Scientific research has consistently demonstrated personality, defined as stable dispositions in how one thinks, feels, and behaves, to be a significant predictor of work-related outcomes. It is now common practice for organizations to use personality assessment tools in their talent management strategies, using such insights to shape how they select, develop, or promote employees, teams, and leaders. Personality assessments are identified to be more scientifically valid in their prediction of job performance and critical work-outcomes over widely used methods such as interviews, resumés, and education. Moreover, insights provided from these tools are free from individual and group biases (Chamorro-Premuzic & Furnham, 2010).
While many organizations use personality assessments in their talent management strategies, the most popular tools are confounded by their length, ineffective use of technology, and inaccessible insights. Together, this creates an inadequate user experience where respondents find the process of taking the assessment cumbersome and often struggle to fully understand their feedback report. In addition, these factors restrict an organization’s ability to deploy personality assessments at scale. As a result, an organization-wide view of talent is often overlooked and undeveloped, resulting in leaders making ill-informed human capital decisions and leaving many without critical feedback and awareness into their behavior. Overcoming these limitations was the driving force behind the development of the Core Drivers Diagnostic.
The Deeper Signals Core Drivers Diagnostic is a personality inventory based on the Five Factor Model — the most scientifically validated and defensible model of personality. The Core Drivers Diagnostic consists of 90 items, where respondents choose an adjective from a pair that best describes themselves and respond to short behavioral statements. Each scale of the Core Drivers Diagnostic comprises of five facets or “Sub Drivers”, empowering coaches and leaders with deep and nuanced talent insights. The diagnostic is administered on a modern, secure, and security-compliant technology platform (“The Deeper Signals Platform”). Upon completion respondents are presented with an interactive and intuitive report that describes their results and provides personalized development plans through a “Digital Coach”.
The Core Drivers Diagnostic was developed using over 150,000 working adults, psychometric techniques, and machine learning methods. This article describes the diagnostic’s psychometric properties. Specifically, the scales’ reliability, factor structure and intercorrelations, alongside construct (convergent &discriminant), concurrent, and criterion validity. Scores on the Core Drivers Diagnostic correlate with many popular psychometric constructs and inventories, including The Big Five, Dark Triad, NEO PI-R, and the Hogan Personality Inventory, in addition to employee engagement, counter-productive work behaviors and manager ratings of job performance. The diagnostic was also tested for adverse impact, of which there is none. Moreover, the diagnostic does not discriminate between protected groups.
The Core Drivers Diagnostic was designed for working adults, in individual contributor, management, and leadership roles. The tool can be used to inform talent management decisions, coaching, team development, learning and development, training, people analytics, and to support organizational change.
This section describes the key characteristics of the Core Drivers Diagnostic, specifically how the assessment can be used, intended audiences and an overview of the report insights. This section has been created to help practitioners and stakeholders evaluate the assessment and assess it suitability within their organizations.
The Core Drivers Diagnostic contains six scales that are based on the Five Factor model of personality, the most scientifically supported personality model (Chamorro-Premuzic & Furnham, 2010). While the next section describes the scales in more detail, the below provides an overview of the six dimensions:
- Agreeableness: The tendency to be candid or considerate.
- Conscientious: The tendency to be flexible or organized.
- Drive: The tendency to be laid back or driven.
- Extraversion: The tendency to be reserved or outgoing.
- Openness: The tendency to be pragmatic or curious.
- Emotional Stability: The tendency to be passionate or stable.
In addition to the six dimension, 30 facets or “Sub-Drivers” constitute each scale (five per dimension). This allows for greater nuance and understanding around an individual’s dispositions. To complete the diagnostic, users indicate the extent to which 30 behavioral statements and 60 force-choice adjectives best describe themselves.
The Core Drivers Diagnostic sets itself apart from other talent assessments with its short format which allows users to complete the diagnostic in less than ten minutes. In addition, the unique, conversational language improves accessibility and provides an engaging user experience. More importantly, this unique experience is delivered without compromising the scientific robustness of the tool. The Core Drivers Diagnostic is scientifically reliable, valid, and free of bias. It meets all industry standards and predicts critical work behaviors and outcomes.
Users can access and complete the assessment on the Deeper Signals platform. This self-service platform features a modern user interface and is designed for mobile devices, while providing a safe, secure, and private online experience.
Compared to other commercial assessments, the Core Drivers can be deployed at scale, enabling organizations to support their leaders and employees with deep, meaningful insights quickly and effortlessly. Further, the assessment is powered by a robust analytics suite that enables HR leaders to identify talent trends at the team and organizational level.
Using the Core Drivers Diagnostic, organizations have the unique opportunity to empower leaders, build strong teams, fuel innovation, and drive large-scale change through self-awareness.
How can the core Drivers Diagnostic be Used?
As humans, one of our biggest challenges lies in our inability to assess ourselves objectively. The benefits associated with possessing self-awareness are well documented. Those who demonstrate a strong sense of self are regarded as higher performers (Church, 2005), have better chances of getting promoted (Bass & Yammarino, 1991), improved job satisfaction (Sy, Tram, & O’Hara, 2006), more productive among direct reports (Moshavi, Brown, & Dodd, 2003), and tend to be better at decision making (Fallon et al., 2014). Yet studies show that most people, across cultures, overestimate their competency across nearly all skills and attributes (Dunning, 2011; Dunning, Johnson, Ehrlinger, & Kruger, 2003; Zell, Strickhouser, Sedikides, & Alicke, 2019).
Since insight and feedback are the main force for change and development, the Core Drivers Diagnostic is designed to provide self-awareness in a variety of ways. Organizations would want to consider using the Core Drivers Diagnostic to:
- Apply scientifically validated and unbiased insights to talent management. Humans are inherently biased decision makers. The Core Drivers Diagnostic can be used to bring evidence-based, data driven decision making to the forefront of talent management processes.
- Empower individual change at all levels within the organization. Self-awareness is a catalyst for lasting behavioral change (Eurich, 2017). The Core Drivers Diagnostic facilitates this process by highlighting behaviors with the biggest potential for impact and change. This enables individuals to gain insight into their unique strengths and gaps and target effort in the most opportune areas. Further, with our commitment to democratize self-awareness, the tool is carefully designed to be accessible to employees at all levels within an organization, across industries. Equipping both leaders and team members with powerful insights to help them deeply understand one another and support them in reaching their full potential.
- Identify critical gaps for training and development. Most learning and development programs practice a “one size fits all” approach to cater to a breadth of employees when in reality this fits no one. Openness to change, learning styles and amount of training required vary significantly between individuals. The most effective development programs are personalized. The Core Drivers Diagnostic take a more strategic and targeted approach to supporting employees in their development journeys.
- Drive instant and sustained behavioral change. Contrary to long standing beliefs, recent evidence demonstrates that behavioral dispositions can be impacted by well-designed interventions (Roberts et al., 2017). The Core Drivers Diagnostic supports individuals with an intelligent digital coach that equips them with tailored micro-actions, nudges and learning experiences. They are inspired to instantly turn awareness into action upon completing the assessment. Moreover, nudges and reminders from the digital coach enable individuals to stay accountable and track progress towards their behavioral goals. As the old adage has it, you cannot manage what you don’t measure.
- Improve strategic workforce planning decisions. In today’s shifting landscape, organizations are seeking to up skill or reskill their workforce to adapt to changing circumstances. Though personality traits and dispositions play a critical role in an individual’s approach and capacity to change, these considerations are often overlooked in the decision-making process. The Core Drivers Diagnostic provides an organization-wide snapshot of the capabilities of its workforce, enabling leaders to identify areas in the organization where critical abilities are lacking and strategically plan for how to overcome these gaps across its talent pipeline. Insights provided from the Core Drivers Diagnostic also help determine where upskilling efforts would be most effective and deliver maximum value.
- Foster cognitive diversity. Diversity of thought is a recognized characteristic of high-performing teams. As today’s workplaces deal with increasingly complex and ambiguous challenges, the fresh perspectives and varied approaches to change brought about by cognitively diverse teams help them solve problems faster (Bell, 2007; Reynolds & Lewis, 2017). The Core Drivers Diagnostic supports organizations take a proactive approach to embracing diversity of talent and leading more inclusively. The platform’s analytics tool provides powerful insights on team cognitive diversity. Leaders can easily identify team strengths and opportunity areas as well as model various team compositions to determine how best to harness the full potential of their talent.
- Inform change management interventions. The successful planning and execution of organizational change initiatives is highly dependent on the individual dispositions of stakeholders who are impacted by the change and their capacity to change. Assessing for these abilities early in the process helps effectively plan for these efforts and identify required interventions. This Core Drivers Diagnostic could be used to help inform change readiness assessments and determine suitable candidates to recruit as change agents and early adopters to champion the change.
The Core Drivers Diagnostic was designed to be used in talent management contexts. Specifically, it can be used to inform talent decisions for those working in individual contributor, management and leadership roles. The assessment is industry agnostic, and scores are not impacted by previous vocational or educational experiences. The assessment was not designed to be used by anyone under the age of 18. The assessment was developed for English speakers and now available in other languages (i.e.,Spanish, Portuguese, Italian, German, French, Bulgarian, Korean & Chinese). When translating the assessment, replication studies are conducted to ensure there is measurement invariance between English and non-English versions.
Report Feedback & Insights
Upon completion of the short assessment, every user instantly receives an online, interactive report that provides engaging, deep insights into how they feel, work, and relate. The report has four components – Core Drivers, Risks, Teamwork and Digital Coach. The following section describes each of these sections.
The diagnostic is built upon a scientific and empirical model, while leveraging the intuitiveness and ease of tools such as the Myers-Briggs Type Indicator. Rather than overwhelming users with multiple scales and percentage scores, feedback is only provided on their most extreme three scores — the individual’s “Core Drivers”. This is done to focus the individual’s attention to the aspects of their personality profile that make them most unique and have the biggest impact on how they live and work. This approach helps users quickly get to insight and understanding.
Data on the other scales is labelled as “Secondary Drivers” and presented elsewhere in this section of the report. While information on these drivers is important, they are considered to be less practically consequential aspects of one’s personality. Users who are interested in understanding these aspects of their personality however, have access to this information.
An individual’s Core Drivers are determined by comparing their scores to our normative global database and identifying the three scales that deviate furthest from the sample mean. As a result, respondents are given three adjectives that describe their Core Drivers. For example, an individual with very low Agreeableness and Openness scores, and high Emotional Stability scores would be described as Candid, Pragmatic and Stable (see next section for a list of all possible Core Drivers and a full description of the underlying theoretical model).
This section of the report starts with describing an individual’s three Core Drivers through the lens of them being a strength. That is, how they positively impact their life at work. A deeper dive into their personality profile is then presented revealing their Core and SecondaryDrivers, alongside the associate Sub-Drivers. These insights are particularly helpful for coaches, trainers and talent management professionals. Finally, an overview of the theoretical model that underpins the tool and responses to frequently asked questions are also included in this section of the report.
