In our previous video we were working with these data on height of males and females,
and we did a frequency count, which showed us how many males and females there were,
and also how many people of each height there were.
But, I mentioned that a frequency count might not be the most interesting thing for the
height data.
Instead, we might want to calculate things like means and standard deviations to determine
what the average height is, and how spread out the heights are around that average.
So these are called measures of central tendency and measures of variability.
Collectively they're called descriptive statistics, because they kind of describe what a set of
numbers look like.
Let's start simple.
Once you have this data entered in, and remember this gender information, it looks like it
says female and male, it's actually numbers that I have value labels for.
So if you're at all unsure about that watch my previous video, see how I did that.
Once you have these data entered in, go to analyze, and go under descriptive statistics
and choose descriptives.
When this window pops up move over any variable you want to analyze.
So, I want to start with height and move that over.
And, while we're here I wanna click on options to specify what kind of calculations we want
to run.
Each one of these boxes represents a different descriptive statistic we can run.
Some common ones like mean, standard deviation, minimum, maximum, those are checked by default.
There are a few others you could do some on the variance and the range and the error,
I think the ones we have now are good so let's just continue the way it is.
Continue and now hit 'OK'
You'll see a table pop up, and I'm going to scroll down so I can focus on things here.
This is the table we got for or descriptive statistics.
It tells us of all the different scores, first off, how many of them are there?
We had ten different height scores.
The smallest one was 61 inches, the highest one was 80 inches.
The Average of all the heights was about 69.2.
We also over here had the standard deviation, which tells us about how spread out the score
are around that mean.
So let me show you something cool.
Go through this again.
Go up to Analyze, and under Descriptive Stats, and Descriptives, there's a little box down
here that says "save standardized values as variables."
I'm going to check that and hit "OK."
I get exactly the same table the second time, but if you go back to your spreadsheet you'll
notice a new variable up here.
It's called Z height, and do you know what these are?
These are the z scores.
It converts each height measurement into a standardized score that says how many standard
deviations units away from the mean, this score is.
And so negative means it's a little bit below the average.
A positive score means it's a little bit above average.
So that's a quick and easy way to get z scores in SPSS.
Now let me show you something else.
Try getting the descriptive statistics for gender.
I'll just hit reset and now move gender over.
Click "ok."
Remember, even though these say male and female, they're really ones and twos.
So back to the output window, this table right here at the bottom, it's telling us that there
are ten different scores.
The minimum is one and the maximum is two, which is kind of a "duh" because we only have
two numbers, ones and twos, and the average score is 1.4.
Well, I don't really think calculating averages or even standard deviations is appropriate
here because these are nominal data.
If ones represents male, and two represents female, what does 1.4 represent exactly?
I don't even want to think about that, so let's do another kind of analysis.
Instead, I think it might be more interesting to compare the heights of males against the
heights of females.
So, instead of kind of sorting and separating these scores into males and females, we can
have SPSS just report the average of all the heights next to female versus all the heights
next to male.
So to do that we'd go up to "analyze" go down to "compare means" and pick "means."
Another window is going to pop up with boxes for dependent and independent.
Dependent is where you move the values that you want analyzed.
So if you want to know the average heights, you ought to moved height over to the dependent
list.
Likewise, independent is like the grouping variable, and we're grouping the height scores
into males and females, so let's move gender down here to independent.
When we have that all entered in, I think we can just click "ok."
Now we see a few more tables.
this first one I don't think is very informative or interesting, I'm going to focus more on
the second one, "report."
So it tells us the average height for all females, at about 68.33 inches tall.
How many females there were, six, and the standard deviation of all the female heights.
And right below that we have the average male height, about 70.5 inches.
There were four males in that data set, or in that sample and a standard deviation of
about 5.91.
And finally there's total, which says the average height for all our people regardless
of gender, how many of them there were in the standard deviation.
If you'll notice this is actually the same thing as we got above here, where is that.
I think this is it.
Here's the average height 69.2 inches for all ten people.
So that's kind of cool, but it separates it into male and female.
So that's how you do some simple descriptive stats used to compare groups.
I think the next step is to compare different groups statistically using T tests, and I'm
going to cover that in later videos.