Bayesrel (version 0.7.8)

omegasCFA: Estimate reliability estimates for multidimensional scales in the frequentist framework

Description

When supplying a data set that is multidimensional the function estimates the reliability of the set by means of omega_total and the general factor saturation of the set by means of omega_hierarchical The procedure entails fitting a hierarchical factor model using a CFA. The second-order (hierarchical, higher-order), the bi-factor, and the correlated factor model can be used in the CFA. The CFA is performed using lavaan 'Yves Rosseel', <https://CRAN.R-project.org/package=lavaan>. Coefficients omega_t and omega_h (only for second-order and bi-factor model) can be computed from the factor model parameters.

Usage

omegasCFA(
  data,
  n.factors = NULL,
  model = NULL,
  model.type = "second-order",
  interval = 0.95,
  missing = "fiml",
  fit.measures = FALSE
)

Value

The point estimates and the Wald-type confidence intervals for omega_t and omega_h (for the second-order and bi-factor model)

Arguments

data

A matrix or data.frame containing multivariate observations, rows = observations, columns = variables/items

n.factors

A number specifying the number of group factors that the items load on

model

A string that by default NULL (=balanced) distributes the items evenly among the number of group factors. This only works if the items are a multiple of the number of group factors and the items are already grouped in the data set, meaning, e.g., items 1-5 load on one factor, 6-10 on another, and so on. A model file can be specified in lavaan syntax style (f1=~.+.+.) to relate the items to the group factors. The items' names need to equal the column names in the data set, aka the variable names

model.type

A string denoting if the model that should be fit is the second-order or bi-factor model or the correlated factor model. This comes down to the researcher's theory about the measurement and the model fit.

interval

A number specifying the confidence interval, which is Wald-type

missing

A string denoting the missing data handling, can be "fiml" (full information ML) or "listwise". Specifying "pairwise" will defulat to "fiml"

fit.measures

A logical denoting if fit.measures from the CFA should be computed, the output then contains the chisq statistic, chisq df, chisq p-value, cfi, tli, rmsea, rmsea 90% ci lower, rmsea 90% ci upper, rmsea<.05 p-value, aic, bic, unbiased srmr, unbiased srmr 90% ci lower, unbiased srmr 90% ci upper, unbiased srmr<.05 p-value

Examples

res <- omegasCFA(upps, n.factors = 5, model = NULL, model.type = "bi-factor",
missing = "listwise")

# or with specified model syntax relating the group factors to the items:
model <- "f1 =~ U17_r + U22_r + U29_r + U34_r
f2 =~ U4 + U14 + U19 + U27
f3 =~ U6 + U16 + U28 + U48
f4 =~ U23_r + U31_r + U36_r + U46_r
f5 =~ U10_r + U20_r + U35_r + U52_r"
res <- omegasCFA(upps, n.factors = 5, model = model, model.type = "second-order",
missing = "listwise")