Research Methods in AL

✅What is Type III Sum of Squares (SS) in output tables of many tests?

In SPSS, Type III Sum of Squares (SS) refers to a method used in ANOVA (Analysis of Variance) or general linear models (GLM) to partition the variability in the dependent variable among the different effects (main effects and interactions) in your model.

💡In ANOVA or regression, the total sum of squares represents the total variation in the data. It is partitioned into:

1-Model sum of squares (variation explained by the model),

2-Error sum of squares (residuals, unexplained variation),

3-And sometimes, further divided among the individual factors and their interactions.


✅ What does it do? Why is it important to be chosen?

Type III Sum of Squares tests each effect after accounting for all other effects in the model. This means:

🛑It evaluates the unique contribution of each factor or interaction, while holding all other variables constant, and it assumes the model is balanced or uses least squares adjustment for imbalance. This is why we cannot tolerate extremely unbalanced designs when using ANOVA or other related multivariate counterparts like MANOVA.

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447 viewsHessameddin Ghanbar, edited  May 27 at 16:17

✅What is Type III Sum of Squares (SS) in output tables of many tests?

In SPSS, Type III Sum of Squares (SS) refers to a method used in ANOVA (Analysis of Variance) or general linear models (GLM) to partition the variability in the dependent variable among the different effects (main effects and interactions) in your model.

💡In ANOVA or regression, the total sum of squares represents the total variation in the data. It is partitioned into:

1-Model sum of squares (variation explained by the model),

2-Error sum of squares (residuals, unexplained variation),

3-And sometimes, further divided among the individual factors and their interactions.


✅ What does it do? Why is it important to be chosen?

Type III Sum of Squares tests each effect after accounting for all other effects in the model. This means:

🛑It evaluates the unique contribution of each factor or interaction, while holding all other variables constant, and it assumes the model is balanced or uses least squares adjustment for imbalance. This is why we cannot tolerate extremely unbalanced designs when using ANOVA or other related multivariate counterparts like MANOVA.

BY Research Methods in AL