The misunderstood relationship between validity and reliability

The foundational psychometric mistake is that they behave as though—and perhaps believe that—reliability is a sort of fertilizer for validity. That is, in practice the mistaken disciplinary view seems to be that reliability leads to validity. But that has the causal relationship backwards. In fact, validity leads to reliability. But validity is not the only factor that can lead to reliability, and that is where the problems come in. Efforts to increase reliability can be orthogonal to validity, or even come at the expense of validity.

Read More

Now Think?


There’s this moment that still looms large in my memory and in my current thinking from back when I was a sophomore in high school (i.e., back in the dark ages). I had just switched to a different French class mid-semester and I really liked my new teacher. This was my least favorite subject in high school, and the one I struggled with most. Madam asked a question of a student and while he was formulating his answer, she turned to the rest of the class and said, “Maintenant, pensez!” Or maybe it was, “Maintenance pensez, tout le mond!” Now think, everyone. I THINK she went on to say, in French, that she might call on anyone next.

This moment has stuck with me because it had not previously occurred to me that people might not be trying to figure out the answer for themselves while our classmate struggled. Weren’t we all already thinking? I thought that her reminder was funny, for being so unnecessary. 

Last week, I was listening to a Ezra Klein Show podcast with guest Ethan Mollick on how to use various AI and LLM tools, today. They were talking about merely wanting a correct or plausible answer, as compared to the hard work of thinking through a problem. Ezra keeps mentioning from episode to episode that generating an early draft is about thinking, and having an AI or intern do the work could give him a draft but it wouldn’t help him to figure out what he thinks or how he should think or whether he needs to rethink something. Ethan remarked, “People who like thinking like thinking.” 

This is a challenge in my work, has always been a challenge in my work. This was true when I was teaching. This was true when I worked in IT. And it is true as a researcher and in my assessment development work. It most definitely is true as a coach. How do we get people who might not actually like to think to actually think?

What is our future, with expanded artificial intelligence tools? It would be great if they could take some of the drudgery from our plates, but it seems that many people hope that AI can do the thinking for them. It seems that many people think that others would rather not think either, and the best thing that AI can do for us is to take responsibility for thinking and then just give us answers, results and shortcuts. That is not what I mean by drudgery. Of course, I—and all of my favorite people—like to think.

I don’t seen any gain for society by catering to people’s reticence to actually think. We need more thinking, not less. We need more care and deliberation in assessment development, and most every other field. 

Why We Don't Love ECD Evidence Statements

RTD is inspired by ECD, and we love the idea of thinking about item and test results as evidence—and so much that it implies. And yet, we do not love ECD’s structure of evidence statements (i.e., descriptions of what evidence of the targeted cognition might look like.)

The biggest problem that we see in ECD is that it calls for evidence, but does not offer any theory of evidence. Hence, RTD had to develop its own Small Theory of Evidence, the quality of evidence produced by an item or test is inversely proportional to its ability to produce or support Type I and/or Type II errors. That is, assessments and their items should not support false positive inferences or false negative inferences. 

Unfortunately, evidence statements—often inspired by ECD—do not account for the quality of the evidence they describe. Yes, such traits or qualities in test takers’ work products could be evidence of proficiency with the targeted cognition, but is it actually strong evidence in this case? Or, in this case, is it instead evidence of some other cognition. For example, is it instead evidence that the test taker recognized that they could plug the answer options back into the equation to see which one worked (i.e., back solving) instead of evidence that they solved the equation using the targeted cognition?

Evidence is often merely suggestive, instead of being proof in itself. Evidence is often ambiguous, and for that can be useful—to a limited degree. Evidence is rarely proof, instead it really needs corroboration to disentangle the ambiguities it suggests. This is the continuum of evidence quality. 

However, evidence statements do not acknowledge this ambiguity and are often confused with descriptions of proof of proficiency with the targeted cognition. Then, they understandably supplant the targeted cognition as assessment targets. Once that happens, Campbell’s Law kicks in. The evidence statement proxy replaces the underlying construct, and item developers target the proxy in whatever most convenient and efficient way they can. 

Efficiently targeting a proxy can improve reliability, but it comes at the expense of validity because the most efficient route to a proxy can be one that does not go through the actual construct. That is, the efficiency requirements of larger scale standardized tests hone that efficiency in addressing the wrong target, seriously degrading the validity of the inferences and decisions made based upon such an assessment. 

Evidence statements can help to identify potential evidence in a large volume of test taker work product, but that process then requires some other construct or procedure to evaluate that potential evidence for its actual quality. Alas, ECD does not offer that second structure, and test developers’ drive for efficiency can ride evidence statements to rather questionable levels of validity. Retrofitting the evidence statement structure to address this problem (i.e., what we call robust evidence statements) is cumbersome—likely beyond any practical use.

Thus, if evidence statements enable increasing reliability at the expense of validity, test developers need a structure that focuses on validity—on producing evidence of the targeted cognition. This is where RTD item logic comes in.

Who is to Blame for Test Results?

Not that long ago, we were caution by a very smart and thoughtful expert not to report—or perhaps even look for evidence of—misunderstanding or misapplications of the construct or targeted cognition of an assessment. They were concerned that doing so would have the effect of blaming the test taker (or student) for their lack of proficiency. We hear this idea from time to time, that tests should only report what test takers cando or what they do know.

