About Theo

Founder and Executive Director of Lectica, Inc. and founder of the DiscoTest initiative.

Dr. Dawson is an award-winning scholar, researcher, educator, and test developer. She has been studying how people learn and how people think about learning for over two decades. Her dissertation, which explored the way people of different ages conceptualize education, learning, testing, and teaching introduced a new set of methods for documenting learning sequences. This work, along with her studies in psychometrics, have provided the basis for a new model of assessment—one that focuses on helping teachers identify the learning needs of individual students.

Through the DiscoTest initiative, Dawson and her colleagues have shown that it is possible to design standardized educational assessments that not only help teachers identify the learning needs of individual students, but turn the testing experience into a rich learning experience in which students practice their thinking, communication, and evaluation skills.

Scholarly articles by Dawson can be found on the articles page of the <a href="https://dts.lectica.org/_about/articles.php"Lectica site.

Statistics for all: Estimating confidence

In the first post in this series, I promised to share a quick and dirty trick for determining how much confidence you can have in a test score. I will. But first, I want to show you a bit more about what estimating confidence means when it comes to educational and psychological tests.

Let’s start with a look at how test scores are usually reported. The figure below shows three scores, one at level 8, one at level 6, and one at level 4. Looking at this figure, most of us would be inclined to assume that these scores are what they seem to be—precise indicators of the level of a trait or skill.

How test scores are usually presented

But this is not the case. Test scores are fuzzy. They’re best understood as ranges rather than as points on a ruler. In other words, test scores are always surrounded by confidence intervals. A person’s true score is likely to fall somewhere in the range described by the confidence interval around a test score.

In order to figure out how fuzzy a test score actually is, you need one thing—an indicator of statistical reliability. Most of the time, this is something called Cronbach’s Alpha. All good test developers publish information about the statistical reliability of their measures, ideally in refereed academic journals with easy to find links on their web sites! If a test developer won’t provide you with information about Alpha (or its equivalent) for each score reported on a test, it’s best to move on.

The higher the reliability (usually Alpha) the smaller the confidence interval. And the smaller the confidence interval, the more confidence you can have in a test score.

The table below will help to clarify why it is important to know Alpha (or its equivalent). It shows the relationship between Alpha (which can range from 0 to 1.0) and the number of distinct levels (strata) a test can be said to have. For example, an assessment with a reliability of .80, has 3 strata, whereas an assessment with a reliability of .94 has 5.

Reliability Strata
.70 2
.80 3
.90 4
.94 5
.95 6
.96 7
.97 8
.98 9

Strata have direct implications for the confidence we can have in a person’s score on a given assessment, because they tell us about the range within which a person’s true score would fall—its confidence interval—given the score awarded.

Imagine that you have just taken a test of emotional intelligence with a score range of 1 to 10 and a reliability of .95. The number of strata into which an assessment with a reliability of .95 can be divided is about 6, which means that each strata equals about 1.75 points on the 10 point scale (10 divided by 6). If your score on this test was 8, your true score would likely be somewhere between 7.13 and 8.88—your score’s confidence interval.

The figure below shows the true score ranges for three test takers, CB, RM, and PR. The fact that these ranges don’t overlap gives us confidence that the emotional intelligence of these test-takers is actually different**.

If these scores were closer together, their confidence intervals would overlap. And if that was the case—for example if you were comparing two individuals with scores of 8 and 8.5—it would not be correct to say the scores were different form one another. In fact, it would be incorrect for a hiring manager to consider the difference between a score of 8 and a score of 8.5 in making a choice between two job candidates.

By the way, tests with Alphas in the range of .94 or higher are considered suitable for high-stakes use (assuming that they meet other essential validity requirements). What you see in the figure below is about as good as it gets in educational and psychological assessment.

estimating confidence when alpha is .95

Most assessments used in organizations do not have Alphas that are anywhere near .95. Some of the better assessments have Alphas as high as .85. Let’s take a look at what an Alpha at this level does to confidence intervals.

If the test you have taken has a score range of 1–10 and an Alpha (reliability) of .85, the number of strata into which this assessment can be divided is about 3.4, which means that each strata equals about 2.9 (10 divided by 3.4) points on the 10 point scale. In this case, if you receive a score of 8, your true score is likely to fall within the range of 6.6 to 9.5*.

