] = 2.138, p < .001), company size (F [76, 5484] = 1.670, p < .001), industry

(F [323, 23528] = 1.158, p < .027), function (F [399, 26334] = 1.768, p < .001), and years of experience

(F [19, 1368] = 16.302, p < .001) all have nonparallel lines.

Gender and ethnicity profiles are also not parallel, (F [19, 1368] = 1.523, p < .001) and

(F [114, 8238] = 1.523, p < .001), respectively.

Table 13. MANCOVA sample sizes for group differences analyses for ethnicity and gender across KFALP measures.

Terms df F p Partial h2

Equal levels, between-groups main effect

Management level x Centroid 1 97.179 .000 .066

Ethnicity x Centroid 6 1.765 .103 .008

Gender x Centroid 1 7.661 .006 .005

Education x Centroid 7 6.061 .000 .030

Company size x Centroid 4 5.849 .000 .017

Industry x Centroid 17 1.683 .040 .020

Function x Centroid 21 3.963 .000 .057

Full-time employment x Centroid 1 30.764 .000 .022

Flatness, within-groups main effect 19 2.558 .000 .034

Parallelism, multivariate interactions

Management level x Profile 19 32.966 .000 .314

Ethnicity x Profile 114 1.523 .000 .021

Gender x Profile 19 6.981 .000 .088

Education x Profile 133 2.138 .000 .029

Company size x Profile 76 1.670 .000 .023

Industry x Profile 323 1.158 .027 .016

Function x Profile 399 1.768 .000 .026

Full-time employment x Profile 19 16.302 .000 .185



Post-hoc contrasts for gender reveal seven significant effect sizes/standardized mean differences, as shown

in Table 14. Whether significant or not, however, only one pairwise contrast exceeds the 4/5ths conventional

threshold beyond which differences are seen as practically problematic; all other impact ratios are > .85 for

females. On eight scales, the impact ratio favors females. All but one effect size is ≤ .25. Trait-based mean

and median effect sizes are -.03 and -.02 respectively, indicating very small gender differences in aggregate.

Experience mean and median effect sizes are -.09 and -.08 respectively, perhaps indicating small differences

between men and women in the opportunity to gain experiences.

Table 14. Gender contrasts on KFALP variables.

Females N = 526

Scale ES p IR 2SD

Advancement drive -0.13 .016 0.90 -1.99

Role preferences 0.02 .728 1.02 0.29

Core experience -0.08 .106 0.93 -1.40

Perspective -0.05 .353 0.96 -0.76

Key challenges -0.14 .006 0.88 -2.41

Self-awareness 0.02 .753 1.01 0.26

Situational self-awareness -0.17 .001 0.87 -2.68

Mental agility -0.11 .031 0.91 -1.76

People agility 0.08 .110 1.07 1.29

Change agility -0.19 .000 0.85 -2.95

Results agility -0.08 .138 0.94 -1.22

Focus 0.00 .984 1.00 0.02

Persistence 0.02 .736 1.01 0.28

Tolerance of ambiguity -0.18 .001 0.87 -2.70

Assertiveness -0.10 .061 0.92 -1.53

Optimism 0.07 .172 1.06 1.13

Raven’s APM -0.06 .277 0.95 -0.92

Volatile 0.29 .000 0.78 4.58

Micromanaging 0.00 .973 1.00 0.03

Closed -0.02 .671 1.02 0.35

Note. Male participants served as the reference group (n = 919). ES = Cohen’s δ effect size. IR = Impact ratio, 2SD = Pooled two-sample z-test.



We examine contrasts between Caucasians and all other ethnic groups on each scale in Table 15. Ten of the

60 pairwise contrasts had p < .05. Of these, two favored the traditionally disadvantaged group. Of the eight

remaining, only three suggested a potential impact ratio < .80 for a historically disadvantaged group. Raven’s

APM for African Americans had an effect size of -.80 and an impact ratio of .60, values consistent with the

literature on cognitive tests. Focus for Asians and Micromanaging for African Americans just exceeded the

impact ratio threshold at .79 and .78 respectively, representing effect sizes of -.26 and -.27. The mean and median

effect sizes for traditionally disadvantaged groups were -.05 and -.07 for trait-based scales and -.05 and .02 for

Experience scales. When evaluated in aggregate, as recommended, KFALP results have generally very low group

differences and are unlikely to substantially disadvantage any group.

Table 15. Ethnicity contrasts on KFALP variables.

