DIFplus (version 1.1)

ContigencyTables: Function to create contigency tables

Description

This function creates contigency tables by strata for each item. Both dichotomous and polytomous item responses are allowed. It also handles missing responses and returns a cleaned data set with no missing data.

Usage

ContigencyTables (Response.data, Response.code=c(0,1), 
       Group, group.names=NULL, Stratum=NULL, Cluster=NULL, 
       missing.code="NA", missing.impute="LW", print.information=TRUE)

Arguments

Response.data

A scored item responses matrix in the form of matrix or data frame. This matrix should not include any other variables (group, stratum, cluser, etc.).

Response.code

A numerical vector of all possible item responses. By default, Response.code=c(0,1).

Group

The variable of group membership (e.g., gender). Its length should be equal to the sample size of the item response matrix.

group.names

Names for each defined group (e.g., c('Male','Female')). This argument is optional. By default, group.names=NULL. If not provided, group names of "Group.1, Group.2, etc." will be automatically generated.

Stratum

The matching variable. By default, Stratum=NULL. If not provided, the observed total score will be used.

Cluster

The cluster variable. Its length should be equal to the sample size of the item response matrix. By default, Cluster=NULL. This variable will not be used to generate contigency tables. It will be included in the returned data set for DIF analysis.

missing.code

Indication of how missing values were defined in the data. By default, missing.code="NA".

missing.impute

The approach selected to handle missing item responses. By default, missing.impute="LW", indicating the list-wise deletion will be used. Other options include: "PM" (person mean or row mean imputation),"IM" (item mean or column mean imputation), "TW" (two-way imputation), "LR" (logistic regression imputation), and EM (EM imputation). Check the package "TestDataImputation" (https://cran.r-project.org/package=TestDataImputation) for more details. Note. If any missing data are detected on group, cluster, or stratum variables, listwise deletion will be used before handling missing item responses.

print.information

Indicator of whether function running information is printed on screen. By default, print.information=TRUE.

Value

A list of strata statistcs, contigency tables, etc.

Strata.stats

Summary statistics for each item: n.valid.strata, n.valid.category, and also sample sizes for each stratum across items.

c.table.list.all

A list that contains all contigency tables across items and strata.

c.table.list.valid

A list that contains only valid contigency tables across items and strata. Strata that have missing item response categories or zero marginal means are removed.

data.out

A cleaned data set with variables "Group", "Group.factor","Cluster", "Stratum", and all item responses (with missing data handled).

Details

This function creats contigency tables.

Examples

# NOT RUN {
#Specify the item responses matrix
data(data.adult)
Response.data<-data.adult[,2:13]
#Run the function with specifications      
c.table.out<-ContigencyTables(Response.data, Response.code=c(0,1), 
                              Group=data.adult$Group, group.names=NULL, 
                              Stratum=NULL, Cluster=NULL, missing.code="NA", 
                              missing.impute= "LW",print.information = TRUE)
#Obtain results
c.tables.all<-c.table.out$c.table.list.all
c.tables.valid<-c.table.out$c.table.list.valid
c.table.out$Strata.stats
data.use<-c.table.out$data.out
# }