Download presentation

Presentation is loading. Please wait.

Published byBrooke Poole Modified over 3 years ago

1
Page 1 Measuring Survey Quality through Representativity Indicators using Sample and Population based Information Chris Skinner, Natalie Shlomo, Barry Schouten, Jelke Bethlehem Li-Chun Zhang,

2
Page 2 Representativity Indicators for Survey Quality collaboration between: national statistical institutes of Netherlands, Norway and Slovenia & universities of Leuven & Southampton

3
Page 3 Representativity Indicators quality indicators for survey non-response to supplement response rate to measure how well respondents represent population tools for use at different stages of survey process (data collection+)

4
Page 4 Aim of paper to consider estimation of representativity indicators using either: sample-based information (microdata for both respondents and nonrespondents), or population-based information (microdata for respondents and aggregate data for population)

5
Page 5 How to define representativity indicator? two approaches both based on idea of response propensity

6
Page 6 Response propensity Idea: R-indicator measures homogeneity of response propensities: = probability of response for unit i given values of auxiliary variables

7
Page 7 R-indicator where if constant if Schouten, Cobben & Bethlehem (2009, Survey Methodology)

8
Page 8 Alternative Indicator where proposed by Särndal and Lundström (2008, JOS) in the context of selecting auxiliary variables for weighting adjustment

9
Page 9 Estimated R-indicator Sample-based – auxiliary variables recorded for whole sample (1)estimate response propensities using e.g. logistic regression (2)

10
Page 10 Estimated R-indicator Population-based – auxiliary variables only measured on respondents and in aggregated form for population (1)Estimate response propensities using ordinary least squares if population covariance matrix known. Estimate from respondents if only population mean vector known

11
Page 11 Estimated R-indicator Population based (continued) (2)

12
Page 12 Simulation Study Samples from Israel census data on 753000 individuals realistic sampling with fractions 1:50, 1:100, 1:200 realistic non-response based on type of locality, household size, children in household – overall response rate 82%

13
Page 13 Simulation Means of for Sample (S), Population with Known Covariance matrix (PC) and Population with Unknown Covariance Matrix (PUC) of Auxiliary Variables

14
Page 14 for Sample (S), Population with Known Covariance Matrix (PC) and Population with Unknown Covariance Matrix (PUC) of Auxiliary Variables – True Model

15
Page 15 for Sample (S), Population with Known Covariance Matrix (PC) and Population with Unknown Covariance Matrix (PUC) of Auxiliary Variables – Less Complex Model

16
Page 16 Simulation Means of for Sample (S) and Population (P) Based Auxiliary Variables

17
Page 17 for Sample (S) and Population (P) Based Auxiliary Variables

18
Page 18 Conclusions representativity indicators defined in terms of response propensities can estimate accurately given either sample- based or population-based information have also applied to surveys from RISQ countries and have examined bias-corrected estimators and estimators of standard errors of estimators

Similar presentations

OK

1 First EMRAS II Technical Meeting IAEA Headquarters, Vienna, 19–23 January 2009.

1 First EMRAS II Technical Meeting IAEA Headquarters, Vienna, 19–23 January 2009.

© 2017 SlidePlayer.com Inc.

All rights reserved.

Ads by Google

Ppt on water resources download Ppt on group development definition Ppt on importance of science and technology Ppt on statistics and probability notes Ppt on logo quiz Ppt on abstract art ideas Ppt on javascript events ios Ppt on 60 years of indian parliament fight Elaine marieb anatomy and physiology ppt on cells Ppt on seven ages of life