Do researchers understand their own explanations?

Have you ever been asked to fill out a survey that included some statement (e.g., “I experience back pain”, “I have difficulty concentrating”, “I write blogs nobody in their right mind would read”, etc.) and an “ordered” set of responses (e.g., “Strongly Agree, Agree, Neutral, Disagree, Strongly Disagree”, “Always, Almost Always, Occasionally, Almost Never, Never” etc.)? These kinds of questions and response formats are referred to as “Likert Scales” or “Likert-type scales”. They are extremely common in fields ranging from neuroscience to managerial science. Individual responses are almost always treated as a single value, ranging from 1-5 or 1-7 or 1-however many possible options there are. Then, statistical tests are used to analyze responses to determine things about certain subpopulations of people (say, bloggers or those who are depressed) or even all people. One problem many researchers have found with this method is the idea that particular response such as “agree” are EXACTLY one “unit” more than “Strongly Agree” and one less than “Neutral”. Numerous proposals for better approaches exist in the literature, including the focus for this post:

Yusoff, R., & Janor, R. M. (2014). Generation of an Interval metric scale to measure attitude. SAGE Open, 4(1), 2158244013516768.

However, I’m not interested in Likert-type response data here. I’m interested in something FAR more problematic across scientific fields. Namely, the use of statistical methods and their underlying assumptions by those who don’t understand basic probability and statistics adequately enough to be entrusted to calculate the probability of a fair coin toss (ok, that’s not fair; they can be trusted with such a task and even with calculating the probability that a roll of dice will equal some number between 2 and 12). The authors in the aforementioned study are proposing a superior method to Likert-scales for evaluating variables of the kind that thousands of scientists do. In the study, they make sure to define the relevant, basic “facts” about the kinds of variables involved here. For example, a variable (such as number of children or number of grades completed from kindergarten through graduate education) are considered discrete. The authors define discrete as follows:

“According to Mann (2001), a discrete variable assumes values that are obtained from counting”

This “Mann (2001)” they refer to is a textbook, Introductory Statistics, by Prem S. Mann. I don’t have the 2001 edition, but I have a later edition and it provides the following definition:

Discrete Variable A variable whose values are countable is called a discrete variable. In other words, a discrete variable can assume only certain values with no intermediate values.
For example, the number of cars sold on any given day at a car dealership is a discrete variable because the number of cars sold must be 0, 1, 2, 3, . . . and we can count it.”

Naturally, one would think, the authors of the study are justified in saying that “[t]o obtain the values of a discrete variable, all one has to do is to count; hence, the operational procedure is counting.”

There’s just a tiny little problem here: this is absolutely, ludicrously, and completely wrong. But it isn’t the researchers’ fault: after all, Mann’s text is similar here to many other introductory and even graduate level statistics textbooks: it assumes the reader has little or no mathematical background and notions like continuity are foreign, so it provides a simplification that ends up being used in technical, peer-reviewed research.

In actuality, a discrete variable is “countable’ in the following sense: either there are finitely many values it can assume (such as the number of cars sold on a Tuesday from Bernoulli Family Dealership) or there are countably infinitely many values it can assume. I’ve previously  provided an informal account of the difference between countably infinite and uncountably infinite, so I won’t bother to do so here. All I wish to point out is something pretty obvious: you can’t count to infinity. Countable, in the sense Mann and mathematicians more generally mean, doesn’t actually mean that there is ANY way one can handle all discrete variables by using “counting” as “the operational procedure”.

What is the opposite of a discrete variable? A continuous variable: “continuous variables are obtained by measuring and thus, assumes any value contained in an interval, for example”.

Of course, the “obtained by measuring” bit is nonsense, because we “measure” discrete variables. Far worse, however, is how the researchers seem to understand “continuous”, which they describe in greater detail as follows: “values of a continuous variable are obtained using a measuring tool or scale that implies the existence of a more elaborate operational procedure that must be clearly defined as the basis for measurement. Because of the dependence on a measuring instrument, values obtained will be subjected to measurement errors, not exact, and fall within an interval that consists of infinite points.”

Here’s the takeaway/key point: there exists a set which you’ve been using at least since you were a teenager that consists of infinitely many points in ANY interval and which is COUNTABLE. This set is called the “rational numbers”. According to the researchers (and even many statistics textbooks), the rational numbers are continuous. It turns out that if one tries to apply the statistical methods used by researchers in all kinds of fields on a variable that is at most as “big” and dense as the rational numbers (i.e., is countably infinite and can assume infinitely many values between any two values), they won’t just give the wrong answer. This is because continuous variables require something from calculus called integration or integrals (actually, they really involve limits, but because integrals require limits and the calculation of continuous variables require integration, I’m simplifying). In order for a function or a variable in probability theory/statistics to be integrable, it cannot be limited to countably many values/outcomes.

To make this a bit clearer, consider the “bell curve” or “normal distribution”. Like any continuous distribution, in order to use it to calculate probabilities or for statistical tests that assume a variable is normally distributed (i.e., all the most common tests), whether or not some experiment yields a statistically significant outcome, or whether two variables are correlated, or any number of uses of this distribution, depends upon integration of a particular normal distribution. Trying to integrate a variable that can take on at most countably infinite values and that is everywhere dense (i.e., between any two possible values there are infinitely many other possible values) is impossible. Ergo, if the researchers actually had to do more than look up in some appendix or use some statistical software package to calculate statistical significance using their definition of discrete and continuous, they couldn’t even get a wrong answer; just no answer.

