Purpose: Key considerations in designing multilevel experimental studies are to efficiently use resources and to determine the sample size allocation such that designs have adequate statistical power. The utility of optimal sampling and power analysis results depends on accurate information about design parameters, such as intraclass correlations (ICCs) that summarize the partitioning of variance at different levels (Hedges & Hedberg, 2007, 2013; Konstantopoulos, 2008a, 2008b). The purpose of this study is to compile empirical values of ICC that can be used to design efficient and effective three-level experimental studies evaluating the effects of teacher empowerment programs (such as teacher professional development and innovative curriculum initiatives; Hill et al., 2020; Jennings et al., 2017; Kelcey et al., 2019; Lynch et al., 2019; Nye et al, 2004) on student academic outcomes. Related Literature: Even though literature has reported empirical values of ICCs for a wide range of outcomes and continental regions (e.g., Bloom et al., 2007; Hedges & Hedberg, 2007, 2013; Kelcey et al., 2016; Kelcey & Phelps, 2013; Raudenbush et al., 2007; Westine et al., 2013), there is still a lack of empirical ICCs in settings of students nested within teachers nested within schools. For example, prior literature has reported empirical values of ICCs and percentages of adjusted variances in settings of students nested within schools (e.g., Bloom et al., 2007; Hedges & Hedberg, 2007; Kelcey et al., 2016) and students nested within schools nested within districts (e.g., Hedges & Hedberg, 2013; Westine et al., 2013). However, most of these studies focused on designs without teachers as a level of nesting. To our knowledge, there have been only three studies that provided empirical values of ICCs with students, teachers, and schools as the levels of nesting (Dong et al., 2016; Jacob et al., 2010; Zhu et al., 2012). These studies covered ICCs for a limited range of grades and outcome domains and were estimated mostly from experimental studies. Data and Measures: This study draws on the Early Childhood Longitudinal Study, Kindergarten Class of 1998-99 (ECLS-K), which is a nationally representative sample of kindergarten children during the academic year of 1998-99. ECLS-K study follows students from their kindergarten year through eighth grade, and the ECLS-K data includes information collected in the fall and spring of kindergarten (1998-99), the fall and spring of first grade (1999-2000), the spring of third grade (2002), the spring of fifth grade (2004), and the spring of eighth grade (2007; Tourangeau et al., 2006). The ECLS-K mathematics and reading examinations were developed through consultation with experts in child development, elementary education, and content experts. The mathematics assessments covered the following: number sense, properties and operations, measurement, geometry and spatial sense, data analysis, statistics and probability, pattern, algebra, and functions. The reading assessment evaluated recognition of sight words, reading words in context, making inferences from a text, using clues and background knowledge to make inferences, and evaluating texts to make connections between a narrative and life experiences (Tourangeau et al., 2006). In this study, we focus on outcome measures of reading and mathematics achievement near the end of each academic year (i.e., spring 1999, spring 2000, spring 2002, spring 2004, and spring 2006) as it is more common to evaluate students' performance near the end of an academic year. Consistent with previous studies (e.g., Jacob et al., 2010), we use IRT scores for the analysis. The sample sizes used in the analysis for each grade and outcome measure vary across different design dimensions. For example, the average sample sizes used for mathematics achievement are 13,411 students with 4,198 teachers in 2,107 schools and the average sample sizes used for reading achievement are 13,193 students with 4,005 teachers in 2,099 schools. Design Dimensions: Our report focuses on ICCs for designs investigating teacher empowerment programs with random assignment at the teacher or school level. Similar with the previous report (Hedges & Hedberg, 2007) that considered four design dimensions, we consider five design dimensions by additionally including the level of randomization. The first dimension is the achievement domain (e.g., mathematics or reading). The second dimension is the grade level of students. The third dimension is the set of covariates, if any, that will be used in the study design and analysis. The fourth dimension is the achievement status of schools among the overall population of schools. The fifth dimension is which type of design investigators intended to use (a three-level cluster-randomized trial or a multisite trial). Analytical Models: We use the R package lme4 (Bates et al., 2015) with the restricted maximum likelihood method to conduct data analyses. Restricted maximum likelihood estimation can help provide more accurate estimation for the variance components than the maximum likelihood estimation. Similar with the previous literature (e.g., Hedges & Hedberg, 2007), our analysis does not use design weights. Doings so, we are able to compute the 95% confidence intervals for unconditional ICCs through the bootstrap parameter estimation method in the package. Specific models are omitted due to space limitations. Results: For all schools, the ICCs at the school level are larger than those at the teacher level (Tables 1 & 3). For low achievement schools, the ICCs at the school level are much smaller (Tables 1 & 3). For both achievement domains, the ICC at the teacher level increases along aging or schooling (Tables 1 & 3). The inclusion of pretest scores can help explain a considerable portion of outcome variance at different levels (Tables 2 & 4). Including demographic covariates provides little extra explanatory power to explain the variances at any level when the pretest scores are already included (Tables 2 & 4). Conclusion: As illustrated, the results of this study can help design efficient and effective three level experimental studies (Tables 5 and 6) under optimal design frameworks (Shen & Kelcey, 2020, 2021, in press). This study also points to the importance of reporting and using grade-specific measures of ICCs to design three-level experiments probing the effects of teacher empowerment programs on student-level academic achievement.