Growth curves vs. individual growth

Individual growth trajectories often don’t stick to statistically determined expectations.

The illustration above depicts the growth trajectory of a woman named Eleanore. Between the ages 12 and 68, she completed two different developmental assessments several times. The first assessment was the LRJA, a test of reflective judgment (critical thinking), which she completed on 8 different occasions. The second assessment was the LDMA, a test of decision-making skills, which she completed four times between the ages of 42 and 68. As you can see, Eleanore has continued to develop throughout adulthood, with periods of more and less rapid growth.

The graph on which Eleanore’s scores are plotted shows several potential developmental curves (A–H), representing typical developmental trajectories for individuals performing in different levels at age 10. You can tell right away that Eleanore is not behaving as expected. Over time, her scores have landed on two different curves (D & E), and she shows considerable growth in age ranges for which no growth is expected — on either curve.

Eleanore, who was born in 1942, was a bright child who did well in school. By the time she graduated from high school in 1960, she was in the top 15% of her class. After attending two years of community college, she joined the workforce as a legal secretary. At 23 she married a lawyer, and at 25 she gave birth to the first of two children. During the next 15 years, while raising her her children, her scores hovered closer to curve E than curve D. When her youngest entered high school, Eleanore decided it was time to complete her bachelor of science degree, which she did, part time, over several years. During this period she grew more quickly than in the previous 10 years, and her LRJA scores began to cluster around curve D.

Sadly, shortly after completing her degree (at age 43), Eleanore learned that her mother had been diagnosed with dementia (now known as Alzheimer’s). For the next 6 years, she cared for her ailing mother, who died only a few days before Eleanore’s 50th birthday. While she cared for her mother, Eleanore learned a great deal about Alzheimer’s — from both personal experience and the extensive research she did to help ensure the best possible care for her mother. This may have contributed to the growth that occurred during this period. Following her mother’s death, Eleanore decided to build upon her knowledge of Alzheimer’s, spending the next 6 years earning a Ph.D. focused on its origins. At the time of her last assessment, she was a respected Alzheimer’s researcher.

And now I must confess. Eleanore is not a real person. She’s a compilation based on 70 years of research in which the growth of thousands of individuals has been measured over periods spanning 8 months to 25 years. Eleanore’s story has been designed to illustrate several phenomena my colleagues and I have observed in these data:

First, although statistics allow us to describe typical developmental trajectories, individual development is usually more or less atypical. Eleanore does not stay on the curve she started out on. In fact she actually drops below this curve for a time, then develops beyond it in later adulthood. She also grew during age-ranges in which no growth at all was expected. Both life events and formal education clearly influenced her developmental trajectory.

Second, many people develop throughout adulthood — especially if they are involved in rich learning experiences (like formal schooling), or when they are coping productively with life crises (like reflectively supporting an ailing parent).

Third, developmental spurts happen. The figure above shows a (real) growth spurt that occurred between the ages of 46 and 51. This highly motivated individual engaged in a sustained and varied learning adventure during this period — just because he wanted to build his interpersonal and leadership skills.

Fourth, developmental growth can happen late in life, given the right opportunities and circumstances. The (real) woman whose scores are shown here responded to a personal life crisis by embracing it as an opportunity to learn more about herself as person and as a leader.

My colleagues and I find the statistically determined growth curves shown on the figures in this article enormously useful in our research, but it’s important to keep in mind that they’re just averages. Many people can jump from one curve to another given the right learning skills and opportunities. On the other hand, these curves are associated with some constraints. For example, we’ve never seen anyone jump more than one of these curves, no matter how excellent their learning skills or opportunities have been. Unsurprisingly, nurture cannot entirely overcome nature.

Growth is predicted by a number of factors. Nature is a big one. How we personally approach learning is also pretty big — with approaches that feature virtuous cycles of learning taking the lead. And, of course, our growth is influenced by how optimally the environments we live, learn, and work in support learning.


Find out how we put this knowledge to work in leader development and recruitment contexts, with LAP-1 and LAP-2.

