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  • Имеет ли метод индикаторной переменной преимущества перед анализом полных наблюдений при обработке пропусков в категориальных регрессорах?
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News
October 5, 2026
‘The Climate Transition Is Not Necessarily a Limitation for Business
Linara Khadimullina works in the field of low-carbon development. In an interview with the Young Scientists of HSE project, she spoke about why nature is not just a beautiful backdrop, her research on the role of sustainable corporate governance in reducing greenhouse gas emissions, and growing plants as a source of inspiration.
October 5, 2026
Africa, Youth, and Civic Dialogue: Public Diplomacy Discussed at HSE University
In late September, HSE University hosted a roundtable discussion titled Civil Society in African Countries and Youth Participation in Public Diplomacy. Representatives of non-governmental organisations from Ghana, Ethiopia, and Russia, along with students from HSE University’s Bachelor’s Programme in Public Administration, discussed how young people without official diplomatic status can influence relations between countries and how the nonprofit sector can remain sustainable amid declining grant funding.
October 1, 2026
HSE Researchers Show How Congenital Motor Disorders Affect Brain Development
Researchers from HSE University’s Institute for Cognitive Neuroscience have synthesised the findings of their previous studies on brain development in children with obstetric brachial plexus palsy and arthrogryposis. Their analysis shows that impaired motor function in early childhood not only limits children’s motor experience but also affects memory, categorical thinking, and information processing. The study has been published in Frontiers in Psychology.

 

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Имеет ли метод индикаторной переменной преимущества перед анализом полных наблюдений при обработке пропусков в категориальных регрессорах?

Мониторинг общественного мнения: Экономические и социальные перемены. 2021. № 4. С. 23–52.
Zhuchkova S., Rotmistrov A., Shabanova E.

If missingness is encountered in a categorical regressor, which approach is preferable: complete case analysis or the missing-indicator method? The former approach implies including in analysis (linear regression in our research) only the cases without missingness across analyzed variables. This approach is embedded in many statistical applications by default, and despite the opinion that its applicability is rather restricted, up-to-date studies provide evidence for its wide applicability – even to missingness not at random. The missing-indicator method, according to which missing data are replaced with a single valid value and a new missing-indicator variable is created, pretends to be an alternative that keeps a full sample available for analysis and, hypothetically, does not lead to the deterioration of parameter estimates. By means of simulated data and a statistical experiment, controlling the factors of missingness mechanism, missingness proportion, and a regression model’s specification, we compare parameter estimates produced by each approach to handling missingness – how biased and inefficient they are. According to the results, no approach leads to crucially biased estimates, but the missing-indicator method produces ineffective estimates.

Research target: Sociology (including Demography and Anthropology
Priority areas: sociology
Language: Russian
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Keywords: пропуски в данныхрегрессионный анализmethod of Monte Carlo simulationregression analysisстатистический экспериментbiasstatistical experimentcategorical datamissing indicator methodкатегориальные данныеметод индикаторной переменнойcoverageслучайные пропускинеслучайные пропускианализ полных наблюденийсимуляции Монте-Карлоcomplete case analysismissingnessmissingness at randommissingness not at randomsimulated data
Publication based on the results of:
Комплексное сравнение методов обработки пропущенных данных в социологических исследованиях (2021)
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