scholarly journals The paths from walk preference to walk behavior: Applying latent factors in structural equation modeling

2012 ◽  
Vol 5 (3) ◽  
Author(s):  
Matthew A. Coogan ◽  
Thomas Adler ◽  
Karla Karash
Keyword(s):  
Structural Equation Modeling ◽  
Structural Equation ◽  
Equation Modeling ◽  
Latent Factors

scholarly journals Associating puberty, metabolism and cognition

2021 ◽  
Author(s):  
Rafaella Sales de Freitas ◽  
Fausto Coutinho-Lourenco ◽  
Hugo Cogo-Moreira ◽  
Allan Chiaratti Oliveira ◽  
Sabine Pompeia
Keyword(s):  
Socioeconomic Status ◽  
Structural Equation Modeling ◽  
Structural Equation ◽  
Block Design ◽  
Metabolic Changes ◽  
Metabolic Status ◽  
Equation Modeling ◽  
Latent Factors ◽  
Intelligence Scale ◽  
Pubertal Status

Cognition is influenced by pubertal and metabolic changes independently. However, these changes co-occur in adolescence and their combined role on cognition is unclear. We used Structural Equation Modeling (SEM) to investigated the association of pubertal and metabolic status latent factors, each composed of a pool of indicators, on intelligence markers in 278 early adolescents, cross-sectionally. The SEM model, controlled for socioeconomic status, included paths between latent factors and from them to the outcomes: two subtests of Wechsler Abbreviated Intelligence Scale (Block Design; Vocabulary). Metabolic status related to pubertal status only in girls, but did not affect performance. Differently, more advanced pubertal status (controled for age) was positively associated with better verbal (Vocabulary) and non-verbal (Block Design) intelligence in both sexes.

scholarly journals Destination Image of Himachal Pradesh: Foreign Tourist Perception

2021 ◽  
Vol 19 (2) ◽  
pp. 112-124
Author(s):  
Sunil Kumar ◽  
◽  
Sandeep Sharma ◽  
Keyword(s):  
Structural Equation Modeling ◽  
Structural Equation ◽  
Major Element ◽  
Himachal Pradesh ◽  
Destination Image ◽  
Equation Modeling ◽  
Latent Factors ◽  
Affective Dimension ◽  
Foreign Nationals ◽  
Present Experience

Destination Image is a psychological set of emotions in a tourist’s mind. This study is an inquiry from 384 Foreign Nationals who included destination Himachal in their visit to India. The latent factors of ‘destination image’ were extracted and validated through structural equation modeling (SEM). Out of the ten explored latent factors ‘affective’ dimension is the major element followed by ‘infrastructure’, ‘culture’ and ‘safety’ are acting as significant persuading forces in destination image formation. The study will help various organizations and agencies to position their tourism products. Future researchers can investigate the change in destination image with other locations having the same geographical conditions. This study is limited to the present experience of tourists and didn’t include the revisit experiences of foreign tourists.

A Structural Equation Modeling Based Approach for Identifying Key Descriptors in Microstructural Materials Design

2015 ◽  
Author(s):  
Yichi Zhang ◽  
Daniel W. Apley ◽  
Wei Chen
Keyword(s):  
Structural Equation Modeling ◽  
Material Properties ◽  
Structural Equation ◽  
Structural Model ◽  
Training Sample ◽  
Sem Analysis ◽  
Equation Modeling ◽  
Latent Factors ◽  
Training Sample Size ◽  
Supervised Ranking

In design of advanced heterogeneous materials system, microstructures play an important role as a link between processing and material properties. An accurate and efficient representation of material microstructures is necessary. Our prior work applied a supervised ranking algorithm to identify key microstructure descriptors, however the approach falls short in identifying redundancy in descriptors and is not reliable when the training sample size is small. In this paper, we propose a Structural Equation Modeling (SEM) based approach to identify significant microstructure descriptors based on either correlation functions (CF) or material properties, or both. By building a reflective structural model, we are able to deal with high correlations among all candidate descriptors, gain more insights into their relations, and identify latent factors for categorizing microstructure features. The proposed approach begins with an Exploratory Factor Analysis (EFA) for grouping and reducing descriptors to determine the proper structure of microstructure descriptors as indicators of latent factors. The SEM analysis is then applied to identify the key descriptors using the Partial Least Squares (PLS) algorithm. The nanodielectric system with epoxy-nanosilica is used as an example to illustrate and validate the proposed approach. The potential use of identified key microstructure descriptors for optimal design of microstructural materials is discussed.

Louder = Larger = Clearer

2020 ◽  
Vol 228 (4) ◽  
pp. 296-300
Author(s):  
Beatrice G. Kuhlmann ◽  
David J. Frank ◽  
Daniel Danner
Keyword(s):  
Structural Equation Modeling ◽  
Word Pair ◽  
Structural Equation ◽  
Past Research ◽  
Equation Modeling ◽  
Font Size ◽  
Latent Factors ◽  
Common Cause ◽  
Confirmatory Factor ◽  
Font Type

Abstract. Past research found robust metamemory illusions about the effects of font type, word-pair identity, volume, and font size on memory that are assumed to share a common cause, such as fluency. The current study simultaneously assessed all four metamemory illusions from vignettes alongside items assessing the belief that fluency benefits memory and that more is generally better. The typical metamemory illusions replicated in all samples. Confirmatory factor and structural equation modeling confirmed that at least the perceptual metamemory illusions (font type, volume, font size) can be explained by one latent factor, which was, however, not related to latent factors capturing the belief that fluency benefits memory or that more is better.

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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