The design of your study, the research questions you’ve posed, and types of data you’ve collected (e.g., quantitative, qualitative) are important considerations in determining the data analysis and reporting choices you make, in order to maximize the impact of your work.
Regardless of the types of data you have, your data analysis should be:
An important step in data analysis is to plan for synthesis of different types or sources of data so that your findings are coherent and inclusive of all of your data collection efforts.
Following analyses, you should focus on interpreting and representing your findings with your intended audience in mind. Clear, logical, and concise reporting is appreciated by all audiences, so consider following the “less is more” rule when sharing findings from your study.
There are a plethora of resources and tools that can assist you in analysing data and reporting findings from your study.
Working with large or complicated data sets and navigating the data analysis process can be a tedious and tricky process. In order to better understand, anticipate, and avoid the potential pitfalls that can occur during the data analysis process, make sure to plan a careful and logical approach to your analyses; take time to make sense of and measure change among your results; and converse with colleagues or other professionals to validate your analyses, and to make sure you are accurately interpreting and visualizing your results.
The basic concept behind data visualization is to use visuals to better understand data and communicate statistical findings. Visuals (e.g., graphs, charts) can help communicate the story behind a data set (e.g., insights and patterns), which might otherwise be missed through textual explanation alone. According to SAS.com, data visualization is the presentation of data in a pictorial or graphical format—and graphical presentations of data can make complex data more digestible, comprehensible, and impactful. In other words, effective data visualization takes into consideration the data, the message, and the medium—"Data visualization makes the invisible, visible" (Albert Cairo).
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