A New Multiprocess IRT Model With Ideal Points for Likert-Type Items

2021 ◽  
pp. 107699862110571
Author(s):  
Kuan-Yu Jin ◽  
Yi-Jhen Wu ◽  
Hui-Fang Chen
Keyword(s):  
Ideal Point ◽  
Reference Points ◽  
Data Sets ◽  
Simulation Studies ◽  
Parameter Recovery ◽  
Irt Model ◽  
Extreme Response ◽  
Ideal Points ◽  
Multiple Reference ◽  
Better Than

For surveys of complex issues that entail multiple steps, multiple reference points, and nongradient attributes (e.g., social inequality), this study proposes a new multiprocess model that integrates ideal-point and dominance approaches into a treelike structure (IDtree). In the IDtree, an ideal-point approach describes an individual’s attitude and then a dominance approach describes their tendency for using extreme response categories. Evaluation of IDtree performance via two empirical data sets showed that the IDtree fit these data better than other models. Furthermore, simulation studies showed a satisfactory parameter recovery of the IDtree. Thus, the IDtree model sheds light on the response processes of a multistage structure.

scholarly journals GGUM-RANK Statement and Person Parameter Estimation With Multidimensional Forced Choice Triplets

2018 ◽  
Vol 43 (3) ◽  
pp. 226-240 ◽  
Author(s):  
Philseok Lee ◽  
Seang-Hwane Joo ◽  
Stephen Stark ◽  
Oleksandr S. Chernyshenko
Keyword(s):  
Parameter Estimation ◽  
Forced Choice ◽  
Future Research ◽  
Simulation Studies ◽  
Scoring Methods ◽  
Mcmc Algorithm ◽  
High Stake ◽  
Parameter Recovery ◽  
Irt Model ◽  
Person Parameter

Historically, multidimensional forced choice (MFC) measures have been criticized because conventional scoring methods can lead to ipsativity problems that render scores unsuitable for interindividual comparisons. However, with the recent advent of item response theory (IRT) scoring methods that yield normative information, MFC measures are surging in popularity and becoming important components in high-stake evaluation settings. This article aims to add to burgeoning methodological advances in MFC measurement by focusing on statement and person parameter recovery for the GGUM-RANK (generalized graded unfolding-RANK) IRT model. Markov chain Monte Carlo (MCMC) algorithm was developed for estimating GGUM-RANK statement and person parameters directly from MFC rank responses. In simulation studies, it was examined that how the psychometric properties of statements composing MFC items, test length, and sample size influenced statement and person parameter estimation; and it was explored for the benefits of measurement using MFC triplets relative to pairs. To demonstrate this methodology, an empirical validity study was then conducted using an MFC triplet personality measure. The results and implications of these studies for future research and practice are discussed.

scholarly journals Comparison between dominance process model and ideal point response model for personality assesment

2012 ◽  
Vol 25 (2) ◽  
pp. 421-452
Author(s):  
DongGun Park ◽  
MyungOk Choi ◽  
WonSun Lee ◽  
HyeMin Lee ◽  
JunHee Lee
Keyword(s):  
Item Response ◽  
Process Model ◽  
Classical Test Theory ◽  
Ideal Point ◽  
Process Models ◽  
Test Theory ◽  
Item Parameter ◽  
Dominance Model ◽  
Irt Model ◽  
Better Than

The present study investigated the utilities of two types of item response process models(dominance model and ideal point model) for personality item parameter estimation and scoring. The authors developed scales for four personality traits(achievement, fairness, cooperation and honesty) using classical test theory, dominance item response theory(IRT) method, and ideal point IRT method and compared the methods in terms of model-data fit, information and criterion validity. Results show that the fit of ideal point IRT model was better than that of dominance IRT model, but the difference between the fit of two models was very slight. The test information functions of ideal point IRT model and dominance IRT model for honesty and cooperation scales were very similar. The criterion-related validity based on individual ability estimates and grades was not significant for the three methods but the validity for the ideal point method is not better than dominant IRT model. Implications and limitations of the findings are discussed.