In the figure below, note that CB’s true score range now overlaps RM’s true score range and RM’s true score range overlaps PR’s true score range. This means we cannot say—with confidence—that CB’s score is different from RM’s score, or that RM’s score is different from PR’s score.

Assessments with Alphas in the .85 range are suitable for classroom use or low-stakes contexts. Yet, every day, schools and businesses use tests with reliabilities in the .85 range to make high stakes decisions—such as who will be selected for advancement or promotion. And this is often done in a way that would exclude RM (yellow circle) even though his confidence interval overlaps CB’s (teal circle) confidence interval.

estimating confidence when alpha is .85

Many tests used in organizations have Alphas in the .75 range. If the test you have taken has a score range of 1–10 and an Alpha of .75, the number of strata into which this assessment can be divided is about 2.2, which means that each strata equals about 4.5 points on the 10 point scale. In this case, if you receive a score of 8, your true score is likely to fall within the range of 6–10*.

As shown in the figure below, scores would now have to differ by at least 4.5 points in order for us to distinguish between two people. CB’s and PR’s scores are different, but RM’s score is uninterpretable.

Tests or sub-scales with alphas in the .75 range are considered suitable for research purposes. Yet, sad to say, schools and businesses now use tests with scales or sub-scales that have Alphas in or below the .75 range, treating these scores as if they provide useful information, when in most cases the scores—like RM’s—are uninterpretable.

estimating confidence when alpha is .75

If your current test providers are not reporting true score ranges (confidence intervals), ask for them. If they only provide Alphas (reliability statistics) you can use the table and figures in this article to calculate true score ranges for yourself. If you don’t want to do the math, no problem. You can use the figures above to get a feel for how precise a score is.

Statistical reliability is only one of the ways in which assessments should be evaluated. Test developers should also ask how well an assessment measures what it is intended to measure. And those who use an assessment should ask whether or not what it measures is relevant or important. I’ll be sharing some tricks for looking at these forms of validity in future articles.

Related Articles

Statistics for all: What the heck is confidence?


*This range will be wider at the top and bottom of the scoring range and a bit narrower in the middle of the range.

**It doesn’t tell us if emotional intelligence is important. That is determined in other ways.


References

Guilford J. P. (1965). Fundamental statistics in psychology and education. 4th Edn. New York: McGraw-Hill.

Kubiszyn T., Borich G. (1993). Educational testing and measurement. New York: Harper Collins.

Wright B. D. (1996). Reliability and separation. Rasch Measurement Transactions, 9, 472.

 

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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.

(Medium doesn’t accommodate html tables and automatically reduces image quality. If you find this table as unreadable as I do, you can view it here.)

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.


Related articles

 

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If a US President thought like a teenager…

In a series of Medium articles, my colleagues and I have been examining the complexity level of national leaders’ thinking — with a newly validated electronic developmental assessment system called CLAS. Since I posted the second article in this series, which focused on the thinking of recent U. S. presidents, I’ve been asked several times to say more about what complexity scores mean and why they matter.

So, here goes. To keep it simple (or a simple as a discussion of complexity can be), I’m going to limit myself to an exploration of the complexity scores of Presidents Trump (mean score = 1054) and Obama, (mean score = 1163).

If you are unfamiliar with complexity levels, I recommend that you start by watching the short video, below. It provides a general explanation of developmental levels that will help get you oriented.

Adult complexity zones

If you’ve read the previous articles in this series (recommended), you’ve already seen the figure below. It shows the four complexity “zones” that are most common in adulthood and describes them in terms of the kinds of perspectives people performing in each zone are likely to be able to work with effectively. The first zone, advanced linear thinking, is the most common among adults in the United States. It’s also fairly common in the later years of high school—though early linear thinking (not shown here) is more common in that age range.

As development progresses, knowledge and thought move through levels of increasing complexity. Each level builds upon the previous level, which means we have to pass through all of the levels in sequence. Skipping a level is impossible, because a level can’t be built unless there is an earlier level to build upon. As we move through these levels, the evidence of earlier levels does not disappear. It leaves traces in language that can be represented as a kind of history of a person’s development. We call this a developmental profile. To produce a score, CLAS’s algorithm compares an individual’s developmental profile to the typical profiles for each possible score on the complexity scale. Right now, the CLAS algorithm is based on 20 years of rigorous research involving over 45,000 scored interviews, observations, and assessments.

