Salīdzināt metodes

	Hierarchical Relational Survey	Daudzlīmeņu modelēšana
Nozare	Pētījuma dizains	Pētniecības statistika
Saime	Process / pipeline	Process / pipeline
Izcelsmes gads	1980s–2002 (modern HLM-based survey tradition)	1992
Autors	Raudenbush & Bryk (multilevel framework); Hox (multilevel survey analysis)	Anthony Bryk and Stephen Raudenbush
Tips	Quantitative survey design with multilevel relational analysis	Method
Pirmavots	Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical Linear Models: Applications and Data Analysis Methods (2nd ed.). Sage. ISBN: 978-0761919049	Bryk, A. S., & Raudenbush, S. W. (1992). Hierarchical Linear Models: Applications and Data Analysis Methods. SAGE Publications.
Citi nosaukumi	nested relational survey, multilevel relational survey, HLM-based relational survey, hierarchical correlational survey	HLM, mixed-effects models, random effects models, MLM
Saistītās	4	3
Kopsavilkums	A hierarchical relational survey combines the correlational goals of relational survey research with a multilevel data structure in which respondents are nested within higher-level units such as classrooms, schools, hospitals, or organizations. The design acknowledges that observations within the same group are not independent, and uses hierarchical linear modeling (HLM) or equivalent multilevel techniques to examine relationships among variables both within and between levels simultaneously.	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.