In this video, I'm going to show you how to do some simple analysis in SPSS.
I already have some data popped in here, but I have explained what they are.
Before I do, pause the video and enter these numbers into your own SPSS
spreadsheet.
These two variables represent gender and height. Variable 1 represents people's
gender information, and variable 2 represents people's height information.
Instead of having to remember this,
let's go into Variable View right now and change the names of these variables.
Call variable 1 "Gender". Call variable 2 "Height"
The data for people's heights are in inches.
We're getting numbers between 60 and 70 inches, which is about five to six
feet.
The data for people's gender are not quite as clear. I'm not typing in "male"
and "female", even though i could, using a string variable.
Because male and female are not numbers, and SPSS needs numbers to analyze,
instead, I'm using a coding scheme of 1's and 2's where 1 represents
female and 2 represents male.
This is an arbitrary code. I could use 0's and 1's, or 5's and 10's or
any two numbers at all. But 1's and 2's are simple, so iIm using those.
Now a quick mention on the scales of measurements.
Gender is an excellent example of the use of a nominal scale because the group
the groups are qualitatively and not quantitatively different. So I can change
my settings to reflect that.
I'm going over to measure and change it from scale to nominal. Height, on the other
hand, is a quantitative variable with fixed interval differences between
scores and a meaningful 0, so we can leave this variable as it is set to
scale, because it's ratio. We're almost set to start analyzing different
aspects of these numbers, but there's one last thing we can do for gender.
It can be a little bit confusing to remember which one was male
and which was female, 1's & 2's...I can't quite remember...
So we can assign value labels to those things. I go back to Variable View and
under Values, I can tell SPSS what each value means. I type in that a value of
1 means female. It knows that 1's are female.
Same thing for 2's and male. Pop those in there and hit OK.
Now if i go back to Data View, I see all those 1's &2's have been replaced by
females and males.
If i want to turn that off and see the numbers again
there's a little button up here on the tool bar called value labels. Click that
they're back to numbers.
Likewise, I can activate it or reactivate it under the view menu.
I just check this box that says value labels and we'll go back to the males and
females.
I think it's more useful to leave it on, so I'm just going to do that. Now I think
we're ready to analyze these data. Probably one of the more simple things
you can do is just count up how often things occur.
It seems easy to do by hand for this data set, but remember if you're dealing
with larger datasets, counting things up by hand can get a little tedious.
So let's have SPSS do it for us. Up at the top
go to Analyze. Analyze in SPSS is going to be your very close friend.
Probably the menu you use most often and all the analysis options are in there.
So under Descriptive Statistics, go to Frequencies
Here we can see how often or how frequent different things occurred.
This window pops up. You're going to see this type of window a lot in SPSS.
Basically, all the different variables we have are on the left and all the
variables we want to analyze go on to the right. So you can decide which
variables you want to analyze just by highlighting them and then moving them
over with this little arrow box. You can move them back if you decide you don't
want to go so just kind of grab a hold of it and then drag it over
whenever you want to analyze so decide whatever you want to analyze, gender is
probably the best right now and when you're ready click OK now your output
window is going to pop up here
I was saying this in the last video but one of the problems with SPSS is that it
often gives you a lot more information than you want.
so it's a bit of a trick looking for the relevant information. Also the outputs
aren't exactly pretty
so we need to kind of figure out what it's showing us here
and luckily this is a pretty simple table. It is telling us first off this
little table, the n
is saying we have 10 valid scores, none are missing
basically we don't have any empty cells in there. This table is probably the
more telling one. It is telling us on the left are different groups, we
have female and male, and in this frequency column
how often are occurring. So we have 6 females, we have 4 males
how often they are occurring. We have 10 miles total. Here's percents which can convert to
percentages: 60, 40, and 100%. Valid percent if we have a few missing cases
it would it would divide it differently of the total number of available cells
instead of all cells, and then cumulative percent which is saying sixty percent of
people are female and a hundred percent are either female or male
You know, we really don't need those things. I think most the relative
information is just frequency, right there. So we could do the same thing for
height to create another frequency table for height, but that would tell us how
many people are 60 inches tall
how many people are 61 inches tall how many are 62 inches tall, etc. that's
probably not going to be very informative to us.
I think a better way to convey the information, or convey just how
tall people are, might be to calculate the average of their height.
So, let's try that.
And just FYI, you can run analysis from the output window, the same menu is there.
Just for simplicity or just for stability, I'm gonna go back to the data
view of the spreadsheet. Go to Analyze and under Descriptive Stats go
to Descriptives. We get the same basic window again. All the variables we have
on the left, all the variables we want to analyze on the right, just pick height
this time and move it over and before we hit OK i want to see what different
options we have. Click on this button in the upper right.
These are all the different types of descriptive stats it could calculate for us.
Mean, standard deviation, minimum and maximum, those are pretty standard things. If we
wanted, we could get the variance, and the range
and the standard error of the mean. The sum is good too, I am going to click on SUM
There are more options on here like what type of distribution we're dealing with.
Display order: if we had multiple variables we were analyzing it once and actually we can
analyze as many variables we want the same time,but we only have one right now
we can specify here
what order they appear in. I really just mostly leave this in variable list.
Hit continue and whenever you're ready just click OK, that's what's gonna pop up
slightly below our last table. So we see here is our variable height
how many scores are in that variable, we had 10, the minimum score is 61,
the shortest person or group. 80 was the highest score. 692 inches...that would be
how long
everyone would be if we kind of stack them up on top of each other, but i think
this mean is the most important thing. The average height for our 10 people:
69.2 inches with a standard deviation of about 6.27 inches, so what we
can do now is we can start to break down height across categories. We can compare
heights of different people from different genders.
The easiest way to do that would be to go to Analyze, instead of
descriptive stats, go to compare means. Pick that first option... means
here we need to specify what are dependent variable is and our
independent variable
We didn't really assign people to condition
Gender would be what's called a quasi-independent variable
let's pretend it's the independent variable because we want to break down
the different categories the different groups
well that's gender, and the dependent list of the things we want to measure, do
calculations on, that would be heights, the inches. So click that and I think we
should be good to go. We could see what's under options
A lot of stuff we don't need. When you're ready, just click OK
Once again we're getting more than we really need from SPSS, this table
I don't really see what it's used for. The second table is I think the one we
really want to see. This is where we see how tall
females are compared to males we have females the average height about 68 . 3
inches and there are six females contributing to that mean standard
deviation about 6.9. Males about 70.5 inches for males. The standard
deviation about 5.9, so it's telling us that on average
males are a little bit taller than females. We also have the total 69.2
inches. That's how tall people are on average. All 10 people.
The standard deviations of about 6.27 that the same number we got up here from
doing descriptive for all those people 69.2. So overall, frequency counts and
descriptive stats or where a good way to take a peek at potential trends in your
data and SPSS made it extremely easy to do so.