How Consistent Are Class Size Effects?

Evidence from Project STAR has suggested that on average small classes increase student achievement. However, thus far researchers have focused on computing mean differences in student achievement between smaller and larger classes. In this study I focus on the distribution of the small class effects at the school level and compute the inconsistency of the treatment effects across schools. I use data from Project STAR and estimated small class effects for each school on mathematics and reading scores from kindergarten through third grade. The results revealed that school-specific small class effects are both positive and negative and that although students benefit considerably from being in small classes in some schools, in other schools being in small classes is a disadvantage. Small class effects were inconsistent and varied significantly across schools. Full time teacher aide effects were also inconsistent across schools and in some schools students benefit considerably from being in regular classes with a full time aide, while in other schools being in these classes is a disadvantage.small classes, treatment variability, meta-analysis

Incorporating Cost in Power Analysis for Three-Level Cluster Randomized Designs

In experimental designs with nested structures entire groups (such as schools) are often assigned to treatment conditions. Key aspects of the design in these cluster randomized experiments include knowledge of the intraclass correlation structure and the sample sizes necessary to achieve adequate power to detect the treatment effect. However, the units at each level of the hierarchy have a cost associated with them and thus researchers need to decide on sample sizes given a certain budget, when designing their studies. This paper provides methods for computing power within an optimal design framework (that incorporates costs of units in all three levels) for three-level cluster randomized balanced designs with two levels of nesting. The optimal sample sizes are a function of the variances at each level and the cost of each unit. Overall, larger effect sizes, smaller intraclass correlations at the second and third level, and lower cost of level-3 and level-2 units result in higher estimates of power.experimental design, statistical power, optimal sampling

Fixed Effects and Variance Components Estimation in Three-Level Meta-Analysis

Meta-analytic methods have been widely applied to education, medicine, and the social sciences. Much of meta-analytic data are hierarchically structured since effect size estimates are nested within studies, and in turn studies can be nested within level-3 units such as laboratories or investigators, and so forth. Thus, multilevel models are a natural framework for analyzing meta-analytic data. This paper discusses the application of a Fisher scoring method in two- and three-level meta-analysis that takes into account random variation at the second and at the third levels. The usefulness of the model is demonstrated using data that provide information about school calendar types. SAS proc mixed and HLM can be used to compute the estimates of fixed effects and variance components.meta-analysis, multilevel models, random effects

The Gender Gap Reloaded: Are School Characteristics Linked to Labor Market Performance?

This study examines the wage gender gap of young adults in the 1970s, 1980s, and 2000 in the US. Using quantile regression we estimate the gender gap across the entire wage distribution. We also study the importance of high school characteristics in predicting future labor market performance. We conduct analyses for three major racial/ethnic groups in the US: Whites, Blacks, and Hispanics, employing data from two rich longitudinal studies: NLS and NELS. Our results indicate that while some school characteristics are positive and significant predictors of future wages for Whites, they are less so for the two minority groups. We find significant wage gender disparities favoring men across all three surveys in the 1970s, 1980s, and 2000. The wage gender gap is more pronounced in higher paid jobs (90th quantile) for all groups, indicating the presence of a persistent and alarming "glass ceiling."Wages, gender differences, school effects, quantile regression

Is the Persistence of Teacher Effects in Early Grades Larger for Lower-Performing Students?

We examined the persistence of teacher effects from grade to grade on lower-performing students using high-quality experimental data from Project STAR, where students and teachers were assigned randomly to classrooms of different sizes. The data included information about mathematics and reading scores and student demographics such as gender, race, and SES. Teacher effects were computed as residual classroom achievement within schools and within grades. Then, teacher effects were used as predictors of achievement in following grades and quantile regression was used to estimate their persistence. Results consistently indicated that all students benefited similarly from teachers. Overall, systematic differential teacher effects were not observed and it appears that lower-performing students benefit as much as other students from teachers. In fourth grade there was some evidence that lower-performing students benefit more from effective teachers. Results from longitudinal analyses suggested that having effective teachers in successive grades is beneficial to all students and to lower-performing students in particular in mathematics. However, having low-effective teachers in successive grades is detrimental to all students and to lower-performing students in particular in reading.quantile regression, low-achievers, teacher effects

