Demonstration 1: Producing a One-Sample T Test In this chapter, we discussed methods of testing differences...

Demonstration 1: Producing a One-Sample T Test In this chapter, we discussed methods of testing differences in means between a sample and a population value. SPSS includes a One-Sample T Test procedure to do this test. SPSS does not compute the test with the Z statistic; instead, it uses the t statistic to test for all mean differences. The One-Sample T Test procedure can be found under the Analyze menu choice, then under Compare Means, where it is labeled One-Sample T Test. The opening dialog box (Figure 8.6) requires that you place at least one variable in the Test Variable(s) box. Then a test value must be specified. We’ll use the GSS2014-B data set for this demonstration. The standard workweek is thought to be 40 hours, so let’s test to see whether American adults work that many hours each week. In this example, place HRS1 in the Test Variable(s) box and “40” in the Test Value box. Then click on OK to run the procedure. The output from the One-Sample T Test procedure is not very extensive (see Figure 8.7). A total of 895 people answered the question about number of hours worked per week. The mean number of hours worked is 41.47, with a standard deviation of 15.039. Below this, SPSS lists the test value, 40. It includes the two-tailed significance, or probability, for the one-sample test. This value is .004, given the calculated t statistic of 2.918, with 894 degrees of freedom. Thus, at the .01 significance level, we would reject the null hypothesis and conclude that American adults work more than 40 hours per week. SPSS also supplies a 95% confidence interval for the mean difference between the test value and the sample mean. Here, the confidence interval runs from 0.48 to 2.45, providing estimates of how much more than 40 hours per week Americans work.

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Demonstration 1: Producing a One-Sample T Test

In this chapter, we discussed methods of testing differences in means between a sample and a population value. SPSS includes a One-Sample T Test procedure to do this test. SPSS does not compute the test with the Z statistic; instead, it uses the t statistic to test for all mean differences. The One-Sample T Test procedure can be found under the Analyze menu choice, then under Compare Means, where it is labeled One-Sample T Test. The opening dialog box (Figure 8.1) requires that you place at least one variable in the Test Variable(s) box. Then a test value must be specified.

We’ll use the GSS2014-B data set for this demonstration. The standard workweek is thought to be 40 hr, so let’s test to see whether American adults work that many hours each week. In this example, place HRS1 in the Test Variable(s) box and “40” in the Test Value box. Then click on OK to run the procedure.

Figure 8.1. One-Sample T Test Dialog Box

The output from the One-Sample T Test procedure is not very extensive (see Figure 8.2). A total of 895 people answered the question about number of hours worked per week. The mean number of hours worked is 41.47, with a standard deviation of 15.039. Below this, SPSS lists the test value, 40. It includes the two-tailed significance, or probability, for the one-sample test. This value is .004, given the calculated t statistic of 2.918, with 894 degrees of freedom. Thus, at the .01 significance level, we would reject the null hypothesis and conclude that American adults work more than 40 hr/week.

SPSS also supplies a 95% confidence interval for the mean difference between the test value and the sample mean. Here, the confidence interval runs from .48 to 2.45, providing estimates of how much more than 40 hr/week Americans work.

Figure 8.2. One-Sample T Test Output

orchestra answered 1 year ago

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