arXiv subjects

Bang Quan Zheng

Publications and source records attributed to Bang Quan Zheng.

7 recordsLinked to original sources

arXiv2026

Racial Comparability in Authoritarianism Scales: Latent Beliefs or Biased Measurement?

Racial differences in authoritarianism are widely used to explain variation in political attitudes, yet it is unclear whether they reflect true latent differences or measurement artifacts. Using anchor-based multi-group confirmatory factor analysis across multiple nationally representative surveys, this paper examines measurement equivalence in the standard child-rearing authoritarianism battery. We find systematic differences in how respondents use response categories across groups. Accounting for this non-invariance alters but does not eliminate racial differences in authoritarianism; African Americans continue to exhibit higher latent authoritarianism under partial scalar invariance. However, conventional multi-item scales substantially attenuate the association between authoritarianism and policy attitudes. These results show that measurement non-invariance is not merely a technical concern but can meaningfully shape substantive inferences about racial differences and their political consequences, underscoring the importance of explicit measurement modeling in studies of public opinion and political behavior.

stat.AP↗

arXiv2026

Identifying Unmeasured Confounders in Panel Causal Models: A Two-Stage LM-Wald Approach

Panel data are widely used in political science to draw causal inferences. However, these models often rely on the strong and untested assumption of sequential ignorability--that no unmeasured variables influence both the independent and outcome variables across time. Grounded in psychometric literature on latent variable modeling, this paper introduces the Two-Stage LM-Wald (2SLW) approach, a diagnostic tool that extends the Lagrange Multiplier (LM) and Wald tests to detect violations of this assumption in panel causal models. Using Monte Carlo simulations within the Random Intercept Cross-Lagged Panel Model (RI-CLPM), which separates within and between person effects, I demonstrate the 2SLW's ability to detect unmeasured confounding across three key scenarios: biased corrections, distorted direct effects, and altered mediation pathways. I also illustrate the approach with an empirical application to real-world panel data. By providing a practical and theoretically grounded diagnostic, the 2SLW approach enhances the robustness of causal inferences in the presence of potential time-varying confounders. Moreover, it can be readily implemented using the R package lavaan.

stat.ME↗

arXiv2025

Rethinking Structural Equation Modeling in Political Science: Challenges, Best Practices, and Future Directions

Structural Equation Modeling (SEM) or Covariance Structure Analysis (CSA) is a versatile and powerful method in the social and behavioral sciences, providing a framework for modeling complex relationships, testing mediation, accounting for measurement error, and analyzing latent constructs. However, SEM remains underutilized in in political science; its application is often marred by misunderstandings, misinterpretations, and methodological pitfalls that can compromise the validity and interpretability of findings. This article examines key challenges in SEM applications within political science, including test statistics and fit indices, model specification, estimator selection, and causal inference. It offers practical recommendations for enhancing methodological rigor and introduces recent advancements in causal inference.

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arXiv2025

Breaking the Balance: Asymmetric Negative Voting in the 2020 Presidential Election

While voters from opposing parties have traditionally exhibited symmetric levels of hostility toward out-party candidates, our analysis of the 2016 and 2020 Nationscape data reveals a notable departure from this pattern. In 2016, negative voting was relatively balanced, with similar levels of hostility directed at Hillary Clinton and Donald Trump. However, by 2020, asymmetric negative voting had emerged. As an incumbent seeking re-election amid a rapidly declining economy, the COVID-19 pandemic, and widespread uncertainty, Trump faced heightened negative perceptions fueled by dissatisfaction with his handling of the economy, race relations, the pandemic, and his leadership style. These factors galvanized younger, educated Democrats and Independents to vote against him in unprecedented numbers. In contrast, Republicans expressed less animosity toward Biden in 2020 than they had toward Clinton in 2016. This shift disrupted the balance in the typical pattern of symmetric negative voting.

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arXiv2024

Improved LM Test for Robust Model Specification Searches in Covariance Structure Analysis

Covariance Structure Analysis (CSA) or Structural Equation Modeling (SEM) is critical for political scientists measuring latent structural relationships, allowing for the simultaneous assessment of both latent and observed variables, alongside measurement error. Well-specified models are essential for theoretical support, balancing simplicity with optimal model fit. However, current approaches to improving model specification searches remain limited, making it challenging to capture all meaningful parameters and leaving models vulnerable to chance-based specification risks. To address this, we propose an improved Lagrange Multipliers (LM) test incorporating stepwise bootstrapping in LM and Wald tests to detect omitted parameters. Monte Carlo simulations and empirical applications underscore its effectiveness, particularly in small samples and models with high degrees of freedom, thereby enhancing statistical fit.

stat.ME↗

arXiv2024

Group Differences in Opinion Instability and Measurement Errors: A G-Theory Analysis of College Students

This study examines opinion instability among individuals from different ethnic groups (White, Latino, and Asian Americans) by analyzing measurement errors in survey measures. Using a multi-wave panel dataset of college students and employing generalizability theory, the study uncovers significant patterns. The results reveal that White students exhibit higher attitude reliability, characterized by larger variances in true opinions and smaller measurement errors. In contrast, Latino and Asian American students display lower attitude stability, with lower variances in true opinions and higher variances in both item-specific and measurement errors. Disparities in political socialization and issue concerns contribute to the observed attitude instability among Latino and Asian American students. Moreover, Asian American and Latino respondents require a greater number of survey items to mitigate measurement error compared to their White counterparts. However, the impact of multiple waves of surveys on improving reliability is limited for Latino and Asian American students compared to White students. These findings deepen our understanding of attitude instability across ethnic groups and underscore the importance of further research in this area.

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arXiv2024

Enhancing Model Fit Evaluation in SEM: Practical Tips for Optimizing Chi-Square Tests

This paper underscores the vital role of the chi-square test within political science research utilizing structural equation modeling (SEM). The ongoing debate regarding the inclusion of chi-square test statistics alongside fit indices in result presentations has sparked controversy. Despite the recognized limitations of relying solely on the chi-square test, its judicious application can enhance its effectiveness in evaluating model fit and specification. To exemplify this, we present three common scenarios pertinent to political science research where fit indices may inadequately address goodness-of-fit concerns, while the chi-square statistic can be effectively harnessed. Through Monte Carlo simulations, we examine strategies for enhancing chi-square tests within these scenarios, showcasing the potential of appropriately employed chi-square tests to provide a comprehensive model fit assessment. Our recommendation is to report both the chi-square test and fit indices, with a priority on precise model specification to ensure the trustworthiness of model fit indicators.

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