Skip to contents

Screen items for problems before factor analysis

Source: R/check_items.R
check_items.Rd

Real-world item sets often contain columns that break or silently degrade a factor analysis: an item everyone answered the same way (no variance), an item dominated by one response with a couple of stray answers, or an item with heavy missingness. On the polychoric basis these are especially costly – a near-empty response category can make psych::polychoric() fail outright (see the correct argument of ackwards()), and a near-constant item can produce a plausible-looking but meaningless factor with no warning.

check_items() reports these problems before you fit, one row per item, so you can collapse rare categories, drop degenerate items, or set cor/correct deliberately rather than debugging a cryptic error. It only reports; it never changes your data. ackwards() runs the same screen internally: it errors on a constant item and warns on a near-degenerate one, naming the offenders.

Usage

check_items(data, cor = c("polychoric", "pearson", "spearman"))

Arguments

data

A data frame or numeric matrix (items in columns).

cor

The correlation basis you plan to use: "polychoric" (default), "pearson", or "spearman". Only affects which problems are flagged – sparse response categories destabilise the polychoric basis but not the others.

Value

A data frame (class check_items) with one row per item and columns item, n_valid, pct_missing, n_distinct, min_count (smallest observed category count), top_prop (proportion of valid responses in the most common value), and flag – one of "ok", "constant", "near-constant", "sparse category", or "high missing". Print it for a grouped summary with guidance; treat it as a plain data frame for the full per-item table.

See also

ackwards() and its correct argument (the polychoric failure this screens for); factorability(), suggest_k(), and comparability(), the other pre-analysis diagnostics.

Examples

# sim16 is clean continuous data -> nothing flagged
check_items(sim16, cor = "pearson")
#> 
#> ── Item quality check (ackwards) ───────────────────────────────────────────────
#> Basis: pearson
#> Items: 16
#> Flagged: 0
#> ✔ No item problems detected.
#> ────────────────────────────────────────────────────────────────────────────────
#> Constant items must be dropped (no variance). A near-constant item (one
#> response dominates) can yield a meaningless factor; a sparse category can make
#> `cor = "polychoric"` fail -- collapse rare categories, try `correct = 0`, or
#> drop the item. Full per-item table: treat this object as a data frame.

# An item dominated by one response with a couple of stray answers is flagged
d <- sim16
d$bad <- c(rep(1L, nrow(d) - 2L), 2L, 3L)
check_items(d)
#> 
#> ── Item quality check (ackwards) ───────────────────────────────────────────────
#> Basis: polychoric
#> Items: 17
#> Flagged: 17
#> 
#> ── Flagged items ──
#> 
#> ! near-constant: "bad"
#> ! sparse category: "i1", "i2", "i3", "i4", "i5", "i6", "i7", "i8", "i9", "i10",
#> "i11", "i12", "i13", "i14", "i15", and "i16"
#> ────────────────────────────────────────────────────────────────────────────────
#> Constant items must be dropped (no variance). A near-constant item (one
#> response dominates) can yield a meaningless factor; a sparse category can make
#> `cor = "polychoric"` fail -- collapse rare categories, try `correct = 0`, or
#> drop the item. Full per-item table: treat this object as a data frame.