Number five: The Frequencies Procedure.
OK so here we're going to look at our first statistical analysis. And a very
common analysis to run is the Frequencies procedure. While frequencies
can be run on any variable, it's most common to run them on categorical
variables, variables that consist of a limited number of categories. And for
those who are familiar with the scales of measurement, they typically make up a
nominal scale of measurement. So we'll run frequencies on gender, since this is
a categorical variable, people are either in the first or second category,
male or female. So to do that we're going to go ahead and select Analyze and then
Descriptive Statistics and then Frequencies. And notice here the three
variables that you see in the Data View window are listed here on the left. We
want to select gender and then click the right arrow button to move it over to
the right. And then go ahead and click OK.
And now what happens is the Viewer window opens here and the output of this
analysis, of the frequencies table, is shown. Our first table indicates that we
have 10 valid values and 0 are missing.
What that means is that we had a gender value reported on every single person. If,
for example, one person we didn't know if they're male or female, it wasn't
recorded, then we would have had nine here under valid and one under missing.
OK to look at the results here briefly we have this indicates the frequency, how
many there are, we have three males and we have seven females. Males makeup
30% of the data set, while females make up 70%. Number
six: The Descriptives Procedure. Now another very common analysis is to run
descriptive statistics using the Descriptives procedure. And they're most
often run on quantitative variables, or variables that consist of a number of
different values where the numbers used communicate meaning about the variable.
In other words, like these exam scores. The value of 80 indicates that
performance, it's really a percentage in this case, but it indicates not only that
they scored eighty percent, but we know they did better than the person who
scored 72, and so on.
These quantitative variables often consist of ratio or interval
level data and sometimes ordinal as well, if we're talking in terms of the scales
of measurement. So to run descriptives, we want to go to Analyze and then
Descriptive Statistics followed by Descriptives. And here we're going to
select exam 1 and exam 2 and I can select both by pressing and holding down
the Ctrl key. Click the right arrow button to move them over and then click
OK. And now our output or viewer window opens again and here we have our table
of output. Notice we have exam 1 in the first row, followed by exam 2. The lowest
score is indicated by the minimum on exam 1 was a 57, and 98 was the highest
score. For exam 2, the low score was 60 and 98 was the highest on exam 2 as well.
Next we have the mean reported. And notice that the performance on exam 2
was slightly better than exam 1, as indicated by the mean or the arithmetic
average. And the standard deviation, a measure of variability, shows that the
scores on exam 1
were slightly more variable or spread out than were the scores on exam 2,
although these two measures are really fairly close.
Number seven: Correlation. Correlation is a very commonly run procedure in SPSS.
So let's go ahead and take a look at those as well. So to run a correlation we
want to Analyze and then Correlate and then select Bivariate. Now here we're
going to run a correlation on exam 1 and exam 2. Correlations always require
two variables at a minimum and what we're going to measure here with the
correlation is is there a relationship between the scores on exam 1 and exam
2.
For example, do people who do well on 1 also tend to do well on exam 2, and
do people who do poorly on exam 1, also tend to do poorly on exam 2?
If that's true, then we should see a positive correlation here. So let's go
ahead and run this analysis and see what we get.
So we want to select both exam 1 and exam 2 variables and we can do that by
pressing and holding the Ctrl key once again. Go ahead and click the right arrow button
and then click OK. And now we get the results of the correlational analysis
here. And in this table, we look where exam 1 and exam 2 intersect or meet. And
it's redundant, these values here, are the same as these values here.
Notice that the correlation here is .819 or about .82, if we round to two
decimal places.
This indicates that there's a strong positive relationship between the two
exam scores, meaning that people who score high on exam 1, tend to score high on
exam 2 as well, people who score low on exam 1, tend to score low on exam 2. I can get
back to my output by selecting window output, and that this relationship is
quite strong as the highest correlation you can have when it's positive, is 1.0.
And in this video we'll go ahead and skip interpreting this significance or
p-value, as we have other resources where we dive into that in detail.