I have a 26 items questionnaire refined by running Common Factor Analysis. As a result, I got 4 factors (f1, f2, f3, f4). Each factor is measured by 6-7 items in the questionnaire. These four extracted factors would be my Independent Variables. I also have two Dependent Variables. What I want to know is the relationship between the 4 IVs and the 2 DVs.
If I use regression analysis, how can I calculate the extracted factor datasets based on the raw data of 6-7 items?
If I use SEM, how can I change 26 items dataset to 4 factors dataset?
Best Answer
I believe you have 3 questions. 1) how to calculate the extracted factor datasets based on the raw data of 6-7 items: you can save factor scores, also known as 'Regression score' in SPSS and R Check this quick SPSS tutorial
2) If you use SEM, how to change 26 items dataset to 4 factors dataset: SEM requires a theoritical support before you can build model. Lets say you come to know that from a previous study. You can however build models based on factor structure found in factor analysis. Check this detailed SEM tutorial
3) Whether to use SEM or regression analysis: Depends on what you want to measure. If you want to measure effects of factors and underlying 6-7 items on both the dependent variable simultaneously, SEM will be ideal. Regression can however measure only one dependent variable at at time. So one model for dependent 1 with 4 factors, another separate model for dependent 2 with 4 factors.
All the best!