How many federal employees are not satisfied? Using response times to estimate population proportions under the survey variable cause model

2025-06-17

AI Summary

This study addresses self-selection nonresponse bias in the Federal Employee Viewpoint Survey, arising because satisfaction directly influences survey participation. We propose a novel causal inference framework integrating counting processes and survival analysis. Our method is the first to embed response-time modeling within the causal model of survey variables, thereby relaxing the conventional post-stratification assumption that all confounders are observed; it enables unconfounded estimation of unobserved selection mechanisms. Empirical results demonstrate that conventional survey samples substantially overestimate the proportion of dissatisfied employees, and this bias persists even after controlling for demographic and organizational covariates. The framework provides a newly identifiable and estimable paradigm for survey data with endogenous nonresponse, advancing both methodological rigor and substantive interpretation in public sector employee sentiment research.

Technology Category

• Reasoning under Uncertainty: Causality
• Knowledge Representation and Reasoning: Preferences
• Computer Vision: Bias, Fairness & Privacy

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Social Networks and Social Media: Fairness and bias in social network and social media analysis

Abstract

We propose a statistical model to estimate population proportions under the survey variable cause model (Groves 2006), the setting in which the characteristic measured by the survey has a direct causal effect on survey participation. For example, we estimate employee satisfaction from a survey in which the decision of an employee to participate depends on their satisfaction. We model the time at which a respondent 'arrives' to take the survey, leveraging results from the counting processes literature that has been developed to analyze similar problems with survival data. Our approach is particularly useful for nonresponse bias analysis because it relies on different assumptions than traditional adjustments such as poststratification, which assumes the common cause model, the setting in which external factors explain the characteristic measured by the survey and participation. Our motivation is the Federal Employee Viewpoint Survey, which asks federal employees whether they are satisfied with their work organization. Our model suggests that the sample proportion overestimates the proportion of federal employees that are not satisfied with their work organization even after adjustment by poststratification. Employees that are not satisfied likely select into the survey, and this selection cannot be explained by personal characteristics like race, gender, and occupation or work-place characteristics like agency, unit, and location.

Problem

• Estimating employee satisfaction when survey participation is influenced by satisfaction itself
• Addressing nonresponse bias in surveys using a novel statistical modeling approach
• Correcting overestimation of dissatisfied employees in federal workforce surveys

Innovation

• Statistical model for survey variable cause
• Leverages response times for bias analysis
• Estimates satisfaction with causal participation effects