The variable physical environment effect, is only a subset of the independent variable ( environmental factors) in my research, there are social and cultural environment effects as well. They are measured in my questionnaire with five questions and the responses are; ( never, rarely, often and always). The dependent variable, student performance, was also measured in the same format as the environmental factors(i.e with five questions and Never, rarely...being the responses). I have coded them into SPSS with the measure; Ordinal. I want to answer the research question; 1. How physical environment affect student performance? 2. How social environment affect student performance? 3. To what extent does cultural environment influence student performance? I've computed the composite score(mean) for the questions, can I use these scores in the ordinal regression analysis? Or is there any other way to compute the questions into a single variable, for both the independent and dependent variables?
In your study, where you have measured the effects of physical, social, and cultural environments on student performance using ordinal scales, you can use ordinal regression analysis to answer your research questions.
To conduct the ordinal regression analysis, you do not necessarily need to compute a composite score for the questions. Instead, you can use each question as a separate predictor in the analysis. Each question represents a different aspect of the environment (physical, social, or cultural), and using them as separate predictors allow you to assess the unique contribution of each aspect to student performance.
Here's a general outline of the steps you can follow:
By using each question as a separate predictor in the ordinal regression analysis, you retain the specificity and granularity of the different aspects of the environment being studied. This approach allows you to explore the individual effects of physical, social, and cultural environments on student performance.
Keep in mind that ordinal regression assumes proportional odds, which means it assumes that the relationship between the predictors and the outcome is consistent across different levels of the outcome variable. It's important to assess this assumption, for example, by conducting tests of parallel lines.
Additionally, be cautious when interpreting the results as causal relationships. While ordinal regression can help identify associations between variables, establishing causal relationships requires rigorous experimental designs or the consideration of other potential confounding factors.
Overall, by using ordinal regression analysis and examining the effects of different environmental factors on student performance separately, you can address your research questions and gain insights into the influence of physical, social, and cultural environments on student performance.
Thanks for your response. I really appreciate. Actually, I just checked that i didn't mention that, my dependent variable; student performance, was also measured with five questions with the same response in (Never, Rarely, Often and Always). So, I did compute the mean scores for the responses for each of the variables, the I carried out a linear regression analysis of each of the independent variables against the dependent variable, to find out the relationship between them separately.
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