Abstract:
The object of this study is the
analysis of questionnaire data using
ontological modeling. The task
relates to the fact that conventional
methods for processing questionnaire
data are often insufficiently effective
when working with large volumes of
information and do not make it possible
to automate many analysis processes.
As a result of the study, an
ontology was designed that structures
and analyzes questionnaire data,
which allows for a more accurate
identification of hidden relationships
between variables. Using these
theoretical provisions, an information
system for assessing the quality of
assimilation of preschool children's
competencies was built. 150 children
from various preschool organizations
were involved in the study as
respondents. The data integration
method proposed in this paper
significantly facilitated the process of
data analysis both for a group and for
an individual respondent.
The key difference of the proposed
methodology is the automation of
routine data analysis operations based
on the ontological structure, which
significantly simplifies the processing
of large volumes of information. This
makes it possible to solve the problem
of limitations in conventional analysis
methods and makes data analysis more
scalable and reproducible.
The practical application of the
results is possible in marketing for
analyzing customer satisfaction,
market segmentation, and evaluating
the effectiveness of advertising
campaigns. In the educational
domain, the ontology could be used
to evaluate the quality of programs
and analyze respondents' opinions,
and in sociology – to analyze public
opinion and conduct research on social
phenomena.
Thus, the proposed ontology
provides an effective tool for analyzing
large volumes of questionnaire data,
allowing organizations to make more
informed decisions and improve their
efficiency
