As much as it pains me to say it, this isn’t the place to offer a detailed defense of personality or clinical psychology. Maybe I’ll make an addendum sometime.

Suffice it to say that Peterson is indeed a personality psychologist, and, amateur beard-stroker’s doubts about the validity of the entire field notwithstanding, personality psychology is indeed an academic discipline, and therefore Peterson is, indeed, an academic. QED. Now I’ve said that he is or was for the most part a mostly unobjectionable kind of researcher in this field and maybe that begs further validation — the most I can offer here is the assurance that the vast majority of his research articles are regular old APA articles about formal scientific studies with research participants and statistical analyses (with some absolute howlers standing out as exceptions to this rule). This is evinced by the fact that when he talks about personality theory strictly, and even the psychometrics that is used to test and (forgive the word) reify the theory, his bullshit-per-sentence ratio drops off precipitously.

And reader, what I’ve said here doesn’t mean that Peterson in fact has some expert platform from which he can make privileged pronouncements about certain subjects; only that he does in fact have some kernel of academic credibility at the very bottom of it all. You’ll be kind enough to recall that the rest of this section is after all dedicated to cataloguing and critiquing positions Peterson maintains as a psychologist; you really don’t really have any excuse if you decide to misconstrue this one

t

r

a

n

s

i

t

i

o

n

[2A] That personality differences explain the pay gap / differences in job placement between men and women

Here is one such critiqued position. I have written at length about this before, and accordingly that previous essay will form the majority of this entry:

I would like to be precise: this short essay is is about minutes 12:00 thru 16:25 of The Debate (almost 7 million views on the original video, at the time of writing) and it is written from the perspective of someone training to join Peterson’s profession. The point of debate in this part of the video is differences in job placement and pay between men and women in the private sector; my main object here is to show that the claims Peterson calmly forwards as psychological or statistical fact in this part of the debate are flawed and disappointments to the science. Those claims are: That the fact that Scandinavia — a region that’s really progressive — still has sex differences throughout sectors of its economy clearly illustrates that there are irreducible differences between the sexes That these irreducible differences — and specifically irreducible differences in personality between the sexes — account for the lion’s share of the disparities in job placement and pay that Newman talks about in this section

Although the essay begins with a very narrow focus on one particular claim of Peterson’s, it expands out into a more robust critique of what turns out to be a pretty consistent claimplication (Def: Not quite a claim, not quite an implication, recently innovated by my main man here) of Peterson’s. That claimplication is that differences in disposition are one of, if not the biggest drivers in the differences between men and women when it comes to job placement and job pay.

Here might be where I expand on this previous argument I’ve made. As of yet none of Peterson’s fans, spelunkers of the master’s cavernous (Def: big, empty) corpus that they are, have jumped on what must seem like an easily-answered criticism of mine:

Peterson touts the importance of multivariate analysis around 5:40 in the video, and derides social scientists who only look for one explanation for the social phenomenon they’re interested in. He does this, granted, in the same breath that he fails to cite the source for his claim that studies have already shown the pay gap doesn’t exist — which isn’t a very academic thing to do — but regardless: Peterson says here, loud and clear, that we shouldn’t narrow our explanations of things to one cause.

Because you see, reader, anyone truly acquainted with Peterson’s body of work would recognize that the “study” he refers to in the section of the video I referred to is almost certainly Warren Farrell’s Why Men Earn More, something Peterson mentioned during his talk with Joe Rogan and at other places besides. The book, at least as Peterson presents it, argues that while there may be a pay gap as such, it’s not because of systematic discrimination so much as the choices men and women make. As the summary offered on the book’s website explains:

Men’s trade-offs include working more hours (women typically work more at home); taking more-hazardous assignments (cab-driving; construction; trucking); moving overseas or to an undesirable location on-demand (women’s greater family obligations inhibit this); and training for more-technical jobs with less people contact (e.g., engineering). Women’s choices appear more likely to involve a balance between work and the rest of life. Women are more likely to balance income with a desire for safety, fulfillment, potential for personal growth, flexibility and proximity-to-home. These lifestyle advantages lead to more people competing for these jobs and thus lower pay.

