Quantitative Methods: Analyzing Quantitative Data

 Week 4: Quantitative Methods: Analyzing Quantitative Data

“Cellphone Cancer Warning: Possible Link to Brain Tumors Cited in Expert Panel’s Review of Past Studies”: This headline appeared on June 1, 2011, in the Wall Street Journal. News agencies began reporting that cell phones can cause certain types of brain cancer before the research study was published. When reviewing the actual study, Stats.org—an organization based out of George Mason University—found that the authors of the research study actually cautioned against a causal interpretation of the study results.
In order to make accurate, straightforward interpretations and reports, researchers should perform careful statistical analyses of the data and variables in their studies. Otherwise, they run the risk of making inappropriate assumptions and generating incorrect conclusions. This week, you begin to develop a foundational knowledge of statistics and the vital role that it plays in research and problem solving. Over the next several weeks, you will complete statistics exercises using SPSS. The knowledge of statistics that is promoted through your work on these exercises will enable you to better contextualize and interpret health care data and its application to evidence-based practice.
This week provides an introduction to descriptive statistics, which can be useful in analyzing data and drawing conclusions about the sample from which that data was taken. You also examine confidence intervals, frequency distributions, and the levels of measurement.

Martin, T., & Hobson, K. (2011, June 1). Cellphone cancer warning: Possible link to brain tumors cited in expert panel’s review of past studies. The Wall Street Journal: Health. Retrieved from http://online.wsj.com/article/SB10001424052702303657404576357571174992448.html

Butterworth, T. (2011, June 3). Cell phones—as (non) cancerous as coffee, firefighting? Statistical Assessment Service. Retrieved from http://stats.org/stories/2011/cellphone_cancer_jun3_11.html

Learning Objectives
Students will:
•	Identify independent and dependent variables
•	Calculate descriptive statistics and interpret the results
•	Construct a table to summarize descriptive statistics using APA format

Assignment 2: Descriptive Statistics

This week, you explore key statistical concepts related to data and problem solving through the completion of the following exercises using SPSS and the information found in your Statistics and Data Analysis for Nursing Research textbook. The focus of this assignment is to become familiar with the SPSS data analysis software and to develop an understanding of how to calculate descriptive statistics and make conclusions based on those calculations. As you formulate your responses, keep in mind that descriptive statistics only allow you to make conclusions and recommendations for the sample at hand—not for the larger population to which that sample may belong.
To prepare:
•	Review the Statistics and Data Analysis for Nursing Research chapters assigned in this week’s Learning Resources. Pay close attention to the examples presented, as they provide information that will be useful when you complete the software exercise this week. You may also wish to review the Research Methods for Evidence-Based Practice video resources to familiarize yourself with the software.
•	Refer to the Week 4 Descriptive Statistics Assignment page and follow the directions to calculate descriptive statistics for the data provided using SPSS software. 
•	Download and save the Polit2SetA.sav data set. You will open the data file in SPSS.
•	Compare your data output against the tables presented in the Week 4 Descriptive Statistics SPSS Output document. This will enable you to become comfortable with defining variables, entering data, and creating tables and graphs.
•	Formulate an initial interpretation of the meaning or implication of your calculations.
To complete:
•	Complete the Part I, Part II, and Part III steps and Assignment as outlined in the Week 4 Descriptive Statistics Assignment page.

IBM SPSS Statistics Standard GradPack (current version). Available in Windows and Macintosh versions. 

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 http://studyhall.laureate.net/research/quantitative-reasoning-and-analysis-resources-rsch-8210/ 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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