1. #UTTribPoll: The Latest Analysis from the UT/Texas Tribune Poll
Budget Endgames and Public Opinion
May 21, 2013: The tripartisanship evident during this week's final budget negotiations pits Democrats, Republicans, and Tea Partiers with vastly different priorities into a game of chicken. Despite the fact that the public doesn't closely follow the budget process, the broad positions of these groups in the Legislature neatly reflect the preferences of those groups in the electorate according to polling data collected at the beginning of the session focusing on the public's perception of the legislative priorities.
Dueling Attitudes on Guns
May 16, 2013: In the context of the gun control debate currently taking place at both the federal and state levels, to say that the Senate was ignoring public opinion in tabling the background check measure - or that the Legislature is accurately reflecting it in expanding gun access - is to ignore the intricacies and ambivalence in public opinion on gun control.
Immigration Reform and the GOP's Self-Interest
May 8, 2013: While many GOP leaders argue that passing comprehensive immigration reform is in the GOP's best interest, some data suggest that the long-term interest of party strategists and the short-term self-interest of members of Congress are not necessarily in sync.
The Calculus of Women's Health
May 3, 2013: While women's health may not have garnered the attention that it did in the 2011 session, here we discuss the connection between women's health and abortion and how the removal of Planned Parenthood from the women's health calculus may have made passing restrictive abortion legislation more difficult in 2013.
Rough Sailing for Water Policy
April 30, 2013: In the wake of last night's failed attempt to pass a comprehensive water plan - a priority of the governor, among many other political elites - Jim Henson takes a look at what public opinion can tell us about why water funding has faced so much difficulty despite the elite consensus behind the issue.
The Tea Party as a Constituency
April 19, 2013: The power and pull of the Tea Party inside the national, and in this case Texas, GOP is a source of interest for many insiders and some outsiders alike. In our latest piece, we examine how the Tea Party has become an acknowledged but understood constituency within the Texas Republican Party, and as a result, less of a force in this legislative session.
Guest Worker Program the Sweet Spot for Texas GOP
April 16, 2013: As an immigration reform package gets unveiled in the Senate today, our polling data suggests that, at least here in Texas, anything even resembling amnesty is a poison pill for Republican voters.
Tea Party Identifiers Outside the Mainstream on Gun Control Proposals
April 11, 2013: As the Senate opens up debate on gun control legislation, it appears that Tea Party members in particular register extreme opposition to these new proposals, far to the right of most Americans, and even most other Texans. Might this help explain the opposition of some Tea Party standard bearers?
Texans Reluctant to Change Conceal Carry Requirements
April 9, 2013: Texans hold ideologically conservative attitudes on most gun control proposals, but literally conservative attitudes on those proposals that seek to reduce requirements for carrying a concealed weapon, a topic that we explore here.
According to Polling Data, Budget Fights Track Closely to Public Opinion
April 4, 2013: If you're really hip like us, you're probably watching the budget fight on the house floor today (either in person, online, or like me, a little of both). See what public opinion can tell us about the budget process so far and why it might get a little more contentious going forward.
No Evidence of Education Backlash Despite Cuts from Last Session
April 2, 2013: Despite all the rhetoric of the calamity that would befall legislators who supported cutting education by $5.4 billion in 2011, we find little evidence of an electorate overwhelming displeased with the Texas education system.
GOP Disapproval of Obama
March 28, 2013: We just think that this is a particularly nifty, and potentially telling, graphic of Texas GOP attitudes about the president.
Shifting Attitudes on Gay Marriage in Texas
March 26, 2013: Gay marriage attitudes in Texas are shifting with the rest of the nation, and this shift poses a potential problem for the Republicans here in Texas as independents, women, suburbanites, and the youngest age cohorts become increasingly support of same sex unions.
1.1 Most Recent UT/Texas Tribune Survey: February, 2013
The February 2013 University of Texas/Texas Tribune Poll surveyed Texans about a wide range of national issues and policy matters facing the 83rd Texas Legislature. In addition to a large battery of questions assessing important political actors, we asked a series of questions about attitudes on topics including:
-Texas budget priorities
-Gun ownership and gun control
-Immigration
-Public education
-Abortion
-Same-sex relationship recognition
-State infrastructure needs
Graphics based on these results will be made available in the feature bar on the right side of these page and in the subject-specific sections listed in the navigation bar on the left side of the page.
The Texas Politics Project will continue to publish comprehensive results, graphics, and cross tabs in the following weeks in this section of the Texas Politics website and other sections in this chapter.
2. Mood of the State
Texans' assessment of the direction of the economy and general path of both the US and Texas show continued concern about both personal situations and the state of the national economy in the February 2013 University of Texas/Texas Tribune poll. Views of whether the state was moving in the right direction or was on the wrong track flipped between May and October and then continued that trend in February with a sharp drop in people expressing no opinion. While in October, 23 percent of respondents had no opinion on whether the state was going in the right direction or on the wrong track, in February, that number had dropped to 16 percent. Of those with an opinion, 45 percent say that the state is headed in the right direction while 39 percent say that we're off on the wrong track. This result marked a slight contraction of the net positive judgment since October, as this graphic tracking trends on this item illustrates.
Following a well-established trend, assessments of the direction of the country were more negative than evaluations of Texas, but were slightly more dour in February 2013 than in October. At 62 percent, the wrong track number for February was a slight increase from October's 58 percent, while the right direction number decreased from 31 to 29 percent.
Perceptions of the national economy remained in net negative territory in the October 2012 poll - 42 percent thought the national economy was worse off than a year ago compared with 32 percent who thought it was better off -- but results indicate an increase in both those who say the economy has gotten better and those who say it has gotten worse since May 2012. (See the trends plotted in this feature.)
2.1 Trends
Retrospective Assessments of the National Economy
Texans' views on the state of the national economy are improving slowly and gradually, as the longitudinal chart comparing assessments as of October 2010 to previous assessments shows. Fifty-six percent of respondents answered that the national economy is worse off than it was a year ago, slightly more than (and statistically very close to) September's 52% , but overall, part of a steady decline for the peak of 86% in October 2008.. Conversely, 28% of respondents answered that the national economy is better off than it was a year ago, a marked increase from 2% (!) in the first July 2008 University of Texas poll.
Retrospective Assessments of Personal Economic Situations
No such clear trend can be discerned from respondents with respect to their personal economic situations. Much like in previous polls, large and statistically equal percentages of respondents say they and their families are either economically worse off or about the same as they were one year ago, while a significantly smaller percentage of respondents say they and their families are better off than they were one year ago.
Right Direction / Wrong Track
The primary characteristic of the responses to the "right direction/wrong track" item continues to be the gap between Texans' perception of the trajectories of the United States and of Texas.
2.3 Most Important Problem Archive
In every poll that we have conducted for the Texas Politics Project (and The Texas Tribune ), we have asked respondents to name the most important problem facing the country and the most important problem facing Texas. For the first three polls that were conducted, these questions were open-ended -- meaning that respondents could write in their responses, which were then coded. Since June 2009, we have provided respondents with a long list of possible answers to both questions. In general, the items on the lists have been consistent, though we have occasionally added items that had recently emerged onto the national or state scene (e.g., the gulf oil spill or Texas's budget shortfall) and deleted items that had become irrelevant.
Results for both the national and state questions have been fairly consistent over time, though there have also been some significant changes. With respect to the most important problem facing the country, answer choices related to the economy or unemployment have always been the most popular. During the initial surveys in 2008 and early 2009, answer choices related to energy issues (gas prices, energy independence, etc.) as well as national security issues (the war in Iraq, terrorism, etc.) were also salient to our respondents, but they have since receded in importance. In their place, concerns about federal spending and the national debt, as well as about political corruption and leadership in Washington, have risen dramatically.
With respect to the most important problem facing Texas, answer choices related to the economy have also been very popular, but they have generally been equally popular to (and have at times been eclipsed by) answer choices related to immigration and border security. In recent surveys, the state's budget shortfall and education have also become frequently-chosen answers.
3. Assessments of Political Leaders: October, 2009
President Obama's ratings differed little from his performance in the general election, with 41 approving and 52 disapproving of his overall job performance. The US Congress job approval rating from Texans was the most negative of all of the assessments: 71% disapproved and only 13% approved. In both of these assessments the most negative option, "disapprove strongly", was the most common choice: 44% strongly disapproved of Obama, and 49% strongly disapproved of Congress.
At the state level, where we solicited approval ratings of Governor Perry, Senator Hutchison, and the Texas state legislature, assessments were less negative though still tepid. Governor Perry's approval numbers were at 36% approve (10% strongly/26% somewhat) versus 44% disapprove (26% strongly / 18% somewhat). Senator Hutchison elicited comparable positives with a 39% approval rating (10% strongly / 29% somewhat), but her disapproval rating was only 27% (10% strongly/17% somewhat) with a larger share choosing the neutral option (24%) than in the Governor's case (15%).
