Estimation of Response Propensities and Indicators of Representative Response Using Population-Level InformationCitation formats

  • Authors:
  • Annamaria Bianchi
  • Natalie Shlomo
  • Barry Schouten
  • Damião N. Da Silva
  • Chris Skinner

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Estimation of Response Propensities and Indicators of Representative Response Using Population-Level Information. / Bianchi, Annamaria; Shlomo, Natalie; Schouten, Barry; Da Silva, Damião N. ; Skinner, Chris .

In: Survey Methodology, Vol. 45, No. 2, 27.06.2019, p. 217-247.

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Vancouver

Bianchi A, Shlomo N, Schouten B, Da Silva DN, Skinner C. Estimation of Response Propensities and Indicators of Representative Response Using Population-Level Information. Survey Methodology. 2019 Jun 27;45(2):217-247.

Author

Bianchi, Annamaria ; Shlomo, Natalie ; Schouten, Barry ; Da Silva, Damião N. ; Skinner, Chris . / Estimation of Response Propensities and Indicators of Representative Response Using Population-Level Information. In: Survey Methodology. 2019 ; Vol. 45, No. 2. pp. 217-247.

Bibtex

@article{29949130147246f996bd05957e3d69ef,
title = "Estimation of Response Propensities and Indicators of Representative Response Using Population-Level Information",
abstract = "In recent years, there has been a strong interest in indirect measures of nonresponse bias in surveys or other forms of data collection. This interest originates from gradually decreasing propensities to respond to surveys parallel to pressures on survey budgets. These developments led to a growing focus on the representativeness or balance of the responding sample units with respect to relevant auxiliary variables. One example of a measure is the representativeness indicator, or R-indicator. The R-indicator is based on the design-weighted sample variation of estimated response propensities. It pre-supposes linked auxiliary data. One of the criticisms of the indicator is that it cannot be used in settings where auxiliary information is available only at the population level. In this paper, we propose a new method for estimating response propensities that does not need auxiliary information for non-respondents to the survey and is based on population auxiliary information. These population-based response propensities can then be used to develop R-indicators that employ population contingency tables or population frequency counts. We discuss the statistical properties of the indicators, and evaluate their performance using an evaluation study basedon real census data and an application from the Dutch Health Survey.",
keywords = "Nonresponse , Missing data, Nonresponse bias, Balanced response",
author = "Annamaria Bianchi and Natalie Shlomo and Barry Schouten and {Da Silva}, {Dami{\~a}o N.} and Chris Skinner",
year = "2019",
month = jun,
day = "27",
language = "English",
volume = "45",
pages = "217--247",
journal = "Survey Methodology",
issn = "1492-0921",
publisher = "Statistics Canada",
number = "2",

}

RIS

TY - JOUR

T1 - Estimation of Response Propensities and Indicators of Representative Response Using Population-Level Information

AU - Bianchi, Annamaria

AU - Shlomo, Natalie

AU - Schouten, Barry

AU - Da Silva, Damião N.

AU - Skinner, Chris

PY - 2019/6/27

Y1 - 2019/6/27

N2 - In recent years, there has been a strong interest in indirect measures of nonresponse bias in surveys or other forms of data collection. This interest originates from gradually decreasing propensities to respond to surveys parallel to pressures on survey budgets. These developments led to a growing focus on the representativeness or balance of the responding sample units with respect to relevant auxiliary variables. One example of a measure is the representativeness indicator, or R-indicator. The R-indicator is based on the design-weighted sample variation of estimated response propensities. It pre-supposes linked auxiliary data. One of the criticisms of the indicator is that it cannot be used in settings where auxiliary information is available only at the population level. In this paper, we propose a new method for estimating response propensities that does not need auxiliary information for non-respondents to the survey and is based on population auxiliary information. These population-based response propensities can then be used to develop R-indicators that employ population contingency tables or population frequency counts. We discuss the statistical properties of the indicators, and evaluate their performance using an evaluation study basedon real census data and an application from the Dutch Health Survey.

AB - In recent years, there has been a strong interest in indirect measures of nonresponse bias in surveys or other forms of data collection. This interest originates from gradually decreasing propensities to respond to surveys parallel to pressures on survey budgets. These developments led to a growing focus on the representativeness or balance of the responding sample units with respect to relevant auxiliary variables. One example of a measure is the representativeness indicator, or R-indicator. The R-indicator is based on the design-weighted sample variation of estimated response propensities. It pre-supposes linked auxiliary data. One of the criticisms of the indicator is that it cannot be used in settings where auxiliary information is available only at the population level. In this paper, we propose a new method for estimating response propensities that does not need auxiliary information for non-respondents to the survey and is based on population auxiliary information. These population-based response propensities can then be used to develop R-indicators that employ population contingency tables or population frequency counts. We discuss the statistical properties of the indicators, and evaluate their performance using an evaluation study basedon real census data and an application from the Dutch Health Survey.

KW - Nonresponse

KW - Missing data

KW - Nonresponse bias

KW - Balanced response

M3 - Article

VL - 45

SP - 217

EP - 247

JO - Survey Methodology

JF - Survey Methodology

SN - 1492-0921

IS - 2

ER -