Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Investigación por contraste de hipótesis× | Investigación de prueba de modelos× | |
|---|---|---|
| Campo | Diseño de investigación | Diseño de investigación |
| Familia | Process / pipeline | Process / pipeline |
| Año de origen≠ | Early 20th century (Fisher 1925; Neyman–Pearson 1933) | 1970s (Joreskog 1969–1973); widely adopted in social sciences by the 1980s–1990s |
| Autor original≠ | Karl Pearson, Ronald A. Fisher, Jerzy Neyman, Egon Pearson | Karl G. Joreskog (SEM/LISREL framework); formalized through structural equation modeling tradition |
| Tipo≠ | Quantitative confirmatory research design | Confirmatory quantitative research design |
| Fuente seminal≠ | Kerlinger, F. N., & Lee, H. B. (1986). Foundations of Behavioral Research (3rd ed.). Holt, Rinehart and Winston. ISBN: 978-0030417603 | Kline, R. B. (2015). Principles and Practice of Structural Equation Modeling (4th ed.). Guilford Press. ISBN: 978-1462523344 |
| Alias | hypothetico-deductive research, confirmatory quantitative research, null hypothesis significance testing, NHST design | model-based research, structural model testing, theory-testing research, MTR |
| Relacionados≠ | 4 | 5 |
| Resumen≠ | 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. | Model testing research is a confirmatory quantitative design in which the researcher specifies a theoretical model — depicting hypothesized relationships among constructs — and then tests how well that model fits empirical data. Drawing primarily on structural equation modeling (SEM) and confirmatory factor analysis (CFA), it evaluates whether the data-implied covariance structure is consistent with the theoretically derived one, yielding fit indices that indicate model-data correspondence. |
| ScholarGateConjunto de datos ↗ |
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