This vignette demonstrates how to use conquestr to document data, report on the quality of data, clean data, and construct item bundles or derived variables based on several variables.
conquestr has a built in system file that we will use for this example.
getCqItanal will return a list of lists,
each list relating to one generalised item from an ‘ACER ConQuest’
itanal output. The list for each item contains the
following information: (1) the item name according to the item label,
(2) a table of item category statistics for the item, and (3) the
item-total and item-rest correlations for the item.
Note that you must use
matrixout in your ‘ACER ConQuest’
itanal to ensure that these objects are available
in the system file from your analysis.
# get default sys file <- ConQuestSys() myEx1Sys #> no system file provided, loading the example system file instead # get itanal lists <- getCqItanal(myEx1Sys) myEx1Sys_itanal # show unformatted list objects for first item print(myEx1Sys_itanal[]) #> $name #>  "item:1 (item one)" #> #> $table #> Category Score Count Percent Pt Bis Ability mean (D1) Ability SD (D1) #> 1 M 0 6 0.6006006 -0.10716121 -0.9039020 0.9999724 #> 2 a 1 644 64.4644645 0.45520912 0.3807850 0.8253835 #> 3 b 0 23 2.3023023 -0.08463114 -0.4970245 0.8507422 #> 4 c 0 47 4.7047047 -0.19873699 -0.8487301 0.8615217 #> 5 d 0 104 10.4104104 -0.23879800 -0.6663719 0.8353948 #> 6 e 0 175 17.5175175 -0.21543829 -0.5583111 0.8250798 #> #> $item_rest_total #> item-total item-rest #> 0.6059588 0.4552091
Following the item-specific list objects, the last element of the
list returned by
getCqItanal contains summary statistics
for the full set of items. The summary statistics include raw and latent
score distribution statistics and Cronbach’s coefficient \(\alpha\).
So far, we have shown how to access the test and item analysis statistics that are available through the itanal command in ‘ACER ConQuest’ and we have shown these without any formatting. One of the many benefits of integrating ‘ACER ConQuest’ output into a markdown document is to permit automated conditional formatting of item analysis output. In this section we show how this conditional formatting can be set up.
Pre-specifying criteria for conditionally formatting item analysis output is a key step in an automated workflow. Any number of metrics from the item analysis can be specified for conditional formatting. Several of these can be passed to conquestr functions as will be illustrated in the following sections.
# set statistical criteria for conditional formatting <- 85 # highlight if facility is GREATER than this value easyFlag <- 15 # highlight if facility is LESS than this value hardFlag <- 0.2 # highlight if item-rest r is LESS than this value irestFlag <- 1.2 # highlight if weighted MNSQ is GREATER than this value underfitFlag <- 0.8 # highlight if weighted MNSQ is LESS than this value overfitFlag <- 0.0 # highlight if non-key ptBis r is MORE than this valueptBisFlag
fmtCqItanal will return a formated version
of the itanal object that we read in earlier. Presently this function
will apply coloured text to any distractor point biserial correlation
that is larger than 0. The following example shows the output for the
fourth item in the current item analysis.
# return a conditionally formatted item category statistics table for the fourth item <- fmtCqItanal(myEx1Sys_itanal, ptBisFlag = ptBisFlag, textColHighlight = "red") myEx1Sys_itanal_f # print table 4]]$tablemyEx1Sys_itanal_f[[
|Category||Score||Count||Percent||Pt Bis||Ability mean (D1)||Ability SD (D1)|
# print summary length(myEx1Sys_itanal_f)]] # the last object is always the summarymyEx1Sys_itanal_f[[
|Standard error of mean||0.08|
|Standard error of measurement||1.43|
This short vignette has illustrated how to access and display itanal output from an ‘ACER ConQuest’ analysis using conquestr. Future vignettes will demonstrate basic and advanced plotting and the production of publication quality item analysis technical reports.