We all have days when we are not at our best. Frustration, stress, excitement, or tiredness can lead us to drop our guard and act without care or thought. These acts can lead to making regretful decisions, damaging important relationships, and harming one’s reputation. Core Risks are considered to be an individual’s Core Drivers taken to their extreme, and describe behavioral gaps or challenges. This perspective is guided by scientific research that confirms that overused strengths harm our ability to lead effectively, make good decisions, and maintain our relationships (Le et al., 2011; Pierce & Aguinis, 2013).
This section of the report reveals an individual’s Core Drivers is related to an area of risk, and explains the impact this may have on their work and those around them. Details around how an individual’s strengths become unproductive or harmful are provided along with practical advice on how they can be managed. Further, insights are included on how these risks can get in the way of them leading effectively, with advice on how they could be managed. The feedback in this section is development focused and designed to raise awareness to potentially derailing behaviors.
This section describes how an individual’s Core Drivers impact the way they interact with others and when working in teams. Practical advice is provided around how they can best add value to team or group contexts. Additional insights are provided on how individuals can manage their relative strengths and risks in team settings. The feedback in this section is designed to equip users with an awareness of their talent in groups contexts, and how to change behaviors that could contribute to poor team performance, group conflict, and general ineffectiveness. Further, in aggregate, this data can be used by managers and leaders to build and develop more cognitively diverse teams.
Designed to turn self-awareness into action and lasting behavior change, the Digital Coach serves users with uniquely curated multimedia content (videos, podcasts, articles, etc.), interactive Learning Journeys that are tailored to an individual’s personality dispositions and micro-action nudges to reinforce and guide change. These resources set themselves apart in the industry as it takes an evidence-based approach grounded in the latest research in personality science which holds that our baseline behavior can change through targeted interventions (Roberts et al., 2017; Stieger et al., 2021).
Users are equipped with personalized development plans and empowered to choose their own learning journeys based on what is most helpful under their individual circumstances. Coaching and learning resources are uniquely curated to help them understand how to lead with their Core Drivers. Learning is most impactful when broken down into easily digestible content, that can be quickly actioned on. Interactive learning modules are supplemented with micro-actions that users can complete in less than five minutes. Progress tools are also provided to help users track their behavioral goals.
Further, the digital coach provides inspiration and promotes accountability through nudges that reinforce the habit formation process. Users are also provided the opportunity to schedule virtual coaching sessions with an accredited Deeper Signals coach, to support them with continuous development.
This multi-pronged approach employs the latest research in personality and behavioral science wherein environmental and contextual factors are harnessed in driving change (Stieger et al., 2021). These science-backed tools have been developed with the constraints of the modern user in mind. With starting small and encouraging consistency, users quickly realize the compounding effects of these new habits with time.
As a social species, understanding others is a key driver of an individual’s success in their personal and professional lives. Without us realizing, as humans, we are continuously trying to understand and predict the preferences, peculiarities, and behaviors of those around us. Often, we do this to make our interactions and relationships more rewarding and meaningful, less stressful, and to improve our chances of influencing others.
Although there are over 7 billion people on the planet, and we all like to think we are unique, science has demonstrated that our behavior varies on a relatively small number of dimensions. Equally, while people change through their life span, an individual's character has a behavioral “center of gravity”. If you are a cheerful, disorganized teenager, you are more than likely to become a cheerful, disorganized pensioner. If you are an irascible and micromanaging marriage partner there is little chance that you would transform into an even-tempered and empowering leader when you arrive at work. Longitudinal scientific studies have tracked people’s personality spanning a 50-year period from the ages of 16 to 60, empirically demonstrate that our personality is largely stable across the lifespan (Damian, Spengler, Sutu, & Roberts, 2019).
Most problems individuals encounter in their professional and personal lives center around a basic lack of understanding.This has driven the growth of popular profiling tools like the Myers-BriggsType Indicator (MBTI), which puts people into one of 16 boxes, and assigns each one a 4-letter code. Although easy to understand, tools like this are flawed and inaccurate. Using data from such tools as input to inform critical decisions could pose significant risks to individuals and organizations. In response, scientists pursued the development of more scientific and data-driven aids to provide insight and understanding of our own and others’ personalities and behaviors.
At Deeper Signals, we have created a tool based on modern science that is easy to use and self-explanatory. This approach provides organizations and individuals with personalized and data-driven feedback to help them unlock their potential, work more productively with others, and create lasting behavioral change. The following chapter describes the theoretical framework and scientific rationale for the Core Drivers Diagnostic.
The Five Factor Model of Personality
Personality traits are defined to be stable, inner, personal dispositions that determine relatively consistent patterns of behavior across different situations (Hogan, 2007). These traits are as unique to an individual as their DNA and play a large part in differentiating their behavior from others in different situations and at work.
In the past twenty years, psychologists have provided compelling evidence that individual differences in personality can be classified based on five broad dimensions, namely Emotional Stability, Extraversion, Agreeableness, Conscientiousness and Openness to Experience. These traits, collectively known as The Five FactorModel (FFM) or the “Big Five” is widely regarded to be the most robust and empirical frameworks of personality (Chamorro-Premuzic & Furnham, 2010). The FFM captures the essence of inter individual variability by providing a general level of description of the person, which can help predict future behavior. Compared to other personality models the FFM serves as a common currency and universal language in personality research. Studies have confirmed its applicability across a wide range of cultures, languages and instruments (Digman, 1990). Accordingly, assessments built upon a “Big Five” model offer practitioners a degree of robustness and versatility when looking to identify, assess and predict future behavior.
The FFM personality traits are not categorical in nature (i.e., either you possess it or you don’t), rather individuals are considered to exhibit varying degrees of these behaviors on a continuum from extremely low to extremely high. Understandably, there are relative behavioral strengths and limitations associated with each of these personality continuums. Scientists call this the “too much of a good thing” effect, whereby demonstrating too much or too little of these specific behaviors characteristics could have negative outcomes (Le et al., 2011). This is discussed further in a later section of this chapter.
The FFM has been found to predict a host of life and work-related outcomes (Chamorro-Premuzic & Furnham, 2010). Here we delve into some of the research that demonstrated the predictive validity of the Five Factor Model.
Table 1: Work-related outcomes predicted by the Five Factor Model
Conscientiousness refers to the extent to which an individual can be described as competent, orderly, dutiful, achievement orientated, disciplined, and determined. This dimension is identified to be strongest predictor of job performance and training proficiency, with meta-analytic coefficients rivaling that of cognitive ability (Barrick & Mount, 1991). To quote these researchers, “individuals who are dependable, persistent, goal directed and organized tend to be higher performers on virtually any job; viewed negatively, those who are careless, irresponsible, low achievement striving and impulsive tend to be lower performers on virtually any job.” (p.851).
Several studies confirm conscientiousness along with agreeableness to be the most important personality traits for workplace success across jobs that span a breadth of complexity, training and experience requirements (Sackett & Walmsley, 2014). Further, high levels of conscientiousness are associated with leadership emergence – the degree to which others perceive an individual to be a leader (Hogan, Curphy, & Hogan, 1994; Judge, Bono,Ilies, & Gerhardt, 2002). On the other end of the spectrum, lower levels of conscientiousness is associated with flexibility and spontaneity (Toegel & Barsoux, 2012), which are also important dispositions in certain roles and contexts (i.e. innovation, navigating change and uncertainty, entrepreneurship and fast-moving environments).
Emotional Stability describes the extent to which an individual is not anxious, impulsive, depressive, or overly self-conscious. Emotional Stability refers to the inverse of what is more widely referred to as Neuroticism. Emotional Stability is linked to job and training performance particularly under stressful conditions (Chamorro-Premuzic, 2007). Although meta-analytic research on the impact of Emotional Stability on work outcomes is mixed, recent theories postulate that the relationship between Emotional Stability and performance is curvilinear, where extremely high or extremely low scores on the dimension lowers performance (Chamorro-Premuzic & Furnham, 2010). This explains why, under some circumstances, notably low situational pressure or tasks that are under-arousing, Neurotic individuals have an advantage over their stable counterparts because they are naturally more alert to potential environmental threats. In line with this, studies on air traffic controllers tend to report superior performance by Neurotic individuals (Smillie, Yeo, Furnham, & Jackson, 2006). Studies also identify emotional stability to be the strongest predictor of job satisfaction (Judge, Heller, & Mount, 2002). Further emotionally stable individuals tend to possess higher levels of emotional intelligence (Chamorro-Premuzic, Bennett, & Furnham, 2007) and studies identify this trait to be exhibited by those perceived to be leaders (Judge, Bono, et al., 2002).
Extraversion describes the extent to which an individual is gregarious, proactive, assertive, excitement seeking and displays positive emotionality. Individuals who score low on this dimension can be described as Introverted. Extraversion scores are found to predict sales performance, managerial effectiveness, and leadership emergence (Mount, Barrick, & Stewart, 1998). Further, some researchers argue that the dimension can be divided into two separate sub-dimensions: one describes the sociability and gregariousness of Extraversion, while the other describes the drive, ambition and proactivity (Deyoung, 2015; J. Hogan & Holland, 2003). Thus, some Extraverts may be characterized more by their tendency to experience positive affect, be sociable and enjoy the company of others (i.e.sociability or gregariousness), whilst in other Extraverts the main trait would be dominance, self-confidence and leadership (i.e. drive or proactivity). Studies indicate that extraverts tend to perform better on tasks that require divided attention (e.g. writing whilst listening to music) whereas introverts have an advantage on tasks that require attentiveness or accuracy. In addition, extraverted individuals demonstrate higher levels of self-efficacy, goal-setting, motivation and absenteeism (Chamorro-Premuzic & Furnham,2010).
Agreeableness describes the extent to which an individual is trusting, altruistic, modest, empathetic and compliant. Agreeableness is advantageous in jobs requiring interpersonal interactions or where getting along is paramount (Mount et al., 1998). A typical case is customer service jobs wherein agreeableness is found to predict higher performance (Hurtz & Donovan, 2000), especially if based on teamwork rather than individualistic tasks (Barrick, Stewart, Neubert, & Mount, 1998). Agreeableness also seems to moderate the effects of Conscientiousness – the strongest personality trait correlate of job performance – or work-related outcomes (Burch & Anderson, 2008). Thus, people who are Conscientious but Disagreeable will tend to have conflicts with others, whereas people who are Conscientious and Agreeable will benefit from the synergistic effects of discipline and cooperation.Continuing this, low Agreeableness scores are related to counterproductive work behaviors and socially deviant behaviors that can disrupt others and harm performance (Mount, Ilies, & Johnson, 2006). Further, leaders who demonstrate high levels of agreeableness are perceived to be transformational leaders (Lim & Ployhart, 2004). Agreeableness and conscientiousness are considered the most important traits for workplace success (Sackett & Walmsley, 2014).