We find this suggestion incredibly destructive of every meaningful purpose for an assessment, including informal assessments.

First, the most important thing that a real teacher can do is to recognize out what a student misunderstands, figure out the nature of their misunderstanding and then provide guidance and support that get them to greater understanding—and even proficiency or mastery. No, mere lecturers and explainers do not have to do this, but that is the difference between a teacher and those far easier roles. Formative assessment is all about looking for those misunderstandings so that teachers can do this special part of their jobs. Assessments must be designed to help teachers with this, and that cannot be done without looking for evidence of those misunderstandings and misapplications.

Second, there is nothing in reporting shortfalls from desired levels of proficiency that assign blame. We do not blame children for being physically short. We do not blame children for not being read to by their parents. We do not blame students for lacking eyeglasses, or for needing them. We do not blame any students for poor instruction, poor curriculum or a lack of appropriate classroom materials.

Yes, it is possible that some students have failed to study or do their homework, and perhaps most of them bear responsibility for that—but not even all of them. Yes, some students are responsible for not paying attention in class, but some distractions are beyond the ability of students to ignore (e.g., an ill family member).

There are so many reasons why a student or test taker might fall short of expectations or our desires for proficiency, and while some of them may fall at the feet of the student or test taker, most of them simply do not. Even disappointing shortcomings in the ease for learning particular types of things (e.g., my own klutziness and lack of straight memorizing abilities) are rarely something to blame students or test takers for.

This gets to the myth of meritocracy we see too often in education. Student success and accomplishments are driven by much more than student effort or even some conception of student ability. Parents, teachers and other influences bear so much responsibility for student successes (and shortcomings) that it would be insane to ascribe it merely to students’ own merit. Moreover, to the extent that there is some sort of innate ability level, it is not as though students earned that.

No, there is no blame involved in looking for evidence of or reporting shortfalls in student proficiency, just as students do not deserve credit for their very really accomplishments that are built upon their lucky advantages.

The Reading Wars and Kanji

Kanji is a writing system used in Japan and grounded in a very similar Chinese system. Rather than a small set of letters based on the sounds of words, it is a vastly larger set of characters based on the meaning of words. Students have to learn 2000-3000 characters in school, but there are upwards of 50,000 different kanji characters—though approximately half of them are more technical or otherwise largely confined to use in narrow contexts.

(Kanji characters can be combined to form a word, as the kanji for “Kyoto” is two characters, “capital” and “city,” because back when Kyoto was first written about, it was the capital of Japan. So, students must not only learn thousands of kanji characters, they must also learn the combinations used to write thousands more words.)

38 Words Written in Kanji

Japan also has a couple of (phonetic-based) alphabets, each larger than our own. But those alphabets have not supplanted kanji. They may be used alongside kanji, but kanji is the foundation of reading and writing in Japanese. (I will stop giving different links for kanji, but it is a fascinating topic that is far far far more complex than I have suggested.)

Now, our reading wars and claims around the so-called (and oft misunderstood) science of reading really boil down to how much instruction should a) lean into the use of our phonetically-based alphabet to sound out words when reading or b) push students to the more advanced recognition of words when reading (i.e., sight reading). I think it that it is pretty obvious that no educators actually advocate for purely-phonics-based instruction, just as none are against the inclusion of phonics; it is question of where the appropriate balance is.

It occurred to me this week that Japan simply cannot have these reading wars. Their primary writing system simply requires pure memorization of thousands of kanji characters. There is no fallback of sounding out words written with a kanji character. There is no fallback of sounding out words written with multiple kanji characters. Yes, one can sound out words written in hiragana or katakana, but not words written in kanji.

I wonder what this does to opportunities for academic success for Japanese students. I wonder if the challenges of memorizing kanji—both for writing and for reading—explains how much studying Japanese and Chinese students are so famous for doing. And I wonder how much we could learn about reading and writing instruction that might inform our reading wars if we looked at reading and writing instruction in Japan and China.

The Exception that Proves the Rule

There are a handful of expressions that used to contain some real wisdom, but in being shortened have become so inane that they even contradict their original wisdom.

For example, the original expression was “Imitation is the sincerest form of flattery that mediocrity can pay to greatness.” (Well, actually that’s Oscar Wilde’s version. Ironically, the original idea came from someone else in a somewhat different form.) Wilde’s expression made clear that the imitator marked themselves as merely mediocre, simply for imitating. This is an enormously condescending insult—Oscar Wilde’s wit, you know. But the shortened modern version, “Imitation is the sincerest form of flattery,” takes away everything insulting and condescending about the original. It takes away the tension in Wilde’s construction and refashions it into a kind of sincerity that means something different. It excuses imitation as being a a good thing—missing the moral valence of the word “flattery.” Heck, on the school yard it attempts to defang imitative mockery into some sort of compliment. Shortening it misses the point—and the wit!

The topical expression this month is “The exception proves the rule.” You see, that its not actually the real expression. The full expression—a legal explanation—is “The exception proves the rule in cases not excepted.” This idea is not the vapid suggestion that if there is a rule that there must be exceptions or that the existence of something that breaks a pattern serves to underscore the existence of a rule or pattern. No, that is all nonsensical.

