Comparer des méthodes
Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| Analyse de contenu quantitative longitudinale× | Recherche longitudinale× | |
|---|---|---|
| Domaine | Conception de la recherche | Conception de la recherche |
| Famille | Process / pipeline | Process / pipeline |
| Année d'origine≠ | 1950s onward; longitudinal application widely adopted in media research by the 1970s–1980s | Late 19th–early 20th century; methodologically codified through the 20th century |
| Auteur d'origine≠ | Developed within communication and media studies; codified by Berelson (1952) and extended by Riffe, Lacy, Fico | No single originator; foundational methodological treatments by Stuart Menard and Judith Singer & John Willett |
| Type≠ | Quantitative observational research design | Quantitative (or mixed) observational research design |
| Source fondatrice≠ | Riffe, D., Lacy, S., Watson, B., & Fico, F. (2019). Analyzing Media Messages: Using Quantitative Content Analysis in Research (4th ed.). Routledge. ISBN: 9781138490536 | Menard, S. (2002). Longitudinal Research (2nd ed.). Sage Publications. ISBN: 978-0761922841 |
| Alias | longitudinal content analysis, repeated-measure content analysis, time-series content analysis, longitudinal QCA | longitudinal study, longitudinal design, prospective longitudinal study, repeated-measures observational study |
| Apparentées≠ | 5 | 4 |
| Résumé≠ | Longitudinal quantitative content analysis systematically codes and counts features of texts, images, or media messages gathered at two or more points in time, enabling researchers to track how content changes, how themes rise or fall in prevalence, and how media or institutional messaging responds to external events. The design merges the structured measurement logic of quantitative content analysis with the temporal tracking power of longitudinal observation. | 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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