Openness to Experience
Openness to Experience describes the extent to which an individual is driven by fantasy, artistic, feelings, inquisitiveness, and has liberal values. Openness is often related to intellectual outcomes and activities, such as curiosity and learning agility, while a predictor of performance for those working artistic roles and environments (Ackerman & Heggestad, 1997; Kaufman, 2013). Furthermore, it is related to holding more tolerant and inclusive attitudes, increased levels of cognitive ability, and producing more innovative output (Akhtar, Humphreys, & Furnham, 2015; Leutner, Ahmetoglu, Akhtar, & Chamorro-Premuzic, 2014; Shin, Kim, Lee, & Bian,2012).
Individuals scoring low on this dimension typically think in more traditional ways, solve problems in a practical and pragmatic way, are uninterested in abstract ideas, and are unlikely to invest time into intellectual activities (Chamorro-Premuzic, 2007). Openness to Experience is found to be the strongest determinant of creative performance in the workplace, especially when individuals are faced with multiple options for solving a problem or performing a task (Zhou & George, 2001). In addition, individuals who are conscientious and open tend more adaptable to change (Le Pine, Colquitt, & Erez, 2000).
The Core Drivers Model
Given the evidence reviewed in the previous section, the Core Drivers model was developed based on the Five Factor Model of personality. To develop the Core Drivers Model, a team of subject matter experts (SMEs), with advanced degrees in Industrial-Organizational psychology and personality science, reviewed a variety of academic and commercial personality frameworks inspired by the Five Factor Model. These included: Costa and McCrae’s NEO PI-R (Costa & McCrae, 2008), The Hogan Personality Inventory (R. Hogan & Hogan, 2007), Lee & Ashton’s HEXACO Inventory (Ashton & Lee, 2009), and DeYoung’s hierarchical interpretation of the Five Factor Model (Deyoung, 2015). Reviewing each model and evaluating its ability to not only describe the full spectrum of human disposition, but also each dimension’s practical utility and relevance to the modern organization, the SMEs synthesized commonalities and reconciled nuanced differences to produce the Core Drivers model of personality.
The Core Drivers model of personality describes six global dispositions. Conceptually, these six dimensions map on to the Five Factor Model, however there are some deviations. First, inspired by DeYoung (2015) and recognizing the practical need to report on one’s level of drive, assertiveness, and ambition, Extraversion was divided into two scales: Extraversion Gregariousness and Extraversion Assertiveness. As a result, the Core Drivers model distinguishes between the outgoing and sociable dispositions of Extraversion and the ambitious, driven and energetic dispositions. This provides organizations, talent managers and coaches a more nuanced insight into how one works and interacts with others.
Second, to deliver on our mission to democratize self-awareness, various facets of the personality assessment experience were scrutinized to identify how to best improve the interpretability of one’s personality. Inspiration was drawn from the popularity of type-based tools like the MBTI which provide intuitive and accessible vocabulary to describe ourselves and each other. The Lexical hypothesis, which in fact served as the foundation of modern personality measurement and science and was used to develop the FFM, was also referenced (Goldberg, 1992). This approach allowed us to integrate robust science with an accessible and intuitive user experience.
Third, it was imperative that users understand that there is no “good” or “bad” personality trait, rather each dimension confers strengths and limitations. Taking these considerations into account, two descriptive labels were developed foreach personality dimension of the Core Drivers model. One label represents the high side of the dimension and the other describes the low side of the dimension. Each label is referred to as a “Driver”, resulting in a total of 12 Drivers. When reporting on an individual’s results, coaches or trainers can use this taxonomy of Drivers to help raise awareness around one’s strengths and gaps, rather than communicating through technical or reductive terminology. A representation of the Core Drivers Model and its 12 Drivers is shown in Table 2. From here on in, when referring to one of the six Core Drivers dimensions, the “high” label is used.
Table 2: The Core Drivers Model
Finally, the need to gain a deeper and more nuanced insight into one’s personality was recognized. While the broad dimensions of the FFM describe one’s dispositions at a global level, to target real behavioral change and development, alongside gain a greater understanding around how one thinks, acts and works, insights at a dimension’s facet level is critical. To this end, each dimension of the Core Drivers model has five facets or “Sub-Drivers”. Each Sub-Driver was identified and developed based on the review and synthesis of the personality frameworks. Emphasis was made on ensuring that the Sub-Drivers are practically relevant, coachable and represent distinct behavioral constructs. A description each Sub-Driver is displayed in Table 3.
Table 3: The Core Drivers and associated Sub-Drivers
Extreme Personality & Dysfunctional Behavior
Gaps in self-awareness lead to otherwise talented individuals to make mistakes, underperform or completely derail. Researchers observe that unconscious behavioral patterns of individuals have contributed to disrupting teams, companies and even countries (R. Hogan, 2007). Overconfidence is an example of one such pattern that has shown to contribute to significant underrepresentation of women in leadership roles, decline in job performance and even driven catastrophic military decisions that cost millions of lives (Chamorro-Premuzic, 2019).
The importance of self-awareness cannot be overstated. A critical part of obtaining these critical self-insights involves understanding that all competent individuals are capable of behaving in seemingly irrational and self-detrimental ways. Here, we will explore in depth the science underpinning these extreme tendencies.
First, extreme traits are often associated with positive outcomes. Individuals who have psychopathic tendencies are found in higher ranks of corporations more often than would be expected by chance (Babiak, Neumann, & Hare, 2010; Mathieu, Neumann, Hare, & Babiak, 2014).These leaders are considered creative, strategic thinkers with strong communication skills. In a similar vein, studies show that narcissistic CEOs are more inclined to be entrepreneurial and attract greater company valuations than those led by more modest leaders (Wales, Patel, & Lumpkin, 2013).
Second, when demonstrated in excess, positive traits have negative effects. Conscientious leaders are valued in the workplace for their reliable, rule-abiding, organized nature. Yet taking these to an extreme could result in being associated with the tendency to be over-critical and micromanaging. There is growing research on the curvilinear relationship of all personality traits (Le et al., 2011). Aristotle summed it up well when he noted that “every virtue is a mean between two extremes, each of which is a vice.” In many contexts, particularly leadership roles, moderate tendencies are preferred, as these individuals are less likely to be disruptive.
Third, social contexts typically require keeping a rein on one’s impulses but when guards are down, behavior could become unrestrained (Furnham, Trickey, & Hyde, 2012; Tamir, 2005). Respectful work and social situations usually necessitate certain restraints. However, when tired, angry, upset or simply past the point of being concerned, individuals can become uninhibited and disruptive. For example, under stress, driven individuals can appear domineering, pushy and insensitive to those around them. Those who are flexible tend to be perceived as disorganized. And curious individuals can appear as idealistic or eccentric. These situations could lead to regretful decisions, damaged relationships and potentially take a toll on one’s reputation.
Finally, behavior is contextual and environmental factors can promote extreme behavior. Moving up seniority levels grant leaders greater influence over decision and delegation rights. Moreover, the positional power that comes with higher rank often cautions direct reports to censor their true viewpoints and act in more compliant ways than is true to their nature. Several researchers observe that power and control tend to dilute inhibition, making allowances for displays of unpleasant behaviors of power and even abusive control (Gaddis & Foster, 2013). If continually exposed to such extreme temperaments, we can expect individuals to have impaired decision-making skills and become unable to collaborate with their colleagues – ultimately taking a toll on individual, team and organizational performance. The damage brought about by toxic leaders can be far-reaching within the individuals and culture of an organization (Padilla, Hogan, & Kaiser, 2007).
When attempting to understand these tendencies in an individual, two frameworks are conventionally used: The Dark Triad and a non-clinical application of DSM-IV personality disorders. The Dark Triad is widely used within academic research, while the latter is most used within applied contexts and define extreme traits in a categorical manner with qualitatively distinct dimensions (R. Hogan, 2009; Paulhus & Williams, 2002). However, there is a growing body of research that suggests that the extreme tendencies of the dimensions in the Five Factor Model could have negative outcomes (Miller, Lynam, Widiger, & Leukefeld, 2001;Widiger, Gore, Crego, Rojas, & Oltmanns, 2016; Widiger & Trull, 2007). Thus, extreme behavior could be defined aspart of a continuum of one’s personality, rather than separate categories of behavior. There is growing evidence that suggests that psychopathology is not limited to people who exhibit extreme behaviors alone (Carter, Miller, & Widiger, 2018). Clinical psychiatry is changing its approach in defining dysfunctional personality disorders, due to the poor reliability of previously determined diagnostic categories. The FFM now forms the backbone of the latest approach set by the American Psychiatric Association (Widiger & Mullins-Sweatt, 2008). Behavioral dispositions which fall at the extremes of the FFM are now considered likely to be dysfunctional, maladaptive, and a strain on interpersonal relationships, while personalities that lie in the middle are more likely to be adaptive and functional.
In practice this means that our extreme tendencies are not wholly different from our day-to-day dispositions or personality. Instead, when we drop our guard or feel stressed, these otherwise helpful characteristics become exaggerated. The same way overusing a muscle causes strain and injury, operating at the extreme of one’s normal self leads to negative outcomes.
To facilitate self-awareness and data-driven talent decisions, the Core Drivers is designed to measure both one’s strengths and risks. Building upon the latest science in understanding extreme dispositions enables an integrated approach to interpreting an individual’s normal and extreme tendencies (Carter et al., 2018) and provides a holistic understanding of one’s talents and challenges.To provide users insights into these aspects of their personality 12 Core Risks were developed to describe behaviors associated with taking each of the 12 Core Drivers to an extreme. A description each Core Risk is described in Table 4.
Table 4: The Core Drivers and Associated Core Risks
The Science of Personality Change
Personality is widely regarded to be a facet of an individual that does not change. The personality trait continuity hypothesis deems that the traits one is born with are deeply ingrained characteristics that remain unchanging throughout the course of one’s life (Roberts & Caspi, 2003). There is growing evidence however that suggests that personality is more malleable and open to change than previously regarded. A longitudinal study that tracked personality changes tracked over five decades revealed that personality is both relatively stable and susceptible to change. The extent to which it changes is different for each individual (Damian et al., 2019).
Further, in recent years, numerous studies demonstrated that that our core dispositions are not fixed and personality can in fact be changed with deliberate effort. Notably, a meta-analysis of over 200 studies concluded that through targeted interventions, moderate shifts in personality are attainable (Roberts et al., 2017). Within the range of FFM traits studied, emotional stability indicated the greatest shift and openness to experience the least. In addition, a recent study demonstrated that targeted, digital interventions can support changes in FFM traits within individuals within a matter of weeks (Stieger et al., 2021). These interventions were considered most effective when individuals had self-awareness of the discrepancy between their actual and desired self. There is therefore sufficient evidence for the plasticity of personality traits — a finding that has considerable implications for growing and developing talent at work.