What the original expression means is that if the law lists some exceptions, then there must be a rule that covered everything else. That is, even if the rule is not listed explicitly, the existence of the explanation of exceptions is enough to prove the existence of the real—though implicit—rule.

Section 3 of the 14th Amendment to the US Constitution reads:

No person shall be a Senator or Representative in Congress, or elector of President and Vice-President, or hold any office, civil or military, under the United States, or under any State, who, having previously taken an oath, as a member of Congress, or as an officer of the United States, or as a member of any State legislature, or as an executive or judicial officer of any State, to support the Constitution of the United States, shall have engaged in insurrection or rebellion against the same, or given aid or comfort to the enemies thereof. But Congress may by a vote of two-thirds of each House, remove such disability.

That last sentence, which I have italicized, is an exception. It explains how to create an exception to the general rule of prohibition. As so many have said, this sentence proves that no legislative action is needed to enforce the rules. In legal terms, the prohibition is self-enacting.

What is ECD's View of Evidence?

However, we’ve come to realize that ECD is actually short when it comes to evidence. I do not mean that it lacks procedures there—after all, it lacks procedures everywhere. Rather, ECD has far too little to say about evidence itself.

How do we know what counts as good evidence? How do we recognize evidence? How do we avoid bad evidence?

ECD’s framework includes Evidence Statements (i.e., descriptions of what evidence of the claims might look like in action). And like Task Models and Domain Models, ECD does not explain what evidence statements should look like. That is left to the individual practitioners and/or project teams. But we’ve come to realize that there are some essential problems with this too vague view of Evidence Statements. 

Simple Evidence Statements have a number of significant weaknesses. This is why RTD suggest more robust evidence statements, and creating them in the context of strong Item Logic. 

First, simple evidence statements often mistake an absence of evidence for evidence of absence. Whether the purpose of an assessment is summative or formative, identifying what students do not know and cannot do is at least as important as identifying what they can do. In fact, with formative assessment, it is even more important. This is not about being negative, rather this is about being instructionally minded. It is important to be sure one does not confuse an absence of affirming evidence for the presence of disconfirming evidence.

Second, simple evidence statements are prone to Type II errors (false negatives). This is in part due to the absence of evidence problem, but it is also due to their inability to disentangle different causes for mistakes or errors.

Third, simple evidence statements are prone to Type I errors (false positives). That characteristic of student or test taker work could be present due to that particular knowledge, skill, and/or ability (KSA), but it might be because the test takers took an alternative path that did not depend on that KSA. 

Simple evidence statements likely work best in the context of some sort of portfolio assessment, in which raters are able to review a broader set of each student's or test taker’s work and look for larger trends and patterns. Taken together, the errors in that noisy data can cancel out and the signal of information can become apparent. This is really just a sample size issue; the noisier the data, the larger a sample size is needed.

However, neither formative assessment nor large scale standardized assessment has access to such large samples of a student's or test taker's work for each assessment target. Therefore, robust evidence statements are needed.

Robust evidence statements must include information about the context in which the evidence appears. What sort of directions or instructions prompted the work? Did they specifically ask for this sort of evidence, or did they merely provide an opportunity to develop it? How much scaffolding was present? Did the task allow for alternative paths? ECD talks about the evidentiary argument, and the importance of the source of and context for evidence is well known in that field of law. Assessment should take those issues just as seriously. 

The Value of Negative Information

One of the most famous Sherlock Holmes stories is Silver Blaze, the one in which he deduces the identity of the guilty party because a dog did not bark. Negative information is often worth as much as positive information.

There certainly have been times in my life, however, when I felt that those who judged me were more concerned with negative information that with positive information. They were more concerned with that I did not do than with what I did do. Or were more concerned with what I could not do than with what I could do. As a teacher, I certainly wanted to identify and celebrate what my students could do.

Positive information feels positive and perhaps celebratory. Negative information feels negative and perhaps even mean.

But negative information can be invaluable.

In some places, there is an explicit effort to focus on what students can do, rather than what they cannot do. This even gets to how some people and organizations talk about assessment. Rather than saying. “Assessments should identify what students can and cannot do,” they say “Assessments should identify what students know and can do.” I really do understand this urge. I used to be a teacher and I cared deeply about my students.

And yet, educators often say that what they really need is high quality formative assessments. That is, they need assessments that help them to provide instruction and to identify where students need more instruction. Of course, formative use of assessment requires times to go back and provide additional instruction and support to students after the assessment (and therefore timely results). But it also requires negative information. It requires tests and items that highlight what students do not know and what they cannot do.

This means that items used in formative assessment must be incredibly careful about false positive results (Type I errors). They cannot provide alternative paths to a successful response that avoids use of the targeted cognition. They cannot be so unstructured that it is not even clear what KSAs a test taker used to produce a successful response. Nor can they focus on the integration of skills such that it is not clear which KSAs broke down when test takers failed to produce a negative response.

The kinds of activities that I would rather teach with and want my students to be able to succeed with are not likely to be very useful for identifying what they need further help with.Yes, many students’ shortfalls might be clear, but many students will be able to steer around their weaknesses and lean further on their strengths. This kind of compensatory approach works to actively hide the information that formative assessments are intended to uncover.

