Day 2 of the master class was a round table discussion with people from a variety of areas human–computer interaction, bio-mechanics, software development, mathematics and urban regeneration.
Several participants introduced a particular topics or probe they are facing with their data and this led to discussions that connected to a wide variety of areas.
Topics we discussed included:
- building explanatory models from questionnaire data
- dealing with relatively small data sets with relatively large numbers of measured values for each data element
- data reduction – principle components and factor analysis
- doing easy understandable analysis for exploring data even if not the perfect model/method for the final publishable analysis
- stratified samples (re-weighting analysis/results based on population statistics)
- deliberately having unbalanced samples that emphasise a particular group
- dangers of spurious results from large volumes of data, Bonferroni correction and other techniques
- SPSS and use of the Laerd resources – I’d not come across this, but highly recommended by participants
- trying multiple models until one ‘works’
- how to make sense of some of the complex models possible in tools such as SPSS, R, etc.
- combining model-driven and data-driven analysis
- making use of large-volume poor-fidelity data with small-volume high-fidelity data
- dealing with one-off data where it is hard or impossible to gather more
- Montey-Hall problem
I will endeavour to write short blogs about each of these ave time … do chase me if there is one that you’d particularly like me to priorities!