Salīdzināt metodes

	Beijesa aptauju pētījumi	Daudzlīmeņu modelēšana
Nozare	Pētījuma dizains	Pētniecības statistika
Saime	Process / pipeline	Process / pipeline
Izcelsmes gads	1980s–2000s (modern applied development)	1992
Autors	Thomas Bayes (theorem, 1763); applied to survey methodology by Donald Rubin, Andrew Gelman, and others (1980s–2000s)	Anthony Bryk and Stephen Raudenbush
Tips	Quantitative observational research design with Bayesian inference	Method
Pirmavots	Gelman, A., & Carlin, J. B. (2007). Some issues on the foundations of statistics. In A. Gelman & J. B. Carlin (Eds.), Data Analysis Using Regression and Multilevel/Hierarchical Models. Cambridge University Press. ISBN: 978-0521686891	Bryk, A. S., & Raudenbush, S. W. (1992). Hierarchical Linear Models: Applications and Data Analysis Methods. SAGE Publications.
Citi nosaukumi	Bayesian survey analysis, Bayesian survey methodology, Bayesian polling, Bayesian questionnaire analysis	HLM, mixed-effects models, random effects models, MLM
Saistītās	4	3
Kopsavilkums	Bayesian survey research applies Bayesian statistical inference to survey data, combining prior knowledge or beliefs about population parameters with observed questionnaire responses to produce posterior probability distributions. Unlike null-hypothesis significance testing, this approach quantifies uncertainty directly, incorporates prior evidence, and yields probabilistic statements about parameters of interest — making it especially powerful for small samples, sequential data collection, and contexts where substantive prior knowledge exists.	Multilevel modeling (also called hierarchical linear modeling, mixed-effects modeling) is a statistical framework for analyzing data organized in nested or clustered structures—students within schools, patients within hospitals, repeated measures within individuals. Developed by Bryk and Raudenbush (1992), it accounts for dependency among observations and partitions variance into levels (within-cluster and between-cluster), enabling valid inference and revealing context effects. Essential in education, medicine, organizational research, and any field where data have natural hierarchies.