scholarly journals Explanatory Item Response Models for Dyadic Data from Multiple Groups

2020 ◽  
Author(s):  
James Murphy
Keyword(s):  
Item Response ◽  
Methodological Approach ◽  
Group Differences ◽  
Tie Strength ◽  
Social Scientists ◽  
Response Models ◽  
Item Response Models ◽  
Mixed Effects Modeling ◽  
Research Areas ◽  
Network Comparisons

Like other quantitative social scientists, network researchers benefit from pooling information from multiple observed variables to infer underlying (latent) attributes or social processes. Appropriate network data for this task is increasingly available. The inherent dependencies in relational data, however, pose unique challenges. This is especially true for those involved in the ascendant tasks of cross-network comparisons or multilevel network analysis. The author draws on item response theory and multilevel (mixed effects) modeling to propose a methodological approach that accounts for these dependencies and allows the analyst to model variation of latent dyadic traits across relations, actors, and groups precisely and parsimoniously. Examples demonstrate the approach’s utility for three important research areas: tie strength in adolescent friendships, group differences in how discussing personal problems relates to tie strength, and the analysis of multiple relations.

scholarly journals Explanatory Item Response Models for Dyadic Data from Multiple Groups

2020 ◽  
pp. 008117502096739
Author(s):  
James P. Murphy
Keyword(s):  
Item Response ◽  
Methodological Approach ◽  
Group Differences ◽  
Tie Strength ◽  
Social Scientists ◽  
Response Models ◽  
Item Response Models ◽  
Mixed Effects Modeling ◽  
Research Areas ◽  
Network Comparisons

Like other quantitative social scientists, network researchers benefit from pooling information from multiple observed variables to infer underlying (latent) attributes or social processes. Appropriate network data for this task is increasingly available. The inherent dependencies in relational data, however, pose unique challenges. This is especially true for the ascendant tasks of cross-network comparisons and multilevel network analysis. The author draws on item response theory and multilevel (mixed effects) modeling to propose a methodological approach that accounts for these dependencies and allows the analyst to model variation of latent dyadic traits across relations, actors, and groups precisely and parsimoniously. Examples demonstrate the approach’s utility for three important research areas: tie strength in adolescent friendships, group differences in how discussing personal problems relates to tie strength, and the analysis of multiple relations.

scholarly journals Scale Alignment in the Between-Item Multidimensional Partial Credit Model

2021 ◽  
pp. 014662162110131
Author(s):  
Leah Feuerstahler ◽  
Mark Wilson
Keyword(s):  
Item Response ◽  
Kindergarten Readiness ◽  
Latent Trait ◽  
Individual Development ◽  
Partial Credit Model ◽  
Partial Credit ◽  
Response Models ◽  
Item Response Models ◽  
Item Parameters ◽  
Polytomous Item Response

In between-item multidimensional item response models, it is often desirable to compare individual latent trait estimates across dimensions. These comparisons are only justified if the model dimensions are scaled relative to each other. Traditionally, this scaling is done using approaches such as standardization—fixing the latent mean and standard deviation to 0 and 1 for all dimensions. However, approaches such as standardization do not guarantee that Rasch model properties hold across dimensions. Specifically, for between-item multidimensional Rasch family models, the unique ordering of items holds within dimensions, but not across dimensions. Previously, Feuerstahler and Wilson described the concept of scale alignment, which aims to enforce the unique ordering of items across dimensions by linearly transforming item parameters within dimensions. In this article, we extend the concept of scale alignment to the between-item multidimensional partial credit model and to models fit using incomplete data. We illustrate this method in the context of the Kindergarten Individual Development Survey (KIDS), a multidimensional survey of kindergarten readiness used in the state of Illinois. We also present simulation results that demonstrate the effectiveness of scale alignment in the context of polytomous item response models and missing data.

Stochastic Approximation Methods for Latent Regression Item Response Models

2010 ◽  
Vol 35 (2) ◽  
pp. 174-193 ◽  
Author(s):  
Matthias von Davier ◽  
Sandip Sinharay
Keyword(s):  
Item Response ◽  
Stochastic Approximation ◽  
Latent Variable ◽  
Reading Assessment ◽  
Mathematics Assessment ◽  
National Assessment ◽  
Variable Model ◽  
Response Models ◽  
Item Response Models ◽  
Latent Regression

This article presents an application of a stochastic approximation expectation maximization (EM) algorithm using a Metropolis-Hastings (MH) sampler to estimate the parameters of an item response latent regression model. Latent regression item response models are extensions of item response theory (IRT) to a latent variable model with covariates serving as predictors of the conditional distribution of ability. Applications to estimating latent regression models for National Assessment of Educational Progress (NAEP) data from the 2000 Grade 4 mathematics assessment and the Grade 8 reading assessment from 2002 are presented and results of the proposed method are compared to results obtained using current operational procedures.

Mixture Item Response Models for Inattentive Responding Behavior

2017 ◽  
Vol 21 (1) ◽  
pp. 197-225 ◽  
Author(s):  
Kuan-Yu Jin ◽  
Hui-Fang Chen ◽  
Wen-Chung Wang
Keyword(s):  
Item Response ◽  
Response Models ◽  
Item Response Models

Supervised item response models for informative prediction

2016 ◽  
Vol 51 (1) ◽  
pp. 235-257 ◽  
Author(s):  
Tsuyoshi Idé ◽  
Amit Dhurandhar
Keyword(s):  
Item Response ◽  
Response Models ◽  
Item Response Models
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