abstract
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Experience sampling is a method aimed primarily at examining within-individual covariation of transient phenomena utilizing repeated measures. It can be applied to test nuanced predictions of extant theories and can provide insights that are otherwise difficult to obtain. It does so by examining the phenomena of interest close to where they occur and thus avoiding issues with recall and similar concerns. Data collected through the experience sampling method (ESM) can, alternatively, be utilized to collect highly reliable data to investigate between-individual phenomena.
A number of decisions need to be made when designing an ESM study. Study duration and intensity (that is, total days of measurement and total assessments per day) represent a tradeoff between data richness and participant fatigue that needs to be carefully weighed. Other scheduling options need to be considered, such as triggered versus scheduled surveys. Researchers also need to be aware of the generally high potential cost of this approach, as well as the monetary and nonmonetary resources required.
The intensity of this method also requires special consideration of the sample and the context. Proper screening is invaluable; ensuring that participants and their context is applicable and appropriate to the design is an important first step. The next step is ensuring that the surveys are planned in a compatible way to the sample, and that the surveys are designed to appropriately and rigorously collect data that can be used to accomplish the aims of the study at hand.
Furthermore, ESM data typically requires proper consideration in regards to how the data will be analyzed and how results will be interpreted. Proper attention to analytic approaches (typically multilevel) is required. Finally, when interpreting results from ESM data, one must not forget that these effects typically represent processes that occur continuously across individuals working liveseffect sizes thus need to be considered with this in mind.