Survey Statistics: Gallup’s Presidential Approval Ratings

· Source: Statistical Modeling, Causal Inference, and Social Science · Field: Science & Research — Research Methodology & Innovation, Social Sciences & Behavioral Studies · Depth: Intermediate, quick

Summary

Gallup has announced it will cease tracking U.S. presidential approval ratings in 2025, concluding an 88-year tradition that began in 1938. This follows their earlier decision in 2012 to stop polling presidential vote choice, which they had conducted since 1936. The move comes amidst increasing difficulty in assessing Gallup's accuracy, partly because vote choice polling, which provided clear election result comparisons, is no longer performed. Challenges include declining response rates, which fell from 28% in 1997 to 7% in 2017 for their GPSS, and the complexities of maintaining consistent survey methodology. Gallup's current methodology for its Gallup Poll Social Series (GPSS) involves a dual-frame random-digit-dial design for landline and cellphone numbers, with weights calibrated to U.S. population targets for demographics like gender, age, and education.

Key takeaway

For data scientists and researchers evaluating long-term public opinion trends, Gallup's cessation of presidential approval tracking highlights the growing challenges in survey methodology. You should critically assess the trade-offs between maintaining consistent survey modes for trend continuity and adapting to new methods to combat declining response rates and nonresponse bias. This shift underscores the need for transparent reporting on response rates and weighting adjustments in any longitudinal survey data you utilize.

Key insights

Gallup is discontinuing presidential approval tracking due to accuracy challenges and methodological shifts.

Principles

Method

Gallup's GPSS uses a dual-frame random-digit-dial design, weighting samples for selection probability, nonresponse, and demographic calibration.

In practice

Topics

Best for: Data Scientist, Research Scientist, Data Analyst

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Editorial summary, takeaway, and curation by AIssential. Original article published by Statistical Modeling, Causal Inference, and Social Science.