The Texas state legislature fared much better than their counterparts in Washington DC: 31% approved of the way the Texas Legislature has been handling its job (3% strongly/28% somewhat). Their state legislature's disapproval the 36% (15% stronglydisapproving/21% somewhat disapproving).
3. Assessments of Political Leaders
The February 2013 UT/Texas Tribune survey finds minor changes in the job performance assessments of Governor Rick Perry and President Barack Obama since the October 2012 survey. The Governor's approval rate was 41 percent, with 40 percent disapproving, exactly the same as the October survey, as the approval rating trends chart illustrates.
President Obama's job approval declined insignificantly, with continuing high disapproval numbers stalled in remarkably negative territory, with 53 percent disapproving -- 46 percent strongly -- as the chart shows, with a slight drop in his positive numbers, which decreased to 39 percent. As this feature illustrates, Republican disapproval of the President's job performance has been high and intense in Texas throughout his presidency.
Looking ahead, we asked respondents whether, if Rick Perry were to choose to run for Governor again in 2014, they would support him, oppose him, or have to wait and see. While only 26 percent of respondents indicated that they would currently support the governor in a reelection attempt, 36 percent indicated they would vote against him, and 33 would have to wait and see - a slight improvement overall from when we first asked the question in October 2012. While most of those who would vote against Perry are in fact Democrats (72 of Democrats), among the partisan groups, 44 percent of Republicans and independents indicated that they will have to wait and see before they decide whether to support the Governor in his fourth run for executive office.
4. 2014 Elections
The February 2013 UT/Texas Tribune Poll took a prospective look at the potential marquee matchup of the 2014 race, the GOP gubernatorial primary, from a few different angles. Rick Perry's standing in 2014 was assessed without a named alternative to gauge his base of support. We also asked respondents whether they would choose Rick Perry or Attorney General Greg Abbott, the most noted challenger to Perry, in a head to head match-up.
5. Education Opinions
While we ask recurring questions to assess Texans' attitudes on the quality of public K-12 schools, the quality of higher education, and the adequacy of education spending here in Texas, in the February 2013 University of Texas/Texas Tribune Poll, we also asked specific questions about proposals that would allow individuals to carry concealed handguns on college campuses and K-12 teachers the ability to carry concealed handguns in their schools.
6. Immigration Opinions
According to our respondents, immigration and border security are the most important problems facing Texas as indicated by our recent surveys. We regularly assess support for comprehensive immigration reform with a pathway to citizenship and whether illegal immigrants should pay in-state or out-of-state tuition when attending Texas colleges and universities. In response to the ongoing debate, in the February 2013 University of Texas / Texas Tribune Survey, we also asked respondents whether they would favor a guest worker program.
7. Social Issue Opinions
In The University of Texas / Texas Tribune Survey, we have regularly asked questions on abortion, same-sex marriage, and respondents' views toward the death penalty, in addition to other social/moral issues that come up from time to time.
8. National Policy Issue Opinions
In the University of Texas/Texas Tribune survey, in addition to our primary focus on Texas, we also ask respondents about pertinent national policy issues to see where Texans stand in today's most important debates, and when possible, how they compare to the nation as a whole. In the February 2013 UT/Texas Tribune survey, we asked a battery of questions about the proposed gun control legislation as well as respondents' support for comprehensive immigration reform and guest worker programs.
9. Methodology, Data, and Graphical Archive
The sub-sections that follow contain code books, data files, cross tabs, and summaries for all of the polls organized chronologically.
The link on the right side of this page will load an excerpt of an interview with Daron Shaw, the polling director for the Texas Politics project and the UT-Austin/Texas Tribune poll, conducted by Jason Embry of the Austin American-Statesman. For a link to the entire podcast, which also discusses poll results and the 2010 election in Texas, see Embry's "First Reading" blog. (The Statesman compiles blog entries at that URL. If you search the page and can't find the entire podcast, you may need search the site for an archived version.)
9.1 July 2008
The July, 2008 UT-Austin Texas Politics Poll was designed by researchers at UT-Austin and conducted by YouGovPolimetrix, a firm with demonstrated success in internet polling. YouGovPolimetrix accomplishes internet polling through a unique sampling procedure known as "matched random sampling." The firm begins with two lists: (1) a list of all consumers in Texas (covering approximately 95 percent of the adult population), and (2) a list of people who have agreed to take YouGovPolimetrix's surveys. For each list, Polimetrix has an extensive set of demographics.
The sampling procedure then progresses in two stages. First, a random sample of consumers is drawn. For each person drawn from this sample a list of key demographics is recorded. In essence, each individual drawn is represented as a cluster of demographic characteristics, including age, income, education, race, gender, longitude and latitude, etc. Second, YouGovPolimetrix uses a matching algorithm to find the PollingPoint panelist who is the closest match to the person drawn off the consumer file. In this way an entire matched random sample is constructed for all people in the sample.
The current poll of 800 adult Texans has a margin of error of +/- 3.46 percentage points at the 95% confidence level. The poll includes interviews with 677 registered voters, with an attendant margin of error of +/- 3.77 percentage points. Response rates are almost 100% given the matching methodology. The YouGovPolimetrix pool includes people who are much less likely to have access to the Internet or a personal computer. They have been especially assiduous at enlisting people with lower incomes and ethnic and racial minorities, part of an attempt to bolster the representativeness of their samples.The poll was administered by YouGov/Polimetrix. Polimetrix interviewed 899 respondents who were then matched down to a sample of 800 to produce the final data set. The respondents were matched on gender, age, race, education, party identification and political interest. YouGov/Polimetrix then weighted the matched set of survey respondents to known marginals for the general population of Texas from the 2006 American Community Survey. Those marginals are:
Age:
18-34: 33.45%
35-54: 39.08%
55+: 27.53%
Gender:
Male: 48.97%
Female: 51.03%
Race:
White/Other: 57.63%
Black: 10.94%
Hispanic: 31.43%
Education:
HS or less: 49.23%
Some College: 28.49%
College Graduate: 15.23%
Post-graduate: 7.50%
9.2 October 2008
The October 2008 UT-Austin Texas Politics Poll was designed by researchers at UT-Austin and conducted by YouGov/Polimetrix, a firm with demonstrated success in internet polling. YouGov/Polimetrix accomplishes internet polling through a unique sampling procedure known as "matched random sampling." The firm begins with two lists: (1) a list of all adult "consumers" in Texas (covering approximately 95 percent of the adult population), and (2) a list of people who have agreed to take YouGov/Polimetrix's surveys. For each list, Polimetrix has an extensive set of demographics.
The sampling procedure then progresses in two stages. First, a random sample of consumers is drawn. For each person drawn from this sample a list of key demographics is recorded. In essence, each individual drawn is represented as a cluster of demographic characteristics, including age, income, education, race, gender, longitude and latitude, etc. Second, YouGov/Polimetrix uses a matching algorithm to find the PollingPoint panelist who is the closest match to the person drawn off the consumer file. In this way an entire "matched" random sample is constructed for all people in the "drawn" sample.
The October 2008 poll consists of 613 adult Texans, and has a margin of error of +/- 3.98 percentage points at the 95% confidence level. The poll includes interviews with 550 registered voters, with an attendant margin of error of +/- 4.20 percentage points. Response rates are almost 100% given the matching methodology. The YouGov/Polimetrix pool includes people who are much less likely to have access to the Internet or a personal computer. YouGov/Polimetrix has been especially assiduous at enlisting ethnic and racial minorities, as well as people who are less affluent, as part of their attempt to ensure the representativeness of their samples.The poll was administered by YouGov/Polimetrix. Polimetrix interviewed 899 respondents who were then matched down to a sample of 613 to produce the final data set. The respondents were matched on (among other items) gender, age, race, education, party identification and political interest. YouGov/Polimetrix then weighted the matched set of survey respondents to known marginals for the general population of Texas from the 2006 American Community Survey. Those marginals are:
Age:
18-34: 33.45%
35-54: 39.08%
55+: 27.53%
Gender:
Male: 48.97%
Female: 51.03%
Race:
White/Other: 57.63%
Black: 10.94%
Hispanic: 31.43%
Education:
HS or less: 49.23%
Some College: 28.49%
College Grad.: 15.23%
Post-graduate: 7.50%
9.3 February-March 2009
The March 2009 UT-Austin Texas Politics Poll was designed by researchers at UT-Austin and conducted by YouGov/Polimetrix, a firm with demonstrated success in internet polling. YouGov/Polimetrix accomplishes internet polling through a unique sampling procedure known as "matched random sampling." The firm begins with two lists: (1) a list of all adult "consumers" in Texas (covering approximately 95 percent of the adult population), and (2) a list of people who have agreed to take YouGov/Polimetrix's surveys. For each list, Polimetrix has an extensive set of demographics.