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National Leaders’ thinking: Australian Prime Ministers

How complex are the interview responses of the last four Australian prime ministers? How does the complexity of their responses compare to the complexity of the U.S. presidents’ responses?

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

This is the 4th in a series of articles on the complexity of national leaders’ thinking, as measured with CLAS, a newly validated electronic developmental scoring system. This article will make more sense if you begin with the first article in the series.

Just in case you choose not to read or revisit the first article, here are a few things to keep in mind:

  • I am an educational researcher and the CEO of a nonprofit that specializes in measuring the complexity level of people’s thinking skills and supporting the development of their capacity to work with complexity.
  • The complexity level of leaders’ thinking is one of the strongest predictors of leader advancement and success. See the National Leaders Intro for evidence.
  • Many of the issues faced by national leaders require principles thinking (level 12 on the skill scale/LecticalScale), illustrated in the figure below). See the National Leaders Intro for the rationale.
  • To accurately measure the complexity level of someone’s thinking (on a given topic), we need examples of their best thinking. In this case, that kind of evidence wasn’t available. As an alternative, my colleagues and I have chosen to examine the complexity level of prime ministers’ responses to interviews with prominent journalists.

Benchmarks for complexity scores

  • Most high school graduates perform somewhere in the middle of level 10.
  • 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 average complexity score (reported in our National Leaders Study) for President Trump was 1053.
  • 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.)

The data

In this article, we examine the thinking of the four most recent prime ministers of Australia—Julia Gillard, Kevin Rudd, Tony Abbott, and Malcolm Turnbull. For each prime minister, we selected 3 interviews, based on the following criteria: They

  1. were conducted by prominent journalists representing respected news media;
  2. included questions that requested explanations of the Prime Minister’s perspective; and
  3. were either conducted within the Prime Minister’s first year in office or were the earliest interviews we could locate that met the first two criteria.

As noted in the introductory article of this series, we do not imagine that the responses provided in these interviews necessarily represent competence. It is common knowledge* that prime ministers and other leaders typically attempt to tailor messages to their audiences, so even when responding to interview questions, they may not show off their own best thinking. Media also tailor writing for their audiences, so to get a sense of what a typical complexity level target for top media might be, we used CLAS to score 11 articles from Australian news media on topics similar to those discussed by the four presidents in their interviews. We selected these articles at random—literally selecting the first ones that came to hand—from recent issues of the Canberra Times, The Age, the Sydney Morning Herald, and Adelaide Now. Articles from all of these newspapers landed in the lower range of the early systems thinking zone, with a mean score of 1109 (15 points lower than the mean for the U.S. media sample) and a range of 45 points.

Hypothesis

Based on the mean media score, and understanding that politicians generally attempt, like media, to tailor messages for their audience, we hypothesized that prime ministers would aim for a similar range. Since the mean score for the Australian media sample was lower by 15 points than the mean score for the U. S. media sample, we anticipated that the average score received by Australian prime ministers would be a bit lower than the average score received by U. S. presidents.

The results

The Table below shows the complexity scores received by the four prime ministers. (Contact us if you would like a copy of the interviews.) Complexity level scores are shown in the same order as interview listings.

All of the scores received by Australian prime ministers fell well below the complexity level of many of the problems faced by national leaders. Although we cannot assume that the interview responses we scored are representative of these leaders’ best thinking, we can assert that we can see no evidence in these interviews that these prime ministers have the capacity to grasp the full complexity of many of the issues they faced (or are currently facing) in office. Instead, their scores suggest levels of skill that are more appropriate for mid- to upper-level managers in large organizations.

