Finding DORI: Using Item Response Theory to Measure Difficulty of Registration in the U.S. and Its Impact on Voters

2021 ◽  
pp. 1532673X2110550
Author(s):  
Joshua M. Jansa ◽  
Matthew Motta ◽  
Rebekah Herrick
Keyword(s):  
Item Response Theory ◽  
Item Response ◽  
Voter Registration ◽  
Response Theory ◽  
Political Equality ◽  
Racially Diverse ◽  
Young Voters ◽  
Irt Model ◽  
Key Dimensions ◽  
The U.S

How do states differ in how difficult they make voter registration, and what effect does this have on voters? We propose and validate a new Difficulty of Registration Index (DORI) calculated via an item response theory (IRT) model of five key dimensions of registration (automaticity, portability, deadline, mode, and preregistration) for each state from 2004 to 2020. Since 2004, most states eased registration processes, with Democratic statehouses in racially diverse and young states leading the way. Using CCES data, we find that DORI is associated with increased probability that voters experience problems registering and failing to turnout (in both self-reported and validated turnout data). These effects are pronounced for young voters. This study holds lessons for how restrictive registration procedures can change the shape of the electorate and make it harder to achieve political equality.

Clustering students according to their proficiency: a comparison between different approaches based on item response theory models

2021 ◽  
pp. 43-48
Author(s):  
Rosa Fabbricatore ◽  
Francesco Palumbo
Keyword(s):  
Item Response Theory ◽  
Item Response ◽  
Clustering Algorithm ◽  
Latent Class ◽  
Latent Class Models ◽  
Response Theory ◽  
Homogeneous Groups ◽  
Final Grade ◽  
Irt Model ◽  
Irt Models

Evaluating learners' competencies is a crucial concern in education, and home and classroom structured tests represent an effective assessment tool. Structured tests consist of sets of items that can refer to several abilities or more than one topic. Several statistical approaches allow evaluating students considering the items in a multidimensional way, accounting for their structure. According to the evaluation's ending aim, the assessment process assigns a final grade to each student or clusters students in homogeneous groups according to their level of mastery and ability. The latter represents a helpful tool for developing tailored recommendations and remediations for each group. At this aim, latent class models represent a reference. In the item response theory (IRT) paradigm, the multidimensional latent class IRT models, releasing both the traditional constraints of unidimensionality and continuous nature of the latent trait, allow to detect sub-populations of homogeneous students according to their proficiency level also accounting for the multidimensional nature of their ability. Moreover, the semi-parametric formulation leads to several advantages in practice: It avoids normality assumptions that may not hold and reduces the computation demanding. This study compares the results of the multidimensional latent class IRT models with those obtained by a two-step procedure, which consists of firstly modeling a multidimensional IRT model to estimate students' ability and then applying a clustering algorithm to classify students accordingly. Regarding the latter, parametric and non-parametric approaches were considered. Data refer to the admission test for the degree course in psychology exploited in 2014 at the University of Naples Federico II. Students involved were N=944, and their ability dimensions were defined according to the domains assessed by the entrance exam, namely Humanities, Reading and Comprehension, Mathematics, Science, and English. In particular, a multidimensional two-parameter logistic IRT model for dichotomously-scored items was considered for students' ability estimation.

Interval Estimation of Latent Variable Scores in Item Response Theory

2017 ◽  
Vol 43 (3) ◽  
pp. 259-285 ◽  
Author(s):  
Yang Liu ◽  
Ji Seung Yang
Keyword(s):  
Item Response Theory ◽  
Item Response ◽  
Latent Variable ◽  
Interval Estimation ◽  
Random Variable ◽  
Item Parameter ◽  
Response Theory ◽  
Inference Problem ◽  
Irt Model ◽  
Item Parameter Estimation

The uncertainty arising from item parameter estimation is often not negligible and must be accounted for when calculating latent variable (LV) scores in item response theory (IRT). It is particularly so when the calibration sample size is limited and/or the calibration IRT model is complex. In the current work, we treat two-stage IRT scoring as a predictive inference problem: The target of prediction is a random variable that follows the true posterior of the LV conditional on the response pattern being scored. Various Bayesian, fiducial, and frequentist prediction intervals of LV scores, which can be obtained from a simple yet generic Monte Carlo recipe, are evaluated and contrasted via simulations based on several measures of prediction quality. An empirical data example is also presented to illustrate the use of candidate methods.

