# BIFIE.waldtest: Wald Tests for BIFIE Methods In BIFIEsurvey: Tools for Survey Statistics in Educational Assessment

## Description

This function performs a Wald test for objects of classes `BIFIE.by`, `BIFIE.correl`, `BIFIE.crosstab`, `BIFIE.freq`, `BIFIE.linreg`, `BIFIE.logistreg` and `BIFIE.univar`.

## Usage

 ```1 2 3 4``` ```BIFIE.waldtest(BIFIE.method, Cdes, rdes, type=NULL) ## S3 method for class 'BIFIE.waldtest' summary(object,digits=4,...) ```

## Arguments

 `BIFIE.method` Object of classes `BIFIE.by`, `BIFIE.correl`, `BIFIE.crosstab`, `BIFIE.freq`, `BIFIE.linreg`, `BIFIE.logistreg` or `BIFIE.univar` (see `parnames` in the Output of these methods for saved parameters) `Cdes` Design matrix C (see Details) `rdes` Design vector r (see Details) `type` Only applies to `BIFIE.correl`. In case of `type="cov"` covariances instead of correlations are used for parameter tests. `object` Object of class `BIFIE.waldtest` `digits` Number of digits for rounding output `...` Further arguments to be passed

## Details

The Wald test is conducted for a parameter vector \bold{θ}, specifying the hypothesis C \bold{θ}=r. Statistical inference is performed by using the D_1 and the D_2 statistic (Enders, 2010, Ch. 8).

For objects of class `bifie.univar`, only hypotheses with respect to means are implemented.

## Value

A list with following entries

 `stat.D` Data frame with D_1 and D_2 statistic, degrees of freedom and p value `...` More values

## References

Enders, C. K. (2010). Applied missing data analysis. Guilford Press.

## See Also

`survey::regTermTest`, `survey::anova.svyglm`, `car::linearHypothesis`

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91``` ```############################################################################# # EXAMPLE 1: Imputed TIMSS dataset ############################################################################# 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: Linear regression res1 <- BIFIEsurvey::BIFIE.linreg( bdat, dep="ASMMAT", pre=c("one","books","migrant"), group="female" ) summary(res1) #*** Wald test which tests whether sigma and R^2 values are the same res1\$parnames # parameter names pn <- res1\$parnames ; PN <- length(pn) Cdes <- matrix(0,nrow=2, ncol=PN) colnames(Cdes) <- pn # equality of R^2 ( R^2(female0) - R^2(female1)=0 ) Cdes[ 1, c("R^2_NA_female_0", "R^2_NA_female_1" ) ] <- c(1,-1) # equality of sigma ( sigma(female0) - sigma(female1)=0) Cdes[ 2, c("sigma_NA_female_0", "sigma_NA_female_1" ) ] <- c(1,-1) # design vector rdes <- rep(0,2) # perform Wald test wmod1 <- BIFIEsurvey::BIFIE.waldtest( BIFIE.method=res1, Cdes=Cdes, rdes=rdes ) summary(wmod1) ## Not run: #****************** #*** Model 2: Correlations # compute some correlations res2a <- BIFIEsurvey::BIFIE.correl( bdat, vars=c("ASMMAT","ASSSCI","migrant","books")) summary(res2a) # test whether r(MAT,migr)=r(SCI,migr) and r(MAT,books)=r(SCI,books) pn <- res2a\$parnames; PN <- length(pn) Cdes <- matrix( 0, nrow=2, ncol=PN ) colnames(Cdes) <- pn Cdes[ 1, c("ASMMAT_migrant", "ASSSCI_migrant") ] <- c(1,-1) Cdes[ 2, c("ASMMAT_books", "ASSSCI_books") ] <- c(1,-1) rdes <- rep(0,2) # perform Wald test wres2a <- BIFIEsurvey::BIFIE.waldtest( res2a, Cdes, rdes ) summary(wres2a) #****************** #*** Model 3: Frequencies # Number of books splitted by gender res3a <- BIFIEsurvey::BIFIE.freq( bdat, vars=c("books"), group="female" ) summary(res3a) # test whether book(cat4,female0)+book(cat5,female0)=book(cat4,female1)+book(cat5,female5) pn <- res3a\$parnames PN <- length(pn) Cdes <- matrix( 0, nrow=1, ncol=PN ) colnames(Cdes) <- pn Cdes[ 1, c("books_4_female_0", "books_5_female_0", "books_4_female_1", "books_5_female_1" ) ] <- c(1,1,-1,-1) rdes <- c(0) # Wald test wres3a <- BIFIEsurvey::BIFIE.waldtest( res3a, Cdes, rdes ) summary(wres3a) #****************** #*** Model 4: Means # math and science score splitted by gender res4a <- BIFIEsurvey::BIFIE.univar( bdat, vars=c("ASMMAT","ASSSCI"), group="female") summary(res4a) # test whether there are significant gender differences in math and science #=> multivariate ANOVA pn <- res4a\$parnames PN <- length(pn) Cdes <- matrix( 0, nrow=2, ncol=PN ) colnames(Cdes) <- pn Cdes[ 1, c("ASMMAT_female_0", "ASMMAT_female_1" ) ] <- c(1,-1) Cdes[ 2, c("ASSSCI_female_0", "ASSSCI_female_1" ) ] <- c(1,-1) rdes <- rep(0,2) # Wald test wres4a <- BIFIEsurvey::BIFIE.waldtest( res4a, Cdes, rdes ) summary(wres4a) ## End(Not run) ```

BIFIEsurvey documentation built on Oct. 17, 2018, 9:06 a.m.