Mobile Experience Sampling (MES) is a promising tool for understanding youth digital media use and its effects. Unfortunately, the method suffers from high levels of missing data. Depending on whether the data is randomly or non-randomly missing, it can have severe effects on the validity of findings. For this reason, we investigated predictors of non-response in an MES study on displacement effects of digital media use on adolescents’ well-being and academic performance ( N = 347). Multilevel binary logistic regression identified significant influencing factors of response odds, such as afternoon beeps and being outside. Importantly, adolescents with poorer school grades were more likely to miss beeps. Because this missingness was related to the outcome variable, modern missing data methods such as multiple imputation should be applied before analyzing the data. Understanding the reasons for non-response can be seen as the first step to preventing, minimizing, and handling missing data in MES studies, ultimately ensuring that the collected data is fully utilized to draw accurate conclusions.

中文翻译：

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发现缺失的部分：青少年媒体影响的移动体验抽样研究中无反应的预测因素

移动体验采样 (MES) 是了解青少年数字媒体使用及其影响的一个很有前途的工具。不幸的是，该方法存在大量数据缺失的情况。根据数据是随机丢失还是非随机丢失，它可能会对研究结果的有效性产生严重影响。为此，我们在一项关于数字媒体使用对青少年幸福感和学业成绩的位移影响的 MES 研究中调查了不回应的预测因素 (N = 347)。多级二元逻辑回归确定了响应几率的重要影响因素，例如下午的嘟嘟声和在外面。重要的是，学校成绩较差的青少年更有可能错过嘟嘟声。由于这种缺失与结果变量相关，因此在分析数据之前应应用多重插补等现代缺失数据方法。了解无响应的原因可以被视为预防、最小化和处理 MES 研究中缺失数据的第一步，最终确保收集到的数据得到充分利用以得出准确的结论。