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Crosstabs in JASP!
Oct 3, 2025
How to do crosstabs in JASP, how to figure out what it means, how to read the results a bit more easily by rounding the percentages... and why to do it!
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0:00
So here we are again and this time we're
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going to do cross tabs or
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crosstabulations
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or as JASP and Jamovi call it
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contingency tables and these are just
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simple tables with statistics. So let's
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take a look. I've already got here
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loaded car sales database. This is a
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sample database. I think it's actually
0:22
from SPSS
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and we will uh we're already in
0:26
analysis. So, let's find the cross tabs.
0:30
You might think they're here in
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descriptives, but they're not.
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Instead, we find them under frequencies.
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You go down and here they are for
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contingency tables. And these are
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really, really handy if you want to
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explain stuff to people because not
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everybody gets statistics, but you can
0:51
make it sound well. Let's show you.
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Let's say that we're going to take
0:56
automakers as our columns. No, let's
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make it rows. It'll be neater if it's
1:03
rows because they go down and you can
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get it on one page. We'll take vehicle
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type as the column. So, which one you do
1:12
doesn't really matter. It's just which
1:13
is easier to look at. If you put it in
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reverse order,
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then you see that it goes right off the
1:20
screen and it's hard to read. So, we'll
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put it back here.
1:25
And keep in mind you can also do this
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you see layers that's to add another
1:30
variable. So if you wanted to do this
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within uh let's say that you wanted to
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do
1:38
maker versus brand.
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Then you can do this for automobiles and
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then for trucks separately. So some
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people would call that
1:48
three-dimensional.
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Let's see. So, let's move brand out of
1:52
there and move type back up here.
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And this is a very simple chart and it's
1:59
easy to see. It takes the makers of
2:02
various vehicles as in 1997 when I
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believe this table is uh from and you
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see that Chrysler made 14 things that
2:11
the EPA defined as automobiles which is
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to say sedans and coups and convertibles
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and that and 10 trucks which back then
2:20
did include minivans
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and you can see GM made 33 cars and four
2:25
trucks Ford 18 and 8 and so on and you
2:28
can keep on going down. Uh Volkswagen,
2:31
this is so long ago. Volkswagen made 10
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uh sorry, 12 car models and no trucks.
2:37
And yeah, I don't see Porsche there. I
2:40
was looking for them
2:42
here. And then your statistics at the
2:44
bottom. And this is a a kai square test
2:47
on this particular table. That's the
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default. If we want to uh if there's a
2:53
reason for it, we can show the
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likelihood ratio.
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And that is needed when you're not
2:59
really fitting the kaiquare assumptions.
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Very often you'll have very similar
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results here under P. In this case the
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likelihood ratio actually gives you a
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much uh better. So you can see the
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kaiquare test p is 0.03 03 which roughly
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means that uh only 3% of of the time
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would we expect to see a pattern that's
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this different from randomly distributed
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due solely to chance. So what this is
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looking at is differences across the
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entire table,
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not one thing versus another. It just
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tells you that the way that the table
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came out is probably not due to random
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chance. That these things are probably
3:48
in some ways interacting that the
3:50
automaker
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different automakers have different uh
3:54
proportions of cars and trucks is really
3:56
all it's telling you in that language.
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So the chances are rather that different
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automakers had different ratios of cars
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to trucks.
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Basically that's what it's telling you.
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Now you look at this and you try to
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explain this to somebody and it's a
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little hard, right? So we can do things
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by going to cells
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and we can put in percentages.
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So let's do row percentage now says
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percent within row. And what this is
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telling you that among GM 89% of the car
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name plates that they have are cars. 11%
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are trucks. Rounding a little bit.
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Uh for Ford. Actually, we can shut off
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the rounding.
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Let's see. We'll go to zero decimals
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here and we'll go back. So, uh here you
4:53
can see. So now it's much easier to
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read, right? So Ford 69% of their
4:58
vehicles were cars and Chrysler 58%
5:01
42% were trucks again including
5:04
minivans. So you see looking at this and
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probably including Jeeps. So you see
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looking at this you see that um of the
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uh automakers here Toyota, Honda,
5:17
Chrysler had the highest percentage of
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vehicles that they made being trucks.
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The other way to do this is to show
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columns.
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So here you say that of the automobile
5:30
name plates available in the US, GM made
5:33
28% of them, Ford made 16%, Chrysler
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made 12%.
5:38
That's basically what it's saying. So
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what some people do is they put in row
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and column. And this is a good way to
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drive yourself crazy and to make
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mistakes. It's not that hard to make to
5:50
do this differently. But the thing is,
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if you're making a point, it's easier to
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say it something like
5:59
uh Chrysler uh 42% of the vehicles
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Chrysler sold were classified as trucks
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compared to GM where 11% were classified
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as trucks. You understand what that
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means intuitively. It's easy to say.
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Kaiquare is great for talking to
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scientists
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um but it's not great for talking to
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other people.
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Let's just do one more for illustration
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and go to the general social survey
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again. We go to frequencies
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contingency tables and let's compare.
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Let's do back pain.
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Whether or not people have back pain
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with their belief in what the Bible
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really is. So here you can see we'll go
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to cells and we'll put in the
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percentages.
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What is this 25%.
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What is this 25% over here?
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It is the percentage
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of people who have back pain
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who believe that the Bible is the word
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of God. So of people who have back pain,
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48% believe the Bible is the inspired
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word of God. 26% believe it's a book of
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fables. Now if you look at these
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differences, they're very, very small.
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You would almost think that having back
7:30
pain does not influence
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what you think of the Bible. So, let's
7:35
take a look. We'll go back to
7:38
preferences and we'll turn the uh
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turn the percentages back on.
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And we see P is 74. So, actually
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statistically what this table is telling
7:53
us is that these things are arranged
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about as we'd expect them if it was just
7:59
random chance.
8:01
But if that wasn't the case, it was a
8:03
big difference, then it would still be
8:05
handy to know the percentages. And again
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we can go to cells we switch to column
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and here you see that of those who
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believe that the Bible is the word of
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God
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26% had back pain and 74% didn't
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which is the same as the numbers for
8:25
people believe that the Bible is the
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inspired word of God
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and is actually pretty close to people
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believe that the Bible is a book of
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fables.
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So there you have it. That's basically
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contingency tables. Uh you can usually
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ignore the values of the kiquare and for
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that matter if you're showing that if
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you're using the likelihood ratio
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instead you can ignore that value
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because these numbers are computed with
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the degrees of freedom. So you can look
8:53
them up in the table and find out what
8:55
the level p is. The computer does that
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for you. So you just need really this
9:00
one column and you need to know how many
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were involved so that you know that
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you've got at least uh five cases per
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cell which you obviously do. All right,
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that's it. Let's move on to something
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else.
#Statistics