Process of Data Input and Calculation for Two-Way ANOVA in SPSS
Performing a Two-Way ANOVA in SPSS (Statistical Package for the Social Sciences) requires correctly inputting data and then analyzing it. In the steps below, we will understand with an example how to enter data in SPSS and perform a Two-Way ANOVA. We will use the previous example (effect of teaching method and gender).
Example Data
We have the following data:
| Teaching Method | Gender | Score (5 Students per Group) |
|---|---|---|
| Lecture | Male | 75, 78, 80, 82, 85 |
| Lecture | Female | 70, 72, 74, 76, 78 |
| Online | Male | 85, 88, 90, 92, 95 |
| Online | Female | 80, 82, 84, 86, 88 |
Objective: To examine the effect of teaching method (Lecture, Online) and gender (Male, Female) on exam scores and their interaction effect.
Step-by-Step Process
Step 1: Inputting Data in SPSS
1. Open SPSS:
- Open SPSS software and check the Data View and Variable View tabs.
- In Variable View, define the variables, and in Data View, enter the data.
2. Define Variables in Variable View:
- Go to the Variable View tab and create the following variables:
- Variable 1: Teaching_Method
- Name:
Teaching_Method - Type: Numeric
- Label: Teaching Method
- Values:
{1 = "Lecture", 2 = "Online"}
- Name:
- Variable 2: Gender
- Name:
Gender - Type: Numeric
- Label: Gender
- Values:
{1 = "Male", 2 = "Female"}
- Name:
- Variable 3: Score
- Name:
Score - Type: Numeric
- Label: Exam Score
- Values: None (this is a continuous variable)
- Name:
3. Enter Data in Data View:
- Go to the Data View tab and enter the data as follows:
- Each row will represent one student’s score.
- For example:
| Teaching_Method | Gender | Score |
|---|---|---|
| 1 | 1 | 75 |
| 1 | 1 | 78 |
| 1 | 1 | 80 |
| 1 | 1 | 82 |
| 1 | 1 | 85 |
| 1 | 2 | 70 |
| 1 | 2 | 72 |
| … | … | … |
| 2 | 1 | 85 |
| 2 | 1 | 88 |
| 2 | 2 | 80 |
| … | … | … |
There will be a total of 20 rows (4 groups × 5 students).
Step 2: Running Two-Way ANOVA
1. Go to Analyze Menu:
- In the SPSS menu bar, click on .
2. Univariate Dialog Box:
- Dependent Variable: Add
Scoreto the Dependent Variable box. - Fixed Factor(s): Add
Teaching_MethodandGenderto the Fixed Factor(s) box.
3. Plots Button:
- Click on Plots.
- Add
Teaching_Methodto Horizontal Axis*/- Add
Genderto Separate Lines (or vice versa).- Click Add, then Continue.
- Add
4. Post Hoc Button (Optional):
- If any factor has 3 or more levels (in our example, there are only 2 levels), click on Post Hoc and select Tukey.
- Click Continue.
5. Options Button:
- Click on Options.
- Select the following options:
- Descriptive statistics
- Estimates of effect size
- Homogeneity tests (for Levene’s Test)
- Click Continue.
6. Press OK:
- Click OK, and the results will appear in the Output Window.
Step 3: Interpreting Results
SPSS will generate several tables and graphs. The key outputs are:
1. Descriptive Statistics:
- Shows the mean, standard deviation, and count for each group (e.g., Lecture-Male, Online-Female).
- Example:
- Lecture, Male: Mean = 80
- Online, Female: Mean = 84
2. Levene’s Test for Homogeneity of Variances:
- This checks whether the variances across groups are equal.
- If the Sig. value > 0.05, the assumption is met.
- Example: If Sig. = 0.155, the variances are equal.
3. Tests of Between-Subjects Effects:
- This is the main table, showing the F-value, p-value, and effect size (Partial Eta Squared).
- Example results:
- Teaching_Method: F = 40.82, Sig. = 0.000 (significant)
- Gender: F = 14.69, Sig. = 0.001 (significant)
- Teaching_Method*Gender: F = 0, Sig. = 1.000 (not significant)
- Conclusion:
- Teaching method and gender have a significant effect on scores.
- There is no significant interaction effect between them.
4. Profile Plot:
- This graph shows how mean scores vary based on teaching method and gender.
- If the lines are not parallel, there may be an interaction effect.
Step 4: Reporting Results (APA Style)
A Two-Way ANOVA was conducted to determine whether teaching method (Lecture, Online) and gender (Male, Female) affect exam scores. The results revealed that teaching method had a significant effect on scores, F(1, 16) = 40.82, p < 0.001, η² = 0.72. Gender also had a significant effect, F(1, 16) = 14.69, p = 0.001, η² = 0.48. However, no significant interaction effect was found between teaching method and gender, F(1, 16) = 0, p = 1.000.
Important Notes
1. Checking Assumptions:
- Normality: Run the Shapiro-Wilk test in .
- Homogeneity of Variances: Check the Levene’s Test results.
- If assumptions are not met, consider non-parametric tests or data transformation.
2. Data Import:
- If your data is in Excel or CSV, import it using . Ensure columns are correctly defined.
3. Post-Hoc Tests:
- If a factor has 3 or more levels, run Post Hoc tests (e.g., Tukey HSD) to identify which groups differ.
4. Software Resources:
- Check online resources (e.g., YouTube or Laerd Statistics) for SPSS tutorials.
- For example, Laerd Statistics provides detailed guides for Two-Way ANOVA.
Problems and Solutions
Problem: Error in data input.
Solution: Ensure correct variable definitions in Variable View and accurate data entry in Data View.
Problem: Levene’s Test shows Sig. < 0.05.
Solution: Consider Welch ANOVA or non-parametric tests (e.g., Kruskal-Wallis).
Problem: Difficulty understanding result interpretation.
Solution: Focus on the Sig. column in the Tests of Between-Subjects Effects table. If p < 0.05, the effect is significant.
If you need further assistance with data input or analysis in SPSS, or clarification on any specific step, let me know and Contact me!
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