Thank your very much professor Crowson. Is it possible that you give us more clarification on how to use R extensions on SPSS28 and 29, and how to download R packages needed for various analyses? In addition, How we can use these versions of SPSS in doing robust statistics, such as factorial ANNOVA and multiple linear regression?
Thanks for your video. I have a question. Factorial ANOVA includes two sets of main effect of each independent variable on the dependent variable, with one interaction effect. I found that the finding of the main effect from the factorial ANOVA differ from the one-way ANOVA. Would you let me know why that occurs?
Hi there. Thanks for visiting and for your question. The reason for the difference is the one-way ANOVA involves only a single independent variable, whereas the factorial ANOVA (practically speaking) includes three independent variables: your focal independent variable, moderator (the second IV), and an interaction variable. Without getting too complicated, we can simply say that in models containing multiple independent variables, those variables will often will be incorrelated at a non-zero level. When your IV's are correlated in a model, their effects cannot be treated as additive. As a result, the F-tests you see associated with the factorial ANOVA reflect the effect of each variable (the IV, moderator, and interaction) controlling for the other effects in the model. This can have the effect of yielding a main effect in your factorial ANOVA that appears non-significant, where it was significant in the one-way ANOVA. Another factor to consider is that the error term (i.e., MSerror) in the one-way ANOVA will typically be larger than the error term in the factorial ANOVA since the latter model will often explain more variation by virtue of having more IV's. Since the F-value for your tests of the main and interaction effects in the factorial ANOVA is formed as a ratio of MSeffect/MSerror, it is also possible you could see an increase the power of the test of your main effect. So in short, yes the test of an independent variable using one-way ANOVA can yield a different result from that associated with the main effect test of that same variable in the context of a factorial ANOVA. I hope this helps. Cheers!
@@mikecrowson2462 Thank you very much for your comments!! One thing that I am confused is that "will be incorrelated at a non-zero level" What do you mean by "incorrelated"?
Thank your very much professor Crowson. Is it possible that you give us more clarification on how to use R extensions on SPSS28 and 29, and how to download R packages needed for various analyses? In addition, How we can use these versions of SPSS in doing robust statistics, such as factorial ANNOVA and multiple linear regression?
Thanks for your video. I have a question. Factorial ANOVA includes two sets of main effect of each independent variable on the dependent variable, with one interaction effect. I found that the finding of the main effect from the factorial ANOVA differ from the one-way ANOVA. Would you let me know why that occurs?
Hi there. Thanks for visiting and for your question. The reason for the difference is the one-way ANOVA involves only a single independent variable, whereas the factorial ANOVA (practically speaking) includes three independent variables: your focal independent variable, moderator (the second IV), and an interaction variable. Without getting too complicated, we can simply say that in models containing multiple independent variables, those variables will often will be incorrelated at a non-zero level. When your IV's are correlated in a model, their effects cannot be treated as additive. As a result, the F-tests you see associated with the factorial ANOVA reflect the effect of each variable (the IV, moderator, and interaction) controlling for the other effects in the model. This can have the effect of yielding a main effect in your factorial ANOVA that appears non-significant, where it was significant in the one-way ANOVA. Another factor to consider is that the error term (i.e., MSerror) in the one-way ANOVA will typically be larger than the error term in the factorial ANOVA since the latter model will often explain more variation by virtue of having more IV's. Since the F-value for your tests of the main and interaction effects in the factorial ANOVA is formed as a ratio of MSeffect/MSerror, it is also possible you could see an increase the power of the test of your main effect. So in short, yes the test of an independent variable using one-way ANOVA can yield a different result from that associated with the main effect test of that same variable in the context of a factorial ANOVA. I hope this helps. Cheers!
@@mikecrowson2462 Thank you very much for your comments!! One thing that I am confused is that "will be incorrelated at a non-zero level" What do you mean by "incorrelated"?
@@adamssam6380 it was a typo. I meant intercorrelated. Cheers!