That my initial ignorance of this fact hasn’t been used to dismiss my entire case out of hand is endlessly surprising. Other things become more surprising still as I think about them. Because you see, reader, I’m realizing now that someone who was not only truly acquainted with Peterson’s body of work but also a striving little social-scientist-cum-essayist like yours truly could have made a substantive objection to my entire essay based only off this one overlooked tidbit. I’ll try to be brief:

As I’ve already said, I explain over the course of the essay that Peterson tends to reduce the pay and placement gaps between men and women to differences in personality between men and women. I talk about how this is generally at odds with the subject matter of the psychology Peterson and I share, and also provide studies that illustrate how it is at odds: personality trait scores alone do not explain much of the variance of the difference between men and women’s pay or occupying of executive positions. Now what another aspirant might have jumped on is how distal personality trait scores themselves are from things like pay and hiring, how far removed they are. They might have argued that the trait scores might not be correlated highly with the end result of income itself, but instead with something more middling like an attitude (e.g., “Desire for material success,”) which itself correlates highly with income. And this is what Peterson and Dr. Farrell have been arguing all along!

Ah the trench warfare of social science.

So first it would be prudent to point out that this, still, reduces the phenomena under discussion to differences in personality, differences in disposition between the sexes. If men and women do make different decisions about their careers in a systematic way, the cause of that, Peterson and Farrell argue one way or the other, is basic differences between their priorities, values, attitudes —differences in their personalities. Introducing some middling mechanisms between the personality traits themselves and these divergent outcomes still keeps (mostly) everything downstream from personality.

Second, it’s important to point this out because while personality could very well be one of the contributing factors to these different priorities about life and career, a variety of other, much more SJW-y explanations still persist. In other words: it may be perfectly true that men and women make different decisions about their jobs in a way like Warren Farrell describes; while at the same time it still being true that the patriarchy and everything else attached is the primary reason they do this and the pay gap exists. (You can just plug ‘patriarchy’ into the place above ‘personality’ in Peterson and Farrell’s explanation of the pay gap, basically). In yet more words: maybe the distribution of dispositions to certain behaviors is different for men and women— but this doesn’t mean that this is the case because of essential or categorical differences between the two. Rather, it could be that, yes, the social, political, and economic structures we live in condition or acculturate men and women to do these things, feel these ways.

If that were the case it would have very different implications for the problem: that would mean that it was malleable, solvable. Regardless, I’ve yet to see any studies that actually make the counterargument I sketched above, and my own essay includes evidence that suggests the difference between men and women’s personality traits themselves is quite modest. For these reasons and others the argument that disposition is the blunt, inert reason for the inequality we see in the workplace looks as dim as ever.

t

r

a

n

s

i

t

i

o

n

[3A] That if you don’t accept the idea of general intelligence (IQ, g) you may as well reject modern psychology

Most of this entry responds to claims made in this excerpted video of one of Peterson’s lectures at the U of T, about intelligence:

*chuckles wisely* “An ‘excerpted’ video … heh … I wish you could get what Dr. Peterson says through ‘excerpted’ videos, friend, — it would have saved me hours — but”

Oh stuff it. It’s 13 minutes long and he has plenty of time to articulate the positions this entry responds to. The titular claim of this entry is made around 1:18 in the video; his justifications for this claim are that:

IQ was originally ‘discovered’ and validated through methods now foundational to correlational psychology today, so that to reject it would be to reject these foundational methods IQ is one of the most well-defined and well-validated constructs in psychology today, so that if we reject it we have reason to reject most other psychological constructs a fortiori

We’ll start with 1, and along the way to responding to it we’ll talk in not-too-great detail about what these foundational methods are, some important critiques of them, and some very basic points about, you know, like logic and stuff.

For starters, Peterson is right that the same man is responsible both for our idea of a general intelligence factor and for many of the most treasured statistical methods in modern correlational psychology; this man is of course Charles Spearman.

Spearman is known for having made a variety of contributions to psychological statistics, but the one Peterson is most likely referencing is factor analysis, which is indeed an indispensable tool for a wide variety of psychological researchers (though far from all of them.) A thumbnail description of factor analysis would be “A statistical method used to evaluate matrices of correlation coefficients for patterns.”

A correlation matrix is actually something you’ve doubtlessly seen before: they look like this:

Author’s own example

The intersection between a row and a column displays the correlation coefficient between the things labelled in each. Naturally things correlate with themselves completely (“1”). Because of how the matrix is constructed the other half usually isn’t filled in for articles for publication, because it is simply a mirror of the other.

