방법 비교
선택한 방법을 나란히 검토하세요. 서로 다른 행은 강조 표시됩니다.
| 종단 가설 검정 연구× | 반복측정 분산분석× | |
|---|---|---|
| 분야≠ | 연구설계 | 통계학 |
| 계열≠ | Process / pipeline | Hypothesis test |
| 기원 연도≠ | Consolidated as a formal design framework in the 1960s–1980s | 1992 |
| 창시자≠ | Synthesized from longitudinal design traditions (Lazarsfeld, 1940s) and classical hypothesis testing (Fisher, Neyman-Pearson, 1920s–1930s) | Girden (textbook treatment); Field (2013) |
| 유형≠ | Quantitative longitudinal research design | Parametric within-subjects mean comparison |
| 원전≠ | Singer, J. D., & Willett, J. B. (2003). Applied Longitudinal Data Analysis: Modeling Change and Event Occurrence. Oxford University Press. ISBN: 978-0195152968 | Field, A. (2013). Discovering Statistics Using IBM SPSS Statistics (4th ed., Ch. 14). SAGE. ISBN: 978-1446249185 |
| 별칭 | longitudinal confirmatory study, repeated-measures hypothesis testing, prospective hypothesis testing, longitudinal inferential research | within-subjects ANOVA, repeated measures analysis of variance, rm-ANOVA, Tekrarlı Ölçüm ANOVA |
| 관련≠ | 5 | 4 |
| 요약≠ | 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. | 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). |
| ScholarGate데이터셋 ↗ |
|
|