Prime minister

Interview by date

Complexity level scores

Mean complexity level

Mean zone

Julia Gillard (2010-2013)

Laurie Oakes, Weekend Today, 6/27/2010; Jon Faine, ABC 774, 6/29/2010; Deborah Cameron, ABC Sydney, 7/07/2010

1108, 1113, 1113

1111

Early systems thinking

Kevin Rudd (2013-2013)

Kerry O’Brien, ABC AM, 4/24/2008; Lyndal Curtis, ABC AM, 5/30/2008; Jon Faine, ABC 774 Brisbane, 6/06/2008

1133, 1138, 1129

1133

Early systems thinking

Tony Abbott (2013-2015)

Alison Carabine, ABC Radio National, 12/16/2013; Ray Hadley, 1/29/2014; Chris Uhlman, ABC AM, 9/26/2014

1133, 1129, 1117

1126

Early systems thinking

Malcolm Turnbull (2015-)

Michael Brissendon, ABC AM, 9/21/2015; Several journalists, 12/1/2015; Steve Austin, ABC Radio Brisbane, 1/17/2017

1133, 1138, 1113

1128

Early systems thinking

Comparison of U.S. and Australian results

There was less variation in the complexity scores of Australian prime ministers than in the complexity scores of U. S. presidents. Mean scores for the U. S. presidents ranged from 1054–1163 (109 points), whereas the range for Australian prime ministers was 1111–1133 (22 points). If we exclude President Trump as an extreme outlier, the mean score for U. S. Presidents was 12 points higher than for Australian prime ministers.

You may notice that the scores of two of the prime ministers who received a score of 1133 on their first interview, had dropped by the time of their third interview. This is reminiscent of the pattern we observed for President Obama.

The mean score for all four prime ministers was 14 points higher than the mean for sampled media. Interestingly, if we exclude President Trump as an extreme outlier, the difference between the average score received by U. S. presidents is almost identical at 13 points. Almost all of the difference between the mean scores of prime ministers and presidents (excluding President Trump) could be explained by media scores.

Country

Complexity score range

Complexity score difference

Leader average

Media average

Leader average – media average

USA

1054–1163

109

1116

1137 (without P. Trump

1124

-8

13

Australia

1111–1133

22

1125

1111

14

The sample sizes here are too small to support a statistical analysis, but once we have conducted our analyses of the British and Canadian prime ministers, we will be able to examine these trends statistically—and find out if they look like more than a coincidence.

Discussion

In the first article of this series, I discussed the importance of attempting to “hire” leaders whose complexity level scores are a good match for the complexity level of the issues they face in their roles. I then posed two 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?”

We now have a third question to add:

  • What is the relation between the complexity level of National Leaders’ interview responses and the complexity level of respected media?

So far, we have learned that when national leaders explain their positions on complex issues, they do not — with the possible exception of President Obama — demonstrate that they are capable of grasping the full complexity of these issues. On average, their explanations do not rise to the mean level demonstrated by executive leaders in Lectica’s database.

We have also learned that when national leaders explained their positions on complex issues to the press, their explanations were 13–14 points higher on the Lectical Scale than the average complexity level of sampled media articles. We will be following this possible trend in upcoming articles about the British and Canadian leaders.

Interestingly, the Lectical Scores of two prime ministers whose average scores were above the media average dropped closer to the media average in their third interviews. We observed the same pattern for President Obama. It’s too soon to declare this to be a trend, but we’ll be watching.

As noted in the article about the thinking of U. S. presidents, the world needs leaders who understand and can work with highly complex issues, and particularly in democracies, we also need leaders whose messages are accessible to the general public. Unfortunately, the drive toward accessibility seems to have led to a situation in which candidates are persuaded to simplify their messages, leaving voters with one less way to evaluate the competence of our future leaders. How are we to differentiate between candidates whose capacity to comprehend complex issues is only as complex as that of a mid-level manager and candidates who have a high capacity to comprehend and work with these issues but feel compelled to simplify their messages? And in a world in which people increasingly seem to believe that one opinion is as good as any other, how do we convince voters of the critical importance of complex thinking and the expertise it represents?


*The speeches of presidents are generally written to be accessible to a middle school audience. The metrics used to determine reading level are not measures of complexity level. They are measures of sentence, word length, and sometimes the commonness of words. For more on reading level see: How to interpret reading level scores.


