Create table with Rasch dichotomous model item fit values for each item.

Defaults to using conditional estimates for MSQ values (Müller, 2020) estimated using the iarm package. Use method = "unconditional" for the "old" unconditional MSQ values (using eRm).

Usage

RIitemfitRM(
  dfin,
  samplesize,
  nsamples,
  zstd_min = -1.96,
  zstd_max = 1.96,
  msq_min = 0.7,
  msq_max = 1.3,
  fontsize = 15,
  fontfamily = "Lato",
  output = "table",
  tbl_width = 65,
  method = "conditional"
)

Arguments

dfin

Dataframe with item data only

samplesize

Desired sample size in multisampling (recommended range 250-500)

nsamples

Desired number of samples (recommended range 10-50)

zstd_min

Lower cutoff level for ZSTD

zstd_max

Upper cutoff level for ZSTD

msq_min

Lower cutoff level for MSQ

msq_max

Upper cutoff level for MSQ

fontsize

Set font size for table

fontfamily

Set font family for table

output

Defaults to output a table. Optional "dataframe" or "quarto"

tbl_width

Set table width in percent

method

Defaults to "conditional". Optional "unconditional"

Details

ZSTD is inflated with large samples (N > 500). Optional function to reduce sample size and run analysis using multiple random samples to get average ZSTD If you are using Quarto/Rmarkdown, "cache: yes" will be a useful chunk option to speed things up. 50 samples seems to give stable output, but 4-8 is probably sufficient for a quick look at the approximate ZSTD statistics. It is recommended to use sample size 200-500, based on Hagell & Westergren, 2016.