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.
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
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
16. J. R. (2003). The handling of1. Dooley, L. M., & Lindner,
nonresponse error. Human Resource Development
Quarterly, 14(1), 99-110.
2. Roth, P. L. (1994). Missing data: A conceptual review for
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