A Multilevel Bayesian Item Response Theory Method for Scaling Socioeconomic Status in International Studies of Education

2006 ◽  
Vol 31 (1) ◽  
pp. 63-79 ◽  
Author(s):  
Henry May
Keyword(s):  
Socioeconomic Status ◽  
Item Response Theory ◽  
Item Response ◽  
International Studies ◽  
Response Theory ◽  
Irt Model ◽  
National Differences ◽  
Proposed Model ◽  
Conventional Models ◽  
Imputation Approach

A new method is presented and implemented for deriving a scale of socioeconomic status (SES) from international survey data using a multilevel Bayesian item response theory (IRT) model. The proposed model incorporates both international anchor items and nation-specific items and is able to (a) produce student family SES scores that are internationally comparable, (b) reduce the influence of irrelevant national differences in culture on the SES scores, and (c) effectively and efficiently deal with the problem of missing data in a manner similar to Rubin’s (1987) multiple imputation approach. The results suggest that this model is superior to conventional models in terms of its fit to the data and its ability to use information collected via international surveys.

scholarly journals Analisis Kualitas Tes Buatan Guru Melalui Pendekatan Item Response Theory (IRT) Model Rasch

2020 ◽  
Vol 7 (1) ◽  
pp. 61-70
Author(s):  
Dinar Pratama
Keyword(s):  
Item Response Theory ◽  
Item Response ◽  
Theory Model ◽  
Item Response Theory Model ◽  
Response Theory ◽  
Irt Model ◽  
Item Fit

Tujuan utama penelitian ini dilakukan adalah untuk menganalisis dan mendeskripsikan karakteristik khusus tes buatan guru Akidah Akhlak melalui pendekatan Item Response Theory (IRT) model Rasch. Jenis penelitian ini termasuk penelitian  kuantitatif deskriptif. Subjek pada penelitian ini berjumlah 67 pola respon siswa terhadap tes dengan lima alternatif jawaban. Perangkat tes buatan guru ini diambil dari hasil pelaksanaan Ujian Akhir Semester tahun pelajaran 2018/2019 melalui teknik dokumentasi. Analisis data kuantitatif dilakukan melalui pendekatan IRT model Rasch dengan bantuan software QUEST. Berdasarkan hasil analisis, dari 30 item terdapat 28 item fit dengan model Rasch dengan nilai OUTFIT t ≤ 2.00. Ditinjau dari tingkat kesulitan item, terdapat 7 item atau sebesar 25% dengan kategori sangat sulit. Item dengan kategori sulit sebanyak 6 item atau 21.4%, kategori item sedang sebanyak 2 item atau sebesar 7.14%, kategori mudah sebanyak 13 item atau sebesar 46.4%, dan 0% untuk kategori item soal sangat mudah. Rentang nilai tingkat kesukaran berkisar antara -2.94 sampai 4.18. Nilai reliability of item estimate sebesar 0.94 dengan kategori baik sekali dan nilai reliability of case estimate sebesar 0.38 dengan kategori lemah. Berdasarkan nilai reliability of case estimate, tes ini perlu dilakukan revisi agar sesuai dengan kemampuan peserta tes. Kata Kunci: Tes, Item Response Theory, Model Rasch

On Longitudinal Item Response Theory Models: A Didactic

2019 ◽  
Vol 45 (3) ◽  
pp. 339-368 ◽  
Author(s):  
Chun Wang ◽  
Steven W. Nydick
Keyword(s):  
Item Response Theory ◽  
Item Response ◽  
Real Data ◽  
Measurement Model ◽  
Higher Order ◽  
Response Theory ◽  
Latent Growth ◽  
Multidimensional Irt ◽  
Irt Model ◽  
Irt Models

Recent work on measuring growth with categorical outcome variables has combined the item response theory (IRT) measurement model with the latent growth curve model and extended the assessment of growth to multidimensional IRT models and higher order IRT models. However, there is a lack of synthetic studies that clearly evaluate the strength and limitations of different multilevel IRT models for measuring growth. This study aims to introduce the various longitudinal IRT models, including the longitudinal unidimensional IRT model, longitudinal multidimensional IRT model, and longitudinal higher order IRT model, which cover a broad range of applications in education and social science. Following a comparison of the parameterizations, identification constraints, strengths, and weaknesses of the different models, a real data example is provided to illustrate the application of different longitudinal IRT models to model students’ growth trajectories on multiple latent abilities.

scholarly journals The Impact of Markov Chain Convergence on Estimation of Mixture IRT Model Parameters

2020 ◽  
Vol 80 (5) ◽  
pp. 975-994
Author(s):  
Yoonsun Jang ◽  
Allan S. Cohen
Keyword(s):  
Markov Chain ◽  
Item Response Theory ◽  
Item Response ◽  
Monte Carlo Algorithm ◽  
Accurate Estimation ◽  
Model Parameters ◽  
Response Theory ◽  
Irt Model ◽  
Mixture Irt ◽  
Item Response Theory Models

