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# Essentials of Applied Quantitative Methods for Health Services Managers PowerPoint PPT Presentation

Essentials of Applied Quantitative Methods for Health Services Managers. Class Slides. Chapter 2: Working with Numbers. Learning Objectives: To Be Able to Calculate and Use Descriptive Statistics

Essentials of Applied Quantitative Methods for Health Services Managers

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## Essentials of Applied Quantitative Methods for Health Services Managers

Class Slides

### 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

### Functions of Managerial Statistics

• Describe certain data elements

• Compare two points of data

• Predict data

### 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

### 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 United13 United

7 BC/BS14 MBCA

### 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

Range – Difference between Maximum and Minimum Value

Standard Deviation – Average Distance of a Given

Data Point to the Mean

### 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

### 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

### 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

### The Normal Distribution

62” 64” 66” 68” 70” 72” 74”

68-95-99.7 Rule

### 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)

### The t-test

Compare Differences between Means between Groups

Types:

- Paired

- Assuming Equal Variances

- Assuming Unequal Variances

### 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

### The Chi-Square Formula

X2 = Σ((Observed – Expected)2)

Expected

Where the Expected Count Is

Row Total * Column Total

n