Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Pēctīgās konfirmācijas izpētēs metodes× | Longitudinālie pētījumi× | |
|---|---|---|
| Nozare | Pētījuma dizains | Pētījuma dizains |
| Saime | Process / pipeline | Process / pipeline |
| Izcelsmes gads≠ | 1970s onward; consolidated in SEM literature from 1990s | Late 19th–early 20th century; methodologically codified through the 20th century |
| Autors≠ | Synthesized from longitudinal design traditions (e.g., Baltes & Nesselroade, 1979) and confirmatory analytic frameworks (Joreskog, 1969) | No single originator; foundational methodological treatments by Stuart Menard and Judith Singer & John Willett |
| Tips≠ | Quantitative research design | Quantitative (or mixed) observational research design |
| Pirmavots≠ | Singer, J. D., & Willett, J. B. (2003). Applied Longitudinal Data Analysis: Modeling Change and Event Occurrence. Oxford University Press. ISBN: 978-0195152968 | Menard, S. (2002). Longitudinal Research (2nd ed.). Sage Publications. ISBN: 978-0761922841 |
| Citi nosaukumi | longitudinal confirmatory study, confirmatory longitudinal design, longitudinal hypothesis-testing design, longitudinal CFA design | longitudinal study, longitudinal design, prospective longitudinal study, repeated-measures observational study |
| Saistītās≠ | 5 | 4 |
| Kopsavilkums≠ | Longitudinal confirmatory research combines the temporal depth of longitudinal design with the hypothesis-driven logic of confirmatory analysis. The researcher specifies a priori hypotheses or structural models about how variables change or remain stable over time, then tests those predictions against data collected at two or more time points. It is the design of choice when theory is mature enough to make specific predictions about developmental, causal, or stability processes. | Longitudinal research is an observational design in which the same participants, groups, or units are measured repeatedly over an extended period. Rather than capturing a single snapshot, it tracks change, stability, and temporal sequencing of variables — making it the primary non-experimental strategy for studying development, growth, decline, and the unfolding of causal processes across time. |
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