sfa_jinglejangle: Detect Jingle and Jangle Fallacies Across Scales

In semanticfa: Semantic Factor Analysis of Language Model Embeddings

Detect Jingle and Jangle Fallacies Across Scales

Description

Compares whole scales by the meaning of their items versus the meaning of their names to surface two classic measurement problems (Wulff & Mata, 2025, 2026): jingle (scales with similar names but dissimilar content) and jangle (scales with dissimilar names but similar content).

Usage

sfa_jinglejangle(
  scales,
  labels = NULL,
  embed = "sbert",
  model = NULL,
  flag = 0.2,
  item_embeddings = NULL,
  label_embeddings = NULL
)

Arguments

scales	A named list; each element is a character vector of the scale's item texts. The names are used as scale labels unless labels is given.
labels	Optional character vector of scale names (construct labels), one per scale, overriding the list names.
embed, model	Embedding backend and model (default the package default sbert model).
flag	Absolute content-minus-label similarity difference at which to flag a pair (default 0.20). The single-difference rule and its 0.20 default are this package's convenience heuristic, not Wulff & Mata's criterion (they flag pairs with quantile-derived dual cutoffs on the two similarities separately); tune it to your scale set.
item_embeddings, label_embeddings	Optional precomputed embeddings: a named list of per-scale item-embedding matrices, and a matrix of label embeddings (one row per scale). Use when no embedding backend is available.

Details

Each scale is represented by a content vector (the mean of its item embeddings) and a label vector (the embedding of its name). For every pair of scales the function compares content similarity with label similarity; large divergences flag the two fallacies.

Value

An object of class "sfa_jinglejangle": a list with the content_sim and label_sim scale-by-scale matrices and a flags data frame (scale_a, scale_b, content_sim, label_sim, divergence, type).

References

Wulff, D. U., & Mata, R. (2025). Semantic embeddings reveal and address taxonomic incommensurability in psychological measurement. Nature Human Behaviour, 9(5), 944–954. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1038/s41562-024-02089-y")}

Wulff, D. U., & Mata, R. (2026). Escaping the jingle-jangle jungle: Increasing conceptual clarity in psychology using large language models. Current Directions in Psychological Science, 35(2), 59–65. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1177/09637214251382083")}

See Also

sfa_anchor

Examples

data(big5)
scales <- list(
  Extraversion = big5$items[big5$factors == "Extraversion"],
  Sociability  = big5$items[big5$factors == "Extraversion"],  # same content, new name
  Neuroticism  = big5$items[big5$factors == "Neuroticism"])

# precomputed embeddings so the example needs no backend
ie <- lapply(scales, function(items)
  big5$embeddings[match(items, big5$items), , drop = FALSE])
le <- big5$embeddings[match(c("E1", "C31", "N11"), big5$codes), , drop = FALSE]
sfa_jinglejangle(scales, item_embeddings = ie, label_embeddings = le)

## Not run: 
# with a live backend, pass the scales and their names are embedded directly:
sfa_jinglejangle(scales)

## End(Not run)

semanticfa documentation built on Sept. 2, 2026, 1:07 a.m.