The limitations of testing

It is important for those of us who use assessments to ensure that they (1) measure what we say they measure, (2) measure it reliably enough to justify claimed distinctions between and within persons, and (3) are used responsibly. It is relatively easy for testing experts to create assessments that are adequately reliable (2) for individual assessment, and although it is more difficult to show that these tests measure the construct of interest (1), there are reasonable methods for showing that an assessment meets this standard. However, it is more difficult to ensure that assessments are used responsibly (3).

Few consumers of tests are aware of their inherent limitations. Even the best tests, those that are highly reliable and measure what they are supposed to measure, provide only a limited amount of information. This is true of all measures. The more we hone in on a measureable dimension—in other words, the greater our precision becomes—the narrower the construct becomes. Time, weight, height, and distance are all extremely narrow constructs. This means that they provide a very specific piece of information extremely well. When we use a ruler, we can have great confidence in the measurement we make, down to very small lengths (depending on the ruler, of course). No one doubts the great advantages of this kind of precision. But we can’t learn anything else about the measured object. Its length usually cannot tell us what the object is, how it is shaped, its color, its use, its weight, how it feels, how attractive it is, or how useful it is. We only know how long it is. To provide an accurate account of the thing that was measured, we need to know many more things about it, and we need to construct a narrative that brings these things together in a meaningful way.

A really good psychological measure is similar. The LAS (Lectical Assessment System), for example, is designed to go to the heart of development, stripping away everything that does not contribute to the pure developmental “height” of a given performance. Without knowledge of many other things—such as the ways of thinking that are generally associated with this “height” in a particular domain, the specific ideas that are associated with this particular performance, information from other performances on other measures, qualitative observations, and good clinical judgment—we cannot construct a terribly useful narrative.

And this brings me to my final point: A formal measure, no matter how great it is, should always be employed by a knowledgeable mentor, clinician, teacher, consultant, or coach as a single item of information about a given client that may or may not provide useful insights into relevant needs or capabilities. Consider this relatively simple example: a given 2-year-old may be tall for his age, but if he is somewhat under weight for his age, the latter measure may seem more important. However, if he has a broken arm, neither measure may loom large—at least until the bone is set. Once the arm is safely in a cast, all three pieces of information—weight, height, and broken arm—may contribute to a clinical diagnosis that would have been difficult to make without any one of them.

It is my hope that the educational community will choose to adopt high standards for measurement, then put measurement in its place—alongside good clinical judgment, reflective life experience, qualitative observations, and honest feedback from trusted others.

What is a holistic assessment?

Thirty years ago, when I was a hippy midwife, the idea of holism began to slip into the counter-culture. A few years later, this much misunderstood notion was all the rage on college campuses. By the time I was in graduate school in the nineties there was a impassable division between the trendy postmodern holists and the rigidly old fashioned modernists. You may detect a slight mocking tone, and rightly so. People with good ideas on both sides made themselves look pretty silly by refusing, for example, to use any of the tools associated with the other side. One of the more tragic outcomes of this silliness was the emergence of the holistic assessment.

Simply put, the holistic assessment is a multidimensional assessment that is designed to take a more nuanced, textured, or rich approach to assessment. Great idea. Love it.

It’s the next part that’s silly. Having collected rich information on multiple dimensions, the test designers sum up a person’s performance with a single number. Why is this silly? Because the so-called holistic score becomes pretty-much meaningless. Two people with the same score can have very little in common. For example, let’s imagine that a holistic assessment examines emotional maturity, perspective taking, and leadership thinking. Two people receive a score of 10 that may be accompanied by boilerplate descriptions of what emotional maturity, perspective taking, and leadership attitudes look like at level 10. However, person one was actually weak in perspective-taking and strongest in leadership, and person two was weak in emotional maturity and strongest in perspective taking. The score of 10, it turns out, means something quite different for these two people. I would argue that it is relatively meaningless because there is no way to know, based on the single “holistic” score, how best to support the development of these distinct individuals.

