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.
profile_tables(parms, domains, item_property, design = NULL) profiles( dataSrc, parms, item_property, predicate = NULL, merge_within_persons = FALSE )
An object returned by
fit_enorm or a data.frame of item parameters
data.frame with column item_id and a column with name equal to
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
data.frame with columns item_id and optionally booklet_id
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
An optional expression to subset data in dataSrc, if NULL all data is used
whether to merge different booklets administered to the same person.
a data.frame with columns person_id, booklet_id, booklet_score, <item_property>, domain_score, expected_domain_score
a data.frame with columns booklet_id, booklet_score, <item_property>, expected_domain_score
When using a unidimensional IRT Model like the extended nominal response model in
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.
Verhelst, N. D. (2012). Profile analysis: a closer look at the PISA 2000 reading data. Scandinavian Journal of Educational Research, 56 (3), 315-332.