A first course in structural equation modeling, 2nd ed.

By Tenko Raykov, George A. Marcoulides, N. Mahwah. 2006

449 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/56c654dbf58d3cd207d5cbc18bc16a6cc334af23

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Citations in the corpus

Factors Affecting the Algebra I Achievement of Academically Talented Learners (2008, background): “Further, SEM allowed us to explore both structural and measurement variables simultaneously (Musil et al., 1998), as well as allowing for multiple meditational paths (Garson, 2008; Raykov & Marcoulides, 2006).”
The Development of English and Mathematics Self-Efficacy: A Latent Growth Curve Analysis (2012, methodology): “In particular, the use of latent growth modeling (LGM) procedures (Bollen & Curran, 2006; Duncan, Duncan, Stycker, Fuzhong, & Alpert, 1999; Raykov & Marcoulides, 2006) has provided a strong methodological premise for additional research into the developmental Address correspondence to Huy P. Phan,…”
New Ways to Evaluate Goodness of Fit: A Note on Using Equivalence Testing to Assess Structural Equation Models (2017, background): “…we refer readers to their manuals and various related publications (e.g., Arbuckle, 2012; Bentler, 2006; Boker et al., 2011; Byrne, 2001; Jöreskog & Sörbom, 1993; Muthén & Muthén, 2014; Pritikin, Hunter, & Boker, 2015; R Development Core Team, 2011; Raykov & Marcoulides, 2006; Rosseel, 2012).”
Multivariate latent change modeling of developmental decline in academic intrinsic math motivation and achievement: Childhood through adolescence (2007, methodology): “…and incorporated into a latent change model in which they can also be studied, the model is more commonly referred to as a multivariate model (the term multivariate latent curve model is also used to define such a model, see Bollen & Curran, 2006; Duncan et al., 1999; Raykov & Marcoulides, 2006).”
Parents’ Conceptions of School Readiness, Transition Practices, and Children's Academic Achievement Trajectories (2015, background): “…investigation of the predictive relationship between static latent factors (parent measures) and both the intercept and slope of continuous outcomes (children’s achievement in reading and mathematics), thus capturing individual variation around the average trajectory (Raykov & Marcoulides, 2006).”
Evidence Regarding the Internal Structure: Confirmatory Factor Analysis (2017, methodology): “Several authors (Brown, 2015; Hair et al., 2006; Raykov & Marcoulides, 2006; Schumacher & Lomax, 2010) have proposed relatively clear step-by-step procedures for conducting a CFA; for the purpose of the application in this article, the steps provided by Schumacker and Lomax are primarily applied.”
Methods and Rule-of-Thumbs in The Determination of Minimum Sample Size When Appling Structural Equation Modelling: A Review (2020, background): “The number of free parameters in the model also considered determining the sample size (Raykov, 2006).”
Estimation of Generalizability Coefficients Via a Structural Equation Modeling Approach to Scale Reliability Evaluation (2006, background): “Widely recognized reasons for this popularity are that SEM offers a readily applicable approach to the quantification and testing of theories and explicitly takes into account the nearly ubiquitous measurement error in these disciplines (e.g., Raykov & Marcoulides, 2006).”
Longitudinal Pathways From Math Intrinsic Motivation and Achievement to Math Course Accomplishments and Educational Attainment (2013, methodology): “The correlation between the Level and Shape factors, respectively, for math intrinsic motivation and for math achievement simply reflect their degree of overlap (Raykov & Marcoulides, 2006, 2008).”
Structural Equation Modeling in Language Testing and Learning Research: A Review (2011, methodology): “To calculate the minimum desirable sample size for SEM analyses, the number of free parameters in each model was calculated and multiplied by 10, following Raykov and Marcoulides (2006).”
Appearance-Related Social Networking Sites and Body Image in Young Women: Testing an Objectification-Social Comparison Model (2020, unknown): “As recommended by Raykov and Marcoulides (2006), a minimum sample size would be at least 10 times the number of free model parameters, referred to as the N: q ≥ 10 rule.”