Package 'rccme'

ESTRESS data

Description

ESTRESS data

Usage

estress

Format

estress

A data frame with 7 variables, 262 cases:

tenure

Company Tenure

estress

Economic stress

affect

Depressed affect

withdraw

Withdrawal intentions

sex

Male (1) or Female (0)

age

age

ese

Entrepreneurial self efficacy

Source

Pollack, J., VanEpps, E. M., & Hayes, A. F. (2012). The moderating role of social ties on entrepreneurs' depressed affect and withdrawal intentions in response to economic stress. Journal of Organizational Behavior, 33, 789-810.

Hayes, A. (2017). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach (2nd edn, p. 692). Guilford Press.

Moon K (2023). processR: Implementation of the 'PROCESS' Macro. R package version 0.2.8, commit c6c817b5b716f2615b9814cd5022aef1cbc5c09d, https://github.com/cardiomoon/processR.

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Protest data

Description

Protest data

Usage

protest

Format

protest

A data frame with 6 variables, 129 cases:

subnum

subject number

protest

experimental condition, 0 = no protest, 1 = individual protest, 2 = group protest

sexism

perceived pervasiveness of sex discrimination. Means of an 8 item Modern Sexism Scale

angry

anger toward the attorney. "I feel angry towards Catherine"

liking

liking of the attorney. Mean rating of 6 liking ratings of the target

respappr

appropriateness of response. Mean of four items of appropriateness of the target's response

Source

Garcia, D. M., Schmitt, M. T., Branscombe, N. R., & Ellemers, N. (2010). Women’s reactions to ingroup members who protest discriminatory treatment: The importance of beliefs about inequality and response appropriateness. European Journal of Social Psychology, 40(5), 733–745. https://doi.org/10.1002/ejsp.644

Hayes, A. (2017). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach (2nd edn, p. 692). Guilford Press.

Moon K (2023). processR: Implementation of the 'PROCESS' Macro. R package version 0.2.8, commit c6c817b5b716f2615b9814cd5022aef1cbc5c09d, https://github.com/cardiomoon/processR.

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Calibrate scores under measurement error

Description

Calibrate scores under measurement error

Usage

rccme_calib_me(
  w_mat,
  rel_vec = NULL,
  w_se_vec = NULL,
  w_se_mat = NULL,
  z_mat = matrix(0, nrow = nrow(w_mat), ncol = 0),
  rescale = TRUE,
  standard = FALSE
)

Arguments

w_mat	(matrix) a matrix of trait estimates for n respondents on p latent variables
rel_vec	(vector) a vector of marginal reliability for p latent variables
w_se_vec	(vector) a vector of standard errors of measurement for p latent variables
w_se_mat	(matrix) a matrix of trait estimates standard-errors for n respondents on p latent variables
z_mat	(matrix) a matrix of error-free covariates for n respondents and q covariates
rescale	(logical) Should the trait estimates and their standard errors be re-scaled? Default is TRUE. The variables are rescaled under the assumption that the trait estimates were not conditioned on any background variables and that the marginal variance of the latent trait is 1. This is true for standardised latent variables in CFA or the default prior variance assumption of 1 in IRT score estimates. This rescaling ensures the trait coefficients are correct for the standardised traits.
standard	(logical) Only relevant when passing reliability. If TRUE, attempt to return the standardised version of the calibrated scores If FALSE (default), do not attempt standardisation.

Value

Calibrated trait estimates.

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