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Emi Tamaki - One of the best experts on this subject based on the ideXlab platform.
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do low survey response rates bias results evidence from japan
Demographic Research, 2015Co-Authors: Ronald R Rindfuss, Minja K Choe, Noriko O Tsuya, Larry L Bumpass, Emi TamakiAbstract:Background: In developed countries, response rates have dropped to such low levels that many in the population field question whether the data can provide unbiased results. Objective: The paper uses three Japanese surveys conducted in the 2000s to ask whether low survey response rates bias results. A secondary objective is to bring results reported in the survey response literature to the attention of the demographic research community. Methods: Using a longitudinal survey as well as paradata from a cross-sectional survey, a variety of statistical techniques (chi square, analysis of variance (ANOVA), logistic regression, ordered probit or ordinary least squares regression (OLS), as appropriate) are used to examine response-rate bias. Results: Evidence of response-rate bias is found for the Univariate Distributions of some demographic characteristics, behaviors, and attitudinal items. But when examining relationships between variables in a multivariate analysis, controlling for a variety of background variables, for most dependent variables we do not find evidence of bias from low response rates. Conclusions: Our results are consistent with results reported in the econometric and survey research literatures. Low response rates need not necessarily lead to biased results. Bias is more likely to be present when examining a simple Univariate Distribution than when examining the relationship between variables in a multivariate model. Comments: The results have two implications. First, demographers should not presume the presence or absence of low response-rate bias; rather they should test for it in the context of a specific substantive analysis. Second, demographers should lobby data gatherers to collect as much paradata as possible so that rigorous tests for low response-rate bias are possible.
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do low survey response rates bias results evidence from japan
Demographic Research, 2015Co-Authors: Ronald R Rindfuss, Minja K Choe, Noriko O Tsuya, Larry L Bumpass, Emi TamakiAbstract:AbstractBACKGROUNDIn developed countries, response rates have dropped to such low levels that many in the population field question whether the data can provide unbiased results.OBJECTIVEThe paper uses three Japanese surveys conducted in the 2000s to ask whether low survey response rates bias results. A secondary objective is to bring results reported in the survey response literature to the attention of the demographic research community.METHODSUsing a longitudinal survey as well as paradata from a cross-sectional survey, a variety of statistical techniques (chi square, analysis of variance (ANOVA), logistic regression, ordered probit or ordinary least squares regression (OLS), as appropriate) are used to examine response-rate bias.RESULTSEvidence of response-rate bias is found for the Univariate Distributions of some demographic characteristics, behaviors, and attitudinal items. But when examining relationships between variables in a multivariate analysis, controlling for a variety of background variables, for most dependent variables we do not find evidence of bias from low response rates.CONCLUSIONSOur results are consistent with results reported in the econometric and survey research literatures. Low response rates need not necessarily lead to biased results. Bias is more likely to be present when examining a simple Univariate Distribution than when examining the relationship between variables in a multivariate model.COMMENTSThe results have two implications. First, demographers should not presume the presence or absence of low response-rate bias; rather they should test for it in the context of a specific substantive analysis. Second, demographers should lobby data gatherers to collect as much paradata as possible so that rigorous tests for low response-rate bias are possible.1. IntroductionSample surveys (cross-sectional and longitudinal) have become the dominant data source used by population researchers. Response rates, both the initial response rate and attrition rates in longitudinal studies, have historically been an important rough-andready yardstick to judge data quality. Response rates6 have been declining in urbanized, high-income countries to the point where many cross-sectional surveys now have response rates below 50% (Atrostic et al. 2001; Brick and Williams 2013; de Leeuw and de Heer 2002; Groves 2011; Singer 2006).The survey literature has long recognized that low response rates only indicate potential bias (e.g., Lessler and Kalsbeck 1992), yet the almost automatic response among most in the population field has been to equate low response rates with poor data quality. Low response rates produce bias only to the extent that there are differences between responders and non-responders on the estimate(s) of interest, and then only if such differences cannot be eliminated or controlled for through the use of observable and available characteristics of responders and non-responders. The difficulty in evaluating non-response bias is that measures of the variable(s) of interest and characteristics of non-responders are generally not observed, and hence the population field has tended to rely on the presumption that low response rates necessarily mean low data quality.In this paper, using surveys designed by demographers and variables that have often been used as dependent variables in demographic research, we examine this common presumption of demographers. We also place our results in the context of the survey research literature in which there are numerous indications that low response rates need not mean the results are biased and, if there is bias within the survey, it varies considerably from variable to variable. It has been unfortunate that the research literature on response rates and bias has tended to be published in journals aimed at survey research experts. These are journals that tend not to be read by population researchers and hence the persistent belief that low response rates equate to low-quality, biased data. …
Ronald R Rindfuss - One of the best experts on this subject based on the ideXlab platform.
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do low survey response rates bias results evidence from japan
Demographic Research, 2015Co-Authors: Ronald R Rindfuss, Minja K Choe, Noriko O Tsuya, Larry L Bumpass, Emi TamakiAbstract:Background: In developed countries, response rates have dropped to such low levels that many in the population field question whether the data can provide unbiased results. Objective: The paper uses three Japanese surveys conducted in the 2000s to ask whether low survey response rates bias results. A secondary objective is to bring results reported in the survey response literature to the attention of the demographic research community. Methods: Using a longitudinal survey as well as paradata from a cross-sectional survey, a variety of statistical techniques (chi square, analysis of variance (ANOVA), logistic regression, ordered probit or ordinary least squares regression (OLS), as appropriate) are used to examine response-rate bias. Results: Evidence of response-rate bias is found for the Univariate Distributions of some demographic characteristics, behaviors, and attitudinal items. But when examining relationships between variables in a multivariate analysis, controlling for a variety of background variables, for most dependent variables we do not find evidence of bias from low response rates. Conclusions: Our results are consistent with results reported in the econometric and survey research literatures. Low response rates need not necessarily lead to biased results. Bias is more likely to be present when examining a simple Univariate Distribution than when examining the relationship between variables in a multivariate model. Comments: The results have two implications. First, demographers should not presume the presence or absence of low response-rate bias; rather they should test for it in the context of a specific substantive analysis. Second, demographers should lobby data gatherers to collect as much paradata as possible so that rigorous tests for low response-rate bias are possible.
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do low survey response rates bias results evidence from japan
Demographic Research, 2015Co-Authors: Ronald R Rindfuss, Minja K Choe, Noriko O Tsuya, Larry L Bumpass, Emi TamakiAbstract:AbstractBACKGROUNDIn developed countries, response rates have dropped to such low levels that many in the population field question whether the data can provide unbiased results.OBJECTIVEThe paper uses three Japanese surveys conducted in the 2000s to ask whether low survey response rates bias results. A secondary objective is to bring results reported in the survey response literature to the attention of the demographic research community.METHODSUsing a longitudinal survey as well as paradata from a cross-sectional survey, a variety of statistical techniques (chi square, analysis of variance (ANOVA), logistic regression, ordered probit or ordinary least squares regression (OLS), as appropriate) are used to examine response-rate bias.RESULTSEvidence of response-rate bias is found for the Univariate Distributions of some demographic characteristics, behaviors, and attitudinal items. But when examining relationships between variables in a multivariate analysis, controlling for a variety of background variables, for most dependent variables we do not find evidence of bias from low response rates.CONCLUSIONSOur results are consistent with results reported in the econometric and survey research literatures. Low response rates need not necessarily lead to biased results. Bias is more likely to be present when examining a simple Univariate Distribution than when examining the relationship between variables in a multivariate model.COMMENTSThe results have two implications. First, demographers should not presume the presence or absence of low response-rate bias; rather they should test for it in the context of a specific substantive analysis. Second, demographers should lobby data gatherers to collect as much paradata as possible so that rigorous tests for low response-rate bias are possible.1. IntroductionSample surveys (cross-sectional and longitudinal) have become the dominant data source used by population researchers. Response rates, both the initial response rate and attrition rates in longitudinal studies, have historically been an important rough-andready yardstick to judge data quality. Response rates6 have been declining in urbanized, high-income countries to the point where many cross-sectional surveys now have response rates below 50% (Atrostic et al. 2001; Brick and Williams 2013; de Leeuw and de Heer 2002; Groves 2011; Singer 2006).The survey literature has long recognized that low response rates only indicate potential bias (e.g., Lessler and Kalsbeck 1992), yet the almost automatic response among most in the population field has been to equate low response rates with poor data quality. Low response rates produce bias only to the extent that there are differences between responders and non-responders on the estimate(s) of interest, and then only if such differences cannot be eliminated or controlled for through the use of observable and available characteristics of responders and non-responders. The difficulty in evaluating non-response bias is that measures of the variable(s) of interest and characteristics of non-responders are generally not observed, and hence the population field has tended to rely on the presumption that low response rates necessarily mean low data quality.In this paper, using surveys designed by demographers and variables that have often been used as dependent variables in demographic research, we examine this common presumption of demographers. We also place our results in the context of the survey research literature in which there are numerous indications that low response rates need not mean the results are biased and, if there is bias within the survey, it varies considerably from variable to variable. It has been unfortunate that the research literature on response rates and bias has tended to be published in journals aimed at survey research experts. These are journals that tend not to be read by population researchers and hence the persistent belief that low response rates equate to low-quality, biased data. …
Larry L Bumpass - One of the best experts on this subject based on the ideXlab platform.
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do low survey response rates bias results evidence from japan
Demographic Research, 2015Co-Authors: Ronald R Rindfuss, Minja K Choe, Noriko O Tsuya, Larry L Bumpass, Emi TamakiAbstract:Background: In developed countries, response rates have dropped to such low levels that many in the population field question whether the data can provide unbiased results. Objective: The paper uses three Japanese surveys conducted in the 2000s to ask whether low survey response rates bias results. A secondary objective is to bring results reported in the survey response literature to the attention of the demographic research community. Methods: Using a longitudinal survey as well as paradata from a cross-sectional survey, a variety of statistical techniques (chi square, analysis of variance (ANOVA), logistic regression, ordered probit or ordinary least squares regression (OLS), as appropriate) are used to examine response-rate bias. Results: Evidence of response-rate bias is found for the Univariate Distributions of some demographic characteristics, behaviors, and attitudinal items. But when examining relationships between variables in a multivariate analysis, controlling for a variety of background variables, for most dependent variables we do not find evidence of bias from low response rates. Conclusions: Our results are consistent with results reported in the econometric and survey research literatures. Low response rates need not necessarily lead to biased results. Bias is more likely to be present when examining a simple Univariate Distribution than when examining the relationship between variables in a multivariate model. Comments: The results have two implications. First, demographers should not presume the presence or absence of low response-rate bias; rather they should test for it in the context of a specific substantive analysis. Second, demographers should lobby data gatherers to collect as much paradata as possible so that rigorous tests for low response-rate bias are possible.
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do low survey response rates bias results evidence from japan
Demographic Research, 2015Co-Authors: Ronald R Rindfuss, Minja K Choe, Noriko O Tsuya, Larry L Bumpass, Emi TamakiAbstract:AbstractBACKGROUNDIn developed countries, response rates have dropped to such low levels that many in the population field question whether the data can provide unbiased results.OBJECTIVEThe paper uses three Japanese surveys conducted in the 2000s to ask whether low survey response rates bias results. A secondary objective is to bring results reported in the survey response literature to the attention of the demographic research community.METHODSUsing a longitudinal survey as well as paradata from a cross-sectional survey, a variety of statistical techniques (chi square, analysis of variance (ANOVA), logistic regression, ordered probit or ordinary least squares regression (OLS), as appropriate) are used to examine response-rate bias.RESULTSEvidence of response-rate bias is found for the Univariate Distributions of some demographic characteristics, behaviors, and attitudinal items. But when examining relationships between variables in a multivariate analysis, controlling for a variety of background variables, for most dependent variables we do not find evidence of bias from low response rates.CONCLUSIONSOur results are consistent with results reported in the econometric and survey research literatures. Low response rates need not necessarily lead to biased results. Bias is more likely to be present when examining a simple Univariate Distribution than when examining the relationship between variables in a multivariate model.COMMENTSThe results have two implications. First, demographers should not presume the presence or absence of low response-rate bias; rather they should test for it in the context of a specific substantive analysis. Second, demographers should lobby data gatherers to collect as much paradata as possible so that rigorous tests for low response-rate bias are possible.1. IntroductionSample surveys (cross-sectional and longitudinal) have become the dominant data source used by population researchers. Response rates, both the initial response rate and attrition rates in longitudinal studies, have historically been an important rough-andready yardstick to judge data quality. Response rates6 have been declining in urbanized, high-income countries to the point where many cross-sectional surveys now have response rates below 50% (Atrostic et al. 2001; Brick and Williams 2013; de Leeuw and de Heer 2002; Groves 2011; Singer 2006).The survey literature has long recognized that low response rates only indicate potential bias (e.g., Lessler and Kalsbeck 1992), yet the almost automatic response among most in the population field has been to equate low response rates with poor data quality. Low response rates produce bias only to the extent that there are differences between responders and non-responders on the estimate(s) of interest, and then only if such differences cannot be eliminated or controlled for through the use of observable and available characteristics of responders and non-responders. The difficulty in evaluating non-response bias is that measures of the variable(s) of interest and characteristics of non-responders are generally not observed, and hence the population field has tended to rely on the presumption that low response rates necessarily mean low data quality.In this paper, using surveys designed by demographers and variables that have often been used as dependent variables in demographic research, we examine this common presumption of demographers. We also place our results in the context of the survey research literature in which there are numerous indications that low response rates need not mean the results are biased and, if there is bias within the survey, it varies considerably from variable to variable. It has been unfortunate that the research literature on response rates and bias has tended to be published in journals aimed at survey research experts. These are journals that tend not to be read by population researchers and hence the persistent belief that low response rates equate to low-quality, biased data. …
Noriko O Tsuya - One of the best experts on this subject based on the ideXlab platform.
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do low survey response rates bias results evidence from japan
Demographic Research, 2015Co-Authors: Ronald R Rindfuss, Minja K Choe, Noriko O Tsuya, Larry L Bumpass, Emi TamakiAbstract:Background: In developed countries, response rates have dropped to such low levels that many in the population field question whether the data can provide unbiased results. Objective: The paper uses three Japanese surveys conducted in the 2000s to ask whether low survey response rates bias results. A secondary objective is to bring results reported in the survey response literature to the attention of the demographic research community. Methods: Using a longitudinal survey as well as paradata from a cross-sectional survey, a variety of statistical techniques (chi square, analysis of variance (ANOVA), logistic regression, ordered probit or ordinary least squares regression (OLS), as appropriate) are used to examine response-rate bias. Results: Evidence of response-rate bias is found for the Univariate Distributions of some demographic characteristics, behaviors, and attitudinal items. But when examining relationships between variables in a multivariate analysis, controlling for a variety of background variables, for most dependent variables we do not find evidence of bias from low response rates. Conclusions: Our results are consistent with results reported in the econometric and survey research literatures. Low response rates need not necessarily lead to biased results. Bias is more likely to be present when examining a simple Univariate Distribution than when examining the relationship between variables in a multivariate model. Comments: The results have two implications. First, demographers should not presume the presence or absence of low response-rate bias; rather they should test for it in the context of a specific substantive analysis. Second, demographers should lobby data gatherers to collect as much paradata as possible so that rigorous tests for low response-rate bias are possible.
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do low survey response rates bias results evidence from japan
Demographic Research, 2015Co-Authors: Ronald R Rindfuss, Minja K Choe, Noriko O Tsuya, Larry L Bumpass, Emi TamakiAbstract:AbstractBACKGROUNDIn developed countries, response rates have dropped to such low levels that many in the population field question whether the data can provide unbiased results.OBJECTIVEThe paper uses three Japanese surveys conducted in the 2000s to ask whether low survey response rates bias results. A secondary objective is to bring results reported in the survey response literature to the attention of the demographic research community.METHODSUsing a longitudinal survey as well as paradata from a cross-sectional survey, a variety of statistical techniques (chi square, analysis of variance (ANOVA), logistic regression, ordered probit or ordinary least squares regression (OLS), as appropriate) are used to examine response-rate bias.RESULTSEvidence of response-rate bias is found for the Univariate Distributions of some demographic characteristics, behaviors, and attitudinal items. But when examining relationships between variables in a multivariate analysis, controlling for a variety of background variables, for most dependent variables we do not find evidence of bias from low response rates.CONCLUSIONSOur results are consistent with results reported in the econometric and survey research literatures. Low response rates need not necessarily lead to biased results. Bias is more likely to be present when examining a simple Univariate Distribution than when examining the relationship between variables in a multivariate model.COMMENTSThe results have two implications. First, demographers should not presume the presence or absence of low response-rate bias; rather they should test for it in the context of a specific substantive analysis. Second, demographers should lobby data gatherers to collect as much paradata as possible so that rigorous tests for low response-rate bias are possible.1. IntroductionSample surveys (cross-sectional and longitudinal) have become the dominant data source used by population researchers. Response rates, both the initial response rate and attrition rates in longitudinal studies, have historically been an important rough-andready yardstick to judge data quality. Response rates6 have been declining in urbanized, high-income countries to the point where many cross-sectional surveys now have response rates below 50% (Atrostic et al. 2001; Brick and Williams 2013; de Leeuw and de Heer 2002; Groves 2011; Singer 2006).The survey literature has long recognized that low response rates only indicate potential bias (e.g., Lessler and Kalsbeck 1992), yet the almost automatic response among most in the population field has been to equate low response rates with poor data quality. Low response rates produce bias only to the extent that there are differences between responders and non-responders on the estimate(s) of interest, and then only if such differences cannot be eliminated or controlled for through the use of observable and available characteristics of responders and non-responders. The difficulty in evaluating non-response bias is that measures of the variable(s) of interest and characteristics of non-responders are generally not observed, and hence the population field has tended to rely on the presumption that low response rates necessarily mean low data quality.In this paper, using surveys designed by demographers and variables that have often been used as dependent variables in demographic research, we examine this common presumption of demographers. We also place our results in the context of the survey research literature in which there are numerous indications that low response rates need not mean the results are biased and, if there is bias within the survey, it varies considerably from variable to variable. It has been unfortunate that the research literature on response rates and bias has tended to be published in journals aimed at survey research experts. These are journals that tend not to be read by population researchers and hence the persistent belief that low response rates equate to low-quality, biased data. …
Minja K Choe - One of the best experts on this subject based on the ideXlab platform.
