Latent growth modeling with domain-specific outcomes comprised of mixed response types in intervention studies.
OBJECTIVE
When several continuous outcome measures of interest are collected across time in experimental studies, the use of standard statistical procedures, such as multivariate analysis of variance or growth curve modeling, can be properly used to assess treatment effects. However, when data consist of mixed responses (e.g., continuous and ordered categorical [ordinal] responses), traditional modeling approaches are no longer appropriate. The purpose of this article is to illustrate the use of a more suitable modeling procedure when mixed responses are collected in longitudinal intervention studies.


METHODS
Problems with traditional analyses of such data are discussed, as are potential advantages provided by the proposed modeling approach. The application of the multiple-domain latent growth modeling approach with mixed responses is illustrated for experimental designs with data from the SeniorWISE study (McDougall et al., 2010). This multisite randomized trial assessed memory functioning of 265 elderly adults across a 26-month period after receiving either a memory or health promotion training program.


RESULTS
The latent growth models illustrated allow one to examine treatment effects on the growth of multiple mixed outcomes while incorporating associations among multiple responses, which allows for better missing data treatment, greater power, and more accurate control of Type I error. The interpretation of parameters of interest and treatment effects is discussed using the SeniorWISE data.


CONCLUSIONS
Multiple-domain latent growth modeling with mixed responses is a flexible statistical modeling tool that can have substantial benefits for applied researchers. As such, the use of this modeling approach is expected to increase.
Department of Educational Psychology, University of Texas at Austin.
Tiffany A
TA
Whittaker
Department of Educational Psychology, University of Texas at Austin.
Keenan A
KA
Pituch
The University of Alabama Capstone College of Nursing.
Graham J
GJ
McDougall
D000203:Activities of Daily Living; D000368:Aged; D003627:Data Interpretation, Statistical; D016002:Discriminant Analysis; D005260:Female; D006293:Health Promotion; D006801:Humans; D007858:Learning; D016014:Linear Models; D008137:Longitudinal Studies; D008297:Male; D000067489:Metacognition; D015233:Models, Statistical; D015999:Multivariate Analysis; D017063:Outcome Assessment, Health Care; D063868:Patient Outcome Assessment; D012107:Research Design; D020377:Self Efficacy; D016896:Treatment Outcome