The sampling procedure then progresses in two stages. First, a random sample of consumers is drawn. For each person drawn from this sample a list of key demographics is recorded. In essence, each individual drawn is represented as a cluster of demographic characteristics, including age, income, education, race, gender, longitude and latitude, etc. Second, YouGov/Polimetrix uses a matching algorithm to find the PollingPoint panelist who is the closest match to the person drawn off the consumer file. In this way an entire "matched" random sample is constructed for all people in the "drawn" sample.
The March 2009 poll consists of 800 adult Texans, and has a margin of error of +/- 3.46 percentage points at the 95% confidence level. The poll includes interviews with 715 registered voters, with an attendant margin of error of +/- 3.66 percentage points. Response rates are almost 100% given the matching methodology. The YouGov/Polimetrix pool includes people who are much less likely to have access to the Internet or a personal computer. YouGov/Polimetrix has been especially assiduous at enlisting ethnic and racial minorities, as well as people who are less affluent, as part of their attempt to ensure the representativeness of their samples. Surveys were completed between February 24 and March 6, 2009.The poll was administered by YouGov/Polimetrix. Polimetrix interviewed 899 respondents who were then matched down to a sample of 800 to produce the final data set. The respondents were matched on gender, age, race, education, party identification and political interest. YouGov/Polimetrix then weighted the matched set of survey respondents to known marginals for the general population of Texas from the 2006 American Community Survey. Those marginals are:
Age:
18-34: 33.45%
35-54: 39.08%
55+: 27.53%
Gender:
Male: 48.97%
Female: 51.03%
Race:
White/Other: 57.63%
Black: 10.94%
Hispanic: 31.43%
Education:
HS or less: 49.23%
Some College: 28.49%
College Graduate: 15.23%
Post-graduate: 7.50%
Surveys were completed between February 25 and March 6.
9.4 June 2009
The June 2009 UT-Austin Texas Politics Poll was designed by researchers in the UT-Austin Department of Government and conducted by YouGov/Polimetrix, a firm with demonstrated success in internet polling. YouGov/Polimetrix accomplishes internet polling through a unique sampling procedure known as "matched random sampling." The firm begins with two lists: (1) a list of all adult "consumers" in Texas (covering approximately 95 percent of the adult population), and (2) a list of people who have agreed to take YouGov/Polimetrix's surveys. For each list, Polimetrix has an extensive set of demographics.
The sampling procedure then progresses in two stages. First, a random sample of consumers is drawn. For each person drawn from this sample a list of key demographics is recorded. In essence, each individual drawn is represented as a cluster of demographic characteristics, including age, income, education, race, gender, longitude and latitude, etc. Second, YouGov/Polimetrix uses a matching algorithm to find the PollingPoint panelist who is the closest match to the person drawn off the consumer file. In this way an entire "matched" random sample is constructed for all people in the "drawn" sample.
The June 2009 poll consists of 924 adult Texans, and has a margin of error of +/- 3.22 percentage points at the 95% confidence level. The poll includes interviews with 791 registered voters, with an attendant margin of error of +/- 3.66 percentage points. Response rates are almost 100% given the matching methodology. The YouGov/Polimetrix pool includes people who are much less likely to have access to the Internet or a personal computer. YouGov/Polimetrix has been especially assiduous at enlisting ethnic and racial minorities, as well as people who are less affluent, as part of their attempt to ensure the representativeness of their samples. Surveys were completed between June 11 and June 22, 2009.
Polimetrix interviewed 924 respondents who were then matched down to a sample of 800 to produce the final data set. The respondents were matched on gender, age, race, education, party identification and political interest. YouGov/Polimetrix then weighted the matched set of survey respondents to known marginals for the general population of Texas from the 2006 American Community Survey. Those marginals are:
Age:
18-34: 33.45%
35-54: 39.08%
55+: 27.53%
Gender:
Male: 48.97%
Female: 51.03%
Race:
White/Other: 57.63%
Black: 10.94%
Hispanic: 31.43%
Education:
HS or less: 49.23%
Some College: 28.49%
College Graduate: 15.23%
Post-graduate: 7.50%
9.5 October 2009
The October 2009 Texas Tribune/UT-Austin Texas Politics Poll was designed by researchers at UT-Austin and conducted by YouGov/Polimetrix, a firm with demonstrated success in internet polling. YouGov/Polimetrix accomplishes internet polling through a unique sampling procedure known as "matched random sampling." The firm begins with two lists: (1) a list of all adult "consumers" in Texas (covering approximately 95 percent of the adult population), and (2) a list of people who have agreed to take YouGov/Polimetrix's surveys. For each list, Polimetrix has an extensive set of demographics.
The sampling procedure then progresses in two stages. First, a random sample of consumers is drawn. For each person drawn from this sample a list of key demographics is recorded. In essence, each individual drawn is represented as a cluster of demographic characteristics, including age, income, education, race, gender, longitude and latitude, etc. Second, YouGov/Polimetrix uses a matching algorithm to find the PollingPoint panelist who is the closest match to the person drawn off the consumer file. In this way an entire "matched" random sample is constructed for all people in the "drawn" sample.
The October 2009 poll consists primarily of 800 adults who are registered voters in Texas, and has a margin of error of +/-3.46 percentage points at the 95% confidence level. The YouGov/Polimetrix pool includes people who are much less likely to have access to the Internet or a personal computer. YouGov/Polimetrix has been especially assiduous at enlisting ethnic and racial minorities, as well as people who are less affluent, as part of their attempt to ensure the representativeness of their samples. Surveys were completed between October 20 and October 27, 2009.
Polimetrix interviewed 1152 respondents who were then matched down to
a sample of 800 to produce the final dataset. The respondents were matched ongender, age, race, education, party identification, ideology and political interest.
Polimetrix then weighted the matched set of survey respondents to known
marginals for the registered voters of Texas from the 2008 Current Population Survey. Those marginals are shown below.
Age:
18-34: 33.45%
35-54: 39.08%
55+: 27.53%
Gender:
Male: 48.97%
Female: 51.03%
Race:
White/Other: 57.63%
Black: 10.94%
Hispanic: 31.43%
Education:
HS or less: 49.23%
Some College: 28.49%
College Graduate: 15.23%
Post-graduate: 7.50%
Because Hank Gilbert was inadvertently left off the list of Democratic gubernatorial nominees in the original round of surveying, YouGov/Polimetrix re-interviewed 269 respondents to the October 2009 survey who had indicated that they intended to vote in the Texas Democratic gubernatorial primary. These respondents were then weighted to the distribution of Democratic gubernatorial primary voters in the initial survey on the following variables: gender, race, age, education, party ID, political interest, ideology, and national origin.
9.6 February 2010
Sampling and Weighting Methodology for the February 2010 Texas Statewide Study
Survey Panel Data
The PollingPoint panel, a proprietary opt-in survey panel, is comprised of 1.6 million U.S. residents who have agreed to participate in YouGov Polimetrix's Web surveys. At any given time, YouGov Polimetrix maintains a minimum of five recruitment campaigns based on salient current events.
Panel members are recruited by a number of methods and on a variety of topics to help ensure diversity in the panel population. Recruiting methods include Web advertising campaigns (public surveys), permission-based email campaigns, partner sponsored solicitations, telephone-to-Web recruitment (RDD based sampling), and mail-to-Web recruitment (Voter Registration Based Sampling).
The primary method of recruitment for the PollingPoint Panel is Web advertising campaigns that appear based on keyword searches. In practice, a search in Google may prompt an active PollingPoint advertisement soliciting opinion on the search topic. At the conclusion of the short survey respondents are invited to join the PollingPoint panel in order to receive and participate in additional surveys. After a double opt-in procedure, where respondents must confirm their consent by responding to an email, the database checks to ensure the newly recruited panelist is in fact new and that the address information provided is valid.
Additionally, YouGov Polimetrix augments their panel with difficult to recruit respondents by soliciting panelists in telephone and mail surveys. For example, in the fall and winter of 2006, YouGov Polimetrix completed telephone interviews using RDD sampling and invited respondents to join the online panel. Respondents provided a working email where they could confirm their consent and request to receive online survey invitations. YouGov Polimetrix also employed registration based sampling, inviting respondents to complete a pre-election survey online. At the conclusion of that survey, respondents were invited to become PollingPoint members and receive additional survey invitations at their email address.
