qmethod (version 1.0)

qzscores: Q methodology: z-scores from loadings

Description

Calculates factor characteristics, z-scores, and rounded scores, provided a matrix of loadings and a matrix of flagged Q-sorts.

Usage

qzscores(dataset, nfactors, nstat, nqsorts, rotation="unknown",
        loa=loa, flagged=flagged, forced=TRUE, distribution=NA)

Arguments

dataset

a matrix or a data frame containing raw 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.

nstat

number of statements in the study.

nqsorts

number of Q-sorts in the study.

rotation

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

loa

matrix or data frame of nqsorts rows and nfactors columns, with values of factor loadings for Q-sorts, calculated using, e.g., principal(...)$loadings.

flagged

matrix or data frame of nqsorts rows and nfactors columns, with TRUE values for Q-sorts that are flagged. Automatic flagging can be aplied using qflag function. Manual f

forced

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

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).

Value

• brief
  summary of the analysis.
• rawdata
  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 obtained from qfcharact.

Details

In order to implement manual flagging, use a manually created data frame for flagged. The loadings from principal(...)$loadings can be explored to decide upong flagging. The loa data frame should have Q-sorts as rows, and factors as columns, where TRUE are the flagged Q-sorts.

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

data(lipset)
library(psych)
loa <- as.data.frame(unclass(principal(lipset, 
                    nfactors=3, rotate="varimax")$loadings))
flagged <- qflag(nqsorts=9, nstat=33, loa=loa)
qmzsc <- qzscores(lipset, nstat=33, nfactors=3, nqsorts=9, 
                    flagged=flagged, loa=loa, 
                    rotation="varimax")
qmzsc