© Amy Gamble 4/30/01
All Rights Rerserved
The Dummy’s Guide to
Data Analysis Using
SPSS
Mathematics 57
Scripps College
Amy Gamble
April, 2001
ii
TABLE OF CONTENTS
PAGE
Helpful Hints for All Tests ................................................................................................1
Tests for Numeric Data
1. Z-Scores .................................................................................................................1
2. Helpful Hints for All T-Tests.................................................................................2
3. One Group T-Tests.................................................................................................2
4. Independent Groups T-Test ...................................................................................3
5. Repeated Measures (Correlated Groups or Paired Samples) T-Test .....................3
6. Independent Groups ANOVA................................................................................4
7. Repeated Measures (Correlated Groups or Paired Samples) ANOVA .................4
8. Correlation Coefficient ..........................................................................................4
9. Linear Regression..................................................................................................5
Tests for Ordinal Data
1. Helpful Hints for All Ordinal Tests .......................................................................7
2. Kruskal – Wallis H.................................................................................................7
3. Friedman’s .............................................................................................................7
4. Spearman’s.............................................................................................................7
Tests for Nominal Data
1. Helpful Hints for All Nominal Tests .....................................................................8
2. Chi-Square Goodness-of-Fit ..................................................................................8
3. Chi-Square Independence ......................................................................................8
4. Cochran’s Q ...........................................................................................................8
5. Phi or Cramer’s V (Correlations for Nominal Data) .............................................9
SPSS Guide to Data Analysis Page 1 of 8
For All Tests
• Remember that the Significance (or Asymp. Sig. in some cases) needs to be less
than 0.05 to be significant.
• The Independent Variable is always the variable that you are predicting
something about (i.e. what your Ha predicts differences between, as long as your
Ha is correct). The Dependent Variable is what you are measuring in order to tell
if the groups (or conditions for repeated measures tests) are different. For
correlations and for Chi-Square, it does not matter which one is the Independent
or Dependent variable.
• H
a
always predicts a difference (for correlations, it predicts that r is different from
zero, but another way of saying this is that there is a significant correlation) and
H
o
always predicts no difference. If your H
a
was directional, and you find that it
was predicted in the wrong direction (i.e. you predicted A was greater than B and
it turns out that B is significantly greater than A) you should still accept H
o
, even
though H
o
predicts no difference, and you found a difference in the opposite
direction.
• If there is a WARNING box on your Output File, it is usually because you used
the wrong test, or the wrong variables. Go back and double check.
Tests For Numeric Data
Z-Scores (Compared to Data )
Analyze àà Descriptive Statistics àà Descriptives
• Click over the variable you would like z-scores for
• Click on the box that says Save Standardized Values as Variables. This is
located right below the box that displays all of the variables.
• If means and standard deviations are needed, click on Options and click on the
boxes that will give you the means and standard deviations.
• The z-scores will not be on the Output File!!!
• They are saved as variables on the Data File. They should be saved in the
variable that is to the far right of the data screen. Normally it is called z, and then
the name of the variable (e.g. ZSLEEP)
• Compare the z-scores to the critical value to determine which z-scores are
significant. Remember, if your hypothesis is directional (i.e. one-tailed), the
critical value is + or – 1.645. If your hypothesis is non-directional (i.e. two-
tailed), the critical value is + or – 1.96.
SPSS Guide to Data Analysis Page 2 of 9
Z-Scores Compared to a Population Mean and Standard Deviation:
• The methodology is the same except you need to tell SPSS what the population
mean and standard deviation is (In the previous test, SPSS calculated it for you
from the data it was given. Since SPSS cannot calculate the population mean and
standard deviation from the class data, you need to plug these numbers into a
formula).
• Remember the formula for a z-score is:
σ
µ
−
=
X
z
• You are going to transform the data you got into a z-score that is compared to the
population by telling SPSS to minus the population mean from each piece of data,
and then dividing that number by the population standard deviation. To do so, go
to the DATA screen, then:
Transform àà Compute
• Name the new variable you are creating in the Target Variable box
(ZUSPOP is a good one if you can’t think of anything).
• Click the variable you want z-scores for into the Numeric Expression box.
Now type in the z-score formula so that SPSS will transform the data to a US
population z-score. For example, if I am working with a variable called Sleep,
and I am told the US population mean is 8.25 and that the US population
standard deviation is .50, then my Numeric Expression box should look like
this:
(SLEEP – 8.25)/.50
• Compare for significance in the same way as above.
For All T-Tests
• The significance that is given in the Output File is a two-tailed significance.
Remember to divide the significance by 2 if you only have a one-tailed test!
For One Group T-Tests
Analyze àà Compare Means àà One-Sample T Test
• The Dependent variable goes into the Test Variables box.
• The hypothetical mean or population mean goes into the Test Value box. Be
Careful!!! The test value should be written in the same way the data was
entered for the dependent variable. For example, my dependent variable is
“Percent Correct on a Test” and my population mean is 78%. If the data for
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