Missing data in surveys: Key concepts, approaches, and applications.
Explore this paper's citation graph
Summary
The concept of missing data in survey research, how missing data is classified, common techniques to account for missingness and how to report on missing data are discussed and methods for analysing data with missing values, such as deletion, imputation and likelihood methods are introduced.
- Type
- article
- Published
- 2021-03-19
- Cited by
- 238
- References
- 38
- OpenAlex
- https://openalex.org/W3139174110
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:232409864
Keywords
Missing data, Imputation (statistics), Computer science, Data mining, Checklist
References
- Calibration as a Standard Method for Treatment of Nonresponse
- Missing by Design: Planned Missing-Data Designs in Social Science
- Review of inverse probability weighting for dealing with missing data
- INFERENCE AND MISSING DATA
- Survey Nonresponse Adjustments for Estimates of Means
- Inverse Probability Weighting with Missing Predictors of Treatment Assignment or Missingness
- Incentives increased return rates but did not influence partial nonresponse or treatment outcome in a randomized trial.
- Income non-reporting: implications for health inequalities research
- Analysis of multivariate polychoric correlation models with incomplete data
- Don't know responses in surveys: Analyses and interpretational consequences
- Planned Missing Data Designs for Developmental Researchers
- Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper
- Integrated methodology for multiple systems estimation and record linkage using a missing data formulation
- How can I deal with missing data in my study?
- A Test of Missing Completely at Random for Multivariate Data with Missing Values
- Statistical Methods in Psychology Journals: Guidelines and Explanations
- Strengthening the Reporting of Observational Studies in Epidemiology (STROBE): Explanation and Elaboration
- Response burden and questionnaire length: is shorter better? A review and meta-analysis.
- Multiple imputation: a primer
- Principled missing data methods for researchers
Cited by
- Use of the Professional Fulfillment Index in Pharmacists: A Confirmatory Factor Analysis
- Intention to buy organic fish among Danish consumers: Application of the segmentation approach and the theory of planned behaviour
- Perception of community pharmacists about the work process of drug dispensing: a cross-sectional survey study
- Exploring the moderating role of job resources in how job demands influence burnout and professional fulfillment among U.S. pharmacists.
- Self-organizing maps for exploration of partially observed data and imputation of missing values
- The behaviour of rank correlation coefficients for incomplete data
- Relationship between teacher fidelity to an early childhood obesity prevention program and the Child care center nutrition and physical activity environment
- Prevent Problematic Missing Data.
- Whose rights are being violated when receiving HIV and sexual and reproductive health services in Nigeria?
- Childhood exposure to parental violence, attachment insecurities, and intimate partner violence perpetration among Arab adults in Israel.
- Ensemble Generative Adversarial Imputation Network with Selective Multi-Generator (ESM-GAIN) for Missing Data Imputation
- Perception and Deception in Nurses’ Clinical and Work-Related Professional Autonomy: Case Study for a Hospital in Romania
- The role of perceived service quality and price competitiveness on consumer patronage of and intentions towards community pharmacies.
- Psychosocial Factors Associated with Memory Complaints during the First Wave of the COVID-19 Pandemic: A Multi-Country Survey
- The Impact of COVID-19 on the Emotion of People Living with and without HIV
- What drives job satisfaction among community pharmacists? An application of relative importance analysis
- HIV Private Care Services in Nigeria Expose Constraints on Healthcare Systems during the Pandemic
- Transformer-enabled generative adversarial imputation network with selective generation (SGT-GAIN) for missing region imputation
- Early Predictors of Sensory Processing Sensitivity in Members of the Birth to Twenty Plus Cohort
- Managing missing and erroneous data in nurse staffing surveys.
Related papers
- Application of SOLAS to the Multiple Imputation for Missing Data
- A reinforcement learning-based approach for imputing missing data
- Guided Multiple Imputation of Missing Data: Using a Subsample to Strengthen the Missing-at-Random Assumption
- Missing Values Imputation Based on Iterative Learning
- The Sin of Missing Data: Is All Forgiven by Way of Imputation?
- [Imputation of missing data].
- Machine learning-based imputation soft computing approach for large missing scale and non-reference data imputation
- Three‐step imputation of missing values in condition monitoring datasets
- Missing Data in a Long Food Frequency Questionnaire