dmacs_summary_single: Summary of measurement nonequivalence effects for a single...
In dmacs: Measurement Nonequivalence Effect Size Calculator

Description Usage Arguments Details Value References Examples

dmacs_summary_single returns a summary of measurement non-equivalence effects given parameters for a focal and reference group.

1 2	dmacs_summary_single(LambdaR, ThreshR, LambdaF, ThreshF, MeanF, VarF, SD, categorical = FALSE, ...)

`LambdaR`	is the factor loading matrix (or dataframe) for the reference group.
`ThreshR`	is a vector of indicator intercepts (for continuous indicators) or a list, indexed by items, of vectors of thresholds (for categorical indicators) for the reference group. For categorical indicators, do not provide a matrix of thresholds.
`LambdaF`	is the factor loading matrix (or dataframe) for the focal group.
`ThreshF`	is a vector of indicator intercepts (for continuous indicators) or a list, indexed by items, of vectors of thresholds (for categorical indicators) for the focal group. For categorical indicators, do not provide a matrix of thresholds.
`MeanF`	is a vector of factor means for the focal group
`VarF`	is a vector of factor variances for the focal group.
`SD`	is a vector of indicator observed standard deviations used as the denominator of the dmacs effect size. This will usually either be pooled standard deviations or the standard deviation of the reference group.
`categorical`	is a Boolean variable declaring whether the variables in the model are ordered categorical. Models in which some variables are categorical and others are continuous are not supported. If no value is provided, categorical defaults to `FALSE`, although if multiple thresholds are provided for an item, categorical will be forced to `TRUE`. A graded response model with probit link (e.g., DWLS in lavaan or WLSMV in Mplus) is used for categorical variables. If you desire for other categorical models (e.g., IRT parameterization) to be supported, e-mail the maintainer.
`...`	other parameters to be used in functions that `dmacs_summary_single` calls, most likely `stepsize` for the `item_dmacs` and `delta_mean_item` functions.

dmacs_summary_single is called by dmacs_summary, which in turn is called by lavaan_dmacs and mplus_dmacs, which are the only functions in this package intended for casual users

A list of measurement nonequivalence effects from Nye and Drasgow (2011), including dmacs, expected bias in the mean score by item, expected bias in the mean total score, and expected bias in the variance of the total score. Expected bias in the variance of the total score is only supplied for unidimensional models in the current version of this package

Nye, C. & Drasgow, F. (2011). Effect size indices for analyses of measurement equivalence: Understanding the practical importance of differences between groups. Journal of Applied Psychology, 96(5), 966-980.

LambdaF <- matrix(c(1.00, 0.74,  1.14, 0.92), ncol = 1)
LambdaR <- matrix(c(1.00, 0.76,  1.31, 0.98), ncol = 1)
ThreshF <- c(0.00, 1.28, -0.82, 0.44)
ThreshR <- c(0.00, 0.65, -0.77, 0.47)
MeanF   <- 0.21
VarF    <- 1.76
SD      <- c(2.12, 1.85,  1.12, 3.61)
dmacs_summary_single(LambdaR, ThreshR, LambdaF, ThreshF, MeanF, VarF, SD)