Simulated five-category ordered item data

An ordered-categorical version of nomo_demo_continuous. The same latent item responses (before missing values were introduced) were cut into five ordered response categories, as is typical of Likert-type rating scales. This dataset supports polychoric factor-retention, ordinal EFA, WLSMV CFA, and ordinal reliability examples.

Usage

nomo_demo_ordinal

Format

A data frame with 500 rows and 10 ordered factors with levels "1" < "2" < "3" < "4" < "5":

a1, a2, a3, a4, a5

Ordered indicators written for factor A (a5 cross-loads on factor B).

b1, b2, b3, b4, b5

Ordered indicators written for factor B (b5 is weak).

Source

Simulated with a fixed seed by data-raw/nomo_demo.R in the package source repository.

Details

Latent responses were thresholded at -1.80, -0.80, 0.20, and 1.20, so the observed distributions lean toward the upper categories. The dataset has no missing values, which keeps the first ordinal examples focused on estimator and correlation choices rather than on missing-data handling for categorical models.

See also

nomo_demo_continuous, nomo_demo_network

Examples

str(nomo_demo_ordinal)
#> 'data.frame':	500 obs. of  10 variables:
#>  $ a1: Ord.factor w/ 5 levels "1"<"2"<"3"<"4"<..: 2 3 4 2 5 3 3 2 3 4 ...
#>  $ a2: Ord.factor w/ 5 levels "1"<"2"<"3"<"4"<..: 1 2 3 2 3 5 3 3 4 5 ...
#>  $ a3: Ord.factor w/ 5 levels "1"<"2"<"3"<"4"<..: 2 4 3 3 5 4 3 3 3 5 ...
#>  $ a4: Ord.factor w/ 5 levels "1"<"2"<"3"<"4"<..: 2 3 4 3 3 3 3 3 4 4 ...
#>  $ a5: Ord.factor w/ 5 levels "1"<"2"<"3"<"4"<..: 1 3 4 4 3 4 3 3 4 2 ...
#>  $ b1: Ord.factor w/ 5 levels "1"<"2"<"3"<"4"<..: 2 3 5 4 3 2 3 3 3 2 ...
#>  $ b2: Ord.factor w/ 5 levels "1"<"2"<"3"<"4"<..: 4 3 3 3 3 2 4 2 2 3 ...
#>  $ b3: Ord.factor w/ 5 levels "1"<"2"<"3"<"4"<..: 2 4 4 4 2 2 3 3 3 2 ...
#>  $ b4: Ord.factor w/ 5 levels "1"<"2"<"3"<"4"<..: 4 3 4 4 4 2 4 4 2 2 ...
#>  $ b5: Ord.factor w/ 5 levels "1"<"2"<"3"<"4"<..: 3 4 5 4 3 2 4 5 3 2 ...
table(nomo_demo_ordinal$a1)
#> 
#>   1   2   3   4   5 
#>  13 105 179 145  58