Missing Data Analysis in Peer
Relations Research
Todd D. Little
University of Kansas
Director, Quantitative Training Program
Director, Center for Research Methods and Data Analysis
Director, Undergraduate Social and Behavioral Sciences Methodology Minor
Member, Developmental Psychology Training Program
crmda.KU.edu
Workshop presented 03-30-2011 @
Peer Relations Preconference
crmda.KU.edu
1
Road Map
• Briefly review the different types of missing data and
how the missing data process can be recovered
• Remember: imputing missing data is not cheating
• NOT imputing missing data is more likely to lead to errors in
generalization!
• Introduce intentionally missing designs
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Types of missing data
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Effects of imputing missing data
No Association
with Observed
Variable(s)
An Association
with Observed
Variable(s)
No Association
with Unobserved
/Unmeasured
Variable(s)
MCAR
•Fully
recoverable
•Fully unbiased
MAR
• Partly to fully
recoverable
• Less biased to
unbiased
An Association
with Unobserved
/Unmeasured
Variable(s)
NMAR
• Unrecoverable
• Biased (same
bias as not
estimating)
MAR/NMAR
• Partly
recoverable
• Same to
unbiased
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4
Modern Missing Data Analysis
MI or FIML
•
In 1978, Rubin proposed Multiple Imputation (MI)
•
•
•
•
An approach especially well suited for use with large public-use
databases.
First suggested in 1978 and developed more fully in 1987.
MI primarily uses the Expectation Maximization (EM) algorithm
and/or the Markov Chain Monte Carlo (MCMC) algorithm.
Beginning in the 1980’s, likelihood approaches developed.
•
•
Multiple group SEM
Full Information Maximum Likelihood (FIML).
• An approach well suited to more circumscribed models
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Missing Data and Estimation:
Missingness by Design
•
Assess all persons, but not all variables at each
time of measurement
•
•
McArdle, Graham
Control entry into study: estimate and control for
retesting effects, increase validity, decrease costs,
increase power, etc.
•
•
Randomly assign participants to their entry into a
longitudinal study and/or to the occasions of assessment
Key to providing unbiased estimates of growth or
change
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6
3-Form Intentionally Missing Design
Common
Form Variables
Variable
Set A
Variable
Set B
Variable
Set C
1
¼ of
Variables
¼ of
Variables
¼ of
Variables
None
2
¼ of
Variables
¼ of
Variables
none
¼ of
Variables
3
¼ of
Variables
none
¼ of
Variables
¼ of
Variables
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3-Form Protocol II
Common
Form Variables
Variable
Set A
Variable
Set B
Variable
Set C
1
Marker
Variables
1/3 of
Variables
1/3 of
Variables
None
2
Marker
Variables
Marker
Variables
1/3 of
Variables
none
1/3 of
Variables
none
1/3 of
Variables
1/3 of
Variables
3
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Expansions of 3-Form Design
(Graham, Taylor, Olchowski, & Cumsille, 2006)
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Expansions of 3-Form Design
(Graham, Taylor, Olchowski, & Cumsille, 2006)
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2-Method Planned Missing Design
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Controlled Enrollment
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Growth Curve Design II
Group
Time 1
Time 2
Time 3
Time 4
Time 5
1
x
x
x
x
x
2
x
x
x
missing
missing
3
x
x
missing
x
missing
4
x
missing
x
x
missing
5
missing
x
x
x
missing
6
x
x
missing
missing
x
7
x
missing
x
missing
x
8
missing
x
x
missing
x
9
x
missing
missing
x
x
10
missing
x
missing
x
x
11
missing
missing
x
x
x
crmda.KU.edu
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Growth Curve Design II
Group
Time 1
Time 2
Time 3
Time 4
Time 5
1
x
x
x
x
x
2
x
x
x
missing
missing
3
x
x
missing
x
missing
4
x
missing
x
x
missing
5
missing
x
x
x
missing
6
x
x
missing
missing
x
7
x
missing
x
missing
x
8
missing
x
x
missing
x
9
x
missing
missing
x
x
10
missing
x
missing
x
x
11
missing
missing
x
x
x
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14
Combined Elements
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The Sequential Designs
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Transforming to Accelerated Longitudinal
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Transforming to Episodic Time
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Missing Data Analysis in Peer
Relations Research
Thanks for your attention!
Questions?
crmda.KU.edu
Talk presented 03-30-2011 @
Peer Relations Preconference
crmda.KU.edu
19
Update
Dr. Todd Little is currently at
Texas Tech University
Director, Institute for Measurement, Methodology, Analysis and Policy (IMMAP)
Director, “Stats Camp”
Professor, Educational Psychology and Leadership
Email: yhat@ttu.edu
IMMAP (immap.educ.ttu.edu)
Stats Camp (Statscamp.org)
www.Quant.KU.edu
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