Daily life research and educational resources

Here you can find several resources for intensive longitudinal methods (ILM) for daily life research. ILMs allow researchers to study people's thoughts, behaviours, feelings and physiology as they occur in real daily life using either (paper-and-pencil) diaries, mobile apps and/or wearable sensors. Depending on the specific discipline, study aims and types of variables measured, ILMs are referred to by different names, including experience sampling, ecological momentary assessment, ambulatory assessment, and real-time data capture. Nonetheless, these methods all share the fundamental characteristic of intensive repeated (or even continuous) measurement of a set of variables over several days, weeks, or even months.

With ILMs it is possible to study the association between, for example, thoughts and behaviour over time, or to try to predict behaviour. ILMs are becoming increasingly popular in psychological research. First of all because the accelerating technological developments have made them much more easy to perform in real-world settings and diverse populations (e.g., by using mobile apps for asking questions and wearables for novel passive sensing). More importantly, however, ILMs hold several assumed advantages over more traditional methods such as surveys or laboratory methods. For instance, they are assumed to provide more realistic and less biased self-reports, and can provide insight into the relevance of fluctuations in behaviours or feelings and the (changes in) context of the respondents (e.g., what they are doing or who they are with). Moreover, the intensive nature of the data allows to separate between-person and within person associations and when you have enough measurements you can even model distinct change processes or associations for each individual. Of course, ILMs also have disadvantages, for instance relating to ethics (e.g., how frequent can you ask people on what they are doing and how they are feeling?). Also, the (statistical) analysis of this type of data brings along additional challenges.