SATcoaching

Effect sizes from studies on the effects of SAT coaching,
reported in Kalaian and Raudenbush (1996)

datasets

Provides several cluster-robust variance estimators (i.e.,
sandwich estimators) for ordinary and weighted least squares linear regression
models, including the bias-reduced linearization estimator introduced by Bell
and McCaffrey (2002)
<https://www150.statcan.gc.ca/n1/pub/12-001-x/2002002/article/9058-eng.pdf> and
developed further by Pustejovsky and Tipton (2017)
<DOI:10.1080/07350015.2016.1247004>. The package includes functions for estimating
the variance- covariance matrix and for testing single- and multiple-
contrast hypotheses based on Wald test statistics. Tests of single regression
coefficients use Satterthwaite or saddle-point corrections. Tests of multiple-
contrast hypotheses use an approximation to Hotelling's T-squared distribution.
Methods are provided for a variety of fitted models, including lm() and mlm
objects, glm(), geeglm() (from package 'geepack'), ivreg() (from package 'AER'), ivreg() (from package 'ivreg' when
estimated by ordinary least squares), plm() (from package 'plm'), gls() and
lme() (from 'nlme'), lmer() (from `lme4`), robu() (from 'robumeta'), and rma.uni()
and rma.mv() (from 'metafor').

James Pustejovsky

clubSandwich

Cluster-Robust (Sandwich) Variance Estimators with Small-Sample
Corrections

SATcoaching function

A data frame with 67 rows and 11 variables:<dl>
 <dt>study</dt>
<dd>Study identifier</dd> <dt>year</dt>
<dd>Year of publication</dd>
 
 <dt>test</dt>
<dd>Character string indicating whether effect size corresponds to outcome on verbal (SATV) or math (SATM) test</dd> <dt>d</dt>
<dd>Effect size estimate (Standardized mean difference)</dd>
 
 <dt>V</dt>
<dd>Variance of effect size estimate</dd>
 
 <dt>nT</dt>
<dd>Sample size in treatment condition</dd>
 
 <dt>nC</dt>
<dd>Sample size in control condition</dd>
 
 <dt>study_type</dt>
<dd>Character string indicating whether study design used a matched, non-equivalent, or randomized control group</dd>
 
 <dt>hrs</dt>
<dd>Hours of coaching</dd>
 
 <dt>ETS</dt>
<dd>Indicator variable for Educational Testing Service</dd>
 
 <dt>homework</dt>
<dd>Indicator variable for homework</dd>
 
 
</dl>

Format

Randomized experiments on SAT coaching — SATcoaching

A data frame with 67 rows and 11 variables:<dl>
 <dt>study</dt>
<dd>Study identifier</dd>

 <dt>year</dt>
<dd>Year of publication</dd>
 
 <dt>test</dt>
<dd>Character string indicating whether effect size corresponds to outcome on verbal (SATV) or math (SATM) test</dd>

 <dt>d</dt>
<dd>Effect size estimate (Standardized mean difference)</dd>
 
 <dt>V</dt>
<dd>Variance of effect size estimate</dd>
 
 <dt>nT</dt>
<dd>Sample size in treatment condition</dd>
 
 <dt>nC</dt>
<dd>Sample size in control condition</dd>
 
 <dt>study_type</dt>
<dd>Character string indicating whether study design used a matched, non-equivalent, or randomized control group</dd>
 
 <dt>hrs</dt>
<dd>Hours of coaching</dd>
 
 <dt>ETS</dt>
<dd>Indicator variable for Educational Testing Service</dd>
 
 <dt>homework</dt>
<dd>Indicator variable for homework</dd>
 
 
</dl>

SATcoaching: Randomized experiments on SAT coaching

Description

Usage

Arguments

Format

References