 Other articles in this series

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Fit-to-role, well-being, & productivity

How to recruit the brain’s natural motivational cycle—the power of fit-to-role.

People learn and work better when the challenges they face in their roles are just right—when there is good fit-to-role. Improving fit-to-role requires achieving an optimal balance between an individual’s level of skill and role requirements. When employers get this balance right, they increase engagement, happiness (satisfaction), quality of communication, productivity, and even cultural health.

video version

Here’s how it works.

In the workplace, the challenges we’re expected to face should be just big enough to allow for success most of the time, but not so big that frequent failure is inevitable. My colleagues and I call this balance-point the Goldilocks zone, because it’s where the level of challenge is just right. Identifying the Goldilocks zone is important for three reasons:

First, and most obviously, it’s not good for business if people make too many mistakes.

Second, if the distance between employees’ levels of understanding and the difficulty of the challenges they face is too great, employees are less likely to understand and learn from their mistakes. This kind of gap can lead to a vicious cycle, in which, instead of improving or staying the same, performance gradually deteriorates.

Third, when a work challenge is just right we’re more likely to enjoy ourselves—and feel motivated to work even harder. This is because challenges in the Goldilocks zone, allow us to succeed just often enough to stimulate our brains to release pleasure hormones called opioids. Opioids give us a sense of satisfaction and pleasure. And they have a second effect. They also trigger the release of dopamine—the striving hormone—which motivates us to reach for the next challenge (so we can experience the satisfaction of success once again).

The dopamine-opioid cycle will repeat indefinitely in a virtuous cycle, but only when enough of our learning challenges are in the zone—not too easy and not too hard. As long as the dopamine-opioid cycle keeps cycling, we feel engaged. Engaged people are happy people—they tend to feel satisfied, competent, and motivated. [1]

People are also happier when they feel they can communicate effectively and build understanding with those around them. When organizations get fit-to-role right for every member of a team, they’re also building a team with members who are more likely to understand one another. This is because the complexity level of role requirements for different team members are likely to be very similar. So, getting fit to role right for one team member means building a team in which members are performing within a complexity range that makes it relatively—but not too—easy for members to understand one another. Team members are happiest when they can be confident that—most of the time and with reasonable effort—they will be able to achieve a shared understanding with other members.

A team representing a diversity of perspectives and skills, composed of individuals performing within a complexity range of 10–20 points on the Lectical Scale is likely to function optimally.

Getting fit-to-role right, also ensures that line managers are slightly more complex thinkers than their direct reports. People tend to prefer leaders they can look up to, and most of us intuitively look up to people who think a little more complexly than we do. [2] When it comes to line managers, If we’re as skilled as they are, we tend to wonder why they’re leading us. If we’re more skilled than they are, we are likely to feel frustrated. And if they’re way more skilled than we are, we may not understand them fully. In other words, we’re happiest when our line managers challenge us—but not too much. (Sound familiar?)

Most people work better with line managers who perform 15–25 points higher on the Lectical Scale than they do.

Unsurprisingly, all this engagement and happiness has an impact on productivity. Individuals work more productively when they’re happily engaged. And teams work more productively when their members communicate well with one another.[2]

The moral of the story

The moral of this story is that employee happiness and organizational effectiveness are driven by the same thing—fit-to-role. We don’t have to compromise one to achieve the other. Quite the contrary. We can’t achieve either without achieving fit-to-role.

Summing up

To sum up, when we get fit to role right—in other words, ensure that every employee is in the zone—we support individual engagement & happiness, quality communication in teams, and leadership effectiveness. Together, these outcomes contribute to productivity and cultural health.

Getting fit-to-role right requires top-notch recruitment and people development practices, starting with the ability to measure the complexity of (1) role requirements and (2) people skills.

