Screening for Depression during Initial Visit and Annually in Primary Care Settings

Research statement: Screening for Depression during Initial Visit and Annually in Primary Care Settings

There are many different methods for analyzing quantitative data; each method is dependent on the type of data gathered as well as the research question being addressed. The first step in analyzing data is to determine what kind of data you have—the level of measurement. Determining the level of measurement is a method of classifying the variables within a research study. Classifying a variable into its appropriate level of measurement helps a researcher determine the most appropriate statistical analysis for those data and to interpret the data the variable generates.
In this Discussion, you identify independent and dependent variables in your research problem, which you identified in the Week 2 Discussion. You classify these variables into their appropriate levels of measurement and determine suitable ways of analyzing the data generated by each variable.
To prepare:
•	Review the materials presented in Chapter 1 of the Polit textbook.
•	Consider Dr. Pothoff’s comments in this week’s media presentation on data analysis.
•	Recall your research problem statement developed for the Week 2 Discussion. Based on your problem statement, develop a research question to address the problem. (see research statement above)
•	Ask yourself:
o	Do the research question involve a comparison of groups or the relationship of variables?
o	How many independent variables do I have? Dependent variables? What are they?
o	Is the independent variable categorical or continuous?
o	Is the dependent variable categorical or continuous?
o	What might be the advantages, or disadvantages, of each variable’s level of measurement?
Post a cohesive summary of the following:
•	Post your research question. Describe the independent and dependent variables.
•	Identify the level of measurement of both the independent and dependent variables. Provide a brief rationale for your classification of each variable.
•	Discuss considerations of analyzing data related to each variable based on its level of measurement. Identify any advantages or challenges you might encounter in your statistical analysis of each variable.
•	Read a selection of your colleagues’ responses.

Learning Resources
Required Media
Laureate Education, Inc. (Executive Producer). (2011). Research methods for evidence-based practice: Quantitative research: Data analysis. Baltimore, MD: Author.
Note: The approximate length of this media piece is 10 minutes. See attachment
In this week’s video, the presenter describes challenges to collecting and utilizing quantitative data and offers suggestions for assessing and improving data quality. The strategic use of quantitative data by health care organizations is also considered.

Laureate Education (Producer). (2016). Descriptive statistics [Video File]. Baltimore, MD: Author. Retrieved from see attachment

Required Readings
Gray, J.R., Grove, S.K., & Sutherland, S. (2017). Burns and Grove’s the practice of nursing research: Appraisal, synthesis, and generation of evidence (8th ed.). St. Louis, MO: Saunders Elsevier.
•	Chapter 21, “Introduction to Statistical Analysis”
This chapter discusses the concepts of statistical analysis with regard to hypothesis testing. The chapter also identifies and defines common statistical terminology.
Polit, D. (2010). Statistics and data analysis for nursing research (2nd ed.). Upper Saddle River, NJ: Pearson Education Inc.
•	Chapter 1, “Introduction to Data Analysis in an Evidence-Based Practice Environment”
This chapter provides an introduction to quantitative and qualitative data in evidence-based practice. The chapter introduces levels of measurement and types of statistical analyses relevant to different types of research studies.
•	Chapter 2, “Frequency Distributions: Tabulating and Displaying Data”
Chapter 2 discusses frequency distributions as well as the different methods of presenting data, especially when data are very extensive. The chapter includes information on the use of bar charts, pie charts, histograms, and frequency polygons.
•	Chapter 3, “Central Tendency, Variability, and Relative Standing”
This chapter examines the many ways data distribution for a quantitative variable can be described through shape, central tendency, and variability.
Bilheimer, L. T., & Klein, R. J. (2010). Data and measurement issues in the analysis of health disparities. Health Services Research, 45(5), 1489–1507. doi:10.1111/j.1475-6773.2010.01143.x

Granberg-Rademacker, J. S. (2010). An algorithm for converting ordinal scale measurement data to interval/ratio scale. Educational & Psychological Measurement, 70(1), 74–90.

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