Transcript PowerPoint Templates

STATISTICAL METHODS FOR
REDUCING BIAS IN WEB SURVEYS
13rd September 2012
Myoung Ho Lee
 Introduction
 Web surveys
 Methodology
- Propensity Score Adjustment
- Calibration (Rim weighting)
 Case Study
 Discussion and Conclusion
Contents
2
• Trends in Data Collection
Paper and Pencil => Telephone => Computer
=> Internet (Web)
• Internet penetration
Introduction
3
 Pros and Cons of Web surveys
• Pros
- Low cost and Speed
- No interviewer effect
- Visual, flexible and interactive
- Respondents convenience
• Cons
- Quality of sample estimates
 Web surveys may be solutions! But, Problems!!!
Introduction
4
 Previous Studies
• Harris Interactive (2000 ~ )
• Lee (2004), Lee and Valliant (2009)
• Hur and Cho (2009)
• Bethlehem (2010), etc.
 Lee and Valliant (2009) : good performance in simulation
 But, most other results do not seem to be so good.
- Malhotra and Krosnick (2007), Huh and Cho (2009)
Introduction
5
 Volunteer Panel Web Survey Protocol (Lee, 2004)
Under-coverage
Self-selection
Non-response
 Challenge: Fix anticipated biases in web survey estimates that
result from under-coverage, self-selection and non-response
Web surveys
6
Proposed Adjustment Procedure for Volunteer Panel Web surveys (Lee, 2004)
Methodology
7
 Propensity Score Adjustment (PSA)
• Original idea : Comparison of two groups, treatment and control,
in observational studies (Rosenbaum and Rubin, 1983)
- by weighting using all auxiliary variables that are thought to
account for the differences
• In context of web surveys, this technique aims to correct for
differences between offline people and online people
- by certain inclinations of people who participate in the volunteer
panel web survey
Methodology
8
• “Webographic” : overlapping variables between
web and reference survey
- To capture the difference between online and
offline populations (Schonlau et al., 2007)
- For example, “Do you feel alone?”, “In the last month
have you read a book?”…… (Harris Interactive)
Methodology
9
• Propensity score :
It is assumed that zi are independent given a set of covariates (xi)
• ‘Strong ignorability assumption’ : Response variable is conditionally
independent of treatment assignment given the propensity score.
Methodology
10
 Logistic regression model :
 Variable Selection
• Include variables related to not only treatment assignment
but also response in order to satisfy the ‘strong ignorability
assumption’
(Rosenbaum and Rubin, 1984; Brookhart et al., 2006)
Methodology
11
 Variable Selection
• In practice, stepwise selection method has been often used to
develop good predictive models for treatment assignment
• Most previous web studies : Use of all available covariates (5-30)
• Huh and Cho (2009) : 9 or 7 out of 123 covariates were chosen
by their “subjective” views
Methodology
12
 Variable Selection
• Stepwise logistic regression using SIC
- large number of covariates, little theoretical guidance
• LASSO (PROC GLMSELECT in SAS)
- a good alternative to stepwise variable selection
• Boosted tree (“gbm” in R)
- determine a set of split conditions
Methodology
13
 Applying methods for PSA
• Inverse propensity scores as weights
- weights :
- then, multiply them with sampling weights
• Subclassification (Stratification)
- subgrouping homogenous people into each stratum
Methodology
14
• Subclassification (Stratification)
1.
Combine both reference and web data into one
2.
Estimate each propensity score from the combined sample
3.
Partition those units into C subclasses according to ordered
values, where each subclass has about the same number of units
4.
Compute adjustment factor, and apply to all units in the cth
subclass.
5.
Multiply the factor with sampling weights to get PSA weights
Methodology
15
 Calibration (Rim weighting)
•
Matching sample and population characteristics only with
respect to the marginal distributions of selected covariates
• Little and Wu (1991)
- Iterative algorithm to alternatively adjust weights according to
each covariates’ marginal distribution until convergence
Methodology
16
 Case Study
• Reference survey : “2009 Social Survey” by Statistics Korea
- Culture & Leisure, Income & Consumption, etc.
- All persons aged +15 in 17,000 households
- Sample size : 37,049
- Face-to-face mode
- Post-stratification estimation
- Assumed to be “True”
Case Study
17
• Web survey
- Recruiting volunteers from web sites (6,854 households)
- Systematic sampling with non-equal selection probabilities
(inverse of rim weights using region, age, gender)
- Sample size : 1,500 households and 2,903 respondents
- Overlapping covariates : 123
Case Study
18
M1 = Stepwise(22), M2 = Stepwise(17), M3 = LASSO(12), M4 = Boosted tree(18)
Case Study – Model Selecion
19
 Assessment methods
• 16 combinations : (Model 1, 2, 3 and 4) × (Inverse weighting
and Subclassification) × (No Calibration and Rim weighting)
• 12 response variables
• Percentage of bias reduction
Case Study
20
Percentage of bias reduction
PSA alone
Calibration
Inverse weighting Subclassification Inverse weighting Subclassification
M1
M2
M3
M4 M1
M2
M3
M4
M1
M2
M3
M4
M1
M2
M3
M4
• Why PSA doesn’t work well alone ???
Propensity scores for each survey in 5 strata in Model 1
Discussion
22
 What are the possible solutions to fix poor PSA?
• Setting maximum value of weight
• Different subclassification algorithm
- Formula for the variance of weights that depends on both the
number of cases from each group within a stratum and the
variability of propensity scores with the stratum
• Matching PSA
- limited number of treated group members and a larger number
of control group members
Discussion
23
• Violation of some assumptions
- ‘Strong ignorability assumption’
- Missing at random (MAR)
- Mode effects
• Variable selection (What are webographic variables?)
- Models affect the performance of PSA significantly
- Maybe expert knowledge, not statistical approach
- Further studies are needed
Discussion
24
• Web surveys have attractive advantages
• However, bias from self-selection, under-coverage, non-responses
• According to my case study results,
=> It seems to be difficult to apply PSA to “real world” just now
• Further researches on webographic variables and different PSA
methods are needed
Conclusion
25