For example, a relevant "piece" of qualitative data might be interspersed with portions of an interview transcript, multiple excerpts from a set of field notes, or a comment or cluster of comments from a focus group. By contrast, qualitative data most often occur in more embedded and less easily reducible or distillable forms than quantitative data. In quantitative analysis of survey results, for example, frequency distributions of responses to specific items on a questionnaire often structure the discussion and analysis of findings. However, the data are apt to be in formats that are unusual for quantitative evaluators, thereby complicating this task. Qualitative analysis is fundamentally an iterative set of processes.Īt the simplest level, qualitative analysis involves examining the assembled relevant data to determine how they answer the evaluation question(s) at hand. Part of what distinguishes qualitative analysis is a loop-like pattern of multiple rounds of revisiting the data as additional questions emerge, new connections are unearthed, and more complex formulations develop along with a deepening understanding of the material. Moreover, the different processes involved in qualitative analysis also overlap in time. By contrast, in qualitative evaluation, data collection and data analysis are not temporally discrete stages: as soon as the first pieces of data are collected, the evaluator begins the process of making sense of the information. Quantitative evaluation is more easily divided into discrete stages of instrument development, data collection, data processing, and data analysis. Timing also works quite differently in qualitative evaluation. If not "objective" in the strict positivist sense, qualitative analysis is arguably replicable insofar as others can be "walked through" the analyst's thought processes and assumptions. Their stance has fed a fundamentally mistaken but relatively common idea of qualitative analysis as unsystematic, undisciplined, and "purely subjective."Īlthough distinctly different from quantitative statistical analysis both in procedures and goals, good qualitative analysis is both systematic and intensely disciplined. That qualitative analysts will not specify uniform procedures to follow in all cases draws critical fire from researchers who question whether analysis can be truly rigorous in the absence of such universal criteria in fact, these analysts may have helped to invite this criticism by failing to adequately articulate their standards for assessing qualitative analyses, or even denying that such standards are possible. This relative lack of standardization is at once a source of versatility and the focus of considerable misunderstanding. We have few agreed-on canons for qualitative data analysis, in the sense of shared ground rules for drawing conclusions and verifying their sturdiness (Miles and Huberman, 1984).
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By contrast, qualitative analysis deals in words and is guided by fewer universal rules and standardized procedures than statistical analysis.
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In quantitative analysis, numbers and what they stand for are the material of analysis. However, all share several common characteristics that distinguish them from quantitative analytic approaches.
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The varieties of approaches - including ethnography, narrative analysis, discourse analysis, and textual analysis - correspond to different types of data, disciplinary traditions, objectives, and philosophical orientations.
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Meaningfulness is determined by the particular goals and objectives of the project at hand: the same data can be analyzed and synthesized from multiple angles depending on the particular research or evaluation questions being addressed. Qualitative modes of data analysis provide ways of discerning, examining, comparing and contrasting, and interpreting meaningful patterns or themes. Part II: Chapter 4: Analyzing Quantitative Data Chapter 4