The complexity of national leaders’ thinking: How does it measure up?

Special thanks to my Australian colleague, Aiden Thornton, for his editorial and research assistance.

This is the first in a series of articles on the complexity of national leaders’ thinking. These articles will report results from research conducted with CLAS, our newly validated electronic developmental scoring system. CLAS will be used to score these leaders’ responses to questions posed by prominent journalists.

In this first article, I’ll be providing some of the context for this project, including information about how my colleagues and I think about complexity and its role in leadership. I’ve embedded lots of links to additional material for readers who have questions about our 100+ year-old research tradition, Lectica’s (the nonprofit that owns me) assessments, and other research we’ve conducted with these assessments.

Context and research questions

Lectica creates diagnostic assessments for learning that support the development of mental skills required for working with complexity. We make these learning tools for both adults and children. Our K-12 initiative—the DiscoTest Initiative—is dedicated to bringing these tools to individual K-12 teachers everywhere, free of charge. Its adult assessments are used by organizations in recruitment and training, and by colleges and universities in admissions and program evaluation.

All Lectical Assessments measure the complexity level (aka, level of vertical development) of people’s thinking in particular knowledge areas. A complexity level score on a Lectical Assessment tells us the highest level of complexity—in a problem, issue, or task—an individual is likely to be able to work with effectively.

On several occasions over the last 20 years, my colleagues and I have been asked to evaluate the complexity of national leaders’ reasoning skills. Our response has been, “We will, but only when we can score electronically—without the risk of human bias.” That time has come. Now that our electronic developmental scoring system, CLAS, has demonstrated a level of reliability and precision that is acceptable for this purpose, we’re ready to take a look.​

Evaluating the complexity of national leaders’ thinking is a challenging task for several reasons. First, it’s virtually impossible to find examples of many of these leaders’ “best work.” Their speeches are generally written for them, and speech writers usually try to keep the complexity level of these speeches low, aiming for a reading level in the 7th to 9th grade range. (Reading level is not the same thing as complexity level, but like most tests of capability, it correlates moderately with complexity level.) Second, even when national leaders respond to unscripted questions from journalists, they work hard to use language that is accessible to a wide audience. And finally, it’s difficult to identify a level playing field—one in which all leaders have the same opportunity to demonstrate the complexity of their thinking.

Given these obstacles, there’s no point in attempting to evaluate the actual thinking capabilities of national leaders. In other words, we won’t be claiming that the scores awarded by CLAS represent the true complexity level of leaders’ thinking. Instead, we will address the following questions:

  1. When asked by prominent journalists to explain their positions on complex issues, what is the average complexity level of national leaders’ responses?
  2. How does the complexity level of national leaders’ responses relate to the complexity of the issues they discuss?

Thinking complexity and leader success

At this point, you may be wondering, “What is thinking complexity and why is it important?” A comprehensive response to this question isn’t possible in a short article like this one, but I can provide a basic description of complexity as we see it at Lectica, and provide some examples that highlight its importance.

All issues faced by leaders are associated with a certain amount of built-in complexity. For example:

  1. The sheer number of factors/stakeholders that must be taken into account.
  2. Short and long-term implications/repercussions. (Will a quick fix cause problems downstream, such as global unrest or catastrophic weather?)
  3. The number and diversity of stakeholders/interest groups. (What is the best way to balance the needs of individuals, families, businesses, communities, states, nations, and the world?)
  4. The length of time it will take to implement a decision. (Will it take months, years, decades? Longer projects are inherently more complex because of changes over time.)
  5. Formal and informal rules/laws that place limits on the deliberative process. (For example, legislative and judicial processes are often designed to limit the decision making powers of presidents or prime ministers. This means that leaders must work across systems to develop decisions, which further increases the complexity of decision making.)

Over the course of childhood and adulthood, the complexity of our thinking develops through up to 13 skill levels (0–12). Each new level builds upon the previous level. The figure above shows four adult complexity “zones” — advanced linear thinking (second zone of level 10), early systems thinking (first zone of level 11), advanced systems thinking (second zone of level 11), early principles thinking (first zone of level 12). In advanced linear thinking, reasoning is often characterized as “black and white.” Individuals performing in this zone cope best with problems that have clear right or wrong answers. It is only once individuals enter early systems thinking, that we begin to work effectively with highly complex problems that do not have clear right or wrong answers.

