In Chapter 19, “Processes of Quantitative Data Analysis and Interpretation,” the steps taken in analyzing quantitative data are described. In the preanalysis phase, the raw data is checked, software is selected for analysis, and data is entered and verified into a computer. In the preliminary assessments phase, missing values are troubleshot, data quality and bias are assessed. In the preliminary actions phase, the required transformations and recodings are performed as well as other peripheral analyses. In the principal analyses phase, the actual statistical analysis takes place, whether descriptive, bivariate or multivariate, and the post hoc tests are also administered. Finally, the interpretive phase synthesizes the results of the analysis and supplementary interpretive analyses (like power analyses) are performed. Other methods include subgroup mean substitutions, which involve using mean values for a relevant subgroup to better estimate the missing value during data analysis. All of these statistical methods are performed in various stages of data analysis in order to assess a study’s credibility and to test inferences to guide the researchers in an interpretation.
It is absolutely vital for nurses to understand the basis of these statistical methods in order to better comprehend the research they are studying. When studying a piece of research, the nurse must think critically about the interpretation the researcher provides and whether or not it holds up to sufficient rigor. For example, nurses must notice if their statistical methods do not address certain limitations; if they are not mentioned by the researcher, it is possible they did not take those limitations into account, and thus the results can be called into question. By knowing exactly how the researchers conducted their statistical data analysis, it may help nurses know whether or not the data is credible.
References
McGonigle, D., & Mastrian K. (2011). Nursing informatics and the foundation of knowledge.
Jones & Bartlett Learning.
