Supplementary Appendix To: Higher-Order Multi-Cumulant Factor Analysis

2021 ◽  
Author(s):  
Wanbo Lu ◽  
Guanglin Huang ◽  
Kris Boudt
Keyword(s):  
Factor Analysis ◽  
Higher Order ◽  
Supplementary Appendix

The Factor Structure and Psychometric Properties of the Resiliency Scale in Chinese Undergraduates

2010 ◽  
Vol 26 (3) ◽  
pp. 162-171 ◽  
Author(s):  
Lixia Cui ◽  
Xiujie Teng ◽  
Xupei Li ◽  
Tian P.S. Oei
Keyword(s):  
Factor Analysis ◽  
Psychometric Properties ◽  
Factor Structure ◽  
Undergraduate Students ◽  
Concurrent Validity ◽  
Higher Order ◽  
Criterion Variable ◽  
Confirmatory Factor ◽  
High Level ◽  
Chinese Undergraduates

The current study examined the factor structure and the psychometric properties of Sandra Prince-Embury’s Resiliency Scale for Adolescents (RESA) in Chinese undergraduates. A total of 726 undergraduate students were randomly divided into two subsamples: Sample A was used for the exploratory factor analysis (EFA) and Sample B was used for the confirmatory factor analysis (CFA). The EFA revealed that 56 items and a model of 10 factors with 3 higher order factors (as described by Sandra) were to be retained; CFA with Sample B confirmed this result. The overall scale and the subscales of the Chinese-RESA demonstrated a high level of internal consistency. Furthermore, concurrent validity was demonstrated by the correlation of the scale with other instruments such as the PANAS and the CSS, and the predictive validity was confirmed via three multiple regression analyses using the PANAS as a criterion variable: one for the 10 subscales of the C-RESA, one for the 3 higher order scales, and one for the total C-RESA. We concluded that the C-RESA may be used for research into Chinese undergraduates’ adaptive behaviors.

scholarly journals A multi-group higher-order factor analysis for studying the gender-effect in Teacher Job Satisfaction

METRON ◽  
2021 ◽  
Author(s):  
Carlo Cavicchia ◽  
Pasquale Sarnacchiaro
Keyword(s):  
Job Satisfaction ◽  
Factor Analysis ◽  
Secondary School Teachers ◽  
Estimation Method ◽  
Likelihood Estimation ◽  
Higher Order ◽  
Teacher Job Satisfaction ◽  
Public Secondary School ◽  
Order Factor ◽  
Maximum Likelihood Estimation Method

AbstractTeachers’ performances also depend on whether and how they are satisfied with their job. Therefore, Teacher Job Satisfaction must be considered as the driver of teachers’ accomplishments. To plan future policies and improve the overall teaching process, it is crucial to understand which factors mostly contribute to Teacher Job Satisfaction. A Common Assessment Framework and Education questionnaire was administered to 163 Italian public secondary school teachers to collect data, and a second-order factor analysis was used to detect which factors impact on Teacher Job Satisfaction, and to what extent. This model-based approach guarantees to detect factors which respect important properties: unidimensionality and reliability. All the coefficients are estimated according to the maximum likelihood estimation method in order to make inference on the parameters and on the validity of the model. Moreover, a new multi-group test for higher-order factor analysis was proposed and implemented. Finally, we analyzed in detail whether the factors impacting Teacher Job Satisfaction are characterized by gender.

scholarly journals Confirmatory Factor Analysis for a Service-Learning Outcomes Measurement Scale (S-LOMS)

2020 ◽  
Vol 32 (1) ◽  
pp. 3-34
Author(s):  
Ka Hing Lau ◽  
Robin Stanley Snell
Keyword(s):  
Factor Analysis ◽  
Service Learning ◽  
Confirmatory Factor Analysis ◽  
Student Learning ◽  
Learning Outcomes ◽  
Student Learning Outcomes ◽  
Higher Order ◽  
Measurement Scale ◽  
Outcomes Measurement ◽  
Confirmatory Factor

Service-learning was introduced into Hong Kong over a decade ago, yet there is a research gap about the self-perceived student learning outcomes, partly due to the lack of a reliable measurement instrument across disciplines and service-learning types. This study evaluated a recently created Service-Learning Outcomes Measurement Scale (S-LOMS) through confirmatory factor analysis with 629 students. S-LOMS measures self-perceived student learning outcomes with 56 items covering various learning outcome domains under four higher-order categories: knowledge application; personal and professional skills; civic orientation and engagement; and self-awareness. Alternative measurement models were compared, with the results indicating that although a model with 11 domains and without higher-order categories was preferred, there was also support for a model with 10 domains subsumed under the four higher-order categories. Multi-sample analyses indicated that both models were stable across gender.

Higher-order factor analysis of an instrument with dichotomous data

2005 ◽  
Vol 28 (5) ◽  
pp. 431-440 ◽  
Author(s):  
Kathleen A. Baldwin ◽  
Margaret S. Grinslade ◽  
Lynn C. Baer ◽  
Parris Watts ◽  
Mary K. Dinger ◽  
...  
Keyword(s):  
Factor Analysis ◽  
Higher Order ◽  
Dichotomous Data ◽  
Order Factor

Helping Orientations: Four Dimensions

1989 ◽  
Vol 69 (3_suppl) ◽  
pp. 1371-1378 ◽  
Author(s):  
Joseph F. Michlitsch ◽  
Stanley Frankel
Keyword(s):  
Factor Analysis ◽  
Factor Structure ◽  
Helping Behavior ◽  
Higher Order ◽  
Convenience Sample ◽  
Four Dimensions ◽  
Underlying Factor ◽  
Target Sample ◽  
Order Factor ◽  
And Coping

The underlying factor structure of four helping and coping orientations was investigated. The four orientations are the foundations of four models of helping and coping. Each model consists of a person's orientations toward helping behavior and the set of likely factors and behaviors related to the orientations. The orientations are believed to influence how a person interacts with others in a helping situation and how that person attempts to help himself (cope). Factor analysis in two studies supports the prediction of four helping orientations across a convenience and a target sample. Secondary, higher-order factor analysis further provides support based on the convenience sample, but not the target sample.

Personality Assessment Inventory: A Confirmatory Factor Analysis

1994 ◽  
Vol 79 (3_suppl) ◽  
pp. 1441-1442 ◽  
Author(s):  
Gregory J. Boyle ◽  
James Ward ◽  
Tania J. Lennon
Keyword(s):  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Personality Assessment ◽  
Personality Assessment Inventory ◽  
Higher Order ◽  
Self Report ◽  
Proposed Model ◽  
Confirmatory Factor ◽  
Order Factor ◽  
Report Measure

The Personality Assessment Inventory is a recently constructed multidimensional self-report measure of personality traits. Morey reported the results of a scale factoring, claiming that the instrument measures four separate higher-order dimensions; however, in an independent Australian study of the psychometric properties of the inventory, Boyle and Lennon found five higher-order dimensions, using factor analytic procedures intended to maximize simple structure. The present paper reports the results of a confirmatory factor analysis for the proposed model based on the Australian data. The results indicate that the model does not provide a satisfactory fit, raising questions about the higher-order factor structure.

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