Shortly after the President passed the Montreal Cognitive Assessment, a reader emailed with two questions:
Does this mean that the President has the cognitive capacity required of a national leader?
How does a score on this test relate to the complexity level scores you have been describing in recent posts?
A high score on the Montreal Cognitive Assessment dos not mean that the President has the cognitive capacity required of a national leader. This test result simply means there is a high probability that the President is not suffering from mild cognitive impairment. (The test has been shown to detect existing cognitive impairment 88% of the time .) In order to determine if the President has the mental capacity to understand the complex issues he faces as a National Leader, we need to know how complexly he thinks about those issues.
The answer to the second question is that there is little relation between scores on the Montreal Cognitive Assessment and the complexity level of a person’s thinking. A test like the Montreal Cognitive Assessment does not require the kind of thinking a President needs to understand highly complex issues like climate change or the economy. Teenagers can easily pass this test.
The difference between 1053 and 1137 generally represents a decade or more of sustained learning. (If you’re a new reader and don’t yet know what a complexity level is, check out the National Leaders Series introductory article.)
 JAMA Intern Med. 2015 Sep;175(9):1450-8. doi: 10.1001/jamainternmed.2015.2152. Cognitive Tests to Detect Dementia: A Systematic Review and Meta-analysis. Tsoi KK, Chan JY, Hirai HW, Wong SY, Kwok TC.
How complex are the ideas about immigration expressed in President Trump’s recent comments to congress?
On January 9th, 2018, President Trump spoke to members of Congress about immigration reform. In his comments, the President stressed the need for bipartisan immigration reform, and laid out three goals.
secure our border with Mexico
end chain migration
close the visa lottery program
I have analyzed President Trump’s comments in detail, looking at each goal in turn. But first, his full comments were submitted to CLAS (an electronic developmental assessment system) for an analysis of their complexity level. The CLAS score was 1046. This score is in what we call level 10, and is a few points lower than the average score of 1053 awarded to President Trump’s arguments in our earlier research.
Here are some benchmarks for complexity scores:
The average complexity score of American adults is in the upper end of level 10, somewhere in the range of 1050-1080.
The average complexity score for senior leaders in large corporations or government institutions is in the upper end of level 11, in the range of 1150-1180.
The average complexity score (reported in our National Leaders Study) for the three U. S. presidents that preceded President Trump was 1137.
The difference between 1046 and 1137 represents a decade or more of sustained learning. (If you’re a new reader and don’t yet know what a complexity level is, check out the National Leaders Series introductory article.)
President Trump’s first goal was to increase border security.
Drugs are pouring into our country at a record pace and a lot of people are coming in that we can’t have… we have tremendous numbers of people and drugs pouring into our country. So, in order to secure it, we need a wall. We…have to close enforcement loopholes. Give immigration officers — and these are tremendous people, the border security agents, the ICE agents — we have to give them the equipment they need, we have to close loopholes, and this really does include a very strong amount of different things for border security.”
This is a good example of a level 10, if-then, linear argument. The gist of this argument is, “If we want to keep drugs and people we don’t want from coming across the border, then we need to build a wall and give border agents the equipment and other things they need to protect the border.”
As is also typical of level 10 arguments, this argument offers immediate concrete causes and solutions. The cause of our immigration problems is that bad people are getting into our country. The physical act of keeping people out of the country is a solution to the this problem.
Individuals performing in level 11 would not be satisfied with this line of reasoning. They would want to consider underlying or root causes such as poverty, political upheaval, or trade imbalances—and would be likely to try to formulate solutions that addressed these more systemic causes.
Side note: It’s not clear exactly what President Trump means by loopholes. In the past, he has used this term to mean “a law that lets people do things that I don’t think they should be allowed to do.” The dictionary meaning of the term would be more like, “a law that unintentionally allows people to do things it was meant to keep them from doing.”
President Trump’s second goal was to end chain migration. According to Wikipedia, Chain migration (a.k.a., family reunification) is a social phenomenon in which immigrants from a particular family or town are followed by others from that family or town. In other words, family members and friends often join friends and loved ones who have immigrated to a new country. Like many U. S. Citizens, I’m a product of chain migration. The first of my relatives who arrived in this country in the 17th century, later helped other relatives to immigrate.
President Trump wants to end chain migration, because…
“Chain migration is bringing in many, many people with one, and often it doesn’t work out very well. Those many people are not doing us right.”