African American N = 41 Hispanic-Latino N = 103 Asian N = 81

Scale ES p IR 2SD ES p IR 2SD ES p IR 2SD

Advancement drive -0.22 .144 0.91 -2.61 -0.16 .098 0.85 -2.66 -0.11 .324 0.92 -1.52

Role preferences -0.17 .264 0.89 -3.13 0.04 .717 1.03 0.51 0.04 .693 1.04 0.63

Core experience 0.12 .436 1.03 0.77 -0.02 .804 0.97 -0.45 -0.02 .854 0.98 -0.31

Perspective 0.03 .850 0.98 -0.60 -0.04 .681 0.97 -0.57 0.07 .540 1.06 1.10

Key challenges -0.28 .059 0.85 -4.32 -0.19 .054 0.82 -3.32 -0.14 .190 0.88 -2.21

Self-awareness 0.49 .001 1.15 3.12 0.13 .189 1.13 2.19 -0.10 .360 0.92 -1.43

Situational self-awareness

0.25 .102 1.11 2.41 0.08 .434 1.07 1.21 -0.05 .627 1.00 0.10

Mental agility -0.02 .869 0.98 -0.46 -0.24 .015 0.81 -3.34 0.01 .928 1.04 0.79

People agility 0.06 .686 1.11 2.48 -0.20 .044 0.83 -3.09 -0.04 .714 0.97 -0.63

Change agility -0.07 .621 0.93 -2.08 0.02 .831 1.02 0.34 -0.01 .944 0.99 -0.13

Results agility -0.24 .105 0.90 -2.93 0.08 .433 1.07 1.24 -0.01 .893 0.99 -0.23

Focus -0.16 .275 0.91 -2.56 -0.04 .685 0.97 -0.61 -0.26 .016 0.79 -3.67

Persistence 0.12 .413 1.03 0.71 0.16 .104 1.15 2.65 0.14 .198 1.12 2.07


-0.31 .038 0.85 -4.42 -0.21 .028 0.81 -3.45 -0.23 .034 0.82 -3.31

Assertiveness -0.09 .560 0.95 -1.37 0.13 .192 1.11 1.95 -0.17 .124 0.85 -2.67

Optimism -0.20 .188 0.89 -3.14 0.16 .096 1.14 2.53 -0.17 .122 0.85 -2.61

Raven’s APM -0.80 .000 0.60 -12.30 -0.12 .220 0.91 -1.67 -0.11 .321 0.92 -1.51

Volatile 0.44 .004 1.38 6.93 -0.22 .025 1.21 3.58 0.05 .670 1.04 0.70

Micromanaging -0.27 .071 0.78 -4.40 0.06 .507 0.94 -1.04 -0.19 .080 0.86 -2.53

Closed -0.03 .828 0.98 -0.49 -0.07 .454 1.07 1.28 0.07 .503 1.07 1.16

Note. Caucasian participants served as the reference group (n = 1172). ES = Cohen’s δ effect size. IR = Impact ratio, 2SD = Pooled two-sample z-test.

NormsKorn Ferry Assessment of Leadership Potential (KFALP) norms have expanded since the inception of the

assessment. There are now six target level norms used in scoring the KFALP. They are defined briefly in Table 16

below. Complete norm demographic descriptions are found in Appendix C “Norm descriptions.”

Current KFALP norms are global, with approximately 70% of persons included in the norms working outside of

North America. The approximate split is one-third North America, one-third Europe/Middle East, and one-third

Asia/Pacific. This varies somewhat by level and is detailed in Appendix C.



Clients should carefully choose a target level, typically two to three levels up from the current level. The goal is

to provide differentiation of talent which requires a relatively “high bar.” There is little value in specifying First

level leader on down as a target level.

Individual contributors are not leaders, so no Individual contributor target level norm is available. Individual

contributor norms are used for scoring Experience for current Individual contributors. For most leaders and

experienced Professional/Individual contributors, the Team lead and First level leader target levels are probably

not appropriate. However, where entry level Individual contributors are the population, those norms may be

appropriate. However, a more demanding, higher level norm is recommended.

Table 16. Target level definitions.

Target level Definition Norm

Chief executive officer/top organizational executive

Serves as the senior most leader of a business or organization. In the public sector, includes the Director, Secretary, or Administrator of an agency or department.

Chief executive officer/top organizational executive


Serves as the senior most leader of a large business line or group of business lines (e.g., service lines, product lines, divisions, regions), or as a C-suite executive (e.g., Chief Financial Officer, Chief Operating Officer). In the public sector, includes senior executives who lead large groups of service lines or products directly related to the mission, or top-tier senior executive roles (e.g., Deputy Director, Under Secretary, or Chief Operating Officer of an agency or division).



Serves as the senior most leader of a business support or mission support function (e.g., Finance, HR, Facilities).



Leads a line of business (e.g., service line, product line, division, region). In the public sector, includes senior executives who lead large service or product groups directly related to the agency mission.


Functional leader Leads a function (e.g., Accounting, Compensation & Benefits, Marketing, IT), as opposed to a line of business (e.g., service line, product line, region). In the public sector, includes senior executives who play key leadership roles within mission support groups (e.g., Finance, HR, Procurement, Security).

Functional leader

Mid-level leader Supervises one or more first-line supervisors/managers. Mid-level leader

First level leader Guides the direction of a group or team of Individual contributors and serves as their formal manager/supervisor.

First level leader

Team lead Guides the direction of a team of Individual contributors, or serves as its key subject-matter expert, without having formal supervisory/managerial responsibilities for team members.

First level leader


Supports a business or organization without supervising others as part of his/her role.

