This essay concentrates on ways of analyzing quantitative and qualitative data. On one hand, there are two primary ways of analyzing qualitative information. First, the researcher can explore their findings with a pre-determined study framework that reflects the aims and objectives of the study. This method allows the researcher to concentrate on particular response descriptions and ignores the rest. The second approach is the thematic network analysis. This approach requires that the researcher codes all data and allow for quantitative interpretations (Abeyasekera, 2006).
However, there exist numerous ways of analyzing quantitative data. Some of them are as follows. First, the researcher can use frequency distributions to describe the number of times attributes occur within a particular interval. Second, percentile analysis serves as a good way of comparing different groups and time periods within an intervention. Third, ratios are useful in presenting the numerical relationship that exists between groups. Fourth, measures of central tendencies and dispersion are vital in describing the normalcy and dispersion existing among the collected data pairs. Fifth, non-parametric tests would be useful in testing study hypothesis on the statistical significance of intervention among others. Finally, regression analysis could be useful in the determination of the extent of a causal relationship between variables (CDC, 2009).
As earlier stated, the needs assessment fits a series of data collection methods among them informant interviews, surveys, and literature reviews. These methods would employ both quantitative and qualitative methods of data analysis. The qualitative data collected will require the use of both thematic network and framework analysis. Contrarily, the assessment will use percentile analysis, frequency distributions, non-parametric tests, measures of central tendencies to analyze quantitative data. The main aim of these mixed methods is to best present the participants’ perspectives and views on the statistical significance of reduced length of days/excuse days (Barnett, 2011).
Abeyasekera, S. (2006). Quantitative Analysis Approaches to Qualitative Data: Why When How. Reading, UK: Statistical Services Center.
Barnett, K. (2011). Best Practices for Community Health Needs Assessment and Implementation Strategy Development: A review of Scientific Methods, Current Practices, and Future Potential. Atlanta Georgia: The Centers for Disease Control and Prevention.
CDC. (2009). Analyzing Quantitative Data for Evaluation . Atlanta, GA: Centers for Disease Control and Prevention .