When my colleagues and I think about the future of recruitment and people development, we envision healthy, effective organizations characterized by engaged, happy, productive, and constantly developing employees & teams. We help organizations achieve this vision by…

  • reducing the cost of recruitment so that best practices can be employed at every level in an organization;
  • improving predictions of fit-to- role;
  • broadening the definition of fit-to-role to encompasses the role, the team, and the position of a role in the organizational hierarchy; and
  • promoting the seamless integration of recruitment with employee development strategy and practice.

[1] Csikszentmihalyi, M., Flow, the psychology of happiness. (2008) Harper-Collins.

[2] Oishi, S., Koo, M., & Akimoto, S. (2015) Culture, interpersonal perceptions, and happiness in social interactions, Pers Soc Psychol Bull, 34, 307–320.

[3] Oswald, A. J., Proto, E., & Sgroi, D. (2015). Happiness and productivity. Journal of labor economics, 33, 789-822.

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How to interpret reading level scores

Fleisch Kincaid and other reading level metrics are sometimes employed to compare the arguments made by politicians in their speeches, interviews, and writings. What are these metrics and what do they actually tell us about these verbal performances?

Fleisch Kincaid examines sentence, word length, and syllable number. Texts are considered “harder” when they have longer sentences and use words with more letters, and “easier” when they have shorter sentences and use words with fewer letters. For decades, Fleisch Kincaid and other reading level metrics have been used in word processors. When you are advised by a grammar checker that the reading level of your article is too high, it’s likely that this warning is based on word and sentence length.

Other reading level indicators, like Lexiles, use the commonness of words as an indicator. Texts are considered to be easier when the words they contain are more common, and more difficult when the words they contain are less common.

Because reading-level metrics are embedded in most grammar checkers, writers are continuously being encouraged to write shorter sentences with fewer, more common words. Writers for news media, advertisers, and politicians, all of whom care deeply about market share, work hard to create texts that meet specific “grade level” requirements. And if we are to judge by analyses of recent political speeches, this has considerably “dumbed down” political messages.

Weaknesses of reading level indicators

Reading level indicators look only at easy-to-measure things like length and frequency. But length and frequency are proxies for what they purport to measure—how easy it is to understand the meaning intended by the author.

Let’s start with word length. Words of the same length or number of syllables can have meanings that are more or less difficult to understand. The word, information has 4 syllables and 12 letters. The word, validity has 4 syllables and 8 letters. Which concept, information or validity, do you think is easier to understand? (Hint, one concept can’t be understood without a pretty rich understanding of the other.)

How about sentence length? These two sentences express the same meaning. “He was on fire.” “He was so angry that he felt as hot as a fire inside.” In this case, the short sentence is more difficult because it requires the reader to understand that it should be read within a context presented in an earlier sentence—”She really knew how to push his buttons.”

Finally, what about commonness? Well, there are many words that are less common but no more difficult to understand than other words. Take “giant” and “enormous.” The word, enormous doesn’t necessarily add meaning, it’s just used less often. It’s not harder, just less popular. And some relatively common words are more difficult to understand than less common words. For example, evolution is a common word with a complex meaning that’s quite difficult to understand, and onerous is an uncommon word that’s relatively easy to understand.

I’m not arguing that reducing sentence and word length and using more common words don’t make prose easier to understand, but metrics that use these proxies don’t actually measure understandability—or at least they don’t do it very well.

How reading level indicators relate to complexity level

When my colleagues and I analyze the complexity level of a text, we’re asking ourselves, “At what level does this person understand these concepts?” We’re looking for meaning, not word length or popularity. Level of complexity directly represents level of understanding.

Reading level indicators do correlate with complexity level. Correlations are generally within the range of .40 to .60, depending on the sample and reading level indicator. These are strong enough correlations to suggest that 16% to 36% of what reading-level indicators measure is the same thing we measure. In other words, they are weak measures of meaning.[1] They are stronger measures of factors that impact readability, but are not related directly to meaning—sentence and word length and/or commonness.

Here’s an example of how all of this plays out in the real world: The New York Times is said to have a grade 7 Fleisch Kincaid reading level, on average. But complexity analyses of their articles yield scores of 1100-1145. In other words, these articles express meanings that we don’t see in assessment responses until college and beyond. This would explain why the New York Times audience tends to be college educated.

