To understand psychological distress during COVID-19, we need to ensure that the same construct is measured over time and investigate how much of the variance in distress is attributable to chronic time-invariant variance compared to transient time-varying variance. We conducted secondary data analyses of Understanding Society, a U.K. probability-based longitudinal study of adults, using prepandemic (2015-2020) and pandemic data (N = 17,761, April 2020-March 2021). Using the General Health Questionnaire-12 (GHQ-12), analyses encompassed (a) five annual waves before COVID-19 plus the first survey wave during COVID-19 and (b) eight (bi)monthly waves during COVID-19. We investigated (a) longitudinal measurement invariance of distress, (b) time-invariant and time-varying variance components of distress using latent trait-occasion modeling, and (c) predictors of these different variance components. In all analyses, unique measurement invariance in distress was established, indicating the same unidimensional construct was measured using the GHQ before and during COVID-19. Time-varying variance was higher at the first COVID-19 lockdown (April 2020, 61.2%) compared to before COVID-19 (∼50%), suggesting increased fluctuations in distress at the start of the pandemic. Sensitivity analyses with equal time lags pre- and during COVID-19 confirmed this interpretation. During the pandemic, the highest distress time-varying variance (40.7%) was detected in April 2020, decreasing to 29.0% (July 2020) after restrictions eased. Despite mean-level fluctuations, time-varying variance remained stable during subsequent lockdowns, indicating more rank-order stability after this first major disruption. Loneliness most strongly predicted time-varying variance during the first lockdown. Life dissatisfaction and financial difficulties were associated with both variance components throughout the pandemic. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

中文翻译：

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英国家庭纵向研究（了解社会）中 COVID-19 期间心理困扰的时变和时不变成分的预测因子。

为了了解 COVID-19 期间的心理困扰，我们需要确保随着时间的推移测量相同的结构，并调查与瞬态时变方差相比，有多少痛苦的方差可归因于慢性时不变方差。我们使用大流行前（2015-2020）和大流行数据（N = 17,761，2020年4月-2021年3月）对理解社会（英国基于概率的成人纵向研究）进行了二次数据分析。使用一般健康调查问卷 12 (GHQ-12)，分析涵盖 (a) COVID-19 之前的每年五波以及 COVID-19 期间的第一波调查和 (b) COVID-19 期间的八（双）月波。我们使用潜在特征事件模型研究了（a）痛苦的纵向测量不变性，（b）痛苦的时不变和时变方差分量，以及（c）这些不同方差分量的预测因子。在所有分析中，都建立了独特的遇险测量不变性，表明在 COVID-19 之前和期间使用 GHQ 测量了相同的一维结构。与 COVID-19 之前（约 50%）相比，第一次 COVID-19 封锁时的时变方差（2020 年 4 月，61.2%）更高，这表明在大流行开始时痛苦的波动增加。在 COVID-19 之前和期间进行的具有相同时间滞后的敏感性分析证实了这一解释。疫情期间，2020 年 4 月检测到的痛苦时变方差最高（40.7%），限制放松后降至 29.0%（2020 年 7 月）。尽管平均水平存在波动，但在随后的封锁期间，随时间变化的方差仍然保持稳定，这表明在第一次重大中断之后，排名顺序更加稳定。孤独感最有力地预测了第一次封锁期间随时间变化的方差。在整个大流行期间，生活不满和经济困难与这两个差异因素有关。（PsycInfo 数据库记录 (c) 2023 APA，保留所有权利）。