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Interpreting SPSS Output for T-Tests and ANOVAs (F-Tests)

I. T-TEST INTERPRETATION: Notice that there is important information displayed in the output: The
Ns indicate how many participants are in each group (N stands for “number”). The bolded numbers in the
first box indicate the GROUP MEANS for the dependent variable (in this case, GPA) for each group (0 is
the No Preschool group, 1 is the Preschool Group).

Group Statistics

Std. Error
preschool N Mean Std. Deviation Mean
GPA-COL 0 11 3.2055 .35328 .10652
1 29 3.2866 .37537 .06970

Now in the output below, we can see the results for the T-test. Look at the enlarged numbers under the
column that says “t” for the t-value, “df” for the degrees of freedom, and “Sig. (2-tailed) for the p-value.
(Notice that the p-value of .539 is greater than our “.05” alpha level, so we fail to reject the null hypothesis.
(if your p-value is very small (<.05), then you would reject the null hypothesis.

If you were to have run the following analysis for a study, you could describe them in the results section as

The mean College GPA of the Preschool group was 3.29 (SD = .38) and the mean College GPA of the No
Preschool group was 3.21 (SD = .35). According to the t-test, we failed to reject the null hypothesis. There
was not enough evidence to suggest a significant difference between the college GPAs of the two groups of
students, t(38) = -.619, p > .05.
Independent Samples Test

Levene's Test for

Equality of Variances t-test for Equality of Means
95% Confidence
Interval of the
Mean Std. Error Difference
F Sig. t df Sig. (2-tailed) Difference Difference Lower Upper
GPA-COL Equal variances
.333 .567 -.619 38 .539 -.08110 .13091 -.34611 .18391
Equal variances
-.637 19.144 .532 -.08110 .12730 -.34740 .18520
not assumed

NOTE: Don’t be confused if your t-value is .619 (a positive number), this can happen simply by inputting
the independent variable in reverse order.

II. ANOVA INTERPRETATION: The interpretation of the Analysis of Variance is much like that of the
T-test. Here is an example of an ANOVA table for an analysis that was run (from the database example) to
examine if there were differences in the mean number of hours of hours worked by students in each ethnic
Group. (IV = Ethnic Group, DV = # of hours worked per week)

Sum of
Squares df Mean Square F Sig.
Between Groups 1504.926 3 501.642 3.529 .024
Within Groups 5116.974 36 142.138
Total 6621.900 39

If you were to write this up in the results section, you could report the means for each group (by running
Descriptives – see the first Lab for these procedures). Then you could report the actual results of the
Analysis of Variance.

According to the Analysis of Variance, there were significant differences between the ethnic groups in the
mean number of hours worked per week F(3, 36) = 3.53 p < .05.