input_manual_nested: Input Manual Nested
In NilsPetras/IPV: Item Pool Visualization

input_manual_nested

R Documentation

Input Manual Nested

Description

Generates manual data input for a nested model with several tests.

Usage

input_manual_nested(
  construct_name,
  test_names,
  items_per_test,
  item_names,
  construct_loadings,
  test_loadings,
  correlation_matrix
)

Arguments

`construct_name`	character; the name of the overall construct.
`test_names`	character; the names of the tests in correct order.
`items_per_test`	integer; number of items per test in correct order (determined by test_names), if all tests have the same number of items a single number can be used, e.g. 10 instead of c(10, 10, 10).
`item_names`	character or integer; the names of the items in correct order (determined by test_names).
`construct_loadings`	integer; vector of the factor loadings from the single factor model of the construct in correct order (determined by item_names).
`test_loadings`	integer; vector of the factor loadings on the test factors from the group factor model in correct order (determined by item_names).
`correlation_matrix`	matrix containing the latent correlations between tests, pay attention to the order of rows and columns, which is determined by test_names.

Details

Pay attention to the order of tests and items, it has to be coherent throughout the whole data. test_names and items_per_test determine which test is listed first and how many items are listed for that test. item_names, construct_loadings and test_loadings have to match that order. The correlation matrix uses the order in test_names for rows and columns.

This function only lists the name of the tests in output$tests. For each of those tests, the data on the facets needs to be added using input_manual_simple. Every test for which you do not provide this data will be treated as having no facets.

Visually inspect the returned object before continuing with input_manual_process!

Value

list containing "raw" data. The data on the facets of the tests needs to be added using input_manual_simple. Afterwards, the whole data needs to be pre-processed using input_manual_process.

Examples

# these data can also be seen in self_confidence, the example data of
# this package
mydata <- input_manual_nested(
construct_name = "Self-Confidence",
test_names = c("DSSEI", "SMTQ", "RSES"),
items_per_test = c(20, 14, 10),
item_names = c(
 1,  5,  9, 13, 17, # DSSEI
 3,  7, 11, 15, 19, # DSSEI
 16,  4, 12,  8, 20, # DSSEI
 2,  6, 10, 14, 18, # DSSEI
 11, 13, 14,  1,  5,  6, # SMTQ
 3, 10, 12,  8, # SMTQ
 7,  2,  4,  9, # SMTQ
 1,  3,  4,  7, 10, # RSES
 2,  5,  6,  8,  9), # RSES
construct_loadings = c(
 .5189, .6055, .618,  .4074, .4442,
 .5203, .2479, .529,  .554,  .5144,
 .3958, .5671, .5559, .4591, .4927,
 .3713, .5941, .4903, .5998, .6616,
 .4182, .2504, .4094, .3977, .5177, .4603,
 .3271,  .261, .3614, .4226,
 .2076, .3375, .5509, .3495,
 .5482, .4627, .4185, .4185, .5319,
 .4548, .4773, .4604, .4657, .4986),
test_loadings = c(
 .5694, .6794, .6615, .4142, .4584, # DSSEI
 .5554, .2165, .5675, .5649, .4752, # DSSEI
 .443 , .6517, .6421, .545 , .5266, # DSSEI
 .302 , .6067, .5178, .5878, .6572, # DSSEI
 .4486, .3282, .4738, .4567, .5986, .5416, # SMTQ
 .3602, .2955, .3648, .4814, # SMTQ
 .2593, .4053, .61  , .4121, # SMTQ
 .6005, .4932, .4476, .5033, .6431, # RSES
 .5806, .5907, .6179, .5899, .6559), # RSES
correlation_matrix = matrix(data = c(  1, .73, .62,
                                      .73,   1, .75,
                                      .62, .75,   1),
                           nrow = 3,
                           ncol = 3))
mydata

NilsPetras/IPV documentation built on July 19, 2023, 9:12 p.m.