Ideal Point Model Research Articles

ANALISIS SIKAP PEMBELIAN DAN KEPUASAN KONSUMEN TERHADAP ATRIBUT PRODUK IKAN KALENG DI KECAMATAN RUNGKUT KOTA SURABAYA

The continued development of the food industry led to a lot of food production very practical, economical, attractive and durable eg food packaged in cans, it caused many consumers are interested to consume so that the production of canned food is increasing. The purpose of this study are: (1) To identify the characteristics of consumers in considering the brand and product attributes Canned Fish purchased (2) to analyze the attitude of Figures Ideal consumers to attribute Products Canned Fish (3) to analyze the level of customer satisfaction Products canned fish. East Java Province. In this study using purposive sampling method (deliberately) that sampling based on specific criteria or considerations, appropriate research purposes. Data analysis methods used in this research is descriptive analysis with the help of descriptive tabulations, method of Ideal Point Model Analysis, and Customer Satisfaction Index (CSI). Ideal attributes include attributes according to consumer prices, Clarity Expiration Date, Brand Canned Condition, Nutritional content, size Saji, Varean Rasa, as well as the Department of Health and MUI Permit for the result that the average gap or no more than 1, which means that atrbut- Canned Fish Products attributes are already close to the ideal or desired in accordance with the consumer. Attributes that result is not ideal that the attributes of packaging design and composition because there is a gap of more than 1. Value Customer Satisfaction Index (CSI) is approximately 0.674, or 67.4%, which means consumers are satisfied in buying the canned fish products DOI : https://doi.org/10.33005/adv.v8i1.1613

Examining the item response process to personality measures in high‐stakes situations: Issues of measurement validity and predictive validity

AbstractWe conducted two experimental studies with between‐subjects and within‐subjects designs to investigate the item response process for personality measures administered in high‐ versus low‐stakes situations. Apart from assessing measurement validity of the item response process, we examined predictive validity; that is, whether or not different response models entail differential selection outcomes. We found that ideal point response models fit slightly better than dominance response models across high‐ versus low‐stakes situations in both studies. Additionally, fitting ideal point models to the data led to fewer items displaying differential item functioning compared to fitting dominance models. We also identified several items that functioned as intermediate items in both the faking and honest conditions when ideal point models were fitted, suggesting that ideal point model is “theoretically” more suitable across these contexts for personality inventories. However, the use of different response models (dominance vs. ideal point) did not have any substantial impact on the validity of personality measures in high‐stakes situations, or the effectiveness of selection decisions such as mean performance or percent of fakers selected. These findings are significant in that although prior research supports the importance and use of ideal point models for measuring personality, we find that in the case of personality faking, though ideal point models seem to have slightly better measurement validity, the use of dominance models may be adequate with no loss to predictive validity.

SIKAP KONSUMEN TERHADAP PRODUK UNGGULAN SEKTOR MAKANAN KOTA MATARAM (STUDI PADA PRODUK TAHU ABIAN TUBUH)

This research is aimed at determining the attitude of consumers towards one of the superior products of Mataram city, that is tahu Abian Tubuh. The research is descriptive which using sample survey method. In order to answer the problem proposed, the analytical tool is Ideal-Point Model. The model measures a difference between what is desired and what is perceived by consumers. The result of this study shows that consumer attitudes are very good. It is indicated by attitude value of 13,764 which is lied at the highest interval in linear numerical scale 0 < Ab < 22,56 (very good). It means that the Tahu Abian Tubuh is in accordance with consumer’s desire. Key words: consumer’s attitude, tahu Abian Tubuh.