We would say that by reducing sentence and word length, New York Times writers avoid making complex ideas harder to understand.

Summing up

Reading level indicators are flawed measures of understanding. They are also dinosaurs. When these tools were developed, we couldn’t do any better. But advances in technology, research methods, and the science of learning have taken us beyond proxies for understanding to direct measures of understanding. The next challenge is figuring out how to ensure that these new tools are used responsibly—for the good of all.

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President Trump on climate change

How complex are the ideas about climate change expressed in President Trump’s tweets? The answer is, they are even less complex than ideas he has expressed about intelligence, international trade, and immigration—landing squarely in level 10. (See the benchmarks, below, to learn more about what it means to perform in level 10.)

The President’s climate change tweets

It snowed over 4 inches this past weekend in New York City. It is still October. So much for Global Warming.
2:43 PM – Nov 1, 2011

 

It’s freezing in New York—where the hell is global warming?
2:37 PM – Apr 23, 2013

 

Record low temperatures and massive amounts of snow. Where the hell is GLOBAL WARMING?
11:23 PM – Feb 14, 2015

 

In the East, it could be the COLDEST New Year’s Eve on record. Perhaps we could use a little bit of that good old Global Warming…!
7:01 PM – Dec 28, 2017

Analysis

In all of these tweets President Trump appears to assume that unusually cold weather is proof that climate change (a.k.a., global warming) is not real. The argument is an example of simple level 10, linear causal logic that can be represented as an “if,then” statement. “If the temperature right now is unusually low, then global warming isn’t happening.” Moreover, in these comments the President relies exclusively on immediate (proximal) evidence, “It’s unusually cold outside.” We see the same use of immediate evidence when climate change believers claim that a warm weather event is proof that climate change is real.

Let’s use some examples of students’ reasoning to get a fix on the complexity level of President Trump’s tweets. Here is a statement from an 11th grade student who took our assessment of environmental stewardship (complexity score = 1025):

“I do think that humans are adding [gases] to the air, causing climate change, because of everything around us. The polar ice caps are melting.”

The argument is an example of simple level 10, linear causal logic that can be represented as an “if,then” statement. “If the polar ice caps are melting, then global warming is real.” There is a difference between this argument and President Trump’s argument, however. The student is describing a trend rather than a single event.

Here is an argument made by an advanced 5th grader (complexity score = 1013):

“I think that fumes, coals, and gasses we use for things such as cars…cause global warming. I think this because all the heat and smoke is making the years warmer and warmer.”

This argument is also an example of simple level 10, linear causal logic that can be represented as an “if,then” statement. “If the years are getting warmer and warmer, then global warming is real.” Again, the difference between this argument and President Trump’s argument is that the student is describing a trend rather than a single event.

I offer one more example, this time of a 12th grade student making a somewhat more complex argument (complexity score = 1035).

“The temperature has increased over the years and studies show that the ice is melting in the north and south pole, so, yes humans are causing climate change.”

This argument is also an example of level 10, linear causal logic that can be represented as an “if,then” statement. “If the temperature has increased and studies show that the ice at the north and south poles are melting, then humans are causing climate change. But in this case, the student has mentioned two trends (warming and melting) and explicitly uses scientific evidence to support her conclusion.

Based on these comparisons, it seems clear that President Trump’s Tweets about climate change represent reasoning at the lower end of level 10.

“Humans have caused a lot of green house gasses…and these have caused global warming. The temperature has increased over the years and studies show that the ice is melting in the north and south pole, so, yes humans are causing climate change.

This argument is also an example of level 10, linear causal logic that can be represented as an “if,then” statement. “If the temperature has increased and studies show that the ice at the north and south poles are melting, then humans are causing climate change. In this case, the student’s argument is a bit more complex than in previous examples. She has mentioned two variables (warming and melting) and explicitly uses scientific evidence to support her conclusion.