These findings offer organizations a radically different approach to employing personality assessments. Rather than limiting their use for selection purposes and predicting employee behavior, they could be used to inform employee development and coaching programs.Individualized development plans, informed by personality diagnostics can empower individuals to expedite their professional growth and transform organizational performance. The Core Drivers Diagnostic and accompanying interventions, disseminated by the Digital Coach, is built upon this latest science.
Assessment Development & Psychometric Overview
The following section describes the methodology used to develop the Core Drivers Diagnostic. This is followed by the diagnostic’s psychometric properties, specifically descriptive statistics, estimates of internal consistency, and factor structure. The presented evidence demonstrates the Core Drivers Diagnostic to meet or exceed accepted industry psychometric standards.
Item Development & Validation
A robust development process was used to create the Core Drivers Diagnostic, integrating a well-established foundation of scientific research concerning individual differences and behavior at work, industry-standard psychometric methods, and innovative machine-learning techniques.
First, four Subject Matter Experts (SMEs)with advanced degrees in I-O psychology and psychometric assessments reviewed the scientific literature on the Five Factor model of personality, alongside more recent taxonomies such as the HEXACO model (Costa & McCrae, 2008; Deyoung, 2015; Goldberg, 1992; Goldberg et al., 2006; R. Hogan & Hogan, 2007; Lee & Ashton, 2004; Roberts, Kuncel, Shiner, Caspi, & Goldberg, 2007). This led to the identification of six behavioral dimensions that are most predictive of professional success: the tendency to be Considerate, Disciplined, Driven, Outgoing, Curious and Stable. Integrating the aforementioned personality taxonomies, the SMEs identified a further five sub-facets per dimension that described narrow elements of each disposition.
Second, utilizing this behavioral taxonomy, the SMEs generated a pool of items hypothesized to measure the affect, cognitions and behaviors characteristic of the six behavioral domains. The diagnostic contains two sets of item types: person-descriptive adjective pairs and behavioral statements. The adjectives pairs were designed so that participants choose between one of two adjectives that best describe themselves. Each adjective represents each end of the behavioral continuum. Examples include: “irritable” v “docile” (Stable),“quiet” v “talkative” (Outgoing), “collaborative” or “competitive” (Driven), “disorganized” v “obsessive” (Disciplined), “straightforward” v “respectful” (Considerate), “pragmatic” v “theoretical” (Curious). Example behavioral statements include: “It is ok to bend the rules to get ahead” and “I am always trying to improve how things work”.
The SMEs decided that the Core Drivers Diagnostic should predominantly comprise of forced-choice adjectives, rather than behavioral statements. This is because they offer an improved and conversational user experience and counter the tendency for some to “fake good” (Meade, Pappalardo, Braddy, & Fleenor, 2020). Behavioral statements were used to capture contextual aspects of one’s personality or internal cognitions that could not be accurately described by person-descriptor adjectives.
The developed adjective pairs and behavioral statements were tested by the SMEs for face and content validity, critiqued, refined and winnowed down to a pool of 300 items comprising around 50 for each for the six domains.
Third, attempts were made to reduce the size of the item pool. This was achieved using a two-step process: First, machine-learning methods were used to identify items that optimized scale reliability and convergent validity. Second, data was collected from a large sample of U.S. working adults to remove skewed and socially desirable items.
To achieve this first step, Genetic Algorithms were used (Sahdra, Ciarrochi, Parker, & Scrucca, 2016). Genetic algorithms are a machine-learning method that uses evolutionary principles to select features that maximize “fitness”. In this case, the optimal combination of personality items that produce the largest correlation with a target personality scale. Within the domain of personality assessment development, this methodology is growing in popularity as it keeps the number of items in a scale low, while ensuring the scale has optimal convergent validity. This is best demonstrated in Yarkoni (2010) who used the algorithm to configure a pool of 200 items to accurately measure 200 personality constructs.
Like most machine-learning algorithms, there are numerous parameters to configure when building genetic algorithms. In this case: item cost was set to .001; the maximum number of items that could be selected per scale was 20; the maximum number of algorithm iterations was 200; and the dataset was cross-validated on a training and test set (for information on these parameters, see Yarkoni, 2010).
The algorithm was run six times, once for each of the Core Driver dimensions. For each of the six models developed, the features were the items generated by Deeper Signals’ SMEs (around 50) and the criterion was an analogous Five-Factor scale developed by the International PersonalityItem Pool (IPIP; Goldberg et al., 2006). To develop the models, data was collected from over 2,000 US working adults. Optimizing for convergent validity and reliability, the use of genetic algorithms reduced the total item pool down to 120 (20 items per dimension), with reliability estimates being greater than .60 and out-of-sample convergent validity estimates ranging between .38 (Considerate) and .70 (Outgoing).
To achieve the second step in the item reduction process, data was collected from over 15,000 U.S. working adults. Given the dichotomous nature of the adjective pairs, it was critical that each adjective was equally desirable or undesirable. Adjectives pairs that had significant skew and desirability were removed. This further reduced the item pool to 90.
Further investigation was conducted to establish the psychometric properties of this item pool. This was achieved by using data from a sample of 1,051 working adults and statistical techniques (i.e.,descriptive statistics, item correlations, measures of internal consistency, exploratory & confirmatory factor analysis). This process revealed a clear factor structure, with six scales each consisting of 15 items and demonstrating good levels of internal consistency.
Next, the diagnostics’ construct and concurrent validity were tested. To achieve this, data from five samples of working adults was collected (Sample 1 N = 374, Sample 2 N = 2,218, Sample 3 N = 928, Sample 4 N = 132, Sample 5 N = 389). All samples completed the Core Drivers Diagnostic, alongside a battery of psychometric inventories. Specifically, measures of the Five Factor Model, the Dark Triad, vocational interests and work engagement. To test the diagnostics’ predictive validity, manager ratings of job performance were collected from three organizations (Technology N = 203, Consumer Retail 2 N = 214, Energy N =202). Collecting data from multiple samples allowed for testing the stability and generalizability of the diagnostic’s psychometric properties and validity.
The final step involved building a global normative database so that an individual’s scores can be compared, benchmarked against are presentative population, and tested for adverse impact. To achieve this, all complete cases of data that had been collected during the development of the Core Drivers Diagnostic were aggregated, alongside data collected from commercial uses of the tool covering regions such as South America, Africa, Asia, and Europe.
Over the course of the Core Drivers Diagnostic’s development and validation, data was collected from over 150,000 working adults. The following sections describes the results of these analyses and the psychometric properties of each scale.
Descriptive Statistics & Scale Reliability
The descriptive statistics for the six scales are presented in Table 5. For each scale, the mean score and its standard deviation are presented alongside the minimum and maximum scores, and an estimate of the scale’s reliability.
The Core Drivers Diagnostic uses two sets of items: force-choice adjective pairs (keyed “0” or “1”) and behavioral statements (responded via a 4-point Likert scale ranging between Strongly Disagree to Strongly Agree, numerically keyed 0 to 1). With 15 items per scale, scores can range between zero and 15. As an example, individuals selecting positive for every item from the Considerate scale will obtain a score of 15, implying that they are friendly, warm and act with integrity, whereas a score of zero would indicate that the individual is candid, critical, and strong minded.
As can be seen in Table 5, the average score for each scale falls within similar ranges and the standard deviations are comparable. Respective minimum and maximum scores indicate that a wide range of scores were reflected. Further, the scales are normally distributed as evidenced by the Skewness and Kurtosis scores, with a slight negative skew in Discipline scores. Finally, the scales display acceptable levels of internal consistency (estimates greater than >.60 are desirable) suggesting that participants respond to each item in a consistent manner (Cronbach, 1951).
Table 5: Descriptive Statistics & Reliabilities
Table 6 displays the descriptive statistics for the 30 Sub-Drivers. Each Sub-Driver is comprised of three items, with scores ranging between 0 and 3. Studying the descriptive statistics, scores are mostly normally distributed. The reliability of scales ranges between .30 to .67. Although reliability estimates less than .60 are typically considered undesirable, it is important to explain why this is not of practical importance in this context.
First, reliability estimates are prone to increase as a function of the number of items within scale (Peters, 2014). Given that each Sub-Driver only has three items, low reliability can be expected. Second, the Sub-Drivers are designed to provide practitioners with a deeper and more nuanced coaching insight, not to be used for decision-making purposes, hence we prioritize content validity and practical utility. Third, sub-scale reliability estimates in popular commercial assessments follow similar trends (i.e. The Hogan Personality Inventory’s “HICS” reliability estimates range between .22 and .76; R. Hogan & Hogan, 2007). Finally, internal consistency can be determined through a variety of statistical procedures. In our analyses, exploratory factor analyses demonstrate a single factor solution for each Sub-Driver.
Table 6: Sub-Driver Descriptive Statistics & Reliabilities
Table 7 displays the correlation between the six scales. These correlations were computed to understand how scores on the six scales are related to each other. Replicating other analyses that have correlated the Five Factor model (Rushton & Irwing, 2008), the Core Drivers’ scales share some correlations with each other. There are some relationships that are worth discussing.
First, Driven is negatively correlated with Considerate scores, while positively correlated with Outgoing scores. Given that the Driven scale samples a lower order behavioral domain, such relationships were hypothesized (Deyoung, 2015). Second, Disciplined is negatively correlated with Curious scores.This suggests that organized and dependable individuals are some what unlikely to be curious and creative, a relationship supported by existing literature (Furnham, Zhang, & Chamorro-Premuzic, 2005). Third, the Outgoing, Driven and Curious scales are positively correlated with each other suggesting that sociable individuals are more ambitious and open minded. Last, the positive correlation between the Considerate and Stable scales suggest that friendly and empathetic individuals are emotionally stable and not prone to feelings of anxiety.
Table 8 displays the correlations between Core Driver and Sub-Driver scores. As expected, high correlations were found between a Core Driver and its constituent Sub-Drivers. This provides further evidence for the high-level of internal consistency of the assessment.
Table 7: Scale Correlations
Table 8: Sub-Driver Correlations
Principal Components Analysis
Table 9 displays the results of a Principal Components Analysis (PCA) with a varimax rotation. As standard practice, a Scree plot was inspected to identify the number of factors to extract. This indicated five components. Upon extracting five components and inspecting the rotated pattern matrix, Sub-Drivers loaded on distinct components as hypothesized and represent the FFM of which the assessment is built upon.
Investigating the pattern matrix, it is important to explain why the Sub-Drivers belonging to the Driven scale cross-loaded across the Outgoing (positively) and Considerate (negatively)components. This was expected as the scale is a lower order behavioral dimension and represents proactive and competitive tendencies — characteristics positively associated with Extraversion and negatively correlated withAgreeableness, respectively (Fong, Zhao, & Smillie, 2021).