Beyond a Bag of Words: Using PULSAR to Extract Judgments on Specific Human Rights at Scale

Abstract Sentiment, judgments and expressed positions are crucial concepts across international relations and the social sciences more generally. Yet, contemporary quantitative research has conventionally avoided the most direct and nuanced source of this information: political and social texts. In contrast, qualitative research has long relied on the patterns in texts to understand detailed trends in public opinion, social issues, the terms of international alliances, and the positions of politicians. Yet, qualitative human reading does not scale to the accelerating mass of digital information available currently. Researchers are in need of automated tools that can extract meaningful opinions and judgments from texts. Thus, there is an emerging opportunity to marry the model-based, inferential focus of quantitative methodology, as exemplified by ideal point models, with high resolution, qualitative interpretations of language and positions. We suggest that using alternatives to simple bag of words (BOW) representations and re-focusing on aspect-sentiment representations of text will aid researchers in systematically extracting people’s judgments and what is being judged at scale. The experimental results below show that our approach which automates the extraction of aspect and sentiment MWE pairs, outperforms BOW in classification tasks, while providing more interpretable parameters. By connecting expressed sentiment and the aspects being judged, PULSAR (Parsing Unstructured Language into Sentiment-Aspect Representations) also has deep implications for understanding the underlying dimensionality of issue positions and ideal points estimated with text. Our approach to parsing text into aspects-sentiment expressions recovers both expressive phrases (akin to categorical votes), as well as the aspects that are being judged (akin to bills). Thus, PULSAR or future systems like it, open up new avenues for the systematic analysis of high-dimensional opinions and judgments at scale within existing ideal point models.

A Multinomial Framework for Ideal Point Estimation

This paper creates a multinomial framework for ideal point estimation (mIRT) using recent developments in Bayesian statistics. The core model relies on a flexible multinomial specification that includes most common models in political science as “special cases.” I show that popular extensions (e.g., dynamic smoothing, inclusion of covariates, and network models) can be easily incorporated whilst maintaining the ability to estimate a model using a Gibbs Sampler or exact EM algorithm. By showing that these models can be written and estimated using a shared framework, the paper aims to reduce the proliferation of bespoke ideal point models as well as extend the ability of applied researchers to estimate models quickly using the EM algorithm. I apply this framework to a thorny question in scaling survey responses—the treatment of nonresponse. Focusing on the American National Election Study (ANES), I suggest that a simple but principled solution is to treat questions as multinomial where nonresponse is a distinct (modeled) category. The exploratory results suggest that certain questions tend to attract many more invalid answers and that many of these questions (particularly when signaling out particular social groups for evaluation) are masking noncentrist (typically conservative) beliefs.

A two-dimensional analysis of seventy years of United Nations voting

International relations scholars frequently use roll-call votes on resolutions in the United Nations General Assembly (UNGA) to measure similarity in the foreign policy ideologies of states. They then correlate those measures with consequential outcomes, such as development lending, trade, or military disputes. Dynamic ideal point models of UNGA voting thus far have been restricted to a single dimension. We examine the existence of a stable, important, and interpretable second dimension underlying contestation in the UN. From the mid-1960s to the mid-1980s, North–South conflict constitutes a stable second dimension, shaped heavily by the agenda-setting powers of the so-called Non-aligned Movement and the Group of 77. In the periods before and after, the second dimension neither is stable nor easily interpretable, though it is sometimes important. We suggest that in most applications, our original one-dimensional estimates have conceptual advantages with minimal losses in explanatory value. We illustrate that conclusion with an analysis that correlates ideal point changes with militarized interstate disputes. Yet, our findings also suggest that scholars interested in specific issues, such as the Middle East, human rights, or arms control, might benefit from more specifically tailored ideal point estimates.

GGUM: An R Package for Fitting the Generalized Graded Unfolding Model.

In this article, the newly created GGUM R package is presented. This package finally brings the generalized graded unfolding model (GGUM) to the front stage for practitioners and researchers. It expands the possibilities of fitting this type of item response theory (IRT) model to settings that, up to now, were not possible (thus, beyond the limitations imposed by the widespread GGUM2004 software). The outcome is therefore a unique software, not limited by the dimensions of the data matrix or the operating system used. It includes various routines that allow fitting the model, checking model fit, plotting the results, and also interacting with GGUM2004 for those interested. The software should be of interest to all those who are interested in IRT in general or to ideal point models in particular.

Item Parameter Estimation With the General Hyperbolic Cosine Ideal Point IRT Model.

