Essentials of Applied Quantitative Methods for Health Services Managers

2. Essentials of Applied Quantitative Methods for Health Services Managers Class Slides

3. Chapter 2: Working with Numbers • Learning Objectives: • To Be Able to Calculate and Use Descriptive Statistics • To Be Able to Compare Different Types of Data Using Statistical Inference and Hypothesis Testing • To Be Able to Present Data Effectively and Efficiently in Visual Form

4. Functions of Managerial Statistics • Describe certain data elements • Compare two points of data • Predict data

5. Types of Data Variables • Nominal – non-overlapping categories, no ranking, and mutually • exclusive; e.g., eye color • Ordinal – measure categories, but categories have ranks; e.g., satisfaction surveys • Interval/Ratio – continuously measured, with equal distance between categories

6. Descriptive Statistics with One Variable Insurance type by patient 1 United 8 BC/BS 2 Medicare 9 Medicaid 3 Medicaid 10 Uninsured 4 Medicare 11 Medicare 5 BC/BS 12 Uninsured 6 United 13 United 7 BC/BS 14 MBCA

8. Measures of Central Tendency Mean – Mathematical Center (Average) Median – Center of a Distribution of Data, When Arranged from Lowest to Highest Mode – Most frequently reported data point

9. Measures of Spread Range – Difference between Maximum and Minimum Value Standard Deviation – Average Distance of a Given Data Point to the Mean

10. Working with Samples Samples are Inherently More Variable than Populations Impossible to Know the “Truth” about Current and/or Future Population Data – Create an Interval that We Can Say with Some Level of Confidence Contains the True Population Mean Formula for Constructing a Confidence Interval: Mean = +/- 1.96 * Standard Error, Where Standard Error = Standard Deviation/√n

11. Working with Bivariate Data Hypothesis Testing Null Hypothesis: The Hypothesis of No Association or Difference Alternative Hypothesis: The Converse of the Null Hypothesis; i.e., There Is Some Association or Difference - When the Direction of the Difference Doesn’t Matter  A Two-Tailed Test. If Direction Does matter, the Test Is One-Tailed Test

12. More on Hypothesis Testing Can Never Be Certain What Relationship Truly IS Between Two Variables So, We Use Hypothesis Testing and Statistics to Make Probabilistic Inferences about Relationships

13. The Normal Distribution 62” 64” 66” 68” 70” 72” 74” 68-95-99.7 Rule

14. Comparing Continuous Data Correlation: A Statistical Measure of Association between Two Phenomena – Not a Causal Relationship r = Correlation Coefficient R = +1.0 = Perfectly Positive Correlation R = - 1.0 = Perfectly Negative Correlation Can Apply Principles of Hypothesis Testing to Correlation to Assess if There Is a Relationship. (Use Table of Critical Values (Table 2-4)

15. The t-test Compare Differences between Means between Groups Types: - Paired - Assuming Equal Variances - Assuming Unequal Variances

16. Comparative Monthly Births

17. Sample t-test Report

18. Comparing Categorical Data • Often Measured in Rates or Proportions • Chi-Square Statistic (X2): Compares Observed Differences • in Proportions with What Would Be Expected if Proportions • Were Equal

19. 2 X 2 Contingency Table

20. Patient SatisfactionComparison Using Chi Square

21. The Chi-Square Formula X2 = Σ((Observed – Expected)2) Expected Where the Expected Count Is Row Total * Column Total n

22. Chi-Square Calculations forPatient Satisfaction Data

23. Summary of Methods

24. Percent of Patients Overweight orObese by BMI Score

25. A Bar Chart

26. Another Bar Chart

27. Raw Data

28. Pie Chart

29. Raw Data

30. Line Graft

31. Raw Data

32. Dual Axis Graft