latentFactoR (version 0.0.7)

add_method_factors: Adds Methods Factors to simulate_factors Data

Description

Adds methods factors to simulated data from simulate_factors. See examples to get started

Usage

add_method_factors(
  lf_object,
  proportion_negative = 0.5,
  proportion_negative_range = NULL,
  methods_factors,
  methods_loadings,
  methods_loadings_range = 0,
  methods_correlations,
  methods_correlations_range = NULL
)

Value

Returns a list containing:

data

Biased data simulated data from the specified factor model

unbiased_data

The corresponding unbiased data prior to replacing values to generate the (biased) data

parameters

Bias-adjusted parameters of the lf_object input into function

original_results

Original lf_object input into function

Arguments

lf_object

Data object from simulate_factors. Data must be categorical. If data are not categorical, then there function with throw an error

proportion_negative

Numeric (length = 1 or factors). Proportion of variables that should have negative (or flipped) dominant loadings across all or each factor. Accepts number of variables as well. The first variables on each factor, up to the corresponding proportion, will be flipped. Set to 0 to not have any loadings flipped. Defaults to 0.50

proportion_negative_range

Numeric (length = 2). Range of proportion of variables that are randomly selected from a uniform distribution. Accepts number of number of variables as well. Defaults to NULL

methods_factors

Numeric

methods_loadings

Numeric

methods_loadings_range

Numeric

methods_correlations

Numeric

methods_correlations_range

Numeric

Author

Alexander P. Christensen <[e-mail]>, Luis Eduardo Garrido <[e-mail]>

References

Garcia-Pardina, A., Abad, F. J., Christensen, A. P., Golino, H., & Garrido, L. E. (2024). Dimensionality assessment in the presence of wording effects: A network psychometric and factorial approach. Behavior Research Methods.

Examples

# Generate factor data
two_factor <- simulate_factors(
  factors = 2, # factors = 2
  variables = 6, # variables per factor = 6
  loadings = 0.55, # loadings between = 0.45 to 0.65
  cross_loadings = 0.05, # cross-loadings N(0, 0.05)
  correlations = 0.30, # correlation between factors = 0.30
  sample_size = 1000, # number of cases = 1000
  variable_categories = 5 # 5-point Likert scale
)

# Add methods factors
two_factor_methods_effect <- add_method_factors(
  lf_object = two_factor,
  proportion_negative = 0.50,
  methods_loadings = 0.20,
  methods_loadings_range = 0.10
)