quest (version 0.2.0)

ucfa: Unidimensional Confirmatory Factor Analysis

Description

ucfa conducts a unidimensional confirmatory factor analysis on a set of variables/items. Unidimensional meaning a one-factor model where all variables/items load on that factor. The function is a wrapper for cfa and returns an object of class "lavaan": lavaan. This then allows the user to extract statistical information from the object (e.g., lavInspect). For details on all the arguments see lavOptions.

Usage

ucfa(
  data,
  vrb.nm,
  std.ov = FALSE,
  std.lv = TRUE,
  ordered = FALSE,
  meanstructure = TRUE,
  estimator = "ML",
  se = "standard",
  test = "standard",
  missing = "fiml",
  ...
)

Value

object of class "lavaan" lavaan

providing the return object from a call to cfa.

Arguments

data

data.frame of data.

vrb.nm

character vector of colnames from data providing the variables/items

std.ov

logical vector of length 1 specifying if the variables/items should be standardized

std.lv

logical vector of length 1 specifying if the latent factor should be standardized resulting in all factor loadings being estimated. If FALSE, then the first variable/item in data[vrb.nm] is fixed to a factor loading of 1.

ordered

logical vector of length 1 specifying if the variables/items should be treated as ordered categorical items where polychoric correlations are used.

meanstructure

logical vector of length 1 specifying if the mean structure of the factor model should be estimated. This would be the variable/item intercepts (and latent factor mean if std.lv = FALSE). Note, this must be true to use Full Information Maximum Likelihood (FIML) to handle missing data via missing = "fiml".

estimator

character vector of length 1 specifying the estimator to use for parameter estimation. Popular options are 1) "ML" = maximum likelihood estimation based on the multivariate normal distribution, 2) "DWLS" = diagonally weighted least squares which uses the diagnonal of the weight matrix, 3) "WLS" for weighted least squares whiches uses the full weight matrix (often results in computational problems), 4) "ULS" for unweighted least squares that doesn't use a weight matrix. "DWLS", "WLS", and "ULS" can each be used with ordered categorical items when ordered = TRUE.

se

character vector of length 1 specifying how standard errors should be calculated. Popular options are 1) "standard" for conventional standard errors from inverting the information matrix, 2) "robust.sem" for robust standard errors, 3) "robust.huber.white" for sandwich standard errors.

test

character vector of length 1 specifying how the omnibus test statistic should be calculated. Popular options are 1) "standard" for the conventional chi-square statistic, 2) "Satorra-Bentler" for the Satorra-Bentler test statistic, 3) "Yaun.Bentler.Mplus" for the version of the Yuan-Bentler test statistic that Mplus uses, 4) "mean.var.adjusted" for a mean and variance adjusted test statistic, 5) "scaled.shifted" for the version of the mean and variance adjusted test statistic Mplus uses.

missing

character vector of length 1 specifying how to handle missing data. Popular options are 1) "fiml" = Full Information Maximum Likelihood (FIML), 2) "pairwise" = pairwise deletion, 3) "listwise" = listwise deletion.

...

any other named arguments available in the cfa function. See lavOptions for the list of arguments.

See Also

summary_ucfa cfa lavaan

Examples

dat <- psych::bfi[1:250, 16:20] # nueroticism items
ucfa(data = dat, vrb.nm = names(dat))
ucfa(data = dat, vrb.nm = names(dat), std.ov = TRUE)
ucfa(data = dat, vrb.nm = names(dat), meanstructure = FALSE, missing = "pairwise")
ucfa(data = dat, vrb.nm = names(dat), estimator = "ML", # MLR
   se = "robust.huber.white", test = "yuan.bentler.mplus", missing = "fiml")
ucfa(data = dat, vrb.nm = names(dat), estimator = "ML", # MLM
   se = "robust.sem", test = "satorra.bentler", missing = "listwise")
ucfa(data = dat, vrb.nm = names(dat), ordered = TRUE, estimator = "DWLS", # WLSMV
   se = "robust", test = "scaled.shifted", missing = "listwise")