Output refers to the statistical measures released by a national statistical agency or by another data producer. These can take the form of tables of aggregate data such as average results by group, micro-data files, interactive data cubes or other forms. The key distinction between output and analysis is that output does not, in itself, answer a research question. Instead, it provides the base information that is analysed in order to produce the answer.
In some cases, the answer may be directly evident from the output, requiring only limited interpretation, comment and caveats, while in other cases extensive statistical analysis may be required.
Because output forms the basis for all subsequent analysis, it provides the key link between specific survey questions and the use of the data in analysis. The required output must therefore be clearly specified before appropriate questions can be designed. Some key issues to consider when specifying the desired output for information on subjective well-being include:
- • Will the analysis require tabular output of averages or proportions, or is micro-data needed? Simple comparisons of how different population groups compare with each other can be accomplished via tabular output, but understanding the drivers of such differences will require a much finer level of detail.
- • Will the analytic techniques used treat the data as ordinal or cardinal? This makes little difference if micro-data is required (since users can decide for themselves), but will influence how summary measures of central tendency and distribution are presented in tabular form. Information on a cardinal variable2 can be presented via techniques that add and average scores (e.g. mean, standard deviation), while ordinal data will need to be reported by category.
- • How important is it to present measures of the central tendency of the data (e.g. mean, median, mode) as opposed to the dispersal (e.g. standard deviation) or full distribution of the data (e.g. proportion responding by category)?
In planning a measurement exercise, the aim should be to clearly specify the desired output, and the data items required to produce this, before considering question design. This will involve, at a minimum, defining the measures to be used and the break-downs and cross-classifications required. In many cases, particularly if multivariate analysis is proposed, more detailed information may be required.