Some Experiences in Computing Estimates and Their Variances Using Data from Complex Survey Designs

Author(s):  
M. A. Hidiroglou ◽  
D. G. Paton
Author(s):  
Erin Hartman ◽  
Ines Levin

This chapter focuses on methods for analyzing data from Internet surveys with complex survey designs in order to draw inferences that can be generalized to a target population of interest. We first review the central design issues and approaches for dealing with representativeness challenges that researchers commonly face when using online polling for persuasion research. Then, using data from a survey experiment on support for immigration reform, we demonstrate the importance of the careful choice of auxiliary information used when constructing weights for ensuring the generalizability of findings from non-representative Internet surveys.


2018 ◽  
Vol 190 (11) ◽  
Author(s):  
L. A. H. Starcevich ◽  
T. McDonald ◽  
A. Chung-MacCoubrey ◽  
A. Heard ◽  
J. Nesmith ◽  
...  

2019 ◽  
Vol 20 (1) ◽  
pp. 67-86
Author(s):  
Raghunath Arnab ◽  
Dahud Kehinde Shangodoyin ◽  
Antonio Arcos

2018 ◽  
Vol 16 (1) ◽  
pp. 16-23
Author(s):  
Janice M. Pogoda ◽  
Galilea Patricio ◽  
Archana J. McEligot

Background and Purpose: Caffeine is ubiquitous in foods, supplements, and medications and has been hypothesized to be associated with several health-related outcomes, including mental health disorders such as anxiety. We explored a possible relationship between caffeine consumption and depression using data from the National Health and Nutrition Examination Survey (NHANES). Methods: Data from 1,342 adult NHANES participants were included. Statistical software for complex survey sample designs was used to perform two multivariable logistic regressions with a binary indicator of depression as the dependent variable: one using dietary caffeine consumption and one using the caffeine metabolite AAMU as the independent variable. Both analyses were adjusted for gender, race/ethnicity, smoking status, and use of anti-depressants. Results: We observed a descriptive, albeit non-significant (p = 0.12), pattern of increasing odds of depression with increasing levels of the AAMU caffeine metabolite. Conclusion: Our finding of a possible association between caffeine metabolite level and depression is compelling because it is independent of self-reported caffeine consumption. Prospective studies are warranted to further explore the temporal relationship.


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