Contents

• Introduction
• Installation
• Assistant usage
  • Step 1 : the data selection
  • Step 2 : the data type definition
    • Binary type
    • Multiple choice type
    • Graded type
  • Step 3 : the model or method choice
    • The one parameter logistic model
    • The two parameters logistic model
    • The three parameters logistic model
    • The nominal esponse model
    • The graded response model
    • The kernel estimator
    • The penalized maximum marginal likelihood estimator
  • Step 4 : the report options
    • The classical test theory statistics
    • The test of fit
    • The test of local independance
    • The item parameters
    • The ability estimates
    • The item and option characteristic curves
    • The information functions
    • The standard errors
    • All the data points from the plots
  • The report
    • The classical test theory statistics
    • The estimation summary
    • The test of fit
    • The test of local independance
    • The item parameter
    • The ability estimates
    • The item and option characteristic curves
    • The information functions
    • All the data points from the plots
    • The pages header and footer
  
  
• Settings
  • The language
  • The priors
    • The slope prior
    • The threshold prior
    • The asymptote prior
  • The algorithm
    • The number of EM iteration
    • The number of Newton iteration
    • The precision
    • The kernel smoothing factor
    • The penalization smoothing factor
    • The data aggregation
  • The quadratures
    • The number of quadrature
    • The first middle point
    • The last middle point
  
  
• Copyright
• Index