Regression Towards the Mean David A. Kenny University of Connecticut .

20
Regression Towards the Mean David A. Kenny University of Connecticut http://davidakenny.net/old/series

Transcript of Regression Towards the Mean David A. Kenny University of Connecticut .

Page 1: Regression Towards the Mean David A. Kenny University of Connecticut .

Regression Towards the Mean

David A. Kenny

University of Connecticut

http://davidakenny.net/old/series/primer.htm

Page 2: Regression Towards the Mean David A. Kenny University of Connecticut .

2

Page 3: Regression Towards the Mean David A. Kenny University of Connecticut .

3

An Opening Quote “The list of studies in which the

regression factor has been neglected grows monotonous as well as distressing” (Rulon, 1941).

Page 4: Regression Towards the Mean David A. Kenny University of Connecticut .

4

Page 5: Regression Towards the Mean David A. Kenny University of Connecticut .

5

Page 6: Regression Towards the Mean David A. Kenny University of Connecticut .

6

Galton Squeeze Diagram

• Two vertical axes.• left line the pretest

• right line the posttest

• A line from the the pretest score to the mean on the posttest.

Page 7: Regression Towards the Mean David A. Kenny University of Connecticut .

7

Page 8: Regression Towards the Mean David A. Kenny University of Connecticut .

8

Page 9: Regression Towards the Mean David A. Kenny University of Connecticut .

9

Definition A person’s score on a variable that

is extreme (in the sense of being far away from the mean) tends to be less extreme in standard deviation units when that person is measured on another variable.

Page 10: Regression Towards the Mean David A. Kenny University of Connecticut .

10

Common Misconceptions

• RTM results in mediocrity.

• RTM is unidirectional.

• RTM is entirely due to measurement error.

• RTM is some mysterious force.

Page 11: Regression Towards the Mean David A. Kenny University of Connecticut .

11

Illustrations

• The Sophomore Jinx

• The Gifted and Not So Gifted

• Spontaneous Remission

Page 12: Regression Towards the Mean David A. Kenny University of Connecticut .

12

Is There a Sophomore Jinx?

• 39 Hitting Winners from 1970 and 1994

• Rookie average .285

• Sophomore average .266

• 28 declined and 11 improved

Page 13: Regression Towards the Mean David A. Kenny University of Connecticut .

13

Jinx?: Scatter-plot

Page 14: Regression Towards the Mean David A. Kenny University of Connecticut .

14

The Gifted and Not So Gifted

• Extreme Score Used for Treatment Selection• educational programs

• medical treatments

• What is the probability that the score will remain extreme after re-testing?

Page 15: Regression Towards the Mean David A. Kenny University of Connecticut .

15

A Computer Simulation• Specifications

• randomly pick a score from a normal distribution

• consider only scores greater than two standard deviations above the mean.

• reliability of .8

• What is the probability that the score will remain extreme (> 2sd’s) after re-testing?

Page 16: Regression Towards the Mean David A. Kenny University of Connecticut .

16

How Much?

• only 43%

• research• dementia: 39%

• ADHD: 45%

• depression: 40%

Page 17: Regression Towards the Mean David A. Kenny University of Connecticut .

17

True Score Correction

• The most important but forgotten formula in psychometrics.

• Given a score with a reliability of , what is the best guess of the true score or XTʹ?

• XTʹ = (X - MX) + MX

• Correlation of XTʹ with X is one.

Page 18: Regression Towards the Mean David A. Kenny University of Connecticut .

18

Spontaneous Remission

Change in the control group using Cohen’s d (over-time mean difference divided by the pretest standard deviation). depression: -0.35 pain: -0.58

Page 19: Regression Towards the Mean David A. Kenny University of Connecticut .

19

Final Points• RTM is not so mysterious:

• change happens.

• RTM is bidirectional.

• Whether the score is above or below the mean in applications may not be clear.

Page 20: Regression Towards the Mean David A. Kenny University of Connecticut .

20

Concluding Quote fromFrancis Galton (1889)

“Some hate the very name of statistics but I find them full of beauty and interest. Whenever they are not brutalized, but handled by higher methods, and warily interpreted, their power of dealing with complicated phenomena is extraordinary.”