Qualitative data analysis entails using the qualitative data to provide a detailed explanation and interpretation of people and situations under investigation (Monette, Sullivan & DeJong, 2011). Conversely, quantitative data analysis encompasses using appropriate techniques to convert data to numerical forms to analyze them statistically. When preparing for analysis of qualitative data, the researcher starts with general open-ended questions. As more information emerges, the researcher will move towards greater precision (Monette, Sullivan & DeJong, 2011). With quantitative data analysis, the researcher identifies the key explanatory and outcome variable in advance. Identifying exploratory and outcome variables in advance encourages the evaluator to code all data; hence modifying the plan.

Second, in qualitative data analysis, you do not need to identify the pre-defined variables in advance. The pre-defined framework reflects the goal and interest of the researcher (Monette, Sullivan & DeJong, 2011). Conversely, in quantitative data analysis, you need to identify and control the contextual variable. The difference that exists concerns pre-defined and contextual variables. The difference increases, the likelihood that the evaluation program will succeed because with a pre-defined framework, the evaluator will focus on one approach and abandon the rest.

Lastly, when preparing for qualitative data analysis you should know that preliminary analysis is an existing part of collecting data. On the other hand, for the case of quantitative data analysis, data collection and analysis are distinct separate phases. The difference between the two methods concerns data collection and analysis, which affects the time to implement the program.

Reference

Monette, D. R., Sullivan, T. J., & DeJong, C. R. (2011). Applied social research: A tool for the human services. Australia: Brookscole.

 
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