President Trump

In the second article of this series, I reported that President Trump’s average score (1054) was in the advanced linear thinking zone. Thinking in this zone is abstract and linear. People performing in this zone link ideas in chains of (more or less) logical relations. Reasoning has a “black and white” quality, in the sense that there is a strong preference for simple correct or incorrect answers. Although individuals performing in this level can often see that a situation or problem involves multiple factors, the only way they can organize their thinking about these factors is in chains of logical statements, usually with an “if, then” structure. President Trump, in his interview with The Wall Street Journal on the 25th of July, 2017, provided a typical “if, then” argument when asked about trade with the UK. He argued:

…we’re going to have a very good relationship with the U.K. And we do have to talk to the European Union, because it’s not a reciprocal deal, you know. The word reciprocal, to me, is very important. For instance, we have countries that charge us 100 percent tax to sell a Harley-Davidson into that country. And yet, they sell their motorcycles to us, or their bikes, or anything comparable, and we charge them nothing. There has to be a reciprocal deal. I’m all about that.

The complexity level of an argument can be seen in its structure and the meanings embedded in that structure. This argument has an “if, then” structure, and points to the meaning of reciprocity, which for the President seems to mean an equal exchange—”If you tax at a certain level, then we should tax at that level too.”  This kind of “tit for tat” thinking is common in level 10 and below. It’s also a form of thinking that disappears above level 10. For example, in level 11, an individual would be more likely to argue, “It’s more complex than that. There are other considerations that need to be taken into account, like the impact a decision like this is is likely to have on international relations or our citizens’ buying power.” President Trump, in his response, does not even mention additional considerations. This is one of the patterns in his responses that contributed to the score awarded by CLAS.

In the results reported here, a Democrat scored higher than a Republican. We have no reason to believe that conservative thinking is inherently less complex than liberal thinking. In fact, in the past, we have identified highly complex thinking in both conservative and liberal leaders.


A couple of side notes

Upon reading President Trump’s statement above, you may have noticed that, without any framing or context, the President jumped to a discussion of reciprocity. This lack of framing is a ubiquitous feature of President Trump’s arguments. I did not mention it in my discussion of complexity because it is not a direct indicator of thinking complexity. It’s more strongly connected to logical coherence, which correlates with complexity but is not fully explained by complexity.

I’d also like to note that it was difficult to find a single argument in President Trump’s interviews that contained an actual explanation. When asked to explain a position, President Trump was far more likely to (1) tell a story, (2) deride someone, (3) point out his own fame or popularity, or (3) claim that another perspective was a lie or fake news. These were the main ways in which he “backed up” his opinions. Like the absence of framing, these behaviors are not direct indicators of thinking complexity, though they may be correlated with complexity. They are more strongly related to disposition, values, and personality.

These flaws in President Trump’s thinking, combined with the complexity level of his interview responses, should raise considerable alarm. If the President Trump we see is showing us his best thinking—and a casual examination of other examples of his thinking suggests that this is likely to be the case—he clearly lacks the thinking skills demanded by his role. In fact, mid-level management roles generally require better thinking skills than those demonstrated by President Trump.


President Obama

President Obama’s mean score (1163) was in the advanced systems thinking zone. Thinking in this zone is multivariate and non-linear. People performing in this zone link ideas in complex webs of relations, connecting these webs of relations to one another through common elements. For example, they view individuals as complex webs of traits & behaviors, and groups of individuals as complex webs that include not only the intersections of the webs of their members, but their own distinct properties. Thinking in this zone is very different from thinking in the advanced linear thinking zone. Where individuals performing in the advanced linear thinking zone are concerned about immediate outcomes and proximal causes, individuals performing in the advanced systems thinking zone concern themselves with long term outcomes and systemic causes. Here is an example from President Obama’s interview with the New York Times on March 7th, 2009, in which he explains his approach to economic recovery following the onset of the great recession:

…people have been concerned, –understandably, about the decline in the market. Well, the reason the market’s declining is because the economy’s declining and it’s generating a lot of bad news, not surprisingly. And so what I’m focused on is fixing the underlying economy. That’s ultimately what’s going to fix the markets. …in the interim you’ve got some folks who would love to see us artificially prop up the market by just putting in more taxpayer money, which in the short term could make bank balance sheets look better, make creditors and bondholders and shareholders of these financial institutions feel better and you could get a little blip. But we’d be in the exact same spot as we were six, eight, 10 months [ago]. So, what I’ve got to do is make sure that we’re focused on the underlying economy, and … if we do that well …we’re going to get this economy moving again. And I think over the long term we’re going to be much better off.