Individual contributor/professional (Experience only)



Appendix A. Frequently asked questions

1. What is the research behind Item Response Theory (IRT)?It is a complex technology. In Classical Test Theory (CTT), for self-report personality assessment, it is typical to

use Likert ratings (responding on an item or question on a 1-to-5 or 1-to-7 ordered scale, e.g., Strongly agree to

Strongly disagree) despite a number of limitations:

• Response styles (e.g., acquiescence bias, social desirability bias, preferring the center of the scale vs. the extremes of the scale).

• Scale anchors may be interpreted differently by respondents.

• Intentional faking in an effort to “game” cannot be controlled.

• Interpretation of scores is dependent on a particular norm and on the specific scale content.

• The estimated reliability and true score estimate error are the same for all participants.

• Item difficulty is not taken into account in scoring.

Tests based on IRT can overcome these issues by:

• Removing response styles and scale anchor issues by eliminating the rating scale.

• Thwarting faking by eliminating the transparency of the Likert rating scale.

• Freeing interpretation from specific scale content by estimating true trait level.

• Providing true score estimates of error at the individual level.

• Taking item content into consideration when estimating true score, thus improving fidelity of the score to the trait construct.

Methodologies developed at the University of Barcelona by Brown and Maydeu-Olivares (2011) have opened

up an IRT methodology for forced-choice items formats using a Thurstonian paired-comparison measurement

model. This model presents items in multi-item blocks (2 or more items) and asks respondents to rank the

items. Traditionally, such response formats in CTT produced results that were ipsative (items dependent on one

another) with clearly detrimental effects. The state-of-the-art FC-IRT overcomes these issues.

The FC-IRT method of scoring uses a two-stage approach. It begins with applying mathematical model for

preferences. If a person prefers or ranks item 1 higher than item 2, then that person’s preference for item 1 is

higher than item 2. For a given block of four items, there are six possible comparisons to make between the

items. After a series of blocks are administered, we can use the information provided by these comparisons to

fit an IRT model. We first use the IRT model to obtain information about how difficult the items are to endorse

(called item difficulty), and how well the items relate to the traits being measured (called item loadings).

Then, using these two pieces of information, we can accurately estimate a person’s true score for each of the

underlying traits measured by the assessment. By abstracting the ranking information into two separate yet

linked statistical models, this two-stage approach allows us to take forced-choice item responses and produce

normative scale scores free of the ipsative limitations of traditional forced-choice measurement. Using Forced-

Choice Item Response Theory, we can take advantage of the bias and faking resistance of the forced-choice item

response format while eliminating the psychometric limitations of the classical test theory scoring methods.



Korn Ferry scientists completed extensive simulations and pilots to:

• Test the underlying assumptions of the model and methods.

• Ensure that the new model did indeed overcome the prior psychometric insufficiencies of forced-choice methods.

• Develop extensions of the model to test the effects of intra- and inter-block “item cross talk” to better understand when item parameters may not be robust.

• Ensure that error estimates are meaningful.

• The results from these statistical simulations and from live pilot data affirmed that the FC-IRT methodology is a superior technology in the ways described above.

2. Why do we select levels based on current vs. target vs. aspiration categorizations? How do each of these show up in reporting? Whose responsibility is it to select each categorization?

KFALP is an assessment of the capacity and interest to develop the qualities required for effective performance

in significantly more challenging leadership roles. Inherent in that is the assumption that this potential will play

out over many years or even decades.

A person’s Aspiration is their current idea of what they would like to be doing someday. The person provides

this as part of the assessment. It does not influence scoring, rather, it provides information useful in feedback.

Today’s aspirations can change, sometimes quickly, with regard to level aspirations, mobility, and areas of

professional interest. They are often linked to current situation such as children’s education needs or the needs

of other family members.

A person’s Current Level is simply where they are situated today. It is used only to provide norms for

Experience scales. This is because it would be unreasonable to expect persons at a First Level Leader level to

have experiences normally gained further along in a career. Current level defaults to the level provided by the

participant during the assessment, however, it can also be set for a group of participants by the client.

Target Level provides a common reference norm for clients to use to compare participants. It is important

to select a target level that is two or three levels above the person’s current level. This sets a high bar and

contemplates the possibility—even the likelihood—of that person advancing two or three or more levels beyond

current level over the course of several years.

3. Do the Key challenges remain the same in the instrument regardless of the target level selected by the client?

Yes, the Key challenges remain the same. It is the amount and depth of participation in such challenges that vary

with level.

4. Why are scores on viaEDGE® and KFALP different for Learning agility?By any standard, though different technologies, the two versions of Learning agility are quality measures of the

same traits, and they share a very large majority of their items.

There are several possible causes of observed differences that may apply to a specific circumstance:

1) Though both measures are quite solid measures of Learning agility, different response scales are used in viaEDGE® (Likert) and KFALP (IRT)—they are different technologies. In large samples, Likert and IRT scores are correlated very highly (> .75)—as highly as can be expected by the reliabilities of the two measures. Still, since they are not perfectly correlated, a noticeable number of scores will not align. Even with this high correlation, approximately 10% of persons in the top quartile will drop to the bottom half of scores, and vice versa. This is normal, even for first-class measures such as these.



