input_manual_simple: Input Manual Simple

In IPV: Item Pool Visualization

Input Manual Simple

Description

Generates manual data input for a simple model with one test.

Usage

input_manual_simple(
  test_name,
  facet_names,
  items_per_facet,
  item_names,
  test_loadings,
  facet_loadings,
  correlation_matrix
)

Arguments

test_name	character; the name of the test.
facet_names	character; the names of the facets in correct order.
items_per_facet	integer; number of items per facet in correct order (determined by facet_names), if all facets have the same number of items a single number can be used, e.g. 5 instead of c(5, 5, 5, 5).
item_names	character or integer; the names of the items in correct order (determined by facet_names).
test_loadings	integer; vector of the factor loadings from the single factor model of the test or a group factor model of multiple tests in correct order (determined by item_names).
facet_loadings	integer; vector of the factor loadings on the facet factors from the group factor model in correct order (determined by item_names).
correlation_matrix	matrix containing the latent correlations between facets, pay attention to the order of rows and columns, which is determined by facet_names.

Details

Pay attention to the order of facets and items, it has to be coherent throughout the whole data. facet_names and items_per_facet determine which facet is listed first and how many items there are listed for that facet. item_names, test_loadings and facet_loadings have to match that order. The correlation matrix uses the order in facet_names for rows and columns.

Visually inspect the returned object before continuing with input_manual_process!

Value

list containing "raw" data, that needs to be pre-processed using input_manual_process.

See Also

input_manual_nested input_manual_process

Examples

# these RSES data can also be seen in self_confidence, the example data of
# this package
mydata <- input_manual_simple(
test_name = "RSES",
facet_names = c("Ns", "Ps"),
items_per_facet = 5,
item_names = c(2, 5, 6, 8, 9,
              1, 3, 4, 7, 10),
test_loadings = c(.5806, .5907, .6179, .5899, .6559,
                    .6005, .4932, .4476, .5033, .6431),
facet_loadings = c(.6484, .6011, .6988, .6426, .6914,
                       .6422, .5835, .536, .5836, .6791),
correlation_matrix = matrix(data = c(1, .69,
                                    .69, 1),
                           nrow = 2,
                           ncol = 2))
mydata
input_manual_process(mydata)

IPV documentation built on Sept. 30, 2022, 5:08 p.m.