2 pages , one page for each question
3 references each.
1. Formulate effective strategies for descriptive analysis that contribute to evidence-based quality improvement
2.Formulate effective strategies in planning and selection of appropriate statistical analysis that contribute to evidence-based quality improvement.
My teachers note on each question, hope these help
1. Descriptive analysis is used to define the fundamental qualities of a study’s data (Tochrim, 2006). It is a simple summation describing the sample and measures of a particular research project (Tochrim, 2006). “Together with simple graphics analysis, they form the basis of virtually every quantitative analysis of data” (Tochrim, 2006). This is an essential tool because a descriptive analysis helps identify prospective issues such as skewness, randomization, and attrition (Melnyk & Fineout-Overholt, 2015, p. 536).
Two categories of descriptive statistics include central tendency (which then can be allocated into the measures of mean, median, and mode) and dispersion (which can be further categorized into range, variance, and standard deviation) (Statistic Solutions, 2017). If the study design is poor and these measures vary greatly, the findings will be unusable.
Without descriptive analysis, the research endeavor can produce confusing and/or overwhelming results. A descriptive analysis summarizes raw statistics, making them more understandable (Descriptive, Predictive, and Prescriptive Analytics Explained, 2016). This makes the descriptive analysis the foundation of a solid research study.
References:
Descriptive, Predictive, and Prescriptive Analytics Explained. (2016). Retrieved June 10, 2017, from https://halobi.com/2016/07/descriptive-predictive-and-prescriptive-analytics-explained/
Melnyk, B. M., & Fineout-Overholt, E. (2015). Evidence-Based Practice in Nursing and Healthcare (3rd ed.). Philadelphia, PA: Wolters Kluwer.
Trochim, W. (2006). Retrieved June 10, 2017, from https://www.socialresearchmethods.net/kb/statdesc.php
Statistic Solutions. (2017). Descriptive Statistics and Interpreting Statistics. Retrieved June 10, 2017, fromhttp://www.statisticssolutions.com/descriptive-statistics-and-interpreting-statistics/ (Links to an external site.)Links to an external site.
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2. Researchers look for evidence using statistical analysis which assists in organizing, interpretation, and communication of statistical information (Polit & Beck, 2016). The purpose of a statistical analysis is to clarify if an observed effect occurred related to the intervention or was the effect caused by chance (Melnyk & Fineout-Overholt, 2015). One of the first steps in statistical analysis involves defining the level of measurement for variables included in the analysis and should include both independent and dependent variables (Polit & Beck, 2016). Typically, researchers use linear, ordinal or multinomial regressions for statistical analysis when the outcome variables are interval, ordinal, or categorical variables (Melnyk & Fineout-Overholt, 2015).
Statistical analysis can be descriptive or inferential; descriptive statistics is descriptive and is used to synthesize data, whereas, inferential statistics makes conclusion or deductions about a population (Polit & Beck, 2016). A standard deviation assists researchers in describing a distribution and interpretation of individual score using a variability index (Polit& Beck, 2016). The standard deviation, along with, the mean and mode are called univariate descriptive statistics which explain a single variable, thus, most of research deals with relationships involving variables (Polit & Beck, 2016). One fundamental data analysis decision researchers must make is choosing between parametric and nonparametric tests (Harwell, 1988). This decision will crucial as the researcher should always choose the statistical test which will control type I errors, produce good power, and provide significant interpretation of data (Harwell, 1988). Researchers must have a broad understanding and knowledge of the numerous approaches available to assist with statistical analysis.
References:
Harwell, M. R. (1988). Choosing between parametric and nonparametric tests. Journal of Counseling & Development, 67(1), 35.
Melnyk, B. & Fineout-Overholt, E. (2015). Evidence-Based Practice in Nursing & Healthcare; A Guide to Best Practice. (3rd ed.). Philadelphia, PA: Wolters Kluwer Health.
Polit, D. F. & Beck, C. T. (2016). Nursing research: Generating and assessing evidence for nursing practice (10th ed.). Philadelphia, PA: Lippincott Williams & Wilkins.
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