Rather than offering a pre-determined solution or focusing a single element of the economic crisis, President Obama anchors on the economic system as a system, advocating a comprehensive long-term solution rather than band-aid solutions that might offer some positive immediate results, but would be likely to backfire in the long term. Appreciating that the economic situation presents “a very complex set of problems,”  he employs a decision-making process that is “constantly… guided by evidence, facts, talking through all the best arguments, drawing from all the best perspectives, and then talking the best course of action possible.”

The complexity level of president Obama’s thinking as represented in the press interviews analyzed for our study, is a reasonable fit for high office. Of course, we were not able to determine if his scores in this context represent his full capabilities. An informal examination of some of his written work suggests that the “true” complexity level of his thinking may be even higher.

Discussion

Thinking complexity is not the only factor that plays a role in a president’s success. As president, Obama experienced both successes and failures, and as is usually the case, it’s difficult to say to what extent his solutions contributed to these successes or failures. But, even in the face of this uncertainty, isn’t it a no brainer that a complex problem that’s adequately understood is more likely to be resolved than a complex problem that’s not even recognized?

In his interview with the Wall Street Journal, President Trump claimed that Barack Obama, “didn’t know what the hell he was doing.” Our results suggest that it may be President Trump who doesn’t know what Obama was doing.

 


Other articles in this series

  1. The complexity of national leaders’ thinking: How does it measure up?
  2. The complexity of national leaders’ thinking: U.S. Presidents
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The US presidents: Are they equipped to cope with the complexity of their role?

pictures of the last 4 US presidents

The results are in, and they have profound implications for the USA, democracy, and the world. To view the results of our research on the thinking complexity of US Presidents, click here: The complexity of national leaders’ thinking: U.S. Presidents

 


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

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

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

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

Context and research questions

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

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

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

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

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

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

Thinking complexity and leader success

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

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

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

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

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

Complexity level and leadership—the evidence

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

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

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

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

Predictive power graph

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

Coming next…

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

 


Appendix

Predictive validity of various types of assessments used in recruitment

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

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

References

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

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

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

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

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

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

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

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

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

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

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

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

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Dear Sir Ken Robinson

 

This morning, I received a newsletter from Sir Ken Robinson, a popular motivational speaker who focuses on education. There was a return email address, so I wrote to him. Here's what I wrote:

Dear Sir Ken,

"I love your message. I'm one of the worker bees who's trying to leverage the kind of changes you envision.

After 20+ years of hard work, my colleagues and I have reinvented educational assessment. No multiple choice. No high stakes. Our focus is on assessment for learning—supporting students in learning joyfully and deeply in a way that facilitates skills for learning, thinking, inquiring, relating and otherwise navigating a complex world. Our assessments are scalable and standardized, but they do not homogenize. They are grounded in a deep study of the many pathways through which students learn key skills and concepts. We're documenting, in exquisite (some would say insane) detail, how concepts and skills develop over time so we can gain insight into learners' knowledge networks. We don't ask about correctness. We ask about understanding and competence and how they develop over time. And we help teachers meet students "where they're at."

We've accumulated a strong base of evidence to support these claims. But now that we're ready to scale, we're running up against hostility toward all standardized assessment. It's difficult to get to the point where we can even have a conversation with our pedagogical allies. Ouch!

Lectica is organized as a nonprofit so we can guarantee that the underprivileged are served first. We plan to offer subscriptions to our assessments (learning tools) without charge to individual teachers everywhere. 

We've kept our heads down as we've developed our methods and technology. Now we're scaling and want to be seen. We know we're part of the solution to today's educational crisis—perhaps a very big part of the solution. I'm hoping you'd like to learn more."

My email was returned with this message: "The email account that you tried to reach does not exist." How frustrating.

So, I thought I'd pen this post and ask my friends and colleagues to help me get access to Sir Ken's ear. If you know him, please forward this message. I'm certain he'll be interested in what we're doing for learning and development. Where are you Sir Ken Robinson? Can you hear me? Are you out there? 

