Spss Handbook

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Running Head: USING SPSS TO PERFORM HYPOTHESIS TESTS 1

Using SPSS to Perform Hypothesis Tests

Cameron Koontz

Longwood University



USING SPSS TO PERFORM HYPOTHESIS TESTS 2

Single Sample t -Test

Use a single sample t -test if:

- the goal is to see if the sample group is significantly different from the population

-

either or is unknown- the variable is normally distributed with n< 30

Performing a Single Sample t -Test

1. When in SPSS, enter into ³Variable View.´

a. Enter a name for the Dependent Variable in the first cell.

2. Enter back into ³Data View.´

a. Enter the data in the column with the DV.

3. Go to Analyze Compare Means One-Sample T Test

4. Select the variable, and click on

the arrow to put it in the ³Test

Variable(s)´ box.

a. In the area labeled ³Test

Value,´ type the given

to test the sample against. b. Under ³Options´, change

the ³Confidence Interval´

to the correct value

depending on your alpha

level.




5. Click OK to analyze the data.

a. A new window will pop up. It will include some descriptive statistics as well as

the results of running the t -test.

b. In the first column, your DV will be shown. The second column will show the t obt.

This, in addition to the degrees of freedom in the third column, can be used to

compare to the t crit to see if the results fall within the rejection range.

Alternatively, the fourth column will also display the significance.

Independent t -Test

Use an independent t -test if:

- the goal is to find if an experimental group is significantly different than the control

group

- population information is unknown

Performing an Independent t -Test

1. W

hen in SPSS, enter into ³Variable View.´a. For the name of the first row, type ³Group´ or some other way to distinguish your

control from experimental.

b. Within this ³Group´ row, click

the cell under ³Values,´ then

click the button on the right

side of the cell.

i. This is where the

experimental and

control group will be

distinguished. In the³Value´ box, type a

number to be associated

with the control group (usually ³1´). Under ³Label,´ call it the name of the

control group. Click ³Add´ to add it to the box below.

ii. Repeat this for the experimental group, using a different value number and

label, then press ³OK.´




c. In the second row in the ³Name´ column, type in a name to call the dependent

variable.

2. Go back into ³Data View.´ In the ³Group´ column, type

the number that corresponds with the label for both

groups, making one row for each data point. In the

column for the DV, put in all the data. Make sure that

the data is paired with the correct value label.

3. Go to Analyze Compare Means Independent-

Samples T Test.a. Click in the DV label, and click the arrow to put

it in the ³Test Variables´ box.

b. Click on ³Groups´ and click on the bottom arrow

to put it in the ³Grouping Variable´ box.

i. The groups need to be defined, and this

can be accomplished

by clicking ³Define

Groups.´ Put the

value for the first

group in ³Group 1´

and the value for the

second group in

³Group 2´

c. Clicking ³Options´ will

allow you to change the

confidence interval.

d. Click OK to analyze the data.

4. A new window will pop up, displaying the results of the t -test. It is important to look at

the ³Equal variances assumed´ row.a. The t obt is shown as well as the degrees of freedom, which gives the necessary

information for looking at a table to find the t crit. Alternatively, the ³Sig. (2

tailed)´ will display the significance.




Dependent t -Test

Use a dependent t -test if:

- each participant is in every group

- there are two groups to compare to each other

Performing a Dependent t -Test


a. In the first row for the ³Name´ column, type a label for the first group.

b. In the second row for the ³Name´ column, type a label for the second group.

2. Enter into ³Data View´ and enter the data

into each row, making sure that the data for

each column is matched with the other.

3. To analyze the data, go to Analyze

Compare Means Paired-Samples T Test.

a. Click the first group name, and then

click the arrow to send it over to the

³Variable 1´ cell.

b. Click the second group name, and

then click the arrow to send it over to

the ³Variable 2´ cell.

c. Click ³Options´ to change the


d.

Click OK to run the test.

4. In order to interpret the test, look at the ³Paired Samples Test´ box.

a. In the three rightmost columns is the t -score, degrees of freedom, and

significance.




One-Way ANOVA

Use a one-way ANOVA if:

- there is one independent variable with three or more levels

-

the experiment is a between groups design- the goal is to assess equivalence or difference among sample means

- the hypothesis is not directional

Performing a one-way ANOVA


a. For the first row in the ³Name´ column, type ³Participants.´ This will allow for

individual labeling of the participants.

b. In the second row in the

³Name´ column, type a name

for the independent variable.

i. In this row, go to the

³Values´ column, click

the cell, and then click

the button on the right

side of the cell.

ii. Choose a value and a

label for each level of

the IV and add them.

c.

