Analyzing EMA Data

I designed a study to assess 50 college students’ motivations to use alcohol and its correlates during their first semester. The most innovative part of this study was that I collected data with smart phones that beeped at several random times on every Thursday, Friday, and Saturday throughout the semester. Now that I’ve collected the data, I’m overwhelmed by how rich the data are and don’t know where to start! My first thought is to collapse the data to weekly summary scores and model those using growth curve analysis. Is there anything more I can do with the data? — Signed, Swimming in Data


Dear Swimming:

You did indeed collect an amazing dataset! With technological advances, the collection of intensive longitudinal data, such as ecological momentary assessments (EMA), is becoming popular among researchers hoping to better understand dynamic processes related to mood, cigarette or alcohol use, physical activity, and many other states or behaviors. Some of the most compelling research questions in these studies often have to do with effects of time-varying predictors.


One familiar way to approach the analysis of EMA data is to reduce the data, summarizing within-day or within-week assessments to a single measure, so that growth curve models may be fit to estimate an average trend and predictors of the intercept and slope. However, this approach disregards the richness of the data that were so carefully collected. Further, EMA studies are typically designed in order to capture something more dynamic than what could be captured as a linear function of time.


A more common approach to the analysis of EMA data is multilevel models, where within- and between-individual variability can be separated. This approach is helpful for understanding, for example, the degree of stability of processes. However, these methods typically impose important constraints, such as the assumption that the effects of covariates on an outcome are stable over time.


New methods for the analysis of intensive longitudinal data have been proposed in the statistical literature, and hold immense promise for addressing important questions about dynamic processes such as the factors driving alcohol use during the freshman year of college. For example, the time-varying effect model (TVEM) is a flexible approach that allows the effects of covariates to vary with time. A detailed introduction to time-varying effect models for audiences in psychological science will appear in Tan, Shiyko, Li, Li, & Dierker (in press).


A demonstration of this approach will appear in an article by Methodology Center researchers and colleagues (Shiyko et al., in press). The authors analyzed data collected as part of a smoking-cessation trial and found that individuals with a successful quit attempt had a rapid decrease in craving within the first few days of quitting, whereas those who eventually relapsed did not experience this decrease. Eventual relapsers had low levels of confidence in their abilities to abstain on cravings early in their quit attempt, but among successful quitters the association with confidence in ability to abstain was significantly weaker.


Any researcher with access to EMA data can fit a TVEM using the %TVEM SAS macro, which is freely available at /downloads/tvem. Give it a try so that you can explore the timevarying effects of individual factors, such as residing in a dorm, and contextual factors, such as excitement about an upcoming sporting event, on motivations to use alcohol.



Shiyko, M. P., Lanza, S. T., Tan, X., Li, R., & Shiffman, S. (2012). Using the time-varying effect model (TVEM) to examine dynamic associations between negative affect and self confidence on smoking urges: Differences between successful quitters and relapsers. Prevention Science. PMCID: PMC3171604

Tan, X., Shiyko, M. P., Li, R., Li, Y., & Dierker, L. (2012). A time-varying effect model for intensive longitudinal data. Psychological Methods, 17(1), 61-77. PMCID: PMC3288551

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