Constructing a More Powerful Test in Two-Level Block Randomized Designs

A more powerful test is proposed for the treatment effect in two-level block randomized designs where random assignment takes place at the first level. When clustering at the second level is assumed to be known, the proposed test produces higher estimates of power than the typical test

Using Power Tables to Compute Statistical Power in Multilevel Experimental Designs

Power computations for one-level experimental designs that assume simple random samples are greatly facilitated by power tables such as those presented in Cohen’s book about statistical power analysis. However, in education and the social sciences experimental designs have naturally nested structures and multilevel models are needed to compute the power of the test of the treatment effect correctly. Such power computations may require some programming and special routines of statistical software. Alternatively, one can use the typical power tables to compute power in nested designs. This paper provides simple formulae that define expected effect sizes and sample sizes needed to compute power in nested designs using the typical power tables. Simple examples are presented to demonstrate the usefulness of the formulae. Accessed 18,608 times on https://pareonline.net from May 20, 2009 to December 31, 2019. For downloads from January 1, 2020 forward, please click on the PlumX Metrics link to the right

Reporting randomised trials of social and psychological interventions: the CONSORT-SPI 2018 Extension.

BACKGROUND: Randomised controlled trials (RCTs) are used to evaluate social and psychological interventions and inform policy decisions about them. Accurate, complete, and transparent reports of social and psychological intervention RCTs are essential for understanding their design, conduct, results, and the implications of the findings. However, the reporting of RCTs of social and psychological interventions remains suboptimal. The CONSORT Statement has improved the reporting of RCTs in biomedicine. A similar high-quality guideline is needed for the behavioural and social sciences. Our objective was to develop an official extension of the Consolidated Standards of Reporting Trials 2010 Statement (CONSORT 2010) for reporting RCTs of social and psychological interventions: CONSORT-SPI 2018. METHODS: We followed best practices in developing the reporting guideline extension. First, we conducted a systematic review of existing reporting guidelines. We then conducted an online Delphi process including 384 international participants. In March 2014, we held a 3-day consensus meeting of 31 experts to determine the content of a checklist specifically targeting social and psychological intervention RCTs. Experts discussed previous research and methodological issues of particular relevance to social and psychological intervention RCTs. They then voted on proposed modifications or extensions of items from CONSORT 2010. RESULTS: The CONSORT-SPI 2018 checklist extends 9 of the 25 items from CONSORT 2010: background and objectives, trial design, participants, interventions, statistical methods, participant flow, baseline data, outcomes and estimation, and funding. In addition, participants added a new item related to stakeholder involvement, and they modified aspects of the flow diagram related to participant recruitment and retention. CONCLUSIONS: Authors should use CONSORT-SPI 2018 to improve reporting of their social and psychological intervention RCTs. Journals should revise editorial policies and procedures to require use of reporting guidelines by authors and peer reviewers to produce manuscripts that allow readers to appraise study quality, evaluate the applicability of findings to their contexts, and replicate effective interventions

Incorporating cost in power analysis for three-level cluster randomized designs

Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in Incorporating Cost in Power Analysis for Three-Level Cluster Randomized Designs Spyros Konstantopoulos D I S C U S S I O N P A P E R S E R I E S ABSTRACT Incorporating Cost in Power Analysis for Three-Level Cluster Randomized Designs In experimental designs with nested structures entire groups (such as schools) are often assigned to treatment conditions. Key aspects of the design in these cluster randomized experiments include knowledge of the intraclass correlation structure and the sample sizes necessary to achieve adequate power to detect the treatment effect. However, the units at each level of the hierarchy have a cost associated with them and thus researchers need to decide on sample sizes given a certain budget, when designing their studies. This paper provides methods for computing power within an optimal design framework (that incorporates costs of units in all three levels) for three-level cluster randomized balanced designs with two levels of nesting. The optimal sample sizes are a function of the variances at each level and the cost of each unit. Overall, larger effect sizes, smaller intraclass correlations at the second and third level, and lower cost of level-3 and level-2 units result in higher estimates of power. JEL Classification: I2