The PollingPoint panel currently has over 55,000 active panelists who are registered voters in Texas. These panelists cover a wide range of demographic characteristics.
Sampling and Sample Matching
Sample matching is a methodology for selection of "representative" samples from non-randomly selected pools of respondents. It is ideally suited for Web access panels, but could also be used for other types of surveys, such as phone surveys. Sample matching starts with an enumeration of the target population. For general population studies, the target population is all adults, and can be enumerated through the use of the decennial Census or a high quality survey, such as the American Community Survey. In other contexts, this is known as the sampling frame, though, unlike conventional sampling, the sample is not drawn from the frame. Traditional sampling, then, selects individuals from the sampling frame at random for participation in the study. This may not be feasible or economical as the contact information, especially email addresses, is not available for all individuals in the frame and refusals to participate increase the costs of sampling in this way.
Sample selection using the matching methodology is a two-stage process.
First, a random sample is drawn from the target population. We call this sample the target sample. Details on how the target sample is drawn are provided below, but the essential idea is that this sample is a true probability sample and thus representative of the frame from which it was drawn.
Second, for each member of the target sample, we select one or more matching members from our pool of opt-in respondents. This is called the matched sample. Matching is accomplished using a large set of variables that are available in consumer and voter databases for both the target population and the opt-in panel.
The purpose of matching is to find an available respondent who is as similar as possible to the selected member of the target sample. The result is a sample of respondents who have the same measured characteristics as the target sample. Under certain conditions, described below, the matched sample will have similar properties to a true random sample. That is, the matched sample mimics the characteristics of the target sample. It is, as far as we can tell, "representative" of the target population (because it is similar to the target sample).
When choosing the matched sample, it is necessary to find the closest matching respondent in the panel of opt-ins to each member of the target sample. Polimetrix employs the proximity matching method to find the closest matching respondent. For each variable used for matching, we define a distance function, d(x,y), which describes how "close" the values x and y are on a particular attribute. The overall distance between a member of the target sample and a member of the panel is a weighted sum of the individual distance functions on each attribute. The weights can be adjusted for each study based upon which variables are thought to be important for that study, though, for the most part, we have not found the matching procedure to be sensitive to small adjustments of the weights. A large weight, on the other hand, forces the algorithm toward an exact match on that dimension.
Sampling Frame and Target Sample
YouGov/Polimetrix constructed a national sampling frame from the 2007 American Community Survey, including data on age, race, gender, education, marital status, number of children under 18, family income, employment status, citizenship, state, and metropolitan area. The frame was constructed by stratified sampling from the full 2007 ACS sample with selection within strata by weighted sampling with replacements (using the person weights on the public use file). Data on voter registration status and turnout were matched to this frame using the November 2006 Current Population Survey. Data on interest in politics and party identification were then matched to this frame from the 2007 Pew Religious Life survey, using the following variables for the match: age, race, gender, education, marital status, number of children under 18, family income, employment status, citizenship, state. The target sample of 800 Texas registered voters was selected with stratification by age, race, gender, education, and with simple random sampling within strata.
Weighting
Because matching is approximate, rather than exact, and response rates vary by group, the sample of completed interviews normally shows small amounts of imbalance that can be corrected by post-stratification weighting.
Raking, first proposed by Deming and Stephan (1940), adjusts an initial set of weights to match a known set of population marginals, using a method of iterative proportional fitting (see Bishop, Fienberg and Holland, 1975 for details). In this procedure, the weights are adjusted sequentially to match the marginal distribution of each weight variable. The process proceeds until all marginals are matched. It does not require any information about the joint distribution of the variables (though, if these data are available and believed to be important, they can be employed by defining a marginal distribution involving a cross-classification of two variables).
You Gov Politmetrix calculated post-stratification weights by raking the completed interviews to known marginals for the general population of Texas from the November 2006 Current Population Survey and Pew Religious Life survey for the following variables: age, race, gender, education, and ideology.
9.7 May 2010
Sampling and Weighting Methodology for the February 2010 Texas Statewide Study
Sampling Frame and Target Sample
YouGov/Polimetrix constructed a national sampling frame from the 2007 American Community Survey, including data on age, race, gender, education, marital status, number of children under 18, family income, employment status, citizenship, state, and metropolitan area. The frame was constructed by stratified sampling from the full 2007 ACS sample with selection within strata by weighted sampling with replacements (using the person weights on the public use file). Data on voter registration status and turnout were matched to this frame using the November 2006 Current Population Survey. Data on interest in politics and party identification were then matched to this frame from the 2007 Pew Religious Life survey, using the following variables for the match: age, race, gender, education, marital status, number of children under 18, family income, employment status, citizenship, state. The target sample of 800 Texas registered voters was selected with stratification by age, race, gender, education, and with simple random sampling within strata.
Weighting
Because matching is approximate, rather than exact, and response rates vary by group, the sample of completed interviews normally shows small amounts of imbalance that can be corrected by post-stratification weighting.
Raking, first proposed by Deming and Stephan (1940), adjusts an initial set of weights to match a known set of population marginals, using a method of iterative proportional fitting (see Bishop, Fienberg and Holland, 1975 for details). In this procedure, the weights are adjusted sequentially to match the marginal distribution of each weight variable. The process proceeds until all marginals are matched. It does not require any information about the joint distribution of the variables (though, if these data are available and believed to be important, they can be employed by defining a marginal distribution involving a cross-classification of two variables).
Post-stratification weights are calculated by raking the completed interviews to known marginals for the general population of Texas from the November 2006 Current Population Survey and Pew Religious Life survey for the following variables: age, race, gender, and education.
9.8 September 2010
For the University of Texas / Texas Tribune survey, YouGovPolimetrix interviewed 906 respondents between September 3 and 8, 2010, who were then matched down to a sample of 800 to produce the final dataset. The respondents were matched on gender, age, race, education, party identification, ideology and political interest. YouGovPolimetrix then weighted the matched set of survey respondents to
known marginals for the registered voters of Texas from the 2008 Current Population survey and the 2007 Pew Religious Life Survey.
Sampling Frame and Target Sample
YouGovPolimetrix constructed a national sampling frame from the 2007 American Community
Survey, including data on age, race, gender, education, marital status, number of children under 18,
family income, employment status, citizenship, state, and metropolitan area. The frame was
constructed by stratified sampling from the full 2007 ACS sample with selection within strata by
weighted sampling with replacements (using the person weights on the public use file). Data on
voter registration status and turnout were matched to this frame using the November 2008
Current Population Survey. Data on interest in politics and party identification were then matched
to this frame from the 2007 Pew Religious Life survey, using the following variables for the match:
age, race, gender, education, marital status, number of children under 18, family income,
employment status, citizenship, state. The target sample of 800 Texas registered voters was
selected with stratification by age, race, gender, education, and with simple random sampling
within strata.
Weighting
Because matching is approximate, rather than exact, and response rates vary by group, the sample
of completed interviews normally shows small amounts of imbalance that can be corrected by
post-stratification weighting.
Raking, first proposed by Deming and Stephan (1940), adjusts an initial set of weights to match a
known set of population marginals, using a method of iterative proportional fitting (see Bishop,
Fienberg and Holland, 1975 for details). In this procedure, the weights are adjusted sequentially to
match the marginal distribution of each weight variable. The process proceeds until all marginals
are matched. It does not require any information about the joint distribution of the variables
(though, if these data are available and believed to be important, they can be employed by defining
a marginal distribution involving a cross-classification of two variables).
Post-stratification weights are calculated by raking the completed interviews to known marginals
for the general population of Texas from the November 2008 Current Population Survey for the
following variables: age, race, gender, and education.
9.9 October 2010
Sampling and Weighting Methodology for the October 2010 Texas Statewide Study
For the survey, YouGovPolimetrix interviewed 914 respondents between October 11 and 19, 2010, who were then matched down to a sample of 800 to produce the final dataset. The respondents were matched on gender, age, race, education, party identification, ideology and political interest. YouGovPolimetrix then weighted the matched set of survey respondents to
known marginals for the registered voters of Texas from the 2008 Current Population survey and the 2007 Pew Religious Life Survey.
Sampling Frame and Target Sample
YouGovPolimetrix constructed a national sampling frame from the 2007 American Community Survey, including data on age, race, gender, education, marital status, number of children under 18, family income, employment status, citizenship, state, and metropolitan area. The frame was constructed by stratified sampling from the full 2007 ACS sample with selection within strata by weighted sampling with replacements (using the person weights on the public use file). Data on voter registration status and turnout were matched to this frame using the November 2008 Current Population Survey. Data on interest in politics and party identification were then matched
to this frame from the 2007 Pew Religious Life survey, using the following variables for the match: age, race, gender, education, marital status, number of children under 18, family income, employment status, citizenship, state. The target sample of 800 Texas registered voters was selected with stratification by age, race, gender, education, and with simple random sampling within strata.
