# Nonresponse problems

Assistant Professor em Dow University of Health Sciences
28 de Jan de 2019
1 de 16

### Nonresponse problems

• 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
• 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