Another instance in which random sampling is not important is when a researcher is conducting a qualitative study. In qualitative studies, depth of information from selected units of analysis is given more importance rather than the number and representativeness of the target group or population. Thus, for a researcher conducting in-depth interviews of household mothers (for example), the characteristics/selection criteria set in identifying the target group (household mothers) are more important than the randomly selecting participants/members of the target group, which could result to household mothers who do not fit the criteria or characteristics set by the researcher. In effect, the type of information that would be generated from randomly sampled household mothers would not be as insightful as it would have...

However, I have realized that the relevance and importance of implications of a study is hinged primarily on the research framework. The framework serves as the roadmap from which the researcher will be guided throughout the research process. Without the framework, the researcher can get "lost" in the large amounts of data and information generated during data collection, and cannot develop actionable results and recommendations from the data analysis stage. The framework provides and helps maintain the research study's and researcher's focus.