Reading a regression model without losing sight of the observed data

library(tabxplor)
library(dplyr)

# html tables in RStudio/Positron Viewer (recommended)
options(tabxplor.print = "html")

Une version française de ce document est disponible : Interpréter un modèle de régression sans perdre de vue les pourcentages.

This article teaches one thing: how to build a regression model on social sciences survey data or administrative data, and how to read one, without ever losing sight of the percentages you started from.

That is not a stylistic preference. A regression is often taught as a machine you feed variables into, and the numbers that come out — coefficients, odds ratios — have no obvious relation to anything you could see in the data. Students then learn to recite “all else equal” without being able to say what was held equal, or what difference it made. This article takes the opposite route. Every model here sits next to the cross-table it came from, and the point of the exercise is always the comparison between the two: what did holding the other variables equal actually change?

There are two guides to regression here, and this is the slow one. vignette("tabxplor-reg") is the reference: every family, every argument, the full grid of what each model can report, the weighting rules, the plots. Read it when you need to look something up. This one walks a single analysis from a first cross-table to a finished sentence, and refers you there for the details.

Everything here is also available without writing R code, as a point-and-click Regressions analysis in the tabxplor module for jamovi.

What we are and are not doing

A word on what a model of this kind is for, because it changes how you read every number below.

We are not predicting. Nothing here is about guessing an individual’s outcome. We are not proving causes either: no amount of adjustment turns survey data or administrative data into a real experiment, and this article will show you two cases where “controlling for” a variable makes the answer worse.

What we are doing is untangling correlations. Social variables come in bundles — income travels with education, which travels with occupation, which travels with age. When you find that two things go together, the honest first question is whether they still go together among people who resemble each other on the rest of the bundle. A regression on survey data answers that question, and only that question. Richard Berk put the modest version well: conventional regression analysis does nothing more than produce, from a data set, a collection of conditional means and conditional variances. The interest lies in comparing them.

So the recurring gesture of this article is a round trip: from the observed percentage, out to a model, and back to a percentage. If you cannot get back, something has gone wrong.

The words, in plain language

Seven words do most of the work. Each is defined by what you do with it.

the word	what it means here	what you do with it
outcome `outcome`	the thing you are trying to explain	you count it, or average it — it is the number in the cells
predictor `predictors`	a thing you compare people on	you split the population by it and compare the pieces
reference `ref`	the group everything else is compared with	you choose a reference level for each predictor; a good choice makes the table readable
deviation `measure`	how far a group sits from the reference	you pick how to measure it — a difference, a ratio, or an odds ratio
effect `effect`	a deviation attributed to one predictor	crude when that predictor is alone, adjusted when the others are in the model
adjusted `Model_…`	measured among people who match on the other predictors	you read it against the crude number in the `Obs_…` column, never alone
all else equal	the other variables in your list have been held level	never the ones you left out, or the ones the survey never measured

The code name beside each word is the argument you actually type — in tab_reg() and in the jamovi module alike, since the options carry the same names.

Three of these deserve a warning now rather than later.

“All else equal” never means all else. It means the variables you listed, measured the way your data measured them. Angrist and Pischke put it exactly: regression is a way to make other things equal, but equality is generated only for the variables included as controls — without them, there is no ceteris paribus. A table cannot tell you what is missing from it. The honest long form, the one worth writing out every time, is “all the other chosen predictors being equal”.

“Controlling for” over-promises. You have not controlled anything; nothing in the world was held still. You have compared people who happen to resemble each other. Andrew Gelman makes the point that regression is about comparisons, not about how a variable responds to change — it is a structured way of computing average comparisons in data. Whenever “controlling for X” starts to sound like an experimental intervention on the real world, translate it back to “comparing two people who differ in this, but look alike on the rest of the list”. It is longer, and it is what the number actually means.

“Twice as likely” is not one quantity. Ordinary English uses that phrase for two different things. It can be a risk ratio — “black arrestees were released 1.16 times less often”. Or it can be about the chance an event happens rather than not happening, its odds, in which case comparing two groups gives an odds ratio — “2.11 times the odds of having been released rather than held”. The two numbers are far apart, and Section 2 is about telling them apart.

The data

Four data sets, each chosen because it makes one thing visible.

Three of them ship with tabxplor, each named after the package it comes from — car_arrests and car_salaries from carData (John Fox, Sanford Weisberg and Brad Price), questionr_hdv from questionr (Julien Barnier, François Briatte and Joseph Larmarange), which carries INSEE’s Histoire de vie survey. With thanks to all of them; ?car_arrests credits each in full. The fourth is in base R.

ucb <- as.data.frame(UCBAdmissions) |>                    # base R, no package needed
  tidyr::uncount(Freq) |> tibble::as_tibble() |>
  dplyr::mutate(Admit  = factor(Admit,  levels = c("Admitted", "Rejected")),
         Gender = factor(Gender, levels = c("Male", "Female")))

One habit worth acquiring immediately: put the level you care about first. tabxplor keeps the first level of a factor as the one it models and the one it shows — which is why ucb above names "Admitted" first, and why the three shipped data sets already read "Yes", "White" and "Oui" first. Without it every sentence in this article would be about people who were not released.

The running example is car_arrests: 5 226 people arrested in Toronto for possession of a small quantity of marijuana, between 1997 and 2002, from data gathered for a series in the Toronto Star. The outcome is whether the person was released with a summons rather than held. The data records the arrestee’s race in a column called colour, whether they were employed, whether they were a citizen, their sex and age, and checks — the number of police databases, out of six, on which their name already appeared. Keep that last one in mind; half of this article turns on it.

1. Start from what you can see

Before any model, the table.

tab(car_arrests, colour, released, pct = "row", ref = "first",
    color = "difference", color_signif = "grey_non_signif")

	released
colour	Yes	No	Total
colour	<row%>		<row% (n)>
White	86%	14%	100% (3 938)
Black	74%	26%	100% (1 288)
Total	83%	17%	100% (5 226)
Percentage points (risk) difference: cell ≥ the reference category (in bold) +5; +10; +20; +30 points; cell ≤ ref -5; -10; -20; -30 points. Uncoloured: not significantly different from the reference category (Newcombe score interval, 95% confidence) or under the first colour threshold (±5 points).

86 % of white arrestees were released, against 74 % of black arrestees: a gap of about twelve points. That is the finding. Everything that follows is an attempt to understand it — not to replace it.

Now the other three candidate predictors, in one table:

tab(car_arrests, c(colour, sex, employed, citizen), released, pct = "row",
    color = "difference", color_signif = "grey_non_signif", ref = "first")

		released
	levels	Yes	No	Total
	levels	<row%>		<row% (n)>
colour	White	86%	14%	100% (3 938)
	Black	74%	26%	100% (1 288)
	Total	83%	17%	100% (5 226)
sex	Female	86%	14%	100% ( 443)
	Male	83%	17%	100% (4 783)
	Total	83%	17%	100% (5 226)
employed	Yes	87%	13%	100% (4 111)
	No	69%	31%	100% (1 115)
	Total	83%	17%	100% (5 226)
citizen	Yes	85%	15%	100% (4 455)
	No	73%	27%	100% ( 771)
	Total	83%	17%	100% (5 226)
Percentage points (risk) difference: cell ≥ the reference category (in bold) +5; +10; +20; +30 points; cell ≤ ref -5; -10; -20; -30 points. Uncoloured: not significantly different from the reference category (Newcombe score interval, 95% confidence) or under the first colour threshold (±5 points).

Being employed and being a citizen both go with being released, and strongly. Which raises the obvious question, and it is the question a regression exists to answer: black arrestees are less often employed and less often citizens — so is the race gap just those two gaps in disguise?

2. Turn a percentage into a comparison

A percentage on its own says very little. 74 % is high or low depending on what you hold it against. The whole of regression is built on comparisons, so the first real step is to turn a pair of percentages into one number that compares them: the deviation of a group from its reference.

There are three ways to measure one deviation, they are three readings of the same two numbers, and a plain cross-table is enough to compute all three:

car_arrests |>
  tab(colour, released, pct = "row", ref = "first", stars = TRUE,
      color = "difference", color_signif = "grey_non_signif") |>
  mutate(difference = set_display(Yes, "difference"),
         ratio      = set_display(Yes, "ratio"),
         odds_ratio = set_display(Yes, "odds_ratio") )

	released
colour	Yes	No	Total	difference	ratio	odds_ratio
colour	<row%>		<n>	<row%-diff>	<row%-ratio>	<row%-OR>
White	86%	14%	(3 938)	ref:86%	1	1
Black	74%***	26%***	(1 288)	-12%***	÷1.16***	1/2.11***
Total	83%***	17%***	(5 226)	-3%***	÷1.03***	1/1.24***
Percentage points (risk) difference: cell ≥ the reference category (in bold) +5; +10; +20; +30 points; cell ≤ ref -5; -10; -20; -30 points. Uncoloured: not significantly different from the reference category (Newcombe score interval, 95% confidence) or under the first colour threshold (±5 points). *: significantly different from the reference category (in bold) at the 99% confidence level; : at the 95% level; *: at the 90% level; no star: not significant.

Each of the last three columns is one measure of the deviation. From 86 % and 74 %, you get:

a difference of −12 percentage points — subtract one from the other;
a ratio of ÷1.16 — black arrestees were released 1.2 times less often (a risk ratio);
an odds ratio of 1/2.11 — black arrestees had 2.11 times lower odds than white ones of having been released rather than held, which is the same thing as 2.11 times higher odds of having been held rather than released. An odds ratio is strictly symmetric that way.

In tab_reg() this choice has a name — measure — and it is worth reading it in full, as the measure of deviation. Not an abstract “scale”, and not a unit either: it asks which kind of deviation you want reported — subtract, divide, or divide the odds. It is the one argument you will regularly set, and it is the equivalent of color = in tab().

Odds is the bookmaker’s word. It comes from the racetrack: saying a horse is 3 to 1 says its chance of winning is three times its chance of losing. Odds put an asymmetric situation into a single ratio — the win on top, the loss underneath. Here: of 100 white arrestees, 86 were released and 14 were not, odds of 6.1 to 1; of 100 black arrestees, 74 against 26, odds of 2.8 to 1. The ratio of those two quantities is the odds ratio: $2.8/6.1 = 0.46$ . Because it falls on the wrong side of 1, tabxplor prints its inverse, 1/2.11 — and the sentence turns over with the number: 2.11 times fewer odds.

They are three measures of the same deviation, and they do not read alike at all: the odds ratio makes the gap sound roughly five times louder than the ratio does. That is not a distortion — one divides odds, the other divides percentages. But it is the single most common misreading in the social sciences, and it is worth being blunt about now: an odds ratio of 2 does not mean “twice as often”.

There are two ways to state an odds ratio in English without it being heard as a risk ratio:

name the opposite outcome — “black arrestees were 2.11 times more likely to be held rather than released”. It is the tail of that sentence, and only the tail, that separates the two quantities for an informed reader — while leaving an uninformed one none the wiser.
use the word odds — “their odds of being held were 2.11 times those of white arrestees”. The word carries the distinction on its own: rigorous, and not very vivid.

An odds ratio is hard to say and hard to picture: an abstract quantity carrying a double comparison, and therefore a double reference. A risk ratio needs no such care — “released 1.16 times less often than white arrestees”, and you are done.

