I'm formulating a questionnaire about recycling behaviour for my school's research project and I want to use the questionnaire to do relationship analysis between different variables or factors that influence recycling behaviour. So in my questionnaire there are sections asking about attitude towards recycling, recycling behaviour, obstacles to recycling etc. But since different sections ask questions differently and therefore have different scales (e.g section 1: 5 point likert scale, section 2: 4 point likert scale, section 3: choose options from A to E) Can I do relationship analysis with questions that have different scales? I'm especially concerned about whether my analysis is valid when comparing likert scale quesitons with categorical questions (choose A-E options).
The scales you have are ordinal, not interval, so you can certainly use ordinal statistics with no problem: i.e. daily > 2x > 1x > less than 1 > don't. For correlations, this would be Spearman correlations.
You could also do crosstabulations and test via Chi-square, and get a measure of the magnitude of the correlation with Cramer's V or a similar statistic.
If you want to compare two subsets (e.g. the opinion of men vs women) you could do this by using a common survey metric: comparing the % "top box" or % "top 2 box" (e.g. % strongly agree, or % strongly agree + % agree).
No problem with any of that. If you want to use interval statistics, though (e.g. t-tests) you will need to assume the points are equally spaced. So you could assume daily=5 > 2x=4 > 1x=3 > less than 1=2 > don't=1, but that's arbitrary. Why this instead of daily=8 > 2x=5 > 1x=4 > less than 1=2 > don't=0? Since this is arbitrary, I'd try to do your school project without making these arbitrary assumptions.
You can do relationship analysis with quantities that have different scales but for the results to be highly accurate you would have to normalize/standardize your data. Normalizing is converting all your analysis inputs to the same scale.
You can convert categorical values A-E to numerical values that make sense before your analysis.
Hope this helps.