Features and Common Practices of Qualitative Research

Qualitative research is quite broad, and to avoid boxing in a definition that excludes certain types of research or disciplines, it is helpful to consider features of qualitative research.

 

Understanding People’s Lives:  Using real-world conditions, qualitative research seeks to understand the meaning of people’s lives.  Without research inquiry or artificial research procedures, people are uninhibited in expressions using sources including diaries, journals, or photography.  Any social interaction is not limited to a research questionnaire, and people respond to the environment with limited interference.

 

Representing Views of Others:  Capturing perspectives of a group can be a significant purpose of a qualitative study.  Meanings of events which manifest over time can provide an interpretation of the values and standards of behavior of participants.

 

Uncovering  Context:  Determining specific institutional, environmental, and social context of people’s lives has an important influence on the human experience.  Qualitative research is particularly important here because many other social sciences do not address context thoroughly and provide the opportunity to control for conditions in quantitative experimental settings.

 

Explaining Behavior:  Qualitative research desires to explain and clarify life events to produce insights in real-world context.  This feature of the research is critical in delineating between explaining events and merely documenting events.  From this portion of qualitative research, it can be possible to explain new inquiries, concepts, or social processes.

 

Multiple Sources of Evidence:  All research must have multiple sources of evidence.  Adding to the credibility of qualitative research includes triangulating data from interviews, document inspection, and observation of diverse participants.

 

Common practices around qualitative research include the use of flexible as opposed to a fixed research design and collecting field-based data.  In addition, analyzing non-numeric data involves a choice around separate varieties of computer software to avoid established generalizations or social stereotypes.

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