1crmda.KU.edu 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 Peer Relations Preconference Missing Data Analysis in Peer Relations Research

2crmda.KU.edu 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

3crmda.KU.edu Types of missing data

4crmda.KU.edu 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

5crmda.KU.edu Modern Missing Data Analysis 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 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 MI or FIML

6crmda.KU.edu 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

7crmda.KU.edu Form Common Variables Variable Set A Variable Set B Variable Set C 1¼ of Variables None 2¼ of Variables none¼ of Variables 3 none¼ of Variables 3-Form Intentionally Missing Design

8crmda.KU.edu Form Common Variables Variable Set A Variable Set B Variable Set C 1Marker Variables 1/3 of Variables None 2Marker Variables 1/3 of Variables none1/3 of Variables 3Marker Variables none1/3 of Variables 3-Form Protocol II

Expansions of 3-Form Design (Graham, Taylor, Olchowski, & Cumsille, 2006) crmda.KU.edu9

Expansions of 3-Form Design (Graham, Taylor, Olchowski, & Cumsille, 2006) crmda.KU.edu10

11 2-Method Planned Missing Design crmda.KU.edu

12 Controlled Enrollment crmda.KU.edu

Growth Curve Design II GroupTime 1Time 2Time 3Time 4Time 5 1xxxxx 2xxxmissing 3xx x 4x xx 5 xxx 6xx x 7x x x 8 xx x 9x xx 10missingx xx 11missing xxx 13crmda.KU.edu

Growth Curve Design II GroupTime 1Time 2Time 3Time 4Time 5 1xxxxx 2xxxmissing 3xx x 4x xx 5 xxx 6xx x 7x x x 8 xx x 9x xx 10missingx xx 11missing xxx 14crmda.KU.edu

15crmda.KU.edu Combined Elements

16crmda.KU.edu The Sequential Designs

17crmda.KU.edu Transforming to Accelerated Longitudinal

18crmda.KU.edu Transforming to Episodic Time

19crmda.KU.edu Thanks for your attention! Questions? crmda. KU.edu Talk presented Peer Relations Preconference Missing Data Analysis in Peer Relations Research

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 IMMAP (immap.educ.ttu.edu) Stats Camp (Statscamp.org) 20www.Quant.KU.edu