profiles function

Profile analysis

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
)

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.

Returns

• 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

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.

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.