Functions to Facilitate the Simulation of Large Scale Assessment Data
Generate an ANOVA table for LSASIM clusters
Attribute Labels in Hierarchical Structure
Generate regression coefficients
Assignment of test items to blocks
Assignment of item blocks to test booklets
Assignment of test booklets to test takers
Generate replicates of a dataset using Balanced Repeated Replication
Calculate ñ
Calculate replicate weights and summary statistics
Calculate Standard Error of Intraclass Correlation
Calculate variance between classes
Calculate the total variance
Calculate variance within classes
Check if an error condition is satisfied
Checks if provided parameters are ignored
Check class of n or N
Check if List is Valid
Generate cluster samples with individual questionnaires
Generate cluster samples with lowest-level questionnaires
Generate cluster sample
Print messages about clusters
Convert Vector to Expanded List
Generation of random correlation matrix
Generation of covariance matrices
Generate latent regression covariance matrix
Setup full YXW covariance matrix
Generate analytical covariance matrix
Customize Summary
Prints welcome message on package load
Draw Cluster Structure
Generates cat_prop for questionnaire_gen
Randomly generate the quantity of background variables
Generate n_X and n_W for clusters
Intraclass correlation
Simulate item responses from an item response model
Generation of item parameters from uniform distributions
Generate replicates of a dataset using Jackknife
Label respondents
Randomly generate a matrix of factor loadings
lsasim: A package for simulating large scale assessment data
Pluralize words
Print the ANOVA table
Generation of random cumulative proportions
Analytical point-biserial conversion
Generation of ordinal and continuous variables
Generation of ordinal and continuous variables
Generation of ordinal and continuous variables
Defines vector as range
Recalculate final weights
Sampling variance of the mean for replications
Generation of item response data using a rotated block design
Generate data from a Zero-truncated Poisson
Sample from population structure
Sample from range
Transform regular vector into selection vector
Split variables in cat_prop
Dataset summary statistics
Summarizes clusters
Trim sample
Wrapper-functions for check_condition
Weight responses
Whitelist message
Provides functions to simulate data from large-scale educational assessments, including background questionnaire data and cognitive item responses that adhere to a multiple-matrix sampled design. The theoretical foundation can be found on Matta, T.H., Rutkowski, L., Rutkowski, D. et al. (2018) <doi:10.1186/s40536-018-0068-8>.