Mastering ANCOVA Using SPSS Statistics
ANCOVA (Analysis of Covariance) is a blend of ANOVA and regression that allows you to compare one or more mean scores, while controlling for the variability of other variables known as covariates. This statistical technique is particularly useful when you want to compare the mean of different groups while accounting for variance caused by other variables.
When to Use ANCOVA
Use ANCOVA when you need to:
- Compare the means of different groups while controlling for one or more covariates.
- Adjust the means of the dependent variable for differences on the covariate.
- Increase statistical power by reducing within-group error variance.
Variables Involved in ANCOVA
In ANCOVA, the variables involved are:
- Dependent Variable: The outcome you are interested in. For example, Test Scores.
- Independent Variable: The grouping variable. For example, Teaching Method.
- Covariate: The variable you want to control for. For example, Hours Studied.
Steps to Perform ANCOVA in SPSS
- Open SPSS Statistics and load your dataset.
- Navigate to Analyze > General Linear Model > Univariate…
- Select the dependent variable (Test Scores) and add it to the Dependent Variable box.
- Select the independent variable (Teaching Method) and add it to the Fixed Factor(s) box.
- Select the covariate (Hours Studied) and add it to the Covariate(s) box.
- Click OK to run the analysis.
Interpreting the Results
The output provided by SPSS Statistics will include several tables. The key table is the Tests of Between-Subjects Effects table, which shows the significance of the covariate and the independent variable after controlling for the covariate.
Example Output
| Source | Type III Sum of Squares | df | Mean Square | F | Significance (p-value) |
|---|---|---|---|---|---|
| Corrected Model | 1234.567 | 3 | 411.522 | 5.678 | 0.002 |
| Intercept | 7890.123 | 1 | 7890.123 | 108.345 | 0.000 |
| Hours Studied | 567.890 | 1 | 567.890 | 7.812 | 0.010 |
| Teaching Method | 876.543 | 2 | 438.272 | 6.543 | 0.003 |
| Error | 4567.890 | 80 | 57.099 | ||
| Total | 23456.789 | 84 | |||
| Corrected Total | 5802.457 | 83 |
In the example above, the covariate (Hours Studied) is significant, F(1, 80) = 7.812, p = 0.010, indicating it has a significant effect on Test Scores. The main effect of Teaching Method is also significant, F(2, 80) = 6.543, p = 0.003.
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