In summary, qualitative data analysis involves much disparate data and transforms them into a coherent whole or story to provide meaning about the human experience. The results are displayed in such a manner that the reader can understand and validate the conclusions that the researcher has drawn through the use of diagrams, tables, charts, direct quotations from the participants, and rich descriptions of the findings. As themes emerge, logical connections and relationships between the themes are identified to form a whole picture.
The emergent themes are then verified through member checking. Although the methods may differ, the text is coded to search for themes and categories through a process of data reduction. Researchers become immersed in the data they listen over and over to the interviews, read and reread the transcripts, and spend substantial time in the field. For example, analysis is conducted alongside the data collection, and in most cases the two processes are interrelated. Regardless of the study's research method, several commonalities exist among methods used in qualitative data analysis. Making conceptual or theoretical coherence (linking the findings into an overarching "how" and "why" of the phenomenon under study) Building a logical chain of evidence (validating each of the relationships identified)ġ3. Finding intervening variables (discerning other variables that may link findings together)ġ2. Noting relationships between variables (depicting the relationships between the findings)ġ1. Factoring (generating words to express common findings)ġ0. Subsuming particulars into the general (using a higher level of abstraction)ĩ. Partitioning variables (breaking down the themes into smaller units)Ĩ. Making contrasts or comparisons (comparing sets of things)ħ. Counting (noting that something is happening a number of times)Ħ. Making metaphors (using a literary device in which different things are compared to make sense of the experience)ĥ. Clustering (grouping together things that seem to share characteristics)Ĥ. Evaluating HyperResearch with Clearfind A clear and effortless decision framework 1.
Hyperresearch software software#
Seeing plausibility (realizing that the finding or conclusion sounds true or makes sense)ģ. The worlds first software evaluation and consolidation engine. Noting patterns and themes (repetitive or recurring patterns among many separate pieces of data)Ģ. List the following 13 tactics for drawing meaning from the data (pp.
0 kommentar(er)
