Transcript The Effect Size

The Effect Size
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The effect size (ES) makes meta-analysis possible
The ES encodes the selected research findings on a
numeric scale
There are many different types of ES measures, each
suited to different research situations
Each ES type may also have multiple methods of
computation
Practical Meta-Analysis -- D. B. Wilson
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Examples of Different Types of Effect Sizes
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Standardized mean difference
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Odds-ratio
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Group contrast research
 Treatment groups
 Naturally occurring groups
Inherently continuous construct
Group contrast research
 Treatment groups
 Naturally occurring groups
Inherently dichotomous construct
Correlation coefficient
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Association between variables research
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Examples of Different Types of Effect Sizes
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Risk ratio
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Group differences research (naturally occurring groups)
Commonly used by epidemiologist and medical meta-analyses
Inherently dichotomous construct
Easier to interpret than the odds-ratio
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Examples of Different Types of Effect Sizes
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Proportion
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Standardized gain score
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Central tendency research
 HIV/AIDS prevalence rates
 Proportion of homeless persons found to be alcohol
abusers
Gain or change between two measurement points on the
same variable
 Reading speed before and after a reading improvement
class
Others?
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What Makes Something an Effect Size
for Meta-analytic Purposes
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The type of ES must be comparable across the
collection of studies of interest
This is generally accomplished through standardization
Must be able to calculate a standard error for that type of
ES
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The standard error is needed to calculate the ES weights, called
inverse variance weights (more on this latter)
All meta-analytic analyses are weighted
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The Standardized Mean Difference
X  X G2
ES  G1
s pooled
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s pooled 
s12 n1  1  s22 n2  1
n1  n2  2
Represents a standardized group contrast on an
inherently continuous measure
Uses the pooled standard deviation (some situations use
control group standard deviation)
Commonly called “d” or occasionally “g”
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The Correlation Coefficient
ES  r
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Represents the strength of association between two
inherently continuous measures
Generally reported directly as “r” (the Pearson product
moment coefficient)
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The Odds-Ratio
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The odds-ratio is based on a 2 by 2 contingency
table, such as the one below
Frequencies
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Success
Failure
Treatment Group
a
b
Control Group
c
d
ad
ES 
bc
The Odds-Ratio is the odds of success in the
treatment group relative to the odds of success in the
control group.
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The Risk Ratio
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The risk ratio is also based on data from a 2 by 2
contingency table, and is the ratio of the probability of
success (or failure) for each group
a / ( a  b)
ES 
c / (c  d )
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Unstandardized Effect Size Metric
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If you are synthesizing are research domain that using a
common measure across studies, you may wish to use
an effect size that is unstandardized, such as a simple
mean difference (e.g., dollars expended)
Multi-site evaluations or evaluation contracted by a
single granting agency
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Effect Size Decision Tree for Group
Differences Research (Page 58 of Book)
All dependent variables are
inherently dichotomous
Difference between proportions
as ES [see Note 1]
Group contrast on dependent variable
Odds ratio; Log of the odds ratio
as ES
All dependent variables are
inherently continuous
All dependent variables measured
on a continuous scale
All studies involve same
measure/scale
Unstandardized mean difference
ES
Studies use different
measures/scales
Standardized mean difference
ES
Some dependent variables measured
on a continuous scale, some
artificially dichotomized
Some dependent variables are
inherently dichotomous, some are
inherently continuous
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Standardized mean difference ES;
those involving dichotomies
computed using probit [see Note 2]
or arcsine [see Note 3]
Do separate meta-analyses for
dichotomous and continuous
variables
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Methods of Calculating the
Standardized Mean Difference
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The standardized mean difference probably has more
methods of calculation than any other effect size type
See Appendix B of book for numerous formulas and
methods.