Leadership at the national level requires exceptional skills for managing complexity, including the ability to deal with the most complex problems faced by humanity (Helbing, 2013). Needless to say, a national leader regularly faces issues at or above early principles thinking.

Complexity level and leadership—the evidence

In the workplace, the hiring managers who decide which individuals will be put in leadership roles are likely to choose leaders whose thinking complexity is a good match for their roles. Even if they have never heard the term complexity level, hiring managers generally understand, at least implicitly, that leaders who can work with the complexity inherent in the issues associated with their roles are likely to make better decisions than leaders whose thinking is less complex.

There is a strong relation between the complexity of leadership roles and the complexity level of leaders’ reasoning. In general, more complex thinkers fill more complex roles. The figure below shows how lower and senior level leaders’ complexity scores are distributed in Lectica’s database. Most senior leaders’ complexity scores are in or above advanced systems thinking, while those of lower level leaders are primarily in early systems thinking.

The strong relation between the complexity of leaders’ thinking and the complexity of their roles can also be seen in the recruitment literature. To be clear, complexity is not the only aspect of leadership decision making that affects leaders’ ability to deal effectively with complex issues. However, a large body of research, spanning over 50 years, suggests that the top predictors of workplace leader recruitment success are those that most strongly relate to thinking skills, including complexity level.

The figure below shows the predictive power of several forms of assessment employed in making hiring and promotion decisions. The cognitive assessments have been shown to have the highest predictive power. In other words, assessments of thinking skills do a better job predicting which candidates will be successful in a given role than other forms of assessment.

Predictive power graph

The match between the complexity of national leaders’ thinking and the complexity level of the problems faced in their roles is important. While we will not be able to assess the actual complexity level of the thinking of national leaders, we will be able to examine the complexity of their responses to questions posed by prominent journalists. In upcoming articles, we’ll be sharing our findings and discussing their implications.

Coming next…

In the second article in this series, we begin our examination of the complexity of national leaders’ thinking by scoring interview responses from four US Presidents—Bill Clinton, George W. Bush, Barack Obama, and Donald Trump.

 


Appendix

Predictive validity of various types of assessments used in recruitment

The following table shows average predictive validities for various forms of assessment used in recruitment contexts. The column “variance explained” is an indicator of how much of a role a particular form of assessment plays in predicting performance—it’s predictive power.

Form of assessment Source Predictive validity Variance explained  Variance explained (with GMA)
Complexity of workplace reasoning (Dawson & Stein, 2004; Stein, Dawson, Van Rossum, Hill, & Rothaizer, 2003) .53 28% n/a
Aptitude (General Mental Ability, GMA) (Hunter, 1980; Schmidt & Hunter, 1998) .51 26% n/a
Work sample tests (Hunter & Hunter, 1984; Schmidt & Hunter, 1998) .54 29% 40%
Integrity (Ones, Viswesvaran, and Schmidt, 1993; Schmidt & Hunter, 1998) .41 17% 42%
Conscientiousness (Barrick & Mount, 1995; Schmidt & Hunter, 1998). .31 10% 36%
Employment interviews (structured) (McDaniel, Whetzel, Schmidt, and Mauer, 1994; Schmidt & Hunter, 1998) .51 26% 39%
Employment interviews (unstructured) (McDaniel, Whetzel, Schmidt, and Mauer, 1994 Schmidt & Hunter, 1998) .38 14% 30%
Job knowledge tests (Hunter and Hunter, 1984; Schmidt & Hunter, 1998) .48 23% 33%
Job tryout procedure (Hunter and Hunter, 1984; Schmidt & Hunter, 1998) .44 19% 33%
Peer ratings (Hunter and Hunter, 1984; Schmidt & Hunter, 1998) .49 24% 33%
Training & experience: behavioral consistency method (McDaniel, Schmidt, and Hunter, 1988a, 1988b; Schmidt & Hunter, 1998; Schmidt, Ones, and Hunter, 1992) .45 20% 33%
Reference checks (Hunter and Hunter, 1984; Schmidt & Hunter, 1998) .26 7% 32%
Job experience (years) Hunter, 1980); McDaniel, Schmidt, and Hunter, 1988b; Schmidt & Hunter, 1998) .18 3% 29%
Biographical data measures Supervisory Profile Record Biodata Scale (Rothstein, Schmidt, Erwin, Owens, and Sparks, 1990; Schmidt & Hunter, 1998) .35 12% 27%
Assessment centers (Gaugler, Rosenthal, Thornton, and Benson, 1987; Schmidt & Hunter, 1998; Becker, Höft, Holzenkamp, & Spinath, 2011) Note: Arthur, Day, McNelly, & Edens (2003) found a predictive validity of .45 for assessment centers that included mental skills assessments. .37 14% 28%
EQ (Zeidner, Matthews, & Roberts, 2004) .24 6% n/a
360 assessments Beehr, Ivanitskaya, Hansen, Erofeev, & Gudanowski, 2001 .24 6% n/a
Training &  experience: point method (McDaniel, Schmidt, and Hunter, 1988a; Schmidt & Hunter, 1998) .11 1% 27%
Years of education (Hunter and Hunter, 1984; Schmidt & Hunter, 1998) .10 1% 27%
Interests (Schmidt & Hunter, 1998) .10 1% 27%

