When analyzing collected data, the key is to apply approaches that best align with the research design and questions being addressed. Extracting usable information often involves identifying patterns, correlations, or causal relationships while ensuring that the analysis remains valid and reliable. For my research, I selected a descriptive statistical approach, as it allows me to summarize the data clearly and highlight trends that directly address the research problem.

Two alternative analysis methods worth comparing are regression analysis and content analysis. Regression analysis is particularly strong for examining causal relationships between variables. If I had chosen regression over descriptive statistics, the results could reveal predictive patterns and stronger evidence of cause-and-effect relationships, rather than simply describing outcomes. On the other hand, content analysiscommonly used in qualitative studieswould yield a very different set of results, focusing on themes, meanings, and context within textual or interview data. Compared to descriptive statistics, content analysis would provide a richer narrative perspective but may lack the precision of quantifiable patterns.

Published studies illustrate these methods well. For example, Angrist and Pischke (2009) demonstrated how regression analysis can establish causality in economics research by isolating the impact of policy interventions on social outcomes. In contrast, Neuendorf's (2017) work on content analysis showed how systematic coding of qualitative data can reveal recurring themes and causal links in communication studies. Both methods, when applied rigorously, establish causality but do so through very different lensesquantitative versus qualitative. This highlights the importance of selecting an approach that best fits the research design while being aware of the alternative perspectives other methods may provide.