Those questions I posted weren't all part of a single study - they were examples from separate small scale investigations I've done in the last couple of years.My question is how would you collect sufficient data with sufficient variables to a level that you could analyse it such that it would be satistically significant.
If you collect some set of statistics but not enough to tease out any confounding variables you might see a correlation between A and B for which you cannot find any causal relationship because the causation is C->A and C->B and C wasn't captured.
If you have lots of data you are going to have to be fairly clever in terms of the analysis to sift it, though. I know a bit about some forms of analysis, but you'd need a good statistician.
You'd need good design for the statistics capture too, to avoid bias due to the capture, or even over capture of statistics that relies on people filling out forms can lead to either people being turned off the process and just making up responses on question 356 onwards of the 1000 question form, or in a longitudinal study people changing how they fill out forms because they understand what is being asked better, meaning that a change in response might not be due at all to what you might think it is.
For any folks interested (probably not many!), my current research area is as follows (title, followed by the RQs):
"Digital natives becoming teachers - how do they use technology to support their learning and professional development"
What factors influence the range of technology supported learning (TSL) activities that today's trainee teachers engage with in formal and informal learning situations?
What are these students' perspectives on their use of TSL in the context of their Initial Teacher Education (ITE) courses?
To what extent do institutional policies and strategies for TSL reflect the actual practices adopted by students in this category?
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