selectStatements: Select Significant Statements from Concept Map Data

In cmAnalysis: Process and Visualise Concept Mapping Data

Select Significant Statements from Concept Map Data

Description

This function selects statements from a concept map dataset based on their significance in terms of co-occurrence. It applies a chi-squared test on the co-occurrence matrix of the statements to identify those that are statistically significant (i.e., those that co-occur more frequently than would be expected by chance).

Usage

selectStatements(CMData, significanceThreshold = 0.05, verbose = TRUE, ...)

Arguments

CMData	A data frame containing concept map data. The data should have at least the following columns: statement: The text of the statement. sorterID: The identifier for the sorter. stackID: The identifier for the stack.
significanceThreshold	A numeric value representing the significance threshold for the chi-squared test. Statements with p-values less than this threshold are considered significant. Default is 0.05.
verbose	Logical, if TRUE, additional information about the processing steps is printed to the console.
...	Additional arguments to be passed to the chi-squared test (optional).

Value

A data frame with the same structure as the input, but with non-significant statements removed (if any). If all statements are significant, the original data frame is returned unchanged.

See Also

chisq.test for chi-squared test functionality.

Examples

# Simulate data with custom parameters:
set.seed(1)
myCMData <- simulateCardData(nSorters=40, pCorrect=.70, attributeWeights=c(1,1,1,1))

# Subject the data to sorter cluster analysis
myCMDataBySorters <- sorterMapping(myCMData)

# Select significant statements
mySelectedStatementsSorterCluster3 <- selectStatements(myCMDataBySorters[[1]])

cmAnalysis documentation built on April 4, 2025, 4:27 a.m.