Relevance & Research question: Smartphones are becoming increasingly important and widely-used for survey completion. Smartphones offer many new possibilities for survey research: We can, for example, send pop-up questions in real-time, for instance to measure participants' feelings, and record sensor data. However, as nice as these new opportunities are, the questions we ask can get increasingly intrusive, and we risk over-asking participants, who may choose to drop-out in response. Nonresponse and nonresponse bias may be different in different phases (f.e. survey, pop-up questions, consent to record sensor data) of the research, because of the different intrusiveness of every phase. Fundamental, methodological knowledge about nonresponse in smartphone-only studies is lacking, but very important to understand selection bias. Therefore, our main research question is: How can we predict nonresponse at different phases in a smartphone-only survey?

Methods & data: We studied an innovative smartphone-only Time Use Survey. The Dutch Institute for Social Research conducted their Time Use Survey in 2013 through an app on a smartphone at two randomly chosen days of the week.

The study consisted of four phases, specifically:

1. Invitation to participate in the study (n = 2154)

2. Participation in the Time Use Survey (n = 1610).

3. Answer pop-up questions (n = 1407).

4. Give permission to record sensor data (f.e. GPS locations and call data) (n = 1004).

Results: We documented the nonresponse and estimated nonresponse bias for each of the four phases. Because the data were collected in a panel, we can use predictors from earlier waves. Therefore, we do not only have the typical (age, sex, income) information, but also more interesting variables (personality, participation history, smartphone usage, survey attitude), which we can use as covariates and predictors of nonresponse.

Added value: This study provides us with knowledge about bias in smartphone-only studies, a field which remains relatively unexplored. We used participants from the LISS-panel, which aims to be representative of the Dutch population. Therefore, this knowledge about who does and who doesn’t participate, and how smartphone studies may be biased, can be very valuable for all online researchers who consider such a study.