Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Investigación de pruebas de modelos comparativos× | Investigación por contraste de hipótesis× | |
|---|---|---|
| Campo | Diseño de investigación | Diseño de investigación |
| Familia | Process / pipeline | Process / pipeline |
| Año de origen≠ | 1969–2000s | Early 20th century (Fisher 1925; Neyman–Pearson 1933) |
| Autor original≠ | Rooted in structural equation modeling traditions; formalized through Jöreskog (1969) and extended by Vandenberg & Lance (2000) | Karl Pearson, Ronald A. Fisher, Jerzy Neyman, Egon Pearson |
| Tipo≠ | Quantitative confirmatory-comparative research design | Quantitative confirmatory research design |
| Fuente seminal≠ | Kline, R. B. (2015). Principles and Practice of Structural Equation Modeling (4th ed.). Guilford Press. ISBN: 978-1462523344 | Kerlinger, F. N., & Lee, H. B. (1986). Foundations of Behavioral Research (3rd ed.). Holt, Rinehart and Winston. ISBN: 978-0030417603 |
| Alias | comparative model comparison, cross-group model testing, competing model comparison research, comparative structural model evaluation | hypothetico-deductive research, confirmatory quantitative research, null hypothesis significance testing, NHST design |
| Relacionados | 4 | 4 |
| Resumen≠ | Comparative model testing research is a quantitative design in which two or more theoretically motivated models — or the same model evaluated across distinct groups or conditions — are systematically tested and compared using fit indices, likelihood-ratio tests, or information criteria. The goal is to determine which model better represents the data structure, or whether a model's parameter structure holds equally across comparison groups. | Hypothesis testing research is a quantitative design in which the investigator derives one or more explicit, falsifiable propositions from theory, translates them into a null hypothesis (H0) and an alternative hypothesis (H1), collects empirical data, and then applies an inferential statistical test to decide whether the evidence is sufficient to reject H0. The approach is the dominant paradigm for confirmatory science across the social, behavioral, health, and natural sciences. |
| ScholarGateConjunto de datos ↗ |
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