Prepare Data for IRT Analyses

Documentation for package ‘eatPrep’ version 0.2.6

aggregateData	Aggregate Datasets With Several Kinds of Missing Values
asNumericIfPossible	Convert Columns of a Data Frame Into Numeric Values if Possible
automateDataPreparation	Automate Data Preparation using Functions from Package eatPrep
catPbc	Calculate Item Discrimination for Each Category of Categorical Variables
checkData	Check Datasets for Missing Values and Invalid Codes
checkDesign	Check Datasets for Deviations From Test Design
collapseMissings	Recode Character Missings of Different Types to 0 or 'NA'
inputDat	List of Three Datasets from Educational Assessment
inputList	Data Frames with Code, Subunit and Unit Information for Datasets in 'inputDat'
mergeData	Merge Data Frames, Check For Inconsistencies, and Replace NA Values
mnrCoding	Recode Missing by Intention to Missing not Reached
readDaemonXlsx	Read xlsx-Files Produced by ZKDaemon
readSpss	Read SPSS Data Files and Truncate Space in String Variables and Change Column Width
recodeData	Recode Datasets with Several Kinds of Missing Values
scoreData	Score Datasets with Several Kinds of Missing Values
set.col.type	Set the Class of Columns in a Data Frame
writeSpss	Export Datasets to SPSS