In my model, I have one dependent variable (Continuous) and multiple independent (dichotomous) variables; hence I feel a One-way ANCOVA analysis would be appropriate. Now, I would like to know, how I can compare which factors (Q6, Q7, Q8 ....Q16) have more effect than the others? Is it possible to compare the Type III Sum of Squares and the Mean Square from the SPSS output and consider that the higher of these values refer to a higher effect on the dependent variable? Please consider the file attached if required to answer. And
MANY THANKS IN ADVANCE TO WHO IS ANSWERING!!
Hi. I've been a statistician for 40 years and I still hate SPSS output. In any case look at the link and scroll down to where the interpretation starts. Follow the directions there and you should be OK. Best wishes, David Booth
https://www.scalelive.com/ancova.html
Hi Muhammad Rehan Masoom ,
maybe they are also useful:
https://statistics.laerd.com/spss-tutorials/ancova-using-spss-statistics.php
https://www.researchgate.net/post/How_strict_should_we_be_with_the_assumptions_for_ANCOVA
Pre‐test‐post‐test control group designs are well suited to investigating effects of innovations and are common in research. They are frequently analysed by means of an ANOVA on change scores, or, what amounts to the same thing, a repeated measures ANOVA to test the treatment by occasion interaction. Although the analysis of change scores has intuitive appeal, such analyses are often inappropriate. An ANCOVA on post‐test scores, with pre‐test scores as co‐variate usually provides a more appropriate and informative analysis.
Muhammad Rehan Masoom wrote: "In my model, I have one dependent variable (Continuous) and multiple independent (dichotomous) variables; hence I feel a One-way ANCOVA analysis would be appropriate."
ANCOVA models (in the statistical dialect I speak) have at least one categorical explanatory variable and one quantitative variable (i.e., a covariate), and the covariate does not interact with the categorical variable. That is not what you describe above. You have multiple dichotomous (i.e., categorical) explanatory variables, which makes it ANOVA, not ANCOVA. You have not said anything about interactions in the model, so presumably it is a main effects only ANOVA model.
Bruce Weaver I have been honored to have you address my query. Became a fan of your answers when searching for the right tools for the data of my PhD dissertation. Yes, I made that BIG mistake and corrected it.
Now, the model has One one dependent variable (Continuous), Two covariates (Continuous and not highly correlated with the dependent variable/ less than r=0.30, I guess absolutely no significance is not required), Fifteen independent variables, Remove all the outliers, but now the problem stands,
Levene's test for homogeneity of variances is found significant.
Hence, Can you PLEASE suggest to me, whether I can justify my result? Because if it can be justified, it would be immensely helpful to construct a theory related to the Sociology of Lived Religion.
Thank you very much for your response and looking forward to your help once again.
David Eugene Booth Thank you very much for sharing it. I have ignored that website before, and after your suggestion, I found that really has some helpful information.
Roberto Molteni Thanks to you. Though Laerd has always been the first place for me to learn assumptions, but thank you for making me feel that the site is being referred by a scholar as well.
Pradeep Paraman Thank you for your response. Yes, ANOVA would have been the right choice if there is no covariate.
1. The covariate and outcome variables should meet the assumption of normality.
2. There should be homogeneity of variance between the independent groups.
3. The covariate should not be correlated at higher than .80 to the outcome. If so, a different covariate should be chosen.
4. The strength and direction of the association between the covariate and the outcome variable must be similar in each independent group. This assumption is known as homogeneity of regression. Thus, your covariate variable needs to be precise and accurate enough to properly control for the variance associated with the relationship between the predictor and outcome variable when using ANCOVA.
The steps for conducting ANCOVA in SPSS
1. The data is entered in a between-subjects fashion.
2. Click Analyze.
3. Drag the cursor over the General Linear Model drop-down menu.
4. Click Univariate.
5. Click on the continuous outcome variable to highlight it.
6. Click on the arrow to move the variable into the Dependent Variable: box.
7. Click on the "grouping" or categorical predictor variable to highlight it.
8. Click on the arrow to move the variable into the Fixed Factor(s): box.
9. Click on the continuous covariate variable to highlight it.
10. Click on the arrow to move the variable into the Covariate(s): box.
11. Click on the Options button.
12. In the Estimated Marginal Means, look in the Factor(s) and Factor Interactions: box.
13. Click on the "grouping" or categorical predictor variable to highlight it.
14. Click on the arrow to move the variable into the Display Means for: box.
15. Click on the Compare main effects box to select it.
16. Click on the Descriptive statistics, Estimates of effect size, Observed power, and Homogeneity tests boxes to select them.
17. Click Continue.
18. Click OK.
The steps for interpreting the SPSS output for ANCOVA
1. Look in the Levene's Test of Equality of Error Variances, under the Sig. column. This is the p-value that is interpreted.
If the p-value is LESS THAN .05, then researchers have violated this assumption and should check for outliers or run non-parametric tests.
If the p-value is MORE THAN .05, then researchers have met the assumption and continue with the analysis.
2. Look in the Tests of Between-Subjects Effects, under the Sig. column. These are the p-values that are interpreted. Look first at the row denoting the covariate variable.
If the p-value is LESS THAN .05, then the covariate significantly adjusts the association between the predictor and outcome variable.
If the p-value is MORE THAN .05, then the covariate does NOT adjust the association between the predictor and outcome variable.
3. Look at the p-value associated with the "grouping" or categorical predictor variable.
If the p-value is LESS THAN .05, then there was a statistically significant difference between the groups or levels of the variable.
If the p-value is MORE THAN .05, then there was NOT a statistically significant difference between the groups or levels of the variable.
4. If the covariate was significant, and the "grouping" or predictor variable was significant, then researchers have evidence that there was a statistically significant difference between the groups or levels when controlling for the covariate.
If the covariate is significant, but the "grouping" or predictor variable is not, then the ANCOVA has shown evidence that it does not adjust the association.
If the covariate is not significant, but the "grouping" or predictor variable is significant, then the covariate does not adjust the association between the predictor and outcome variable.
Quick Steps
Click on Analyze -> Compare Means -> One-Way ANOVA
Drag and drop your independent variable into the Factor box and dependent variable into the Dependent List box
Click on Post Hoc, select Tukey, and press Continue
Click on Options, select Homogeneity of variance test, and press Continue
Press the OK button, and your result will pop up in the Output viewer
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