Presented byJohn Ruggiero, MPA, PhD

Vice President, Education and OutcomesInstitute for Continuing Healthcare Education

Philadelphia, PA

Adjunct Professor of Graduate BiostatisticsDrexel University

College of Nursing and Health ProfessionsPhiladelphia, PA

© ICHE J.Ruggiero, PhD. 2009

Disclosures

John Ruggiero has no interest in selling technology, a program, product, and/or service to CME professionals. There are no

financial disclosures to report.

STATISTICS?! WHY SHOULD I BE INTERESTED IN THAT? Statistics create order from chaos Statistics empower one to consider and

complete the larger picture Statistics help us become better citizens Statistics create outcomes—historical

reports that evoke decisions

A BIT OF HISTORY

THE REALITY

Every person in an organization should understand his/her individual and organizational expected goal(s) for success.

The information should be used to measure and improve effectiveness.

LEARNING OBJECTIVES

At the end of this lecture, learners should be

able to:

1. Generally explain the terminology used with statistics

2. Analyze the information presented3. Discern the relevant information

from the irrelevant information

KEY COMPONENTS TO A STUDY Population ( µ ) v. Sample ( x ) Collection of Data Independent variable v. Dependent

variableExample: Correlation between education

and a commitment-to-change performance

DESIGNING A STUDY

DESCRIPTIVE STATISTICSDescribing a situation –

The collection of data occurs before the analysis

INFERENTIAL STATISTICSHypotheses describe a situation – The researcher makes educated

guesses, collects the data and then analyses whether or not the hypotheses were correct

SAMPLING: HOW TO GET THE DATA Random Sampling

Samples are chosen without rhyme or reason Systematic Sampling

Samples are chosen by every kth number Stratified Sampling

Samples are divided into groups and then randomly chosen from those groups

Clustered SamplingSamples are chosen from a specific cluster for

purposes of the study design

MAKING OBSERVATIONS Collect the data

○ Rating learning objectives○ Rating faculty○ Was the education fair and balanced?

Measure your Central Tendency and DispersionMean, Median, and ModeStandard Deviation

EXAMPLE 1

Using a scale of 1-4 (ORDINAL DATA):1. Learning Objective was not met

2. Learning Objective was partially met

3. Learning Objective was met

4. Learning Objective exceeded expectations

The mean of learning objective 1 is collected among 10 learners from a small regional dinner activity

EXAMPLE 1 ANALYSIS Using a scale of 1-4 (ORDINAL DATA):

1. Learning Objective was not met

2. Learning Objective was partially met

3. Learning Objective was met

4. Learning Objective exceeded expectations

At a mean of “3”, learners from this activity believed that Learning Objective 1 was met. The margin of error is (±1.247).

OBJ 1

0

5

10

15

20

1 2 3 4

OBJ 1

70%

10%

0%

20%Physicians

Nurses

Pharmacists

Other

HISTOGRAMS

A histogram, or normal distribution curve, is used to graphically represent the normalcy of the mean related to all other data values from a study

ENGLISH!

Most of your data centers around the

mean

Extraneous data falls here

Histogram on Practice Change

02468

1012

1 2 3 4

Bins

Series1

DOSE OF REALITY

Pretty charts, animated graphs, and clean presentations don’t mean a thing, unless…You measure the results against a previous

educated guessThe study can be repeated

HYPOTHESES:THE EDUCATED GUESS Alternate v. Null

H1 (Alternate)○ The research hypothesis○ An observed effect is genuine – there is a

definite change

H0 (Null)○ There is no change to the study

HYPOTHESES EXAMPLE (Figures are fictitious). Let’s assume that the

national mean for victims of domestic violence is reported at 7%. This can be assumed because a cluster sample of 1500 people who had entered a medical facility emergency room in the past 12 months was completed.

My educated guess is that 7% is too low. I therefore believe that after educating targeted emergency room medical staff, and agreeing that every patient (regardless of visitation cause) is directly asked if they are a victim of domestic violence, the national mean will increase.

HYPOTHESES EXAMPLE Cont’d H0 (Null)

µ = 7%

H1 (Alternate)µ ≠ 7%

This is a two-tailed testRule of thumb: When hypotheses are written

with equality statements (= to) a two-tailed test can be assumed. When hypotheses are written with inequality statements (>,<) a one-tailed test can be assumed.

HYPOTHESES TESTING Declare an alpha (α) level of significance

○ Usually .01, .05 or .10○ This becomes known as the Critical Value○ Critical Value is compared to the z table. z-score

is then identified Recognize the p-value

○ The probability of getting values of the test statistic as extreme, or more extreme than, that observed if the null is true.

Statistical Significance○ If the p-value is less than the alpha, or when

completing a test value, the value falls within the Critical Value (beyond the z-score), one rejects the null hypothesis

P-value is = .05 while the observed value is 1.645. If the result is greater than 1.645, or in the critical region (5%), then you reject the null.

RELATING HYPOTHESES TO THE CME INDUSTRY

How could you use alternate and null hypotheses to assist you with one of your CME initiatives?

CONFIDENCE INTERVALS

Can the study be repeated at a specific level of confidence?Statisticians will usually choose either 99%,

95%, or 90% confidence percentages○ Example: If you claim 95% confidence with

your results, you are basically saying that no matter how many times you repeat a study, 95% of the time the mean and all other results will be similar.

Why this is important for CME

CONFIDENCE INTERVAL TESTS CI Test of the Means

○ (n > or = 30)

T-test ○ (n < or = 29)

CORRELATION & REGRESSION Correlation is the association between

two quantitative variables Association is linear The correlation coefficient is measured

on a scale that varies from + 1 to -1. Symbol for correlation is r

Correlation Graphs

Le, C.T. (2001).

Correlation Example

Le, C.T. (2001).

Regression (The Trend)

Ruggiero, J. based on Le, C.T. (2001).

LOOK FAMILIAR?

Survival Curves Survival curves illustrate prognosis. The

percentage of patients reaching an endpoint (for example: death, recurrence of disease, or cure) is plotted on the y (vertical) axis against time on the x (horizontal) axis.

The Kaplan-Meier method is preferred unless there is an extremely large number of patients being studied

MISUSES OF STATISTICS

Loaded-questions Self-interest Precise numbers Voluntary response

APLLICABLE TO CME? Yes For professionals, statistics can be

considered the science ofCollecting dataSummarizing dataDrawing practical conclusions

Statistics assist with Outcomes Measurements

Tracking Change in Practice and Behavior Tracking Change in Knowlegde

Resources Le, C.T. (2001). Health & numbers: A problems-based

introduction to biostatistics. (2nd Edition). NY: Wiley-Liss.

Levine, David M., Mark L Berenson, David Stephan. (1999) Statistics for managers: using Microsoft Excel. Upper Saddle River, New Jersey. Prentice-Hall

Motulsky, Harvey. (1995) Intuitive biostatistics. Oxford University Press Inc.

Patten, Mildred L. (2002). Understanding research methods: An Overview of the essentials (3rd ed.). Los Angeles: Pyrczak Publishing.

Swinscow, TDV. (2006). Statistics at square one. Ninth edition. BMJ Publishing Group.

QUESTIONS?

THANK YOU

John Ruggiero, MPA, PhD

E-mail: jruggiero@iche.edu

TEL: (215) 446-8088

ext 1440