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).
According to Smeeton and Goda (2003), “Statistical advice should be obtained at the stage of initial planning of an investigation so that, for example, the method of sampling and design of questionnaire are appropriate”.
The chief aim of analysis is to distinguish between an event occurring as either reflecting a true effect versus a false one.Any bias occurring in the collection of the data, or selection of method of analysis, will increase the likelihood of drawing a biased inference.Bias can occur when recruitment of study participants falls below minimum number required to demonstrate statistical power or failure to maintain a sufficient follow-up period needed to demonstrate an effect (Altman, 2001).If it the study is exploratory in nature, the investigator should make this explicit so that readers understand that the research is more of a hunting expedition rather than being primarily theory driven.Although a researcher may not have a theory-based hypothesis for testing relationships between previously untested variables, a theory will have to be developed to explain an unanticipated finding.Resnik (2000) states that it is prudent for investigators to follow these accepted norms.Resnik further states that the norms are ‘…based on two factors: (1) the nature of the variables used (i.e., quantitative, comparative, or qualitative), (2) assumptions about the population from which the data are drawn (i.e., random distribution, independence, sample size, etc.). Statistical, practical, clinical: How many types of significance should be considered in counseling research? While the conventional practice is to establish a standard of acceptability for statistical significance, with certain disciplines, it may also be appropriate to discuss whether attaining statistical significance has a true practical meaning, i.e., ‘clinical significance’. Jeans (1992) defines ‘clinical significance’ as “the potential for research findings to make a real and important difference to clients or clinical practice, to health status or to any other problem identified as a relevant priority for the discipline”. Kendall and Grove (1988) define clinical significance in terms of what happens when “… troubled and disordered clients are now, after treatment, not distinguishable from a meaningful and representative non-disturbed reference group”.