What generalises, and what does not

The stars and the greyed-out cells answer a different question from the colours, and it is worth keeping the two apart.

Your data is a sample. You are not interested in these 5 226 people; you are interested in whether what you see among them is a feature of the population they came from, or an accident of who happened to be arrested and recorded. That is all a confidence interval is: the range of values for the population that would be consistent with what you observed.

Worth noting in passing: this is administrative data, not a representative sample, so the biases in how these facts came to be recorded weigh a great deal more than the mathematisable uncertainty due to the luck of the draw.

tab(car_arrests, colour, released, pct = "row", ref = "first", ci = "ref",
    color = "difference", color_signif = "grey_non_signif")

	released
colour	Yes	No	Total
colour	<row%>		<row% (n)>
White	86%	14%	100% (3 938)
Black	74%	26%	100% (1 288)
Total	83%	17%	100% (5 226)
Percentage points (risk) difference: cell ≥ the reference category (in bold) +5; +10; +20; +30 points; cell ≤ ref -5; -10; -20; -30 points. Uncoloured: not significantly different from the reference category (Newcombe score interval, 95% confidence) or under the first colour threshold (±5 points).

A cell is significant when that interval excludes the neutral value — the one that would mean no deviation at all: 0 for a difference, 1 for a ratio or an odds ratio. The printed bracket, the stars, and the greying all read that same interval, so they cannot disagree with each other.

And then significance stops being interesting. This is the part that standard teaching gets wrong for social scientists. Think of an experiment run by doctors or psychologists on 100 people, one arm on a drug and one on a placebo: there, the stars are the result — they are what shows the drug does anything at all. In the social sciences, on a survey with 5 000 respondents, almost every real gap will be significant, including gaps far too small to write a sentence about; on 300 respondents a large and real gap may not be. Significance is a permission slip: it tells you whether you may generalise the observation beyond your sample. Once you have that permission, the only interesting question is how big the effect is — and, in this article, how much the adjustment moved it.

The word itself is a trap in ordinary English, where “significant” means sizeable, or important. A statistically significant effect can be utterly minuscule.

color_signif = "grey_non_signif" is the setting that encodes this: it colours cells by the size of the effect, and it greys out both the ones you have no right to generalise (at a 95 % confidence level) and the ones too small to be worth a sentence. What is left in colour is what you may talk about. Its counterpart color_signif = "guaranteed_effect", useful on small samples, colours every significant deviation instead.

3. Hold the other variables equal

Going from the cross-table to the regression is, put plainly, modelling the kind of deviation you have just chosen — all the other chosen predictors being equal.

Each kind of variable, and so each family of model, has its own base way of measuring that deviation. But as we will see, you can always keep the model that suits your outcome and still report the deviation you care about, through marginal effects.

Choosing a model: what kind of number is the outcome?

tab_reg() needs to know what sort of quantity it is modelling, because each family of model works natively on one particular measure of deviation.

kind	what the outcome is	family	the base measure of deviation
categorical	a yes/no answer	`binomial`	an odds ratio (OR)
—	a choice among 3+ options	`multinomial`	an odds ratio — one column per option
—	a choice among ordered options	`ordinal`	one odds ratio shared by every cut of the scale
numeric	a continuous quantity (money, age, …)	`gaussian`	a difference of means
—	a count (hours of `x`, number of `x`)	`poisson`	a rate ratio — “1.4 times more `x` per day”
—	a score counting yes/no items	`binomial`	an odds ratio

For a categorical variable — a factor in R’s sense — the exact model follows from the number of levels and whether they are ordered: binomial for two, multinomial for three or more, ordinal for three or more that are ordered. tabxplor works this out from the outcome itself.

All three are “logistic” regressions: they model odds, and ratios between odds, $\log(\text{odds}) = \log\!\left(\frac{\%_{\text{held}}}{100 - \%_{\text{held}}}\right)$ . That transform is also called a logit link, and it is what gives the family its name. Which means that the commonest model, applied to the commonest kind of variable in the social sciences, works natively in the hardest deviation to read.

For a numeric variable there is a real choice to make: gaussian, Poisson or binomial?

The sociologist’s way to think about it is not “which statistical distribution does my variable follow?” — a question about mathematics — nor “which test should I run?” — a question about statistics — but “what kind of quantity am I counting?” A continuous measurement, a proper count and a proportion are three different things, and each has its own natural way of saying that one group sits higher than another.

Suppose that instead of released we wanted to model checks — in how many of six police databases the arrested person’s name already appeared. Which models could we choose? It is a count, but it is also a score out of six, and it could be treated as a continuous quantity. Each choice gives the deviation between two groups of a predictor a different shape:

as a count — all the other chosen variables being equal, black arrestees appear in 1.35 times as many databases as white arrestees (a rate ratio, IRR);
as a score out of six — for any one database, their odds of being on it rather than not are 1.55 times higher (an odds ratio);
as a continuous quantity — they appear in 0.5 more databases on average, which is a difference of means, so a subtraction.

Each reading is one model:

tab_reg(car_arrests, "checks", c("colour", "employed", "citizen"), family = "gaussian")
tab_reg(car_arrests, "checks", c("colour", "employed", "citizen"), family = "poisson" )
tab_reg(car_arrests, "checks", c("colour", "employed", "citizen"), family = "binomial", trials = 6)

Put the three side by side and you can see they are three ways of looking at the same thing:

car_arrests |>
  mutate(checks_gaussian = checks, checks_poisson = checks, checks_binomial = checks) |>
  tab_reg(c("checks_gaussian", "checks_poisson", "checks_binomial"),
          family = c("gaussian", "poisson", "binomial"), trials = c(checks_binomial = 6),
          predictors = c("colour", "employed", "citizen"),
          stats = NULL, empirical = FALSE
         )

Regression models: checks_gaussian, checks_poisson +1 more by colour, employed +1 more

			checks_gaussian	checks_poisson	checks_binomial
	levels	n	Model_diff	Model_IRR	Model_OR
	levels	<n>	<diff>	<ratio>	<OR>
Constant	Reference profile	2 883	1.3	1.3	1/3.48***
colour	White	3 938	0	1	1
colour	Black	1 288	+0.5***	×1.35***	1.55***
employed	Yes	4 111	0	1	1
employed	No	1 115	+0.9***	×1.58***	1.96***
citizen	Yes	4 455	0	1	1
citizen	No	771	-0.1	÷1.05	1/1.07
Model (checks_gaussian): linear regression; diff: mean difference (vs the reference category). Model (checks_poisson): Poisson regression; IRR: incidence-rate ratio (vs the reference category). Model (checks_binomial): logistic regression; OR: odds ratio (vs the reference category). checks_gaussian — diff ≥ +0.1; +0.2; +0.4; +0.8 SD; diff ≤ -0.1; -0.2; -0.4; -0.8 SD. Uncoloured: not significantly different from the reference category (Wald interval, 95% confidence) or under the first colour threshold (±0.1 SD). checks_poisson — IRR ≥ ×1.1; ×1.2; ×1.5; ×2; IRR ≤ ÷1.1; ÷1.2; ÷1.5; ÷2. Uncoloured: not significantly different from the reference category (Wald interval on the log rate-ratio, 95% confidence) or under the first colour threshold (×1.1). checks_binomial — OR ≥ 1.2; 1.5; 2; 4; OR ≤ 1/1.2; 1/1.5; 1/2; 1/4. Uncoloured: not significantly different from the reference category (Wald interval on the log odds-ratio, 95% confidence) or under the first colour threshold (×1.2). *: significantly different from the reference category (in bold) at the 99% confidence level (from 1 for the Constant); : at the 95% level; *: at the 90% level; no star: not significant.

Same data, same predictors, three measures of deviation, three sentences to write.

You could argue about which of them fits best — the linear regression uses a gaussian distribution, which sits badly with the fact that “on no database at all” is 35 % of the sample. You could argue about the assumptions each one makes: the binomial treats being on each of the six databases as six equal and independent chances. But the point to take away here is that each family has a preferred quantity, a habitual way of measuring the deviation between the levels of a predictor.

What can I ask of this outcome?

Rather than memorise the grid, ask:

reg_measures(car_arrests, "checks")

#> # A tibble: 10 × 6
#>    family   link       measure    effect                header      reads_as    
#>    <chr>    <chr>      <chr>      <chr>                 <chr>       <chr>       
#>  1 gaussian difference difference conditional           Model_diff  mean differ…
#>  2 gaussian (any)      difference marginal|at_reference Model_mdiff marginal me…
#>  3 gaussian (any)      ratio      marginal|at_reference Model_mRoM  marginal ra…
#>  4 binomial odds_ratio odds_ratio conditional           Model_OR    odds ratio  
#>  5 binomial (any)      difference marginal|at_reference Model_mRD   marginal ri…
#>  6 binomial (any)      ratio      marginal|at_reference Model_mRR   marginal ri…
#>  7 binomial (any)      odds_ratio marginal|at_reference Model_mOR   marginal od…
#>  8 poisson  ratio      ratio      conditional           Model_IRR   incidence-r…
#>  9 poisson  (any)      difference marginal|at_reference Model_mdiff marginal me…
#> 10 poisson  (any)      ratio      marginal|at_reference Model_mIRR  marginal in…

Every family comes with its own default link, its preferred way of measuring a deviation. That is its conditional effect: choosing to read it means reading the model’s own coefficients directly.

The output has one block per family, and each block has two parts. The first is the model itself: its link, and the measure that model’s own coefficients carry. The second is what can be read off the model’s predictions instead — and those rows say link = "(any)", because a marginal effect averages fitted values and does not care which link produced them. Section 4 is about that second part: it is what lets you keep the model that fits your data and still report a deviation you can say out loud — turning an odds ratio, hard to interpret, into something plainer.

So link is the one choice that matters, and it decides only which measure comes with a coefficient: everything else is available whichever model you fit. measure then picks a row, effect is the override knob for the two prediction forms, and header is the column name you will get.

By default only each family’s own model is listed — the one it fits unless told otherwise. Every family can be fitted at other links, which are specialist choices asked for by name; reg_measures(car_arrests, "checks", link = "all") adds them, and marks the family’s own with base_link.

Only what can be built is listed: a measure this kind of outcome does not have simply has no row.

The bridge: the observed column is the cross-table

Here is the move that this whole article rests on. Here is the logistic model of our outcome released — binomial, because the variable is binary — and by default, as you will see, tabxplor puts the deviation observed in the data next to each deviation the model estimates (set empirical = FALSE to drop it):

model <- tab_reg(car_arrests, "released", c("colour", "sex", "employed", "citizen", "checks"))
model

Logistic regression: released by colour, sex +3 more

			released: Yes
	levels	n	Obs_OR	Model_OR
	levels	<n>	<(obs%) OR>	<OR (adj%)>
Constant	Reference profile			8.31*** (89%)
colour	White	3 938	(86%) 1	1 (84%)
colour	Black	1 288	(74%) 1/2.11***	1/1.48*** (79%)
sex	Female	443	(86%) 1	1 (83%)
sex	Male	4 783	(83%) 1/1.26*	1.01 (83%)
employed	Yes	4 111	(87%) 1	1 (86%)
employed	No	1 115	(69%) 1/2.99***	1/2.12*** (75%)
citizen	Yes	4 455	(85%) 1	1 (84%)
citizen	No	771	(73%) 1/2.11***	1/1.77*** (76%)
checks	per 3.08 (2SD), at 1.64 (mean)		1/3.71***	1/3.06***
Model fit	N			5 226
	Dispersion (robust/model SE)			1.02
	Collinearity (max VIF)			1.08
	Influence (max dfbetas)			0.14
	LR vs null			<0.01%
	McFadden R2			0.100
	AIC			4 311
	BIC			4 351
Model: logistic regression; OR: odds ratio (vs the reference category); obs%: observed proportion; adj%: adjusted/predicted proportion. Obs_OR, Model_OR — OR ≥ 1.2; 1.5; 2; 4; OR ≤ 1/1.2; 1/1.5; 1/2; 1/4. Uncoloured: not significantly different from the reference category (Wald interval on the log odds-ratio, 95% confidence; matching Woolf interval on the observed column) or under the first colour threshold (×1.2). *: significantly different from the reference category (in bold) at the 99% confidence level (from 1 for the Constant); : at the 95% level; *: at the 90% level; no star: not significant.