2) Normal error of measurement. Normal error of measurement comes into play when comparing scores on two similar assessments, or on repeated use of the exact same assessment—even top-notch assessments like viaEDGE® and KFALP. You can expect to see:

• Exactly the same score is extraordinarily rare.

• Scores just a few points apart will actually be rare as well.

• In any large-sample administrations of the two instruments, a large majority of scores will be similar, that

is, within about a fourth of the full range of raw scores from each other. This is all that can be expected

from even state-of-the-art assessments.

• A smaller number will naturally vary to a degree greater than that.

• A few will be wildly different, just due to measurement error. This is a normal part of using assessments.

3) Percentiles can mislead by exaggerating the difference in raw score differences. Percentiles are very desirable to explain what an individual score means to the person (70 means you scored better than 70% of the people in the norm). However, the dark side of percentiles is that they very much exaggerate or stretch the raw score differences, especially toward the middle of the natural normal distribution—where most persons’ raw scores are. Percentiles are intended to help understanding of single scale scores for individuals, and it accomplishes that objective. However, it is a source of misunderstanding when comparing two or more scores.

4) Different norms are used in viaEDGE® (single norm) and KFALP (level-specific norms). Different norm groups will result in different reported scores.

The bottom line is that each score is an estimate of true score that contains error. No one can determine which

one is more “right” for any individual; however, reliability of KFALP scales is slightly higher than the viaEDGE®

scales. That is one reason we are switching to that technology.

5. Can client organizations choose to opt out of Capacity while using the KFALP?Yes. Thus, only six signposts will be reported in the reports, rather than seven.

6. What signposts are likely to stay constant over time for an individual? What are the biggest areas to see the most change?

None are constant, as all can change over time, but at different rates.

Experience is the most directly malleable through proactively seeking broader experiences.

Capacity, on the other hand, is probably the one that is least malleable. The Raven’s APM measures the raw core

of reasoning. However, as people move through their careers, they rely less and less on narrow raw reasoning

and more and more on domain knowledge and learned heuristics for judgment and success. Also, the people we

assess are already in the top third to top half of the cognitive ability distribution.

Drivers can change with career and life stage as leaders decide to pursue more or less challenge or try a new

path and discover a new passion.

The Awareness, Learning agility, Leadership traits, and Derailment risks signposts are trait-based. Traits change

within an individual as they mature, faster than Capacity, but perhaps more slowly than Experience or Drivers.

It is important to remember that personality is not fixed and does not directly determine behavior. It has a big

influence on behavior, as does work situation, learned self-regulation skills, fatigue, and other state factors.



7. What, if any, differences in norms have we seen in regards to different regions, countries, cultures? Do results look similar across different regions, countries, or cultures?

Regions are in some sense arbitrary and we do not offer Region-based norms. We may offer geography-based

norms at some point in the future if sufficient sample sizes demonstrate meaningful difference may occur in

decisions made about participants.

We are keenly interested, though, in understanding any differences across cultures. We expect the FC-IRT

technology to limit differences due to cultural differences in use of a rating scale—eliminating culturally-based

response sets—which we know plague classical test theory assessments.

In general, based on very early analysis:

• Differences appear small, that is, there are far greater differences between persons within culture than

between averages for different cultures.

• Geographically-based differences are generally small relative to other important factors (management

level).

• Profiles are quite similar across geographies.

We await larger samples across more countries to adequately represent any differences found. We expect to

launch a research program on these issues as soon as sufficient data are available.

8. What qualifies signposts to be categorized as Strong (green), Needs development (yellow), or orange?In this case “Strong” is relative to the target level (except Experience)—usually a high bar.

Signposts are marked “green” based on the number of sub-dimensions on which an individual has reached a

green threshold:

• Drivers – any two of three sub-dimensions.

• Experience – any two of three sub-dimensions.

• Awareness – both sub-dimensions.

• Learning agility – any three of four sub-dimensions.

• Leadership traits – any four of five sub-dimensions.

• Capacity – one (of one) sub-dimension.

• Derailment risks – none of the three Derailment risks factors.

If these qualifications aren’t met, then you score Needs development (yellow or orange).

The green and yellow signpost markers are provided as an “at a glance” view of the participants’ results. They

are also used to assist in sorting the Talent Grid Report. No signpost appearing yellow should be considered on its own as a “knock out” factor for consideration of potential. The KFALP data must be considered as a whole-

person view and in conjunction with everything job-relevant that is known about the person.

Users should look further than the signposts. More interesting data are available at the scale level. At the scale

level, the meaning of yellow and green is whether or not the individual scored above or below the average of

persons who have already advanced to the target level. One should keep in mind that the scoring is against a

high target.



