Topic
Item tree analysis
About: Item tree analysis is a research topic. Over the lifetime, 21 publications have been published within this topic receiving 287 citations.
Papers
TL;DR: In this paper, the authors describe a data-analytic method which allows one to derive implications between test items from observed response patterns to these items, which is a further development of item tree analysis.
33 citations
TL;DR: This paper investigates the method known as inductive item tree analysis and introduces some corrections and improvements to it, resulting in two newly proposed algorithms.
32 citations
TL;DR: The R package DAKS is introduced for performing basic and advanced operations in knowledge space theory and implements three inductive item tree analysis algorithms for deriving quasi orders from binary data, the original, corrected, and minimized corrected algorithms, in sample as well as population quantities.
Abstract: Knowledge space theory is part of psychometrics and provides a theoretical framework for the modeling, assessment, and training of knowledge. It utilizes the idea that some pieces of knowledge may imply others, and is based on order and set theory. We introduce the R package DAKS for performing basic and advanced operations in knowledge space theory. This package implements three inductive item tree analysis algorithms for deriving quasi orders from binary data, the original, corrected, and minimized corrected algorithms, in sample as well as population quantities. It provides functions for computing population and estimated asymptotic variances of and one and two sample Z tests for the diff fit measures, and for switching between test item and knowledge state representations. Other features are a function for computing response pattern and knowledge state frequencies, a data (based on a finite mixture latent variable model) and quasi order simulation tool, and a Hasse diagram drawing device. We describe the functions of the package and demonstrate their usage by real and simulated data examples.
24 citations
TL;DR: A computer program is described called ITA 2.0 which implements both of the algorithms available to perform an Item Tree Analysis and is shown with a concrete data set how the program can be used for the analysis of questionnaire data.
Abstract: Item Tree Analysis (ITA) is an explorative method of data analysis which can be used to establish a hierarchical structure on a set of dichotomous items from a questionnaire or test. There are currently two different algorithms available to perform an ITA. We describe a computer program called ITA 2.0 which implements both of these algorithms. In addition we show with a concrete data set how the program can be used for the analysis of questionnaire data.
13 citations
TL;DR: This result allows for building quasi ordinal assessment spaces from data through a generalized version of a well-established procedure, known as item tree analysis, as it allows for describing dependencies between positive and negative answers to the items in a questionnaire.
10 citations
Related Topics (5)
Performance Metrics
| Year | Papers |
|---|---|
| 2021 | 1 |
| 2019 | 1 |
| 2018 | 1 |
| 2017 | 2 |
| 2016 | 2 |
| 2015 | 2 |