Statistical views of data collecting

Subject description


  • Approaches to statistical inference.
  • Classical sampling theory and basic sample designs (simple random sample, stratification, cluster sampling, multiphase sampling, panels).
  • Variance estimation approaches: direct methods and replication method, design effect, weights.
  • Specifics related to research type (academic, official, business, international research), target population (institutions, households, persons, transactions etc.) and survey mode (telephone, web, face-to-face, mail and mixed mode surveys).
  • Nonprobability sampling: types, approaches to design and analyses.
  • Mean squared error, total survey error, data quality indicators and criteria.



  • Concepts, mechanisms and approaches
  • Classic approaches to missing data: ignoring, imputation, weighting.
  • Modelling: Bayesian approach, maximum likelihood method, EM algorithm, multiple imputations.
  • Data matching and data fusion: statistical and ethical issues.



  • Data preparation process: data editing, controls, coding, recoding,
  • Automatization of controls, analysis, analytics and integration.
  • Combining  surveys, big data and administrative data.

Cost-error optimisation in data collection approaches.

The subject is taught in programs

Objectives and competences

Students will gain competences with statistical approaches in sample design and analysis, missing data, as well as with cost optimisation and process automatization of data collection.

Teaching and learning methods

Lectures, project work, presentations, consultations 

Part of the pedagogical process will be carried out with the help of ICT technologies and the opportunities they offer.

Expected study results

Knowledge and understanding:

  • main sample design strategies,
  • approaches to calculate sampling variance,
  • concepts and techniques to deal with missing data,
  • cost optimisation in data collections,
  • process automatization in data collection and analysis.

Basic sources and literature

  1. Kalton, Vehovar (2001). Vzorčenje v anketah. FDV.
  2. Vehovar (2020). Manjkajoči podatki v anketah. FDV.
  3. Callegaro, Lozar-Manfreda, Vehovar (2015). Web survey methodology. Sage.
  4. Rassler (2002): Data Matching. Springer.
  5. Little, Rubin (2019): Statistical Analysis with Missing Data. WIley.

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