profiles: Profile analysis

profile_tablesR Documentation

Profile analysis

Description

Expected and observed domain scores, conditional on the test score, per person or test score. Domains are specified as categories of items using item_properties.

Usage

profile_tables(parms, domains, item_property, design = NULL)

profiles(
  dataSrc,
  parms,
  item_property,
  predicate = NULL,
  merge_within_persons = FALSE
)

Arguments

parms

An object returned by fit_enorm or a data.frame of item parameters

domains

data.frame with column item_id and a column with name equal to item_property

item_property

the name of the item property used to define the domains. If dataSrc is a dexter db then the item_property must match a known item property. If datasrc is a data.frame, item_property must be equal to one of its column names. For profile_tables item_property must match a column name in domains.

design

data.frame with columns item_id and optionally booklet_id

dataSrc

a connection to a dexter database or a data.frame with columns: person_id, item_id, item_score, an arbitrarily named column containing an item property and optionally booklet_id

predicate

An optional expression to subset data in dataSrc, if NULL all data is used

merge_within_persons

whether to merge different booklets administered to the same person.

Details

When using a unidimensional IRT Model like the extended nominal response model in dexter (see: fit_enorm), the model is as a rule to simple to catch all the relevant dimensions in a test. Nevertheless, a simple model is quite useful in practice. Profile analysis can complement the model in this case by indicating how a test-taker, conditional on her/his test score, performs on a number of pre-specified domains, e.g. in case of a mathematics test the domains could be numbers, algebra and geometry or in case of a digital test the domains could be animated versus non-animated items. This can be done by comparing the achieved score on a domain with the expected score, given the test score.

Value

profiles

a data.frame with columns person_id, booklet_id, booklet_score, <item_property>, domain_score, expected_domain_score

profile_tables

a data.frame with columns booklet_id, booklet_score, <item_property>, expected_domain_score

References

Verhelst, N. D. (2012). Profile analysis: a closer look at the PISA 2000 reading data. Scandinavian Journal of Educational Research, 56 (3), 315-332.


dexter documentation built on May 25, 2022, 9:10 a.m.