1. Chapter 12: Other nonresponse correction techniques
Handbook: chapter 10
Call-back Approach (Hansen & Hurvitz)
Basic Question Approach
Estimating contact probabilities (Politz & Simmons)

2. Call-back Approach Hansen & Hurvitz (1946)
A probability sample is selected from the nonrespondents and then re-approached
In a different data collection mode, or
By specially trained interviewers,
Offering (more) incentives.
Under the Fixed Response Model, the Call-back approach is:
Draw a simple random sample from the nonresponse stratum
IF everybody responds OR if the nonresponse in the call-back is ignorable it is possible to compute unbiased estimates

3. Call-back approach
Select a sample of size m for the call-back. Denote the number of respondents in the call-back approach by mR.
Main survey
Follow-up survey
Response
Nonresponse

4. Call-back Approach Estimation
Let denote the mean of the mR values of the responding elements.
Then
is an unbiased estimator of the mean of the population mean of Y.
However, we do not know NR / N and NNR / N so we have to replace them by their unbiased estimates nR / n and nNR / n:
Estimator is unbiased in case of complete response, or if nonresponse is MCAR or MAR.

5. The Call Back approach Example: Labour Force Survey 2005
After 1 week, non-contacts, refusals and unprocessed cases were selected for a follow-up survey.
Specially selected interviewers, additional training.
Same questionnaire, same mode (face-to-face).
Interviewers could offer incentive.
Also interviewers could get a bonus.
Longer fieldwork period (2 months
Response
Percentage
Main survey
62 %
Follow-up survey
43 %
Overall
78 %

6. The Basic Question Approach
Method of Basic Questions, Kersten and Bethlehem (1984)
Ask non-respondents to answer small number of questions
Use key survey questions, so-called basic questions.
Basic idea
Non-respondents inclined to give quick response
Additional response can be used to improve estimates
Examples
Housing Demand Survey: Do you intend to move within two years?
Labour Force Survey: How many people in this household have a paid job?
Holiday Survey: Have you been on holiday during the last 12 months?
Family Planning Survey: Taking into account your present circumstances, and your expectations of the future, how many children do you think to get from this moment on?

7. The Basic Question Approach
From literature it is known that wording and design of questionnaire may affect answers to questions.
Use wording of original questionnaire
Select questions from start of questionnaire
Test and compare answers
Example
Dutch Housing Demand Survey 1981
Call-back survey among all respondents, including those who did the Basic Question Approach.
Basic question
Call-back
Intends to move
Does not intend to move
8.6 %
5.1 %
12.1 %
74.1 %

8. The Basic Question approach
Issues
Amount of additional response
Number of basic questions.
Questionnaire effects
Strategy
Different interviewer
Different interview mode
Timing
Multiple sets of basic questions

9. The Basic Question approach
Example, LFS 2005
Non-contacts, refusals and unprocessed cases received basic question approach after one week using telephone, paper and web.
Overall response rate:
 (0.60   0.23) = 0.77
LFS
Response
R = 65%
Nonresponse
R = 35%
Telephone
n = 573
Paper/web
n = 280
Response
R = 55%
Nonresponse
R = 45%
Response
R = 23%
Nonresponse
R = 77%

10. The Basic Question approach
Example, LFS 2005
CATI if registered fixed-line phone available
PAPI/CAWI if registered fixed-line phone not available
One member of household (next birthday selection method)
Two short questionnaires, CATI and PAPI/CAWI
Proxi interviewing
Basic questions about employment status (employed, unemployed or non-labour force)

11. The Basic Question approach
Estimation under the Fixed Response Model
Differentiate nonresponse stratum with respect to nonresponse types
Assume strong relation between basic question variables and other target variables in the survey
Assume response to basic question approach to be a random subsample of nonresponse within nonresponse strata
Notation under the Fixed Response Model
Response stratum with elements
Nonresponse strata with elements
Target variable Y and basic question variable Z

12. The Basic Question approach
Estimator for basic question variable Z
Estimators for target variable Y
Use Z as auxiliary variable in the estimation for Y
In case Z is qualitative use for instance post-stratification.
Say Z has M categories, is the estimated proportion of persons in category m and is the mean of Y for respondents that answered in category m.
In case Z is quantitative, use for instance regression estimator

13. The Basic Question approach
Example
Proportion of paid jobs in response: 50.2%
Proportion of people looking for paid job in response: 12.5%
Estimate for proportion of paid jobs:
Estimate for proportion of people looking for paid job:
Result
Proportion of sample
Has a paid job
Response
65 %
50.2 %
Non-contact
10 %
59.7 %
Refusal
20 %
38.7 %
Unprocessed
5 %
51.0 %
Response
Looking for a job
Not looking for a job
Paid job
5 %
95 %
No paid job
20 %
80 %

14. Call-back and Basic Question Approach compared
Response rates and response composition
R-indicator
Maximum bias
Root Mean Square Error

15. Call-back and Basic Question Approach compared
LFS 2005
Response rates and response composition – full population
Response rates and response composition – registered phones
Response
Response rate
R-indicator
Max bias
Max RMSE
LFS
62%
79%
8,4%
8,5%
LFS + BQ
76%
77%
7,6%
LFS + CB
85%
4,9%
Response
Response rate
R-indicator
Max bias
Max RMSE
LFS
68%
85%
5,5%
LFS + BQ
83%
87%
3,9%
4,0%

16. Estimating contact probabilities
Method of Politz & Simmons (1949)
Correct for bias due to not-at-home.
Other types of nonresponse are ignored.
Useful if relation between not-at-home and target variable.
Basic idea
Estimate probability of being at home.
Apply post-stratification by these probabilities.
No bias within strata, because response probabilities are (approximately) the same.
Estimation of at-home probability
Simply ask (also for previous evenings):
Would you mind telling me whether or not you happened to be at home last night at just this time?
Or (less reliable):
How many nights out of the last five where you at home at just this time?

17. Estimating contact probabilities
Period of L days, including the day of the interview.
Let tk be the number of times someone was home at previous L-1 days.
Estimate of at-home probability
Possible values: 1/L, 2/L, …, L/L
Post-stratification by at-home probability requires stratum sizes N1, N2, …, NL to be known.
They are unknown, so the have to be estimated.
Let nh be the number persons in the response with at-home probability h/L.
Estimate for total number of people with at-home probability h/L:

18. Estimating contact probabilities
This techniques is restricted to people with positive at-home probability. Those with probability 0 are excluded.
Required: total size of population. This is estimated by:
Post-stratification estimator:
Complications
At-home probabilities in household surveys difficult to estimate
Assumption: interviewers make contact at randomly chosen moment. This may be violated in case of appointments or announcement letters.

19. Estimating contact probabilities
Example: 1981 Labour Force Survey.
One town, sample of 105 households from 3500 households.
Question: On how many of the past 6 days was somebody at home around this time?
Results:
N’ =4200. Estimates of at-home probabilities are wrong.
Number of times someone was at home during the last 6 days
Frequency
At-home probability 1
1/7 2
2/7
3/7 3 6
4/7 4 9
5/7 5 10
6/7 77
7/7
Total
105