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Data interpretation
The questions as to when values should be interpreted as low or high and which conclusions should be drawn from the collected data depend on the circumstances and questions of the individual project. A generalisation is therefore not possible. (This refers especially to the data that is gathered by content-analytical methods.) Nevertheless, we would like to give some examples.
Scale (mean) values from questionnaire data can be interpreted, for example, as a function of the meaning of the scale description. For example “Students assess xy on average as 'good'", etc. Interpretation of results can also occur by taking a comparison approach:
- among variables or data (e.g. comparison of the number of projects that pursue a certain goal, with weighting of these goals)
- among student groups
- among different points in time (e.g. comparison of data obtained from network analysis before and after a network meeting)
- among courses / classes
- among software tools
- among projects
Sample
“A sample is a subset of a (=>) population, often taken for the purpose of statistical inference” (From: Connexions, Populations and samples).
When interpreting of the values, the size and the composition of the sample should also be considered. For program evaluation, data should preferably be obtained from all projects!
Interpretation of correlations / associations
Correlations – irrespective of their level - provide no information about causalities. Whether a leads to b or vice-versa cannot be explained by correlational methods.