Fast Estimation of Ideal Points with Massive Data

2016 ◽  
Vol 110 (4) ◽  
pp. 631-656 ◽  
Author(s):  
KOSUKE IMAI ◽  
JAMES LO ◽  
JONATHAN OLMSTED
Keyword(s):  
Ideal Point ◽  
Simulated Data ◽  
Large Data ◽  
Monte Carlo Algorithm ◽  
Estimation Methods ◽  
Massive Data ◽  
Data Sets ◽  
Em Algorithms ◽  
Fast Estimation ◽  
Ideal Points

Estimation of ideological positions among voters, legislators, and other actors is central to many subfields of political science. Recent applications include large data sets of various types including roll calls, surveys, and textual and social media data. To overcome the resulting computational challenges, we propose fast estimation methods for ideal points with massive data. We derive the expectation-maximization (EM) algorithms to estimate the standard ideal point model with binary, ordinal, and continuous outcome variables. We then extend this methodology to dynamic and hierarchical ideal point models by developing variational EM algorithms for approximate inference. We demonstrate the computational efficiency and scalability of our methodology through a variety of real and simulated data. In cases where a standard Markov chain Monte Carlo algorithm would require several days to compute ideal points, the proposed algorithm can produce essentially identical estimates within minutes. Open-source software is available for implementing the proposed methods.

scholarly journals Generalized Ideal Point Models for Time-Varying and Missing-Data Inference

2019 ◽  
Author(s):  
Robert Kubinec
Keyword(s):  
Missing Data ◽  
Ordinal Data ◽  
Ideal Point ◽  
Autoregressive Process ◽  
Joint Modeling ◽  
Mixed Data ◽  
Data Sets ◽  
Time Varying ◽  
Ideal Points ◽  
Data Inference

This paper presents an item-response theory parameterization of ideal points that unifies existing approaches to ideal point models while also extending them. For time-varying inference, the model permits ideal points to vary in a random walk, in a stationary autoregressive process, or in a semi-parametric Gaussian process. For missing data, the model implements a two-stage selection adjustment to account for non-ignorable missingness. In addition, the ideal point model is extended to handle new distributions, including continuous, positive-continuous and ordinal data. To enable modeling of datasets with mixed data (discrete and continuous), I incorporate joint modeling of different distributions. Finally, I also address ways of implementing Bayesian inference with big data sets, including variational inference and within-chain MCMC parallelization.

Modeling Self-Determination Theory Motivation Data by Using Unfolding IRT

2020 ◽  
pp. 1-9
Author(s):  
Philipp A. Freund ◽  
Annette Lohbeck
Keyword(s):  
Ideal Point ◽  
Self Determination Theory ◽  
Self Determination ◽  
Dominance Model ◽  
Irt Model ◽  
Autonomous Behavior ◽  
Location Parameters ◽  
Unfolding Models ◽  
Using Data ◽  
Item Location

Abstract. Self-determination theory (SDT) suggests that the degree of autonomous behavior regulation is a characteristic of distinct motivation types which thus can be ordered on the so-called Autonomy-Control Continuum (ACC). The present study employs an item response theory (IRT) model under the ideal point response/unfolding paradigm in order to model the response process to SDT motivation items in theoretical accordance with the ACC. Using data from two independent student samples (measuring SDT motivation for the academic subjects of Mathematics and German as a native language), it was found that an unfolding model exhibited a relatively better fit compared to a dominance model. The item location parameters under the unfolding paradigm showed clusters of items representing the different regulation types on the ACC to be (almost perfectly) empirically separable, as suggested by SDT. Besides theoretical implications, perspectives for the application of ideal point response/unfolding models in the development of measures for non-cognitive constructs are addressed.

Multiple reference points in pay satisfaction assessments

1997 ◽  
Author(s):  
Lisa Ordonez ◽  
Terry Connolly ◽  
Richard Coughlan
Keyword(s):  
Reference Points ◽  
Pay Satisfaction ◽  
Multiple Reference

scholarly journals Investigating the Impact of Noneffortful Responses on Individual-Level Scores: Can the Effort-Moderated IRT Model Serve as a Solution?

2021 ◽  
pp. 014662162110138
Author(s):  
Joseph A. Rios ◽  
James Soland
Keyword(s):  
Classification Accuracy ◽  
Negative Impact ◽  
Parameter Recovery ◽  
Individual Level ◽  
Irt Model ◽  
Ability Estimates ◽  
Misclassification Rates ◽  
Individual Scores ◽  
3Pl Model ◽  
The Impact

