English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

UTILITY OF A GOODNESS-OF -FIT INDEX FOR THE GRADED RESPONSE MODEL WITH SMALL SAMPLE SIZES: A MONTE CARLO INVESTIGATION Christina R. Studts March 28, 2012 Item response theory (lR T) is expanding to diverse research settings, without accompanying access to easily implemented model fit methods. One simple model fit approach involves lldjratios. However, its utility is not known across several conditions salient to recent applied IR T research. A Monte Carlo simulation was implemented to investigate the effects of several factors (sample size, adjustment condition, type of misfit, and proportion ofmisfitling items) on x21djratios in the context of the Graded Response Model. Results suggested that: (a) adjusted x21djratios were appropriate for the largest sample size condition (N=1 0000), but were extremely inflated for small (N=400) and medium (N=1500) conditions; (b) lId/ratios were differentially affected across sample sizes by type and amount of misfit; and (c) sensitivity of the x2ldj> 3 cut point for identifying misfit in single items was notably low across all study conditions. Implications, limitations, and future directions are discussed.

Utility of a goodness-of-fit index for the graded response model with small sample sizes : a Monte Carlo investigation.