What type of analysis would likely be used to interpret survey data?

Statistical analysis is the appropriate method for interpreting survey data because surveys typically yield quantifiable information that can be systematically analyzed. This type of analysis involves applying mathematical techniques and statistical methods to summarize and draw inferences from the numerical data collected from respondents. As surveys often include close-ended questions that produce measurable responses, statistical analysis allows researchers to identify trends, correlations, and significant patterns within the data.

Statistical techniques, such as regression analysis, t-tests, or ANOVA, can be employed to assess differences or relationships among variables. This allows for a more objective evaluation of the responses, helping researchers to make data-driven conclusions and recommendations based on the findings from the survey.

While thematic analysis and qualitative analysis are vital for interpreting qualitative data (like open-ended survey responses), they do not suit the quantitative nature of typical survey data. Longitudinal analysis is focused on data collected over time, which is not inherently applicable to the immediate interpretation of standard survey results. Thus, statistical analysis stands out as the most effective method for interpreting survey data.