Does pls have advantages for small sample size or non-normal data?

MIS Quarterly: Management Information Systems

Volumn 36, Issue 3, 2012, Pages 981-1001

  Goodhue, Dale L ^a   Lewis, William ^c   Thompson, Ron ^b  

^a UNIVERSITY OF GEORGIA   (United States)

^b WAKE FOREST UNIVERSITY   (United States)

^c NONE

Author keywords

Monte Carlo simulation; Non normal distributions; Partial least squares; PLS; Regression; Small sample size; Statistical power; Structural equation modeling

Indexed keywords

INTELLIGENT SYSTEMS; LEAST SQUARES APPROXIMATIONS; NORMAL DISTRIBUTION; REGRESSION ANALYSIS; SAMPLING;

NON-NORMAL DISTRIBUTION; PARTIAL LEAST SQUARE (PLS); REGRESSION; SMALL SAMPLE SIZE; STATISTICAL POWER; STRUCTURAL EQUATION MODELING;

MONTE CARLO METHODS;

EID: 84864956746     PISSN: 02767783     EISSN: 21629730     Source Type: Journal    
DOI: 10.2307/41703490     Document Type: Article

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