The backlash against standardized tests is based upon many ideas, but one of them is surely that standardized tests and their results can feel mean. Such tests are often designed to designed to reveal shortcomings, deficits and lack of proficiency. Alternatives to traditional standardized tests, therefore often focus on the kinds of activities that I would rather teach my students with and with which I want my students to be successful. Such tasks seem to have more potential to feel celebratory. But such test simply cannot provide high quality information for formative purposes (and the information is not really valid for summative purposes either).

Formative assessment is just as demanding as summative assessment. It requires just as much skill and rigor to produce. Though we do not focus on formative assessment in our Rigorous Test Development (RTD) model of practice, just about everything in RTD applies to formative assessment as well as it does to summative assessment.

How and Why Plagiarism Matters in the Academy

Plagiarism is a very important issue in academia—far more important than in other contexts. This is a very different issue than copyright, which is about the law and perhaps money. Plagiarism is something else. 

Plagiarism is about using the ideas or the expression of ideas of someone else without crediting them for it. (I was taught long ago that it also includes the organization of ideas, but I have never seen that really developed.) It is not a matter of using someone else’s idea or words, rather it is using them in an uncredited fashion. The exact same behavior—even the same exact case—can be meaningless and harmless in other contexts, but a major violation in a scholarly context. 

There are two reasons for this. 

First, academia is about what I call the scholarly conversation. This is where we build on the work of others, crediting them for their contributions and then extending, applying or refuting them. Because we are building on the work of others, they have already shown how, why, where and in what circumstances those ideas apply, what they put together to get there, and perhaps laid out some caveats and/or restrictions. We do not replicate all of that work ourselves, because they have already done it. Unless our specific goal is to replicate their work—in order to verify it, perhaps in a new context—we should not try to replicate it. Instead, we give our readers a shortcut, by letting them know where they can find that earlier work.

This allows readers to evaluate the validity of the foundations we are building upon by considering the credibility of those earlier scholars. There are people whom I respect so much that I would likely just accept their conclusions, without needing to go and investigate how they got there. There are people whose work I have previously found so problematic that I do not trust anything built upon it. But most scholars? Well, if I am unsure about the meaning or breadth or application of an idea, I might want to go and learn more about it. Citations to others’ work allow me, the reader, to evaluate the precursors to the work I am reading—and to do so in the fashion I choose. 

Second, citing those who came before allows me to evaluate the scholar and work I am reading. If I can see that they know that they are building on the work of these previous scholars, I can better be assured that they have already considered or taken into account the issues that those previous scholars raised. I can see that they are, in fact, building on those other people’s work. This means that they should not be making mistakes already warned against, retreading on infertile ground, or simply doing more elementary work. If they show me that they already know that previous work, and how they are building on it, I can take them and their work more seriously. 

More subtly, by citing previous scholars, I can usually see the disciplinary, methodological and substantive direction that a work is coming from. It helps me to understand the kinds of concerns that will be explored, the kinds of tools that might be brought to bear and the classes of themes that might be recognized. That is, it gives me notice of what schema I should be activating so that I can more easily make more sense of what I am reading and will get to in this work. 

Now, both of these two reasons matter enormously for those with expertise to recognize and understand the citations in a work. They can seem like minor things to those who couldn’t make use of the citations for these sorts of purposes. But academic writing is aimed that just such an audience of experts. Obviously, this serves as a barrier to the larger public understanding academic work. This is why writing for the broader public is just so very different than writing scholarly works. But that is a different audience, and different audiences should be approached differently. 

Clearly, neither of these two reasons really addresses the importance of correctly indicating when someone else’s words are being used. That is mostly about just politeness. But there is value in clear and/or efficient expression of an idea. We ought to give credit, rather than steal credit, for well crafted explanations of ideas. But in student work, including doctoral dissertations, there is another very important reason to be a stickler for properly crediting the expression of ideas. You see, explaining something in your own words is often how you show that you actually understand what something means, or why it is important. This is why when quoting an extended passage—even the best written extended passages—it is still important to explain its significance. Yes, this helps the reader to pick out the parts you mean to build on, but (perhaps more importantly), it shows the reader that you actually understand what you are referring to. 

Because scholars in the academy therefore must be stringent on this issue with their students, it becomes incumbent upon them to model the behavior they expect from students in their own work. Even a mild paraphrase can be introduced with “As [scholar] explained,…”  And, honestly, I would feel taken advantage of if someone took credit for my phrasings (which I am sometimes proud of). I am so accustomed to giving credit to others in the scholarly community, I expect others to do the same with me.

With all of these reasons to correctly indicate the sources of the words in a piece and the sources of ideas in that the piece builds upon, why not give proper credit and correctly indicate quotations? I can hardly think of a respectable reason, leaving just laziness and sloppiness—which are hardly decent excuses. 

