Quantitative data management and analysis uses numbers in its methods, while a qualitative approach involves text. According to Norman K. Denzin and Yvonna S. Lincoln, the authors of "Handbook of Qualitative Research," qualitative methods also emphasize “the socially constructed nature of reality, the intimate relationship between the researcher and what is studied, and the situational constraints that shape inquiry.” The goal of qualitative data management and analysis is to order, structure and give meaning to collected data. In classic content analysis, categories of meaning are assigned to text that represent participants’ beliefs and experiences -- such as employment’s effect on happiness, most enjoyable job or past professional training. As an increasing number of cases are examined, recurring themes that are essential to validating or challenging the researcher’s hypotheses are identified.

Data Management

Qualitative data are most often derived from in-depth interviews or focus groups that explore issues related to your research hypotheses (observations and document reviews are additional data collection techniques). A semi-structured guide or module of questions elicits verbal responses from subjects, either one on one or in small groups. On average, exchanges take place from one to two hours. Recordings are then transcribed by the researcher or by an outside service, with no names or identifying information. Transcribed text is also cleaned (quality reviewed against the original audio). Finalized content is stored in word processing files on password protected computers. Files can be securely uploaded into a variety of qualitative analysis programs, such as NVivo and Atlas.ti.

Structural Coding

The first step of qualitative analysis is structural -- or open -- coding. Codes and subsequent sets are created in a separate file from data. Structural codes are associated with responses