9. What is the calculation for the subscales that contribute to the ranking in the Talent Grid Report?1) Number of green signposts (7,6,5…), then ties broken by

2) Number of scales 50th percentile or above, then ties broken by

3) Number of scales 91st and above, except Derailment risks (that is the black square with the up arrow

icon), then ties broken by

4) Number of scales 50th to 90th, except Derailment risks, (that is the black square icon), then ties broken by

5) Number of scales 11th to 49th, except derailers (that is the clear square icon), then ties broken by

6) Number of scales 10th and below (that is the clear square with the down arrow icon), then ties broken by

7) Order in the report left to right.

The result is sort of an overall heat map with the generally higher scores rising.

10. Say more about the Capacity measure, why it is used, and its limitations.The Raven’s APM Version 2 was chosen because it is the only effectively language-free test available on the market, a feature important to global organizations.

The Raven’s APM predicts job performance when Problem solving is a main part of the job and when people must depend on their raw reasoning to do problem solving. In cases where persons have been pre-selected for problem solving or are already known to be of high mental ability, it does not predict so well. We have studies where it does predict and studies where it does not. The studies where it does not predict are with highly educated populations where success is based on what the leaders have learned more than raw reasoning. The studies where it does predict are in leadership populations less pre-selected and where leaders “worked their way up.”

Raven’s APM is a measure of raw reasoning ability. People rely less on this ability as they progress through their work life and more on heuristics, tacit knowledge, teams, etc., for problem solving. The degree to which raw reasoning is required will depend on the work a leader is expected to do.

As is true of all tests of cognitive ability, the Raven’s APM has the possibility of introducing adverse impact against historically disadvantaged groups. Adverse impact occurs only when employee selection procedures used in making employment decisions have the effect of selecting persons belonging to a historically disadvantaged group at a rate that is substantially lower than that of the group with the higher selection rate. Adverse impact may occur due to the characteristics of an assessment tool or other components included in the selection process, or, due to characteristics of the labor pool, recruitment practices, or other process factors.

Use Raven’s scores only as a starting point for consideration of a person’s problem-solving skills.

• Persons in the leadership pipeline typically are already among the top half of the cognitive ability distribution and the norms we use are a “high bar.”

• Persons may have developed extremely valuable tacit knowledge, judgment heuristics, or domain knowledge, or rely on their teams such that they perform at a higher problem-solving level than those who have not developed similar approaches—better even than those with higher Capacity scores.

• It is important to verify test scores through inquiring about what has been observed about the person’s practical problem-solving skills.

While an individual can over-depend on extremely high cognitive ability, there is really no evidence that such

high scores result in failure or derailment of leaders so long as other skills are present and used. And as always,

consider Problem solving just a part of each person’s tool kit. People are much more than their mental engine.

11. Does it pay to guess on the Raven’s APM?It does not hurt to guess, however, the odds of it helping are small, given that the likelihood of a correct pure guess

is only 1 in 8. For the entire test, guessing would, on average, get a score of 3—sometimes less, sometimes more. It

is assumed that any participant may narrow the options on an item and guess if they cannot resolve a final answer.



Appendix B. Inter-method correlations

Table 17. Correlation of CTT and FC-IRT versions of KFALP trait scales.

N = 2022

Awareness

Self-awarenessSituational self-awareness

.62

.52

Learning agility

Mental agilityPeople agilityChange agilityResults agility

.69

.71

.80

.73

Leadership traits

FocusPersistenceTolerance of ambiguityAssertivenessOptimism

.76

.74

.78

.77

.71



Appendix C. Norm descriptions

General norm descriptions for the Korn Ferry Assessment of Leadership Potential.

First level leader N = 2001

Mean age: 38.6, SD = 6.88

Gender: Males 65.6%, Females 34.4%

Highest level of education completed:

