An individual’s rate of development is affected by a wide range of factors. Twin studies suggest that about 50% of the variation in Lectical growth trajectories is likely to be predicted by genetic factors. The remaining variation is explained by environmental factors, including the environment in the womb, the home environment, parenting quality, educational quality & fit, economic status, diet, personal learning habits, and aspects of personality.
Each Lectical Level takes longer to traverse than the previous level. This is because development through each successive level involves constructing increasingly elaborated and abstract knowledge networks. Don’t be fooled by the slow growth, though. A little growth can have an important impact on outcomes. For example, small advances in level 11 can make a big difference in an individual’s capacity to work effectively with complexity and change—at home and in the workplace.
The graphs above show possible learning trajectories, first, for the lifespan and second, for ages 10-60. Note that the highest age shown on these graphs is 60. This does not mean that individuals cannot develop after the age of 60.
The yellow circle in each graph represents a Lectical Score and the confidence interval around that score. That’s the range in which the “true score” would most likely fall. When interpreting any test score, you should keep the confidence interval in mind.
Within individuals, growth is not tidy
When we measure the development of individuals over short time spans, it does not look smooth. The kind of pattern shown in the following graph is more common. However, we have found that growth appears a bit smoother for adults than for children. We think this is because children, for a variety of reasons, are less likely to do their best work on every testing occasion.
People don’t grow at the same rate in every knowledge area
An individual’s rate of growth depends on the level of their immersion in particular knowledge areas. A physicist may be on one trajectory when it comes to physics and quite a different trajectory when it comes to interpersonal understanding.
Factors that affect the rate of development
Genetics & socio-economic status.
A test-taker’s current developmental trajectory. For example, as time passes, a person whose history places her on the green curve in the first two graphs is less and less likely to jump to the blue curve.
The amount of everyday reflective activity (especially VCoLing) the individual typically engages in (less reflective activity > less growth)
Participation in deliberate learning activities that include lots of reflective activity (especially VCoLing)
Participating in supported learning (coaching, mentoring) after several years away from formal education (can create a growth spurt).
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. 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.
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.
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
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.
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 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.)
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.
How complex are the ideas about intelligence expressed in President Trump’s tweets?
President Trump recently tweeted about his intelligence. The media has already had quite a bit to say about these tweets. So, if you’re suffering from Trump tweet trauma this may not be the article for you.
But you might want to hang around if you’re interested in looking at these tweets from a different angle. I thought it would be interesting to examine their complexity level, and consider what they suggest about the President’s conception of intelligence.
In the National Leaders Study, we’ve been using CLAS — Lectica, Inc.’s electronic developmental scoring system—to score the complexity level of several national leaders’ responses to questions posed by respected journalists. Unfortunately, I can’t use CLAS to score tweets. They’re too short. Instead, I’m going to use the Lectical Dictionary to examine the complexity of ideas being expressed in them.
If you aren’t familiar with the National Leaders series, you may find this article a bit difficult to follow.
The Lectical Dictionary is a developmentally curated list of about 200,000 words or short phrases (terms) that represent particular meanings. (The dictionary does not include entries for people, places, or physical things.) Each term in the dictionary has been assigned to one of 30 developmental phases, based on its least complex possible meaning. The 30 developmental phases span first speech (in infancy) to the highest adult developmental phase Lectica has observed in human performance. Each phase represents 1/4 a level (a, b, c, or d). Levels range from 5 (first speech) to 12 (the most complex level Lectica measures). Phase scores are named as follows: 09d, 10a, 10b, 10c, 10d, 11a, etc. Levels 10 through 12 are considered to be “adult levels,” but the earliest phase of level 10 is often observed in middle school students, and the average high school student performs in the 10b to10c range.
In the following analysis, I’ll be identifying the highest-phase Lectical Dictionary terms in the President’s statements, showing each item’s phase. Where possible, I’ll also be looking at the form of thinking—black-and-white, if-then logic (10a–10d) versus shades-of-gray, nuanced logic (11a–11d)—these terms are embedded in.
The President’s statements
The first two statements are tweets made on 01–05–2018.
“…throughout my life, my two greatest assets have been mental stability and being, like, really smart.
The two most complex ideas in this statement are the notion of having personal assets (10c), and the notion of mental stability (10b).
“I went from VERY successful businessman, to top T.V. Star…to President of the United States (on my first try). I think that would qualify as not smart, but genius…and a very stable genius at that!”
This statement presents an argument for the President’s belief that he is not only smart, but a stablegenius (10b-10c). The evidence offered consists of a list of accomplishments—being a successful (09c) businessman, being a top star, and being elected (09b) president. (Stable genius is not in the Lectical Dictionary, but it is a reference back to the previous notion of mental stability, which is in the dictionary at 10b.)
The kind of thinking demonstrated in this argument is simple if-then linear logic. “If I did these things, then I must be a stable genius.”
Later, at Camp David, when asked about these Tweeted comments, President Trump explained further…
“I had a situation where I was a very excellent student, came out, made billions and billions of dollars, became one of the top business people, went to television and for 10 years was a tremendous success, which you’ve probably heard.”
This argument provides more detail about the President’s accomplishments—being an excellent (08a) student, making billions and billions of dollars, becoming a top business person, and being a tremendous success (10b) in television. Here the president demonstrates the same if-then linear logic observed in the second tweet, above.
The President has spoken about his intelligence on numerous occasions. Across all of the instances I’ve identified, he makes a strong connection between intelligence and concrete accomplishments — most often wealth, fame, or performance (for example in school or in negotiations). I could not find a single instance in which he attributed any part of these accomplishments to external or mitigating factors — for example, luck, being born into a wealthy family, having access to expert advice, or good employees. (I’d be very interested in seeing any examples readers can send my way!)
President Trump’s statements represent the same kind of logic and meaning-making my colleagues and I observed in the interview responses analysed for the National Leaders’ series. President Trump’s logic in these statements has a simple, if-then structure and the most complex ideas he expresses are in the 10b to10c range. As yet, I have seen no evidence of reasoning above this range.
The average score of a US adult is in the 10c–10d range.
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.
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.
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.