However, I would add that non-experts might not recognize when an expression is really just a standard way to explain an idea. In fact, most of my quantitative methodology classes focused a shocking amount of attention on how to explain in words what quantitative data, results and/or analysis signified. This was taken so seriously that if two people in the same class were give them same data, graph or statistical output, we could very easily independently write the exact same sentences to describe them, and our peers (and other experts) would immediately recognize what is—if not essentially boilerplate plate sort of language—the style what a particular group has been intentionally acculturated into using. I wish that my qualitative methodology courses were as careful about steering us clear of overstating or misstating what our data showed. 

I would also add that this blog is not written in a scholarly fashion or for a scholarly audience. While I sometimes write with lots of citations, I do that much less here. Different form for a different audience, with different expectations. However, I try hard to attribute quotations properly, even here.

Cognitive Complexity: Uncertainty and Deliberation

While cognitive complexity can describe many things, the RTD approach to cognitive complexity is firmly grounded in the assessment industry’s dominant model, Norman Webb’s Depth of Knowledge (wDOK). As we read it, the central thrust of wDOK is the continuum of deliberation-to-automaticity, with the greater cognitive work of more deliberative cognitive paths being more cognitively complex, and the lesser work of greater automaticity—often earned through practice and greater proficiency—being less cognitively complex. (No, this is not the only way to think about cognitive complexity, but we based our rDOK approach on wDOK because it is so dominant in the industry. See our writing on rDOK (revised Depth of Knowledge) to examine how we think this plays out in the various content areas.)

One of our colleagues, a former science educator and now science assessment expert, wisely asked about the relationship between uncertainty and deliberation. Well, there are many kinds of uncertainty, and not all of them are tied to the kind of deliberation that DOK is about. Nonetheless, uncertainty often does lead to greater deliberation and a more cognitive complex path.

  • There is the uncertainty of not even knowing where to start, or whether to start. That is not deliberation. That is just indecision—often paralyzing indecision. It is a general, and common, nervousness that can be a barrier to focused effort. Teachers and tutors are familiar with this and an important part of their role is to help their students to develop the confidence to overcome this kind of uncertainty and take that first step.

  • There is the uncertainty of lack of confidence in one’s execution, which can be entirely rational. Perhaps more people should have this, as it leads to various sorts of proofreading. That is, they review their work for little mistakes in execution, even though this does not include rethinking the whole approach they took. Math teachers say “Check your work,” meaning the the mathematics equivalent of proofreading. This uncertainly is not advanced deliberation, and the greater work it prompts is not indicative of great cognitive complexity. Rather, it is essentially repetition of earlier work.

  • There is the uncertainty of not being sure what to do next when in the middle of the problem, or even not being sure what to do first. That is, once past the paralysis that keeps one from even being able to truly try to make sense of the task, one might still be unsure about the first step. This question of “What do I do next?” is a form of deliberation. It can be answered simply by trying to remember the next step in a (perhaps poorly) memorized procedure. It might instead be answered by trying to (re)discover or (re)invent a good next step. This latter response constitutes reasoning and the kind of deliberation at the focus of both wDOK and rDOK. Indeed, uncertainty is often what creates the opportunity for deliberation. 

  • An even more careful deliberation can be prompted by initial uncertainty. One might try to figure out more than just the initial step, instead trying to work out a longer plan before diving into the work of the first step. This is not necessarily a different kind of uncertainty than mentioned above, but one’s response to it can be less or more carefully and deliberative—and therefore more cognitively complex. 

  • There is also a second kind of uncertainty after completing a task. One might ask oneself, “Was that even the right thing to do?” and revisit/question the reasoning that led to the steps taken. This differs from merely proofreading/checking one’s work, though both are prompted by uncertainty after the fact. Proofreading revisits execution, whereas this revisiting of reasoning is more cognitively complex.

Of course, one might be uncertain before a task and i) carefully develop a plan to help break through initial paralysis, ii) execute the plan, iii) revisit the reasoning of the plan but decide it was a good approach, and iv) when check one’s work. Uncertainly can drive all of this. All of that careful deliberation can still lead to bad plans poorly executed with errors that were missed when proofreading. No amount of deliberation can guarantee success, and the highly proficient can often achieve success without any conscious deliberation. 

Uncertainty can be a product of a range of factors. It might come from genuine ignorance or other lack of necessary skills. It can from insufficient practice or arise due to being faced with novel situations. It can be a product poor instruction or lack of effort to learn by a students. Some people are by character more confident and some are by character less confident—and either be justified or not in this. But regardless of the source of uncertainty, the question of cognitive complexity (i.e., either rDOK or wDOK) is answered by looking at the response to uncertainty. 

On the other hand, a lack of certainty obviously inhibits deliberation. It makes deliberation of any sort far less likely, which is often detrimental to producing high quality work. Ideally, intellectual humility would put an upper limit on certainty and lower limit on deliberation of various sorts.

Writing Multiple Choice Items is Harder than it Looks

Writing multiple choice (MC) items is extraordinarily more difficult than just writing good constructed response (CR) questions. CR questions give the test taker the freedom to take any path they want and to demonstrate their understandings and misunderstandings without the scaffolding and limitations of the set of answer options that are the distinguishing characteristic of MC items. 

When we use MC items, it is because for all the initial difficulty in writing and refining them, they are faster and cheaper to score than CR items. Machines have been able to do it quickly and for virtually no cost for decades. That’s why we used to use #2 pencils—that’s the kind of pencil that the machines were make to read most easily. That long term cost saving is truly the only reason to use them.

