Krahasoni metodat

	Mostrat e Përvojës Mobile nga Shumë Burime	Modelimi Shumë-Nivelësh
Fusha	Metodologjia e anketave	Statistika e hulumtimit
Familja	Process / pipeline	Process / pipeline
Viti i origjinës	2000s–2010s	1992
Krijuesi	Developed from ESM (Csikszentmihalyi & Larson, 1983) and extended to multi-informant intensive longitudinal designs by Bolger, Laurenceau, and colleagues	Anthony Bryk and Stephen Raudenbush
Lloji	Intensive longitudinal multi-informant data collection technique	Method
Burimi themelues	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.
Emërtime të tjera	multi-informant ESM, dyadic ESM, multi-respondent ecological momentary assessment, MSESM	HLM, mixed-effects models, random effects models, MLM
Të lidhura	6	3
Përmbledhja	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.