References

Arthur, W., Day, E. A., McNelly, T. A., & Edens, P. S. (2003). A meta‐analysis of the criterion‐related validity of assessment center dimensions. Personnel Psychology, 56(1), 125-153.

Becker, N., Höft, S., Holzenkamp, M., & Spinath, F. M. (2011). The Predictive Validity of Assessment Centers in German-Speaking Regions. Journal of Personnel Psychology, 10(2), 61-69.

Beehr, T. A., Ivanitskaya, L., Hansen, C. P., Erofeev, D., & Gudanowski, D. M. (2001). Evaluation of 360 degree feedback ratings: relationships with each other and with performance and selection predictors. Journal of Organizational Behavior, 22(7), 775-788.

Dawson, T. L., & Stein, Z. (2004). National Leadership Study results. Prepared for the U.S. Intelligence Community.

Dawson, T. L. (2017, October 20). Using technology to advance understanding: The calibration of CLAS, an electronic developmental scoring system. Proceedings from Annual Conference of the Northeastern Educational Research Association, Trumbull, CT.

Dawson, T. L., & Thornton, A. M. A. (2017, October 18). An examination of the relationship between argumentation quality and students’ growth trajectories. Proceedings from Annual Conference of the Northeastern Educational Research Association, Trumbull, CT.

Gaugler, B. B., Rosenthal, D. B., Thornton, G. C., & Bentson, C. (1987). Meta-analysis of assessment center validity. Journal of Applied Psychology, 72(3), 493-511.

Helbing, D. (2013). Globally networked risks and how to respond. Nature, 497, 51-59.

Hunter, J. E., & Hunter, R. F. (1984). The validity and utility of alternative predictors of job performance. Psychological Bulletin, 96, 72-98.

Hunter, J. E., Schmidt, F. L., & Judiesch, M. K. (1990). Individual differences in output variability as a function of job complexity. Journal of Applied Psychology, 75, 28-42.

Johnson, J. (2001). Toward a better understanding of the relationship between personality and individual job performance. In M. R. R. Barrick, Murray R. (Ed.), Personality and work: Reconsidering the role of personality in organizations (pp. 83-120).

McDaniel, M. A., Schmidt, F. L., & Hunter, J., E. (1988a). A Meta-analysis of the validity of training and experience ratings in personnel selection. Personnel Psychology, 41(2), 283-309.

McDaniel, M. A., Schmidt, F. L., & Hunter, J., E. (1988b). Job experience correlates of job performance. Journal of Applied Psychology, 73, 327-330.

McDaniel, M. A., Whetzel, D. L., Schmidt, F. L., & Maurer, S. D. (1994). Validity of employment interviews. Journal of Applied Psychology, 79, 599-616.

Rothstein, H. R., Schmidt, F. L., Erwin, F. W., Owens, W. A., & Sparks, C. P. (1990). Biographical data in employment selection: Can validities be made generalizable? Journal of Applied Psychology, 75, 175-184.

Stein, Z., Dawson, T., Van Rossum, Z., Hill, S., & Rothaizer, S. (2013, July). Virtuous cycles of learning: using formative, embedded, and diagnostic developmental assessments in a large-scale leadership program. Proceedings from ITC, Berkeley, CA.

Tett, R. P., Jackson, D. N., & Rothstein, M. (1991). Personality measures as predictors of job performance: A meta-analytic review. Personnel Psychology, 44, 703-742.

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