Data analysis is key for discovering credible findings from implementing nursing studies. Discussion and conclusions can be made about the meaning of the findings from the data analysis.
Share what you learned about descriptive analysis (statistics), inferential analysis (statistics), and qualitative analysis of data; include something that you learned that was interesting to you and your thoughts on why data analysis is necessary for discovering credible findings for nursing.
Compare clinical significance and statistical significance; include which one is more meaningful to you when considering application of findings to nursing practice.
Descriptive Analysis (Statistics)
Nurses need to understand basic scientific procedures and research activities (QSEN, 2020). Some of this information you should recognize from completing your pre-requisites of math/statistics before taking this course. The purpose of descriptive statistics is to describe; one useful goal descriptive statistics is used is to quickly understand the characteristics of the sample (participants/subjects/people) in the study. Researchers can categorize descriptive data analysis many differing ways (Houser, 2018; Nieswiadomy & Bailey, 2018; Schmidt & Brown, 2019):
Descriptive analysis of data is helpful in determining the characteristics of subjects and variables (Houser, 2018). Being able to competently read descriptive analysis will prove to be a valuable skill when considering if the results can be applicable to a particular practice setting.
Pie charts
Histograms
Line graphs
Bar charts
Scatter plots
Tables
Diagrams
Scattergrams
Percentile Rank
Quartiles
Standardized scores
Correlational coefficients
Range
Standard deviation
Variance
Mean
Median
Mode
Frequency (table, distribution)
Counts
Percentages
Proportions
Rates
Quantitative Analysis (Inferential Statistics)
The purpose is to determine the strength and the applicability of findings to the general population (Houser, 2018; Polit & Beck, 2018). The calculation processes and procedures are organized, systematic, and mathematical. “Inferential statistical tests are based on the assumption that chance or sampling error/random errors is the only explanation; however, researchers want to establish that chance/error is not the reason” (Nieswiadomy & Bailey, 2018, p. 250). For nursing studies, incorporating this type of statistical testing is important in rendering strong, sound, reliable results.
Inferential statistics yields robust, scientific findings. By using inferential stats, results can be determined to be statistically and/or clinically significant (Houser, 2018). Read Houser chapter 13 for more information.
Being able to competently read inferential analysis will prove to be a valuable skill when reading research studies and trying to determine if the findings are significant and applicable to practice.
Qualitative Analysis (Results)
Evidence-based practice emphasizes equality within all areas of scientific methodology, clinical experience, and patient preferences; therefore, one does not trump the other in terms of significance. Qualitative studies are valuable to nursing practice as these studies are designed to provide insight towards the latter two, both clinical experience and patient preferences.
When reading research studies that describes the meaning of an experience, the methods or procedures should be clearly described; yet may seem to unfold during the description. “Some qualitative research may or may not report the type/category of qualitative design used in the study; therefore, the study may be labeled qualitative or content analysis” (Houser, 2018, p. 411); some research articles may be labeled by the classification of qualitative design such as ethnography, grounded theory, phenomenology, or case research. All qualitative studies should outline some of the following methods (Houser, 2018; Polit & Beck):
Sampling procedures
Inclusion criteria
Data collection techniques
Overview of the analysis
Data analysis (should begin with data collection)
Interpreting data collected
comprehening
synthesizing
theorizing
recontextualizing
Data management (should begin early)
Content analysis
catalog, code, codebook
reviewed for themes/patterns/common threads
analysis—uses an analytic method
report themes with supporting quotes
main themes and sub-themes should be reported
interpretation of themes
conclusions are based on themes
overall implications for nursing practice is described in terms of understanding the lived experience
Includes efforts to establish/enhance qualitative validity:
Trustworthiness
Credibility
Dependability
Confirmability
Bracketing
Purposive sampling
Saturation
Triangulation, member checking, peer debriefing
Prolonged engagement
External checks
Inter-rater and inter-coder reliability
Inquiry audit/Audit trail
Decision trail
Deep level detail
Even though qualitative studies may be challenging due to the use of unfolding techniques and the large amounts of word data, findings are valuable in helping to understand and determine the patient or informant’s perceptions, feelings, and experiences. These types of studies are just as valuable as quantitative studies in helping to establish an evidence-based nursing practice.