Now look at the Obs_OR column, and compare it with the odds ratios computed straight from the cross-table:

tab(car_arrests, c(colour, sex, employed, citizen), released, pct = "row", ref = "first",
    stars = TRUE, color = "odds_ratio", color_signif = "grey_non_signif", display = "OR")

		released
	levels	Yes	No	Total
	levels	<row%-OR>		<n>
colour	White	1	1	(3 938)
	Black	1/2.11***	2.11***	(1 288)
	Total	1/1.24***	1.24***	(5 226)
sex	Female	1	1	( 443)
	Male	1/1.26*	1.26*	(4 783)
	Total	1/1.24	1.24	(5 226)
employed	Yes	1	1	(4 111)
	No	1/2.99***	2.99***	(1 115)
	Total	1/1.35***	1.35***	(5 226)
citizen	Yes	1	1	(4 455)
	No	1/2.11***	2.11***	( 771)
	Total	1/1.14**	1.14**	(5 226)
Odds ratio: OR ≥ 1.2; 1.5; 2; 4; OR ≤ 1/1.2; 1/1.5; 1/2; 1/4. Uncoloured: not significantly different from the reference category (Wald interval on the log odds-ratio, 95% confidence; Woolf closed form) or under the first colour threshold (×1.2). *: significantly different from the reference category (in bold) at the 99% confidence level; : at the 95% level; *: at the 90% level; no star: not significant.

The same numbers, the same stars. The observed column is not an approximation of the cross-table or a summary of it: it is the cross-table — the odds ratio computed directly on the observed percentages, printed next to the model. The stars were added to say in so many words what the greying already encodes: a level with no star at all is not significantly different from its reference level, even at the 90 % confidence level the lowest star marks. (The two ladders are not the same one: the stars run 90 / 95 / 99 %, while the greying is decided at 95 %.)

That is what makes the comparison fair, and it is worth stating as a rule: in tabxplor, the observed deviation is always the same quantity as the modelled one, computed on the same people, on the same scale, with the same confidence intervals. (Here nothing is missing from the data, so the two populations coincide on their own; in general the observed column is computed on the model’s complete cases, so that they always do.)

The main difference is that the observed deviation holds for one predictor at a time, and is read predictor by predictor, while the modelled deviation concerns the whole table, and is read all the other chosen predictors being equal.

The distance between the two columns measures the model’s adjustment: the movement, small or large, that the model brings to the deviations already in the data. The model adjusts the observed numbers — nudging them, most of the time, sometimes shifting them hard. It is that pair, and not the numbers taken one at a time, that you have to learn to read. A cross-table always gives its results all things unequal : the two crossed variables sit there in a dense context, tied to every other variable, including the ones the survey never measured. A model gives its own all the other chosen predictors being equal. The two columns carry exactly that difference, and nothing more. Section 5 is about reading it.

Reading a row from left to right

model

Logistic regression: released by colour, sex +3 more

			released: Yes
	levels	n	Obs_OR	Model_OR
	levels	<n>	<(obs%) OR>	<OR (adj%)>
Constant	Reference profile			8.31*** (89%)
colour	White	3 938	(86%) 1	1 (84%)
colour	Black	1 288	(74%) 1/2.11***	1/1.48*** (79%)
sex	Female	443	(86%) 1	1 (83%)
sex	Male	4 783	(83%) 1/1.26*	1.01 (83%)
employed	Yes	4 111	(87%) 1	1 (86%)
employed	No	1 115	(69%) 1/2.99***	1/2.12*** (75%)
citizen	Yes	4 455	(85%) 1	1 (84%)
citizen	No	771	(73%) 1/2.11***	1/1.77*** (76%)
checks	per 3.08 (2SD), at 1.64 (mean)		1/3.71***	1/3.06***
Model fit	N			5 226
	Dispersion (robust/model SE)			1.02
	Collinearity (max VIF)			1.08
	Influence (max dfbetas)			0.14
	LR vs null			<0.01%
	McFadden R2			0.100
	AIC			4 311
	BIC			4 351
Model: logistic regression; OR: odds ratio (vs the reference category); obs%: observed proportion; adj%: adjusted/predicted proportion. Obs_OR, Model_OR — OR ≥ 1.2; 1.5; 2; 4; OR ≤ 1/1.2; 1/1.5; 1/2; 1/4. Uncoloured: not significantly different from the reference category (Wald interval on the log odds-ratio, 95% confidence; matching Woolf interval on the observed column) or under the first colour threshold (×1.2). *: significantly different from the reference category (in bold) at the 99% confidence level (from 1 for the Constant); : at the 95% level; *: at the 90% level; no star: not significant.

Take the colour block of that table. It contains four numbers, and they are arranged in the order in which you would think them:

you read	you get	it means
the count `n`	3 938 / 1 288	how many people each row rests on
`Obs_OR`, in brackets	(86 %) / (74 %)	what you actually counted on the data
`Obs_OR`, the estimate	`1/2.11***`	the observed comparison you made from it
`Model_OR`, the estimate	`1/1.48***`	the same comparison, among people alike on the rest
`Model_OR`, in brackets	(84 %) / (79 %)	what the model says the percentages would then be

The two estimates sit next to each other in the middle, which is deliberate: that is the comparison you are here for. The two percentages sit at the outer edges — observed on the far left, adjusted on the far right. (On a table of means those two would be means; tabxplor calls that outer number the base, whatever it happens to be.) A row read left to right is the modelling operation itself: this is what I counted → this is the comparison gap → this is the gap among comparable people on the chosen predictors → this is what the percentages become.

And the sentence, at last:

Of the people arrested for possession in Toronto, 86 % of white arrestees were released and 74 % of black arrestees — odds about 2.1 times lower. Among people alike in sex, employment, citizenship and prior police record, the gap narrows but does not close: odds about 1.5 times lower, or 84 % against 79 % in adjusted percentages.

Note what the adjusted percentages let you say that the odds ratio does not. 1/1.48 is an abstract number; 84 % against 79 % is a statement about people: what the percentages would be if people were really alike on all other chosen predictors.

And what is the “Model fit” block sitting below every table? It is, quite literally, the model’s own report card:

how many people it was fitted on (N);
how it is holding up:
- Dispersion — can you trust its stars? (Is the model’s standard error comparable with the one a robust method gives, allowing for unequal variances between groups?)
- Influence — is one single respondent carrying the result?
- Collinearity — are two predictors in fact measuring the same thing, which distorts everything?
is the model with predictors significantly better than the null model with none (LR vs null)?
how much of the outcome’s variability does it account for (McFadden R2)?

This article never uses it, because its question is what adjustment did, not whether the model is a good one. Both matter, and vignette("tabxplor-reg") explains every row of it.

A predictor with no levels

checks — in how many of six police databases the person’s name already appears — is a number, so it has no categories to compare. Two things change.

tab_reg(car_arrests, "released", c("colour", "checks"), stats = NULL)

Logistic regression: released by colour, checks

			released: Yes
	levels	n	Obs_OR	Model_OR
	levels	<n>	<(obs%) OR>	<OR (adj%)>
Constant	Reference profile			6.51*** (87%)
colour	White	3 938	(86%) 1	1 (85%)
colour	Black	1 288	(74%) 1/2.11***	1/1.72*** (77%)
checks	per 3.08 (2SD), at 1.64 (mean)		1/3.71***	1/3.47***
Model: logistic regression; OR: odds ratio (vs the reference category); obs%: observed proportion; adj%: adjusted/predicted proportion. Obs_OR, Model_OR — OR ≥ 1.2; 1.5; 2; 4; OR ≤ 1/1.2; 1/1.5; 1/2; 1/4. Uncoloured: not significantly different from the reference category (Wald interval on the log odds-ratio, 95% confidence; matching Woolf interval on the observed column) or under the first colour threshold (×1.2). *: significantly different from the reference category (in bold) at the 99% confidence level (from 1 for the Constant); : at the 95% level; *: at the 90% level; no star: not significant.

outcome	numeric predictor	observed range	observed shape (central 95%)
p = %_Yes ; log(p/(1-p))	checks	61-91% (OR 6.7)

First, the effect is per unit — and the unit is a choice. One extra database out of six is a large step; one extra year of age is a small one. So tabxplor reports the effect per two standard deviations by default, and the row says both how far it steps and from where: per 3.08 (2SD), at 1.64 (mean). In a normal, bell-shaped distribution, 95 % of people sit within ±2 SD of the mean, which is roughly the span a binary predictor covers — and that is what makes a continuous row and a two-level row comparable at a glance. Here it reads: someone two standard deviations above the mean — on about 4.7 databases — has about 3.5 times the odds of being held rather than released that someone at the mean (1.64) has. Except that the unit is absurd here, because nobody is on 1.64 databases. Better to anchor at 0 with ref and step by one database with multiplier:

tab_reg(car_arrests, "released", c("colour", "checks"),
        ref = c(checks = 0), multiplier = c(checks = 1),
        empirical = FALSE, stats = NULL)

Logistic regression: released by colour, checks

			released: Yes
	levels	n	Model_OR
	levels	<n>	<OR>
Constant	Reference profile		12.61***
colour	White	3 938	1
colour	Black	1 288	1/1.72***
checks	per 1, at 0		1/1.50***
Model: logistic regression; OR: odds ratio (vs the reference category). OR ≥ 1.2; 1.5; 2; 4; OR ≤ 1/1.2; 1/1.5; 1/2; 1/4. Uncoloured: not significantly different from the reference category (Wald interval on the log odds-ratio, 95% confidence) or under the first colour threshold (×1.2). *: significantly different from the reference category (in bold) at the 99% confidence level (from 1 for the Constant); : at the 95% level; *: at the 90% level; no star: not significant.

outcome	numeric predictor	observed range	observed shape (central 95%)
p = %_Yes ; log(p/(1-p))	checks	61-91% (OR 6.7)

Now it reads per single database: each extra database divides the odds of release by 1.50.

Second, look at the curve below the main table. That is the outcome plotted against the numeric predictor in bins, with no model in it at all, but on the model’s own scale (here, log-odds). The observed range column gives the extremes present in the data: varying the number of police databases gives release rates spread from 61 % to 91 % — the outcome’s own scale — which is an odds ratio of 6.7 between the lowest and the highest bin, on the model’s scale. The curve is there so you can check what a regression always assumes without saying so: that the effect is linear all the way along. Here the curve is close to a line — only jagged — so the assumption holds.

