We examine longitudinal extensions of the two-method measurement design, which uses planned missingness to optimize cost-efficiency and validity of hard-to-measure constructs. These designs use a combination of two measures: a "gold standard" that is highly valid but expensive to administer, and an inexpensive (e.g., survey-based) measure that contains systematic measurement bias (e.g., response bias). Using simulated data on 4 measurement occasions, we compared the cost-efficiency and validity of longitudinal designs where the gold standard is measured at one or more measurement occasions. We manipulated the nature of the response bias over time (constant, increasing, fluctuating), the factorial structure of the response bias over time, and the constraints placed on the latent variable model. Our results showed that parameter bias is lowest when the gold standard is measured on at least two occasions. When a multifactorial structure was used to model response bias over time, estimation difficulties were common. Almost all parameters in all conditions displayed high relative efficiency, suggesting that the 2-method design is an effective way to reduce costs and improve power and accuracy in longitudinal research.
Keywords missing data, planned missingness, two-method measurement, intentionally missing data, structural equation modeling, simsem
|Advisor:||Deboeck, Pacal R.|
|Commitee:||Johnson, David K., Little, Todd D.|
|School:||University of Kansas|
|School Location:||United States -- Kansas|
|Source:||MAI 52/04M(E), Masters Abstracts International|
|Keywords:||Intentionally missing data, Missing data, Planned missingness, Simulated structural equation, Structural equation modeling, Two-method measurement|
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