eefAnalytics (version 1.0.6)

srtFREQ: Analysis of Simple Randomised Education Trial using Linear Regression Model.

Description

srtFREQ perfoms analysis of educational trials under the assumption of independent errors among pupils. This can also be used with schools as fixed effects.

Usage

srtFREQ(formula, intervention, nBoot = NULL, nPerm = NULL, data)

Arguments

formula

the model to be analysed. It is of the form y~x1+x2+.... Where y is the outcome variable and Xs are the predictors.

intervention

a string variable specifying the "intervention variable" as appeared in the formula. See example below

nBoot

number of bootstraps required to generate bootstrap confidence interval. Default is NULL.

nPerm

number of permutations required to generate permutated p-value. Default is NULL.

data

data frame containing the data to be analysed.

Value

S3 object; a list consisting of

• Beta. Estimates and confidence intervals for the predictors specified in the model.
• ES. Hedges' g effect size for the intervention(s). If nBoot is not specified, the confidence intervals are 95
• sigma2. Residual variance.
• Perm. A vector containing permutated effect sizes under null hypothesis. It is produced only if nPerm is specified.
• Bootstrap. A vector containing bootstrapped effect sizes. It is prduced only if nBoot is specified.

Examples

if(interactive()){

data(mstData)

###################################################################
## Analysis of simple randomised trials using Hedges Effect Size ##
###################################################################

output1 <- srtFREQ(Posttest~ Intervention+Prettest,
		intervention="Intervention",data=mstData )
ES1 <- output1$ES
ES1

###################################################################
## Analysis of simple randomised trials using Hedges Effect Size ## 
## with Permutation p-value                                      ##
###################################################################

output2 <- srtFREQ(Posttest~ Intervention+Prettest,
		intervention="Intervention",nPerm=1000,data=mstData )

ES2 <- output2$ES
ES2


#### plot permutated values

plot(output2, group=1)



###################################################################
## Analysis of simple randomised trials using Hedges Effect Size ##
## with non-parametric bootstrap confidence intervals            ##
###################################################################

output3 <- srtFREQ(Posttest~ Intervention+Prettest,
		intervention="Intervention",nBoot=1000,data=mstData)

ES3 <- output3$ES
ES3

### plot bootstrapped values

plot(output3, group=1)

####################################################################
## Analysis of simple randomised trials using Hedges' effect size  ##
##  with schools as fixed effects                                  ##
####################################################################

output4 <- srtFREQ(Posttest~ Intervention+Prettest+as.factor(School),
		intervention="Intervention",data=mstData )

ES4 <- output4$ES
ES4

####################################################################
## Analysis of simple randomised trials using Hedges' effect size ##
## with schools as fixed effects and with permutation p-value     ##
####################################################################

output5 <- srtFREQ(Posttest~ Intervention+Prettest+as.factor(School),
		intervention="Intervention",nPerm=1000,data=mstData )

ES5 <- output5$ES
ES5

#### plot permutated values

plot(output5, group=1)

####################################################################
## Analysis of simple randomised trials using Hedges' effect size ##
## with schools as fixed effects and with permutation p-value      ##
####################################################################

output6 <- srtFREQ(Posttest~ Intervention+Prettest+as.factor(School),
		intervention="Intervention",nBoot=1000,data=mstData)

ES6 <- output6$ES
ES6 

### plot bootstrapped values

plot(output6, group=1)
}