DATA ANALYSIS

Data analysis



Data analysis

Data Analysis

As can likely be seen by now, quantitative researchers frequently have a detailed plan of action that thought out prior to the beginning of a study's taking place. Qualitative researchers, on the other hand, tend to take a more flexible approach to their studies. This holds true for the analysis of data, as well. Whereas in quantitative studies, the data analysis methods, planned out in advance and then occur after the data collected, data analysis typically takes place at the same time as data collection in qualitative studies. To make appropriate subsequent observations, analyses must normally begin after studying one to several early cases. As a result, quantitative researchers are not usually afforded the opportunity to modify their methods of data collection during a project, while qualitative researchers can do so at any point in a project after conducting the initial data analysis. Additionally, although qualitative data are more subjective and sometimes difficult to interpret, quantitative data are easily coded into numerical formats. As a result, it is much easier to enter quantitative data into computer programs, such as Excel and SPSS, than it is to enter qualitative data. Furthermore, there are a number of programs that analyze the statistical data, such as SPSS and Stata. Although programs do exist for the interpretation of qualitative data, they are not used nearly as extensively as those used for quantitative data analysis (Frankfort, 1999, 22).

Finally, whereas quantitative researchers have a variety of means to test the statistical significance and validity of the data that they are analyzing, this is not the case for qualitative researchers. Instead, qualitative researchers must do their best to present a clear, accurate, and convincing analysis of their data. As a result, a topic of much debate between quantitative and qualitative researchers is the validity and reliability of findings produced in studies. Validity is the extent to which measures correspond to the concepts they intended to reflect. Reliability is the consistency with which a measuring instrument allows assignment of values to cases when repeated over time. Although a measure can be reliable without being valid, it cannot be valid without being reliable. Additionally, since one of the main points of conducting quantitative research is to study causal relationships, part of the process involves manipulating various factors that could potentially influence a phenomenon of interest while at the same time controlling for other variables that could affect the outcome. For instance, if a researcher were examining if gender played a role in whether a person received a job, it would be important to control for other variables, such as education or previous work experience, since these factors may also determine why an individual would receive an employment offer. In quantitative analysis, empirical relationships and associations are typically examined by using general linear models, nonlinear models, or factor analysis to understand important information about the relationship between variables, such as the direction of a relationship. However, despite the results that may be produced by these ...