Weighting
Because matching is approximate, rather than exact, and response rates vary by group, the sample of completed interviews normally shows small amounts of imbalance that can be corrected by post-stratification weighting.
Raking, first proposed by Deming and Stephan (1940), adjusts an initial set of weights to match a known set of population marginals, using a method of iterative proportional fitting (see Bishop, Fienberg and Holland, 1975 for details). In this procedure, the weights are adjusted sequentially to
match the marginal distribution of each weight variable. The process proceeds until all marginals are matched. It does not require any information about the joint distribution of the variables (though, if these data are available and believed to be important, they can be employed by defining
a marginal distribution involving a cross-classification of two variables).
Post-stratification weights are calculated by raking the completed interviews to known marginals for the general population of Texas from the November 2008 Current Population Survey for the following variables: age, race, gender, and education.
9.10 February 2011
Sampling and Weighting Methodology for the February 11 Texas Statewide Study
For the February 2011 survey, YouGovPolimetrix interviewed 963 respondents between February 9-18 2011, who were then matched down to a sample of 800 to produce the final dataset. The respondents were matched on gender, age, race, education, party identification, ideology and political interest.
YouGovPolimetrix then weighted the matched set of survey respondents to known marginals for the registered voters of Texas from the 2008 Current Population survey and the 2007 Pew Religious Life Survey.
Sampling Frame and Target Sample
YouGovPolimetrix constructed a national sampling frame from the 2007 American Community Survey, including data on age, race, gender, education, marital status, number of children under 18, family income, employment status, citizenship, state, and metropolitan area. The frame was
constructed by stratified sampling from the full 2007 ACS sample with selection within strata by weighted sampling with replacements (using the person weights on the public use file). Data on voter registration status and turnout were matched to this frame using the November 2008 Current Population Survey. Data on interest in politics and party identification were then matched to this frame from the 2007 Pew Religious Life survey, using the following variables for the match: age, race, gender, education, marital status, number of children under 18, family income, employment status, citizenship, state. The target sample of 800 Texas registered voters was
selected with stratification by age, race, gender, education, and with simple random sampling within strata.
Weighting
Because matching is approximate, rather than exact, and response rates vary by group, the sample of completed interviews normally shows small amounts of imbalance that can be corrected by
post-stratification weighting. Raking, first proposed by Deming and Stephan (1940), adjusts an initial set of weights to match a known set of population marginals, using a method of iterative proportional fitting (see Bishop,
Fienberg and Holland, 1975 for details). In this procedure, the weights are adjusted sequentially to match the marginal distribution of each weight variable. The process proceeds until all marginals are matched. It does not require any information about the joint distribution of the variables
(though, if these data are available and believed to be important, they can be employed by defining a marginal distribution involving a cross-classification of two variables). Post-stratification weights are calculated by raking the completed interviews to known marginals for Texas registered voters from the November 2008 Current Population Survey for the following
variables: age, race, gender, and education.
9.11 May 2011
For the May 2011 University of Texas / Texas Tribune survey, Polimetrix interviewed 891 respondents who were then matched down to a sample of 800 to produce the final dataset. The respondents were matched on gender, age, race, education, party identification, ideology and political interest. Polimetrix then weighted the matched set of survey respondents to known marginals for the registered voters of Texas from the 2008 Current Population survey and the 2007 Pew Religious Life Survey.
Sampling Frame and Target Sample
YouGovPolimetrix constructed a national sampling frame from the 2007 American Community Survey, including data on age, race, gender, education, marital status, number of children under 18, family income, employment status, citizenship, state, and metropolitan area. The frame was constructed by stratified sampling from the full 2007 ACS sample with selection within strata by weighted sampling with replacements (using the person weights on the public use file). Data on voter registration status and turnout were matched to this frame using the November 2008 Current Population Survey. Data on interest in politics and party identification were then matched to this frame from the 2007 Pew Religious Life survey, using the following variables for the match: age, race, gender, education, marital status, number of children under 18, family income, employment status, citizenship, state. The target sample of 800 Texas registered voters was selected with stratification by age, race, gender, education, and with simple random sampling within strata.
Weighting
Because matching is approximate, rather than exact, and response rates vary by group, the sample of completed interviews normally shows small amounts of imbalance that can be corrected by post-stratification weighting. Raking, first proposed by Deming and Stephan (1940), adjusts an initial set of weights to match a known set of population marginals, using a method of iterative proportional fitting (see Bishop, Fienberg and Holland, 1975 for details). In this procedure, the weights are adjusted sequentially to match the marginal distribution of each weight variable. The process proceeds until all marginals are matched. It does not require any information about the joint distribution of the variables (though, if these data are available and believed to be important, they can be employed by defining a marginal distribution involving a cross-classification of two variables). Post-stratification weights are calculated by raking the completed interviews to known marginals for Texas registered voters from the November 2008 Current Population Survey for the following variables: age, race, gender, and education.
9.12 October 2011
Sampling and Weighting Methodology for the October 2011 Texas Statewide Study
For the survey, YouGovPolimetrix interviewed 889 respondents between October 19-26 2011, who were then matched down to a sample of 800 to produce the final dataset. The respondents were matched on gender, age, race, education, party identification, ideology and political interest. YouGovPolimetrix then weighted the matched set of survey respondents to known marginals for the registered voters of Texas from the 2008 Current Population survey and the 2007 Pew Religious Life Survey.
Sampling Frame and Target Sample
YouGovPolimetrix constructed a national sampling frame from the 2007 American Community Survey, including data on age, race, gender, education, marital status, number of children under 18, family income, employment status, citizenship, state, and metropolitan area. The frame was constructed by stratified sampling from the full 2007 ACS sample with selection within strata by weighted sampling with replacements (using the person weights on the public use file). Data on voter registration status and turnout were matched to this frame using the November 2008 Current Population Survey. Data on interest in politics and party identification were then matched to this frame from the 2007 Pew Religious Life survey, using the following variables for the match: age, race, gender, education, marital status, number of children under 18, family income, employment status, citizenship, state. The target sample of 800 Texas registered voters was selected with stratification by age, race, gender, education, and with simple random sampling within strata.
Weighting
Because matching is approximate, rather than exact, and response rates vary by group, the sample of completed interviews normally shows small amounts of imbalance that can be corrected by post-stratification weighting.
Raking, first proposed by Deming and Stephan (1940), adjusts an initial set of weights to match a known set of population marginals, using a method of iterative proportional fitting (see Bishop, Fienberg and Holland, 1975 for details). In this procedure, the weights are adjusted sequentially to match the marginal distribution of each weight variable. The process proceeds until all marginals are matched. It does not require any information about the joint distribution of the variables (though, if these data are available and believed to be important, they can be employed by defining a marginal distribution involving a cross-classification of two variables).
Post-stratification weights are calculated by raking the completed interviews to known marginals for Texas registered voters from the November 2008 Current Population Survey for the following variables: age, race, gender, and education.
Survey Panel Data
The YouGov panel, a proprietary opt-in survey panel, is comprised of 1.2 million U.S. residents who have agreed to participate in YouGov Web surveys. At any given time, YouGov maintains a minimum of five recruitment campaigns based on salient current events.
Panel members are recruited by a number of methods and on a variety of topics to help ensure diversity in the panel population. Recruiting methods include Web advertising campaigns (public surveys), permission-based email campaigns, partner sponsored solicitations, telephone-to-Web recruitment (RDD based sampling), and mail-to-Web recruitment (Voter Registration Based Sampling).
The primary method of recruitment for the YouGov Panel is Web advertising campaigns that appear based on keyword searches. In practice, a search in Google may prompt an active YouGov advertisement soliciting opinion on the search topic. At the conclusion of the short survey respondents are invited to join the YouGov panel in order to receive and participate in additional surveys. After a double opt-in procedure, where respondents must confirm their consent by responding to an email, the database checks to ensure the newly recruited panelist is in fact new and that the address information provided is valid.
Additionally, YouGov augments their panel with difficult to recruit respondents by soliciting panelists in telephone and mail surveys. For example, in 2006 and 2010, YouGov completed telephone interviews using RDD sampling and invited respondents to join the online panel. Respondents provided a working email where they could confirm their consent and request to receive online survey invitations. YouGov also employed registration based sampling, inviting respondents to complete a pre-election survey online. At the conclusion of that survey, respondents were invited to become YouGov members and receive additional survey invitations at their email address.
The YouGov panel currently has nearly 20,000 panelists who are residents of Texas. These panelists cover a wide range of demographic characteristics.