In the third column, type aname for dependent variable.

2. Enter into ³Data View.´

a. In the ³Participants´ column, either write the

participants¶ names, or for the sake of

confidentiality, label them with a number.

b. In the column with the IV, use the value labels to

designate which data point in the DV column

belongs to which level of the IV.

c. In the column with the DV, enter all the data, being

sure to keep the right score with the correct level of IV.

3. Go to Analyze Compare Means One-Way ANOVA.

a. Click on the dependent variable, and click on the

top arrow to send it to the ³Dependent List.´

b. Click on the independent variable, and click on the

bottom arrow to send it to the ³Factor´ box.




c. Click ³Options´ to have the option to make descriptive statistics available.

d. Click ³Post Hoc´ to change

the confidence level, or to

select any post hoc tests to

perform.

e. Click OK to perform the test.

4. The only row of interest is the

³Between Groups´ row. The Fobt and

significance as well as the degrees of

freedom are shown. It is important

to note that the one-way ANOVA

can only assess whether or not there are significant differences between the means, and

cannot show where the differences are.

R epeated Measures ANOVA

Use a repeated measures ANOVA if:

- there is one independent variable with three or more levels

-

each subject is in each group- the experiment is a repeated measures design

Performing a Repeated Measures ANOVA

1. When in SPSS, enter into ³Variable

View.´

a. In the ³Name´ column, type the

name of each level of the

independent variable, using a new

row for each new level.


a. Enter all the data into the cells,

being sure to keep each data point

consistent across the row, so as to

keep its pairs.

3. Go to Analyze General Linear Model Repeated Measures.




a. In the ³Within-Subject Factor Name´ type the name of the IV. In the ³Number of

Levels´ box, type the number of levels of the IV, then click ³Add.´ Click

³Define´ to define the levels.

b. Click each of the

groups, then click the

arrow to send them to

the ³Within-Subjects

Variables´ box.

c. Click ³Options´ to

change the


d. Click OK to perform

the test.

4. To view the results of the

test, look at the ³Tests of Within-Subject Effects´ box.

a. Look at the first row

to see the results.

This will show the Fobt value as well as the degrees of freedom. Also, the degrees

of freedom of the Error is necessary to look in an F table to find the critical level.

Alternatively, look at the rightmost box to see the significance of the results.




Two-Way ANOVA

Use a two-way ANOVA if:

- there are two factors, and there are two or more groups for each factor

- the experiment is a between groups design

Performing a Two-Way ANOVA


a. In the ³Name´ column, type the name of each

factor into a different row.

i. In each row, go to the ³Values´ column

and click the button to the right. Add a

value label for each group within the

factor.

b. Add a final row for the dependent variable. In

the ³Name´ column, type a name for the DV.


a. All the data must be specified to each group that

it belongs to. For each piece of data that is

entered into the DV column, type the number

corresponding to the value label of the group it

belongs to in the factor columns.

3. Go to Analyze General Linear Model Univariate.

a.

Click on the DV, and click on the top arrow tosend it to the ³Dependent Variable´ box.

b. Click on the factors and click

on the second arrow to send

them to the ³Fixed Factor(s)´

box.

c. Click on ³Options´ in order to

change the significance level as

well as to add the descriptive

statistics to the results.

d. Click OK to perform the test.4. The ³Tests of Between-Subjects

Effects´ is of interest. The third row

includes the FA, as well as the

significance and degrees of freedom.

In the fourth row is the FB information.

The fifth row shows the FAB information.




Pearson r

Use a Pearson r if:

- data is interval or ratio- not seeking cause and effect statements

Performing a Pearson r


a. In the ³Name´ column, make one row for each variable.

i. Make sure the first row is the predictor variable, and the second row is the

criterion variable.

2. Enter into ³Data View´

a.

Type the data for each corresponding category.3. Go to Analyze Correlate

Bivariate

a. Click each of the

groups and click the

arrow to send them to

the ³Variables´ box.

b. Make sure the

³Pearson´ check box

is checked.

c. Click OK to performthe test.




4. Look at the first row, which should be the predictor variable. In the rightmost box will be

the correlation and the significance of this correlation.

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