I believe that what the President is saying here is that chain migration is when one person immigrates to a new country and lots of other people known (or related to?) that person are allowed to immigrate too. He is concerned that the people who follow the first immigrant aren’t behaving properly.
To support this claim, President Trump provides an example of the harm caused by chain migration.
“…we have a recent case along the West Side Highway, having to do with chain migration, where a man ran over — killed eight people and many people injured badly. Loss of arms, loss of legs. Horrible thing happened, and then you look at the chain and all of the people that came in because of him. Terrible situation.”
The perpetrator—Sayfullo Saipov—of the attack Trump appears to be referring to, was a Diversity Visa immigrant. Among other things, this means he was not sponsored, so he cannot be a chain immigrant. On November 21, 2017, President Trump claimed that Saipov had been listed as the primary contact of 23 people who attempted to immigrate following his arrival in 2010, suggesting that Saipov was the first in a chain of immigrants. According to Buzzfeed, federal authorities have been unable to confirm this claim.
Like the border security example, Trump’s argument about chain migration is a good example of a level 10, if-then, linear argument. Here, the gist of his argument is that, If we don’t stop chain migration, then bad people like Sayfullo Saipov will come into the country and do horrible things to us. (I’m intentionally ignoring President Trump’s mistaken assertion that Saipov was a chain immigrant.)
Individuals performing in level 11 would not regard a single example of violent behavior as adequate evidence that chain immigration is a bad thing. Before deciding that eliminating chain migration was a wise decision, they they would want to know, for example, whether or not chain immigrants are more likely to behave violently (or become terrorists) than natural born citizens.
The visa lottery (Diversity Visa Program)
The visa lottery was created as part of the Immigration Act of 1990, and signed into law by President George H. W. Bush. Application for this program is free, The only way to apply is to enter your data into a form on the State Department’s website. Individuals who win the lottery must undergo background checks and vetting before being admitted into the United States. (If you are interested in learning more, the Wikipedia article on this program is comprehensive and well-documented.)
President Trump wants to cancel the lottery program
“…countries come in and they put names in a hopper. They’re not giving you their best names; common sense means they’re not giving you their best names. They’re giving you people that they don’t want. And then we take them out of the lottery. And when they do it by hand — where they put the hand in a bowl — they’re probably — what’s in their hand are the worst of the worst.”
Here, President Trump seems to misunderstand the nature of the visa lottery program. He claims that countries put forward names and that these are the names of people they do not want in their own countries. That is simply not the way the Diversity Visa Program works.
To support his anti-lottery position, Trump again appears to mention the case of Sayfullo Saipov (“that same person who came in through the lottery program).”
But they put people that they don’t want into a lottery and the United States takes those people. And again, they’re going back to that same person who came in through the lottery program. They went — they visited his neighborhood and the people in the neighborhood said, “oh my God, we suffered with this man — the rudeness, the horrible way he treated us right from the beginning.” So we don’t want the lottery system or the visa lottery system. We want it ended.”
I think that what President Trump is saying here is that Sayfullo Saipov was one of the outcasts put into our lottery program by a country that did not want him, and that his new neighbors in the U. S. had complained about his behavior from the start.
This is not a good example of a level 10 argument. This is not a good example of an argument. President Trump completely misrepresents the Diversity Immigrant Visa Program, leaving him with no basis for a sensible argument.
The results from this analysis of President Trump’s statements about immigration provides additional evidence that he tends to perform in the middle of level 10, and his arguments generally have a simple if, then structure. It also reveals some apparent misunderstanding of the law and other factual information.
It is a matter for concern when a President of the United States does not appear to understand a law he wants to change.
Why you might want to reconsider using 360s and EQ assessments to predict recruitment success
Measurements are often used to make predictions. For example, they can help predict how tall a 4-year-old is likely to be in adulthood, which students are likely to do better in an academic program, or which candidates are most likely to succeed in a particular job.
Some of the attributes we measure are strong predictors, others are weaker. For example, a child’s height at age 4 is a pretty strong predictor of adult height. Parental height is a weaker predictor. The complexity of a person’s workplace decision making, on its own, is a moderate predictor of success in the workplace. But the relation between the complexly of their workplace decision making and the complexity of their role is a strong predictor.
How do we determine the strength or a predictor? In statistics, the strength of predictions is represented by an effect size. Most effect size indicators are expressed as decimals and range from .00 –1.00, with 1.00 representing 100% accuracy of prediction. The effect size indicator you’ll see most often is r-square. If you’ve ever been forced to take a statistics course—;)—you may remember that r represents the strength of a correlation. Before I explain r-square, let’s look at some correlation data.
