قارن الطرق

	البحث التكويني الطولي	أبحاث اختبار النماذج الطولية
المجال	تصميم البحث	تصميم البحث
العائلة	Process / pipeline	Process / pipeline
سنة النشأة	1970s onward; consolidated in SEM literature from 1990s	1970s–1990s (SEM foundations by Joreskog 1970; longitudinal SEM elaborated through 1990s–2000s)
صاحب الطريقة	Synthesized from longitudinal design traditions (e.g., Baltes & Nesselroade, 1979) and confirmatory analytic frameworks (Joreskog, 1969)	Synthesized from longitudinal panel design and SEM tradition (Joreskog, Bollen, Singer & Willett)
النوع	Quantitative research design	Quantitative, confirmatory, longitudinal design
المصدر التأسيسي	Singer, J. D., & Willett, J. B. (2003). Applied Longitudinal Data Analysis: Modeling Change and Event Occurrence. Oxford University Press. ISBN: 978-0195152968	Singer, J. D., & Willett, J. B. (2003). Applied Longitudinal Data Analysis: Modeling Change and Event Occurrence. Oxford University Press. ISBN: 978-0195152968
الأسماء البديلة	longitudinal confirmatory study, confirmatory longitudinal design, longitudinal hypothesis-testing design, longitudinal CFA design	longitudinal confirmatory modeling, longitudinal SEM, panel model testing, longitudinal structural modeling
ذات صلة	5	6
الملخص	Longitudinal confirmatory research combines the temporal depth of longitudinal design with the hypothesis-driven logic of confirmatory analysis. The researcher specifies a priori hypotheses or structural models about how variables change or remain stable over time, then tests those predictions against data collected at two or more time points. It is the design of choice when theory is mature enough to make specific predictions about developmental, causal, or stability processes.	Longitudinal model testing research combines repeated measurement across time with formal, a priori structural modeling to confirm or disconfirm hypothesized relationships among constructs. Rather than simply describing change, it tests whether a pre-specified theoretical model — typically a structural equation model or growth model — fits observed data collected at two or more time points. This design supports causal inference more convincingly than cross-sectional approaches by capturing temporal ordering of variables.