Secondary/High School Graduate 2.6%

Trade/Technical Education 1.9%

Associate’s Degree/Diploma 3.8%

Undergraduate Degree (Bachelor’s, etc.) 38.4%

Postgraduate Degree (Master’s, etc.) 36.7%

Doctorate/Professional (Ph.D., M.D., etc.) 5.9%

Primary industryAdvanced Technology 1.8%

Consumer Goods 9.7%

Distribution Services .6%

Education & Training 1.6%

Energy & Utilities 14.0%

Financial Services 9.9%

Government .3%

Healthcare & Biological Sciences 26.4%

Industrial & Manufacturing 16.8%

Media & Entertainment .9%

Nonprofit .0%

Professional & Business Services 3.1%

Real Estate & Property Management .2%

Research & Development .3%

Retail 1.9%

Telecommunications 10.0%

Travel, Hospitality & Leisure .7%

Multi-Industry Holding Companies 1.4%

Current functional areaAdministrative Services 1.2%

Communications 1.2%

Consulting 2.9%

Creative .1%

Customer Service 3.5%


Executive & General Management .8%



Finance & Control 9.1%


Healthcare 2.0%

Human Resources 10.6%

Information Technology 4.8%

Legal 1.5%

Manufacturing 2.9%

Marketing 6.9%

Operations 12.6%

Public Safety & Military .3%

Research, Development & Engineering 9.0%

Retail .2%

Sales 16.3%

Strategic Planning 3.5%

Multiple Functions 5.9%

Company typePublicly Traded 61.1%

Subsidiary of Publicly Traded 14.0%

Privately Held 16.8%

Government Organization-Agency 2.2%

Government Owned Enterprise 3.9%

Nonprofit 1.5%

Self-Employed .4%

Global regionAsia 19.1%

Caribbean .2%

Europe 26.6%

Latin America 7.3%

Middle East and North Africa 7.4%

North America 25.5%

Oceania 2.2%

Sub-Saharan Africa 1.7%

No Data 9.9%

Ethnicity (US only, optional)Hispanic or Latino 8.3%

White 71.1%

Black or African American 6.6%

Native Hawaiian or Other Pacific Islander .4%

Asian 9.3%

American Indian or Alaska Native .2%

Two or more races (more than one of the above) 4.0%



Mid-level leader N = 1959

Mean age: 41.85, SD = 6.68



Some Secondary Education .5%








Consumer Goods 11.5%

Distribution Services 2.1%




Government .3%




Nonprofit .2%




Retail 4.4%





Communications 1.3%

Consulting 2.9%

Creative .5%



Executive & General Management 2.7%





Healthcare 1.8%



Legal 1.9%

Manufacturing 3.6%

Marketing 8.6%

Operations 12.7%



Retail .9%

Sales 12.0%







Government Owned Enterprise 1.2%

Nonprofit 1.5%

Self-Employed .1%

Mean years in management: 10.47, SD = 6.44


Caribbean .2%

Europe 22.9%

Latin America 10.5%


North America 24.8%

Oceania 2.4%


No Data 13.7


White 80.8%


Native Hawaiian or Other Pacific Islander 0.0%

Asian 6.0%

American Indian or Alaska Native .4%




Functional leader N = 2218

Mean age: 43.89, SD = 6.97











Consumer Goods 8.0%





Government .1%



Media & Entertainment 1.1%

Nonprofit .2%


Real Estate & Property Management 1.0%


Retail 2.8%




Current functional areaAdministrative Services .6%

Communications 1.4%

Consulting 2.2%

Creative .2%


Education & Training .9%






Healthcare 1.4%



Legal 2.1%

Manufacturing 3.3%

Marketing 8.2%

Operations 12.1%



Retail .4%

Sales 5.9%






Government Organization-Agency .7%

Government Owned Enterprise .4%

Nonprofit 2.3%

Self-Employed .1%



Caribbean .2%

Europe 20.1%

Latin America 8.6%


North America 28.9%

Oceania 5.1%


No Data 14.5%


White 82.1%



Asian 5.8%

American Indian or Alaska Native 0.0%




Business or organizational unit/division leader N = 2218

Mean age: 44.82, SD = 6.77











Consumer Goods 6.3%





Government .4%




Nonprofit .6%




Retail 3.4%





Communications .6%

Consulting 2.3%

Creative .4%






Healthcare 1.5%





Legal 1.3%

Manufacturing 3.1%

Marketing 6.6%

Operations 9.4%


Retail 1.6%

Sales 12.6%








Nonprofit 4.0%

Self-Employed .2%


Global regionAsia 13.4

Caribbean .3

Europe 23.2

Latin America 9.5

Middle East and North Africa 4.4

North America 24.4

Oceania 6.3

Sub-Saharan Africa 2.7

No Data 15.8


White 82.7%


Asian 6.7%




Senior/top functional executive N = 1593

Mean age: 46.43, SD = 6.89




Secondary/High School Graduate .8%

Trade/Technical Education .9%






Consumer Goods 6.5%

Distribution Services 1.4%




Government .4%



Media & Entertainment 2.4%

Nonprofit .8%


Real Estate & Property Management 1.3%


Retail 3.4%


Travel, Hospitality & Leisure 1.6%


Current functional areaAdministrative Services .6%

Communications 1.9%

Consulting 1.3%

Creative .4%


Education & Training .9%



Financial Services .9%

Healthcare .8%





Legal 3.8%

Manufacturing 1.9%

Marketing 5.9%

Operations 9.8%



Retail .2%

Sales 6.7%








Nonprofit 3.1%

Self-Employed .1%



Caribbean 0.0%

Europe 17.1%

Latin America 24.0%


North America 29.0%

Oceania 3.6%


No Data 8.1%


White 86.5%


Asian 4.0%




Top business or organizational group executive N = 595

Mean age: 46.97, SD = 6.57

Gender: Males 79%, Females 21%



Trade/Technical Education .8%





Primary industryAdvanced Technology 6.1

Consumer Goods 5.9

Distribution Services 1.7

Education & Training 3.4

Energy & Utilities 5.0

Financial Services 12.6

Government 1.3

Healthcare & Biological Sciences 22.0

Industrial & Manufacturing 12.1

Media & Entertainment 1.8

Nonprofit .8

Professional & Business Services 2.5

Real Estate & Property Management 1.0

Research & Development .8

Retail 4.7

Telecommunications 15.6

Travel, Hospitality & Leisure 1.3

Multi-Industry Holding Companies 1.2

Current functional areaAdministrative Services .2

Communications 1.3

Consulting 1.2

Creative .3

Customer Service 1.2

Education & Training .5

Executive & General Management 38.8

Finance & Control 7.4

Financial Services 1.0