Suboptimal effort is a major threat to valid score-based inferences. While the effects of such behavior have been frequently examined in the context of mean group comparisons, minimal research has considered its effects on individual score use (e.g., identifying students for remediation). Focusing on the latter context, this study addressed two related questions via simulation and applied analyses. First, we investigated how much including noneffortful responses in scoring using a three-parameter logistic (3PL) model affects person parameter recovery and classification accuracy for noneffortful responders. Second, we explored whether improvements in these individual-level inferences were observed when employing the Effort Moderated IRT (EM-IRT) model under conditions in which its assumptions were met and violated. Results demonstrated that including 10% noneffortful responses in scoring led to average bias in ability estimates and misclassification rates by as much as 0.15 SDs and 7%, respectively. These results were mitigated when employing the EM-IRT model, particularly when model assumptions were met. However, once model assumptions were violated, the EM-IRT model’s performance deteriorated, though still outperforming the 3PL model. Thus, findings from this study show that (a) including noneffortful responses when using individual scores can lead to potential unfounded inferences and potential score misuse, and (b) the negative impact that noneffortful responding has on person ability estimates and classification accuracy can be mitigated by employing the EM-IRT model, particularly when its assumptions are met.

scholarly journals Different algorithms, different models

2021 ◽  
Author(s):  
Martyna Daria Swiatczak
Keyword(s):  
Comparative Analysis ◽  
Real World ◽  
Qualitative Comparative Analysis ◽  
Comparative Methods ◽  
Data Sets ◽  
Simulation Studies ◽  
Threshold Values ◽  
Real World Data ◽  
Software Packages ◽  
Methodological Approaches

AbstractThis study assesses the extent to which the two main Configurational Comparative Methods (CCMs), i.e. Qualitative Comparative Analysis (QCA) and Coincidence Analysis (CNA), produce different models. It further explains how this non-identity is due to the different algorithms upon which both methods are based, namely QCA’s Quine–McCluskey algorithm and the CNA algorithm. I offer an overview of the fundamental differences between QCA and CNA and demonstrate both underlying algorithms on three data sets of ascending proximity to real-world data. Subsequent simulation studies in scenarios of varying sample sizes and degrees of noise in the data show high overall ratios of non-identity between the QCA parsimonious solution and the CNA atomic solution for varying analytical choices, i.e. different consistency and coverage threshold values and ways to derive QCA’s parsimonious solution. Clarity on the contrasts between the two methods is supposed to enable scholars to make more informed decisions on their methodological approaches, enhance their understanding of what is happening behind the results generated by the software packages, and better navigate the interpretation of results. Clarity on the non-identity between the underlying algorithms and their consequences for the results is supposed to provide a basis for a methodological discussion about which method and which variants thereof are more successful in deriving which search target.

The development of nurses’ foundational values

2021 ◽  
pp. 096973302110032
Author(s):  
Sastrawan Sastrawan ◽  
Jennifer Weller-Newton ◽  
Gabrielle Brand ◽  
Gulzar Malik
Keyword(s):  
Professional Training ◽  
Reference Points ◽  
Constructivist Grounded Theory ◽  
Professional Values ◽  
Data Sets ◽  
Value System ◽  
Institutional Values ◽  
Multiple Data ◽  
Human Ethics ◽  
Multiple Data Sets

Background: In the ever-changing and complex healthcare environment, nurses encounter challenging situations that may involve a clash between their personal and professional values resulting in a profound impact on their practice. Nevertheless, there is a dearth of literature on how nurses develop their personal–professional values. Aim: The aim of this study was to understand how nurses develop their foundational values as the base for their value system. Research design: A constructivist grounded theory methodology was employed to collect multiple data sets, including face-to-face focus group and individual interviews, along with anecdote and reflective stories. Participants and research context: Fifty-four nurses working across various nursing settings in Indonesia were recruited to participate. Ethical considerations: Ethics approval was obtained from the Monash University Human Ethics Committee, project approval number 1553. Findings: Foundational values acquisition was achieved through family upbringing, professional nurse education and organisational/institutional values reinforcement. These values are framed through three reference points: religious lens, humanity perspective and professionalism. This framing results in a unique combination of personal–professional values that comprise nurses’ values system. Values are transferred to other nurses either in a formal or informal way as part of one’s professional responsibility and customary social interaction via telling and sharing in person or through social media. Discussion: Values and ethics are inherently interweaved during nursing practice. Ethical and moral values are part of professional training, but other values are often buried in a hidden curriculum, and attained and activated through interactions during nurses’ training. Conclusion: Developing a value system is a complex undertaking that involves basic social processes of attaining, enacting and socialising values. These processes encompass several intertwined entities such as the sources of values, the pool of foundational values, value perspectives and framings, initial value structures, and methods of value transference.

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