BIFIE.univar.test {BIFIEsurvey}

Analysis of Variance and Effect Sizes for Univariate Statistics

Description

Computes a Wald test which tests equality of means (univariate analysis of variance). In addition, the d and \eta effect sizes are computed.

Usage

BIFIE.univar.test(BIFIE.method, wald_test=TRUE)

## S3 method for class 'BIFIE.univar.test'
summary(object,digits=4,...)

Arguments

BIFIE.method

Object of class BIFIE.univar

wald_test

Optional logical indicating whether a Wald test should be performed.

object

Object of class BIFIE.univar.test

digits

Number of digits for rounding output

...

Further arguments to be passed

Value

A list with following entries

stat.F

Data frame with F statistic for Wald test

stat.eta

Data frame with \eta effect size and its inference

stat.dstat

Data frame with Cohen's d effect size and its inference

...

More values

See Also

BIFIE.univar

Examples

#############################################################################
# EXAMPLE 1: Imputed TIMSS dataset - One grouping variable
#############################################################################

data(data.timss1)
data(data.timssrep)

# create BIFIE.dat object
bdat <- BIFIEsurvey::BIFIE.data( data.list=data.timss1, wgt=data.timss1[[1]]$TOTWGT,
           wgtrep=data.timssrep[, -1 ] )

#**** Model 1: 3 variables splitted by book
res1 <- BIFIEsurvey::BIFIE.univar( bdat, vars=c("ASMMAT", "ASSSCI","scsci"),
                    group="books")
summary(res1)
# analysis of variance
tres1 <- BIFIEsurvey::BIFIE.univar.test(res1)
summary(tres1)

#**** Model 2: One variable splitted by gender
res2 <- BIFIEsurvey::BIFIE.univar( bdat, vars=c("ASMMAT"), group="female" )
summary(res2)
# analysis of variance
tres2 <- BIFIEsurvey::BIFIE.univar.test(res2)
summary(tres2)

## Not run: 
#**** Model 3: Univariate statistic: math
res3 <- BIFIEsurvey::BIFIE.univar( bdat, vars=c("ASMMAT") )
summary(res3)
tres3 <- BIFIEsurvey::BIFIE.univar.test(res3)

#############################################################################
# EXAMPLE 2: Imputed TIMSS dataset - Two grouping variables
#############################################################################

data(data.timss1)
data(data.timssrep)

# create BIFIE.dat object
bdat <- BIFIEsurvey::BIFIE.data( data.list=data.timss1, wgt=data.timss1[[1]]$TOTWGT,
                  wgtrep=data.timssrep[, -1 ] )

#**** Model 1: 3 variables splitted by book and female
res1 <- BIFIEsurvey::BIFIE.univar(bdat, vars=c("ASMMAT", "ASSSCI","scsci"),
                  group=c("books","female"))
summary(res1)

# analysis of variance
tres1 <- BIFIEsurvey::BIFIE.univar.test(res1)
summary(tres1)

# extract data frame with Cohens d statistic
dstat <- tres1$stat.dstat

# extract d values for gender comparisons with same value of books
# -> 'books' refers to the first variable
ind <- which(
  unlist( lapply( strsplit( dstat$groupval1, "#"), FUN=function(vv){vv[1]}) )==
  unlist( lapply( strsplit( dstat$groupval2, "#"), FUN=function(vv){vv[1]}) )
        )
dstat[ ind, ]

## End(Not run)