Understanding widespread misconceptions in statistics is essential for anyone working with quantitative data. By recognizing these pitfalls, researchers can more critically evaluate statistical claims, design more robust studies, analyze data more effectively, and report findings with greater accuracy and confidence.
Data Analysis
Have you identified any pre-existing data that could be relevant to your project? When reusing someone else's data, it's crucial to follow key steps to ensure proper documentation and its provenance. This includes detailing its origin, context, and lineage, which helps maintain transparency and traceability throughout your work.
Qualitative coding is the systematic process of categorizing and segmenting textual or visual content into meaningful analytical units to identify patterns and themes within the data. This handout compares deductive and inductive approaches to qualitative coding and highlights campus-licensed and supported tools to aid in this process.