Holism has its roots in system dynamics, where measurements are used to build rich models of systems. All of the measurements are unidimensional. They are never lumped together into “holistic” measures. That would be equivalent to talking about the temperaturelength of a day or the lengthweight of an object*. It’s essential to measure time, weight, and length with appropriate metrics and then to describe their interrelationships and the outcomes of these interrelationships. The language used to describe these is the language of probability, which is sensitive to differences in the measurement of different properties.

In psychological assessment, dimensionality is a challenging issue. What constitutes a single dimension is a matter for debate. For DTS, the primary consideration is how useful an assessment will be in helping people learn and grow. So, we tend to construct individual assessments, each of which represents a fairly tightly defined content space, and we use only one metric to determine the level of a performance. The meaning of a given score is both universal (it is an order of hierarchical complexity and phase on the skill scale) and contextual (it is provided to a performance in a particular domain in a particular context, and is associated with particular content.) We independently analyze the content of the performance to determine its strengths and weaknesses—relative to its level and the known range of content associated with that level—and provide feedback about these strengths and weaknesses as well as targeted learning suggestions. We use the level score to help us tell a useful story about a particular performance, without claiming to measure “lenghtweight”. This is accomplished by the rigorous separation of structure (level) and content.

*If we described objects in terms of their lengthweight, an object that was 10 inches long and 2 lbs could have a lengthweight of 12, but so could an object that was 2 inches long and 10 lbs.

What is a developmental assessment?

A developmental assessment is a test of knowledge and thinking that is based on extensive research into how students come to learn specific concepts and skills over time. All good developmental assessments require test-takers to show their thinking by making written or oral arguments in support of their judgments. Developmental assessments are less concerned about “right” answers and more concerned with how students use their knowledge and thinking skills to solve problems. A good developmental assessment should be educative in the sense that taking it is a learning experience in its own right, and each score is accompanied by feedback that tells students what they are most likely to benefit from learning next.

Test reliability 2: How high should it be?

There is a great deal of confusion in the assessment community about the interpretation of test reliability. This confusion results in part from the different ways in which researchers and test developers approach the issue.

How test scores are usually presented

Researchers learn how to design research instruments which they use to study population trends or compare groups. They evaluate the quality of their instruments with statistics. One of the statistics used is Cronbach's Alpha, an indicator of statistical reliability that ranges from 0 to 1. Researchers are taught that Alphas above .75 or so are acceptable for their instruments, because this level of reliability ensures that their instrument is measuring real differences between people.

Test developers use a special branch of statistics called psychometrics to build assessments. Assessments are designed to evaluate individuals. Like researchers, test developers are concerned about reliability, but for somewhat different reasons. From a psychometric point of view, it's not enough to know that an assessment measures real differences between people. Psychometricians need to be confident that the score awarded to an individual is a good estimate of that particular individual's true score. Because of this, most psychometricians set higher standards for reliability than those set by researchers.

The table below will help to clarify why it is important for assessments to have higher reliabilities than research instruments. It shows the relationship between statistical reliability 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 specific person's score on a given assessment, because they tell us about the range within which a person's true score would fall, given a particular score.

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 this assessment can be divided is about 6, which means that each strata equals about 1.75 points on the 10 point scale. If your score on this test is 8, your true score is likely to fall within the range of 7.1 to 8.9*.

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**.

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).

Alpha equals .95

If the test you have taken has a score range of 1 to 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 points on the 10 point scale. This means 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 classorrom use or low-stakes contexts. Yet, every day, schools and businesses use tests with reliablilites in the .85 range to make high stakes decisions—such as who will be selected for advancement or promotion.

Alpha equals .85

If the test you have taken has a score range of 1 to 10 and an Alpha (reliability) of .85, the number of strata into which this assessment can be divided is about 2.2, which means that each strata equals about .45 points on the 10 point scale. This means your true score is likely to fall within the range of 6 to 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 subscales with alphas in the .75 range are considered suitable for research purposes. Yet, sad to say, schools and businesses now use tests with subscales 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.

