השוואת שיטות
סקרו את השיטות שבחרתם זו לצד זו; שורות שבהן יש הבדל מודגשות.
| MCP רגרסיה עם עונש (MCP Penalized Regression)× | מודל מבנה גורמים חקרני× | |
|---|---|---|
| תחום | פסיכומטריה | פסיכומטריה |
| משפחה | Latent structure | Latent structure |
| שנת המקור≠ | 2010 | 2009 |
| הוגה השיטה≠ | Cun-Hui Zhang | Tihomir Asparouhov, Bengt Muthén |
| סוג≠ | Penalized regression with minimax concave penalty | Hybrid exploratory-confirmatory factor modeling |
| מקור מכונן≠ | Zhang, C. H. (2010). Nearly unbiased variable selection under minimax concave penalty. Annals of Statistics, 38(2), 894-942. DOI ↗ | Asparouhov, T., & Muthén, B. (2009). Exploratory structural equation modeling. Structural Equation Modeling, 16(3), 397-438. DOI ↗ |
| כינויים | MCP | ESEM |
| קשורות≠ | 4 | 5 |
| תקציר≠ | MCP (Minimax Concave Penalty) is a variable selection method developed by Zhang (2010) that uses a concave penalty function for automated feature selection. Like SCAD, MCP addresses bias in lasso by avoiding shrinkage of large coefficients, but uses a different penalty shape that is computationally simpler than SCAD. | Exploratory Structural Equation Modeling (ESEM) is a hybrid approach that combines exploratory factor analysis (EFA) with confirmatory factor analysis (CFA) and path modeling, developed by Asparouhov and Muthén (2009). ESEM relaxes restrictive zero-loading assumptions of traditional CFA, allowing all indicators to load on all factors, which can reveal cross-factor complexity and improve model fit while retaining the ability to test substantive structural theories. |
| ScholarGateמערך נתונים ↗ |
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