e-journal

Combining Decision Trees and Stochastic Curtailment for Assessment Length Reduction of Test Batteries Used for Classification

---

For classification problems in psychology (e.g., clinical diagnosis), batteries of tests are often administered. However, not every test or item may be necessary for accurate classification. In the current article, a combination of classification and regression trees (CART) and stochastic curtailment (SC) is introduced to reduce assessment length of questionnaire batteries. First, the CARTalgorithm provides relevant subscales and cutoffs needed for accurate classification, in the form of a decision tree. Second, for every subscale and cutoff appearing in the decision tree, SC reduces the number of items needed for accurate classification. This procedure is illustrated by post hoc simulation on a data set of 3,579 patients, to whom the Mood and Anxiety Symptoms Questionnaire (MASQ) was administered. Subscales of the MASQ are used for predicting diagnoses of depression. Results show that CART-SC provided an assessment length reduction of 56%, without loss of accuracy, compared with the more traditional prediction method of performing
linear discriminant analysis on subscale scores. CART-SC appears to be an efficient and accurate algorithm for shortening test batteries.

Keywords
classification, test batteries, computerized testing, sequential testing, classification and regression
trees, stochastic curtailment, respondent burden, efficiency

---

Ketersediaan

Tidak ada salinan data

Informasi Detail

Judul Seri

Applied Psychological Measurement

No. Panggil

-

Penerbit

: The Author., 2013

Deskripsi Fisik

Applied Psychological Measurement 38( 1) 3 - 17

Bahasa

English

ISBN/ISSN

-

Klasifikasi

-

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

EKONOMI

Info Detail Spesifik

-

Pernyataan Tanggungjawab

Wati/Agus

Versi lain/terkait

Lampiran Berkas

Komentar

---

Anda harus masuk sebelum memberikan komentar