T3 DQ1

docx

School

Grand Canyon University *

*We aren’t endorsed by this school

Course

832

Subject

Statistics

Date

Jan 9, 2024

Type

docx

Pages

Uploaded by BauerKingston2025

Imagine you are a state-level health policy maker interested in health care access. You want to identify geographic areas and ethnic communities that are underserved to be able to correct problems and ensure fair access. You can only use archival data for your study. What data do you need for your study? What variables will you use? Do you expect any difficulties in obtaining those data? Why or why not? What kind of design would you use? Why? What parametric and nonparametric statistical tests can you use to obtain the information you need? Explain. For this quantitative study, the Ex-post facto would be an appropriate design. Ex-post facto uses secondary data (archival) and examines the difference between two or more groups by one or more categorical variables (Pressman, 2022). To breakdown the geographic and ethnic communities the data needed could include income, gender, age, race, and specific location to then identify and categorize the underserved population. The variables would come from both nominal and ordinal categories. Nominal because they are first level measurements and are classified as unique, such as age, race, gender (Pressman, 2022). Ordinal due to the ability of ranking or organizing sequentially based on value, such as income (Pressman, 2022). One issue that could derive from the use of archival data would be that the information in these datasets may not be applicable to the current research (Pressman, 2022). Based on the ‘which stats test’ responses provided by the learner, a non-parametric, Spearman Rank Correlation analysis would benefit this study. Spearman rank correlation is used to measure the degree of association between two variables and is the appropriate correlation analysis when the variables are measured on an ordinal scale (Pressman, 2022). Because each inferential approach has a list of assumptions that must be met and parametric tests assumes normal distribution, parametric statistical testing may not be appropriate for this research study (Pressman, 2022). The researcher cannot assume equal distribution among race, age, income, gender or location prior to collecting and analyzing the data. REFERENCES: Pressman, M.S. (2022). Comparisons of Qualitative and Quantitative Designs. GCU doctoral research introduction to sampling, data collection, and data analysis. Grand Canyon University.

Discover more documents: Sign up today!

Unlock a world of knowledge! Explore tailored content for a richer learning experience. Here's what you'll get: