Data Archive
February 2015 University of Texas/Texas Tribune Poll
Sampling and Weighting Methodology for the February 2015 Texas Statewide Study
For the survey, YouGov interviewed 1387 respondents between February 6-16, 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 characteristics of registered voters of Texas from the 2012 Current Population survey and the 2007 Pew Religious Landscape Survey.
Sampling Frame and Target Sample
YouGov constructed a national sampling frame from the 2012 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 2012 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 2012 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 3.36%.
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 over 20,000 active 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.
October 2014 University of Texas/Texas Tribune Poll
Sampling and Weighting Methodology for the October 2014 Texas Statewide Study
For the survey, YouGov interviewed 1387 respondents between October 10-20, 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 characteristics of registered voters of Texas from the 2012 Current Population survey and the 2007 Pew Religious Landscape Survey.
Sampling Frame and Target Sample
YouGov constructed a national sampling frame from the 2012 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 2012 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 2012 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 3.06%. The margin of error for likely voters is 3.63%.
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 over 20,000 active 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.
June 2014 University of Texas/Texas Tribune Poll
Sampling and Weighting Methodology for the May-June 2014 Texas Statewide Study
For the survey, YouGov interviewed 1350 respondents between May 30 and June 9, 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 characteristics of registered voters of Texas from the 2010 Current Population survey and the 2007 Pew Religious Landscape Survey.
Sampling Frame and Target Sample
YouGov constructed a national sampling frame from the 2010 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 2010 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 2010 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 3.58%. The margin of error for Republican primary voters is 5.64%. The margin of error for Democratic primary voters is 6.04%.
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 over 20,000 active 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.
February 2014 University of Texas/Texas Tribune Poll
Sampling and Weighting Methodology for the October 2013 Texas Statewide Study
For the survey, YouGov interviewed 1327 respondents between February 7-16, 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 characteristics of registered voters of Texas from the 2010 Current Population survey and the 2007 Pew Religious Landscape Survey.
Sampling Frame and Target Sample
YouGov constructed a national sampling frame from the 2010 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 2010 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 2010 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 3.58%.
The margin of error for Republican primary voters is 5.64%. The margin of error for Democratic primary voters is 6.04%.
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 active 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.
October 2013 University of Texas/Texas Tribune Poll
Sampling and Weighting Methodology for the October 2013 Texas Statewide Study
For the survey, YouGov interviewed 1618 respondents between October 18-29, 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 characteristics of registered voters of Texas from the 2010 Current Population survey and the 2007 Pew Religious Landscape Survey.
Sampling Frame and Target Sample
YouGov constructed a national sampling frame from the 2010 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 2010 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 2010 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 3.1%.
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 active 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.
June 2013 University of Texas/Texas Tribune Poll
Sampling and Weighting Methodology for the May-June 2013 Texas Statewide Study
For the survey, YouGov interviewed 1359 respondents between May 30-June13, 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 characteristics of registered voters of Texas from the 2010 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 2010 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 active 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.
February 2013 University of Texas/Texas Tribune Poll
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.
October 2012 University of Texas/Texas Tribune Poll
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.
May 2012 University of Texas/Texas Tribune Poll
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.
February 2012 University of Texas/Texas Tribune Poll
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.
October 2011 University of Texas/Texas Tribune Poll
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.
May 2011 University of Texas/Texas Tribune Poll
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.
February 2011 University of Texas/Texas Tribune Poll
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.
October 2010 University of Texas/Texas Tribune Poll
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.
September 2010 University of Texas/Texas Tribune Poll
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.
May 2010 University of Texas/Texas Tribune Poll
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.
February 2010 University of Texas/Texas Tribune Poll
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.
October 2009 University of Texas Poll
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.
June 2009 University of Texas Poll
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.
February-March 2009 University of Texas Poll
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.
October 2008 University of Texas Poll
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.
July 2008 University of Texas Poll
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.