Fang Fang Chen: h-index, Total Citations, and Citation Map
Fang Fang Chen's h-index is 26 (31 i10-index, 22,483+ total citations across 5+ publications) according to Google Scholar as of May 2026. Fang Fang Chen is affiliated with University of Delaware.
Fang Fang Chen is a researcher affiliated with University of Delaware, specializing in Social Psychology, Quantitative Psychology. Their work has been cited 22,483 times. This profile visualizes their global influence, highlighting strong citation networks in United States.
Fang Fang Chen's Citation Metrics
Bibliometric impact based on 5 indexed publications.
- H-Index
- 26
- i10-Index
- 31
- Total Citations
- 22,483
- Citing Countries
- 21
As of May 2026.
Fang Fang Chen has an h-index of 26 and 22,483 total citations across 5 publications, with research cited by institutions in 21 countries.
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We've mapped 5,000 of 22,483 citations for Fang Fang Chen
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Top Cited Works
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Sensitivity of Goodness of Fit Indexes to Lack of Measurement Invariance
200714,490
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Visa Evidence Package
Views and exports tuned for EB-1A, O-1A, and EB-2 NIW petitions. Sustained acclaim, geographic reach, and independent-citation filtering are the strongest evidence categories immigration adjudicators look for.
Significant Contributions
Auto-detected research lines — a seminal paper and the follow-up work building on it. Review and edit before using in a petition. Each Free PDF opens in a new tab — EB-1A organises this into the structure USCIS applies to Criterion 5 of 8 CFR § 204.5(h)(3)(v); EB-1B re-frames it under § 204.5(i)(3) (outstanding researcher); NIW presents it under prong 2 of Matter of Dhanasar.
The researcher established foundational methods for testing measurement invariance in second-order factor models, significantly advancing structural equation modeling practices.
The researcher exposed methodological flaws in cross-cultural comparisons, demonstrating how inappropriate analogies distort findings and establishing rigorous standards for valid cross-cultural research.
The researcher established a critical methodological framework for distinguishing bifactor from second-order models in quality of life research, providing a foundational reference for psychometric analysis.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
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