When one straight line is a poor summary, the most readable fix is to stop treating the variable as a number and cut it into categories. shape is what does it:

tab_reg(car_arrests, "released", c("colour", "checks"), shape = c(checks = "quartiles"), stats = NULL)

Logistic regression: released by colour, checks

			released: Yes
	levels	n	Obs_OR	Model_OR
	levels	<n>	<(obs%) OR>	<OR (adj%)>
Constant	Reference profile	1 575		11.77*** (92%)
colour	White	3 938	(86%) 1	1 (85%)
colour	Black	1 288	(74%) 1/2.11***	1/1.75*** (77%)
checks	0	1 851	(91%) 1	1 (91%)
	1 or 2	1 643	(87%) 1/1.62***	1/1.51*** (87%)
	3	953	(75%) 1/3.60***	1/3.26*** (76%)
	4 to 6	779	(65%) 1/5.81***	1/5.27*** (66%)
Model: logistic regression; OR: odds ratio (vs the reference category); obs%: observed proportion; adj%: adjusted/predicted proportion. Obs_OR, Model_OR — OR ≥ 1.2; 1.5; 2; 4; OR ≤ 1/1.2; 1/1.5; 1/2; 1/4. Uncoloured: not significantly different from the reference category (Wald interval on the log odds-ratio, 95% confidence; matching Woolf interval on the observed column) or under the first colour threshold (×1.2). *: significantly different from the reference category (in bold) at the 99% confidence level (from 1 for the Constant); : at the 95% level; *: at the 90% level; no star: not significant.

Now checks is an ordinary factor with its own percentages, effects and colours, and the shape is visible in the observed numbers themselves: 91 %, 87 %, 70 %. Not much happens between zero and one or two databases; the collapse comes later. That is a finding, and a single slope hid it.

shape also takes a square root or a logarithm, to straighten a curve that flattens out as it goes, or a quadratic term for a more complex shape — a hill, a valley, an acceleration.

The reference profile

One row is left to explain, and it is the one students most often skip: Constant.

tab_reg(car_arrests, "released", c("colour", "checks"),
        ref = c(checks = 0), multiplier = c(checks = 1), stats = NULL)

Logistic regression: released by colour, checks

			released: Yes
	levels	n	Obs_OR	Model_OR
	levels	<n>	<(obs%) OR>	<OR (adj%)>
Constant	Reference profile			12.61*** (93%)
colour	White	3 938	(86%) 1	1 (85%)
colour	Black	1 288	(74%) 1/2.11***	1/1.72*** (77%)
checks	per 1, at 0		1/1.53***	1/1.50***
Model: logistic regression; OR: odds ratio (vs the reference category); obs%: observed proportion; adj%: adjusted/predicted proportion. Obs_OR, Model_OR — OR ≥ 1.2; 1.5; 2; 4; OR ≤ 1/1.2; 1/1.5; 1/2; 1/4. Uncoloured: not significantly different from the reference category (Wald interval on the log odds-ratio, 95% confidence; matching Woolf interval on the observed column) or under the first colour threshold (×1.2). *: significantly different from the reference category (in bold) at the 99% confidence level (from 1 for the Constant); : at the 95% level; *: at the 90% level; no star: not significant.

A regression measures everything from somewhere, and the Constant row is that somewhere: the reference profile, the person who has the reference level of every factor and the anchor value of every number, both of them set by ref. Here it is a white arrestee with no prior record at all, and the odds of release for such a person are 12.61 to 1, which is 93 % of chances of being released.

ref is what sets it. For a factor it names the level others are compared with (bold in the table); for a number it names the value the variable is measured from. The default anchor is the mean, because zero is often nonsense — nobody is zero years old — but here, counting police databases, zero is both meaningful and interesting. Anchoring changes the Constant row and nothing else: a straight slope is the same wherever you start reading it from.

Two things are worth knowing. When every predictor is a factor, the reference profile is a real group of people, and the n column counts them — so look at it. It can be tiny: in the model below, the baseline is a white, female, unemployed non-citizen, and the file holds 7 of them. The model still works, but a baseline resting on so few people describes almost nobody, which is worth knowing before you quote it.

tab_reg(car_arrests, "released", c("colour", "sex", "employed", "citizen"), empirical = FALSE, stats = NULL)

Logistic regression: released by colour, sex +2 more

			released: Yes
	levels	n	Model_OR
	levels	<n>	<OR>
Constant	Reference profile	263	10.01***
colour	White	3 938	1
colour	Black	1 288	1/1.72***
sex	Female	443	1
sex	Male	4 783	1/1.24
employed	Yes	4 111	1
employed	No	1 115	1/2.77***
citizen	Yes	4 455	1
citizen	No	771	1/1.67***
Model: logistic regression; OR: odds ratio (vs the reference category). OR ≥ 1.2; 1.5; 2; 4; OR ≤ 1/1.2; 1/1.5; 1/2; 1/4. Uncoloured: not significantly different from the reference category (Wald interval on the log odds-ratio, 95% confidence) or under the first colour threshold (×1.2). *: significantly different from the reference category (in bold) at the 99% confidence level (from 1 for the Constant); : at the 95% level; *: at the 90% level; no star: not significant.

And as soon as a number is among the predictors, that count disappears, as in the table just above. The profile sits at one exact value of a continuous variable, and most of the time nobody sits exactly there. French sociology has known that warning in another form for a long time — Quetelet’s average man never existed — and it is the one Hanmer and Kalkan make about average-case profiles in general. It is one more reason the round trip of Section 4 ends by averaging over real people, unless inventing a typical one is a deliberate, constructed ideal-typical move.

4. Come back to something you can say

Here is the problem with everything above. Try saying out loud to somebody what an odds ratio of 1/1.48 concretely means, in one sentence. You cannot.

An odds ratio makes for a mathematically better model, but it adds a layer of abstraction that can make it hard to interpret. Set against a risk ratio — a plain ratio between two percentages — it usually exaggerates.

By how much, and when? An odds ratio does not depend on which level you name: 1/2.11 for “released” becomes 2.11 for “held” — the same number, turned over. And it does not depend on where you sit on the percentage scale either. That is where the real trap is, because an odds ratio of 2.11 is all of these at once:

from	to	in points	in “times as often”	OR
5 %	10 %	+5 points	×2 — twice as often	2.11
50 %	68 %	+18 points	×1.36	2.11
74 %	86 %	+12 points	×1.16	2.11
90 %	95 %	+5 points	×1.06 — next to nothing	2.11

One single number for four situations nobody would describe alike. Odds are symmetric about 50 %: they treat the event and its opposite outcome identically, so “5 % against 10 %” and “90 % against 95 %” come out the same, though the first pair is a doubling and the second is near-equality.

Hence the reading rule: an odds ratio means nothing until you know which percentage it starts from. Two consequences are worth keeping:

In percentage points (a difference), one and the same odds ratio is worth a great deal in the middle of the scale and almost nothing at either edge: 18 points at 50 %, 5 points at 5 %, 3 points at 95 %.
In “times as often” (a ratio), it is always further from 1 than the risk ratio is, and it only comes close when the event you name is rare. Here the event named is common — 83 % of people are released — so the gap is at its widest: 2.11 as an odds ratio, but “1.16 times more likely to have been held”.

That is exactly why the table prints the observed and the adjusted percentage next to the estimate: they are what puts the odds ratio back on the scale. But rather than compensating after the fact, you can ask the model for another measure directly.

So the last step of the round trip is to come back down to a quantity you could put in a sentence. This costs nothing: it is the same model, fitted once, read several ways.

One model, four readings

tab_reg() asks four questions, and they chain: each one follows from the one before in an “auto” cascade unless you say otherwise.

argument	the question it answers	in practice
`family`	what kind of number is the outcome?	detected for a factor; yours to pick for a number (Section 3)
`link`	which kind of deviation does the model work in?	leave it alone; `vignette("tabxplor-reg")` has the full grid
`measure`	which kind of deviation do you want reported?	this is the one you set
`effect`	where is that number taken from?	leave it alone, except to build ideal types with `"at_reference"`

And one rule ties measure to everything else:

If the measure you ask for is the one the model already works in, you read the model’s own coefficient. If it is not, the model works your measure out from its predictions, for every person in the sample, and averages them.

All you have to do, then, is set measure to the one that suits you.

The odds ratio — the default, because a logistic model works in odds. Comparable with the published literature, hard to say aloud.

tab_reg(car_arrests, "released", c("colour", "sex", "employed", "citizen", "checks"), stats=NULL)

Logistic regression: released by colour, sex +3 more

			released: Yes
	levels	n	Obs_OR	Model_OR
	levels	<n>	<(obs%) OR>	<OR (adj%)>
Constant	Reference profile			8.31*** (89%)
colour	White	3 938	(86%) 1	1 (84%)
colour	Black	1 288	(74%) 1/2.11***	1/1.48*** (79%)
sex	Female	443	(86%) 1	1 (83%)
sex	Male	4 783	(83%) 1/1.26*	1.01 (83%)
employed	Yes	4 111	(87%) 1	1 (86%)
employed	No	1 115	(69%) 1/2.99***	1/2.12*** (75%)
citizen	Yes	4 455	(85%) 1	1 (84%)
citizen	No	771	(73%) 1/2.11***	1/1.77*** (76%)
checks	per 3.08 (2SD), at 1.64 (mean)		1/3.71***	1/3.06***
Model: logistic regression; OR: odds ratio (vs the reference category); obs%: observed proportion; adj%: adjusted/predicted proportion. Obs_OR, Model_OR — OR ≥ 1.2; 1.5; 2; 4; OR ≤ 1/1.2; 1/1.5; 1/2; 1/4. Uncoloured: not significantly different from the reference category (Wald interval on the log odds-ratio, 95% confidence; matching Woolf interval on the observed column) or under the first colour threshold (×1.2). *: significantly different from the reference category (in bold) at the 99% confidence level (from 1 for the Constant); : at the 95% level; *: at the 90% level; no star: not significant.

All the other chosen variables being equal, black arrestees had 1.48 times the odds of being held rather than released — which is to say their odds of being released were 1.48 times lower.

This is where the rule of Section 2 has to be paid for in full: the closing clause, or the word odds. Without one of them, the sentence announces a risk ratio of 1.48 that flatly contradicts the data.

The marginal risk ratio — measure = "ratio". Not the model’s own measure, so it is worked out and averaged, and the header says so: Model_mRR. This time it really is “how many times as often”:

tab_reg(car_arrests, "released", c("colour", "sex", "employed", "citizen", "checks"),
        measure = "ratio", stats=NULL)

Logistic regression: released by colour, sex +3 more

			released: Yes
	levels	n	Obs_RR	Model_mRR
	levels	<n>	<(obs%) ratio>	<ratio (adj%)>
Constant	Population average	5 226		83%
colour	White	3 938	(86%) 1	1 (84%)
colour	Black	1 288	(74%) ÷1.16***	÷1.07*** (79%)
sex	Female	443	(86%) 1	1 (83%)
sex	Male	4 783	(83%) ÷1.04*	×1.00 (83%)
employed	Yes	4 111	(87%) 1	1 (86%)
employed	No	1 115	(69%) ÷1.26***	÷1.15*** (75%)
citizen	Yes	4 455	(85%) 1	1 (84%)
citizen	No	771	(73%) ÷1.17***	÷1.11*** (76%)
checks	per 3.08 (2SD), at 1.64 (mean)		÷1.40***	÷1.29***
Model: logistic regression; mRR: marginal risk ratio (the ratio of adjusted proportions, sample-averaged); obs%: observed proportion; adj%: adjusted/predicted proportion. Obs_RR, Model_mRR — RR ≥ ×1.1; ×1.2; ×1.5; ×2; RR ≤ ÷1.1; ÷1.25; ÷2; ÷4. Uncoloured: not significantly different from the reference category (Wald interval on the log risk-ratio, 95% confidence; matching Katz interval on the observed column) or under the first colour threshold (×1.1). *: significantly different from the reference category (in bold) at the 99% confidence level; : at the 95% level; *: at the 90% level; no star: not significant.