Sampling and Sample Matching
Sample matching is a methodology for selection of "representative" samples from non-randomly selected pools of respondents. It is ideally suited for Web access panels, but could also be used for other types of surveys, such as phone surveys. Sample matching starts with an enumeration of the target population. For general population studies, the target population is all adults, and can be enumerated through the use of the decennial Census or a high quality survey, such as the American Community Survey. In other contexts, this is known as the sampling frame, though, unlike conventional sampling, the sample is not drawn from the frame. Traditional sampling, then, selects individuals from the sampling frame at random for participation in the study. This may not be feasible or economical as the contact information, especially email addresses, is not available for all individuals in the frame and refusals to participate increase the costs of sampling in this way.
Sample selection using the matching methodology is a two-stage process. First, a random sample is drawn from the target population. We call this sample the target sample. Details on how the target sample is drawn are provided below, but the essential idea is that this sample is a true probability sample and thus representative of the frame from which it was drawn.
Second, for each member of the target sample, we select one or more matching members from our pool of opt-in respondents. This is called the matched sample. Matching is accomplished using a large set of variables that are available in consumer and voter databases for both the target population and the opt-in panel.
The purpose of matching is to find an available respondent who is as similar as possible to the selected member of the target sample. The result is a sample of respondents who have the same measured characteristics as the target sample. Under certain conditions, described below, the matched sample will have similar properties to a true random sample. That is, the matched sample mimics the characteristics of the target sample.
When choosing the matched sample, it is necessary to find the closest matching respondent in the panel of opt-ins to each member of the target sample. YouGov employs the proximity matching method to find the closest matching respondent. For each variable used for matching, we define a distance function, d(x,y), which describes how "close" the values x and y are on a particular attribute. The overall distance between a member of the target sample and a member of the panel is a weighted sum of the individual distance functions on each attribute. The weights can be adjusted for each study based upon which variables are thought to be important for that study, though, for the most part, we have not found the matching procedure to be sensitive to small adjustments of the weights. A large weight, on the other hand, forces the algorithm toward an exact match on that dimension.
9.13 February 2012
Sampling and Weighting Methodology for the February 2012 Texas Statewide Study
For the survey, YouGov interviewed 909 respondents between February 8-15, 2012, who were then matched down to a sample of 800 to produce the final dataset. The respondents were matched on gender, age, race, education, party identification, ideology and political interest. YouGov then weighted the matched set of survey respondents to known marginals for the registered voters of Texas from the 2008 Current Population survey and the 2007 Pew Religious Landscape Survey.
Sampling Frame and Target Sample
YouGov constructed a national sampling frame from the 2007 American Community Survey, including data on age, race, gender, education, marital status, number of children under 18, family income, employment status, citizenship, state, and metropolitan area. The frame was constructed by stratified sampling from the full 2007 ACS sample with selection within strata by weighted sampling with replacements (using the person weights on the public use file). Data on voter
registration status and turnout were matched to this frame using the November 2008 Current Population Survey. Data on interest in politics and party identification were then matched to this frame from the 2007 Pew Religious Life survey, using the following variables for the match: age, race, gender, education, marital status, number of children under 18, family income, employment status, citizenship, state. The target sample of 800 Texas registered voters was selected with stratification by age, race, gender, education, and with simple random sampling within strata.
Weighting
Because matching is approximate, rather than exact, and response rates vary by group, the sample of completed interviews normally shows small amounts of imbalance that can be corrected by post-stratification weighting. Raking, first proposed by Deming and Stephan (1940), adjusts an initial set of weights to match a known set of population marginals, using a method of iterative proportional fitting (see Bishop, Fienberg and Holland, 1975 for details). In this procedure, the weights are adjusted sequentially to match the marginal distribution of each weight variable. The process proceeds until all marginals
are matched. It does not require any information about the joint distribution of the variables (though, if these data are available and believed to be important, they can be employed by defining a marginal distribution involving a cross-classification of two variables).
Post-stratification weights are calculated by raking the completed interviews to known marginals for Texas registered voters from the November 2008 Current Population Survey for the following variables: age, race, gender, and education. Survey Panel Data
The YouGov panel, a proprietary opt-in survey panel, is comprised of 1.2 million U.S. residents who have agreed to participate in YouGov Web surveys. At any given time, YouGov maintains a minimum of five recruitment campaigns based on salient current events. Panel members are recruited by a number of methods and on a variety of topics to help ensure diversity in the panel population. Recruiting methods include Web advertising campaigns (public surveys), permission-based email campaigns, partner sponsored solicitations, telephone-to-Web recruitment (RDD based sampling), and mail-to-Web recruitment (Voter Registration Based Sampling).
The primary method of recruitment for the YouGov Panel is Web advertising campaigns that appear based on keyword searches. In practice, a search in Google may prompt an active YouGov advertisement soliciting opinion on the search topic. At the conclusion of the short survey respondents are invited to join the YouGov panel in order to receive and participate in additional surveys. After a double opt-in procedure, where respondents must confirm their consent byr esponding to an email, the database checks to ensure the newly recruited panelist is in fact new and that the address information provided is valid.
Additionally, YouGov augments their panel with difficult to recruit respondents by soliciting panelists in telephone and mail surveys. For example, in 2006 and 2010, YouGov completed telephone interviews using RDD sampling and invited respondents to join the online panel. Respondents provided a working email where they could confirm their consent and request to receive online survey invitations. YouGov also employed registration based sampling, inviting respondents to complete a pre-election survey online. At the conclusion of that survey, respondents were invited to become YouGov members and receive additional survey invitations at their email address. The YouGov panel currently has nearly 20,000 panelists who are residents of Texas. These panelists cover a wide range of demographic characteristics.
Sampling and Sample Matching
Sample matching is a methodology for selection of "representative" samples from non-randomly selected pools of respondents. It is ideally suited for Web access panels, but could also be used for other types of surveys, such as phone surveys. Sample matching starts with an enumeration of the target population. For general population studies, the target population is all adults, and can be enumerated through the use of the decennial Census or a high quality survey, such as the American Community Survey. In other contexts, this is known as the sampling frame, though, unlike conventional sampling, the sample is not drawn from the frame. Traditional sampling, then, selects individuals from the sampling frame at random for participation in the study. This may not be feasible or economical as the contact information, especially email addresses, is not available for all individuals in the frame and refusals to participate increase the costs of sampling in this way.
Sample selection using the matching methodology is a two-stage process. First, a random sample is drawn from the target population. We call this sample the target sample. Details on how the target sample is drawn are provided below, but the essential idea is that this sample is a true probability sample and thus representative of the frame from which it was drawn. Second, for each member of the target sample, we select one or more matching members from our pool of opt-in respondents. This is called the matched sample. Matching is accomplished using a large set of variables that are available in consumer and voter databases for both the target population and the opt-in panel.
The purpose of matching is to find an available respondent who is as similar as possible to the selected member of the target sample. The result is a sample of respondents who have the same measured characteristics as the target sample. Under certain conditions, described below, the matched sample will have similar properties to a true random sample. That is, the matched sample mimics the characteristics of the target sample.
When choosing the matched sample, it is necessary to find the closest matching respondent in the panel of opt-ins to each member of the target sample. YouGov employs the proximity matching method to find the closest matching respondent. For each variable used for matching, we define a distance function, d(x,y), which describes how "close" the values x and y are on a particular attribute. The overall distance between a member of the target sample and a member of the panel is a weighted sum of the individual distance functions on each attribute. The weights can be adjusted for each study based upon which variables are thought to be important for that study, though, for the most part, we have not found the matching procedure to be sensitive to small adjustments of the weights. A large weight, on the other hand, forces the algorithm toward an exact match on that
dimension.
9.14 May 2012
For the survey May 2012 , YouGov interviewed 909 respondents between May 7-13 2012, who were then matched down to a sample of 800 to produce the final dataset. The respondents were matched on gender, age, race, education, party identification, ideology and political interest. YouGov then weighted the matched set of survey respondents to known marginals for the registered voters of Texas from the 2008 Current Population survey and the 2007 Pew Religious Landscape Survey.
Sampling Frame and Target Sample
YouGov constructed a national sampling frame from the 2007 American Community Survey, including data on age, race, gender, education, marital status, number of children under 18, family income, employment status, citizenship, state, and metropolitan area. The frame was constructed by stratified sampling from the full 2007 ACS sample with selection within strata by weighted sampling with replacements (using the person weights on the public use file). Data on voter registration status and turnout were matched to this frame using the November 2008 Current Population Survey. Data on interest in politics and party identification were then matched to this frame from the 2007 Pew Religious Life survey, using the following variables for the match: age, race, gender, education, marital status, number of children under 18, family income, employment status, citizenship, state. The target sample of 800 Texas registered voters was selected with stratification by age, race, gender, education, and with simple random sampling within strata.
