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ORCID
https://orcid.org/0000-0002-5806-6569
Access Type
Open Access Thesis
Document Type
thesis
Degree Program
Political Science
Degree Type
Master of Arts (M.A.)
Year Degree Awarded
2020
Month Degree Awarded
September
Abstract
Even with clear advantages to using internet based survey research, there are still some uncertainties to which survey methods are most conducive to an online platform. Most survey method literature, whether focusing on online, telephone, or in-person formats, tend to observe little to no differences between using various survey modes and survey results. Despite this, there is little research focused on the interaction effect between survey formatting, in terms of design and framing, and public opinion on social issues, specifically child immigration policies - a recent topic of popular debate. This paper examines an anomalous result found within the 2016 Cooperative Congressional Election Study (CCES) public opinion immigration question focusing on a DACA-related policy, where support was evenly split on the typically highly favored policy. To decipher the unprecedented result, an experimental survey design was conducted via Qualtrics by comparing various survey formats (single-style, forced choice, Likert scale) and inclusionary policy details to the original CCES “select all that apply” matrix style. By comparing the experimental polls, the results indicated that the “select all that apply” matrix again produced anomalous results, while the various other methods produced a breakdown similar to typical DACA-related polling data. These findings have necessary implications for future survey designs and those examining public opinion on child immigration policies.
DOI
https://doi.org/10.7275/19162506
First Advisor
Scott Blinder
Second Advisor
Meredith Rolfe
Recommended Citation
Calkins, Saige, "Designing Surveys on Youth Immigration Reform: Lessons from the 2016 CCES Anomaly" (2020). Masters Theses. 955.
https://doi.org/10.7275/19162506
https://scholarworks.umass.edu/masters_theses_2/955
Included in
American Politics Commons, Data Science Commons, Models and Methods Commons, Social Statistics Commons