Comparing people alike in sex, employment, citizenship and record, black arrestees were released 1.07 times less often than white arrestees.

Count the other way round — not the chances of being released but of being held — and black arrestees were 1.34 times more likely. A ratio is not symmetric, which is its own problem, and it always speaks louder on the rare category than on the common one:

tab_reg(car_arrests, "released", c("colour", "sex", "employed", "citizen", "checks"),
        measure = "ratio", stats=NULL, outcome_level = "No")

Logistic regression: released by colour, sex +3 more

			released: No
	levels	n	Obs_RR	Model_mRR
	levels	<n>	<(obs%) ratio>	<ratio (adj%)>
Constant	Population average	5 226		17%
colour	White	3 938	(14%) 1	1 (16%)
colour	Black	1 288	(26%) ×1.82***	×1.34*** (21%)
sex	Female	443	(14%) 1	1 (17%)
sex	Male	4 783	(17%) ×1.22	×1.00 (17%)
employed	Yes	4 111	(13%) 1	1 (14%)
employed	No	1 115	(31%) ×2.37***	×1.76*** (25%)
citizen	Yes	4 455	(15%) 1	1 (16%)
citizen	No	771	(27%) ×1.80***	×1.51*** (24%)
checks	per 3.08 (2SD), at 1.64 (mean)		×2.38***	×2.08***
Model: logistic regression; mRR: marginal risk ratio (the ratio of adjusted proportions, sample-averaged); obs%: observed proportion; adj%: adjusted/predicted proportion. Obs_RR, Model_mRR — RR ≥ ×1.1; ×1.2; ×1.5; ×2; RR ≤ ÷1.1; ÷1.25; ÷2; ÷4. Uncoloured: not significantly different from the reference category (Wald interval on the log risk-ratio, 95% confidence; matching Katz interval on the observed column) or under the first colour threshold (×1.1). *: significantly different from the reference category (in bold) at the 99% confidence level; : at the 95% level; *: at the 90% level; no star: not significant.

The marginal difference, in percentage points — measure = "difference". Usually the most useful of all, because points are the unit people already mostly think in.

tab_reg(car_arrests, "released", c("colour", "sex", "employed", "citizen", "checks"),
        measure = "difference", stats=NULL)

Logistic regression: released by colour, sex +3 more

			released: Yes
	levels	n	Obs_RD	Model_mRD
	levels	<n>	<(obs%) diff>	<diff (adj%)>
Constant	Population average	5 226		82.9%
colour	White	3 938	(86%) 0%	0% (84%)
colour	Black	1 288	(74%) -11.7%***	-5.2%*** (79%)
sex	Female	443	(86%) 0%	0% (83%)
sex	Male	4 783	(83%) -3.1%*	+0.1% (83%)
employed	Yes	4 111	(87%) 0%	0% (86%)
employed	No	1 115	(69%) -18.1%***	-10.9%*** (75%)
citizen	Yes	4 455	(85%) 0%	0% (84%)
citizen	No	771	(73%) -12.2%***	-8.1%*** (76%)
checks	per 3.08 (2SD), at 1.64 (mean)		-23.6%***	-18.5%***
Model: logistic regression; mRD: marginal risk difference (on adjusted proportions, in percentage points, sample-averaged); obs%: observed proportion; adj%: adjusted/predicted proportion. Obs_RD, Model_mRD — RD ≥ +5; +10; +20; +30 points; RD ≤ -5; -10; -20; -30 points. Uncoloured: not significantly different from the reference category (Wald interval, 95% confidence) or under the first colour threshold (±5 points). *: significantly different from the reference category (in bold) at the 99% confidence level; : at the 95% level; *: at the 90% level; no star: not significant.

Comparing people alike in sex, employment, citizenship and record, black arrestees were released 5.2 percentage points less often than white arrestees.

The marginal difference (the m in the header) is also the easiest of all to reconstruct by hand: subtract the bracketed adjusted percentage of the reference level from the bracketed adjusted percentage of the level you are reading.

The adjusted percentages themselves are the far end of the round trip, and you do not have to ask for them: every default display already prints them in brackets beside the estimate.

Keeping the actual distribution of sex, employment, citizenship and record as it is, if everyone in the file had been white, an estimated 84 % would have been released; if everyone had been black, only 79 %.

That last sentence is worth dwelling on, because it is what an adjusted percentage is. It is not the percentage for some average person. It is the whole sample, counted twice: once as if everybody were in one group, once as if everybody were in the other, with everything else left as it actually is. That is why it can be compared with the observed 86 % and 74 % on the same row — same people, different bookkeeping.

A marginal effect is a deviation averaged over the sample

That operation has a name in statistics — a marginal effect — and there is a false friend to watch out for: in French sociology, following Philippe Cibois, effet marginal often means an effect expressed in percentage points. Originally the word means averaged over the sample, and it applies to a ratio every bit as much as to a difference. Sample-averaged effect is the plainer name, and it has the advantage of saying what it does.

The word “marginal” comes from the margins of a cross-table. A margin is the Total row: what you get when you stop splitting people into groups and look at everybody at once — and the operation is identical. Work the effect out for each respondent, at their own combination of characteristics, then average, the way a Total row averages the rows above it. (With one difference, of course: the Total is observed, not adjusted, while the averaged effect comes from the model — each person’s predicted percentage is computed with the other predictors held at that person’s own values.)

This is worth learning, because it is what you will mostly be doing with a logistic regression. For a yes/no factor, the binomial family generally fits best with a logit link; but even when the model works best in odds ratios, you may want to read it in risk ratios — and that is in fact the only way to legitimately write a sentence like “men are 1.4 times less likely to have done x than women”.

Since measure left alone gives you the model’s own coefficient (exponentiated when needed), every other measure you ask for is an averaged effect. So for a yes/no outcome, a logistic regression is most often read through marginal differences (measure = "difference"), for the plainest reading and the one closest to the percentages, or through the marginal risk ratio (measure = "ratio") when you need to be able to write “1.4 times less likely”.

And you never have to remember which you asked for, because the table says so in three places.

where to look	you are reading a coefficient	you are reading an averaged effect
the column header	`Model_OR` — no marker	`Model_mRR` — the `m`
the Constant row	Reference profile	Population average
the footer’s `Model:` line	“odds ratio (vs the reference category)”	“…sample-averaged”

The Constant row is the quickest of the three, and it is not decoration: a coefficient is measured from a starting point, so the table shows you that point — one level of every predictor. An averaged effect is averaged over everybody, so there is no starting point to show, and the row gives the whole sample’s own figure instead.

Which one to report

you want to	ask for	you can then write
compare with published models	the default	“odds ratio 1.48”
say “x times more likely”	`measure = "ratio"`	“1.07 times less likely”
say “how many points”	`measure = "difference"`	“5.2 points less often”
show the percentages	the figures in brackets	“84 % against 79 %”

If you report one number, report the percentage points: it is the one your reader can check against the observed percentages sitting in the same table. If you report two, add the odds ratio for the specialists.

For whom? — `effect`, and the ideal type

measure says which deviation; effect says whose — and in particular at which point the number is worked out. You will rarely set it, because the rule above already picks well: the model’s own coefficient when you ask for its own measure, an averaged effect when you ask for another. What it is for is the third possibility.

`effect`	the number describes…
`"conditional"`	anybody — the model assumes one answer fits all
`"marginal"`	the people you actually surveyed, averaged
`"at_reference"`	one ideal-typical profile, built on purpose

effect = "at_reference" reports the deviation not on average, but at the reference profile — the very profile the model’s Constant row is computed for, the reference level of every factor crossed with the anchor value of every number.

And that third row is the one to reach for, because “reference profile” undersells it. That profile is not an average and not an accident: it is a figure you construct, one level of every predictor, chosen because it is worth thinking about. It is Max Weber’s ideal type: a deliberate simplification, built to think with rather than to describe anything empirical. ref is what builds it, so the young male senior manager and the older woman in an unskilled job are both one argument away, and the model will tell you what it expects of each. An ideal type is built, not found — so that the combination matches only a handful of real people is not in itself a fault, provided its sociological meaning has been carefully thought through.

5. Studying what the adjustment did

Reading the gap between the observed deviations and the modelled ones is what Jérôme Deauvieau calls translating a model’s results back into “the language of the cross-table”. It is becoming the norm in some disciplines: STROBE (STrengthening the Reporting of OBservational studies in Epidemiology) requires unadjusted estimates and confounder-adjusted estimates, so that readers can compare them and judge by how much, and in which direction, they moved. The social sciences, which generally claim to stay closer to the empirical, would gain a good deal from adopting the convention widely.

The five things adjustment can do

Students often arrive expecting adjustment to shrink effects, and it very often does. But there are five outcomes, all of them met in real data, and a reader who knows only one of them will misread the other four. Each row below is a real result commented on somewhere in this article:

what happens	example	crude → adjusted
it holds	age, on going to the cinema (France)	−16.8 → −17.1 points
it shrinks	race, on release (Toronto)	−11.7 → −5.2 points
it vanishes	sex, on release (Toronto)	−3.1 → +0.1 points
it grows	class, on going to the cinema (France)	−21.3 → −24.9 points
it reverses	sex, on Berkeley admissions	−14.2 → +1.9 points

Every row is in percentage points, and that is deliberate: Section 6 shows what happens to this same table when you read it on the odds-ratio scale instead.

It holds. Older people go to the cinema less, and holding social class equal changes that by three tenths of a point. Whatever explains the age effect, it is not class.

It shrinks. The race gap in Toronto loses more than half its size. Part of what looked like a direct race effect was employment, citizenship and prior record — all consequences, in their own way, of one dimension of structural racism. But the other half, the half that holds, could equally be a more direct racialisation playing out in the encounter with the officers — or other factors not measured here.

It vanishes. Women were released slightly more often than men (86 % against 83 %, just significant). Among comparable people, nothing at all — the table below is in percentage points, which is what measure = "difference" asks for.

tab_reg(car_arrests, "released", c("colour", "sex", "employed", "citizen", "checks"),
        measure = "difference", stats = NULL)

Logistic regression: released by colour, sex +3 more

			released: Yes
	levels	n	Obs_RD	Model_mRD
	levels	<n>	<(obs%) diff>	<diff (adj%)>
Constant	Population average	5 226		82.9%
colour	White	3 938	(86%) 0%	0% (84%)
colour	Black	1 288	(74%) -11.7%***	-5.2%*** (79%)
sex	Female	443	(86%) 0%	0% (83%)
sex	Male	4 783	(83%) -3.1%*	+0.1% (83%)
employed	Yes	4 111	(87%) 0%	0% (86%)
employed	No	1 115	(69%) -18.1%***	-10.9%*** (75%)
citizen	Yes	4 455	(85%) 0%	0% (84%)
citizen	No	771	(73%) -12.2%***	-8.1%*** (76%)
checks	per 3.08 (2SD), at 1.64 (mean)		-23.6%***	-18.5%***
Model: logistic regression; mRD: marginal risk difference (on adjusted proportions, in percentage points, sample-averaged); obs%: observed proportion; adj%: adjusted/predicted proportion. Obs_RD, Model_mRD — RD ≥ +5; +10; +20; +30 points; RD ≤ -5; -10; -20; -30 points. Uncoloured: not significantly different from the reference category (Wald interval, 95% confidence) or under the first colour threshold (±5 points). *: significantly different from the reference category (in bold) at the 99% confidence level; : at the 95% level; *: at the 90% level; no star: not significant.