Based on these comparisons, it seems clear that President Trump’s Tweets about climate change represent reasoning at the lower end of level 10.

Reasoning in level 11

Individuals performing in level 11 recognize that climate is an enormously complex phenomenon that involves many interacting variables. They understand that any single event or trend may be part of the bigger story, but is not, on its own, evidence for or against climate change.

Summing up

It concerns me greatly that someone who does not demonstrate any understanding of the complexity of climate is in a position to make major decisions related to climate change.


Benchmarks for complexity scores

  • Most high school graduates perform somewhere in the middle of level 10.
  • 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 average complexity score (reported in our National Leaders Study) for President Trump was 1053.
  • 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.)

 

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President Trump on immigration

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.

  1. secure our border with Mexico
  2. end chain migration
  3. 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.)

Border security

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.”

Chain migration

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.

Summing up

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.

 

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Complexity level—A primer

image of a complex neural network—represents complexity level

What is complexity level? In my work, a complexity level is a point or range on a dimension called hierarchical complexity. In this article, I’m not going to explain hierarchical complexity, but I am going to try to illustrate—in plain(er) English—how complexity level relates to decision-making skills, workplace roles, and curricula. If you’re looking for a more scholarly definition, you can find it in our academic publications. The Shape of Development is a good place to begin.

Background

My colleagues and I make written-response developmental assessments that are designed to support optimal learning and development. All of these assessments are scored for their complexity level on a developmental scale called the Lectical Scale. It’s a scale of increasing hierarchical complexity, with 13 complexity levels (0–12) that span birth through adulthood. On this scale, each level represents a way of seeing the world. Each new level builds upon the previous level, so thinking in a new complexity level is more complex and abstract than thinking at the precious level. The following video describes levels 5–12.

We have five  ways of representing Lectical Level scores, depending on the context: (1) as whole levels (9, 10, 11, etc.), (2) as decimals (10.35, 11.13, etc.), (3) as 4 digit numbers (1035, 1113, etc.), (4) as 1/4 of a level phase scores (10a, 10b, 10c, 10d, 11a, etc.), and (5) as 1/2 of a level zone scores (early level 10, advanced level 10; early level 11, etc.).

Interpreting Lectical (complexity level) Scores

Lectical Scores are best thought of in terms of the specific skills, meanings, tasks, roles, or curricula associated with them. To illustrate, I’m including table below that shows…

  • Lectical Score ranges for the typical complexity of coursework and workplace roles (Role demands & Complexity demands), and
  • some examples of decision making skills demonstrated in these Lectical Score ranges.

In the last bullet above, I highlighted the term skill, because we differentiate between skills and knowledge. Lectical Scores don’t represent what people know, they represent the complexity of the skill used to apply what they know in the real world. This is important, because there’s a big difference between committing something to memory and understanding it well enough to put it to work. For example, in the 1140–1190 range, the first skill mentioned in the table below is the “ability to identify multiple relations between nested variables.” The Lectical range in this row does not represent the range in which people are able to make this statement. Instead, it represents the level of complexity associated with actually identifying multiple relations between nested variables.

Image of table providing information about complexity level. Click on image to go to readable version.

If you want to use this table to get an idea of how skills increase in complexity over time, I suggest that you begin by comparing skill descriptions in ranges that are far apart. For example, try comparing the skill description in the 945–995 range with the skill descriptions in the 1250–1300 range. The difference will be obvious. Then, work your way toward closer and closer ranges. It’s not unusual to have difficulty appreciating the difference between adjacent ranges—that generally takes time and training—but you’ll find it easy to see differences that are further apart.

When using this table as a reference, please keep in mind that several factors play a role in the actual complexity demands of both coursework and roles. In organizations, size and sector matter. For example, there can be a difference as large as 1/2 of a level between freshman curricula in different colleges.

I hope you find this table helpful (even though it’s difficult to read). I’ll be using it as a reference in future articles exploring some of what my colleagues and I have learned by measuring and studying complexity level—starting with leader decision-making.


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