Discover and read the best of Twitter Threads about #aapor
Most recents (18)
Live tweeting session Detection and Mitigation: Developing Monitoring Tools to Ensure Quality in Interviewer-Administered Data Collection at #AAPOR
First is Joe Murphy (RTI) presenting A Dashboard for Field Survey Data Quality Management 
Why an *interactive* dashboard?
Different users, different needs
Gives user control to what they need
Different users, different needs
Gives user control to what they need
Live tweeting the panel of Elections and Nonresponse now here at #AAPOR
First is Cameron McPhee (SSRS) presenting Underestimation or Overcorrection? an Evaluation of Weighting and Likely-Voter Identification in 2022 Pre-Election Polls
2022 Election Polls did really well, with maybe some under-estimation of Democrats
Live tweeting Methods and Election Polling at #AAPOR here
First is Mike Witherly presenting The Effect of Random Ballot Order in the 2018 and 2022 City of Vancouver Municipal Elections
Vancouver municipal elections:
Rare instance of down ballot races
Rare instance of down ballot races
Live tweeting the #AAPOR session The Panel on the Panel: Development and Testing of a Probability-Based, Nationally-Representative Survey Panel for Federal Use
First is Victoria Dounoucus (RTI) presenting Qualitative Work to Inform Contact Materials and Baseline Questions for the Ask U.S. Panel Pilot
Cognitive interview in Microsoft Teams for ~1 hour, with 30 interview (21 in English, 9 in Spanish)
Live tweeting the "Gridded Population Surveys" at #AAPOR
First is Dana Thomson (U of Twente) giving an introduction to gridded population sampling
Gridded population datasets publicly available
Not all are equal
Not all are equal
My next session is “Like it or Not? Survey Recruitment and Data Collection using Social Media”. Presented by @trentbuskirk #AAPOR
Trent starts by saying this survey is a pilot study to conceptually cover individuals who have professional experience about privacy. Trent first starts with a literature review of using social media as a method of recruitment. #AAPOR
The sample was partially sourced by twitter users who tweeted certain keywords about privacy. A random sample of 1680 users were identified. #AAPOR
Tweeting now session "Come In and Stay a While: Recruiting and Measuring Attrition" at #AAPOR
Kyle (SSRS) starts the session talking about chronic-nonrespondents in the SSRS probability panel
Defining chronic nonrespondents -- people who have not responded the survey after at least six invitation.
Xiayoi Deng and Yangyang (ICF) present a new approach to nonresponse analysis using machine learning and multilevel models #AAPOR
They use data from National Youth Behaviour Survey (NYBS) that uses a 3-stage school-based sample.
There are some concerns about NR bias in NYBS due to declining response rates at both district level and school level.
There are some concerns about NR bias in NYBS due to declining response rates at both district level and school level.
Used a multilevel logistic-regression with districts as random effects to model nonresponse with various district level (geography and social-economic) and school level characteristics (school type, race/ethnicity, other social-economic variables)
Michelle Cavazos (RTI) talks about nonresponse bias in Address-Based Samples with an in-person nonresponse follow-up #AAPOR
RQ's: Impact of NRFU on respondents' demographics and on study outcomes.
Sample design: stratified sample by response propensity, oversampling strata with lower propensities.
My next session at #AAPOR is “Things that Divide Us: Ideology, Identification, and Information”. The first talk is “Fake News Interventions: Effective for Both Strong and Weakly Identified Partisans?” presented by Joseph Sandor.
Joseph first explains that while people have a motivation to share accurate information on social media sometimes people share things that are inaccurate. Joseph is presenting the results of an experiment where users rated a headline’s accuracy before sharing. #AAPOR
Next Joseph is walking through the literature on why people share fake news and how sometimes people are motivated to share fake news for political reasons. #AAPOR
I’m excited to be at #aapor this year. The first session I’m attending (and live tweeting) is “Assessing the Polls: Measuring Bias and Vote Choice”.
The first presentation is “It's Not Personal: Evaluating the Impact of Asking for Voters By Name” presented by Travis Brodbeck and Madeline Harland of @SienaResearch.
