TY - JOUR
T1 - Search for efficient complete and planned missing data designs for analysis of change
AU - Wu, Wei
AU - Jia, Fan
AU - Rhemtulla, Mijke
AU - Little, Todd D.
N1 - Publisher Copyright:
© 2015, Psychonomic Society, Inc.
PY - 2016/9/1
Y1 - 2016/9/1
N2 - The design of longitudinal data collection is an essential component of any study of change. A well-designed study will maximize the efficiency of statistical tests and minimize the cost of available resources (e.g., budget). Two families of designs have been used to collect longitudinal data: complete data (CD) and planned missing (PM) designs. This article proposes a systematic and flexible procedure named SEEDMC (SEarch for Efficient Designs using Monte Carlo Simulation) to search for efficient CD and PM designs for growth-curve modeling under budget constraints. This procedure allows researchers to identify efficient designs for multiple effects separately and simultaneously, and designs that are robust to MCAR attrition. SEEDMC is applied to identify efficient designs for key change parameters in linear and quadratic growth models. The identified efficient designs are summarized and the strengths and possible extensions of SEEDMC are discussed.
AB - The design of longitudinal data collection is an essential component of any study of change. A well-designed study will maximize the efficiency of statistical tests and minimize the cost of available resources (e.g., budget). Two families of designs have been used to collect longitudinal data: complete data (CD) and planned missing (PM) designs. This article proposes a systematic and flexible procedure named SEEDMC (SEarch for Efficient Designs using Monte Carlo Simulation) to search for efficient CD and PM designs for growth-curve modeling under budget constraints. This procedure allows researchers to identify efficient designs for multiple effects separately and simultaneously, and designs that are robust to MCAR attrition. SEEDMC is applied to identify efficient designs for key change parameters in linear and quadratic growth models. The identified efficient designs are summarized and the strengths and possible extensions of SEEDMC are discussed.
KW - Efficiency
KW - Growth curve modeling
KW - Longitudinal data collection
KW - Planned missing data designs
UR - http://www.scopus.com/inward/record.url?scp=84984638798&partnerID=8YFLogxK
U2 - 10.3758/s13428-015-0629-5
DO - 10.3758/s13428-015-0629-5
M3 - Article
C2 - 26170055
AN - SCOPUS:84984638798
SN - 1554-351X
VL - 48
SP - 1047
EP - 1061
JO - Behavior Research Methods
JF - Behavior Research Methods
IS - 3
ER -