So what does it mean to look for patterns in a thing like this?

Let’s say you’ve given your newly developed VeriCog™ intelligence inventory to 350 undergraduates, wherever. The VeriCog™ has five sections, each of which contain 10 questions, for a total of 50 questions. Now, when making the VeriCog™, you had in mind to construct a measure of two different psychological ‘things’ (or more professionally: psychological constructs) which you would name “verbal intelligence” and “quantitative intelligence.” OK. (Brief digression: most of the contemporary theory about constructing and interpreting psychological tests is based on latent variable models. This is only to say that psychologists believe that a well-made test is actually testing some latent, not-directly-observable variable (AKA a construct) which is itself responsible for the differences in participants’ test scores. Which is how most people think of tests in an everyday sense anyway but just to give us a more precise vocabulary.) So OK how to make sure that your test is actually measuring something like these things, working properly? Well, if our design held true, one would expect that scores on the Vocab and Reading Comprehension sections would correlate with one another, and that scores on the Arithmetic and Geometry sections would correlate with one another, reflecting the existence of some latent verbal intelligence and quantitative intelligence variables, respectively. And one would expect that something that, on face, tested a very broad kind of intellectual ability (e.g., “Logic Games,”) would correlate in at least some modest way with all other section scores. So it’s just a matter of booting up some statistical software and asking for the correlations between 5 variables (each section score) that you tell the machine are independent of one another. And you might get something like what’s above.

Now to test your two above guesses you can perform what’s very charitably called an “eye test,” and just kind of look at the matrix. And, given the simplicity of the example, we can indeed see that all of our guesses from above seem to pan out. But how might we perform a more rigorous, algorithmic kind of test of our guesses? Spearman’s factor analysis affords us just such a test.

I am still very new to the math myself so I won’t try to claim to be breaking much down here. For people with some grounding in the social sciences or especially psych, this was the single article that helped me the most to understand some of the more detailed aspects of factor analysis. I’ll do my best to communicate something half as helpful and half as commanding of the details:

With a sample size as hefty as the one sitting before you, it would be wise in this case to subject not just the summed section scores but the scores for every one of the 50 items on the test to factor analysis, to see if scores on the individual items relate to one another like they’re supposed to. When you run a factor analysis on the responses you collected from your undergraduate sample, you’re basically testing to see if some items’ responses correlate highly with one another but not so highly with those of other items — you’re looking for items clumping together in the data. The advantage is that the criterion for what in fact clumps with what is determined by quantifiable features of the data you’re putting in (don’t ask me what), and not just the researcher’s own judgement. Items that are found to clump together are said to constitute a ‘factor,’ which is itself a kind of conglomeration of the items that make it up.

So maybe this will make the idea of a factor clearer: factor analysis was originally developed in part just to collapse big matrices of correlations down to more manageable sizes, making them easier to work with. It was a very pragmatic impetus, in the days before digital computing; 350 responses to a 50 question test would take a great deal of time to tabulate and analyze. And so researchers figured that, for example, since each one of the 10 questions testing vocab on the VeriCog are testing the same thing, in the end, there might be some way to summarize the responses to these questions, so that ultimately a researcher would only have to examine the equivalent of participants’ responses to 5 questions instead of 50. And this is what factor analysis does: if the 10 vocab questions do in fact form a factor, at the end of running a factor analysis of the dataset each participant will have been given a ‘score’ on this vocab factor, which itself should be a serviceable stand-in for their scores on the 10 vocab questions themselves. Moreover, your computer will tell you how strongly each item correlates with this factor score (how strongly each item ‘loads on the factor,’) which can give you a sense for what items more directly tap the construct (AKA factor AKA clump AKA psychological thing) you’re trying to test and what items tap it more indirectly.

Okay? We’re getting pretty deep in the mud of the details at this point, but suffice to say that subjecting the responses you collected to the VeriCog to factor analysis will let you test some simple hypothesis which should hold true if your test is measuring what you think it is:

Items in each of the five sections will form a factor with one another The Vocab and Reading Comp. items will form a factor with one another and the Geometry and Arithmetic items will form a factor with one another

3. All of the responses to the items will load a little bit on a general factor

And if all of those things hold up then you have some good first evidence to believe that the VeriCog is a measure of something like quantitative and verbal intelligence. And, that being said: now that we understand the method itself we’re in a better place to understand some of the criticisms of it, as well as what these criticisms imply about our ability to continue employing it.

