What is he best known for?
The fields of study he is best known for:
- Statistics
- Law
- Normal distribution
Social psychology, Statistics, Friendship, Multilevel model and Exponential random graph models are his primary areas of study.
The Social psychology study combines topics in areas such as Developmental psychology, Control, Cognitive psychology and Social network.
His work carried out in the field of Friendship brings together such families of science as Similarity, Agency, Peer group and Advice.
As part of one scientific family, he deals mainly with the area of Multilevel model, narrowing it down to issues related to the Heteroscedasticity, and often Explained variation, Threshold model, Generalized linear model and Categorical variable.
His Algebraic formula for the variance research is multidisciplinary, incorporating perspectives in Marginal model, Econometrics, Homoscedasticity and Variance-based sensitivity analysis.
Tom A. B. Snijders merges many fields, such as Econometrics and Random effects model, in his writings.
His most cited work include:
- Multilevel analysis : an introduction to basic and advanced multilevel modeling (4494 citations)
- Multilevel analysis : an introduction to basic and advanced multilevel modeling (4018 citations)
- Multilevel analysis. An introduction to basic and advanced multilevel modeling, 2nd edition (1st edition 1999). (1351 citations)
What are the main themes of his work throughout his whole career to date?
Tom A. B. Snijders focuses on Statistics, Multilevel model, Social psychology, Econometrics and Social network.
In general Statistics, his work in Regression analysis and Nonparametric statistics is often linked to Exponential random graph models and Random effects model linking many areas of study.
Tom A. B. Snijders interconnects Mathematics education and Network analysis in the investigation of issues within Multilevel model.
His Social psychology research integrates issues from Developmental psychology and Similarity.
His work deals with themes such as Theoretical computer science and Artificial intelligence, which intersect with Social network.
His Statistical model research is multidisciplinary, incorporating elements of Dynamic network analysis and Markov chain Monte Carlo.
He most often published in these fields:
- Statistics (18.61%)
- Multilevel model (17.98%)
- Social psychology (17.98%)
What were the highlights of his more recent work (between 2017-2021)?
- Social psychology (17.98%)
- Social network (12.93%)
- Norm (4.42%)
In recent papers he was focusing on the following fields of study:
Tom A. B. Snijders mainly focuses on Social psychology, Social network, Norm, Network analysis and Multilevel model.
His study on Friendship and Ambivalence is often connected to Social identity theory as part of broader study in Social psychology.
His biological study spans a wide range of topics, including Social support and Artificial intelligence.
Tom A. B. Snijders has researched Artificial intelligence in several fields, including Goodness of fit and Monte Carlo method.
His Network analysis research is multidisciplinary, relying on both Missing data and Computational science.
His Multilevel model study combines topics in areas such as Developmental psychology, Academic achievement, Mathematics education, Classroom climate and Social environment.
Between 2017 and 2021, his most popular works were:
- Forms of Dependence: Comparing SAOMs and ERGMs From Basic Principles: (38 citations)
- Beyond homophily: Incorporating actor variables in statistical network models (14 citations)
- Multiple imputation for longitudinal network data (11 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Law
- Social science
Tom A. B. Snijders mostly deals with Social network, Social network analysis, Network analysis, Artificial intelligence and Order.
Social network and Unsafe Sex are two areas of study in which Tom A. B. Snijders engages in interdisciplinary research.
His research in Social network analysis intersects with topics in Missing data, Selection, Statistical power, Network simulation and Dynamic network analysis.
His studies deal with areas such as Perception, Mathematics education, Classroom climate, Social environment and Social research as well as Network analysis.
His research integrates issues of Goodness of fit and Monte Carlo method in his study of Artificial intelligence.
Tom A. B. Snijders connects Statistical analysis with Exponential random graph models in his research.
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