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Creating a File in SPSS
Create an Excel file that looks like the one below.
Save the Excel program to your computer. Now, open up SPSS. SPSS will ask you if you
want to open an existing program. Click OK.
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Now, use the top bar that says, Look in: to find where your data is located. Make sure
Files of type: says Excel.
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I called my Excel program anova example. Open it by double-clicking (DC).
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You will see this box:
Click OK
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Once you have opened the file, it should look like this:
If you have not already done so, click Edit-Options-Viewer and then click the box that
says “Display commands in the log.”
Now you are ready to run a one-way ANOVA. The first thing we want to do is analyze
our model assumptions. Consider what you would look for to evaluate independence.
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One-way ANOVA
To assess for normality, we will use the Explore command on SPSS.
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You want your dependent variable (DV) to be normally distributed for each level of your
independent variable (IV). Click once on to Group and select the arrow next to Factor
List:
Now, click once on to Score and select the arrow next to Dependent List:
The next step is to select the bottom button labeled Plots…
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Click on the box in the middle that says Normality plots with tests
Click Continue.
Then click OK
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An output page similar to the one below will pop up.
On this page is information related to normality, including the Shapiro-Wilk statistic,
skewness, and kurtosis for each group if you scroll down.
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You can divide the skewness by the standard error. If your value is less than 2.576 (.01
level of significance) or 3.29 (.001 level of significance), then your distribution is
approximately normal. Preferably, we will use the .01 level of significance in this class.
We still have to check our HOV assumption, which we will do when we run the
ANOVA.
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Once again, click Analyze, and then General Linear Model, then Univariate
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Click once on to Group and select the arrow next to Fixed Factor(s):
Now, click once on to Score and select the arrow next to Dependent Variable:
Next, click Options…
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Click the boxes beside Descriptive statistics and Homogeneity tests—this will tell you
whether or not you had unequal variances (HOV).
Click Continue, then click OK on the next screen.
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Your output will include descriptive statistics, the Levene statistic for testing
Homogeneity of Variances, and your ANOVA results.
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Now you are ready to run post hoc analyses.
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Post Hoc Analysis
To run a Tukey post hoc analysis to identify exactly where significant differences may
be, go back to your data and click Analyze, and then General Linear Model, then
Univariate
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Now click Post Hoc…
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Click once on Group and click on the arrow so that it moves underneath Post Hoc Tests
for:
Click the box next to Tukey. Then click Continue.
Then click OK on the next screen.
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Notice your results. An * denotes significant differences between groups at the .05 level
of significance. However, if you have not set your alpha level to be different in SPSS, the
* will not be accurate. So, as a rule of thumb, ignore the *. Regardless of the *, always
compare the p-value to the alpha level you have set for your study.
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20
Contrast Comparisons
Now, we will take a look at running contrast comparisons. For the purposes of this
exercise, we will run two contrasts:
Compare groups 1 and 3 to group 2.
Compare group 2 to group 3.
Click Analyze, and then Compare Means, then One-Way ANOVA…
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Your screen will look like the one below:
Click once on Score and click the arrow next to Dependent List:
Then click once on Group and click the arrow next to Factor:
Then click Contrasts…
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In comparing groups 1 and 3 to group 2, move the cursor to the box that says
Coefficients: and type .5
Then click Add
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Now in the box that says Coefficients: type -1
and click Add
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Now in the box that says Coefficients: type .5
and click Add
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Now in the box that says Coefficients: type 0
and click Add
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Now click Next
and repeat this process using the proper coefficients for the second contrast comparison:
0, 1, -1 and 0
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Your screen should look like the one below:
Now click Continue
And click OK on the next screen
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You will see a results page like the one below:
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Transformations
For the purposes of explaining transformations, some changes in the previous data set
have been made for group four.
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Notice that when the Shapiro-Wilk statistic is evaluated, group four is nonnormal.
Shapiro-Wilk for group four is less than .01 and therefore significantly deviates from a
normal distribution.
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To perform a transformation, decide which procedure would be appropriate to use. For
this example, we will try a logarithmic transformation. Next click Transform then
Compute.
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Create a name for your transformed variable. For this example, the variable name will be
trans_score. Then click the box under numeric expression.
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Using the downward arrow under functions, click on LN (natural log).
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Now click the upward arrow next to Functions. Your screen should look like the one
below.
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Now double-click on Score and click OK
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A new variable called tran_score appears in your data set.
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Now, run your normality statistics again. But, in place of score for your dependent
variable, use tran_score.
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Notice that all of the groups meet the assumption for normality.
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Factorial ANOVA
We will use Lesson 25 Data file 1 on the CD in the Green & Salkind textbook. As in the
one-way ANOVA, you will need to analyze your model assumptions. Review pp. 6-15.
In running a factorial ANOVA, you will put in 2 independent variables instead of 1. For
the IV, Gender and Method were used; gpaimpr is our DV.
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As before, click Analyze, and then General Linear Model, then Univariate
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Click once on to Gender and select the arrow next to Factor List:
Click once on to method and select the arrow next to Factor List:
These are your independent variables.
Note that gender has 2 levels (male and female). method has 3 levels. This would be a 2
by 3 (2 X 3) factorial ANOVA
Click once on to gpaimpr and select the arrow next to Dependent Variable:
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After you evaluate your data and analyze your model assumptions, you have to determine
if there is an interaction effect. First, select Options…
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Your screen should look like the one below:
Click on OVERALL and then click the arrow. It will move your selection to Display
Means for: Repeat this process for gender, method, gender*method.