But what does it take to write an MC item that gathers the information that a CR item does? How do we avoid making the answer too obvious? How do we avoid the little hints that so often appear in MC items? Unfortunately, quite few researchers have really looked at the contents of items clearly enough to offer good guidance, but we do know that each distractor (i.e., incorrect answer option) should represent the result of a misunderstanding or misapplication of whatever knowledge, skills and/or abilities that that item is trying to assess. 

So, let us consider a very simple question: How many states make up the United States of America?

We know that key (i.e., the correct answer) is 50. But can we come up with three or four good distractors?

It seems plausible that some significant share of test takers who get this question wrong will come up with 13, confusing the number of original colonies that rebelled against the British and formed the original collection of state. 13 is clearly a good distractor. 

What are the other mistakes that someone might make? Can you think of any? I don’t think that any other key American government numbers are likely mistakes. 

  • 3 (i.e., branches of government) is too obviously wrong and too low. 

  • 538 (i.e. the number of members in the electoral college) is too obscure and too advanced. Anyone who even knows that that is a significant number clearly will know the number of states.

  • 435 (i.e., the number of members of the house of representatives) is not quite so advanced as 538, but suffers similar problems. 

  • 27 (i.e., the number of constitutional amendments) again suffers similar problems. 

So, we still need two or three or more distractors. What about sheer guessing? I expect that most people who would get this question wrong would simply not know an answer and would simply guess. What are likely guesses? 

I think that 100 is a likely guess, and perhaps attractive to someone who doesn’t know the real answer. I’m concerned that it’s a little large, but a nice round number doesn’t seem crazy. 

But we still need at least one more distractor, at least. Something smaller than 100—which feels a little large. Maybe not a round number, so it feels more precise. Mexico has 31 states, so that might be particularly attractive to Mexican migrants. Of course, that might raise a fairness issue. Maybe they’d be more likely to pick it because they recognize the number, or maybe less likely because they know it is only Mexico’s answer? I’m going to ignore that for now. 

How many states make up the United States of America?

A. 13

B. 31

C. 50

D 100

Now, I don’t love that. I’m always nervous that test takers are more likely to pick middle values (i.e., under the goldilocks principle of juuuuuuust right). Obviously, though, we ought to use every answer position equally when placing the key. I suspect that item developers too often try to offer distractors are smaller and that are greater than the key, and I don’t want to fall into a too common pattern.We could replace 100 with 25, though that is not so round a number, and therefore perhaps not as attractive for guessing. 

How many states make up the United States of America?

A. 13

B. 31

C. 25

D 50

Such a simple question, and with one clearly correct answer. And yet, it’s not obvious which set of distractors is better. We would love it if we could offer good distractors to attract as many guessers and other mistakes as possible, so it just identifies the test takers who really do know the correct answer.

Writing high quality multiple choice items is hard. 


Rise in Absenteeism, Drop in Exams

Two related stories caught my eye this week.

First, the New York Times reports that State of New York might be dropping its quite longstanding end of course (EOC) Regents Exams as a graduation requirement. I first heard of these tests back in the mid-80’s, though I then lived in the DC suburbs. I later taught in New York, and became quite familiar with them. This story also mentions dropping the multiple different sorts of high school diplomas available in New York down to a single diploma, which is related to the use of the Regents Exams, as passing additional Regents Exams (e.g., more science?) was a major requirement for the higher level diplomas.

Second, there has been a steep increase in student absences since before the pandemic. I saw a chart from a DC school district report that showed how bad it’s gotten there, with nearly 50% of students missing at least 10% of school.

That is worse at the high school level, nearly 2/3 of student missing at least 10% of school days and over 25% missing at least 30% of school days. Anyone who has spent days digging out of the hole created from missing a couple days for vacation, due to sickness or just because of a work trip easily understands that the impact of missing a day or two of class stretches far beyond simply the days missed. 

A White House Council of Economic Advisors’ blog post pointed to a larger national study of the pandemic’s impact on absenteeism. It shows nearly a doubling of chronic absenteeism across the country. 

Of course, absenteeism is not evenly distributed across all schools. This is a greater problem in lower SES schools, and often in more minority and English Language Learner dominated schools. This is why DC’s numbers look particularly bad, as they do not include schools from the (more affluent) suburbs.

These stories feel connected to me. I know from my own teaching experience—both in suburban schools and in inner-city schools—that the kids who failed to graduate overwhelmingly were kids who had major attendance problems at least as far back as 9th grade. Most of the kids who had the most trouble passing the required Regents Exams came from the same group of kids, or they were kids who came to school but did not do homework or pay attention—so they were physically present but were not engaged in learning. 

Why are we shifting away from standardized tests? I understand they do not do a good enough job measuring student proficiency with the state learning standards, but I did not see kids who really had those proficiencies and yet could not pass the exams. The bar on the required exams simply was not that high. Yes, there were occasional years when the Physics Regents Exam as outrageously difficult, but that was not a required exam for graduation. Yes, we need better exams, but what do we accomplish by moving away from them?

