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| Longitudinal hypotesetestning× | Bekræftende forskning× | |
|---|---|---|
| Fagområde | Forskningsdesign | Forskningsdesign |
| Familie | Process / pipeline | Process / pipeline |
| Oprindelsesår≠ | Consolidated as a formal design framework in the 1960s–1980s | 1934 (Popper); widely adopted in social sciences from 1960s onward |
| Ophavsperson≠ | Synthesized from longitudinal design traditions (Lazarsfeld, 1940s) and classical hypothesis testing (Fisher, Neyman-Pearson, 1920s–1930s) | Karl Popper (falsificationism); formalized in behavioral sciences by Paul Meehl and others |
| Type≠ | Quantitative longitudinal research design | Quantitative research design |
| Oprindelig kilde≠ | Singer, J. D., & Willett, J. B. (2003). Applied Longitudinal Data Analysis: Modeling Change and Event Occurrence. Oxford University Press. ISBN: 978-0195152968 | Popper, K. R. (1959). The Logic of Scientific Discovery. Hutchinson. ISBN: 978-0415278447 |
| Aliasser | longitudinal confirmatory study, repeated-measures hypothesis testing, prospective hypothesis testing, longitudinal inferential research | hypothesis-testing research, deductive research, theory-testing research, confirmatory study |
| Relaterede≠ | 5 | 4 |
| Resumé≠ | Longitudinal hypothesis testing research combines a longitudinal design — measuring the same units repeatedly over time — with formal null-hypothesis significance testing to determine whether observed changes exceed what chance alone can explain. It is widely used in education, medicine, psychology, and social science to test directional predictions about change, stability, or group differences that emerge over a defined time span. | Confirmatory research is a deductive quantitative design in which the researcher specifies hypotheses derived from existing theory before data collection, then tests whether the data support or refute those hypotheses. Unlike exploratory approaches that generate ideas from data, confirmatory research begins with an established theoretical framework, pre-registers predictions, and applies statistical tests to evaluate those predictions against empirical evidence. It is the backbone of hypothesis-driven social, behavioral, and health science inquiry. |
| ScholarGateDatasæt ↗ |
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