Over the last decade, researchers have come to recognize the benefits of ideal point item response theory (IRT) models for noncognitive measurement. Although most applied studies have utilized the Generalized Graded Unfolding Model (GGUM), many others have been developed. Most notably, David Andrich and colleagues published a series of papers comparing dominance and ideal point measurement perspectives, and they proposed ideal point models for dichotomous and polytomous single-stimulus responses, known as the Hyperbolic Cosine Model (HCM) and the General Hyperbolic Cosine Model (GHCM), respectively. These models have item response functions resembling the GGUM and its more constrained forms, but they are mathematically simpler. Despite the apparent impact of Andrich's work on ensuing investigations, the HCM and GHCM have been largely overlooked by applied researchers. This may stem from questions about the compatibility of the parameter metric with other ideal point estimation and model-data fit software or seemingly unrealistic parameter estimates sometimes produced by the original joint maximum likelihood (JML) estimation software. Given the growing list of ideal point applications and variations in sample and scale characteristics, the authors believe these HCMs warrant renewed consideration. To address this need and overcome potential JML estimation difficulties, this study developed a marginal maximum likelihood (MML) estimation algorithm for the GHCM and explored parameter estimation requirements in a Monte Carlo study manipulating sample size, scale length, and data types. The authors found a sample size of 400 was adequate for parameter estimation and, in accordance with GGUM studies, estimation was superior in polytomous conditions.

Backlash and Judicial Restraint: Evidence From the European Court of Human Rights

International courts are increasingly facing backlash from consolidated liberal democ- racies. Do international courts become more restrained in their rulings in order to keep their traditional allies on board? We examine this question in the context of the European Court of Human Rights (ECtHR). We evaluate two mechanisms. First, gov- ernments that are critical of the Court may nominate more deferential judges. Second, judges may behave in a more deferential way towards consolidated democracies in order to prevent future backlash. We evaluate these ideas with a new dataset of all ECtHR judgments. We estimate ideal-point models based on dissenting opinions and nd that governments have indeed started to appoint more restrained judges. Five of the Court's six most restrained judges were appointed after the 2012 Brighton conference, which strongly signaled a preference for restraint. We then use matching and a di erence-in- di erences design to estimate changes in the Court's restraint versus the United Kingdom and other consolidated democracies. We nd strong evidence of a new variable geometry, in which consolidated democracies are increasingly given more deference compared to non-consolidated democracies. The United Kingdom is an especially large bene ciary. However, we do not nd that applicants belonging to vulnerable minorities, such as prisoners and refugees, have been disproportionally a ected by the ECtHR's increased restraint towards consolidated democracies.

A Two-Dimensional Analysis of Seventy Years of United Nations Voting

International relations scholars frequently use roll-call votes on resolutions in the United Nations General Assembly (UNGA) to measure similarity in the foreign policy ideologies of states. They then correlate those measures with consequential outcomes, such as development lending, trade, or military disputes. Dynamic ideal point models of UNGA voting thus far have been restricted to a single dimension. We examine the existence of a stable, important, and interpretable second dimension underlying contestation in the UN. From the mid 1960s to the mid-1980s, North-South conflict constitutes a stable second dimension, shaped heavily by the agenda-setting powers of the so-called Non-Aligned Movement and the Group of 77. In the periods before and after, the second dimension neither is stable nor easily interpretable, though it is sometimes important. We suggest that in most applications, our original one-dimensional estimates have conceptual advantages with minimal losses in explanatory value. We illustrate that conclusion with an analysis that correlates ideal point changes with militarized interstate disputes. Yet, our findings also suggest that scholars interested in specific issues, such as the Middle East, human rights, or arms control, might benefit from more specifically tailored ideal point estimates.