Healthcare 1.5

Human Resources 3.7



Information Technology 3.2

Legal 3.0

Manufacturing 1.5

Marketing 3.2

Operations 6.1

Public Safety & Military .2

Research, Development & Engineering 3.0

Retail 1.2

Sales 6.6

Strategic Planning 2.5

Multiple Functions 12.4






Nonprofit 6.2%

Self-Employed .5%



Caribbean 0.0%

Europe 19.0%

Latin America 15.8%


North America 31.9%

Oceania 3.7%


No Data 8.2%


White 80.2%



Asian 6.8%




Appendix D. Global Personality Inventory (GPI) definitions

Thinking Factor

ThoughT AgiliTyThis is a measure of the tendency to be open both to multiple ideas and to using alternative modes of thinking.

innovATion This is a measure of the tendency to produce unique and original things.

ThoughT FocusThis is a measure of the tendency to understand ambiguous information by analyzing and detecting the systematic themes in the data.

vision This is a measure of the tendency to have foresight in one’s thinking.

Planning and Execution Factor

ATTenTion To DeTAil This is a measure of the tendency to be exacting and precise.

Work Focus This is a measure of the tendency to be self-disciplined in one’s approach to work.

Facilitating Leadership Factor

TAking chArge This is a measure of the tendency to take a leadership role.

inFluence This is a measure of the tendency to get others to view and do things in a certain way.

Derailing Leadership Factor

ego cenTereD This is a measure of the tendency to be self-centered and appear egotistical.

MAnipulATion This is a measure of the tendency to be self-serving and sly.

Micro-MAnAgingThis is a measure of the tendency to over-manage once a person has advanced to higher levels of management.

inTiMiDATing This is a measure of the tendency to use power in a threatening way.

pAssive-AggressiveThis is a measure of the tendency to avoid confronting others, conveying acceptance or cooperation and yet appearing to behave in uncooperative and self-serving ways.

Interpersonal Factor

sociAbiliTy This is a measure of the tendency to be highly engaged by any social situation.

consiDerATion This is a measure of the tendency to express care about others’ well-being.

eMpAThyThis is a measure of the tendency to understand what others are experiencing and to convey that understanding to them.

TrusT This is a measure of the tendency to believe that most people are good and well intentioned.

sociAl AsTuTenessThis is a measure of the tendency to accurately perceive and understand the meaning of social cues and use that information to accomplish a desired goal.

energy level This is a measure of the tendency to be highly active and energetic.

iniTiATive This is a measure of the tendency to take action in a proactive rather than reactive manner.

Desire For AchieveMenT This is a measure of the tendency to have a strong drive to realize personally meaningful goals.

Self-Management Factor

ADApTAbiliTy This is a measure of the tendency to be open to change and considerable variety.

openness This is a measure of the tendency to accept and respect the individual differences of people.

negATive AFFecTiviTyThis is a measure of the tendency to be generally unsatisfied with many things, including but not limited to work.

opTiMisM This is a measure of the tendency to believe that good things are possible.

eMoTionAl conTrol This is a measure of the tendency to be even-tempered.

sTress TolerAnceThis is a measure of the tendency to endure typically stressful situations without undue physical or emotional reaction.



selF-conFiDence This is a measure of the tendency to believe in one’s own abilities and skills.

iMpressing This is a measure of the tendency to try to make a good impression on others.

selF-AWAreness This is the tendency to be aware of one’s strengths and weaknesses.

inDepenDence This is a measure of the tendency to be autonomous.

coMpeTiTiveness This is a measure of the tendency to evaluate one’s own performance in comparison to others.

risk-TAking This is a measure of the tendency to take chances based on limited information.

Desire For ADvAnceMenTThis is a measure of the tendency to be ambitious in the advancement of one’s career or position in organizational hierarchy.

Collective Orientation Factor

inTerDepenDence This is a measure of the tendency to work well with others.

DuTiFulness This is a measure of the tendency to be filled with a sense of moral obligation.

responsibiliTy This is a measure of the tendency to be reliable and dependable.

Global Personality Inventory (GPI) definitions (continued)



Appendix E. GPI and KFALP correlations

Ad

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Situ

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Self-

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Men

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Peop

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gili

ty

Cha

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agili

ty

Res

ults

ag

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Op

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Focu

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Pers

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Ass

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ss

Mic

rom

anag

ing

Clo

sed

Vol

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Attention to Detail -.23 -.25 .03 .12 -.14 -.05 -.21 .08 -.10 -.26 .61 .17 -.02 .51 .21 -.06

Adaptability .37 .36 .08 -.01 .36 .04 .49 .16 .13 .49 -.24 .01 .13 -.34 -.52 -.09

Social Astuteness .17 .07 .10 .10 .20 .22 .13 .06 .05 .18 .02 .18 .10 -.17 -.20 -.13