The sex row goes from -3.1 %* to +0.1 %. Read that carefully, because it is the most common place to over-claim: this does not say men and women were treated the same. It says the small observed difference is what you would expect from the fact that the men and women in this file differ in employment, citizenship and record — so there is nothing left for sex to explain in this particular case.

It grows, and it reverses — the two cases that are the least taught, and Section 6 has one of each.

Watching it move, one block at a time

The single most useful thing you can do with a regression is to fit it several times, adding predictors one by one, and watch the numbers move. Pass a named list instead of a vector:

model <-
tab_reg(car_arrests, "released",
        list("colour"         = "colour",
             "+ who they are" = c("colour", "sex", "citizen", "employed"),
             "+ prior record" = c("colour", "sex", "citizen", "employed", "checks")),
        measure = "difference", display = "est", stats = NULL)
model

Logistic regressions (models comparison): released, ‘Yes’ (mRD)

	levels	n	Obs_RD	colour	+ who they are	+ prior record
	levels	<n>	<diff>	<diff>	<diff>	<diff>
Constant	Population average	5 226		82.9%	82.9%	82.9%
colour	White	3 938	0%	0%	0%	0%
colour	Black	1 288	-11.7%***	-11.7%***	-7.8%***	-5.2%***
sex	Female	443	0%		0%	0%
sex	Male	4 783	-3.1%*		-2.7%	+0.1%
citizen	Yes	4 455	0%		0%	0%
citizen	No	771	-12.2%***		-7.6%***	-8.1%***
employed	Yes	4 111	0%		0%	0%
employed	No	1 115	-18.1%***		-16.2%***	-10.9%***
checks	per 3.08 (2SD), at 1.64 (mean)		-23.6%***			-18.5%***
Model: logistic regression of released (‘Yes’); mRD: marginal risk difference (on adjusted proportions, in percentage points, sample-averaged). Obs_RD, colour, + who they are, + prior record — RD ≥ +5; +10; +20; +30 points; RD ≤ -5; -10; -20; -30 points. Uncoloured: not significantly different from the reference category (Wald interval, 95% confidence) or under the first colour threshold (±5 points). *: significantly different from the reference category (in bold) at the 99% confidence level; : at the 95% level; *: at the 90% level; no star: not significant.

Read the colour row across: −11.7 points → −11.7 → −7.8 → −5.2. The first two are the same number, and that is the bridge of Section 3 shown once more: a model with colour as its only predictor is the cross-table. Then employment and citizenship account for about a third of the gap; prior record accounts for another quarter; and the five points seen earlier survive everything in the file.

This is the format to write up, if journals can find room for it. It shows the reader what each set of controls bought, instead of asking them to trust a single final number. And it demands a patient model build, which is what stops you keeping, at the end, only the specification that gave the answer you wanted.

Colouring the movement

On a wide table, comparing the adjustment column by column gets tedious. color = "adjustment" colours the movement itself. The reference for the colours then becomes the Obs_RD column — the observed risk difference — in bold:

tab_reg(car_arrests, "released",
        list("colour"         = "colour",
             "+ who they are" = c("colour", "sex", "citizen", "employed"),
             "+ prior record" = c("colour", "sex", "citizen", "employed", "checks")),
        measure = "difference", color = "adjustment", color_signif = "guaranteed_effect",
        stats = NULL)

Logistic regressions (models comparison): released, ‘Yes’ (mRD)

	levels	n	Obs_RD	colour	+ who they are	+ prior record
	levels	<n>	<(obs%) diff>	<diff>	<diff>	<diff>
Constant	Population average	5 226		82.9%	82.9%	82.9%
colour	White	3 938	(86%) 0%	0%	0%	0%
colour	Black	1 288	(74%) -11.7%***	-11.7%***	-7.8%***	-5.2%***
sex	Female	443	(86%) 0%		0%	0%
sex	Male	4 783	(83%) -3.1%*		-2.7%	+0.1%
citizen	Yes	4 455	(85%) 0%		0%	0%
citizen	No	771	(73%) -12.2%***		-7.6%***	-8.1%***
employed	Yes	4 111	(87%) 0%		0%	0%
employed	No	1 115	(69%) -18.1%***		-16.2%***	-10.9%***
checks	per 3.08 (2SD), at 1.64 (mean)		-23.6%***			-18.5%***
Model: logistic regression of released (‘Yes’); mRD: marginal risk difference (on adjusted proportions, in percentage points, sample-averaged). Obs_RD — The observed effect (the reference for the adjustment). colour, + who they are, + prior record — RD further from no effect (0) than the observed column, by +0; +2; +5; +10 points; RD closer to no effect (0) than the observed column (or inversed effect), by -0; -2; -5; -10 points. Uncoloured: not significantly different from the observed effect. Some rows carry no test and are left uncoloured. *: significantly different from the reference category (in bold) at the 99% confidence level; : at the 95% level; *: at the 90% level; no star: not significant.

Shades from yellow to red mean the adjustment pulled the deviation closer to the neutral value (0 = “no effect”, or 1 for a ratio), or even reversed its direction — from a negative difference to a positive one, or from “A is less likely than B” (÷) to “A is more likely than B” (×).

Deepening shades of blue mean the opposite: the adjustment pushed the deviation further from the neutral value. If the observed difference was already below 0, the model widened the gap between the two groups in the same direction.

With color_signif = "guaranteed_effect", everything significant is coloured, and an adjustment that stays grey means the movement is no bigger than noise: at this sample size, the modelled deviation is not significantly different from the observed one.

(The first threshold is a change of ×1.1, the old epidemiological convention that a 10 % change in an estimate is worth noticing. Treat it as a reading aid, not a rule. It entered the literature from a 1993 simulation by Maldonado and Greenland about selecting confounders, and has been argued against as a selection rule ever since — the appropriate cut-off depends on the effect size, the sample size and the correlations involved. As a device for asking “did this move, and which way?”, it is fine. As a rule for deciding which variables belong in your model, it is not.)

You can print both at once: color = c("measure", "adjustment") uses the text colour for each column’s own effect and the background colour for the movement away from the observed one — though the result is not always easy to read.

6. What the model cannot settle

Everything so far was mechanics. This section is the part that decides whether your analysis is any good, and none of it is a computation.

“All else equal” is a sentence about your table

The adjusted number depends entirely on which variables you listed. Not a little — a lot:

tab_reg(car_arrests, "released",
        list("without prior record" = c("colour", "employed", "citizen"),
             "with prior record"    = c("colour", "employed", "citizen", "checks") ),
        measure = "difference", display="est")

Logistic regressions (models comparison): released, ‘Yes’ (mRD)

	levels	n	Obs_RD	without prior record	with prior record
	levels	<n>	<diff>	<diff>	<diff>
Constant	Population average	5 226		82.9%	82.9%
colour	White	3 938	0%	0%	0%
colour	Black	1 288	-11.7%***	-7.9%***	-5.2%***
employed	Yes	4 111	0%	0%	0%
employed	No	1 115	-18.1%***	-16.1%***	-10.9%***
citizen	Yes	4 455	0%	0%	0%
citizen	No	771	-12.2%***	-7.7%***	-8.1%***
checks	per 3.08 (2SD), at 1.64 (mean)		-23.6%***		-18.5%***
Model fit	N			5 226	5 226
	Dispersion (robust/model SE)			1.01	1.02
	Collinearity (max VIF)			1.06	1.07
	Influence (max dfbetas)			0.07	0.07
	LR vs null			<0.01%	<0.01%
	McFadden R2			0.057	0.100
	AIC			4 513	4 309
	BIC			4 539	4 342
	LR vs previous				<0.01%
Model: logistic regression of released (‘Yes’); mRD: marginal risk difference (on adjusted proportions, in percentage points, sample-averaged). Obs_RD, without prior record, with prior record — RD ≥ +5; +10; +20; +30 points; RD ≤ -5; -10; -20; -30 points. Uncoloured: not significantly different from the reference category (Wald interval, 95% confidence) or under the first colour threshold (±5 points). *: significantly different from the reference category (in bold) at the 99% confidence level; : at the 95% level; *: at the 90% level; no star: not significant.

The same race gap is −5.2 points in one column and −7.9 in the other. Both are “all else equal”. They differ by half again, and the only difference is one variable.

So when you write “all else equal”, you are making a claim about a list you chose. The model has no opinion about what is missing from it, and no way to warn you. Which brings us to the question that decides everything.

Adjusting away the thing you are studying

Should checks be in the model at all?

The case for is obvious: someone with a long record is treated differently, and it would be naive to compare a first offender with a repeat one. The case against needs one more table. checks counts police databases — previous arrests, previous convictions, parole status. So ask the question the model cannot ask itself: is prior record itself patterned by race?

tab(car_arrests, colour, checks, pct = "row", color = "difference", ref = 1)

	checks
colour	mean
colour	<mean (cv)>
White	1.5 (cv 102%)
Black	2.1 (cv 73%)
Total	1.6 (cv 94%)
Standardized mean difference: cell ≥ the reference category (in bold) +0.1; +0.2; +0.4; +0.8 SD; cell ≤ ref -0.1; -0.2; -0.4; -0.8 SD.

tab_reg(car_arrests, "checks", c("colour", "sex", "employed", "citizen"),
        family = "binomial", trials = 6)

Logistic regression: checks by colour, sex +2 more

			checks
	levels	n	Obs_OR	Model_OR
	levels	<n>	<(obs mean) OR>	<OR (adj mean)>
Constant	Reference profile	263		1/5.98*** (0.9)
colour	White	3 938	(1.5) 1	1 (1.5)
colour	Black	1 288	(2.1) 1.64***	1.52*** (2.0)
sex	Female	443	(1.1) 1	1 (1.1)
sex	Male	4 783	(1.7) 1.76***	1.80*** (1.7)
employed	Yes	4 111	(1.4) 1	1 (1.4)
employed	No	1 115	(2.4) 2.04***	2.00*** (2.3)
citizen	Yes	4 455	(1.6) 1	1 (1.7)
citizen	No	771	(1.8) 1.13***	1/1.09* (1.6)
Model fit	N			5 226
	Dispersion (robust/model SE)			1.42
	Pearson dispersion (phi)			1.90
	Collinearity (max VIF)			1.06
	Influence (max dfbetas)			0.22
	LR vs null			<0.01%
	McFadden R2			0.048
	AIC			18 837
	BIC			18 869
Model: logistic regression; OR: odds ratio (vs the reference category); obs%: observed proportion; adj%: adjusted/predicted proportion. Obs_OR, Model_OR — OR ≥ 1.2; 1.5; 2; 4; OR ≤ 1/1.2; 1/1.5; 1/2; 1/4. Uncoloured: not significantly different from the reference category (Wald interval on the log odds-ratio, 95% confidence; matching Woolf interval on the observed column) or under the first colour threshold (×1.2). *: significantly different from the reference category (in bold) at the 99% confidence level (from 1 for the Constant); : at the 95% level; *: at the 90% level; no star: not significant.

