q.analysis: Do a Q-Method analysis on the dataset

In openPCI/OpenPCIScoring: Automatic Scoring and Analysis of OpenPCI-items

Description

Do a Q-Method analysis on the dataset

Usage

q.analysis(
  q = NULL,
  q.df = get.q.df(q),
  only.complete = T,
  nfactors = 3,
  rotation = "varimax",
  cor.method = "kendall",
  nsteps = NULL,
  indet = "qindtest"
)

Arguments

q	A, open.pci.q object from get.q()
q.df	Alternatively to q, provide a data.frame from get.q.df()
only.complete	Boolean. Only include persons who have used all statements.
nfactors	The number of factors to extract.
rotation	The type of rotation to use
cor.method	Which method to use for the correlations in cor() ("pearson", "spearmann", "kendall"). Pearson is default in qmethod, but given that the data is ordinal, not interval scaled, Kendall's tau is default in q.analysis.
nsteps	The number of repetitions to use when bootstrapping (to get estimates of standard errors and bias). See qmboots().
indet	Indeterminacy method ("qindtest", "procrustes"). Default is qindtest, use procrustes for more than three factors. See qmboots().

Value

Returns an object of QmethodRes. See qmethod() for explanation.

Note

The analysis is done by the qmethod-package.

See Also

qmethod(), qmboots(), cor(), plot.QmethodRes().

Examples

qjson<-c("[[\"Running\"],[\"Walking\",\"Crawling\"],[\"Jumping\",\"Sprinting\",\"Jogging\"],[\"Strolling\",\"\",\"Standing\",\"Sitting\"]]",
         "[[\"Sitting\"],[\"Strolling\",\"Jogging\"],[\"Standing\",\"Sprinting\",\"Crawling\"],[\"Walking\",\"Running\",\"Jumping\"]]")
q<-get.q(qjson)
q.analysis(q)

openPCI/OpenPCIScoring documentation built on June 10, 2021, 1:18 p.m.