Builds an item-by-item Q-correlation matrix from rating data and runs a simple factor extraction (PCA by default). This legacy comparator is retained for compatibility and exploratory use; it is not part of the recommended sort, rating, or expert-panel workflows.
Usage
qfactor_content(
ratings,
item_col = "item",
rater_col = "rater",
construct_col = "construct",
rating_col = "rating",
k_factors = NULL,
method = c("pca", "pa")
)Arguments
- ratings
A data.frame with columns for item, rater, construct, rating.
- item_col
Name of the item column. Default "item".
- rater_col
Name of the rater column. Default "rater".
- construct_col
Name of the construct column. Default "construct".
- rating_col
Name of the rating column. Default "rating".
- k_factors
Optional integer: number of factors to extract. If
NULL, uses a simple Kaiser > 1 rule on PCA eigenvalues to suggest k.- method
"pca"(default) or"pa"(principal axis; uses SMCs as initial communalities).
Value
A list with components:
cor_Q: item-by-item correlation matrix,eigen: eigenvalues ofcor_Q,k: number of factors used,loadings: matrix of factor loadings,method: the extraction method.
Examples
set.seed(1)
df <- data.frame(
item = rep(paste0("I",1:6), each = 30),
rater = rep(1:10, times = 18),
construct = rep(rep(LETTERS[1:3], each = 10), times = 6),
rating = rnorm(180)
)
qf <- qfactor_content(df)
str(qf$loadings)
#> num [1:6, 1:3] 0.211 -0.756 -0.638 0.177 0.559 ...
#> - attr(*, "dimnames")=List of 2
#> ..$ : chr [1:6] "I1" "I2" "I3" "I4" ...
#> ..$ : chr [1:3] "PC1" "PC2" "PC3"