qmethod (version 1.5.5)

qdc: Q methodology: distinguishing and consensus statements

Description

Indicates the distinguishing and consensus statements. It does so by comparing the z-scores between each pair factors.

Usage

qdc(dataset, nfactors, zsc, sed)

Arguments

dataset

a matrix or a dataframe containing original data, with statements as rows, Q sorts as columns, and grid column values in each cell.

nfactors

number of factors extracted.

zsc

a matrix with the factor z-scores for statements resulting from qzscores.

sed

a matrix with the standard error of differences resulting from qfcharact.

Details

Finds the distinguishing and consensus statements, based on the absolute differences between factor z-scores being larger than the standard error of differences (SED, calculated in qfcharact) for a given pair of factors.

Returns a single data frame with the differences in z-scores between each pair of factors and the variable dist.and.cons, indicating whether each statement is distinguishing or consensus and for which factor(s) it is distinguishing. These are the possible categories in the dist.and.cons variable:

  • Where all the comparisons between each pair of factors are significantly different at p-value < .05 the statement is labelled as "Distinguishes all".

  • Where the comparisons of a given factor with all other factors are significant at p-value < .05, and comparisons between all other factors are not significant, the statement is labeled as "Distinguishes f*".

  • Where none of the comparisons are significantly different, the statement is labeled as "Consensus".

  • Statements that have category "" (empty) are not distinguishing for any of the factors in particularly. They distinguish one or more pairs of factors and the star indications may be inspected to understand their role.

Significant differences at p-values:

  • p >= 0.05 <- "" (i.e. nothing)

  • p < 0.05 <- "*"

  • p < 0.01 <- "**"

  • p < 0.001 <- "***"

  • p < 0.000001 <- "6*"

References

Brown, S. R., 1980 Political subjectivity: Applications of Q methodology in political science, New Haven, CT: Yale University Press.

See further references on the methodology in qmethod-package.

Examples

Run this code
# NOT RUN {
data(lipset)
results <- qmethod(lipset[[1]], nfactors = 3, rotation = "varimax")
sed <- as.data.frame(results[[7]][[3]])
zsc <- results[[5]]
qdc(lipset[[1]], nfactors = 3, zsc = zsc, sed = sed)
# }

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