A Monte Carlo examination of an MTMM model with planned incomplete data structures

Abstract

The classic approach for partitioning and assessing reliability and validity has been through the use of the multitrait-multimethod (MTMM) model. The MTMM approach generally involves 3 different groups (method) evaluating 3 traits. This approach can be reconceptualized for questionnaire evaluation, so that the method becomes 3 different scaling types, which are administered to the same respondents on different occasions to avoid carryover effects. A serious limitation of this MTMM model is that data are required from respondents on at least 3 different occasions, thus placing a heavy burden on the researcher and respondents. Planned incomplete data designs for the purpose of substantially reducing the amount of data required for MTMM models were investigated: 1st, a design that reduces the amount of data collected at the 3rd administration by 22%; and 2nd, a design in which data need only be collected at 2 occasions. The performance of Listwise Deletion, Pairwise Deletion, and the expectation maximization (EM) algorithm at dealing with planned incomplete data are examined through a series of simulations. Results indicate that EM was generally precise and efficient.