Course type: compulsory
Lecturer: Vasja Vehovar
 

For the timeline see Curriculum.

Prerequisites:

• Fulfilling general condition for enrollment.

Content (Syllabus outline):
Sampling

• Sample design: basic sample types (simple random sample, stratification, cluster sampling, multiphase sampling, panels).
• Variance estimation approaches: direct methods and replication method.
• Specific with respect to type of survey (academic, official, business, international), target population (institutions, households, persons, transactions etc.) and mode of data collection (telephone, web, face-to-face, mail).

Incomplete data

• Data editing and controls.
• Missing data mechanism (MAR; MCAR; NMAR)
• Classic approaches to missing data (ignoring, imputation , weighting)
• Model approaches (Bayes, maximum likelihood, EM algorithm, multiple imputations).
• Data fusion: statistical and ethical issues.

Selected topics

• Selected topics in the area related to statistical aspects of designing data collection, performing data collection and editing data

Objectives and competences:
Students will get familiar with basic statistical approaches to designing the data collection (sampling) and editing the data.

Intended learning outcomes:
Understanding of the approaches to data collection, understanding the concept of data quality and practical skills for data collection and editing.