Overall, the PCA explained 53% of the variance. Components one and two explained accounted for the most variance (13%& 12%, respectively), followed by component three and four (10%) and component five (9%). These empirical findings illustrate the factor structure of the Core Drivers Diagnostic and confirm its alignment with the theoretical model that underpins it.
Table 9: Principal Components Analysis
Understanding the validity of a psychometric assessment is critical, without it assessment administrators cannot be sure of what the assessment measures and what work behaviors or outcomes it can predict. In this chapter, we outline evidence of the CoreDrivers Diagnostic’s construct and criterion validity.
First, the question “to what extent do scores on the dimensions correlate with well-established psychological constructs?” is answered (construct validity). When assessing the construct validity of an assessment it is helpful to distinguish the lines of inquiry between convergent and discriminant validity. Where convergent validity tests the extent to which a scale correlates with other variables that are hypothesized to measure a similar behavioral domain, discriminant validity tests the extent to which a scale does not correlate with variables that measure different behavioral domains. Establishing convergent and discriminant validity is important in psychometric construction as it places the scales within a nomological network of psychological constructs. This serves as additional evidence that the scales are measuring the intended behaviors and increases the interpretability of scores.
Second, evidence is provided to demonstrate the scale’s criterion validity. We display evidence for the assessment’s concurrent validity (“to what extent do scores on the dimensions correlate with relevant work behaviors and outcomes?”) and predictive validity (“to what extent do scores on dimensions predict future events or outcomes?”). This evidence is critical in understanding the Core DriversDiagnostic’s ability to correlate or predict meaningful work-related outcomes such as performance, work engagement and effectiveness.
Although the following analyses demonstrate the Core Drivers Diagnostic to hold multiple forms of construct and criterion validity, it is important to follow the American Psychological Association’s guidelines when using the assessments for selection decisions and conduct local validity studies. Like all high-quality psychometric assessments, if the Core Drivers Diagnostic is to be used for selection purposes, it is critical to demonstrate that assessment scores predict job-relevant measures of performance. Failing to do so may violate employee laws and lead to unfair and ineffective hiring decisions. Where necessary, Deeper Signals will partner with organizations to conduct such validation studies to ensure fair and unbiased talent practices.
The below section describes the measures used to test the convergent and discriminant validity of the Core DriversDiagnostic, alongside the presentation and interpretation of these analyses. To test the assessments’ convergent and discriminant validity, inventories that were related to the diagnostic’s theoretical model, and widely validated within research and applied contexts, were chosen.
Revised NEO Personality Inventory (NEO PI-R; Costa & McCrae, 2008)
The NEOPI-R is a widely used assessment that measures the Five-Factor Model ofPersonality (Extraversion, Agreeableness,Conscientiousness, Emotional Stability and Openness). The assessment consists of 240 items and each personality dimension has six facets. The inventory requires individuals to rate themselves on a five-point Likert scale. The technical manual reports the assessment to have desirability psychometric properties (i.e., reliability estimates greater than .70, clear a factor structure, construct, and criterion validity) and generalize across different demographic groups and nationalities. The assessment is widely used by both practitioners and academics alike.
Mini-IPIP Big Five Personality Inventory (Donnellan, Oswald, Baird, & Lucas, 2006)
The Mini-IPIP Big Five inventory is a 20-item version of the widely used IPIP Big Five inventory (Goldberg et al.,2006). It measures five dimensions: Extraversion, Agreeableness, Conscientiousness, Emotional Stability and Openness. The taxonomy has found to predict a host of life and work-related outcomes (Barrick & Mount, 1991). Participants responded to each item using a five-point Likert scale (Strongly Disagree to Strongly Agree).
Hogan Personality Inventory (HPI; Hogan & Hogan, 2007)
The HPI is a popular personality assessment used in selection and development contexts. The HPI has been found to predict a range of relevant work outcomes, such as job performance, leadership effectiveness and innovation (for a review, see Akhtar et al., 2015). The HPI measures seven behavioral dimensions: Adjustment, Ambition, Sociability, Interpersonal Sensitivity, Prudence, Inquisitiveness, and Learning Approach. The inventory consists of 206 items, with participants responding to each item using a force-choice response. The technical reports desirable psychometric properties, robust factor structure and high degree of construct and criterion validity. For this validation study, we used the IPIP version of the HPI.
HEXACO Personality Inventory (Lee & Ashton, 2004)
The HEXACO model of personality consists of six different factors of personality: Honesty/Humility, Emotionality, Extraversion, Agreeableness, Conscientiousness, and Openness along with four facets of each factor. For the purposes of this analysis, participants only completed items from the Honesty/Humility scale. Persons with very high scores on the Honesty-Humility scale avoid manipulating others for personal gain, feel little temptation to break rules, are uninterested in lavish wealth and luxuries, and feel no special entitlement to elevated social status. Conversely, persons with very low scores on this scale will flatter others to get what they want, are inclined to break rules for personal profit, are motivated by material gain, and feel a strong sense of self-importance. Participants were asked their agreement (Strongly Disagree to Strongly Agree) with the statements. The scale consists of 10 items, has high levels of internal consistency and is found to be a valid predictor of life and work outcomes (Lee & Ashton, 2004).
Assessio “Measuring Integrity” Inventory (MINT; Sjöberg,Svensson, & Sjöberg, 2012)
Assessio’s MINT assessment measures the extent to which an individual typically acts and behaves with integrity. The assessment contains 60 items, and participants respond to items using a 4-point Likert scale. The technical manual reports the MINT assessment to have desirable psychometric properties and good construct and criterion validity. For example, higher scores on the MINT assessment are correlated with increased ratings of job performance and organizational citizenship behaviors, while negatively correlated with counterproductive work behaviors, such as bullying and misusing organizational resources.
Assessio’s Measuring and Assessing Individual Potential-Extremes (MAP-X; Akhtar, Ort, Winsborough,& Chamorro-Premuzic, 2019)
Assessio’s MAP-X assessment is a personality inventory based on the DSM-5’s model of personality disorders (Widiger, 2015; Widiger et al., 2016; Widiger & Mullins-Sweatt, 2008). Using the Five-Factor Framework of personality, the MAP-X measures the “dark side” of personality. The MAP-X inventory can be used for screening and selection to predict workplace behavior, at the individual contributor, manager, and leader level. The technical manual reports the assessment to have optimal levels of internal reliability, robust factor structure, and hold good concurrent validity with other psychological constructs and work outcomes.
Dark Triad Dirty Dozen (Jonason & Webster, 2010)
The DirtyDozen is a 12-item inventory for The Dark Triad of personality. The Dark Triad represents three broad social malevolent and agentic dimensions of personality: Psychopathy, Narcissism and Machiavellianism. Individuals who score highly on these three dimensions of personality are likely to be callous, uncaring, and selfish (Psychopathy), egotistical and over-confident (Narcissism), and manipulative and exploitative (Machiavellianism). These dimensions have been found to predict job performance, engagement and work-related behaviors (Furnham, Richards, & Paulhus, 2013). Participants responded to each item using a five-point Likert scale (Strongly Disagree to Strongly Agree).
Social Dominance Orientation (Ho et al., 2015)
Social Dominance Orientation (SDO) measures one’s attitude towards supporting inequality between social groups and has been found to play a central role in range of intergroup attitudes and behaviors. The SDO scale consists of 16 items that are rated by a seven-point Likert scale (Strongly Oppose to StronglySupport) and has high levels of internal consistency (α > .70).
The Deeper Signals Core Values Diagnostic (Akhtar, Ort, Winsborough, & Chamorro-Premuzic, 2019b)
The Deeper Signals’ Core Values Diagnostic measures an individual’s motivations, values, and goal-directed behaviors. The diagnostic is built upon Self-Determination Theory (SDT, (Gagné &Deci, 2005), which describes human motivation to be a product of three motivations: the need for independence, the need for competence, and the need for connection. The Core Values diagnostic is quick, accurate, and provides deep psychological insights. The diagnostic was developed using a sample of 12,000 working adults and robust psychometric techniques, is free of bias and adverse impact, and has been validated against a large battery of psychological constructs and work-related behaviors.
Construct Validity Results
The following sections describe the construct validity of the Core Drivers Diagnostic. Attention is paid to highlighting and interpreting statistically significant correlations. The analyses presented below demonstrate that the six scales have good convergent and discriminant validity. Not only do these analyses place the scales within a psychological taxonomy, the strong correlations also provide evidence that the items are measuring the desired behaviors and overlap with adjacent psychological constructs. When compared to other modern adjective personality assessments, the Core Drivers diagnostic demonstrates equal or superior convergent validity with measures of the FFM (Costa & McCrae, 2008; R. Hogan & Hogan, 2007;Meade et al., 2020).
Table 10 displays the correlations between the Core Drivers and measures of the Five Factor Model of personality, specifically the NEO PI-R, the Mini-IPIP Big Five Personality Inventory, and the Hogan Personality Inventory.
The Considerate scale measures the extent to which individuals are compassionate, cooperative and friendly verses argumentative, tough and suspicious. Based upon the Agreeableness dimension from the FFM, high scorers are typically prosocial, empathetic and seek to avoid conflict. On the other hand, low scores are willing to break the rules, make tough decisions and act in self-serving ways. Studying the correlations, it can be seen that Considerate scores were positively correlated with measures of Agreeableness and Interpersonal Sensitivity. A positive relationship was expected given that all three scales claim to sample the same behavioral domain.
The Disciplined scale measures the extent to which an individual is described as organized, reliable and deliberate, versus flexible, reactive, and impulsive. Based upon the Conscientiousness dimension from the FFM, high scorers are typically efficient, self-disciplined and dutiful. Conversely, low scorers are flexible, spontaneous, reactive and impulsive. When examining the relationship between Disciplined scores and theFive Factor Model inventories, the scale is the scale is positively correlated with Conscientious and Prudence scores. Given that these scales sample the same behavioral domain, high level of convergence was expected.
The Driven scale measures the extent to which an individual is ambitious, risk-taking and goal-orientated, versus laid-back, easy-going and humble. High scorers are likely to be viewed as persistent, hard-working and proactive. Conversely, low scorers will be relaxed, satisfied and humble. Driven scores were positively correlated with measures ofExtraversion and Stability, and negatively correlated with measures ofAgreeableness. Based on these results, it can be said that individuals with high Driven scores are energetic, confident, resilient, strong minded, and critical.High scorers are likely to enjoy positions of leadership, doing things their way and competing with others. Low scorers are likely to be reserved, humble, diplomatic, and cooperative.
