Research apps allow to administer survey questions and passively collect smartphone data, thus providing rich information on individual and social behaviours. Agreeing to this novel form of data collection requires multiple consent steps, and little is known about the effect of non-participation. We invited 4,293 Android smartphone owners from the German Panel Study Labour Market and Social Security (PASS) to download the IAB-SMART app. The app collected data over six months through (a) short in-app surveys and (b) five passive mobile data collection functions. The rich information on PASS members from previous survey waves allows us to compare participants and non-participants in the IAB-SMART study at the individual stages of the participation process and across the different types of data collected. We find that 14.5 percent of the invited smartphone users installed the app, between 12.2 and 13.4 percent provided the different types of passively collected data, and 10.8 percent provided all types of data at least once. Likelihood to participate was smaller among women, decreased with age and increased with educational attainment, German citizenship, and PASS tenure. We find non-participation bias in substantive variables, including overestimation of social media usage and social network size and underestimation of non-working status.

中文翻译：

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不参与使用研究应用程序收集智能手机数据

研究应用程序允许管理调查问题并被动收集智能手机数据，从而提供有关个人和社会行为的丰富信息。同意这种新颖的数据收集形式需要多个同意步骤，而且对不参与的影响知之甚少。我们邀请了来自德国劳动力市场和社会保障小组研究 (PASS) 的 4,293 名安卓智能手机用户下载了 IAB-SMART 应用程序。该应用程序通过 (a) 简短的应用程序内调查和 (b) 五种被动移动数据收集功能收集了六个月的数据。先前调查浪潮中有关 PASS 成员的丰富信息使我们能够在参与过程的各个阶段以及收集的不同类型数据中比较 IAB-SMART 研究的参与者和非参与者。我们发现 14。5% 的受邀智能手机用户安装了该应用程序，12.2% 至 13.4% 的用户提供了不同类型的被动收集数据，10.8% 的用户至少提供了一次所有类型的数据。女性参与的可能性较小，随着年龄的增长而降低，随着教育程度、德国公民身份和 PASS 任期的增加而增加。我们发现实质性变量存在非参与偏差，包括高估社交媒体使用和社交网络规模以及低估非工作状态。