Dataset Information

Item-Fit Statistic Based on Posterior Probabilities of Membership in Ability Groups.

ABSTRACT: A novel approach to item-fit analysis based on an asymptotic test is proposed. The new test statistic, χw2 , compares pseudo-observed and expected item mean scores over a set of ability bins. The item mean scores are computed as weighted means with weights based on test-takers' a posteriori density of ability within the bin. This article explores the properties of χw2 in case of dichotomously scored items for unidimensional IRT models. Monte Carlo experiments were conducted to analyze the performance of χw2 . Type I error of χw2 was acceptably close to the nominal level and it had greater power than Orlando and Thissen's S-x2 . Under some conditions, power of χw2 also exceeded the one reported for the computationally more demanding Stone's χ2∗ .

SUBMITTER: Kondratek B

PROVIDER: S-EPMC9382089 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Item-Fit Statistic Based on Posterior Probabilities of Membership in Ability Groups.

Kondratek Bartosz B

Applied psychological measurement 20220620 6

A novel approach to item-fit analysis based on an asymptotic test is proposed. The new test statistic, χ w 2 , compares pseudo-observed and expected item mean scores over a set of ability bins. The item mean scores are computed as weighted means with weights based on test-takers' <i>a posteriori</i> density of ability within the bin. This article explores the properties of χ w 2 in case of dichotomously scored items for unidimensional IRT models. Monte Carlo experiments were conducted to a ...[more]

PMID: 35991828

Similar Datasets

Project description:The global rise in nature-based recreation increases the need for research on visitor activity use and interaction especially for multi-use trail systems. Conflict often arises during negatively perceived physical encounters (i.e., direct observation) of different user groups. Our study addresses these encounters on a winter multi-use refuge in Fairbanks, Alaska. Our goal was to develop a method that generates spatially and temporally explicit estimates of trail occupancy and encounter probabilities among different user groups. We used trail cameras with optic alteration to protect individual identity. We monitored winter recreational activity from November 2019 to April 2020 (n = 133 days) and sorted users into three user groups: 1) motor-powered, 2) dog-powered, and 3) human-powered. We calculated the total number of occurrences and proportion of activity across all user groups at each camera location. We identified hotspots of activity overlap (e.g., near trail access points), and peak times (14:01-15:00), days (Saturdays and Sundays), and months (December, February, and March) that may have had higher potential for physical encounters and conflict. We used multiplication and addition probability rules to estimate two probabilities: 1) the probability of user groups occupying individual trail segments, and 2) the probability of encounter between different user groups. We scaled up these probability estimates both temporally (hourly and daily) and spatially (refuge quadrant and refuge-wide). Researchers can adapt our novel method to any recreational trail system to identify locations with potential for congestion and conflict. This method can help inform management that improves visitor experience and overall trail user satisfaction.Management implicationsWe provide managers of recreational trail systems with a quantitative, objective, and noninvasive method to monitor activity among trail user groups. This method can be altered both spatially and temporally to fit any recreational trail system's research questions. These questions may involve congestion, trail carrying capacity, or user group and wildlife encounters. Our method advances current knowledge of trail use dynamics by quantifying the extent of activity overlap between different user groups that may be prone to conflict. Managers can use this information to incorporate relevant management strategies to mitigate congestion and conflict for their own recreational trail system.

Dataset Information

Item-Fit Statistic Based on Posterior Probabilities of Membership in Ability Groups.

Publications

Item-Fit Statistic Based on Posterior Probabilities of Membership in Ability Groups.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets