T-tests are used when you want to examine differences but you do not know everything about the population.There are three types of t-tests that you may choose to do:one-sample t-test, independent sample t-test, or dependent sample t-test.You can calculate these by hand, in SPSS, or in Excel.The instructions below can be used for SPSS and your textbook offers instructions for using Excel.

Single-sample t-tests

These tests are used when you want to determine the probability that a sample was drawn from a population with a known mean () but with a standard deviation estimated from the sample.

• Click on analyze, compare means, one-sample t-test
• Copy the variables you want to test into the Test Variables box
• Type the population mean into the Test Value box
• Click on options to get:
• Confidence intervals (95% is default)
• Exclude cases analysis by analysis
• If some data is missing, this will drop the data only in analyses where that data is missing.
• Exclude cases listwise
• If you are doing multiple t-tests and have missing data, this will drop participants who have missing data from all t tests
• Click on continue, ok
• The output will display the t-statistic, degrees of freedom (n-1), significance (two-tailed), and the confidence interval

Independent sample t-test

These tests are used when you want to determine the probability that two samples were drawn from the same population with unknown means and standard deviations; both of which are estimated from the sample.No population parameters are specified.

• The data should be entered in one column and should be named as your dependent variable.
• You will need another column of data to identify each group according to number.So, it is a good idea to have two columns of data (one for the IV and one for the DV).
• For the IV column, you should use two consecutive numbers (I usually use 1 and 2)
• Also, be sure to use variable view to name your variables (otherwise this can become very confusing)

First, we need to calculate means for the purpose of interpretation.

Go to analyze, compare means, means

Put your IV in the grouping variable box and your DV in the dependent variable box.

Click OK to get the means and standard deviations

Now, you need to calculate your t-test

Go to analyze, compare means, independent samples t-test

Your IV is the grouping variable

Click on define range and enter 1-2

Put the DV in the dependent variables box.

You can click on options to change the confidence interval (default is 95%)

Click on OK

The output will show you the t-statistic, the significance level, the standard error of the mean, and the confidence interval.

Dependent samples t-test

We use this t-test when we have a repeated measures design such as the same sample completes a pre and post-test and we want to know if there is a difference from one test to the other.

Go to analyze, compare means, paired sample t-test

Select two variables and move into box

Click on OK

The output will give you means for each trial (or pre-post test measure) as well as the t-statistic and significance level

Let's try a few using the data below.Be sure to attach your printouts and answer the questions below.

1. First, conduct an independent sample t-test for gender (IV) and pretest scores (DV)

Were there significant gender differences?How do you know? Interpret the results statistically and in words.

1. Then conduct an independent sample t-test for gender (IV) and posttest scores (DV)

Were there significant gender differences?How do you know? Interpret the results statistically and in words.

1. Now, conduct a paired (dependent) sample t-test for pretest and posttest scores.

Were there two scores significantly different?How do you know? Interpret the results statistically and in words.

Data Set Homework 2 (*note 1 = males, 2 = females)

GenderPretestPosttest

1.0050.0080.00

1.0050.0070.00

1.0080.0070.00

1.0080.0050.00

1.0070.0050.00

1.0070.0050.00

1.0060.0060.00

1.0060.0080.00

1.0090.0080.00

1.0090.0090.00

1.0090.0080.00

1.0080.0090.00

1.0080.0090.00

1.0070.0070.00

1.0070.0080.00

1.0050.0080.00

1.0050.0070.00

1.0050.0070.00

1.0060.0060.00

1.0070.0080.00

2.0050.0070.00

2.0040.0050.00

2.0040.0080.00

2.0070.0080.00

2.0050.0070.00

2.0050.0080.00

2.0050.0090.00

2.0060.0090.00

2.0070.0090.00

2.0040.0080.00

2.0040.0080.00

2.0030.0070.00

2.0030.0070.00

2.0050.0060.00

2.0060.0080.00

2.0040.0080.00

2.0070.0080.00

2.0050.0070.00

2.0060.0070.00

2.0070.0090.00