The four figures below represent 4 different correlations, from weakest (.30) to strongest (.90). Let’s say the vertical axis (40 –140) represents the level of success in college, and the horizontal axis (50 –150) represents scores on one of 4 college entrance exams. The dots represent students. If you were trying to predict success in college, you would be wise to choose the college entrance exam that delivered an r of .90.
Why is an r of .90 preferable? Well, take a look at the next set of figures. I’ve drawn lines through the clouds of dots (students) to show regression lines. These lines represent the prediction we would make about how successful a student will be, given a particular score. It’s clear that in the case of the first figure (r =.30), this prediction is likely to be pretty inaccurate. Many students perform better or worse than predicted by the regression line. But as the correlations increase in size, prediction improves. In the case of the fourth figure (r =.90), the prediction is most accurate.
What does a .90 correlation mean in practical terms? That’s where r-square comes in. If we multiply .90 by .90 (calculate the square), we get an r-square of .81. Statisticians would say that the predictor (test score), explains 81% of the variance in college success. The 19% of the variance that’s not explained (1.00 -.81 =.19) represents the percent of the variance that is due to error (unexplained variance). The square root of 19% is the amount of error (.44).
Correlations of .90 are very rare in the social sciences—but even correlations this strong are associated with a significant amount of error. It’s important to keep error in mind when we use tests to make big decisions—like who gets hired or who gets to go to college. When we use tests to make decisions like these, the business or school is likely to benefit—slightly better prediction can result in much better returns. But there are always rejected individuals who would have performed well, and there are always accepted individuals who will perform badly.
Let’s get realistic. As I mentioned earlier, correlations of .90 are very rare. In recruitment contexts, the most predictive assessments (shown above) correlate with hire success in the range of .50 –.54, predicting from 25% – 29% of the variance in hire success. That leaves a whopping 71% – 75% of the variance unexplained, which is why the best hiring processes not only use the most predictive assessments, but also consider multiple predictive criteria.
On the other end of the spectrum, there are several common forms of assessment that explain less than 9% of the variance in recruitment success. Their correlations with recruitment success are lower than .30. Yet some of these, like 360s, reference checks, and EQ, are wildly popular. In the context of hiring, the size of the variance explained by error in these cases (more than 91%) means there is a very big risk of being unfair to a large percentage of candidates. (I’m pretty certain assessment buyers aren’t intentionally being unfair. They probably just don’t know about effect size.)
If you’ve read my earlier article about replication, you know that the power-posing research could not be replicated. You also might be interested to learn that the correlations reported in the original research were also lower than .30. If power-posing had turned out to be a proven predictor of presentation quality, the question I’d be asking myself is, “How much effort am I willing to put into power-posing when the variance explained is lower than 9%?”
If we were talking about something other than power-posing, like reducing even a small risk that my child would die of a contagious disease, I probably wouldn’t hesitate to make a big effort. But I’m not so sure about power-posing before a presentation. Practicing my presentation or getting feedback might be a better use of my time.
Summing up (for now)
A basic understanding of prediction is worth cultivating. And it’s pretty simple. You don’t even have to do any fancy calculations. Most importantly, it can save you time and tons of wasted effort by giving you a quick way to estimate the likelihood that an activity is worth doing (or product is worth having). Heck, it can even increase fairness. What’s not to like?
My organization, Lectica, Inc., is a 501(c)3 nonprofit corporation. Part of our mission is to share what we learn with the world. One of the things we’ve learned is that many assessment buyers don’t seem to know enough about statistics to make the best choices. The Statistics for all series is designed to provide assessment buyers with the knowledge they need most to become better assessment shoppers.
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 learningthat 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:
When asked by prominent journalists to explain their positions on complex issues, what is the average complexity level of national leaders’ responses?
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:
The sheer number of factors/stakeholders that must be taken into account.
Short and long-term implications/repercussions. (Will a quick fix cause problems downstream, such as global unrest or catastrophic weather?)
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?)
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.)
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.
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.
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
Variance explained (with GMA)
Complexity of workplace reasoning
(Dawson & Stein, 2004; Stein, Dawson, Van Rossum, Hill, & Rothaizer, 2003)
Aptitude (General Mental Ability, GMA)
(Hunter, 1980; Schmidt & Hunter, 1998)
Work sample tests
(Hunter & Hunter, 1984; Schmidt & Hunter, 1998)
(Ones, Viswesvaran, and Schmidt, 1993; Schmidt & Hunter, 1998)
(Barrick & Mount, 1995; Schmidt & Hunter, 1998).
