The social sciences have long discovered the potential of smartphones to obtain data, implement new research designs, and, thus, tackle new phenomena and research questions. This course focuses on mobile data collection designs in the social sciences and how scholars can benefit from using smartphones and intensive-longitudinal methods in their research. Scholars use smartphones, on the one hand, as a device for taking surveys and conducting intensive-longitudinal survey designs; on the other hand, mobile digital behavioral data (e.g., location, mobility, app usage, health data) is a valuable data source for social scientists. Hence, in principle, we can distinguish mobile survey designs such as experience sampling, ecological momentary assessment, ambulatory assessment, sensor data (e.g. GPS or wearable data), and tracking data (e.g. app/phone usage). This course will focus on intensive longitudinal survey designs. However, depending on the interests of participants and available time, we will also get to know designs, tools, and studies working with mobile tracking (e.g. app usage) or sensing data (e.g. health and wearable data). During the workshop, participants will learn how to implement such designs with the GESIS AppKit.

 

The participants will learn about mobile designs (e.g., experience sampling, ambulatory assessment, diary-style studies), mobile data (mobile survey, tracking, and sensor data), and intensive longitudinal methods in general. The emphasis of this workshop will be on using tools and software for mobile data collection (with the GESIS AppKit) in order to plan and conduct a mobile, intensive-longitudinal survey study. After designing a study and getting their hands on the data collection software, participants will learn how to process and handle mobile intensive-longitudinal data, e.g., different data formats, data visualization, handling missing data, and more. Finally, we will have a first look into analysis, reporting, and linking of mobile surveys and mobile digital behavioral data. (For a deeper dive into the analysis of intensive-longitudinal data, the course “Time Series Analysis for Modeling Intensive Longitudinal Data” by Noémi Schurmann is also recommended).

 

Organizational Structure of the Course

There will be lectures on the basic idea of mobile assessments, the role of the smartphone in the social sciences, and mobile designs. However, especially on days 2 and 3, participants can plan a study and work directly on datasets.

There will be an opportunity for participants to consult the lecturer on individual projects and, if applicable, start working on their own project with the support of the lecturer and the group. If participants plan on conducting a mobile (survey) study, they can also send research questions and information to the lecturer beforehand. Of course, this is not mandatory, and researchers who are new to mobile designs and methods can participate without prior projects, research questions, or data.

More specifically, the first day will be heavier on input from the lecturer, introducing mobile designs, software, and data. We will address questions on what is unique about intensive-longitudinal designs, which research questions can be tackled, and what might be the challenges of mobile research designs, including the development of an own suitable design, if applicable. On the second day, the focus is on planning a study and using the mobile infrastructure (i.e. the GESIS AppKit). On day 3, we will learn how to process and visualize intensive longitudinal data using suitable R packages and toolboxes such as: https://preprocess.esmtools.com/. We conclude with an outlook to the analysis of intensive-longitudinal data.

A good knowledge of traditional (longitudinal) social science methods and survey methods, i.e. knowledge of item types, scales, reliability and validity,

Basic knowledge of the distinction between within- and between-person processes

A solid statistical and methods basis, i.e. regression, ANOVA, hypothesis testing etc.

Knowledge on how to handle data and conduct basic analysis with R, e.g. knowledge of data frames, basic knowledge of visualization, descriptive statistics, and analysis with R. We will get to know and work with a specific toolbox/package for the GESIS AppKit on the last day (see https://preprocess.esmtools.com/ for a similar solution).

 

Software Requirements

Make sure that R and RStudio are installed on your computer. We will install and use the packages that are needed/recommended for data management, visualization, and analysis on day 3.

Information on how to open an account for the GESIS AppKit software will be circulated in due time before the workshop. The software is free to use for all participants.