Here’s the real problem: almost none of the variables that are treated as continuously distributed can assume even countably infinitely many values, but only finitely many. If an infinite set like the rationals is too “small” for the standard integration required for all the most common statistical tests, think of how much worse a finite set fails.

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4 Responses to Do researchers understand their own explanations?

  1. Jeff says:

    ‘Far worse, however, is how the researchers seem to understand “continuous”, which they describe in greater detail as follows: “values of a continuous variable are obtained using a measuring tool or scale that implies the existence of a more elaborate operational procedure that must be clearly defined as the basis for measurement. Because of the dependence on a measuring instrument, values obtained will be subjected to measurement errors, not exact, and fall within an interval that consists of infinite points.”’

    So their problem lies with the measuring instrument? It still doesn’t seem like they accurately defined what they mean by continuous. By continuous do they mean something (data or what not) that continues to show up?

    Like an error in a calculation or bug in a computer program. Meaning that error persists until someone can determine what that error actually is or whether or not it actually exists (meaning it might be human error or bias).

    Seems kind of crazy to relate a measuring instrument to something related to statistics. Although I guess everything has a scale for measuring, even if they are responses to something you want feedback on. Likert Scales are usually good for gathering data, but then you have to depend on the person giving the answers to give accurate and truthful answers or feedback.

    Plus, another problem with Likert Scales is how the person doing the polling or information gathering is asking the questions or conducting the survey. If you have a biased survey that makes assumptions without letting people make any definitive response you will probably get some pretty skewed results. I have conducted some Likert style surveys before, so the people creating the poll usually needs to make a generalized version in my opinion versus a definitive response type, which wouldn’t correlate very well with a discrete type variable. Definitive Responses do not seem to correlate very well with discrete variables. It would be interesting to have a digital version though, that has a sliding scale instead of numbers (1-5). Like where your agreement is either very low to very high or somewhere in the middle. Like very low would be red and very high would be green with no numbers, just a sliding scale. The person conducting the survey would be the only one that knows the numbers and it would be a scale of 0-100. So the only difference would be that the person taking the questionnaire wouldn’t know what the scale is based on. =)

    You are kind of giving away your experiment when you let people know the scale or what the measurements are based on, and a scale with only 5 options doesn’t really seem like much of a choice.

    • “and a scale with only 5 options doesn’t really seem like much of a choice.” Particularly when you treat the 5 options as being uncountably infinite.
      The problem with the definition of continuity is that a large majority of researchers in many sciences learn what this concept is through an introductory/elementary statistics textbook, and corresponding course. Continuity and continuous variables are subtle notions that are addressed (and mostly misunderstood) in calculus courses- hopefully addressed again more accurately in a course in real analysis. Really understanding continuity requires really understanding the real number line, which is deceptively nuanced. So statistics textbooks for researchers at the graduate and undergraduate level can only give very informal definitions of continuity that are often just plain wrong (for example, textbooks for the social and behavioral sciences will often relate continuous variables with infinite possibilities, while many textbooks I’ve used or come across on mathematics for physicists, computational sciences, etc., note that all measurements are discrete; the authors of the study reverse the discrete results that are ensured given any measurement with the underlying continuous processes or outcomes of continuous (random) variables).
      Of course, in “real” probability theory (i.e., measure-theoretic) these distinctions are largely or wholly dispensed with. However, as too many researchers never even take undergraduate probability, and measure theory is an advanced undergraduate or beginning graduate level topic, the discrete/continuous dichotomy must remain in order to use the probability theory that so may tables used for determining whether a result was “statistically significant” depend upon.
      Also, it’s ridiculous to think that every individual who responds to a question with something like “strongly agree” even interprets the question the same, let alone intends the same thing by their response such that it is infinitely precise (i.e., corresponds so completely that there is no real differences that would make coding similar responses as equivalent to the same numbers rather than as numbers despite the fact that between any two of the numbers responses are coded as equivalent two there exist uncountably infinitely many other numbers; in fact, if I respond “strongly agree” and we could somehow show that someone else who responded “strongly agree” really “agreed” .00001 “amount” more, there exists uncountably infinitely many values between my coded response and theirs).
      When I was first tasked with running analyses on Likert-type response data for neuroimaging studies, I proposed using fuzzy set theory rather than statistical methods/measures which assume the variables in question are normally distributed. However, as the individuals I was working with didn’t know anything beyond elementary calculus (and some didn’t even know any calculus either), so fuzzy set theory, multidimensional scaling, and most other alternatives were shot down because the researchers didn’t know what they were, but felt comfortable using what we were taught in undergraduate introductory statistics courses.

  2. Pingback: Weekend reads: Top science excuses; how figures can mislead; a strange disclosure - Retraction Watch at Retraction Watch

  3. I should mention that measure-theoretic probability renders the discrete/continuous distinction basically meaningless, but researchers do not, in general, ever use or are able to understand probability at this level. Also, continuity corrections are controversial at best. But this gets into a level of sophistication/technicality that I try to avoid here whenever possible.

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