Improved DEA Cross Efficiency Evaluation Method Based on Ideal and Anti-Ideal Points

A new model is introduced in the process of evaluating efficiency value of decision making units (DMUs) through data envelopment analysis (DEA) method. Two virtual DMUs called ideal point DMU and anti-ideal point DMU are combined to form a comprehensive model based on the DEA method. The ideal point DMU is taking self-assessment system according to efficiency concept. The anti-ideal point DMU is taking other-assessment system according to fairness concept. The two distinctive ideal point models are introduced to the DEA method and combined through using variance ration. From the new model, a reasonable result can be obtained. Numerical examples are provided to illustrate the new constructed model and certify the rationality of the constructed model through relevant analysis with the traditional DEA model.

Detecting Curvilinear Relationships: A Comparison of Scoring Approaches Based on Different Item Response Models

There is a growing use of noncognitive assessments around the world, and recent research has posited an ideal point response process underlying such measures. A critical issue is whether the typical use of dominance approaches (e.g., average scores, factor analysis, and the Samejima's graded response model) in scoring such measures is adequate. This study examined the performance of an ideal point scoring approach (e.g., the generalized graded unfolding model) as compared to the typical dominance scoring approaches in detecting curvilinear relationships between scored trait and external variable. Simulation results showed that when data followed the ideal point model, the ideal point approach generally exhibited more power and provided more accurate estimates of curvilinear effects than the dominance approaches. No substantial difference was found between ideal point and dominance scoring approaches in terms of Type I error rate and bias across different sample sizes and scale lengths, although skewness in the distribution of trait and external variable can potentially reduce statistical power. For dominance data, the ideal point scoring approach exhibited convergence problems in most conditions and failed to perform as well as the dominance scoring approaches. Practical implications for scoring responses to Likert-type surveys to examine curvilinear effects are discussed.

Consumer confusion from price competition and excessive product attributes under the curse of dimensionality

The purpose of our study is to (i) investigate the effects of the number of products, product attributes, and prices on consumer confusion, (ii) conduct a numerical analysis to check the robustness of the results, and (iii) present an example of the cell phone market in Japan. Following an ideal point model and embedding the number of products and product attributes, we clarify how these factors affect consumer confusion and purchase probability. We show that as the number of product attributes increases, the choice probability of each product becomes equal, implying that consumer confusion occurs. This result is robust to the introduction of prices as strategic variables.

Case-based decision model matches ideal point model:

This paper studies the relationship between a case-based decision theory (CBDT) and an ideal point model (IPM). We show that a case-based decision model (CBDM) can be transformed into an IPM under some assumptions. This transformation can allow us to visualize the relationship among data and simplify the calculations of distance between one current datum and the ideal point, rather than the distances between data. Our results will assist researchers with their product design analysis and positioning of goods through CBDT, by revealing past dependences or providing a reference point. Furthermore, to check whether the similarity function, presented in the theoretical part, is valid for empirical analysis, we use data on the viewing behavior of audiences of TV dramas in Japan and compare the estimation results under the CBDM that corresponds to a standard decision model with similarities and other various similarity functions and without a similarity function. Our empirical analysis shows that the CBDM with a similarity function, presented in this study, best fits the data.

Estimating policy positions of local parties in elections with multi-vote ballots

ABSTRACTThis paper investigates the potential for estimating policy positions from electoral results in elections with multiple votes. When voters can spread their votes across multiple party lists in open list elections, they are more likely to select candidates from parties with similar policy positions. The electoral results can therefore be exploited to infer parties’ preferences based on the structure of vote combinations. The proposed data provide a valuable tool for analysing party behaviour in circumstances where ordinary methods for estimating policy positions fail, most importantly in electoral contexts with local competitors. Applying an ideal point model for count data, party preferences are estimated for a German municipality.

Evaluating Anchor-Item Designs for Concurrent Calibration With the GGUM.

