qmethod (version 1.2.2)

qmethod: Q methodology analysis

Description

This function performs a full Q method analysis using principal components analysis (see Notes). The main results are factor characteristics, statement z-scores and rounded factor scores, and distinguising and consensus statements.

Usage

qmethod(dataset, nfactors, rotation="varimax", forced=T, 
        distribution=NA, cor.method="pearson")

Arguments

dataset

a matrix or a data frame containing original data, with statements as rows, Q-sorts as columns, and the column scores in the distribution in each cell.

nfactors

number of factors to extract.

rotation

rotation method, defaults to "varimax". Other possible rotations allowed in principal function can be used: "none", "varimax", "quartimax", "

forced

logical; Is the ranking of the items forced to match the distributions? Set to TRUE if all respondents ranked the items strictly following the distribution scores, and the values of the distribution are calculated automatically. Set to

distribution

logical; when forced = FALSE, the distribution has to be provided as a vector of numbers, such as c(-2, -1, -1, 0, 1, 1, 2, 2).

cor.method

character string indicating which correlation coefficient is to be computed, to be passed on to the function cor: "pearson" (default), "kendall", or "spearman".

Value

• Returns a list with eight objects:
• brief
  a list with the basic values of the analysis: date ("date"), number of statements ("nstat"), number of Q-sorts ("nqsort"), number of factors extracted ("nfactors"), type or rotation ("rotation"), and method for correlation in the PCA ("cor.method").
• dataset
  original data.
• loa
  factor loadings for Q-sorts.
• flagged
  logical dataframe of flagged Q-sorts.
• zsc
  statements z-scores.
• zsc_n
  statements rounded scores, rounded to the values in the first row of the original dataset.
• f_char
  factor characteristics:

  • "characteristics": data frame with the following values for each factor: average reliability coefficient, number of loading Q-sorts, eigenvalues, percentage of explained variance, composite reliability, standard error of factor scores.

  "cor_zsc": matrix of correlation coefficients between factors z-scores. "sd_dif": matrix of standard errors of differences.

item

qdc

Details

This function wraps together all the steps required for a complete analysis: extracting component loadings (principal); flagging Q-sorts (qflag); calculating weights, z-scores, and rounded scores (qzscores), calculating general characteristics (qfcharact), and finding distinguishing and consensus statements (qdc).

The default qmethod performs automatic flagging and uses varimax rotation. Varimax rotation can be replaced by other methods for rotation allowed in principal from psych package.

If the input data contains row names and variable names, these will be kept throughout the analysis. Input data is validated, and it will give an error if there are non numerical values or either if the number of statements and Q-sorts introduced does not match the input data.

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.

See Also

principal in package psych

Examples

data(lipset)
results <- qmethod(lipset[[1]], nfactors=3, rotation="varimax")
summary(results)
results #shows all results