I have 03 variables in my research, Two independent and one dependent.
Dependent Variable = Instructional Practices (03 factors)
Independent Variable1 = Epistemic Beliefs (04 factors)
Independent Variable2 = Pedagogic Beliefs (02 factors)
I have collected data using questionnaire consisting of 45 items (05 items against each factor) With 08 demographic variables.
I want to find the " relationship between instructional practices and epistemic and pedagogic beliefs of university teachers. Additionally I want to find relationships between dependent variable and both independent variables individually. I also want to calculate relationships between different factors.
Please guide which tests should I apply and how they can be performed through SPSS.
First, report the variables individually. Then, check out the correlation between each pair and report it. In this case, if collinearity, exists, you are able to use only one of the independent variables to build a linear regression model. Finally, use the best regression model (ordinary or polynomial) based on the scatterplots. Good luck. Babak Jamshidi
Hello Mazhar,
Given your description of variable sets and what certainly sounds like proposed latent variables (factors), it sounds to me as if a structural equation model is the analytic framework that would work for you. In SPSS, you can either use: (a) the add-on program, AMOS; or (b) use the R-link to run a comparable SEM library, such as lavaan (which is freely available).
Evaluating the variables individually would be a step along the way, but a key part of the analysis would be verification of the "measurement model," that is, demonstrating that the five target items from the survey do in fact relate to one and only one factor (across the set of 9 total factors).
Good luck with your work.
Mazhar Ali Kazmi
The only thing I would add to what David Morse proposed is that structural analysis requires relatively large sample sizes. The following link may help you:
https://www.danielsoper.com/statcalc/calculator.aspx?id=89
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