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do low survey response rates bias results evidence from japan
Demographic Research, 2015Co-Authors: Ronald R Rindfuss, Minja K Choe, Noriko O Tsuya, Larry L Bumpass, Emi TamakiAbstract:Background: In developed countries, response rates have dropped to such low levels that many in the population field question whether the data can provide unbiased results. Objective: The paper uses three Japanese surveys conducted in the 2000s to ask whether low survey response rates bias results. A secondary objective is to bring results reported in the survey response literature to the attention of the demographic research community. Methods: Using a longitudinal survey as well as paradata from a cross-sectional survey, a variety of statistical techniques (chi square, analysis of variance (ANOVA), logistic regression, ordered probit or ordinary least squares regression (OLS), as appropriate) are used to examine response-rate bias. Results: Evidence of response-rate bias is found for the Univariate Distributions of some demographic characteristics, behaviors, and attitudinal items. But when examining relationships between variables in a multivariate analysis, controlling for a variety of background variables, for most dependent variables we do not find evidence of bias from low response rates. Conclusions: Our results are consistent with results reported in the econometric and survey research literatures. Low response rates need not necessarily lead to biased results. Bias is more likely to be present when examining a simple Univariate Distribution than when examining the relationship between variables in a multivariate model. Comments: The results have two implications. First, demographers should not presume the presence or absence of low response-rate bias; rather they should test for it in the context of a specific substantive analysis. Second, demographers should lobby data gatherers to collect as much paradata as possible so that rigorous tests for low response-rate bias are possible.
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do low survey response rates bias results evidence from japan
Demographic Research, 2015Co-Authors: Ronald R Rindfuss, Minja K Choe, Noriko O Tsuya, Larry L Bumpass, Emi TamakiAbstract:AbstractBACKGROUNDIn developed countries, response rates have dropped to such low levels that many in the population field question whether the data can provide unbiased results.OBJECTIVEThe paper uses three Japanese surveys conducted in the 2000s to ask whether low survey response rates bias results. A secondary objective is to bring results reported in the survey response literature to the attention of the demographic research community.METHODSUsing a longitudinal survey as well as paradata from a cross-sectional survey, a variety of statistical techniques (chi square, analysis of variance (ANOVA), logistic regression, ordered probit or ordinary least squares regression (OLS), as appropriate) are used to examine response-rate bias.RESULTSEvidence of response-rate bias is found for the Univariate Distributions of some demographic characteristics, behaviors, and attitudinal items. But when examining relationships between variables in a multivariate analysis, controlling for a variety of background variables, for most dependent variables we do not find evidence of bias from low response rates.CONCLUSIONSOur results are consistent with results reported in the econometric and survey research literatures. Low response rates need not necessarily lead to biased results. Bias is more likely to be present when examining a simple Univariate Distribution than when examining the relationship between variables in a multivariate model.COMMENTSThe results have two implications. First, demographers should not presume the presence or absence of low response-rate bias; rather they should test for it in the context of a specific substantive analysis. Second, demographers should lobby data gatherers to collect as much paradata as possible so that rigorous tests for low response-rate bias are possible.1. IntroductionSample surveys (cross-sectional and longitudinal) have become the dominant data source used by population researchers. Response rates, both the initial response rate and attrition rates in longitudinal studies, have historically been an important rough-andready yardstick to judge data quality. Response rates6 have been declining in urbanized, high-income countries to the point where many cross-sectional surveys now have response rates below 50% (Atrostic et al. 2001; Brick and Williams 2013; de Leeuw and de Heer 2002; Groves 2011; Singer 2006).The survey literature has long recognized that low response rates only indicate potential bias (e.g., Lessler and Kalsbeck 1992), yet the almost automatic response among most in the population field has been to equate low response rates with poor data quality. Low response rates produce bias only to the extent that there are differences between responders and non-responders on the estimate(s) of interest, and then only if such differences cannot be eliminated or controlled for through the use of observable and available characteristics of responders and non-responders. The difficulty in evaluating non-response bias is that measures of the variable(s) of interest and characteristics of non-responders are generally not observed, and hence the population field has tended to rely on the presumption that low response rates necessarily mean low data quality.In this paper, using surveys designed by demographers and variables that have often been used as dependent variables in demographic research, we examine this common presumption of demographers. We also place our results in the context of the survey research literature in which there are numerous indications that low response rates need not mean the results are biased and, if there is bias within the survey, it varies considerably from variable to variable. It has been unfortunate that the research literature on response rates and bias has tended to be published in journals aimed at survey research experts. These are journals that tend not to be read by population researchers and hence the persistent belief that low response rates equate to low-quality, biased data. …
