In this video, I'm going to show you how to do some simple analyses in SPSS.
I already have some data popped in here, but I have not 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.
I will call this one "gender"; I will call this one "height."
In Data View we'll see the names are changed.
The data for people's heights are in inches.
We're getting a range between 60 and 80 inches which is about
five to seven feet tall.
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,
labeled male and female. But those 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."
Now, 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 I'm using those.
Now, a quick mention on the scales of measurement.
Gender is an excellent example of when to use a nominal scale because
the groups are qualitatively and not quantitatively different, so I can change
my settings under Variable View to reflect that.
Under 'Measure' I can change it from Scale to Nominal. I don't have to do this, but I can.
Height, however, 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 a ratio. Now we're almost set to start analyzing different
aspects of these numbers but there's one last thing we can do.
I'm looking at these 1's and 2's and I'm having trouble remembering
which one was male and which was female.
I can't quite remember, so in order to keep that straight I can create what are called
value labels. If I go back to Variable View, under the Values option, I can tell SPSS what each value means.
Put a value of 1 and a label of "Female" and click 'Add'.
Likewise, a value of 2 gets a label of "Male". Click 'Add' and click 'OK'.
Now if I go back to Data View and click the Value Labels button on the tool bar,
the numbers will turn into words.
I can click the button again to turn that off and see the numbers.
I can also do it under 'View' and 'Value Labels'.
So, there's still numbers there, but they're just showing us what each number means. I think it's more useful to leave it on so I will do that.
Now we're ready to analyze these data, so one of the more simple things
you can do is just count up how often things occur which is called
a frequency count. It seems easy to do by hand, but remember that with
larger datasets, counting things up by hand can be tedious.
So, let's have SPSS do it for us.
Up at the top go to 'Analyze'. Analyze in SPSS is probably the menu
you will use most often and all the analysis options are there.
So under 'Descriptive Statistics' go to 'Frequencies'.
Here we can see how frequently different things occur.
When this window pops up, you're going to see all the variables we have on the left.
All the variables we want to analyze will go on the right.
To choose the variables you want to analyze just highlight them and click the arrow here.
You can also just grab them and drag them over like that.
When you're ready you can just click 'OK'.
So I've talked about this before in the other videos. But, the output in SPSS isn't exactly pretty.
It gives us more than we really want. But we can look around for what
the numbers mean and which think are the most interesting.
So starting with this first table here.
This shows us how many values we have for each variable.
So the 'N' for gender is 10 and the 'N' for height is ten which means
we have ten information units for both gender and height.
The 0's next to 'Missing' just mean that we don't
have any blank cells in those columns.
Let's look down at this table that says "Gender"
This table is a little more interesting.
This table shows the frequencies for our 'Gender' variable.
So in the frequency column we see the number of males and females.
So, we have six females. Which would be 60% of the scores.
Valid percent is calculated differently if we have any missing values.
Then cumulative percent would be if we had multiple types of one variable, it would add them up as we go along.
The most interesting part is the frequency column. We have 6 females, 4 males, 10 different people total.
We can do the same thing for height which is this table down here.
What we see is we have one person who is 61" tall, two people who are 62" tall, two people are 68" tall.
I don't feel like this information is very meaningful as it is right now.
A better way to analyze this type of information would be to get the average
of these heights or to see how spread out these height scores are.
These are measures of central tendency and variability which I will explain in the next video.
A better way to analyze this type of information would be to get the average of these numbers.
This is a measure of central tendency which I will show you in the next video.
can do now is we can start to break down haight across categories we can compare
heights of different people from different genders
so the easiest way to do that would be go to going to analyze instead of
descriptive stats go to compare means and make that first option means now
here we need to specify basically uh what are dependent variable is in our
independent variable
I you know we didn't really assign people to condition
gender 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 it 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 that when you're ready just click OK
now
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 7.5 inches for males going invest in
education 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 transfer your
data and ask estimated extremely easy to do so