Well-chosen covariates boost the design sensitivity of individually and cluster-randomized trials. We provide guidance on covariate selection generating an extensive compilation of single- and multilevel design parameters on student achievement. Embedded in psychometric heuristics, we analyzed (a) covariate types of varying bandwidth-fidelity, namely domain-identical (IP), cross-domain (CP), and fluid intelligence (Gf) pretests, as well as sociodemographic characteristics (SC); (b) covariate combinations quantifying incremental validities of CP, Gf, and/or SC beyond IP; and (c) covariate time lags of 1–7 years, testing validity degradation in IP, CP, and Gf. Estimates from six German samples (1868 ≤ N ≤ 10,543) covering various outcome domains across grades 1–12 were meta-analyzed and included in precision simulations. Results varied widely by grade level, domain, and hierarchical level. In general, IP outperformed CP, which slightly outperformed Gf and SC. Benefits from coupling IP with CP, Gf, and/or SC were small. IP appeared most affected by temporal validity decay. Findings are applied in illustrative scenarios of study planning and enriched by comprehensive Online Supplemental Material (OSM) accessible via the Open Science Framework (OSF; https://osf.io/nhx4w).

精心选择的协变量可以提高个体随机试验和整群随机试验的设计敏感性。我们提供协变量选择指导，生成有关学生成绩的单级和多级设计参数的广泛汇编。嵌入心理测量启发法，我们分析了（a）不同带宽保真度的协变量类型，即域相同（IP）、跨域（CP）和流体智力（Gf）预测试，以及社会人口特征（SC）； (b) 协变量组合，量化 CP、Gf 和/或 SC 超出 IP 的增量有效性； (c) 协变量时滞1-7 年，测试 IP、CP 和 Gf 的有效性退化。对覆盖 1-12 年级各个结果领域的 6 个德国样本 (1868 ≤ N ≤ 10,543) 的估计值进行了荟萃分析，并将其纳入精确模拟中。结果因年级、领域和层级的不同而有很大差异。总体而言，IP 优于 CP，后者略优于 Gf 和 SC。 IP 与 CP、Gf 和/或 SC 耦合的好处很小。 IP 似乎受时间有效性衰减的影响最大。研究结果应用于研究计划的说明性场景，并通过开放科学框架（OSF；https://osf.io/nhx4w）访问综合在线补充材料（OSM）进行丰富。