Black arrestees appear in 2.1 databases on average against 1.5, and the pattern survives adjustment for sex, employment and citizenship (odds 1.52 per database, still highly significant). Prior record is not a neutral background fact. It is a record of previous encounters with the same police force whose behaviour is under study.

If some of those earlier encounters happened for the same reasons the present one is being examined, then “controlling for prior record” removes part of the very phenomenon you are trying to measure. The five points that survive in the adjusted model are then not “the race effect purged of confounding” but the race effect at this arrest, over and above everything the police already recorded about this person — a narrower and more defensible claim, and a different one.

This has a name — overcontrol bias, or conditioning on a mediator — and Cinelli, Forney and Pearl state the rule in one line: block the spurious paths between your variables, and do not disturb the causal ones. A variable that lies between the thing you study and the outcome is on a causal path, and adjusting for it blocks the effect you were looking for.

Nothing in the output tells you which case you are in. The table looks identical either way. This is a question about sociology, not about statistics, and it is why this article began by saying no amount of adjustment turns survey data into a controlled experiment.

The same problem, in a different discipline. These are academic salaries, from a data set a college collected in 2008–2009 precisely to monitor a pay gap. The everyday version of the same trap is the phrase the press reaches for every year: “for the same job”.

tab_reg(car_salaries, "salary",
        list("sex alone"      = "sex",
             "+ field, years" = c("sex", "discipline", "yrs.service"),
             "+ rank"         = c("sex", "discipline", "yrs.service", "rank")),
        family = "gaussian", empirical = FALSE)

Linear regressions (models comparison): salary (diff)

	levels	n	sex alone	+ field, years	+ rank
	levels	<n>	<diff>	<diff>	<diff>
Constant	Reference profile	39	101 002	99 019	66 788
sex	Female	39	0	0	0
sex	Male	358	+14 088***	+8 423*	+4 771
discipline	A	181		0	0
discipline	B	216		+13 034***	+13 473***
yrs.service	per 26 (2SD), at 17.6 (mean)			+21 646***	-2 309
rank	AsstProf	67			0
	AssocProf	64			+14 560***
	Prof	266			+49 160***
Model fit	N		397	397	397
	Dispersion (robust/model SE)		0.87	1.20	1.36
	Collinearity (max VIF)			1.05	1.63
	Influence (max dfbetas)		0.32	0.34	0.51
	F		0.57%	<0.01%	<0.01%
	R2		0.019	0.165	0.448
	Adjusted R2		0.017	0.158	0.441
	Residual SD		30 034.61	27 789.81	22 651.61
	F vs previous			<0.01%	<0.01%
Model: linear regression of salary; diff: mean difference (vs the reference category). diff ≥ +0.1; +0.2; +0.4; +0.8 SD; diff ≤ -0.1; -0.2; -0.4; -0.8 SD. Uncoloured: not significantly different from the reference category (Wald interval, 95% confidence) or under the first colour threshold (±0.1 SD). *: significantly different from the reference category (in bold) at the 99% confidence level; : at the 95% level; *: at the 90% level; no star: not significant.

The raw gap is $14 088 in favour of men. Adding field and seniority takes it to $8 423; adding academic rank — assistant professor, associate professor, full professor — takes it to $4 771, and the stars disappear. A careless reading would be “two thirds of the pay gap is explained, and the rest is not significant”. Both halves of that sentence are traps.

On the rank column, ask the same question as before:

tab(car_salaries, sex, is_prof, pct = "row", color = "difference", ref = 1, color_signif = "grey_non_signif")

	is_prof
sex	Full professor	Not yet	Total
sex	<row%>		<row% (n)>
Female	46%	54%	100% ( 39)
Male	69%	31%	100% (358)
Total	67%	33%	100% (397)
Percentage points (risk) difference: cell ≥ the reference category (in bold) +5; +10; +20; +30 points; cell ≤ ref -5; -10; -20; -30 points. Uncoloured: not significantly different from the reference category (Newcombe score interval, 95% confidence) or under the first colour threshold (±5 points).

46 % of the women are full professors, against 69 % of the men. If promotion is itself unequal, then controlling for rank compares women with the men who were promoted at the same rate — and adjusts away precisely the mechanism through which a pay gap would operate. The $4 771 is the gap within rank: a real quantity, a narrower question, and not “the pay gap corrected”.

On the disappearing stars, remember Section 2. There are 397 people here and 39 of them are women. “Not significant” means this sample can no longer tell a $4 771 gap apart from zero — not that the gap is zero. Print the interval and see:

tab_reg(car_salaries, "salary", c("sex", "discipline", "yrs.service", "rank"),
        family = "gaussian", display = "est_ci", empirical = FALSE)

Linear regression: salary by sex, discipline +2 more

			salary
	levels	n	Model_diff
	levels	<n>	<diff ci>
Constant	Reference profile		66 788
sex	Female	39	0
sex	Male	358	+4 771 [-2 853;+12 396]
discipline	A	181	0
discipline	B	216	+13 473*** [+8 921;+18 026]
yrs.service	per 26 (2SD), at 17.6 (mean)		-2 309 [-8 019;+3 400]
rank	AsstProf	67	0
	AssocProf	64	+14 560*** [+6 503;+22 618]
	Prof	266	+49 160*** [+41 621;+56 698]
Model fit	N		397
	Dispersion (robust/model SE)		1.36
	Collinearity (max VIF)		1.63
	Influence (max dfbetas)		0.51
	F		<0.01%
	R2		0.448
	Adjusted R2		0.441
	Residual SD		22 651.61
Model: linear regression; diff: mean difference (vs the reference category). diff ≥ +0.1; +0.2; +0.4; +0.8 SD; diff ≤ -0.1; -0.2; -0.4; -0.8 SD. Uncoloured: not significantly different from the reference category (Wald interval, 95% confidence) or under the first colour threshold (±0.1 SD). *: significantly different from the reference category (in bold) at the 99% confidence level; : at the 95% level; *: at the 90% level; no star: not significant.

The gap is compatible with anything from $2 853 in favour of women to $12 396 in favour of men. That is not “no difference”; it is a sample too small to tell the difference. With ten times as many people and the same estimate, the very same $4 771 would come out highly significant. Absence of evidence, on 39 women, is very weak evidence of absence — and this is exactly why display = "est_ci" is worth reaching for whenever an important star goes missing.

And there is a reversal hiding in that table, worth a glance: yrs.service goes from +$10 823 crude to −$1 155 adjusted. Seniority strongly predicts salary — until you hold rank equal, at which point extra years are worth nothing at all. Long service pays through promotion, and not otherwise.

When adjustment reverses the answer

The most famous table in the discipline. Berkeley graduate admissions, autumn 1973, the six largest departments:

tab(ucb, Gender, Admit, pct = "row", color = "difference", color_signif = "grey_non_signif")

	Admit
Gender	Admitted	Rejected	Total
Gender	<row%>		<row% (n)>
Male	45%	55%	100% (2 691)
Female	30%	70%	100% (1 835)
Total	39%	61%	100% (4 526)
Percentage points (risk) difference: cell ≥ the Total row +5; +10; +20; +30 points; cell ≤ the Total row -5; -10; -20; -30 points. Uncoloured: not significantly different from the Total row (Newcombe score interval, 95% confidence) or under the first colour threshold (±5 points).

45 % of men were admitted against 30 % of women — a gap of fourteen points, on 4 526 applications. Now hold the departments inside the university equal:

tab_reg(ucb, "Admit", c("Gender", "Dept"), measure = "difference")

Logistic regression: Admit by Gender, Dept

			Admit: Admitted
	levels	n	Obs_RD	Model_mRD
	levels	<n>	<(obs%) diff>	<diff (adj%)>
Constant	Population average	4 526		38.8%
Gender	Male	2 691	(45%) 0%	0% (38%)
Gender	Female	1 835	(30%) -14.2%***	+1.9% (40%)
Dept	A	933	(64%) 0%	0% (65%)
	B	585	(63%) -1.2%	-1.0% (64%)
	C	918	(35%) -29.3%***	-30.5%*** (35%)
	D	792	(34%) -30.5%***	-31.3%*** (34%)
	E	584	(25%) -39.2%***	-40.4%*** (25%)
	F	714	( 6%) -58.0%***	-58.7%*** ( 6%)
Model fit	N			4 526
	Dispersion (robust/model SE)			1.00
	Collinearity (max VIF)			1.38
	Influence (max dfbetas)			0.14
	LR vs null			<0.01%
	McFadden R2			0.142
	AIC			5 201
	BIC			5 246
Model: logistic regression; mRD: marginal risk difference (on adjusted proportions, in percentage points, sample-averaged); obs%: observed proportion; adj%: adjusted/predicted proportion. Obs_RD, Model_mRD — RD ≥ +5; +10; +20; +30 points; RD ≤ -5; -10; -20; -30 points. Uncoloured: not significantly different from the reference category (Wald interval, 95% confidence) or under the first colour threshold (±5 points). *: significantly different from the reference category (in bold) at the 99% confidence level; : at the 95% level; *: at the 90% level; no star: not significant.

−14.2 points becomes +1.9, and no longer significant. Not reduced — reversed in sign. And the mechanism is entirely visible in a cross-table, which is the point:

tab(ucb, Gender, Dept, pct = "row", color = "difference", ref = 1)

	Dept
Gender	A	B	C	D	E	F	Total
Gender	<row%>						<row% (n)>
Male	31%	21%	12%	15%	7%	14%	100% (2 691)
Female	6%	1%	32%	20%	21%	19%	100% (1 835)
Total	21%	13%	20%	17%	13%	16%	100% (4 526)
Percentage points (risk) difference: cell ≥ the reference category (in bold) +5; +10; +20; +30 points; cell ≤ ref -5; -10; -20; -30 points.

Read the two rows against the departments’ admission rates in the previous table (A 64 %, B 63 %, C 35 %, D 34 %, E 25 %, F 6 %). 31 % of the men applied to department A, the easiest, against 6 % of the women; 21 % of the women applied to E and 19 % to F, the two hardest, against 7 % and 14 % of the men. Women applied to departments that admitted fewer people. Within departments, the differences are small and go both ways.

Bickel, Hammel and O’Connell published this in Science in 1975, and it is the standard illustration of a composition effect — what French statisticians call an effet de structure, and what INSEE explains with an example everyone understands: average pay can rise while pay within every occupation stagnates, simply because the well-paid occupations grow. The reversal itself also goes by another name, Simpson’s paradox.

Resist the tidy moral. “The bias was an illusion” is not what this table shows, and the modern literature on it is firm about that. Conditioning on department estimates the gap within departments, which leaves entirely untouched the question of why women applied where they applied, and why the departments women applied to were the ones that could admit fewer of their applicants. The model answered a narrower question, and answered it well — handing us not a verdict but a concrete mechanism to go and study.

When the movement is arithmetic and not confounding

Now the trap that is pure mathematics, and the reason this article keeps insisting on percentages. It is the one already flagged above, about comparing odds ratios.

