Theoretical foundations of computer-assisted interviewing
Computer-assisted interviewing emerged in response to the limitations of paper questionnaires. Automated logical filters reduce human error. Monitoring response time allows inconsistencies to be detected. Automatic validation of variables raises the quality of the final data set.
In research design, the choice between CAPI and CATI depends on the objective, the population and the complexity of the instrument.
Probability and non-probability sampling
National studies use multistage probability sampling. Stratification is applied by region, urban or rural residence and locality size. Households and respondents are selected at random.
In exploratory projects or B2B studies, non-probability methods may be used. Selection is based on predefined criteria. Representativeness is replaced by segmented relevance.
When face-to-face interviews (CAPI) are used
- Sensitive topics: voting intention, financial behaviour, social perceptions.
- Long questionnaires, over 30 minutes.
- The use of visual materials.
- Including population groups with limited access to telephone or internet.
Face-to-face CAPI interviews allow rapport to be built with the respondent and provide context and clarification.
When telephone interviews (CATI) are used
- Rapid monitoring and short opinion polls.
- Satisfaction studies and post-service evaluations.
- Questionnaires of 10-20 minutes.
CATI reduces operational costs and allows accelerated data collection.
Quality control
Quality control is built into every stage of the CAPI and CATI process. Automatic consistency checks are applied. Interview duration is monitored. Telephone re-contacts are made for validation.
For field interviews, GPS location is used. Audio checks are applied to a random sample. The distribution of variables is analysed to detect statistical deviations.
Advanced statistical analysis
The data collected through CAPI and CATI are processed in SPSS, STATA, R and Python. Weighting is applied to adjust the sample distribution. Inferential models are built.
The methods used include linear, logistic and multinomial regression, exploratory and confirmatory factor analysis, cluster analysis, discriminant analysis, logit and probit models, structural equation models and partial correlation analysis.
Statistical significance tests, confidence intervals, bootstrap validation and predictive models are applied.
Data visualisation
Visualisation is an integral part of interpretation. Comparative charts, thematic maps and interactive dashboards make the relationships between variables easier to understand.
Sound analysis does not mean statistical calculation alone. It means structuring the conclusions so that the decision-maker understands the implications.
The depth of the final reports
Reports include a detailed methodological description, weighted distributions, comparative analyses, strategic interpretation and development scenarios.
We build applicable conclusions, not just numerical results. The emphasis is on practical relevance.
Perspicuus expertise
Our approach combines academic rigour with strategic orientation. We apply in-depth statistical methods and multiple validation. We interpret the data in its economic and social context.
CAPI and CATI thus become decision-making tools, not mere technical exercises. Data are turned into clear arguments. Analysis becomes the foundation for positioning and differentiation.