I am just troubled that we are removing the best information for voters, tax payers and community members about the academic performance of the schools, perhaps largely due to wanting to stick our heads in sand. We don’t want to pay attention to the degree of disparities across communities—disparities that have been exacerbated in the last few years. Senator Ted Kennedy agreed to an increase in standardized testing in order to shine more light on the disparities, and I find that reasoning compelling. I don’t see how dumping tests will help students who need the most help or (are supposed to) attend our lowest performing schools. 

What is wrong with the Haladyna Item Writing Rules?

Haladyna et al.’s item writing rules project might have begun as merely a literature review, but by the time any of it was first published (Haladyna & Downing, 1989), it claimed to be “as a complete and authoritative set of guidelines for writing multiple-choice items.”  The 2002 update (Haladyna, Downing & Rodriguez)—the last that claims to be grounded in the work of others—says “for classroom assessment” in the title, but then says, “This taxonomy may also have usefulness for developing test items for large-scale assessments,” in the article’s abstract. Their project has always been quite ambitious, intended to be far more than merely a literature review.

I have no problem with this ambition. The first fundamental problem with the Haladyna Rules is the arrogance. They do not explain reasoning for each rule, and sometimes even go against the literature. Ambition is offering such expansive guidance, arrogance is doing so when you’ve never actually done the work professionally, yourself. Ambition is trying to cover both classroom assessment and large scale standardized assessment, arrogance is mistaking your own opinion and/or preferences for reasoning and/or evidence.

The second fundamental problem with the Haladyna Rules project is that it displays profound ignorance about how items work or what their purpose is. Yes, the two articles (1989, 2002) are rather oriented towards reporting on what others’ have written, but their two major books (2004, 2013) are not. In either case, they could have explained their rules in terms of item functionality and purpose, but they do not. Are these rules focused on items ability to provide the intended information? Or, as we say so very often, valid items elicit evidence of the targeted cognition for the range of typical test takers. Their rules are focused on clarity and even presentation. The feel incredibly shallow, as though little consideration is given to content, and only a hand wave to test takers.

The third problem with the Haladyna rules is their organization. Content, Formatting, Style, Stem, and Choices is simply not a good structure, and they do not even follow it consistently. Some ideas are listed separately in the Stem and Choices sections, and some are listed elsewhere to apply to both parts of an item. The cluing rules uniquely has subparts, but oddly does not include all of the cluing rules/guidelines. This looks like they imposed their own latent thinking, but not mindfully enough to do it well. It’s a mess. Perhaps they hould have a section on Cluing and a section on Clarity, as these themes are common in their list.

The fourth problem with the Haladyna Rules is that it is just sloppy. Despite all the iterations, there are a number of pairs of redundant rules. 13 & 16, 11 & 12, 17 & 27, 29 & 30, 14 & 15, and 23 & 24. Each of these pairs clearly should be consolidated into a single rule, and it is inexplicable to me that they were not. Moreover, the articles offer explanations for just some of their rules, not for all of them. Yes, by 2013 it is less sloppy, but most of the redundancies remain.

From my perspective—from the perspective of me and my frequent co-author—the deepest problem is that lack of understanding of what makes for a high quality item. Items must align with their assessment targets; they should measure what they are supposed to measure, and not something else in the neighborhood. That idea of alignment seems absent from these rules. And for items to be aligned, they must be aligned for the range of test takers, not just the ones like oneself or one’s own students. This is the idea of formal/technical fairness. But their project only has a small hint of that concern, and it is not explicit in any of the actual rules. This is what we refer to as content and cognition.

So, this approach to “Item-Writing Guidelines/Rules/Suggestions/Advice,” as they termed it, was destined to fail. Rather than helping teachers, item writers and/or content development professionals by helping with the hardest parts of item development, they focus on the inane (e.g., “vertically instead of horizontally”), the obvious (e.g., correct spelling), the tautological (e.g., “avoid over specific and over general content”), the false (e.g., “the length of choices about equal”), and the shallow (i.e., just about everything else). It does not acknowledge the importance of item stimulus (e.g., reading passages), or even their existence! Even when there is a decent kernel of an idea, they fall short—usually woefully short.

Could a good list of item writing principles have come out of this project? I don’t know. I really don’t. I do not understand turning to people who lack deep experience in item development and lack a deep understanding of how items function, but it appears that that is the basis for their project. Of course, I understand that that is how scholarship and literature reviews work, but they literally claim that this resulted in a “complete and authoritative” list. Furthermore, their later works do not present this stuff as merely a literature review; these are their guidelines for writing items.

My colleagues and I have seen how these rules have become embedded in the field, in item writing manuals, in style guides, in educational assessment works of scholarship and of instruction. And they are bad. You can review my daily posts last month to see me take each of them down—all but Rule 14—but that’s not entirely necessary. The point is that these rules might help to develop shiny polished item that look good, but they do not really help to develop highly refined items that elicit evidence of the targeted cognition for the range of typical test takers. And it appears that their lists mere existence might have hampered the development of better item writing guidance—what else could be responsible.