So in introducing the general intelligence factor, or g, (which is what IQ tests purport to measure), Spearman made it clear that he was being very precise and scientific about things: g is and only is a factor which obtains between scores on various mental tests — it is simply a statistical conglomeration we can make of these scores, as explained above. To quote:

When asked what G is, one has to distinguish between the meanings of terms and the facts about things. G means a particular quantity derived from statistical operations. Under certain conditions the score of a person at a mental test can be divided into two factors, one of which is always the same in all tests, whereas the other varies from one test to another; the former is called the general factor or G, while the other is called the specific factor. This then is what the G term means, a score-factor and nothing more. But this meaning is sufficient to render the term well defined so that the underlying thing is susceptible to scientific investigation; we can proceed to find out facts about this score-factor, or G factor. We can ascertain the kind of mental operations in which it plays a dominant part as compared with the other or specific factor.

(emphasis added)

Although it’s delivered with an affecting old world locution, this short paragraph by Spearmen commits a few of the critical errors written about at length by philosopher Ned Block in his article “Fictionalism, Functionalism, and Factor Analysis.” For one, in spite of Spearman’s insistence that g is simply a statistical factor, he proceeds to talk about it in a realist way, i.e. as if it were something actually out in the world with the ability to causally interact with other real things. This is not an undue jump to make! Like I said before, modern test theory is based around the idea that latent variables (which are construed to be truly out in the world, one way or another) that these are the things actually being measured by good tests. So talking in a realist way isn’t per se a problem. Rather, as Ned Block writes, the problem here is that Spearman and other psychologists so often play a “double game” with these two different interpretations, quickly retreating to the operationalism of their textbooks (“g strictly speaking is a statistical factor and nothing more”) while still using realist language to talk about it (“g may be the reason some people perform better in the accounting program than others.”)

Now maybe this doesn’t seem too bad. So Spearman lived in the Before Time prior to the publication of 12 Rules for Life, and was in his own way a kind of virtuous pagan, ignorant of the Truth that you should Be Precise in Your Speech. It still remains that, whether g is talked about like its a statistical factor or some real thing in the world, scores on intelligence tests do correlate with one another to make it possible to collapse them into a factor in the first place, and the simplest reason for that would have to be that these tests test the same thing in overlapping ways.

This is the second critical error that Block spends much of the article critiquing: the idea that Correlation Entails Commonality (which he calls the ‘CEC Principle’). This is a principle that belongs not just with Spearman back in the early adolescence of the discipline but is indeed something most psychologists still tend to adhere to today: the idea that if scores on test A correlate with scores on test B to the tune of 0.7, then 0.5 ( ≈0.7 ²) of the variance in scores between the two tests is probably due to some shared latent variable. In other words, if you drew a venn diagram of “Stuff test A measures” and “Stuff test B measures,” half of each would be overlapping with the other — and this is why their scores correlate. Because Ned Block is, you know, a philosopher, I’ll trust him to express his response to this idea more clearly than I can:

In sum, the best explanation of correlation is sometimes commonality, sometimes something else, sometimes in part commonality and in part something else. What is wrong with the CEC Principle is that it insists that correlation of ability tests is always due totally to commonality. It might be replied that while the CEC Principle is false in general, it is true, or nearly true with respect to some very restricted domains: intellectual abilities, for example. It might be said that commonality is the only plausible explanation of the correlation of intellectual abilities. It is easy to see, however, that there are many possible explanations of why abilities might correlate. For example: (1) Perhaps many environmental conditions which nurture one ability tend to nurture a number of abilities. For example, the effect of good health, nutrition, educational and cultural background are likely to be spread over a number of abilities. Personality traits like curiosity and drive for achievement may also affect a number of abilities. (2) Perhaps many environmental conditions which hinder the development of one ability hinder the development of a number of abilities. This is plausible with respect to disease, injury, malnutrition, poor education, lack of encouragement, etc. (3) One ability may sometimes be a prerequisite for — though not a component of — another ability. This could be natural, in the way ability to hear may be required for learning to speak, or societally imposed, as would be the case if successful completion of auditing training depends in part on adhering to certain codes of behavior and dress. (4) People who excel in a number of abilities may tend to marry people who also excel in a number of abilities. Such ‘assortative mating’ has been observed for many traits. If abilities turn out to be heritable, as has often been claimed (though on the basis of poor evidence) they might correlate for genetic reasons.

