Structural Equation Modeling: Applications in Ecological and Evolutionary Biology. Edited by Bruce H  Pugesek, Adrian  Tomer, and , Alexander  von Eye. Cambridge and New York: Cambridge University Press. $75.00. xiii + 409 p; ill.; index. ISBN: 0–521–78133–7. 2003.

2004 ◽  
Vol 79 (3) ◽  
pp. 330-330
Author(s):  
Jack McArdle
Keyword(s):  
New York ◽  
Structural Equation Modeling ◽  
Evolutionary Biology ◽  
Structural Equation ◽  
Equation Modeling ◽  
Cambridge University

Customer satisfaction with bus rapid transit: a study of New York City select bus service applying structural equation modeling

2016 ◽  
Vol 8 (3) ◽  
pp. 497-520 ◽  
Author(s):  
Dan Wan ◽  
Camille Kamga ◽  
Wei Hao ◽  
Aaron Sugiura ◽  
Eric B. Beaton
Keyword(s):  
New York ◽  
New York City ◽  
Structural Equation Modeling ◽  
Customer Satisfaction ◽  
York City ◽  
Structural Equation ◽  
Equation Modeling ◽  
Bus Rapid Transit ◽  
Rapid Transit ◽  
Bus Service

Implementing Structural Equation Modeling on Public Data

2018 ◽  
pp. 178-195
Author(s):  
Yair Cohen ◽  
Elsa-Sofia Morote
Keyword(s):  
New York ◽  
Student Achievement ◽  
Structural Equation Modeling ◽  
Structural Equation ◽  
School Attendance ◽  
New York State ◽  
Fourth Grade ◽  
Equation Modeling ◽  
State Education ◽  
Public Data

The researchers used structural equation modeling (SEM) to create a model predicting fourth-grade student achievement in math by exploring the relationships among: student, household, school, and teacher factors. Public data from the New York State Education Department (NYSED) 2012–13 school report card data, NYSED fiscal reporting system, Census 2010 School District Demographics System, and 2011 Civil Right Data Collection were used from 1,263 schools in New York excluding New York City. Variables were chosen using this convenient sample and supported by our conceptual rationale. The model predicted fourth-grade math achievement with 67 percent of effect size. Household factors had strong predictive, while school attendance rate had medium predictive value for student achievement.

MW-L CHEUNG. Meta-analysis: A Structural Equation Modeling Approach. New York: Wiley

Biometrics ◽  
2017 ◽  
Vol 73 (1) ◽  
pp. 357-358
Author(s):  
Suzanne Jak
Keyword(s):  
New York ◽  
Structural Equation Modeling ◽  
Structural Equation ◽  
Meta Analysis ◽  
Equation Modeling ◽  
Modeling Approach

Testing the Assumption of Uncorrelated Errors for Short Scales by Means of Structural Equation Modeling

2014 ◽  
Vol 35 (4) ◽  
pp. 201-211 ◽  
Author(s):  
André Beauducel ◽  
Anja Leue
Keyword(s):  
Structural Equation Modeling ◽  
Negative Correlation ◽  
Structural Equation ◽  
Error Score ◽  
Classical Test Theory ◽  
Test Theory ◽  
Equation Modeling ◽  
Eysenck Personality Questionnaire ◽  
Personality Questionnaire ◽  
True Score

It is shown that a minimal assumption should be added to the assumptions of Classical Test Theory (CTT) in order to have positive inter-item correlations, which are regarded as a basis for the aggregation of items. Moreover, it is shown that the assumption of zero correlations between the error score estimates is substantially violated in the population of individuals when the number of items is small. Instead, a negative correlation between error score estimates occurs. The reason for the negative correlation is that the error score estimates for different items of a scale are based on insufficient true score estimates when the number of items is small. A test of the assumption of uncorrelated error score estimates by means of structural equation modeling (SEM) is proposed that takes this effect into account. The SEM-based procedure is demonstrated by means of empirical examples based on the Edinburgh Handedness Inventory and the Eysenck Personality Questionnaire-Revised.

The Measurement Invariance of the Short Dark Triad

2020 ◽  
Vol 41 (4) ◽  
pp. 207-218
Author(s):  
Mihaela Grigoraș ◽  
Andreea Butucescu ◽  
Amalia Miulescu ◽  
Cristian Opariuc-Dan ◽  
Dragoș Iliescu
Keyword(s):  
Structural Equation Modeling ◽  
Item Response ◽  
Structural Equation ◽  
Dark Triad ◽  
Theory And Practice ◽  
Equation Modeling ◽  
Scale Level ◽  
High Stake ◽  
Differential Functioning ◽  
Item Functioning

Abstract. Given the fact that most of the dark personality measures are developed based on data collected in low-stake settings, the present study addresses the appropriateness of their use in high-stake contexts. Specifically, we examined item- and scale-level differential functioning of the Short Dark Triad (SD3; Paulhus & Jones, 2011 ) measure across testing contexts. The Short Dark Triad was administered to applicant ( N = 457) and non-applicant ( N = 592) samples. Item- and scale-level invariances were tested using an Item Response Theory (IRT)-based approach and a Structural Equation Modeling (SEM) approach, respectively. Results show that more than half of the SD3 items were flagged for Differential Item Functioning (DIF), and Exploratory Structural Equation Modeling (ESEM) results supported configural, but not metric invariance. Implications for theory and practice are discussed.

Childhood Intelligence Predicts Adult Trait Openness

2016 ◽  
Vol 37 (2) ◽  
pp. 105-111 ◽  
Author(s):  
Adrian Furnham ◽  
Helen Cheng
Keyword(s):  
Structural Equation Modeling ◽  
Structural Equation ◽  
Social Background ◽  
Study Data ◽  
Equation Modeling ◽  
Direct Effects ◽  
Data Set ◽  
National Child Development Study ◽  
Nationally Representative ◽  
The Uk

Abstract. This study used a longitudinal data set of 5,672 adults followed for 50 years to determine the factors that influence adult trait Openness-to-Experience. In a large, nationally representative sample in the UK (the National Child Development Study), data were collected at birth, in childhood (age 11), adolescence (age 16), and adulthood (ages 33, 42, and 50) to examine the effects of family social background, childhood intelligence, school motivation during adolescence, education, and occupation on the personality trait Openness assessed at age 50 years. Structural equation modeling showed that parental social status, childhood intelligence, school motivation, education, and occupation all had modest, but direct, effects on trait Openness, among which childhood intelligence was the strongest predictor. Gender was not significantly associated with trait Openness. Limitations and implications of the study are discussed.

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