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

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Essentials of Applied Quantitative Methods for Health Services Managers

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Essentials of applied quantitative methods for health services managers

Essentials of Applied Quantitative Methods for Health Services Managers

Class Slides


Chapter 2 working with numbers

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

Functions of Managerial Statistics

  • Describe certain data elements

  • Compare two points of data

  • Predict data


Types of data variables

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

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

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


Measures of spread

Measures of Spread

Range – Difference between Maximum and Minimum Value

Standard Deviation – Average Distance of a Given

Data Point to the Mean


Working with samples

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

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

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


The normal distribution

The Normal Distribution

62” 64” 66” 68” 70” 72” 74”

68-95-99.7 Rule


Comparing continuous data

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

The t-test

Compare Differences between Means between Groups

Types:

- Paired

- Assuming Equal Variances

- Assuming Unequal Variances


Comparative monthly births

Comparative Monthly Births


Sample t test report

Sample t-test Report


Comparing categorical data

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


2 x 2 contingency table

2 X 2 Contingency Table


Patient satisfaction comparison using chi square

Patient SatisfactionComparison Using Chi Square


The chi square formula

The Chi-Square Formula

X2 = Σ((Observed – Expected)2)

Expected

Where the Expected Count Is

Row Total * Column Total

n


Chi square calculations for patient satisfaction data

Chi-Square Calculations forPatient Satisfaction Data


Summary of methods

Summary of Methods


Percent of patients overweight or obese by bmi score

Percent of Patients Overweight orObese by BMI Score


A bar chart

A Bar Chart


Another bar chart

Another Bar Chart


Raw data

Raw Data


Pie chart

Pie Chart


Raw data1

Raw Data


Line graft

Line Graft


Raw data2

Raw Data


Dual axis graft

Dual Axis Graft


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