Now, the field of educational measurement’s focus on use of item difficulty and item discrimination statistics to evaluate items and to evaluate item writing rules has not helped. This approach undermines domain models and both content- and construct-validity. It renders claims of evidence from test content less than credible, because it bends what are supposed to be criteria-referenced tests into more effective norm-referenced tests. But that is no excuse for this mess. Haladyna et al. themselves are fully on board with this approach to evaluating items, and approvingly refer to such studies of item writing rules in their own work. But none of that explains why they the field has not dived more deeply into what makes for an actually useful item.

Instead, we have the Haladyna Rules—which definitely need to be replaced.

 

Fisking the Haladyna Rules: The Complete List

Fisking the Haladyna Rules #31: Use humor sparingly

[Each day in October, I analyze one of the 31 item writing rules from Haladyna, Downing and Rodriquez (2002), the super-dominant list of item authoring guidelines.]

Writing the choices: Use humor if it is compatible with the teacher and the learning environment.

And now, the last Haladyna et al. rule. Or guideline. Whatever you want to call it. All their library research to compile the consensus of textbooks, researchers and other authors ends with this one. Is it a good rule?

Ha! This is a bad rule i) by their own official standards, ii) because they seem to contradict themselves and iii) because it their explanation shows how little they understand that challenges of item development.  

First, their 2002 article shows that they do not have even a single source that says that humor is a problem in items. Not one! Only 15% of their sources even mention the idea, and none favor their rule. There is no consensus here, and among the small number of sources who address it, they kinda say, “Meh, whatever.”

Second, their fuller statement of their rule contradicts their shorter statement. That is, their Table 1 says, “Use humor if it is compatible with the teacher and the learning environment,” but their Table 2 says “Use humor sparingly.” Do these two statements mean the same thing? Should it be used sparingly, or is it ok? The longer version seems to be generally supportive, and the shorter version seems generally unsupportive. How is this a rule or guideline or advice? Do they mean that it is ok for classroom assessment but not  for large scale standardized assessment? I can imagine that advice, but that is not what they offer. I can’t tell what they are offering.

Third, the worst thing about this rule is that it is teacher-centric rather than test taker- or student-centric. Test takers vary, and in an enormous number of ways. The problem with humor is that not everyone agrees on what is funny. People have different senses of humor, and attempts at humor in stressful or important situations is only a good idea if everyone gets the joke. But the less homogenous the group, the less likely everyone is to agree that something was actually funny. Haladyna et al. seem neither to understand this basic fact about humor, nor the basic fact about test taker variation—which is essentially the issue of Fairness.

To try to explain this rule, they offer an example. A bad example. It is supposed to be funny, but instead it’s just confusing. And it is double keyed. So, the problem is not the use of humor; the problem is that it is double keyed! In fact, I seriously question whether the example is actually humorous.

In Phoenix, Arizona, you cannot take a picture of a man with a wooden leg. Why not?

A.        Because you have to use a camera to take a picture

B.        A wooden leg does not take pictures

C.        That’s Phoenix for you

There are ­so-oooo many problems with this item, including by their own rules. Not only is it double keyed,  but option C does not even pretend to answer the question. The answer options are not parallel in grammatical structure, they vary enormously in length. It’s not at all clear what the targeted cognition even is. There’s a negative in the stem that is not highlighted in any way. Heck, this is an item that would actually benefit from adding “D. All of the Above.”

Is the problem humor? Is the problem that it attempts humor? Of course  not! It is a bad item for so many other reasons that have nothing to do with humor.

Is this their best argument? Yeah, I suppose it is. Afterall, between even just these four versions of their list of rules (i.e., 1989, 2002, 2004 and 2013), this example is from the final one. They always cautioned against the use of humor, and this was their refined reasoning.

It is the least supported, least defensible and worst rule in their list. Maybe not the dumbest—if only because writing funny is just hard!—but still the worst. Sure, there’s a good reason to avoid trying to use humor, but they do not even wave in that direction.

[Haladyna et al.’s exercise started with a pair of 1989 articles, and continued in a 2004 book and a 2013 book. But the 2002 list is the easiest and cheapest to read (see the linked article, which is freely downloadable) and it is the only version that includes a well formatted one-page version of the rules. Therefore, it is the central version that I am taking apart, rule by rule, pointing out how horrendously bad this list is and how little it helps actual item development. If we are going to have good standardized tests, the items need to be better, and this list’s place as the dominant item writing advice only makes that far less likely to happen.

Haladyna Lists and Explanations

  • Haladyna, T. M. (2004). Developing and validating multiple-choice test items. Routledge.

  • Haladyna, T. M., & Rodriguez, M. C. (2013). Developing and validating test items. Routledge.

  • Haladyna, T., Downing, S. and Rodriguez, M. (2002). A Review of Multiple-Choice Item-Writing Guidelines for Classroom Assessment. Applied Measurement in Education. 15(3), 309-334

  • Haladyna, T.M. and Downing, S.M. (1989). Taxonomy of Multiple Choice Item-Writing Rules. Applied Measurement in Education, 2 (1), 37-50

  • Haladyna, T. M., & Downing, S. M. (1989). Validity of a taxonomy of multiple-choice item-writing rules. Applied measurement in education, 2(1), 51-78.

  • Haladyna, T. M., Downing, S. M., & Rodriguez, M. C. (2002). A review of multiple-choice item-writing guidelines for classroom assessment. Applied measurement in education, 15(3), 309-333.

]