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visual programming tool interactive data visualization

Characteristics of data sample[edit]

In any report or article, the structure of the sample must be accurately described. It is especially important to exactly determine the structure of the sample (and specifically the size of the subgroups) when subgroup analyses will be performed during the main analysis phase.
The characteristics of the data sample can be assessed by looking at:

  • Basic statistics of important variables
  • Scatter plots
  • Correlations and associations
  • Cross-tabulations[32]

Final stage of the initial data analysis[edit]

During the final stage, the findings of the initial data analysis are documented, and necessary, preferable, and possible corrective actions are taken.
Also, the original plan for the main data analyses can and should be specified in more detail or rewritten.
In order to do this, several decisions about the main data analyses can and should be made:

  • In the case of non-normals: should one transform variables; make variables categorical (ordinal/dichotomous); adapt the analysis method?
  • In the case of missing data: should one neglect or impute the missing data; which imputation technique should be used?
  • In the case of outliers: should one use robust analysis techniques?
  • In case items do not fit the scale: should one adapt the measurement instrument by omitting items, or rather ensure comparability with other (uses of the) measurement instrument(s)?
  • In the case of (too) small subgroups: should one drop the hypothesis about inter-group differences, or use small sample techniques, like exact tests or bootstrapping?
  • In case the randomization procedure seems to be defective: can and should one calculate propensity scores and include them as covariates in the main analyses?[3