Weighting
Because matching is approximate, rather than exact, and response rates vary by group, the sample of completed interviews normally shows small amounts of imbalance that can be corrected by post-stratification weighting.
Raking, first proposed by Deming and Stephan (1940), adjusts an initial set of weights to match a known set of population marginals, using a method of iterative proportional fitting (see Bishop, Fienberg and Holland, 1975 for details). In this procedure, the weights are adjusted sequentially to match the marginal distribution of each weight variable. The process proceeds until all marginals are matched. It does not require any information about the joint distribution of the variables (though, if these data are available and believed to be important, they can be employed by defining a marginal distribution involving a cross-classification of two variables).
Post-stratification weights are calculated by raking the completed interviews to known marginals for Texas registered voters from the November 2008 Current Population Survey for the following variables: age, race, gender, and education.
Survey Panel Data
The YouGov panel, a proprietary opt-in survey panel, is comprised of 1.2 million U.S. residents who have agreed to participate in YouGov Web surveys. At any given time, YouGov maintains a minimum of five recruitment campaigns based on salient current events.
Panel members are recruited by a number of methods and on a variety of topics to help ensure diversity in the panel population. Recruiting methods include Web advertising campaigns (public surveys), permission-based email campaigns, partner sponsored solicitations, telephone-to-Web recruitment (RDD based sampling), and mail-to-Web recruitment (Voter Registration Based Sampling).
The primary method of recruitment for the YouGov Panel is Web advertising campaigns that appear based on keyword searches. In practice, a search in Google may prompt an active YouGov advertisement soliciting opinion on the search topic. At the conclusion of the short survey respondents are invited to join the YouGov panel in order to receive and participate in additional surveys. After a double opt-in procedure, where respondents must confirm their consent by responding to an email, the database checks to ensure the newly recruited panelist is in fact new and that the address information provided is valid.
Additionally, YouGov augments their panel with difficult to recruit respondents by soliciting panelists in telephone and mail surveys. For example, in 2006 and 2010, YouGov completed telephone interviews using RDD sampling and invited respondents to join the online panel. Respondents provided a working email where they could confirm their consent and request to receive online survey invitations. YouGov also employed registration based sampling, inviting respondents to complete a pre-election survey online. At the conclusion of that survey, respondents were invited to become YouGov members and receive additional survey invitations at their email address.
The YouGov panel currently has nearly 20,000 panelists who are residents of Texas. These panelists cover a wide range of demographic characteristics.
Sampling and Sample Matching
Sample matching is a methodology for selection of "representative" samples from non-randomly selected pools of respondents. It is ideally suited for Web access panels, but could also be used for other types of surveys, such as phone surveys. Sample matching starts with an enumeration of the target population. For general population studies, the target population is all adults, and can be enumerated through the use of the decennial Census or a high quality survey, such as the American Community Survey. In other contexts, this is known as the sampling frame, though, unlike conventional sampling, the sample is not drawn from the frame. Traditional sampling, then, selects individuals from the sampling frame at random for participation in the study. This may not be feasible or economical as the contact information, especially email addresses, is not available for all individuals in the frame and refusals to participate increase the costs of sampling in this way.
Sample selection using the matching methodology is a two-stage process. First, a random sample is drawn from the target population. We call this sample the target sample. Details on how the target sample is drawn are provided below, but the essential idea is that this sample is a true probability sample and thus representative of the frame from which it was drawn.
Second, for each member of the target sample, we select one or more matching members from our pool of opt-in respondents. This is called the matched sample. Matching is accomplished using a large set of variables that are available in consumer and voter databases for both the target population and the opt-in panel.
The purpose of matching is to find an available respondent who is as similar as possible to the selected member of the target sample. The result is a sample of respondents who have the same measured characteristics as the target sample. Under certain conditions, described below, the matched sample will have similar properties to a true random sample. That is, the matched sample mimics the characteristics of the target sample.
When choosing the matched sample, it is necessary to find the closest matching respondent in the panel of opt-ins to each member of the target sample. YouGov employs the proximity matching method to find the closest matching respondent. For each variable used for matching, we define a distance function, d(x,y), which describes how "close" the values x and y are on a particular attribute. The overall distance between a member of the target sample and a member of the panel is a weighted sum of the individual distance functions on each attribute. The weights can be adjusted for each study based upon which variables are thought to be important for that study, though, for the most part, we have not found the matching procedure to be sensitive to small adjustments of the weights. A large weight, on the other hand, forces the algorithm toward an exact match on that dimension.
9.15 October 2012
Sampling and Weighting Methodology for the October 2012 Texas Statewide Study
For the survey, YouGov interviewed 912 respondents between Oct 15-21 2012, who were then matched down to a sample of 800 to produce the final dataset. The respondents were matched on gender, age, race, education, party identification, ideology and political interest. YouGov then weighted the matched set of survey respondents to known marginals for the registered voters of Texas from the 2008 Current Population survey and the 2007 Pew Religious Landscape Survey.
Sampling Frame and Target Sample
YouGov constructed a national sampling frame from the 2007 American Community Survey, including data on age, race, gender, education, marital status, number of children under 18, family income, employment status, citizenship, state, and metropolitan area. The frame was constructed by stratified sampling from the full 2007 ACS sample with selection within strata by weighted sampling with replacements (using the person weights on the public use file). Data on voter registration status and turnout were matched to this frame using the November 2008 Current Population Survey. Data on interest in politics and party identification were then matched to this frame from the 2007 Pew Religious Life survey, using the following variables for the match: age, race, gender, education, marital status, number of children under 18, family income, employment status, citizenship, state. The target sample of 800 Texas registered voters was selected with stratification by age, race, gender, education, and with simple random sampling within strata.
Weighting
Because matching is approximate, rather than exact, and response rates vary by group, the sample of completed interviews normally shows small amounts of imbalance that can be corrected by post-stratification weighting.
Raking, first proposed by Deming and Stephan (1940), adjusts an initial set of weights to match a known set of population marginals, using a method of iterative proportional fitting (see Bishop, Fienberg and Holland, 1975 for details). In this procedure, the weights are adjusted sequentially to match the marginal distribution of each weight variable. The process proceeds until all marginals are matched. It does not require any information about the joint distribution of the variables (though, if these data are available and believed to be important, they can be employed by defining a marginal distribution involving a cross-classification of two variables).
Post-stratification weights are calculated by raking the completed interviews to known marginals for Texas registered voters from the November 2008 Current Population Survey for the following variables: age, race, gender, and education.
Survey Panel Data
The YouGov panel, a proprietary opt-in survey panel, is comprised of 1.2 million U.S. residents who have agreed to participate in YouGov Web surveys. At any given time, YouGov maintains a minimum of five recruitment campaigns based on salient current events.
Panel members are recruited by a number of methods and on a variety of topics to help ensure diversity in the panel population. Recruiting methods include Web advertising campaigns (public surveys), permission-based email campaigns, partner sponsored solicitations, telephone-to-Web recruitment (RDD based sampling), and mail-to-Web recruitment (Voter Registration Based Sampling).
The primary method of recruitment for the YouGov Panel is Web advertising campaigns that appear based on keyword searches. In practice, a search in Google may prompt an active YouGov advertisement soliciting opinion on the search topic. At the conclusion of the short survey respondents are invited to join the YouGov panel in order to receive and participate in additional surveys. After a double opt-in procedure, where respondents must confirm their consent by responding to an email, the database checks to ensure the newly recruited panelist is in fact new and that the address information provided is valid.
Additionally, YouGov augments their panel with difficult to recruit respondents by soliciting panelists in telephone and mail surveys. For example, in 2006 and 2010, YouGov completed telephone interviews using RDD sampling and invited respondents to join the online panel. Respondents provided a working email where they could confirm their consent and request to receive online survey invitations. YouGov also employed registration based sampling, inviting respondents to complete a pre-election survey online. At the conclusion of that survey, respondents were invited to become YouGov members and receive additional survey invitations at their email address.
The YouGov panel currently has nearly 20,000 panelists who are residents of Texas. These panelists cover a wide range of demographic characteristics.
Sampling and Sample Matching
Sample matching is a methodology for selection of "representative" samples from non-randomly selected pools of respondents. It is ideally suited for Web access panels, but could also be used for other types of surveys, such as phone surveys. Sample matching starts with an enumeration of the target population. For general population studies, the target population is all adults, and can be enumerated through the use of the decennial Census or a high quality survey, such as the American Community Survey. In other contexts, this is known as the sampling frame, though, unlike conventional sampling, the sample is not drawn from the frame. Traditional sampling, then, selects individuals from the sampling frame at random for participation in the study. This may not be feasible or economical as the contact information, especially email addresses, is not available for all individuals in the frame and refusals to participate increase the costs of sampling in this way.