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Dr. Howard Drossman—leadership in environmental education

For several years now, one of our heroes, professor Howard Drossman of Colorado College and the Catamount Center, has been working with Lectical Assessments and helping us build LESA, the Lectical Environmental Stewardship Assessment.

Dr. Drossman's areas of expertise include developmental pedagogy, environmental stewardship, and the development of reflective judgment. His teaching focuses on building knowledge, skill, and passion through deep study, hands-on experience, and reflection.

For example, Dr. Drossman and ACM (Associated Colleges of the Midwest) offered a 10-day faculty seminar on interdisciplinary learning called Contested Spaces. This physically and intellectually challenging expeditionary learning experience provided participants with multiple disciplinary perspectives on current issues of land stewardship in the Pikes Peak region of Colorado. 

A second, ongoing program is offered by Catamount Center and Colorado College is dedicated to inspiring the "next generation of ecological stewards." This program, called TREE (Teaching & Research in Environmental Education), is a 16-week, residential program for undergraduate students who have an interest in teaching and the environment. Program participants live and learn in community at the Catamount Mountain Campus, which is located in a montane forest outside of Woodland Park, Colorado. Through study and practice, they cultivate their own conceptions of environmental stewardship and respect for the natural world, while building skills for creating virtuous cycles of learning and useable knowledge in K-12 classrooms.

Dr. Drossman embeds Lectical Assessments in both of these programs, using them to customize instruction, support individual development, and measure program outcomes. He also is working closely with us on the development of the LESA, which is one of the first assessments we plan to bring online after our new platform, LecticaLive, has been completed. 

 

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World Economic Forum—tomorrow’s skills

The top 10 workplace skills of the future.

Sources: Future of Jobs Report, WEF 2017

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
  • measured with ratings on surveys or on tests of people's ability to provide correct answers.

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.

 

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From Piaget to Dawson: The evolution of adult developmental metrics

I've just added a new video about the evolution of adult developmental metrics to YouTube and LecticaLive. It traces the evolutionary history of Lectica's developmental model and metric.

If you are curious about the origins of our work, this video is a great place to start. If you'd like to see the reference list for this video, view it on LecticaLive.

 

 

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Why measure complexity?

“Why measure growth in complexity level?”* That’s the question a new acquaintance asked me recently, and it took me by surprise.

One lesson I’ve had to learn again and again is that the only way to escape the boundaries of my own perspective is to listen hard to the perspectives of others. These perspectives are often reflected in the questions people ask.

“Why measure growth?” I didn’t realize this question needed an answer. Like other questions that have surprised me, this one relates to something I take for granted — one of the fundamental assumptions that underlie my research.

Why we do it

We measure growth for three primary reasons — to make it visible, to learn how people learn, and to customize learning.

  1. By measuring growth, we make it visible. Being able to see evidence of our own growth motivates us to grow further. Contrast this with receiving grades or conventional test scores. They compare you with other people. If you get good grades, you may be motivated to strive for even better grades. But if you consistently get poor grades, you’re more likely to feel like you’re being punished for your learning efforts. By measuring growth, we provide positive motivation for every learner.
  2. A measure of growth helps us understand how people learn. At Lectica, we’re constantly asking how particular growth scores relate to specific skills and knowledge, and how current skills and knowledge relate to the skills and knowledge we observe at the next level. In other words, we’re systematically and continually documenting the development of knowledge and skills so we can answer questions like, “Is there an optimal way for people to learn this skill?”
  3. A measure of growth allows us to determine what a learner is most likely to benefit from learning next. This is important, because when we get the difficulty of the next learning challenge just right (not to easy and not too hard), we activate the brain’s inborn motivational system. This not only increases motivation in the moment, but supports a lifelong love of learning while increasing the rate of development.

These aren’t the only reasons for measuring growth in complexity level, but they are the reasons at the core of our work. Measures of complexity level can also be used to help match people to roles, as in recruitment, or deciding which political candidate is best equipped to handle the complexity of a particular office.

For a more detailed explanation of my reasons for focusing on growth, see, How I was seduced into trying to fix education.


*When we talk about measuring growth, we don't mean the ability to get more items right on a multiple choice test. We measure developmental growth—growth in the level skill with which people apply their knowledge in complex real-world contexts.

If you found this article helpful, you may also like: What PISA measures. What we measure.

 

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