A data analysis plan is a roadmap for how you’re going to organize and analyze your survey data—and it should help you achieve three objectives that relate to the goal you set before you started your survey: When you were planning your survey, you came up with general research questions that you wanted to answer by sending out a questionnaire.
Remind yourself of your objectives when you start your data analysis plan.
And if enough of them took your survey, they may have lowered your overall scores.
But don’t fret—students were happy with your conference, so you know that your entire event wasn’t awful.
data analysis is going to involve identifying common patterns within the responses and critically analyzing them in order to achieve research aims and objectives.
Data analysis for quantitative studies, on the other hand, involves critical analysis and interpretation of figures and numbers, and attempts to find rationale behind the emergence of main findings.
Because you want to gain a more insightful understanding of what your data means, organize your thoughts by attributing your specific survey questions to each general research question. Overall, do you think the conference provided too much, too little, or about the right amount of networking? In general, how would you rate the food at the conference? Do you feel the temperature in the conference building was too hot, too cold, or just right?
So when it comes to creating an effective final report, you’ll know exactly which data you need to answer your bigger questions.3. You performed an event feedback survey because you wanted to know where you need to make improvements so you can host better future events.
Comparisons of primary research findings to the findings of the literature review are critically important for both types of studies – qualitative and quantitative.
Data analysis methods in the absence of primary data collection can involve discussing common patterns, as well as, controversies within secondary data directly related to the research area.