Concurrent calibration using anchor items has proven to be an effective alternative to separate calibration and linking for developing large item banks, which are needed to support continuous testing. In principle, anchor-item designs and estimation methods that have proven effective with dominance item response theory (IRT) models, such as the 3PL model, should also lead to accurate parameter recovery with ideal point IRT models, but surprisingly little research has been devoted to this issue. This study, therefore, had two purposes: (a) to develop software for concurrent calibration with, what is now the most widely used ideal point model, the generalized graded unfolding model (GGUM); (b) to compare the efficacy of different GGUM anchor-item designs and develop empirically based guidelines for practitioners. A Monte Carlo study was conducted to compare the efficacy of three anchor-item designs in vertical and horizontal linking scenarios. The authors found that a block-interlaced design provided the best parameter recovery in nearly all conditions. The implications of these findings for concurrent calibration with the GGUM and practical recommendations for pretest designs involving ideal point computer adaptive testing (CAT) applications are discussed.

Linking Methods for the Zinnes-Griggs Pairwise Preference IRT Model.

Forced-choice item response theory (IRT) models are being more widely used as a way of reducing response biases in noncognitive research and operational testing contexts. As applications have increased, there has been a growing need for methods to link parameters estimated in different examinee groups as a prelude to measurement equivalence testing. This study compared four linking methods for the Zinnes and Griggs (ZG) pairwise preference ideal point model. A Monte Carlo simulation compared test characteristic curve (TCC) linking, item characteristic curve (ICC) linking, mean/mean (M/M) linking, and mean/sigma (M/S) linking. The results indicated that ICC linking and the simpler M/M and M/S methods performed better than TCC linking, and there were no substantial differences among the top three approaches. In addition, in the absence of possible contamination of the common (anchor) item subset due to differential item functioning, five items should be adequate for estimating the metric transformation coefficients. Our article presents the necessary equations for ZG linking and provides recommendations for practitioners who may be interested in developing and using pairwise preference measures for research and selection purposes.

Fast Estimation of Ideal Points with Massive Data

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.

(How) Can We Estimate the Ideology of Citizens and Political Elites on the Same Scale?

Estimating the ideological positions of political elites on the same scale as those of ordinary citizens has great potential to increase our understanding of voting behavior, representation, and other political phenomena. There has been limited attention, however, to the fundamental issues, both practical and conceptual, involved in conducting these joint scalings, or to the sensitivity of these estimates to modeling assumptions and data choices. I show that the standard strategy of estimating ideal point models using preference data on citizens and elites can suffer from potentially problematic pathologies. This article explores these issues and presents a technique that can be used to investigate the effects of modeling assumptions on resulting estimates and also to impose restrictions on the ideological dimension being estimated in a straightforward way.

Family planning policy in the United States: the converging politics of abortion and contraception

ObjectivesFollowing decades of mainstream bipartisan support, contraception has reemerged as a controversial political issue in the United States. At the same time, opposition to abortion has intensified. State legislatures across the country have enacted highly visible policies limiting access to family planning. Perhaps the most striking example occurred in 2011 in Texas, when legislators instituted unprecedented requirements on abortion providers and cut public funding for contraception by two thirds. Yet, despite popular interpretations of this phenomenon as a simple byproduct of increasing partisan divisions, little is understood about the factors underlying such policy shifts. Study designWe fit Bayesian ideal-point models to analyze correlation patterns in record-vote data in the Texas House of Representatives in the 2003 and 2011 Legislatures. Both sessions had large Republican majorities and saw the passage of restrictive abortion bills, but they differed markedly with respect to public funding for contraception. ResultsWe demonstrate that variation in voting on family-planning issues cannot be fully attributed to partisanship in either session. However, the politics of abortion and contraception have converged over time, and — at least for Democrats — the correlation between constituency characteristics and voting behavior on family-planning legislation is markedly higher in 2011 than in 2003. These shifts have been partly driven by legislators from high-poverty, majority Latino districts near the US–Mexico border. ConclusionsRecent dramatic shifts in family-planning policy go beyond simple partisan divisions. As the politics of abortion and contraception have converged, policies that are increasingly hostile to reproductive health and that disproportionately affect low-income minority women have emerged. ImplicationsRecent shifts in family-planning policy restrict women's access to contraception and abortion, yet little research has examined why such shifts are occurring. This paper analyzes factors underlying voting behavior on restrictive policies in Texas. Identification of these factors helps us to better understand the current political climate surrounding our field.