방법 비교

	코호트 연구	종단 조사	반복측정 분산분석
분야	역학	조사방법론	통계학
계열	Process / pipeline	Process / pipeline	Hypothesis test
기원 연도	Mid-20th century (formal epidemiological design codified ~1950s)	1940s (panel survey tradition); longitudinal designs codified mid-20th century	1992
창시자	Doll & Hill (British Doctors Study, 1951); Snow (cholera, 1854)	Established tradition; formalized in social science by Paul Lazarsfeld and colleagues (1940s panel studies)	Girden (textbook treatment); Field (2013)
유형	Observational longitudinal study design	Quantitative / mixed-methods survey design	Parametric within-subjects mean comparison
원전	Rothman, K. J., Greenland, S., & Lash, T. L. (2008). Modern Epidemiology (3rd ed.). Lippincott Williams & Wilkins. ISBN: 978-0781755641	Menard, S. (2002). Longitudinal Research (2nd ed.). Sage Publications. ISBN: 978-0761922292	Field, A. (2013). Discovering Statistics Using IBM SPSS Statistics (4th ed., Ch. 14). SAGE. ISBN: 978-1446249185
별칭	longitudinal study, follow-up study, panel study, incidence study	panel survey, repeated-measures survey, longitudinal panel study, wave survey	within-subjects ANOVA, repeated measures analysis of variance, rm-ANOVA, Tekrarlı Ölçüm ANOVA
관련	6	3	4
요약	A cohort study assembles a group of individuals who share a common starting point — typically freedom from the outcome of interest — and follows them over time to observe who develops the outcome. By comparing incidence rates between exposed and unexposed subgroups, researchers can estimate relative risk and absolute risk differences. Cohort studies are the gold-standard observational design for measuring disease incidence and establishing temporal relationships between exposure and outcome.	A longitudinal survey collects structured questionnaire data from the same individuals or units at two or more distinct points in time. By tracking the same respondents across waves, researchers can distinguish genuine change from stable individual differences, establish temporal ordering between variables, and model trajectories of attitudes, behaviors, or outcomes in ways that a single cross-sectional snapshot cannot support.	Repeated-measures ANOVA is a parametric hypothesis test that compares three or more measurements taken from the same individuals — typically across time points or conditions — to decide whether their means differ. It extends one-way ANOVA to within-subjects designs, as treated in standard references such as Girden (1992) and Field (2013).