Bootstraping of Q methodology using PCA.
qbstep(subdata, subtarget, indet,
nfactors, nqsorts, nstat,
qmts = qmts, qmts_log = qmts_log,
rotation = "unknown",
flagged = flagged, cor.method="pearson", ...)resampled dataset of Q-sorts.
target matrix, adapted to match the rows of the resampled dataset.
method to solve the double indeterminacy issue when bootstrapping Principal Components Analysis (PCA). "procrustes" for procrustes rotation from GPArotation, "qindtest" for simple solution valid for at least up to 3 factors extracted (see references), "both" for a "qindtest" and a "procrustes" rotation, or "none" for no solution. The latter is not recommended because it introduces inflated variability. If "none" is selected, each replication is rotated using the method set in rotation.
number of factors in the study.
number of Q-sorts in the study.
number of statements in the study.
data frame with two rows and at least one column. This is automatically created when this function is called from qmboots(see Note below).
data frame with two rows and at least one column. This is automatically created when this function is called from qmboots(see Note below).
rotation method, defaults to "none".
matrix or data frame of nqsorts rows and nfactors columns, with TRUE values for the Q-sorts that are flagged. Automatic flagging can be aplied using qflag. Manual flagging can be done by providing a logical matrix with nqsorts rows and nfactors columns to the argument flagged.
character string indicating which correlation coefficient is to be computed, to be passed on to the function cor: "pearson" (default), "kendall", or "spearman".
summary of the analysis.
This function performs a single step within a bootstrap of Q methodology data. It takes one resample, performs the Q method analysis, checks for indeterminacy issues, and corrects them if necessary by calling the function qindtest or qpcrustes.
Zabala, Pascual (2016) Bootstrapping Q Methodology to Improve the Understanding of Human Perspectives. PLoS ONE 11(2): e0148087.