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Experimental designs that randomly assign entire clusters of individuals (e.g., schools and classrooms) to treatments are frequently advocated as a way of guarding against contamination of the estimated average causal effect of treatment. However, in the absence of contamination, experimental designs that randomly assign intact clusters to treatments are less efficient than designs that randomly assign individual units within clusters. The current article considers the case of contamination processes that tend to make experimental and control subjects appear more similar than they truly are. The article demonstrates that, for most parameter values of practical interest, the statistical power of a randomized block (RB) design remains higher than the power of a cluster randomized (CR) design even when contamination causes the effect size to decrease by as much as 10%-60%. Furthermore, from the standpoint of point estimation, RB designs will tend to be preferred when true effect sizes are small and when the number of clusters in the experiment is not too large, but CR designs will tend to be preferred when true effect sizes are large or when the number of clusters in the experiment is large.

An important concern when planning research studies is to obtain maximum precision of an estimate of a treatment effect given a budget constraint. When research designs have a multilevel or hierarchical structure changes in sample size at different levels of the design will impact precision differently. Furthermore, there will typically be differential costs of enrolling additional units at different levels of the hierarchy. The optimal design problem in multilevel research studies involves determining the optimal sample size at each level of the design given specified design parameters and a specified marginal cost of recruitment at each level. The current work extends existing results by considering optimal design for (a) unbalanced random assignment designs and (b) regression discontinuity designs.

Researchers designing multisite and cluster randomized trials of educational interventions will usually conduct a power analysis in the planning stage of the study. To conduct the power analysis, researchers often use estimates of intracluster correlation coefficients and effect sizes derived from an analysis of survey data. When there is heterogeneity in treatment effects across the clusters in the study, these parameters will need to be adjusted to produce an accurate power analysis for a hierarchical trial design. The relevant adjustment factors are derived and presented in the current article. The adjustment factors depend upon the covariance between treatment effects and cluster-specific average values of the outcome variable, illustrating the need for better information about this parameter. The results in the article also facilitate understanding of the relative power of multisite and cluster randomized studies conducted on the same population by showing how the parameters necessary to compute power in the two types of designs are related. This is accomplished by relating parameters defined by linear mixed model specifications to parameters defined in terms of potential outcomes.

Motivation for reading is important to comprehension and has been studied extensively in offline reading contexts. However, little is known about the role of motivation in online reading, a new and increasingly important context for reading, largely because of a lack of valid and reliable instruments to estimate a student’s motivation for online reading. The authors report on the development of the Motivations for Online Reading Questionnaire (MORQ) among 1,798 seventh-grade students in two states. Results from confirmatory factor analysis revealed a three-factor solution for the MORQ: curiosity/ value, self-efficacy, and self-improvement beliefs. Additionally, measurement invariance across female and male students was established. Predictive validity of the MORQ was supported by the positive and significant contribution of the MORQ to the Online Research and Comprehension Assessment, an established measure of online reading comprehension. Results help establish the MORQ as a well-validated instrument for measuring online reading motivation. Results are discussed in relation to theory, research, and practice.

The struggles of students in the United States to comprehend non-fiction science text are well documented Middle school students, in particular, have minimal instruction in comprehending nonfktion and flounder on assessments. This article describes the development process of the Readorium software, an interactive web-basedprogram being developed to assist students with comprehension of science text. The program incorporates research-based recommendations for effective reading comprehension suggested by the Institute of Education Science (ÍES). Efforts to turn the IES recommendations into a coherent software product that motivates learners are described, as is the process of incorporating student and teacherfeedback to improve the usability of the product. Preliminary results suggest that the program operates efficiently, motivates students, and may substantially impact student comprehension of science text. [PUBLICATION ABSTRACT]

The use of cluster randomized experiments to study the effects of treatments on groups of subjects has increased in recent years. Many of these experiments lack the necessary statistical power to detect practically meaningful effects of treatment. One method for improving power in cluster randomized experiments that has been advanced is to use external information about the intracluster correlation coefficient (ICC) to increase the degrees of freedom available to estimate the variance of the estimated treatment effect. This thesis contributes to the discussion about improving power in cluster randomized experiments through the use of external information about the ICC in the following fashion. First, I point out that existing proposals for incorporating external information about the ICC into an analysis of a cluster randomized experiment do not have well studied size and power properties. Secondly, I derive a method for studying the small sample size and power of existing proposals and show that none of these proposals can guarantee that the hypothesis test procedure proposed achieves the nominal type I error rate in small samples. Thirdly, I derive bounds on the amount of power improvement possible through the use of external information about the ICC. Finally, I propose a new method for incorporating external information about the ICC into an analysis of a cluster randomized experiment which results in a hypothesis test that can be shown to have the nominal level in small samples. I show how this method can be extended in order to be utilized for hypothesis testing in any linear model with normal errors and a non-diagonal covariance matrix.