Sienna college polls have been some of the most accurate and are based on the L2 voter file and a likely voter model to determine the likelihood someone will vote. #AAPOR
Attention #AAPOR & #AcademicTwitter: With 19 days left to apply to join the #PRRIPublicFellows 2022-2023 cohort, take a look at some of our fellow's work over the past year. Articles in @RNS, @washingtonpost, @TheConversation and more.
#ResearchMatters
prri.smapply.io
#ResearchMatters
prri.smapply.io
In the @ConversationUS, @profkmichalka, @janehongphd, @laura_e_alex & Luis Romero state that the treatment of Ukrainian vs. other asylum seekers has prompted criticism that the Biden admin is favoring white, European, mostly Christian refugees over others.
theconversation.com/how-race-and-r…
theconversation.com/how-race-and-r…
A @PRRIpoll spotlight by @jcoleysociology found that many Christian colleges discriminate against LGBTQ students, and govt policies allow them to do so.
A recent study found that over 1/3 of LGBTQ students had been bullied or harassed in school.
bit.ly/3pUWxWf
A recent study found that over 1/3 of LGBTQ students had been bullied or harassed in school.
bit.ly/3pUWxWf
#AAPOR ICYMI
@johnmsides and I examined major explanations for polling error in 2020.
The big takeaway:
Evidence that highly engaged Democrats were a big contributor to the problem in 2020. Reweighting by in-cylce measures of participation substantially reduced error.
@johnmsides and I examined major explanations for polling error in 2020.
The big takeaway:
Evidence that highly engaged Democrats were a big contributor to the problem in 2020. Reweighting by in-cylce measures of participation substantially reduced error.
Taking it from the top:
pre-election polling in 2020 was...not great.
pre-election polling in 2020 was...not great.
One line of thought people had directly after the election:
-non-response by those low in social capital/trust might be causing these errors
-reweighting by some measure of social capital/trust might reduce error
-non-response by those low in social capital/trust might be causing these errors
-reweighting by some measure of social capital/trust might reduce error
#JSM2021 panel led by @minebocek on upskilling for a statistician -- how to learn??
@minebocek #JSM2021 @hglanz no shortage of stuff to learn. First identify what you don't know -- that comes from modern media (blogs, twitter, podcasts; groups, communities -- @RLadiesGlobal or local chapters; professional organizations -- @amstatnews ).
@minebocek @hglanz @RLadiesGlobal @AmstatNews #JSM2021 @hglanz What do the job postings require these days? (This is how the content for the @CalPoly stat/data science program was developed.)
Ahead of #2020Elections, here’s a thread of the 2016 election poll evaluation conducted by #AAPOR members
aapor.org/Education-Reso… (1/7)
aapor.org/Education-Reso… (1/7)
National polls were generally correct and accurate by historical standards.
They indicated Clinton had a 3 percentage point lead, and she won the popular vote by 2 points. (2/7)
They indicated Clinton had a 3 percentage point lead, and she won the popular vote by 2 points. (2/7)
State-level polls showed a competitive, uncertain contest, but clearly under-estimated Trump’s support in the Upper Midwest (3/7)
I worked on a couple of projects for #AAPOR that focused on the increasingly strong relationship between sexism and partisan divisions and I thought I’d do a little thread to highlight some major points. Shout out to @scluks who co-authored one of the papers with me. 1/10
First, the work I’ve done in this area uses a subset of items from the hostile sexism battery. Basically, a person’s level of hostile sexism is determined by how much they agree or disagree with statements like these. 2/10
This plot shows the distribution of sexism by party and gender. Note that Republican men and women express very similar levels of sexism, but Democratic men are more sexist than Democratic women. 3/10
Today, @AP follows through on its commitment to transparency with AP VoteCast, the new standard in election research.
Our public use data file with results from our #VoteCast survey of the American electorate in the 2018 midterm elections is now available for download.
Our public use data file with results from our #VoteCast survey of the American electorate in the 2018 midterm elections is now available for download.
@AP The #VoteCast public use file can be accessed at:
The Roper Center for Public Opinion Research
ropercenter.cornell.edu/ap-norc-center…
The Roper Center for Public Opinion Research
ropercenter.cornell.edu/ap-norc-center…
#AAPOR Tim Johnson starts his Presidential Address.
#AAPOR Tim Johnson : We are experiencing attempts to delegitimize survey research
#AAPOR Tim Johnson 25% of US adults don’t trust federal statistics or think that federal data are inaccurate
