Data Analysis is the process of systematically applying statistical and/or logical techniques to describe and illustrate, condense and recap, and evaluate data. According to Shamoo and Resnik (2003) various analytic procedures provide a way of drawing inductive inferences from data and distinguishing the signal (the phenomenon of interest) from the noise (statistical fluctuations) present in the data. While data analysis in qualitative research can include statistical procedures, many times analysis becomes an ongoing iterative process where data is continuously collected and analyzed almost simultaneously. Indeed, researchers generally analyze for patterns in observations through the entire data collection phase (Savenye, Robinson, 2004). The form of the analysis is determined by the specific qualitative approach taken (field study, ethnography content analysis, oral history, biography, unobtrusive research) and the form of the data (field notes, documents, audiotape, videotape). An essential component of ensuring data integrity is the accurate and appropriate analysis of research findings. Improper statistical analyses distort scientific findings, mislead casual readers (Shepard, 2002), and may negatively influence the public perception of research. Integrity issues are just as relevant to analysis of non-statistical data as well. Considerations/issues in data analysis There are a number of issues that researchers should be cognizant of with respect to data analysis. These include: Having the necessary skills to analyze Concurrently selecting data collection methods and appropriate analysis Drawing unbiased inference Inappropriate subgroup analysis Following acceptable norms for disciplines Determining statistical significance Lack of clearly defined and objective outcome measurements Providing honest and accurate analysis Manner of presenting data Environmental/contextual issues Data recording method Partitioning when analyzing qualitative data Training of staff conducting analyses. Reliability and Validity
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