The French Histoire de vie survey of 2003, an extract of which Julien Barnier ships with questionr, asked whether people go to the cinema. Cross it with social class:

tab_reg(questionr_hdv, "cinema", c("qualif", "age"))

Logistic regression: cinema by qualif, age

			cinema: Oui
	levels	n	Obs_OR	Model_OR
	levels	<n>	<(obs%) OR>	<OR (adj%)>
Constant	Reference profile			2.32*** (70%)
qualif	Cadre	260	(65%) 1	1 (67%)
	Ouvrier specialise	203	(22%) 1/6.63***	1/10.75*** (22%)
	Ouvrier qualifie	292	(25%) 1/5.67***	1/7.73*** (27%)
	Technicien	86	(43%) 1/2.50***	1/3.82*** (40%)
	Profession intermediaire	160	(46%) 1/2.25***	1/2.74*** (47%)
	Employe	594	(44%) 1/2.39***	1/3.50*** (42%)
	Autre	58	(47%) 1/2.17***	1/3.12*** (44%)
age	per 31.6 (2SD), at 48.2 (mean)		1/5.87***	1/7.25***
Model fit	N			1 653
	Dispersion (robust/model SE)			1.04
	Collinearity (max VIF)			1.08
	Influence (max dfbetas)			0.24
	LR vs null			<0.01%
	McFadden R2			0.181
	AIC			1 853
	BIC			1 897
Model: logistic regression; OR: odds ratio (vs the reference category); obs%: observed proportion; adj%: adjusted/predicted proportion. Obs_OR, Model_OR — OR ≥ 1.2; 1.5; 2; 4; OR ≤ 1/1.2; 1/1.5; 1/2; 1/4. Uncoloured: not significantly different from the reference category (Wald interval on the log odds-ratio, 95% confidence; matching Woolf interval on the observed column) or under the first colour threshold (×1.2). *: significantly different from the reference category (in bold) at the 99% confidence level (from 1 for the Constant); : at the 95% level; *: at the 90% level; no star: not significant.

65 % of senior managers go to the cinema against 22 % of unskilled workers — a large, thoroughly Bourdieusian class gap. And the odds ratio grows under adjustment for age, from 1/6.63 to 1/10.75. Read naively, that says age was masking part of the class gap, which would be an interesting finding.

It is not a finding. Look at the mean ages:

tab(questionr_hdv, qualif, c(cinema, age), pct = "row", na = "drop_all",
    color = "difference", ref = 1)

	cinema			age
qualif	Oui	Non	Total	mean
qualif	<row%>		<row% (n)>	<mean (cv)>
Cadre	65%	35%	100% ( 260)	50 (cv 30%)
Ouvrier specialise	22%	78%	100% ( 203)	49 (cv 37%)
Ouvrier qualifie	25%	75%	100% ( 292)	50 (cv 31%)
Technicien	43%	57%	100% ( 86)	46 (cv 32%)
Profession intermediaire	46%	54%	100% ( 160)	49 (cv 28%)
Employe	44%	56%	100% ( 594)	47 (cv 34%)
Autre	47%	53%	100% ( 58)	47 (cv 37%)
Total	42%	58%	100% (1 653)	48 (cv 33%)
cinema — Percentage points (risk) difference: cell ≥ the reference category (in bold) +5; +10; +20; +30 points; cell ≤ ref -5; -10; -20; -30 points. age — Standardized mean difference: cell ≥ the reference category (in bold) +0.1; +0.2; +0.4; +0.8 SD; cell ≤ ref -0.1; -0.2; -0.4; -0.8 SD.

The classes are all the same age — between 46 and 50 — and the spread of their answers is close too, a coefficient of variation between 28 % and 37 % (the standard deviation over the mean). There is almost nothing for age to confound. So where did the extra gap come from?

From the odds ratio itself. An odds ratio moves as soon as you add any strong predictor of the outcome to the model, even one with no connection whatsoever to the variable you care about. This property is called non-collapsibility, and it is arithmetic, not sociology. Carina Mood’s warning to sociologists is exactly this: because of it, you cannot compare odds ratios between models with different predictors — which is precisely what a crude and an adjusted column invite you to do.

The way to check is to ask the same question with a measure of deviation that does not have the problem:

tab_reg(questionr_hdv, "cinema", c("qualif", "age"), measure = "difference")

Logistic regression: cinema by qualif, age

			cinema: Oui
	levels	n	Obs_RD	Model_mRD
	levels	<n>	<(obs%) diff>	<diff (adj%)>
Constant	Population average	1 653		41.6%
qualif	Cadre	260	(65%) 0%	0% (67%)
	Ouvrier specialise	203	(22%) -43.2%***	-45.2%*** (22%)
	Ouvrier qualifie	292	(25%) -40.4%***	-39.9%*** (27%)
	Technicien	86	(43%) -22.4%***	-26.7%*** (40%)
	Profession intermediaire	160	(46%) -19.8%***	-19.9%*** (47%)
	Employe	594	(44%) -21.3%***	-24.9%*** (42%)
	Autre	58	(47%) -18.8%***	-22.6%*** (44%)
age	per 31.6 (2SD), at 48.2 (mean)		-28.8%***	-29.3%***
Model fit	N			1 653
	Dispersion (robust/model SE)			1.04
	Collinearity (max VIF)			1.08
	Influence (max dfbetas)			0.24
	LR vs null			<0.01%
	McFadden R2			0.181
	AIC			1 853
	BIC			1 897
Model: logistic regression; mRD: marginal risk difference (on adjusted proportions, in percentage points, sample-averaged); obs%: observed proportion; adj%: adjusted/predicted proportion. Obs_RD, Model_mRD — RD ≥ +5; +10; +20; +30 points; RD ≤ -5; -10; -20; -30 points. Uncoloured: not significantly different from the reference category (Wald interval, 95% confidence) or under the first colour threshold (±5 points). *: significantly different from the reference category (in bold) at the 99% confidence level; : at the 95% level; *: at the 90% level; no star: not significant.

unskilled workers vs senior managers	crude	adjusted	moved by
odds ratio	`1/6.63`	`1/10.75`	×1.6
risk ratio	`÷2.9`	`÷3.1`	×1.07
difference in points	`-43.2`	`-45.2`	2 points

On the two measures that behave, the class gap barely moves at all — the honest finding, and a much duller one: age explains essentially none of the cultural gap between classes. All the drama on the first line was the odds ratio rescaling itself.

This is why tabxplor colours the movement on an odds-ratio column but refuses to test it. You have to know that, because the table does not say so: its legend carries no mention of the refusal. If reading the adjustment is the point of your table — and in this article it always is — work with a measure of deviation where the movement means what you think it means: measure = "difference" for points, measure = "ratio" for risk ratios. The odds ratio is for reporting alongside a literature that uses it.

Three ways to get this wrong

“The adjusted number is the true one.” It is not more true; it answers a different question. Crude says how much do these groups differ, adjusted says how much do they differ among people alike on my list. Both are facts about the world, and the interesting object is the distance between them. This is why the two columns are printed side by side.

“Not significant means no effect.” It means this sample cannot tell it apart from zero. On 39 women in the university example, that is a statement about the sample. Look at the confidence interval — display = "est_ci" prints it — before concluding anything from an absence.

“Each coloured cell is a finding.” Every cell is tested on its own, with no correction for how many tests are on the page. In a table carrying twenty comparisons, about one will look significant by pure chance: a 95 % confidence level is wrong one time in twenty. Read the pattern — a whole block of rows moving the same way — and quote the footer’s per-variable test rather than a single cell.

Where to go next

vignette("tabxplor-reg") — the reference: every family, the full grid of what each family, link, measure and effect combination builds, weighted and survey data, interactions, model checks, forest plots.
vignette("tabxplor") — cross-tables, colours, and the confidence intervals used above.
vignette("tabxplor-weights") — weighted and survey data, for cross-tables and models alike.
reg_measures(data, outcome) lists what a given outcome can be asked; reg_formulas(model) shows the formulas that were actually fitted; tab_columns(model) says what each column estimates and how its interval was built.
?tab_reg documents every argument, grouped by purpose.

Where these ideas come from

On regression as comparison, not intervention:

Richard Berk, Regression Analysis: A Constructive Critique, 2004 (a regression produces conditional means and conditional variances, and nothing more; the interest is in comparing them).
Andrew Gelman et al., Regression and Other Stories, 2020 (regression is about comparisons, not about how a variable responds to intervention).
Angrist and Pischke, Mastering ’Metrics, 2015 (equality is generated only for the variables included as controls; and a coefficient is a weighted average of the very cell-by-cell comparisons a cross-table displays).
Kenneth Rothman (stratified analysis before regression, for “getting in contact with the data”).
Hanmer and Kalkan, 2013 (average over real people rather than over an average-case profile).

On reading a model back into a cross-table — the French tradition this article extends:

Jérôme Deauvieau, “Comment traduire sous forme de probabilités les résultats d’une modélisation logit ?”, Bulletin de méthodologie sociologique, 2010 (translating a model back into “the language of the cross-table” — this article’s own thesis).
Philippe Cibois, Les méthodes d’analyse d’enquêtes, ENS Éditions, 2014, freely readable online (teach “toutes choses égales par ailleurs” after the cross-table, not instead of it; the racetrack image for odds).
Marion Selz and Florence Maillochon, Le raisonnement statistique en sociologie, PUF, 2009 (without a single formula: only a good qualitative researcher can be a good quantitative one — the case this article assumes).
INSEE, the definition of effet de structure (average pay rising while no individual pay moves).

On odds ratios and how to read them:

Carina Mood, “Logistic Regression: Why We Cannot Do What We Think We Can Do, and What We Can Do About It”, European Sociological Review, 2010 (non-collapsibility: why odds ratios cannot be compared between models with different predictors).
Norton and Dowd, Health Services Research, 2018 (average marginal effects are generally preferable for communicating results).

On which controls help and which hurt:

Cinelli, Forney and Pearl, “A Crash Course in Good and Bad Controls”, 2024 (block the spurious paths, do not disturb the causal ones).
Elwert and Winship, 2014 (selection bias: conditioning on a collider).
Maldonado and Greenland, 1993 (the origin of the 10 % threshold — and why it is not a variable-selection rule).
Bickel, Hammel and O’Connell, Science, 1975 (the Berkeley admissions, the canonical illustration of a composition effect).

And the reporting standard:

von Elm et al., STROBE, 2007, item 16(a) (give unadjusted estimates and confounder-adjusted estimates, so readers can compare them).

What we are and are not doing

The words, in plain language

The data

1. Start from what you can see

2. Turn a percentage into a comparison

What generalises, and what does not

3. Hold the other variables equal

Choosing a model: what kind of number is the outcome?

What can I ask of this outcome?

The bridge: the observed column is the cross-table

Reading a row from left to right

A predictor with no levels

The reference profile

4. Come back to something you can say

One model, four readings

A marginal effect is a deviation averaged over the sample

Which one to report

For whom? — effect, and the ideal type

5. Studying what the adjustment did

The five things adjustment can do

Watching it move, one block at a time

Colouring the movement

6. What the model cannot settle

“All else equal” is a sentence about your table

Adjusting away the thing you are studying

When adjustment reverses the answer

When the movement is arithmetic and not confounding

Three ways to get this wrong

Where to go next

Where these ideas come from

For whom? — `effect`, and the ideal type