Home > Data Center > 2016 Time Series Study > Updates & Errata

2016 Time Series: Updates & Errata

Current Dataset Release: December 19, 2017 version

October 30, 2018
Defense spending 7-point scales (variables V161181, V161182, V161183) were correctly administered to respondents although the labels for the endpoint categories in the codebook documentation represent an error in the face-to-face instrument. Endpoint labels in the codebook represent what appeared onscreen to interviewers during administration of the survey. However, although incorrect, they do not represent the endpoint labels which were correct in the Respondent Booklet (used by face-to-face respondents to make a number selection from the scale) and correct in the Web instrument . The 2016 data for these variables should be considered representing questions administered comparably to other years.
October 2, 2018
Web cases in 2016 Time Series Study variable V161317 require a recoding of values as follows: code 0 to 4, code 1 to 0, code 2 to 1, code 3 to 2, and code 4 to 3.
December 19, 2017
The ANES 2016 Time Series data have been re-released with several additional variables, including occupation and industry codes, the timing of the respondent’s voting decision, interviewer observations about the dwelling units in the face-to-face sample, a summary score for “need to evaluate,” and reasons for non-registration to vote.

Already Applied

Previous

May 2, 2017
The May 2, 2017 version of the initial release includes new variables containing interviewer observations, including the interviewer’s observation of the respondent’s skin tone. The data contained in these variables were collected for face-to-face interviews only. Interviewer observations were recorded following both the pre and post election surveys.
April 3, 2017
Updates to the ANES 2016 Time Series study are available. The April 3, 2017 version includes important changes to the data for the following variables: Party ID (V161155 & V16158x), sexual orientation of R (V161511), pre-election survey media variables, party to deal with most important problem (V162117, V162119, V162121), country needs free thinkers (V162168), discrimination in the US (V162357-66), self discrimination (V162367). Corrections were also made to the number of post election cases with no-post interview. Many variable labels and code labels have also been updated.
March 31, 2017
We thank several data users, including Alan Abramowitz, Ismail White, and Gary Jacobson, for noting a problem with the Party Identification data. The problem reflects a data processing error in which Internet questionnaire randomization was not correctly accounted for. The face-to-face data were not affected. We are preparing a corrected dataset for release as soon as possible.
May 2, 2017
The May 2, 2017 version of the initial release includes new variables containing interviewer observations, including the interviewer’s observation of the respondent’s skin tone. The data contained in these variables were collected for face-to-face interviews only. Interviewer observations were recorded following both the pre- and post-election surveys.
April 3, 2017
Updates to the ANES 2016 Time Series study are available. The April 3, 2017 version includes important changes to the data for the following variables: Party ID (V161155 & V16158x), sexual orientation of R (V161511), pre-election survey media variables, party to deal with most important problem (V162117, V162119, V162121), country needs free thinkers (V162168), discrimination in the US (V162357-66), self discrimination (V162367). Corrections were also made to the number of post election cases with no-post interview. Many variable labels and code labels have also been updated.
March 31, 2017
We thank several data users, including Alan Abramowitz, Ismail White, and Gary Jacobson, for noting a problem with the Party Identification data. The problem reflects a data processing error in which Internet questionnaire randomization was not correctly accounted for. The face-to-face data were not affected. We are preparing a corrected dataset for release as soon as possible.

Here you can create the content that will be used within the module.