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Degrees of Approximation to the ES Value
Depending of Method of Computation
Great
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Poor
Good
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Direct calculation based on means and standard deviations
Algebraically equivalent formulas (t-test)
Exact probability value for a t-test
Approximations based on continuous data (correlation coefficient)
Estimates of the mean difference (adjusted means, regression B
weight, gain score means)
Estimates of the pooled standard deviation (gain score standard
deviation, one-way ANOVA with 3 or more groups, ANCOVA)
Approximations based on dichotomous data
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Methods of Calculating the
Standardized Mean Difference
Direction Calculation Method
ES 
X1  X 2
X1  X 2
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2
2
s pooled
s1 (n1  1)  s2 (n2  1)
n1  n2  2
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Methods of Calculating the
Standardized Mean Difference
Algebraically Equivalent Formulas:
n1  n2
ES  t
n1n2
ES 
F (n1  n2 )
n1n2
independent t-test
two-group one-way ANOVA
exact p-values from a t-test or F-ratio can be converted
into t-value and the above formula applied
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Methods of Calculating the
Standardized Mean Difference
A study may report a grouped frequency distribution
from which you can calculate means and standard
deviations and apply to direct calculation method.
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Methods of Calculating the
Standardized Mean Difference
Close Approximation Based on Continuous Data -Point-Biserial Correlation. For example, the correlation
between treatment/no treatment and outcome measured
on a continuous scale.
ES 
2r
1 r 2
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Methods of Calculating the
Standardized Mean Difference
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Estimates of the Numerator of ES -- The Mean
Difference
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difference between gain scores
difference between covariance adjusted means
unstandardized regression coefficient for group membership
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Methods of Calculating the
Standardized Mean Difference
Estimates of the Denominator of ES -Pooled Standard Deviation
s pooled  se n  1
standard error of the mean
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Methods of Calculating the
Standardized Mean Difference
Estimates of the Denominator of ES -Pooled Standard Deviation
s pooled
MS between
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F
MS between 
one-way ANOVA >2 groups
2
X
 j nj 
( X j n j ) 2
k 1
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n
j
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Methods of Calculating the
Standardized Mean Difference
Estimates of the Denominator of ES -Pooled Standard Deviation
s pooled 
s gain
2(1  r )
standard deviation of gain
scores, where r is the correlation
between pretest and posttest
scores
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Methods of Calculating the
Standardized Mean Difference
Estimates of the Denominator of ES -Pooled Standard Deviation
s pooled
MS error df error  1
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2
1  r df error  2
ANCOVA, where r is the
correlation between the
covariate and the DV
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Methods of Calculating the
Standardized Mean Difference
Estimates of the Denominator of ES -Pooled Standard Deviation
s pooled
SS B  SS AB  SSW
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df B  df AB  dfW
A two-way factorial ANOVA
where B is the irrelevant factor
and AB is the interaction
between the irrelevant factor
and group membership (factor
A).
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Methods of Calculating the
Standardized Mean Difference
Approximations Based on Dichotomous Data
ES  probit ( p group1 )  probit ( p group2 )
the difference between the probits transformation
of the proportion successful in each group
converts proportion into a z-value
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Methods of Calculating the
Standardized Mean Difference
Approximations Based on Dichotomous Data
ES 
3
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log Odds  ratio
this represents the rescaling of the logged
odds-ratio (see Sanchez-Meca et al 2004
Psychological Methods article)
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Methods of Calculating the
Standardized Mean Difference
Approximations Based on Dichotomous Data
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ES  2
N 2
ES 
2r
1 r
2
chi-square must be based on
a 2 by 2 contingency table
(i.e., have only 1 df)
phi coefficient
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Formulas for the Correlation Coefficient
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Results typically reported directly as a correlation
Any data for which you can calculate a standardized
mean difference effect size, you can also calculate a
correlation type effect size
See appendix B for formulas
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Formulas for the Odds Ratio
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Results typically reported in one of three forms:
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Frequency of successes in each group
Proportion of successes in each group
2 by 2 contingency table
Appendix B provides formulas for each situation
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Data to Code Along With the ES
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The effect size
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May want to code the data from which the ES is calculated
Confidence in ES calculation
Method of calculation
Any additional data needed for calculation of the inverse variance weight
Sample size
ES specific attrition
Construct measured
Point in time when variable measured
Reliability of measure
Type of statistical test used
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Issues in Coding Effect Sizes
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Which formula to use when data are available for
multiple formulas
Multiple documents/publications reporting the same data
(not always in agreement)
How much guessing should be allowed
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sample size is important but may not be presented for both
groups
some numbers matter more than others
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