The Outgoing scale measures the extent to which an individual is sociable, outwardly, and chatty, versus reserved, quiet and introspective. Based upon the Extraversion dimension of the FFM, high scorers are energetic, talkative and seek stimulation from others. Low scorers are comfortable with their own company, independent, and reflective. As expected, Outgoing scores are strongly correlated with measures of Extraversion and Sociability. Given that all three scales are hypothesized to measure the same behavioral domain, these relationships can be taken as evidence of strong convergent validity.
The Curious scale measures the extent to which an individual is creative, intellectual, and inquisitive, versus pragmatic, practical, and straightforward. Based upon the Open to NewExperiences dimension of the FFM, high scorers are described as imaginative, artistic, and inventive. On the other hand, low scorers are pragmatic, practical and traditional. The Curious scale is positively correlated with measures of Openness, Inquisitive and Learning Approach, indicating a high degree of convergent validity.
The Stable scale measures the extent to which an individual is emotionally resilient, calm, and relaxed, versus passionate, anxious, and self-critical. Based on the Emotional Stability dimension of the FFM, high scorers can be described as calm, confident, and secure. Low scorers can be described as emotionally volatile, self-critical, and prone to feelings of stress and anxiety. Stable scores are negatively correlated with Neuroticism and positively correlated with Adjustment scores.Given that these scales sample the same the behavioral domain, these correlations serve as evidence of convergent validity.
Table 10: The Relationship with Five Factor Model Inventories
Table 11 displays the correlations between the Core Drivers and measures of pro and anti-social dispositions. Specifically, measures of integrity, Honesty-Humility, dysfunctional personality traits (MAP-X), the Dark Triad, and social-dominance orientation.
Integrity, the extent to which individuals behave in an ethical and principled way, was positively correlated with Considerate, Disciplined and Stable scores. These findings suggest that compassionate, rule-binding and emotionally controlled individuals will behave in ways that are fair, practice justice and equity, and seen as trustworthy by their peers. Similar trends were found with measures of Honesty-Humility, with one exception, a negative correlation with Driven scores. This highlights how Considerate and Disciplined individuals are humble and modest, while Driven individuals are likely to be overconfident and bend the rules to get ahead.
As discussed in Chapter 3, modern science now conceptualizes dysfunctional personality traits to be analogous to the FFM where by unhelpful and unproductive dispositions can be found at the extremes of the trait continuums (Hopwood et al., 2018; Trull & Widiger, 2013; Widiger et al., 2016). The MAP-X measures such extreme tendencies. Given that both theCore Drivers and MAP-X are both built upon the FFM, it is unsurprisingly to see a high degree of convergence. These strong correlations demonstrate the Core Drivers Diagnostic’s ability to detect extreme tendencies and confirm how Core Drivers become Core Risks (see Table 4).
The Dark Triad was found to negatively correlate with Considerate, Disciplined and Stable scores, and positively correlate with Driven and Outgoing scores. These findings are in line with existing research that has investigated the relationship between the Dark Triad and the FFM (Furnham et al., 2013). Individuals with low Considerate and Discipline, and high Driven and Outgoing scores, may tend to appear callous, manipulative, deceitful, narcissistic, and self-serving.
Finally, Driven scores were positively correlated with SDO, suggesting that high scorers are likely to have an unhealthy need to control others, difficulty relinquishing power and struggle to adapt their approach and attitude to accommodate others.
Table 11: The Relationship with Measures of Pro- and Anti-Social Dispositions
Table 12 displays the correlations between the Core Drivers and the Deeper Signals Core Values Diagnostic. Investigating the relationship between individuals’ personality and values helps practitioners know what goals, preferences, and motivations an individual may have.
Of the six value dimensions Novelty was correlated with each Core Driver. Specifically, Novelty was positively correlated with Driven, Outgoing, Curious and Stable scores, and negatively correlated with Considerate and Disciplined scores. Interpreting these correlations, it can be said that individuals who value Novelty can be described as energetic, disposed to change, curious, flexible, and resistant to feelings of stress and worry.
The Power value was strongly correlated with Driven and Outgoing scores and negatively correlated with Considerate scores. This finding is in line with previous validity evidence that foundDriven and less Considerate individuals to be status-seeking, drawn to influence and wanting to have autonomy and control in life.
The Connection value was strongly correlated with Outgoing, Stable and Considerate scores. Individuals with this Core Drivers profile are likely to be motivated by affiliation, relationships and social connection.
The Virtue value was most strongly correlated with Considerate scores, reflecting the belief that people should treated fairly and equitably, rules should not be bent or broken, and holding oneself accountable to their principles and morals.
The Mastery value was correlated with Driven and Outgoing scores, reflecting how such individuals are motivated by achievement, self-development and the pursuit of ambitious goals and difficult challenges.
The Inquiry value was most correlated with Curious scores, reflecting how such individuals are interested in learning about the world, appreciating different cultures and ways of thinking, and living a life full of variety and differences.
Table 12: The Relationship with the Core Values Diagnostic
Construct Validity Summary
The presented evidence has demonstrated the Core Drivers Diagnostic to have a high level of construct validity. The listed correlations outline how scores on the Core Drivers Diagnostic converge with other popular and robust psychometric FFM tools, are related to various pro-and anti-social dispositions, and are related to various motivations and values. These analyses have successfully placed the Core Drivers scales within a large nomological net of influential and important psychological characteristics and dispositions.
The following section describes the criterion validity of the Core Drivers Diagnostic. We first describe the measures used to test for both concurrent and criterion validity, and then present correlations between the Core Drivers Diagnostic and these measures.
The Utrecht Work Engagement Survey-9 items (UWES-9; Schaufeli & Bakker, 2006)
The UWES-9 is a 9-item scale measuring work engagement. It is a shorter version of the original 17-item UWES that characterizes work engagement by three sub scales: Vigor, Dedication, and Absorption, which can be totaled to produce a single work engagement score —representing the extent to which an individual is cognitively, emotionally, and physically engaged with, and motivated by, their work. Participants respond to each item using a frequency 7-point Likert scale (1 = Never to 7 = Always).Work engagement has been found to hold a positive relationship with a variety of organizational measures of performance (Saks, 2006).
Counter Productive Work Behaviors (Bennett & Robinson, 2000)
Counterproductive behavior (CWBs) describes employee behavior that goes against the interests of an organization and its incumbents. This can include behaviors such as absenteeism, abuse toward sothers, bullying, loafing, incivility, fraud, sexual harassment, and sabotage (Spector et al., 2006). To measure CWBs we used the 18-item CWB checklist that was developed by Bennet and Robinson (2000). The checklist contains 18 specificCWBs and participants rated the frequency of which they have displayed a given behavior (0 = never, 7 = daily). The scale was found to have acceptable levels of internal consistency and has been used extensively in research contexts.
Manager Performance Ratings
To assess the predictive validity of the Core Drivers Diagnostic, we collected performance ratings from mangers across three different organizations: an international technology company, a global consumer goods company, and the fastest growing renewable energy firm. In each sample, performance ratings were collected from managers on every employee, across all areas of the business (i.e., Operations, Marketing, Product, Engineering, etc.). The performance ratings were based on each organization’s competency framework, allowing us to predict both task and contextual dimensions of job performance. Finally, we took a machine-learning approach to evaluate the predictive validity of the assessment. This involved using cross-validated LASSO regressions to empirically identify the subset of Sub-Drivers that best predicted the target competency rating. To learn more about such modern approaches, see Speer, Christiansen, Robie and Jacobs (2022).
Criterion Validity Results
Table 13 contains the correlation between the Core Drivers Diagnostic and measures of work engagement, interpersonal and organizational counterproductive work behaviors, and multiple ratings of task and contextual performance.
Evidence of the Core Drivers Diagnostics’ concurrent validity can be seen in the positive correlations between Work Engagement scores and the Driven, Outgoing and Stable scales. These relationships can be interpreted as committed, goal-orientated, emotionally balanced individuals that are somewhat outgoing tend to hold more positive attitudes towards their work (i.e. feelings of commitment, energy and satisfaction; Schaufeli & Bakker, 2006). Again, such relationships are also in line with existing literature (Akhtar, Boustani, Tsivrikos, & Chamorro-Premuzic, 2015).
Additional concurrent validity evidence is found in the moderate correlations between CWB scores and the Core Drivers scales. The Disciplined scale was negatively correlated with both CWB measures while the Driven and Outgoing scales held positive correlations. Such relationships are in line with the existing literature and previously shared validity evidence that identified a relationship between the aforementionedCore Drivers scales and anti-social dispositions (Mount et al., 2006).
Finally, to test the predictive validity of the Core Drivers Diagnostic, its correlation with manager ratings of performance were investigated. Using a machine-learning approach, the Core Drivers diagnostic was found to be an accurate predictor of performance ratings explaining about 10-15% of the variance in contextual and task performance. These findings not only demonstrate the assessment’s ability to predict future performance, they are in line with previous research that has identified the psychological determinants of career success (Hogan, Chamorro-Premuzic, & Kaiser, 2013).
Criterion Validity Summary
The presented evidence has demonstrated theCore Drivers Diagnostic to have a high level of criterion validity.Specifically, the presented data demonstrate the assessment’s concurrent validity through its correlation with work engagement and CWBs, and its criterion validity by predicting manager ratings of performance.
It is our view that criterion validity is one of the most important attributes of a psychometric assessment. When using the Core Drivers Diagnostic for selection purposes, we strongly encourage users to partner with us to conduct local validation studies. This is to demonstrate the tool’s ability to predict job performance within your organization. There is no “one size fits all” approach to talent selection and such algorithms must accommodate for organizational nuances to ensure fair, effective, and legally compliant talent management practices.
Table 13: Criterion Validity Results
Group Differences & Adverse Impact
This chapter reports on the extent to which different genders, age and ethnic groups have statistically significant different scores on the Core Drivers diagnostic. Understanding such differences may aid in the interpretation of feedback reports and scores. We then present the result for adverse impact simulations to demonstrate that the scales do not discriminate on the bases of age, gender, and ethnicity.
Independent samples t-tests were conducted to investigate whether males and females, under/over 40-years old, and White and Non-White individuals scored significantly different across the six scales. Cohen’s d was also computed to understand to what extent are such differences practically meaningful. To aid interpretation, scores were normalized and transformed into percentiles before conducting tests of group differences.
Table 14 indicates that there are statistically significant differences in average scores between individuals who are under or over 40 years old. However, upon inspection of Cohen’s d, these differences are small and not practically meaningful. Similarly, Table 15 displays similar pattern of results when studying gender differences.
Finally, Table 16 to Table 18 illustrates the mean differences between White and Non-White individuals. Although many of the differences are statistically significant, Cohen’s d suggests that there are no large differences on the scales.
Although these analyses reveal small to moderate group differences, they do not identify or confirm assessment bias. To empirically test this, adverse impact analyses were conducted.
