dmacs_summary_single {dmacs}  R Documentation 
dmacs_summary_single
returns a summary of measurement nonequivalence
effects given parameters for a focal and reference group.
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 
... 
other parameters to be used in functions that

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), 966980.
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)