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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揭开缺失的碎片：青年媒体效应移动体验抽样研究中无响应的预测因子


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种很有前途的工具。遗憾的是，该方法存在大量缺失数据的问题。根据数据是随机缺失还是非随机缺失，它可能会对结果的有效性产生严重影响。出于这个原因，我们在一项关于数字媒体使用对青少年福祉和学习成绩的位移影响的 MES 研究中调查了无反应的预测因子 （N = 347）。多水平二元 logistic 回归确定了反应比值的显着影响因素，例如下午的哔哔声和外出。重要的是，学习成绩较差的青少年更有可能错过哔哔声。因为这种缺失与结果变量有关，所以在分析数据之前，应该应用现代缺失数据方法，例如多重插补。了解未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，最终确保充分利用收集的数据来得出准确的结论。