Sample selection using the matching methodology is a two-stage process. First, a random sample is drawn from the target population. We call this sample the target sample. Details on how the target sample is drawn are provided below, but the essential idea is that this sample is a true probability sample and thus representative of the frame from which it was drawn.
Second, for each member of the target sample, we select one or more matching members from our pool of opt-in respondents. This is called the matched sample. Matching is accomplished using a large set of variables that are available in consumer and voter databases for both the target population and the opt-in panel.
The purpose of matching is to find an available respondent who is as similar as possible to the selected member of the target sample. The result is a sample of respondents who have the same measured characteristics as the target sample. Under certain conditions, described below, the matched sample will have similar properties to a true random sample. That is, the matched sample mimics the characteristics of the target sample.
When choosing the matched sample, it is necessary to find the closest matching respondent in the panel of opt-ins to each member of the target sample. YouGov employs the proximity matching method to find the closest matching respondent. For each variable used for matching, we define a distance function, d(x,y), which describes how "close" the values x and y are on a particular attribute. The overall distance between a member of the target sample and a member of the panel is a weighted sum of the individual distance functions on each attribute. The weights can be adjusted for each study based upon which variables are thought to be important for that study, though, for the most part, we have not found the matching procedure to be sensitive to small adjustments of the weights. A large weight, on the other hand, forces the algorithm toward an exact match on that dimension.
9.16 February 2013
Sampling and Weighting Methodology for the February 2013 Texas Statewide Study
For the survey, YouGov interviewed 1420 respondents between February 15-25, 2013, who were then matched down to a sample of 1200 to produce the final dataset. The respondents were matched on gender, age, race, education, party identification, ideology and political interest. YouGov then weighted the matched set of survey respondents to known marginals for the registered voters of Texas from the 2008 Current Population survey and the 2007 Pew Religious Landscape Survey.
Sampling Frame and Target Sample
YouGov constructed a national sampling frame from the 2007 American Community Survey, including data on age, race, gender, education, marital status, number of children under 18, family income, employment status, citizenship, state, and metropolitan area. The frame was constructed by stratified sampling from the full 2007 ACS sample with selection within strata by weighted sampling with replacements (using the person weights on the public use file). Data on voter registration status and turnout were matched to this frame using the November 2008 Current Population Survey. Data on interest in politics and party identification were then matched to this frame from the 2007 Pew Religious Life survey, using the following variables for the match: age, race, gender, education, marital status, number of children under 18, family income, employment status, citizenship, state. The target sample of 1200 Texas registered voters was selected with stratification by age, race, gender, education, and with simple random sampling within strata.
Weighting
Because matching is approximate, rather than exact, and response rates vary by group, the sample of completed interviews normally shows small amounts of imbalance that can be corrected by post-stratification weighting.
The matched cases were weighted to the sampling frame using propensity scores. The matched cases and the frame were combined and a logistic regression was estimated for inclusion in the frame. The propensity score function included age, years of education, gender, race/ethnicity, and interest in politics. The propensity scores were grouped into deciles of the estimated propensity score in the frame and post-stratified according to these deciles. Weights larger than 7 were trimmed and the final weights normalized to equal sample size.
The margin of error of the weighted data is 3.3%.
Survey Panel Data
The YouGov panel, a proprietary opt-in survey panel, is comprised of 1.2 million U.S. residents who have agreed to participate in YouGov Web surveys. At any given time, YouGov maintains a minimum of five recruitment campaigns based on salient current events.
Panel members are recruited by a number of methods and on a variety of topics to help ensure diversity in the panel population. Recruiting methods include Web advertising campaigns (public surveys), permission-based email campaigns, partner sponsored solicitations, telephone-to-Web recruitment (RDD based sampling), and mail-to-Web recruitment (Voter Registration Based Sampling).
The primary method of recruitment for the YouGov Panel is Web advertising campaigns that appear based on keyword searches. In practice, a search in Google may prompt an active YouGov advertisement soliciting opinion on the search topic. At the conclusion of the short survey respondents are invited to join the YouGov panel in order to receive and participate in additional surveys. After a double opt-in procedure, where respondents must confirm their consent by responding to an email, the database checks to ensure the newly recruited panelist is in fact new and that the address information provided is valid.
Additionally, YouGov augments their panel with difficult to recruit respondents by soliciting panelists in telephone and mail surveys. For example, in 2006 and 2010, YouGov completed telephone interviews using RDD sampling and invited respondents to join the online panel. Respondents provided a working email where they could confirm their consent and request to receive online survey invitations. YouGov also employed registration based sampling, inviting respondents to complete a pre-election survey online. At the conclusion of that survey, respondents were invited to become YouGov members and receive additional survey invitations at their email address.
The YouGov panel currently has nearly 20,000 panelists who are residents of Texas. These panelists cover a wide range of demographic characteristics.
Sampling and Sample Matching
Sample matching is a methodology for selection of "representative" samples from non-randomly selected pools of respondents. It is ideally suited for Web access panels, but could also be used for other types of surveys, such as phone surveys. Sample matching starts with an enumeration of the target population. For general population studies, the target population is all adults, and can be enumerated through the use of the decennial Census or a high quality survey, such as the American Community Survey. In other contexts, this is known as the sampling frame, though, unlike conventional sampling, the sample is not drawn from the frame. Traditional sampling, then, selects individuals from the sampling frame at random for participation in the study. This may not be feasible or economical as the contact information, especially email addresses, is not available for all individuals in the frame and refusals to participate increase the costs of sampling in this way.
Sample selection using the matching methodology is a two-stage process. First, a random sample is drawn from the target population. We call this sample the target sample. Details on how the target sample is drawn are provided below, but the essential idea is that this sample is a true probability sample and thus representative of the frame from which it was drawn.
Second, for each member of the target sample, we select one or more matching members from our pool of opt-in respondents. This is called the matched sample. Matching is accomplished using a large set of variables that are available in consumer and voter databases for both the target population and the opt-in panel.
The purpose of matching is to find an available respondent who is as similar as possible to the selected member of the target sample. The result is a sample of respondents who have the same measured characteristics as the target sample. Under certain conditions, described below, the matched sample will have similar properties to a true random sample. That is, the matched sample mimics the characteristics of the target sample.
When choosing the matched sample, it is necessary to find the closest matching respondent in the panel of opt-ins to each member of the target sample. YouGov employs the proximity matching method to find the closest matching respondent. For each variable used for matching, we define a distance function, d(x,y), which describes how "close" the values x and y are on a particular attribute. The overall distance between a member of the target sample and a member of the panel is a weighted sum of the individual distance functions on each attribute. The weights can be adjusted for each study based upon which variables are thought to be important for that study, though, for the most part, we have not found the matching procedure to be sensitive to small adjustments of the weights. A large weight, on the other hand, forces the algorithm toward an exact match on that dimension.
10. About These Polls
The UT-Austin Texas Politics Poll was launched in July of 2008, marking a major step forward in the measurement of public opinion for Texas.
In October, 2009, researchers in the Department of Government welcomed The Texas Tribune, a new non-profit, nonpartisan public media organization based in Austin, as collaborators in the project.
At the time the UT Poll was launched, Texas did not have a regularly occurring, non-partisan poll from which data are made available for public use (other large states, such as California and New York, have these sorts of polls). The UT Poll fills this gap by providing a much-needed measure of public opinion among adult citizens of Texas.
The UT-Austin/Texas Tribune poll is notable not just for its substantive contribution but for its research design as well. It is conducted in conjunction with YouGov/Polimetrix, a company that is well-known for its innovative internet-based survey techniques. Unlike most political surveys, the UT-Austin/Texas Tribune Poll will be conducted entirely online. Internet-based polling is rapidly becoming a popular alternative to telephone-based polling; as many have observed, telephone polls are becoming somewhat problematic because of the growth of cellphone-only households and caller ID screening in residential landlines. Thus, the UT-Austin/Texas Tribune Poll represents a cutting-edge approach to ascertaining popular opinion in Texas.
The poll is designed to provide both educational and research resources to students, educators, and the general public. The survey data is and will continue to be made available to the public and to researchers. Graphs and charts are designed to make data not only available but accessible to everyone interested in Texas politics and government.
Contact:
Dr. James Henson
Department of Government and Liberal Arts Instructional Technology Services
The University of Texas at Austin
512 471-0090
j.henson@austin.utexas.edu
Professor Daron Shaw
Government Department
University of Texas at Austin
512 232-7275
dshaw@austin.utexas.edu
