Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Daudzavotu mobilo pieredzes izpēte× | Daudzlīmeņu modelēšana× | |
|---|---|---|
| Nozare≠ | Aptauju metodoloģija | Pētniecības statistika |
| Saime | Process / pipeline | Process / pipeline |
| Izcelsmes gads≠ | 2000s–2010s | 1992 |
| Autors≠ | Developed from ESM (Csikszentmihalyi & Larson, 1983) and extended to multi-informant intensive longitudinal designs by Bolger, Laurenceau, and colleagues | Anthony Bryk and Stephen Raudenbush |
| Tips≠ | Intensive longitudinal multi-informant data collection technique | Method |
| Pirmavots≠ | Bolger, N., & Laurenceau, J.-P. (2013). Intensive Longitudinal Methods: An Introduction to Diary and Experience Sampling Research. Guilford Press. ISBN: 978-1462506781 | Bryk, A. S., & Raudenbush, S. W. (1992). Hierarchical Linear Models: Applications and Data Analysis Methods. SAGE Publications. DOI ↗ |
| Citi nosaukumi | multi-informant ESM, dyadic ESM, multi-respondent ecological momentary assessment, MSESM | HLM, mixed-effects models, random effects models, MLM |
| Saistītās≠ | 6 | 3 |
| Kopsavilkums≠ | Multi-source Mobile Experience Sampling extends the standard ESM design by simultaneously collecting repeated momentary self-reports from two or more linked informant types — such as patient and caregiver, employee and supervisor, or partners in a dyad — via their smartphones. Signals are delivered concurrently across sources, enabling researchers to examine convergences and discrepancies between informants' real-time experiences and to model interpersonal dynamics at the moment they unfold in daily life. | 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. |
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