Table 14: Age Differences
Table 15: Gender Differences
Table 16: Ethnic Differences - Asian
Table 17: Ethnic Differences - Black or African American
Table 18: Ethnic Differences - Hispanic or Latino
Adverse Impact Simulations
Adverse Impact (AI) can be defined as “a substantially different rate of selection in hiring, promotion, or other employment decisions which works to the disadvantage of members of a race, sex or ethnic group” (see section 1607.16 of the Uniform Guidelines on Employee Selection Procedures; Equal Employment Opportunity Commission, Civil Service Commission, U.S. Department of Labor, 1978). The “Four-Fifths rule” can be used to determine whether an assessment has AI. Specifically, when the “selection rate for any race, sex or ethnic group which is less than four-fifths (4/5) (or eighty percent) of the rate for the group with the highest rate will generally be regarded by the Federal enforcement agencies as evidence of adverse impact.” (1978, see section 1607.4 D). Furthermore, given the Age Discrimination in Employment Act states that individuals over 40 years old need protection, assessments should not adversely impact younger or older individuals (Age Discrimination in Employment Act of 1967).
While the previous analyses demonstrated statistically significant, although not meaningful, group differences, AI simulations of the 4/5ths rule were conducted to further demonstrate that six scales do not adversely impact protected groups. To test for AI, we compared the selection rate of protected groups (e.g. females, over 40 year olds & non-white individuals) against the selection rate of non-protected groups (e.g. males, under 40 year olds & white individuals). Ratios greater than or equal to .80 indicate that the assessment as no AI.
Although organizations do not need to conduct validity studies for selection tools that do not adversely impact protected groups, it is best practice that organizations do continually test for AI and continue to build evidence of criterion validity. As such, Deeper Signals recommends that organizations who use the Core Drivers Diagnostic, pilot the tool and collect such evidence before using the assessment to inform their employee selection and development practices. Similarly, Deeper Signals is committed to algorithmic responsibility and conducts regular audits of its scoring algorithms to ensure our assessments continues to meet industry standards of reliability and validity and remains free of adverse impact and bias.
When using the assessment for talent decision-making it is recommended to use normed scores to ease interpretation and candidate comparison, and set the cutoff score for each Core Driver at the 30th percentile. Cutoff scores represent a balance between screening out individuals with the lowest scores and not create adverse impact. While we supply these scores, we stress that low scores do not imply negative, unproductive, or harmful behaviors. Personality lies on a continuum whereby behavioral strengths and challenges can be found at either end (T. Widiger & Mullins-Sweatt, 2008). Accordingly, we recommend organizations conduct a job analysis and local validation studies to identify the most suitable personality profile before using the tool to make talent decisions. Adding to this, organizations are also advised to run their own adverse impact analyses when using the assessment.
Using the cutoff scores listed above, we conducted AI simulations for three demographic dimensions: age, gender, and ethnicity. Table 19 contains the results of our AI analyses for gender groups, Table 20 contains the results of AI analyses for age groups, and Table 21 contains the results of AI analyses for different ethnic groups. Given that the AI ratio was equal to or greater than .80 across each scale and demographic group, we conclude that when using the recommended cutoff scores organizations should not expect to see adverse impact or bias.
Table 19: Selection & Adverse Impact Ratios for Gender
Table 20: Selection & Adverse Impact Ratios for Age
Table 20: Selection & Adverse Impact Ratios for Ethnicity
Ackerman, P.L., & Heggestad, E. D. (1997). Intelligence, personality, and interests: Evidence for overlapping traits. Psychological Bulletin, 121(2),219–245.
Age Discrimination in Employment Act of1967. , Pub. L. No. Pub. L. No. 90-202, et seq (1967).
Akhtar, R., Boustani, L., Tsivrikos, D.,& Chamorro-Premuzic, T. (2015). The engageable personality: Personality and trait EI as predictors of work engagement. Personality and Individual Differences, 73, 44–49.
Akhtar, R., Humphreys, C., & Furnham, A. (2015). Exploring the relationships among personality, values, and business intelligence. Consulting Psychology Journal, 67(3), 258–276.
Akhtar, R., Ort, U., Winsborough, D., &Chamorro-Premuzic, T. (2019a). Measuring and Assessing Individual Potential- Extremes Inventory - Technical Manual. Stockholm, Sweden.
Akhtar, R., Ort, U., Winsborough, D., &Chamorro-Premuzic, T. (2019b). The Deeper Signals Core Values Diagnostics Technical Manual. New York, NY.
Ashton, M. C., & Lee, K. (2009). The HEXACO-60: A short measure of the major dimensions of personality. Journal of Personality Assessment, 91(4), 340–345.
Babiak, P., Neumann, C., & Hare, R. D.(2010). Corporate Psychopathy : Talking the Walk. Behavioral Sciences and the Law, 28(April), 174–193.
Barrick, M. R., & Mount, M. K. (1991).The Big Five Personality Dimensions and Job Performance : A Meta Analysis. PersonnelPsychology, 44(1), 1–26.
Barrick, M. R., Stewart, G. L., Neubert, M.J., & Mount, M. K. (1998). Relating member ability and personality to work-team processes and team effectiveness. Journal of Applied Psychology,Vol. 83, pp. 377–391.
Bass, B. M., & Yammarino, F. J. (1991).Congruence of Self and Others’ Leadership Ratings of Naval Officers forUnderstanding Successful Performance. Applied Psychology, 40(4),437–454.
Bell, S. T. (2007). Deep-level composition variables as predictors of team performance: A meta-analysis. Journal ofApplied Psychology, 92(3), 595–615.
Bennett, R. J., & Robinson, S. L.(2000). Development of a measure of workplace deviance. Journal of AppliedPsychology, 85(3), 349–360.
Burch, G. S. J., & Anderson, N. (2008).Personality as a Predictor of Work-Related Behavior and Performance: RecentAdvances and Directions for Future Research. In G. Hodgkinson & K. Ford(Eds.), International Review of Industrial and Organizational Psychology2008 (pp. 261–305).
Carter, N. T., Miller, J. D., & Widiger, T. (2018). Extreme Personalities at Work and in Life. CurrentDirections in Psychological Science, 27(6), 429–436.
Chamorro-Premuzic, T. (2007). Personality and Individual Differences. Oxford: Blackwell Publishers Inc.
Chamorro-Premuzic, T. (2019). Why do so many incompetent men become leaders? (and what to do about it). Boston, MA:Harvard Business Review Press.
Chamorro-Premuzic, T., Bennett, E., &Furnham, A. (2007). The happy personality: Mediational role of trait emotional intelligence. Personality and Individual Differences, 42(8),1633–1639.
Chamorro-Premuzic, T., & Furnham, A.(2010). The Psychology of Personnel Selection. In Management (Vol. 52).
Church, A. H. (2005). Managerial self-awareness in high-performing individuals in organizations. Journal ofApplied Psychology, 82(2), 281–292.
Costa, P. T., & McCrae, R. R. (2008).The revised NEO personality inventory (NEO-PI-R). In The SAGE Handbook ofPersonality Theory and Assessment: Volume 2 - Personality Measurement andTesting.
Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16(3),297–334.
Damian, R. I., Spengler, M., Sutu, A.,& Roberts, B. W. (2019). Sixteen going on sixty-six: A longitudinal study of personality stability and change across 50 years. Journal of Personality and Social Psychology, 117(3), 674–695.
Deyoung, C. G. (2015). Cybernetic Big Five Theory. Journal of Research in Personality, 56, 33–58.
Digman, J. M. (1990). PersonalityStructure: Emergence of the Five-Factor Model. Annual Review of Psychology,41(1), 417–440.
Donnellan, M. B., Oswald, F. L., Baird, B.M., & Lucas, R. E. (2006). The Mini-IPIP scales: Tiny-yet-effective measures of the Big Five factors of personality. Psychological Assessment, 18(2),192–203.
Dunning, D. (2011). The dunning-kruger effect. On being ignorant of one’s own ignorance. In Advances inExperimental Social Psychology (Vol. 44).
Dunning, D., Johnson, K., Ehrlinger, J.,& Kruger, J. (2003). Why People Fail to Recognize Their Own Incompetence. CurrentDirections in Psychological Science, 12(3), 83–87.
Equal Employment Opportunity Commission, Civil Service Commission, U.S. Department of Labor, & U. S. D. of J.(1978). Uniform guidelines on employee selection procedures. Federal Register, 43, 38290–38309.
Eurich, T. (2017). Insight: Why we’re not as self-aware as we think, and how seeing ourselves clearly helps us succeed at work and in life. Crown Business.
Fallon, C. K., Panganiban, A. R., Wohleber,R., Matthews, G., Kustubayeva, A. M., & Roberts, R. (2014). Emotional intelligence, cognitive ability and information search in tactical decision-making. Personality and Individual Differences, 65,24–29.
Fong, M., Zhao, K., & Smillie, L. D.(2021). Personality and competitiveness: Extraversion, agreeableness, and their aspects, predict self-reported competitiveness and competitive bidding in experimental auctions. Personality and Individual Differences, 169(q),109907.
Furnham, A., Richards, S. C., & Paulhus, D. L. (2013). The Dark Triad of Personality: A 10 Year Review. Social and Personality Psychology Compass, 3(7), 199–216.
Furnham, A., Trickey, G., & Hyde, G.(2012). Bright aspects to dark side traits: Dark side traits associated with work success. Personality and Individual Differences, 52(8),908–913.
Furnham, A., Zhang, J., &Chamorro-Premuzic, T. (2005). The Relationship between Psychometric andSelf-Estimated Intelligence, Creativity, Personality and Academic Achievement. Imagination,Cognition and Personality, 25(2), 119–145.
Gaddis, B. H., & Foster, J. L. (2013).Meta-Analysis of Dark Side Personality Characteristics and Critical WorkBehaviors among Leaders across the Globe: Findings and Implications forLeadership Development and Executive Coaching. Applied Psychology, 64(1),25-54.
Gagné, M., & Deci, E. L. (2005).Self-determination theory and work motivation. Journal of OrganizationalBehavior, 26(4), 331–362.
Goldberg, L. R. (1992). The Development ofMarkers for the Big-Five Factor Structure. Psychological Assessment, 4(1),26–42.
Goldberg, L. R., Johnson, J. A., Eber, H.W., Hogan, R., Ashton, M. C., Cloninger, C. R., & Gough, H. G. (2006). The international personality item pool and the future of public-domain personality measures. Journal of Research in Personality, 40(1), 84–96.
Ho, A. K., Sidanius, J., Kteily, N.,Sheehy-Skeffington, J., Pratto, F., Henkel, K. E., … Stewart, A. L. (2015). TheNature of Social Dominance Orientation: Theorizing and Measuring Preferences for Intergroup Inequality Using the New SDO7 Scale. Journal of Personality and Social Psychology, 109(6), 1003–1028.
Hogan, J., & Holland, B. (2003). Using theory to evaluate personality and job-performance relations: A socioanalytic perspective. Journal of Applied Psychology, 88(1), 100–112.