As Block stresses at the beginning of this quote (though I’m sure it will still be ignored,) he is not saying that the entirety of the correlation between intelligence tests is due to these other explanations; rather he is saying that it would be difficult to separate out what proportion of the correlation is attributable to each explanation in the first place. This is less true in the days of ultra-efficient digital computing, and statistical methods that let us estimate parameters like this with ever greater confidence, but it’s still true: there are a lot of other things besides common identity that the correlations between scores on intelligence tests might reflect.

So let’s take stock of things for a moment here. What is being argued for, argued against? Well I’ll tell you this much: the existence of some kind of general intelligence factor hasn’t been outright denied by myself or anyone else — the proposition I’m denying is, again, the idea that said factor is so well established, is such a prototype of mature psychological science, that to do away with it would be to do away with the discipline more generally.

What I’ve endeavored to show above is:

(1) The nature of the psychological construct that is general intelligence — what it means to say that it is a “score factor” and nothing more

(2) Some fairly straightforward critiques of this conceptualization of intelligence — that it invites us to play a double game as described above; that some of the assumptions leading to its assertion have some clear flaws as quoted directly above

And that’s all. The funny thing is that these points, as complicated to make as they’ve been, are only the framing and context for a much, much simpler point: that just because you reject a particular result of some method doesn’t mean you have to reject the method. Indeed I feel like I could have written this sentence out and rebutted Peterson’s first point right then and there, but then the good people would be a lot less educated about IQ in general, and therefore less able to participate in conversations about IQ, which itself seems to have become a favorite topic among Peterson’s fans. But, uh, yeah: the methods of the matured science of physics produced the luminiferous aether theory of the propagation of light, which had a sound theoretical and, for a while, empirical basis; and people eventually rejected that theory wholesale without rejecting physics. We can produce more examples besides, but this should just be pretty intuitive: rejecting a particular result of some method doesn’t mean you have to reject the method.

Which leads us to the second justification Peterson gave for his original claim that to dismiss IQ is to dismiss psychology and, blissfully, the end of this entry. Peterson says (he doesn’t use the cool Latin I do) but he says that if we reject IQ we should reject pretty much every other psychological construct a fortiori, because it’s one of the best established. What Peterson is talking about here, probably, is the ultra-important notion of construct validity, which as the beginning of that Wiki entry notes is the central organizing concept in modern theories about testmaking. You have already been punished relentlessly so I won’t even begin to try to explain construct validity (in this essay, at least (*winks, and winks only at you*)) but on this front Peterson has more of a point. IQ and general intelligence, from what I know, have built up a good deal of construct validity over the decades, which is a long-term and multifaceted process believe me. But I think the extent to which we have to throw all the other psychological constructs out with IQ depends on how strongly, and in what way, we are actually rejecting IQ.

So someone who rejects IQ in a totally unqualified sense — it doesn’t exist — none of the evidence presented for it is good — this person might be forced to reject a lot of other formal psychological constructs. I don’t think it’s all of them, and I’m sure I could have a little nitpicky argument with someone about what constructs would and wouldn’t get thrown out, — but yes, sure, whatever; this person might be committed, at least partially, to what Peterson says. I don’t think people reject it that fullheartedly though. Most of the time a rejection of the usual conception of IQ amounts to a rejection of it being more than the “score-factor” of Spearman; it usually amounts to an extensive qualification, of the sort offered by Block, rather than a complete denial. People doubt that it is a simple, real entity in the world that inheres in people, or they doubt the mechanism through which some argue it inheres in people, or they doubt the extent of its ability to determine and predict things, or what have you. The point is that usually IQ itself isn’t denied some kind of existence; rather it is a variety of the properties ascribed to it that are denied.

IQ is not a foundational concept in psychology; I don’t deny that it exists, in some way; if you want to though, fine, go ahead; pull out the Jenga block, see the tower still standing.