Alpha equals .75

If your current test provider is not reporting true score ranges, ask for them. If they only provide Alphas (reliability statistics) you can use the table and figures in this article to figure out true score ranges for yourself.

Be particulary wary about test developers that claim to measure multiple diminsions with 10-15 minute tests. It is not possible to detect individual differences reliably under these conditions.

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.

*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 intellignece is important. That is determined in other ways.


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.

A good test

babyIn this post, I explore a way of thinking about testing that would lead to the design of tests that are very different from most of the tests students take today.

Two propositions, an observation, and a third proposition:

Proposition 1. Because adults who do not enjoy learning are at a severe disadvantage in a rapidly changing world, an educational system should do everything possible to nurture children's inborn love of learning.

Proposition 2. In K-12, the specific content of a curriculum is not as important as the development of broadly applicable skills for learning, reasoning, communicating, and participating in a civil society. (The content of the curriculum would be chosen to support the development of these skills and could—perhaps should—differ from classroom to classroom.)

Observation. Testing tends to drive instruction.

Proposition 3. Consequently, tests should evaluate relevant skills and be employed in ways that support students' natural love of learning.

Given these propositions, here is my favorite definition of a "good test."

A good test is part of the conversation between a "student" and a "teacher" that tells the teacher what the student is most likely to benefit from learning next.

I'll unpack this definition and show how it relates to the proposals listed above:

Anyone who has carefully observed an infant in pursuit of knowledge will understand the conversational nature of learning. A parent holds out a shiny spoon and an infant's arms wave wildly. Her hand makes contact with the spoon and a message is sent to her brain, "Something interesting happened!" The next day, her arm movements are a little less random. She makes contact several times, feeling the same sense of satisfaction. Her parents laugh with delight. She coos. In this way, her physical and social environment provide immediate feedback each time she succeeds (or fails). Over time, the infant uses this information to learn how to reach out and touch the spoon at will. Of course, she is not satisfied with merely touching the spoon, and, through the same kind of trial and error, supplemented with a little support from Mom and Dad, she soon learns to bring the spoon to her mouth. And the conversation goes on.

Every attempt to touch the spoon is a kind of test. Every success is an affirmation that the strategy just employed was an effective strategy, but the story does not end here. In her quest to master her environment, the infant keeps moving the bar. Once she can do so at will, touching the spoon is no longer satisfying. She moves on to the next skill—holding the spoon, and the next—bringing it to her mouth, etc. Having observed this process hundreds of times, I strongly suspect that a sense of mastery is the intrinsic reward that motivates learning, while conversation, including both social and physical interactions, acts as the fuel.


A good educational test should have the same quality of conversation, in the form of performance and feedback, that is illustrated in the example above. In an ideal testing situation, the student shows a teacher how he or she understands new concepts and skills, then the teacher uses this information to determine what comes next.

Part of the conversation

However, a good test is part of the conversation—not the entire conversation. No single test (or kind of conversation) will do. For example, the infant reaches for the spoon because she finds it interesting, and she must be interested enough to reach out many dozens of times before she can grasp an object at will. Good parents recognize that she expresses more sustained interest if they provide her with a number of different objects—and don't try to force her to manipulate objects when she would rather be nursing or sleeping. Each act is a test embedded in a long conversation that is further embedded in a broader context.

What comes next?

In the story, I suggest that the spoon must be both interesting and within an infant's reach before it can become part of an ongoing conversation. In the same way, a good test should both be engaging and within a student's reach in order to play its role in the conversation between student and teacher.

An engaging test of appropriate skills can tell us how a student understands what he or she is learning, but this knowledge, by itself, does not tell the teacher (or the student) what comes next. To find out, researchers must study how particular concepts and skills are learned over time. Only when we have done a good job describing how particular skills and concepts are learned can we predict what a student is most likely to benefit from learning next.

So, a good test must not only capture the nature of a particular student's understanding, it must also be connected to knowledge about  the pathways through which students come to understand the concepts and skills of the knowledge area it targets.