Hogan, R. (2007). Personality and theFate Organizations. Lawrence Erlbaum Associates Publishers.
Hogan, R. (2009). The Hogan DevelopmentSurvey Technical Manual. Tulsa, OK: Hogan Assessment Systems.
Hogan, R., & Hogan, J. (2007). HoganPersonality Inventory Manual (3rd ed.). Hogan Assessment Systems.
Hogan, R, Chamorro-Premuzic, T., & Kaiser, R. B. B. (2013). Employability and Career Success: Bridging the GapBetween Theory and Reality. Industrial and OrganizationalPsychology-Perspectives on Science and Practice, 6(1), 3–16.
Hogan, R., Curphy, G., & Hogan, J. (1994). What We Know About Leadership. Review of General Psychology,9(2), 493–504.
Hopwood, C. J., Kotov, R., Krueger, R. F.,Watson, D., Widiger, T., Althoff, R. R., … Zimmermann, J. (2018). The time has come for dimensional personality disorder diagnosis. Personality and MentalHealth, Vol. 12, pp. 82–86.
Hough, L. M., Oswald, F. L., & Ployhart, R. E. (2001). Determinants, Detection and Amelioration of AdverseImpact in Personnel Selection Procedures: Issues, Evidence and Lessons Learned.International Journal of Selection and Assessment, 9(1&2),152–194.
Hurtz, G. M., & Donovan, J. J. (2000).Personality and job performance: The Big Five revisited. Journal of Applied Psychology, 85(6), 869–879.
Jonason, P. K., & Webster, G. D.(2010). The dirty dozen: A concise measure of the dark triad. PsychologicalAssessment, 22(2), 420–432.
Judge, T. A., Bono, J. E., Ilies, R., & Gerhardt, M. W. (2002). Personality and leadership: A qualitative and quantitative review. Journal of Applied Psychology, 87(4),765–780.
Judge, T. A., Heller, D., & Mount, M.K. (2002). Five-factor model of personality and job satisfaction: A meta-analysis. Journal of Applied Psychology, 87(3), 530–541.
Kaufman, S. B. (2013). Opening up Openness to Experience: A Four-Factor Model and Relations to Creative Achievement in theArts and Sciences. The Journal of Creative Behavior, 47(4),233–255.
Le, H., Oh, I. S., Robbins, S. B., Ilies,R., Holland, E., & Westrick, P. (2011). Too Much of a Good Thing: Curvilinear Relationships Between Personality Traits and Job Performance. Journal of Applied Psychology, 96(1), 113–133.
Le Pine, J. A., Colquitt, J. A., & Erez, A. (2000). Adaptability to changing task contexts: Effects of general cognitive ability, conscientiousness, and openness to experience. PersonnelPsychology, 53(3), 563–593.
Lee, K., & Ashton, M. C. (2004).Psychometric Properties of the HEXACO Personality Inventory. MultivariateBehavioral Research, 39(2), 329–358.
Leutner, F., Ahmetoglu, G., Akhtar, R.,& Chamorro-Premuzic, T. (2014). The relationship between the entrepreneurial personality and the Big Five personality traits. Personality and Individual Differences, 63, 58–63.
Lim, B. C., & Ployhart, R. E. (2004).Transformational leadership: Relations to the five-factor model and team performance in typical and maximum contexts. Journal of Applied Psychology,89(4), 610–621.
Mathieu, C., Neumann, C. S., Hare, R. D.,& Babiak, P. (2014). A dark side of leadership: Corporate psychopathy and its influence on employee well-being and job satisfaction. Personality andIndividual Differences, 59, 83–88.
Meade, A. W., Pappalardo, G., Braddy, P.W., & Fleenor, J. W. (2020). Rapid Response Measurement: Development of aFaking-Resistant Assessment Method for Personality. Organizational ResearchMethods, 23(1), 181–207.
Miller, J. D., Lynam, D. R., Widiger, T.,& Leukefeld, C. (2001). Personality disorders as extreme variants of common personality dimensions: Can the five-factor model adequately represent psychopathy? Journal of Personality, 69(2), 253–276.
Moshavi, D., Brown, F. W., & Dodd, N. G.(2003). Leader self-awareness and its relationship to subordinate attitudes and performance. Leadership & Organization Development Journal, 24(7),407–418.
Mount, M. K., Barrick, M. R., &Stewart, G. L. (1998). Five-Factor Model of personality and Performance in JobsInvolving Interpersonal Interactions. Human Performance, 11(2–3),145–165.
Mount, M. K., Ilies, R., & Johnson, E. (2006). Relationship of personality traits and counterproductive work behaviors: The mediating effects of job satisfaction. Personnel Psychology,59(3), 591–622.
Padilla, A., Hogan, R., & Kaiser, R. B. (2007). The toxic triangle: Destructive leaders, susceptible followers, and conducive environments. Leadership Quarterly, 18(3), 176–194.
Paulhus, D. L., & Williams, K. M.(2002). The Dark Triad of personality: Narcissism, Machiavellianism, and psychopathy. Journal of Research in Personality, 36(6), 556–563.
Peters, G.-J. Y. (2014). The Alpha and theOmega of Scale Reliability and Validity: why and how to Abandon Cronbach’s Alpha. European Health Psychologist, 16(2), 56–69.
Pierce, J. R., & Aguinis, H. (2013).The Too-Much-of-a-Good-Thing Effect in Management. Journal of Management,39(2), 313–338.
Reynolds, A., & Lewis, D. (2017,March). Teams solve problems faster when they’re more cognitively diverse.Retrieved from Harvard Business Review website:https://hbr.org/2017/03/teams-solve-problems-faster-when-theyre-more-cognitively-diverse
Roberts, B. W., & Caspi, A. (2003). TheCumulative Continuity Model of Personality Development: Striking a BalanceBetween Continuity and Change in Personality Traits across the Life Course. In UnderstandingHuman Development (pp. 183–214).
Roberts, B. W., Kuncel, N. R., Shiner, R., Caspi, A., & Goldberg, L. R. (2007). The Power of Personality: TheComparative Validity of Personality Traits, Socioeconomic Status, and CognitiveAbility for Predicting Important Life Outcomes. Perspectives onPsychological Science, 2(4), 313–345.
Roberts, B. W., Luo, J., Briley, D. A.,Chow, P. I., Su, R., & Hill, P. L. (2017). A systematic review of personality trait change through intervention. Psychological Bulletin, 143(2),117–141.
Rushton, J., & Irwing, P. (2008). A General Factor of Personality (GFP) from two meta-analyses of the Big Five: Digman (1997) and Mount, Barrick, Scullen, and Rounds (2005). Personality and Individual Differences, 45(7), 679–683.
Sackett, P. R., & Walmsley, P. T.(2014). Which Personality Attributes Are Most Important in the Workplace? Perspectives on Psychological Science, 9(5), 538–551.
Sahdra, B. K., Ciarrochi, J., Parker, P.,& Scrucca, L. (2016). Using Genetic Algorithms in a Large NationallyRepresentative American Sample to Abbreviate the Multidimensional ExperientialAvoidance Questionnaire. Frontiers in Psychology, 7, 189.
Saks, A. M. (2006). Antecedents and consequences of employee engagement. Journal of Managerial Psychology, 21(7),600–619.
Schaufeli, W. B., & Bakker, A. B.(2006). The Measurement of Work Engagement With a Short Questionnaire: ACross-National Study. Educational and Psychological Measurement, 66(4),701–716.
Shin, S. J., Kim, T., Lee, J., & Bian,L. (2012). Cognitive Team Diversity and Individual Team Member Creativity: ACross-Level Interaction. Academy of Management Journal, 55(1),197–212.
Sjöberg, S., Svensson, C., & Sjöberg,A. (2012). Measuring Integrity - Technical Manual. Sweden: Stockholm: Assessio International AB.
Smillie, L. D., Yeo, G. B., Furnham, A. F.,& Jackson, C. J. (2006). Benefits of all work and no play: The relationship between neuroticism and performance as a function of resource allocation. Journal of Applied Psychology, 91(1), 139–155.
Spector, P. E., Fox, S., Penney, L. M.,Bruursema, K., Goh, A., & Kessler, S. (2006). The dimensionality of counter productivity: Are all counterproductive behaviors created equal? Journal of Vocational Behavior, 68(3), 446–460.
Stieger, M., Flückiger, C., Rüegger, D.,Kowatsch, T., Roberts, B. W., & Allemand, M. (2021). Changing personality traits with the help of a digital personality change intervention. Proceedings of the National Academy of Sciences of the United States of America, 118(8).
Sy, T., Tram, S., & O’Hara, L. A.(2006). Relation of employee and manager emotional intelligence to job satisfaction and performance. Journal of Vocational Behavior, 68(3),461–473.
Tamir, M. (2005). Don’t worry, be happy? Neuroticism, trait-consistent affect regulation, and performance. Journal of Personality and Social Psychology, 89(3), 449–461.
Toegel, G., & Barsoux, J. L. (2012). How to become a better leader. MIT Sloan Management Review, Vol. 53, pp.51–60. Retrieved from www.personalitytest.net/ipip/ipipneo1.htm.
Trull, T. J., & Widiger, T. (2013).Dimensional models of personality: The five-factor model and the DSM-5. Dialogues in Clinical Neuroscience, 15(2), 135–146.
Wales, W. J., Patel, P. C., & Lumpkin,G. T. (2013). In pursuit of greatness: CEO narcissism, entrepreneurial orientation, and firm performance variance. Journal of Management Studies,50(6), 1041–1069.
Widiger, T. (2015). Assessment of DSM–5 Personality Disorder. Journal of Personality Assessment, 3891(June),1–11.
Widiger, T., Gore, W. L., Crego, C., Rojas,S. L., & Oltmanns, J. R. (2016). Five Factor Model and PersonalityDisorder (The Oxford; T. A. Widiger, Ed.).
Widiger, T., & Mullins-Sweatt, S.(2008). Five-Factor Model of Personality Disorder: A Proposal for DSM-V. Annual Review of Clinical Psychology, 5(1), 197–220.
Widiger, T., & Trull, T. J. (2007).Plate tectonics in the classification of personality disorder: Shifting to a dimensional model. American Psychologist, 62(2), 71–83.
Yarkoni, T. (2010). The abbreviation of personality, or how to measure 200 personality scales with 200 items. Journal of Research in Personality, 44(2), 180–198.
Zell, E., Strickhouser, J. E., Sedikides,C., & Alicke, M. D. (2019). The Better-Than-Average Effect in Comparative Self- Evaluation: A Comprehensive Review and Meta-Analysis. PsychologicalBulletin, 146(2), 118–149.
Zhou, J., & George, J. M. (2001). When Job Dissatisfaction Leads to Creativity: Encouraging the Expression of Voice. Academy of Management Journal, 44(4), 682–696.