A nonconverged Markov chain can potentially lead to invalid inferences about model parameters. The purpose of this study was to assess the effect of a nonconverged Markov chain on the estimation of parameters for mixture item response theory models using a Markov chain Monte Carlo algorithm. A simulation study was conducted to investigate the accuracy of model parameters estimated with different degree of convergence. Results indicated the accuracy of the estimated model parameters for the mixture item response theory models decreased as the number of iterations of the Markov chain decreased. In particular, increasing the number of burn-in iterations resulted in more accurate estimation of mixture IRT model parameters. In addition, the different methods for monitoring convergence of a Markov chain resulted in different degrees of convergence despite almost identical accuracy of estimation.

scholarly journals A Bayesian Random Block Item Response Theory Model for Forced-Choice Formats

2019 ◽  
Vol 80 (3) ◽  
pp. 578-603
Author(s):  
HyeSun Lee ◽  
Weldon Z. Smith
Keyword(s):  
Item Response Theory ◽  
Item Response ◽  
Model Performance ◽  
Theory Model ◽  
Forced Choice ◽  
Simultaneous Estimation ◽  
Response Theory ◽  
Irt Model ◽  
Measurement Models ◽  
Random Block

Based on the framework of testlet models, the current study suggests the Bayesian random block item response theory (BRB IRT) model to fit forced-choice formats where an item block is composed of three or more items. To account for local dependence among items within a block, the BRB IRT model incorporated a random block effect into the response function and used a Markov Chain Monte Carlo procedure for simultaneous estimation of item and trait parameters. The simulation results demonstrated that the BRB IRT model performed well for the estimation of item and trait parameters and for screening those with relatively low scores on target traits. As found in the literature, the composition of item blocks was crucial for model performance; negatively keyed items were required for item blocks. The empirical application showed the performance of the BRB IRT model was equivalent to that of the Thurstonian IRT model. The potential advantage of the BRB IRT model as a base for more complex measurement models was also demonstrated by incorporating gender as a covariate into the BRB IRT model to explain response probabilities. Recommendations for the adoption of forced-choice formats were provided along with the discussion about using negatively keyed items.

scholarly journals The Bayesian Multilevel Trifactor Item Response Theory Model

2018 ◽  
Vol 79 (3) ◽  
pp. 462-494 ◽  
Author(s):  
Ken A. Fujimoto
Keyword(s):  
Item Response Theory ◽  
Item Response ◽  
Latent Trait ◽  
Theory Model ◽  
Response Theory ◽  
Irt Model ◽  
Response Data ◽  
Proposed Model ◽  
Method Effects ◽  
Bayesian Irt

Advancements in item response theory (IRT) have led to models for dual dependence, which control for cluster and method effects during a psychometric analysis. Currently, however, this class of models does not include one that controls for when the method effects stem from two method sources in which one source functions differently across the aspects of another source (i.e., a nested method–source interaction). For this study, then, a Bayesian IRT model is proposed, one that accounts for such interaction among method sources while controlling for the clustering of individuals within the sample. The proposed model accomplishes these tasks by specifying a multilevel trifactor structure for the latent trait space. Details of simulations are also reported. These simulations demonstrate that this model can identify when item response data represent a multilevel trifactor structure, and it does so in data from samples as small as 250 cases nested within 50 clusters. Additionally, the simulations show that misleading estimates for the item discriminations could arise when the trifactor structure reflected in the data is not correctly accounted for. The utility of the model is also illustrated through the analysis of empirical data.

scholarly journals PROC IRT: A SAS Procedure for Item Response Theory

2017 ◽  
Vol 41 (4) ◽  
pp. 311-320 ◽  
Author(s):  
Ki Matlock Cole ◽  
Insu Paek
Keyword(s):  
Item Response Theory ◽  
Item Response ◽  
Small Scale ◽  
Analysis Software ◽  
Response Theory ◽  
Parameter Recovery ◽  
Irt Model ◽  
Research And Teaching ◽  
Input And Output ◽  
Statistical Analysis Software

This article reviews the procedure for item response theory (PROC IRT) procedure in SAS/STAT 14.1 to conduct item response theory (IRT) analyses of dichotomous and polytomous datasets that are unidimensional or multidimensional. The review provides an overview of available features, including models, estimation procedures, interfacing, input, and output files. A small-scale simulation study evaluates the IRT model parameter recovery of the PROC IRT procedure. The use of the IRT procedure in Statistical Analysis Software (SAS) may be useful for researchers who frequently utilize SAS for analyses, research, and teaching.

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