Back to conversation

I argue above, that in infancy, a sense of mastery is the intrinsic reward that motivates learning, while conversation is the fuel. If conversation is the fuel, tests that do a good job serving the conversational function I outline here are likely to fuel students' natural pursuit of mastery and a lifelong love of learning.

Later: But what about accountability?

Predicting trends, testing people

Mark Forman, in his response to the post entitled, IQ and development, wrote about the difference between predicting trends and testing individuals. I agree that people, including many academics, do not understand the difference between using assessments to predict trends and using assessments to make judgments about individuals. There are two main issues: First, as Mark argues, questions of validity differ, depending upon whether we are looking at individuals or population trends. If we are looking at trends, determining predictive validity is a simple matter of determining if an assessment helps an institution make more successful decisions than it was able to make without the assessment. However, if a test is intended to be useful to individuals (aid in their learning, help them determine what to learn next, help them find the best place to learn, help them decide what profession to pursue, etc.), predictive validity cannot be determined by examining trends. In this case, the predictive validity of an assessment should be evaluated in terms of how well it predicts what individual test-takers can most benefit from learning next, where they can learn it, or what kind of employment they should seek—as individuals.

The second issue concerns reliability. Especially in the adult assessment field, researchers often do not understand that the levels of statistical reliability considered acceptable for studies of population trends are far from adequate for making judgments about individuals. Many of the adult assessments that are on the market today have been developed by researchers who do not understand the reliability criteria for assessments used to test individuals*. As a consequence, the reliability of these assessments is often so low that we cannot be confident that a score on a given assessment is truly different from any other score on that assessment.

*Unfortunately, there is no magic reliability number. But here are some general guidelines. The absolute minimum statistical reliability for an assessment that claims to distinguish two or three levels of performance is an alpha of .85. To claim up to 6 levels, you need an alpha of .95. You will also want to think about the meaning of these distinctions between levels in terms of confidence intervals. A confidence interval is the range in which an individual’s true score is most likely to fall.  For example, in the case of Lectical™ assessments, the statistical reliabilities we have calculated over the last 10 years indicate that the confidence interval around Lectical scores is generally around 1/4 of a level (a phase).

Advice: If statistical reliability is not reported (preferably in a peer reviewed article), don’t use the test.

IQ and development

IQ is a dimension of ability that has been defined using a form of statistical modeling called psychometrics. It is based entirely on psychometric analysis of results from tests consisting of many items, each of which has one correct answer.

IQ scores are arranged along a scale that is based upon the performances of hundreds of people who have taken the same test.

IQ is considered to be a relatively fixed characteristic of a person. People who score higher on an IQ test are considered to be more intelligent than people who score lower.

Cognitive development is a theoretically defined, evidence based dimension. Developmental level is determined by asking individuals to engage in activities that expose their reasoning. Items on developmental assessments are typically open-ended and do not focus on correct answers. They focus on how people go about seeking answers.

A single developmental dimension has been shown to underlie development in a wide range of cognitive domains, making it possible to define a non-arbitrary scale along which development progresses. Individual performances can be placed within a range on this scale.

Cognitive developmental level is not viewed as a fixed trait and is known to vary within persons, depending on knowledge area and a range of contextual variables. Individuals who demonstrate higher levels of cognitive development are viewed as more cognitively developed than those demonstrating lower levels of cognitive development.

The relation between IQ and cognitive development

Children with higher IQ’s learn the kind of knowledge and skills represented in IQ tests earlier than people with lower IQ’s. There is some evidence that cognitive development is likely to be more rapid (and have a higher “endpoint”) in people who have higher IQ’s.

Limitations of testing

The subject matter of IQ tests is limited, and the skill sets that are tested are narrow, so we have to be careful about making generalizations about people based on test results—especially the results of single tests. The same is true for cognitive developmental assessments. Good cognitive developmental assessments are now providing scores with a level of precision similar to that of conventional assessments, but even the most precise and accurate scores apply to performance on a single assessment in a single subject area, and do not capture the full range of capabilities of a test-taker.

