Input \'Qualitative Studies\' Into a Search Engine

Input 'qualitative studies' into a search engine and an abundant and myriad of studies pop up on the screen. What makes the article Rigor in qualitative social work research: A review of strategies used in published articles (Barusch, Gringeri, George, 2011) one that catches the reader's attention is that it is the epitome of a qualitative study accomplished with quantitative results. The difference between qualitative and quantitative studies are that one is measuring feelings, thoughts, perceptions, ideas and the human element of academic studies, while the other is used to quantify material in numerical or statistical context to verify trends, patterns, results, applications and inferences.

The abstract of the Rigor study is a perfect example of mixing the qualitative and quantitative methodologies; the study sough to describe strategies used by social workers to enhance the rigor of their qualitative work. Interestingly enough, the study's quantitative aspects are stated right up front with "sampling rationale (67%), analyst triangulation (59%) and mention of methodological limitations (56%)" (p. 11) as well as "deviant case analysis (8%), external audit (7%), and specification of ontology (6%)" (p. 11).

The quantitative analysis conducted by the study to determine the qualitative enhancements is a perfect example of the strength of quantitative data analysis in a qualitative study. The qualitative study gets its strength through determining how its participants feel about certain subjects, while the quantitative study provides the strength of a numerical foundation to assist the qualitative researcher in portraying those thoughts, feelings and perceptions.

One of the weaknesses of a quantitative study is that it most often provides the numbers, not the why's and wherefores that a qualitative study can provide. One recent quantitative study provides a good example of that weakness. The study "concerns the design and development of online instruction and specifically targets interaction and communication between online learners" (Jain, Jain, Jain, 2011, p. 538). What the study discovered was that the "mean number of interactions per student per week was 4.76, the standard deviation for this variable was 3.89" (p. 544). The study also determined that the strength of relationship between dependent and independent variable "as measured by n2, was strong, with differences in discipline accounting for 225 of the variance of the dependent variable" (p. 544).