Sammenlign metoder
Gjennomgå de valgte metodene side om side; rader som avviker, er uthevet.
| Multivariat kvantitativ innholdsanalyse× | Longitudinal kvantitativ innholdsanalyse× | |
|---|---|---|
| Fagfelt | Forskningsdesign | Forskningsdesign |
| Familie | Process / pipeline | Process / pipeline |
| Opprinnelsesår≠ | 1969–2000s | 1950s onward; longitudinal application widely adopted in media research by the 1970s–1980s |
| Opphavsperson≠ | Rooted in Holsti (1969) and Neuendorf (2002); multivariate extensions developed in communication and political science research from the 1970s onward | Developed within communication and media studies; codified by Berelson (1952) and extended by Riffe, Lacy, Fico |
| Type≠ | Quantitative research design | Quantitative observational research design |
| Opprinnelig kilde≠ | Neuendorf, K. A. (2002). The Content Analysis Guidebook. Sage Publications. ISBN: 978-0761919773 | Riffe, D., Lacy, S., Watson, B., & Fico, F. (2019). Analyzing Media Messages: Using Quantitative Content Analysis in Research (4th ed.). Routledge. ISBN: 9781138490536 |
| Alias | multivariate QCA, multivariate content analysis, MQCA, multivariate text analysis | longitudinal content analysis, repeated-measure content analysis, time-series content analysis, longitudinal QCA |
| Relaterte≠ | 6 | 5 |
| Sammendrag≠ | Multivariate quantitative content analysis (MQCA) is a systematic, replicable approach to measuring multiple attributes of communication content simultaneously and examining how those attributes relate to each other or to external variables. It extends standard content analysis by applying multivariate statistical techniques — such as factor analysis, cluster analysis, regression, or MANOVA — to coded content data, enabling researchers to uncover complex patterns across many variables at once. | 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. |
| ScholarGateDatasett ↗ |
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