Employment interviews (structured)
(McDaniel, Whetzel, Schmidt, and Mauer, 1994; Schmidt & Hunter, 1998)
Employment interviews (unstructured)
(McDaniel, Whetzel, Schmidt, and Mauer, 1994 Schmidt & Hunter, 1998)
Job knowledge tests
(Hunter and Hunter, 1984; Schmidt & Hunter, 1998)
Job tryout procedure
(Hunter and Hunter, 1984; Schmidt & Hunter, 1998)
(Hunter and Hunter, 1984; Schmidt & Hunter, 1998)
Training & experience: behavioral consistency method
(McDaniel, Schmidt, and Hunter, 1988a, 1988b; Schmidt & Hunter, 1998; Schmidt, Ones, and Hunter, 1992)
(McDaniel, Schmidt, and Hunter, 1988a; Schmidt & Hunter, 1998)
Years of education
(Hunter and Hunter, 1984; Schmidt & Hunter, 1998)
(Schmidt & Hunter, 1998)
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.
In a recent blog post—actually in several recent blog posts—I've been emphasizing the importance of building tomorrow's skills. These are the kinds of skills we all need to navigate our increasingly complex and changing world. While I may not agree that all of the top 10 skills listed in the World Economic Forum report (shown above) belong in a list of skills (Creativity is much more than a skill, and service orientation is more of a disposition than a skill.) the flavor of this list is generally in sync with the kinds of skills, dispositions, and behaviors required in a complex and rapidly changing world.
The "skills" in this list cannot be…
developed in learning environments focused primarily on correctness or in workplace environments that don't allow for mistakes; or
These "skills" are best developed through cycles of goal setting, information gathering, application, and reflection—what we call virtuous cycles of learning—or VCoLs. And they're best assessed with tests that focus on applications of skill in real-world contexts, like Lectical Assessments, which are based on a rich research tradition focused on the development of understanding and skill.
Metacognition is thinking about thinking. Metacognitive skills are an interrelated set of competencies for learning and thinking, and include many of the skills required for active learning, critical thinking, reflective judgment, problem solving, and decision-making. People whose metacognitive skills are well developed are better problem-solvers, decision makers and critical thinkers, are more able and more motivated to learn, and are more likely to be able to regulate their emotions (even in difficult situations), handle complexity, and cope with conflict. Although metacognitive skills, once they are well-learned, can become habits of mind that are applied unconsciously in a wide variety of contexts, it is important for even the most advanced learners to “flex their cognitive muscles” by consciously applying appropriate metacognitive skills to new knowledge and in new situations.
Lectica's learning model, VCoL+7 (the virtuous cycle of learning and +7 skills) leverages metacognitive skills in a number of ways. For example, the fourth step in VCoL is reflection & analysis, the +7 skills include reflective disposition, self-monitoring and awareness, and awareness of cognitive and behavioral biases.
Lectical Scale (our developmental scale). The collaboration continuum has emerged from this research.
Many people seem to think of decision making as either top-down or collaborative, and tend to prefer one over the other. But several thousand decision-making leaders have taught us that this is a false dichotomy. We’ve learned two things. First, there is no clear-cut division between autocratic and collaborative decision making—it’s a continuum. And second, both more autocratic and more collaborative decision making processes have legitimate applications.
As it applies to decision making, the collaboration continuum is a scale that runs from fully autocratic to consensus-based. We find it helpful to divide the continuum into 7 relatively distinct levels, as shown below:
Basis for decision
personal knowledge or rules, no consideration of other perspectives
everyday operational decisions where there are clear rules and no apparent conflicts
quick and efficient
personal knowledge, with some consideration of others' perspectives (no perspective seeking)
operational decisions in which conflicts are already well-understood and trust is high
quick and efficient, but spends trust, so should be used with care
personal knowledge, with perspective-seeking to help people feel heard
operational decisions in which the perspectives of well-known stakeholders are in conflict and trust needs reinforcement
time consuming, but can build trust if not abused
personal knowledge, with perspective seeking to inform a decision
operational or policy decisions in which the perspectives of stakeholders are required to formulate a decision
time consuming, but improves decisions and builds engagement
leverages stakeholder perspectives to develop a decision that gives everyone something they want
making "deals" to which all stakeholders must agree
time consuming, but necessary in deal-making situations
leverages stakeholder perspectives to develop a decision that everyone can consent to (even though there may be reservations)
policy decisions in which the perspectives of stakeholders are required to formulate a decision
can be efficient, but requires excellent facilitation skills and training for all parties
leverages stakeholder perspectives to develop a decision that everyone can agree with.
