Directions: Answer each question completely, showing all your work. Refer to the SPSS tutorials as needed (see all attachments). Copy and Paste the SPSS output into the word document for the calculati
S P S S Tu t o r i a l Two-Way Analysis of Variance (ANOVA) – Between Groups 01 A two-way ANOVA is used to test the equality of two or more means when there are two factors of interest. When two factors are of interest, an interaction effect is possible as well. There is an interaction between two factors if the effect of one of the factors changes for different categories of the other factor. There are two different options: between groups and within groups. In be- tween-groups experiments, researchers randomly assign partic - ipants to independent groups and then expose one group to one level of the independent variable and the others to the other levels To explore the two-way ANOVA in SPSS we will use the following example from the Visual Learner Media Piece.Example:
A professor at a local University believes there is a relationship between head size, the major of the students, and the gender of students in her biostatistics classes. She takes a random sample from her three classes. The data is in the following table.
Notice that the sample size for each set of categories is the same. (i.e., female and Pre Med had 4 data values as does male and Pre Med) 02 A two way ANOVA essentially does three different hypothesis test. First test for interaction effect then effect from each of the two factors if there is no interaction effect.
The first step is to enter the data into SPSS.
The two-way ANOVA has two factors and one response variables. This is how the data is entered into SPSS. One column for each factor, gender and major. A third column for the response, head size. The factors must be quantitative, so we need to assign numerical values to each level.
For Gender, let Female = 1 and Male = 2. For Major, let Pre Med = 1, Pre PT = 2, Nursing = 3, and Health Car Admin = 4. You can use any values that you choose but make sure you are consistent and you note the values assigned.
Once the data is entered, the analysis is performed by selecting Analyze – General linear Mode – Univariate . 03 In the Univariate popup box, the factors, Gender and Major need to be put in Fixed Factor(s). The response, Head_Size is put in the Dependent Variable box. There is an option for selection of Post_Hoc test on the right of the screen that gives several options for different Post_Hoc test that can be performed.
Remember that a Post_Hoc test is only need if there is significant difference found.
Therefore, the two-way ANOVA should be run first. Select continue , then interpret the output in the output window. The fist output box gives the sample size for each of the factors.
The second output box gives the two-way ANOVA table. Remember to test for inter- action, looking at Gender*Major first. Then, if there is no significant effect, go on to look for a significant effect due to Gender and Major separately.
Putting all the statistical conclusions together we can see that there is no effect from the interaction of gender and major on the head circumference and there is no effect on head circumference due to major but there is an effect due to gender on head circumference at a statistically significant level of 0.05. 04