Competitiveness .32 .10 -.01 .00 -.04 .01 .13 .18 .04 .10 .12 .17 .29 .14 -.03 -.08

Consideration .05 .05 .08 .02 .14 .25 -.02 .07 .03 .04 .04 -.03 -.01 -.20 -.08 -.02

Desire for Achievement .36 .22 .02 .07 .30 .05 .36 .36 .10 .35 .00 .16 .27 -.11 -.32 -.07

Desire for Advancement .29 .09 .01 -.06 .06 -.01 .14 .14 .02 .13 .15 .13 .20 .10 -.07 -.07

Dutifulness -.04 .01 .11 .05 .07 .01 .01 .10 .11 .03 .15 .12 -.01 .04 -.07 -.10

Emotional Control .11 .02 .23 .11 .21 .05 .17 .03 .19 .21 .04 .13 -.01 -.03 -.18 -.42

Ego Centered -.07 -.17 -.04 -.03 -.07 -.08 -.04 .07 -.15 -.10 .17 .04 .12 .20 .15 .04

Energy Level .35 .25 .04 .06 .18 .12 .32 .38 .18 .28 .01 .22 .23 -.16 -.31 -.07

Empathy .03 .01 .13 .05 .14 .32 -.04 .04 .00 .01 .07 .04 -.02 -.20 -.04 -.11

Impressing -.11 -.12 .06 .00 -.01 .08 -.16 .05 -.03 -.15 .35 .16 -.05 .18 .13 -.09

Independence -.16 -.07 -.01 -.10 -.03 -.20 -.13 -.02 -.12 -.09 .07 -.06 -.06 .21 .16 .01

Influence .27 .13 .14 -.01 .23 .37 .19 .17 .11 .19 .08 .21 .34 -.12 -.11 -.15

Initiative .21 .18 .06 .05 .24 .09 .28 .23 .08 .20 .12 .16 .29 -.06 -.25 -.05

Innovation .14 .12 .14 -.03 .40 .16 .24 .11 .01 .26 .01 .10 .14 -.10 -.21 -.13

Interdependence .08 .06 .07 .01 .13 .10 .15 .02 .06 .08 -.03 .06 .02 -.20 -.21 -.13

Intimidating -.11 -.06 -.03 -.01 -.14 -.07 -.03 -.04 -.09 -.05 .14 .10 .08 .19 .13 .00

Manipulation -.02 -.05 -.12 -.07 -.16 -.11 -.06 -.13 -.16 -.09 .03 -.13 .02 .17 .08 .08

Micro-Managing -.18 -.16 -.06 .04 -.16 -.11 -.24 .06 -.24 -.27 .34 .03 .09 .38 .29 .09

Negative Affectivity -.18 -.17 -.07 -.08 -.15 -.11 -.15 -.10 -.26 -.16 .08 -.15 -.06 .19 .19 .08

Openness .23 .17 .08 .04 .22 .19 .30 .15 .25 .20 -.04 .16 .14 -.21 -.32 -.16

Optimism .25 .18 .11 .03 .19 .18 .23 .22 .20 .22 .05 .22 .11 -.16 -.24 -.14

Passive-Aggressive -.28 -.25 -.10 -.05 -.20 -.14 -.29 -.15 -.18 -.27 .15 -.12 -.16 .26 .31 .13

Responsibility .08 .07 .14 .07 .14 .11 .10 .23 .13 .04 .12 .23 .04 .00 -.07 -.17

Risk-taking .30 .22 .05 -.03 .26 .06 .40 .15 .00 .32 -.07 .16 .18 -.16 -.31 -.07

Self-Awareness .09 .02 .22 .13 .10 .08 .08 .08 .12 .04 .09 .29 .13 .02 -.08 -.23

Self-Confidence .16 .12 .15 .01 .21 .05 .15 .24 .11 .12 .11 .17 .19 .05 -.16 -.15

Sociability .33 .18 .10 .06 .13 .34 .19 .15 .22 .14 .02 .16 .33 -.21 -.20 -.14

Stress Tolerance .17 .13 .26 .04 .11 .09 .24 .07 .30 .22 -.02 .19 .01 -.08 -.22 -.39

Thought Agility .04 -.01 .08 .06 .22 .11 .08 .04 .00 .11 .13 .19 .04 -.03 -.13 -.14

Taking Charge .31 .18 .11 .02 .15 .12 .16 .22 .07 .19 .14 .18 .53 -.01 -.11 -.13

Thought Focus .10 .05 .15 .08 .29 .10 .12 .16 -.01 .18 .07 .16 .12 -.04 -.08 -.17

Trust .10 .09 .10 .03 .14 .17 .02 .09 .18 .09 -.02 .10 .04 -.22 -.04 -.05

Vision .08 .04 .14 .04 .27 .15 .17 .13 -.05 .20 .07 .15 .11 -.05 -.13 -.18

Work Focus -.07 -.14 .21 .16 -.01 -.01 -.09 .15 .06 -.12 .35 .31 .04 .25 .05 -.29



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