decisions in which complete agreement is required to formulate a decision
requires strong relationships, useful primarily when decision-makers are equal partners
As the table above shows, all 7 forms of decision making on the collaboration continuum have legitimate applications. And all can be learned in any adult developmental level. However, the most effective application of each successive form of decision making requires more developed skills. Inclusive, consent, and consensus decision making are particularly demanding, and generally require formal training for all participating parties.
The most developmentally advanced and accomplished leaders who have taken our assessments deftly employ all 7 forms of decision making, basing the form chosen for a particular situation on factors like timeline, decision purpose, and stakeholder characteristics.
(The feedback in our LDMA [leadership decision making] assessment report provides learning suggestions for building collaboration continuum skills. And our Certified Consultants can offer specific practices, tailored for your learning needs, that support the development of these skills.)
Our learning model, the Virtuous Cycle of Learning and its +7 skills (VCoL+7) is more than a way of learning—it's a set of tools that help students build a relationship with knowledge that's uniquely compatible with democratic values.
Equal opportunity: In the company of good teachers and the right metrics, VCoL makes it possible to create a truly level playing field for learning—one in which all children have a real opportunity to achieve their full learning potential.
Freedom: VCoL shifts the emphasis from learning a particular set of facts, vocabulary, rules, procedures, and definitions, to building transferable skills for thinking, communicating, and learning, thus allowing students greater freedom to learn essential skills through study and practice in their own areas of interest.
Pursuit of happiness: VCoL leverages our brain's natural motivational cycle, allowing people retain their inborn love of learning. Thus, they're equipped not only with skills and knowledge, but with a disposition to adapt and thrive in a complex and rapidly changing world.
Citizenship: VCoLs build skills for (1) coping with complexity, (2) gathering, evaluating, & applying information, (3) perspective seeking & coordination, (4) reflective analysis, and (5) communication & argumentation, all of which are essential for the high quality decision making required of citizens in a democracy.
Open mindset: VCoLs treat all learning as partial or provisional, which fosters a sense of humility about one's own knowledge. A touch of humility can make citizens more open to considering the perspectives of others—a useful attribute in democratic societies.
All of the effects listed here refer primarily to VCoL itself—a cycle of goal setting, information gathering, application, and reflection. The +7 skills—reflectivity, awareness, seeking and evaluating information, making connections, applying knowledge, seeking and working with feedback, and recognizing and overcoming built in biases—amplify these effects.
VCoL is not only a learning model for our times, it could well be the learning model that helps save democracy.
This morning, while doing some research on leader development, I googled “vertical leadership” and “coaching.” The search returned 466,000 results. Wow. Looks like vertical development is hot in the coaching world!
Two hours later, after scanning dozens of web sites, I was left with the following impression:
Vertical development occurs through profound, disruptive, transformative insights that alter how people see themselves, improve their relationships, increase happiness, and help them cope better with complex challenges. The task of the coach is to set people up for these experiences. Evidence of success is offered through personal stories of transformation.
But decades of developmental research contradicts this picture. This body of evidence shows that the kind of transformative experiences promised on these web sites is uncommon. And when it does occur it rarely produces a fairytale ending. In fact, profound disruptive insights can easily have negative consequences, and most experiences that people refer to as transformational are really just momentary insights. They may feel profound in the moment, but don’t actually usher in any measurable change at all, much less transformative change.
"The good news is, you don’t have to work on transforming yourself to become a better leader."
The fact is, insight is fairly easy, but growth is slow, and change is hard. Big change is really, really hard. And some things, like many dispositions and personality traits, are virtually impossible to change. This isn’t an opinion based on personal experience, it’s a conclusion based on evidence from hundreds of longitudinal developmental studies conducted during the last 70 years. (Check out our articles page for some of this evidence.)
The good news is, you don’t have to work on transforming yourself to become a better leader. All you need to do is engage in daily practices that incrementally, through a learning cycle called VCoL, help you build the skills and habits of a good leader. Over the long term, this will change you, because it will alter the quality of your interactions with others, and that will change your mind—profoundly.