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Your screen should look like the one below. Also, click the boxes next to Descriptive
statistics, Homogeneity tests, Estimates of effect size.
Click Continue
On the next screen click OK
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Notice on your output that you have a non-significant interaction, as evidenced by the
results of gender*method: F (2, 54) = 2.921, p = .062.
In this particular case, you would report the main effects of each independent variable
and conduct a Tukey post hoc analysis on any IV with 3 or more levels. So, we would
need to do a Tukey for method. Follow the steps on pp. 16-19.
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For an example of using SPSS when there is a significant interaction, we will use lesson
25 data file 2 on the CD in Green & Salkind.
Follow the same steps outlined on pp. 31-35.
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This time, however, notice that you have a significant interaction, as evidenced by the
results of gender*method: F (2, 54) = 10.543, p < .0001.
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At this point in time, you want to plot the interaction. You can do this by charting the
means from the output below:
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You can also ask SPSS to plot the interaction. Click Analyze, and then General Linear
Model, then Univariate. Then click Plots…
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Click once on method and then click the arrow next to Horizontal Axis.
Click once on Sex and then click the arrow next to Separate lines:
Then click Add
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Your screen should look like the one below.
The numbers on the vertical axis represent scores for gpaimpr. You can see by the chart
that males and females perform differently across note-taking methods.
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Since we have a significant interaction, we need to test for simple effects by sorting our
data and running an analysis for each level of an IV. This can be done in one of two
ways.
In the first method, we will use SPSS coding. SPSS can perform the proceeding steps using
the following coding formula in SPSS Syntax:
UNIANOVA DV BY IV1 IV2
/EMMEANS = TABLES (IV1 * IV2) COMP (IV2)
Click Analyze, and then General Linear Model, then Univariate. Then click Paste…
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Now type your SPSS code using the formula above, highlight it, and click the run button.
The univariate tests below with the post hoc tests above show your results for the simple
effects.
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The next method is more involved, but it replicates the results of the first method, and it
also shows you how the results were derived. In other words, it replicates the SPSS code.
First, click Data and Select Cases…
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Click the second selection If the condition is satisfied. Then click If…
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Click once on gender and then click the arrow.
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On the key pad (as shown below) click = 1.
Click Continue. Then on the next screen click OK.
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Now, SPSS will only select cases in which gender = 1 (males only).
Now, run your ANOVA following steps on pp.11-19. You will need to consult your
lecture notes to interpret the output.
Follow steps on pp. 53-57 to run the analysis for females (sex = 2).
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Repeated Measures
We will be using Lesson 28 data file 1 from the CD in Green & Salkind.
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Click Analyze, and then General Linear Model, then Repeated Measures…
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In this particular example, we will test to see if there are significant differences among
test scores on the Desire to Express Worry Scale after 0, 5, 10, and 15 years of marriage.
You will see this screen below:
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Where it says factor1 by the box next to Within Subject Factor Name: erase and type
Time. Then type 4 in the box next to Number of Levels: Now click Add
Now click Define
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Your box should look like the one below:
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Click time1 and then the arrow for Within Subject Variables. Repeat this step for
time2, time3, and time4.
Click Options
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Click once on OVERALL and then click the arrow to move the selection to Display
Means for. Repeat this step for time
Click the box next to Descriptive Statistics and then click Continue
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On the next screen click OK. Your output should look like the following:
Review your course notes for interpretation.
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To do a post hoc analysis, click Analyze, Compare Means, and then Paired Samples
T-Test.
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Click one pair times to compare. In this example, I am comparing time1 to time2.
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Click the arrow beside Paired Variables:
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Now repeat this process for all possible comparisons.
Then click OK
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Use your notes to interpret the results. Make sure you do a Bonferroni adjustment.
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Multiple Regression
We will be using Lesson 33 Exercise file 1 on the CD of Green & Salkind. We want to
examine the extent of the relationship between stateexam (criterion variable) from
mathtest and engtest (predictor variables).
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Click Analyze, Regression, Linear
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Your screen should show the following:
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Click once on math aptitude and then click the arrow by Independent(s):
Repeat this for English aptitude.
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Then click on Average percentage and click the arrow next to Dependent:
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Click Statistics and click the boxes next to Descriptives, Part and partial correlations,
and Collinearity diagnostics. Estimates and Model fit should already be selected.
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Click Continue.
On the next screen click OK.
Refer to your notes for interpretation of output.
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Evaluating model assumptions in multiple regression
From the Linear Regression screen click Plots
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Insert the standardized predicted (ZPRED) and residual (ZRES) values in the X & Y
axes. We will use this graph to determine the linearity and homoscedascity assumptions.
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Now click on Save
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Now click Standardized Residuals. This will create a new column in our data set. We
will use this variable to examine the normality of our error variances.
Now click Continue and the OK.
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Notice you have a new column for your standardized residuals (ZRE_1). Now click
Analyze—Descriptive Statistics--Explore.
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We will now run normality tests for the criterion variable, which must be normally
distributed, and for our error variances, which should also be normally distributed. Click
on the criterion variable (which in this case is the average percentage on the exam) and
the standardized residuals and place them in the Dependent List. Then click Plots.
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Because multiple regression is a large sample procedure, we will focus on the appearance
of the box plots to determine if the criterion variable and error variances are normally
distributed. Be sure to click Normality plots with tests and then Continue followed by
OK.
This concludes the procedures for analyzing model assumptions. The sample output will
be helpful in examining the procdures conducted.
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