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Summary
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This study analyzes survey data integration and inference using text mining and bibliometric analysis. It identifies current trends and future research directions in non-probability sampling and big data integration.

Keywords:
Bibliometric analysisNew data sourcesNonprobability samplesSurvey data integrationThematic analysis

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Area of Science:

  • Statistics
  • Data Science
  • Information Science

Background:

  • Survey data integration and inference from non-probability samples are increasingly important.
  • Cost-effectiveness drives interest in combining probabilistic surveys with auxiliary data.
  • Emerging data sources like big data present new challenges for statistical inference.

Purpose of the Study:

  • To describe and understand the evolution of research in survey data integration and inference.
  • To identify contemporary research trends and future investigation directions.
  • To propose a research agenda addressing identified gaps.

Main Methods:

  • Text mining and bibliometric analysis of publications.
  • Utilizing the Scopus database to retrieve relevant documents.
  • Analysis of a collection of 1023 documents.

Main Results:

  • Characterization of the existing literature on survey data integration and inference.
  • Identification of current research trends in the field.
  • Discovery of potential future research avenues.

Conclusions:

  • The study provides a comprehensive overview of the research landscape.
  • It highlights the need for further investigation into emerging challenges.
  • A proposed research agenda aims to guide future studies in this domain.