The inability of any single assessment (or type of assessment) to provide an accurate account of the capabilities of an individual suggests that the best (most ethical) use of assessments involves repeated measurements across a wide range of subject areas over time.

Testing as part of learning 1

Learning isn’t easy

Yet all healthy babies pursue it with dogged determination, spending hour after hour exploring—and learning to master—their own bodies, as well as their physical and social environments.

Natural testing

When infants and young children engage their environments, they receive constant feedback about what does and does not work. For example, babies spend months learning how to control the movements of their hands. An infant will spend several weeks just learning how to bring an object to her mouth. She’ll use what she learns from successes and failures to do better next time. Feedback is instant and accurate, and the results of each attempt tell her what to try next.

Babies often act like they are addicted to learning. They will tolerate an amazing amount of failure. But without prompt feedback from their external environment, they wouldn’t get far. The same is true for older children.

Testing in schools

Ideally, educational tests model natural testing by providing students with timely and accurate feedback that tells them (and their teachers) what to try next.

Construct and ecological validity

Test developers face a tension betwen construct and ecological validity. If a test is (1) measuring what it intends to measure (construct validity) and (2) what it is measuring is of value (ecological validity), it is considered to be a valid test. Sounds pretty straightforward, but it's not. That's partly because construct and ecological validity often compete with one another—and it is a challenge to find the right balance.

For example, it seems pretty obvious that math items should be about math and reading comprehension items should be about reading comprehension. So, to make sure a math test has construct validity—is about math—you ought to limit the amount of reading required to understand your test items, right?

But what if what you really want to know is how students tackle real-world math problems, which often require the ability to understand the context in which mathematical problems are encountered. After all, there are good reasons to think that a skill a student can apply in real-world contexts is superior to a skill a student can only exhibit on a test that is stripped of context. If you followed this line of reasoning and composed your test of questions that reflect how knowledge is used in the world outside of the classroom, it would have ecological validity.

Here lies the tension between construct and ecological validity: While including context in your math test would increase its ecological validity, doing so would increase the risk of reducing its construct validity by making it less clear exactly what is being measured. This might be reflected in lowered scores for students who can do math but aren't good readers or are unfamiliar with the kind of situations described in test questions. A result like this can look a lot like discrimination—especially when the stakes are high.

In sum, the more you strip away context, the more you risk lowering ecological validity. The more context you add, the more you risk lowering construct validity. Today, there is a strong tendency to prioritize construct validity over ecological validity, primarily because the stakes of many tests are very high, which increases our focus on anything that seems to interfere with fairness. Without intending to, test developers, policy-makers, parents, and teachers have contributed to the creation of tests with decreasing ecological validity—and there is no doubt that teachers are teaching to these tests. The implication? What students are learning in our public schools is increasingly irrelevant to competence in the real world. 

This is a cause for concern.

Test reliability 1: Confidence in test scores

How much confidence should you have in your test score?

When you measure the height of a table, you can be pretty confident that the measurement you make is correct. Rulers are well-calibrated measures that we can use with great confidence if we use them correctly. Scores on tests are not like points on a ruler. They always have bands around them called confidence intervals. A confidence interval is the range around your score in which your "true ability" is likely to reside. Usually, a confidence interval represents a likelihood somewhere between 70% and 95%.

The overall level of confidence we can have in a test score is represented in the test's statistical reliability. (A reliability of 1 is perfect.) As a general rule, no test that is used to evaluate individuals (as opposed to group trends) should have a statistical reliability below .85. Also, the higher the stakes, the higher the reliability. For example, the SAT and GRE have reliabilities in the .95 range.

There is a close relation between confidence intervals and reliability. If you place a series of 95% confidence intervals end-to-end along the scale of a really good standardized test—imagine putting pieces of string end to end along the length of a ruler—you won't be able to fit more than 4 to 6 of them on the scale without allowing them to overlap. This means that the test can distinguish only 4 to 6 truly different levels of performance.

So, why do scores get reported on scales that span more than 4 to 6 levels?