Claims

Latent profile analysis identifies three career adapt-abilities subgroups (low, moderate, high) among English majors

Claim · All claims
Evidence · 1 study · associational r2 · q3
Currency · newest study 2026 · not yet checked against its sources · not reviewed by a person · recheck by 2031: baselines and conditions change

Subclaims

Reading the evidence codes

q is the design tier, i the impact, n the sample, and the kind with its r the rigour judged within that kind: what each code means.

This claim: no standardized effect is recorded in observations/ for it yet, so no benchmark reading is possible.

q3 i? A three-profile solution was optimal: low (41.0%), moderate (51.2%), and high (7.8%) career adapt-abilities groups, with LMR-LRT non-significant beyond three profiles. → Wang X 2026

Evidence

Wang X 2026

Wang X (2026) The impact of career adapt-abilities on AI anxiety among English majors: a dual perspective analysis based on core self-evaluations at the person- and variable-centered. Front. Psychol. 17:1767791. https://doi.org/10.3389/fpsyg.2026.1767791

q3 · i? · associational · r2

Latent profile analysis in Mplus 8.3 with the 24 Career Adapt-Abilities Scale items as indicators; models with 2 to 5 profiles were fitted. The "3-profile model had proportions of 41.0, 51.2, and 7.8%" and the highest entropy (0.952).

"Regarding profile sizes, the 3-profile model had proportions of 41.0, 51.2, and 7.8%, all exceeding the 5% minimum threshold for meaningful interpretation."

Discussion

Page metadata
Field Value
type claim
id three-career-adapt-abilities-latent-profiles-english-majors
title Latent profile analysis identifies three career adapt-abilities subgroups (low, moderate, high) among English majors
description Latent profile analysis identifies three career adapt-abilities subgroups (low, moderate, high) among English majors
status review
evidence_strength moderate
newest_evidence 2026
recheck_by 2031
generated by process:wiki-ingest on 2026-10-11
trust unverified — no one has confirmed this page's content
sources 1 — wang-x-2026

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