1. Nonresponse Problems
By
Syed Yousaf Shah
Assistant Professor ION-DUHS

2. Nonresponse
• Elements that are selected in the sample, and
that are also eligible for the survey, do not
provide the required.

3. Types of Nonresponse
• Unit nonresponse: a selected element does
not provide any information at all.
• Item nonresponse: a selected element does
answer some questions, but not all of them.

4. Why is nonresponse a problem?
• Smaller sample size.
• Nonresponse bias due to selective
nonresponse.

5. Causes of nonresponse
• No-contact
• Refusal
• Not-able
• Unprocessed

6. Response rates
• Proportion of eligible elements in the sample for which a
questionnaire has been completed:
• nr/nE
• Notation
• nE = Number of eligible elements in the sample
• nR = Number of eligible respondents
• nE = nR + nNC + nRF + nNA
• Initial sample size n = nE + nOC.
• So nE = n – nOC.
• Problem: over-coverage unknown for non-contacts.

7. Method 1: Comparison of Early to Late Respondents
 Extrapolation based on statistical inferences
 Operationally define ‘Late Respondents’
 Last wave of respondents: Late Respondents
 Compare early and late respondents based on key
variables of interest.
 If no difference, results can be generalized to larger
population.
METHODS FOR HANDLING
NON-RESPONSE

8. Method 2: Using “Days to Respond” as a Regression
Variable
 “Days to respond” is coded as continuous variable and
used as IV in regression equation.
 Primary variables of interest are regressed on variable
“Days to Respond”.
 If not statistically significant: Assume that respondents
are not different from non-respondents.
METHODS FOR HANDLING
NON-RESPONSE

9. Method 3: Compare Respondents to Non-Respondents
Compute differences by sampling nonrespondents
and working extra diligently to get their responses.
Minimum 20% of responses from nonrespondents
should be obtained.
If fewer than 20% responses are obtained, Method 1
or 2 should be used by combining the results.
METHODS FOR HANDLING
NON-RESPONSE

10. Method 4: Compare Respondents on Characteristics
known a priori
 Compare respondents to population or
characteristics known in advance
 Describe similarities and differences.
Method 5: Ignore Non-Response as a Threat to External
Validity
 If above methods are you can choose to ignore.
METHODS FOR HANDLING
NON-RESPONSE

11.  Missing data can be:
 Due to preventable errors, mistakes, or lack of foresight by the
researcher
 Due to problems outside the control of the researcher
 Deliberate, intended, or planned by the researcher to reduce
cost or respondent burden
 Due to differential applicability of some items to subsets of
respondents Etc.
Missing data

12. • Non-Response v/s Missing Data
• Missing Data: Where valid values on one or more
variables are not available for analysis.
identify the
the missing
• Researchers primary concern is to
patterns and relationships underlying
data.
• we need to understand process leading to missing
data to take appropriate course of action.
• Common in Social Research
• More acute in experiments and surveys
• Best way is to avoid it by planning and conscientious
data collection.
• Not uncommon to have some level of missing data.
MISSING DATA

13. Lost data
Reduces Statistical Power
Meaningfully diminishes sample size
Bias Parameter Estimates
Correlations biased downwards
Predictor scores affected
Restrict Variance
Central Tendency Biased
PRIMARY PROBLEMS

14.  The data can be missing at three levels:
1. Item- level missingness
2. Construct- level missingness
3. Person-level missingness
LEVELS OF MISSINGNESS

15. DETERMINE THE TYPE OF MISSING DATA
 Is it under the control of researcher?
 Is it ignorable?
 Ignorable Missing Data
 Expected
 Remedies not needed
 Allowance for missing data are inherent in the technique
 Missing data is operating at random
 Non—Ignorable Missing Data
 Known to researchers: Some remedies if random
 Unknown missing data: Process less easy, but remedies
available
PROCESS FOR IDENTIFYING MISSING
DATA AND APPLYING REMEDIES

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applied psychologists. Personnel psychology , 47(3), 537-560.
3. Blair, E., & Zinkhan, G. M. ( 2006). Nonresponse and
generalizability in academic research. Journal of the Academy of
Marketing Science , 34(1), 4-7.
4. Newman, D. A. (2014). Missing data five practical
guidelines. Organizational Research Methods , 17(4), 372-411 .
5. Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L.
(2006). Multivariate data analysis 6th Edition. New Jersey:
Pearson Education .
REFERENCES