Effective data analysis support involves more than running statistical software. It starts with understanding the analysis objectives and checking the dataset, then ends with outputs that can be understood and reviewed confidently.
Reviewing objectives and data
The process begins with the analysis objectives, dataset, and questionnaire structure. This review can reveal coding problems and missing values and establish each variable's measurement level before tests are selected.
Support for questionnaire data
Support may include coding responses, reviewing reverse-scored items, calculating scale scores, preparing demographic tables, assessing reliability such as Cronbach's alpha, and choosing comparisons aligned with the analysis objectives.
Support for medical and health data
Clinical variables and patient characteristics can be organized, unusual values reviewed, groups compared, and risk factors or relationships between measurements examined, with both significant and nonsignificant findings reported clearly.
Selecting analyses and tools
Methods are selected according to variable types, sample size, data structure, and statistical assumptions. SPSS is used primarily, while jamovi, Python, or Power BI may be used when they suit the data and required outputs.
Interpreting and preparing outputs
Support continues beyond tables by connecting results to the analysis objectives and explaining p-values, effect sizes, associations, and regression models. Tables and charts are organized to support clear output review with the project team.
Need help analyzing your data?
Send your analysis objectives and data file for clear guidance on a suitable approach.
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