title: Sample Size Requirements for Estimation of Item Parameters in the Multidimensional Graded Response Model.
abstract: Likert types of rating scales in which a respondent chooses a response from an ordered set of response options are used to measure a wide variety of psychological, educational, and medical outcome variables. The most appropriate item response theory model for analyzing and scoring these instruments when they provide scores on multiple scales is the multidimensional graded response model (MGRM) A simulation study was conducted to investigate the variables that might affect item parameter recovery for the MGRM. Data were generated based on different sample sizes, test lengths, and scale intercorrelations. Parameter estimates were obtained through the flexMIRT software. The quality of parameter recovery was assessed by the correlation between true and estimated parameters as well as bias and root-mean-square-error. Results indicated that for the vast majority of cases studied a sample size of N = 500 provided accurate parameter estimates, except for tests with 240 items when 1000 examinees were necessary to obtain accurate parameter estimates. Increasing sample size beyond N = 1000 did not increase the accuracy of MGRM parameter estimates.
authors.affiliation: Psychology, University of Minnesota Twin Cities Minneapolis, MN, USA.
authors.forename: Shengyu
authors.initials: S
authors.lastname: Jiang
authors.affiliation: Psychology, University of Minnesota Twin Cities Minneapolis, MN, USA.
authors.forename: Chun
authors.initials: C
authors.lastname: Wang
authors.affiliation: Psychology, University of Minnesota Twin Cities Minneapolis, MN, USA.
authors.forename: David J
authors.initials: DJ
authors.lastname: Weiss
keywords: graded response model; item parameters; multidimensionality; parameter recovery; sample size