Higher-Order Factor Analysis and Functional Regionalization: A Case Study in South Wales, 1971

1980 ◽  
Vol 12 (6) ◽  
pp. 685-701 ◽  
Author(s):  
W K D Davies
Keyword(s):  
Factor Analysis ◽  
Higher Order ◽  
Factorial Approach ◽  
South Wales ◽  
Functional Regionalization ◽  
Order Factor ◽  
Journey To Work ◽  
The Stability

A method for functional regionalization based on higher-order factor analysis is proposed. The utility of the method when using alternative factorial procedures is described by reference to a study of journey-to-work flows in South Wales in 1971, in which the stability of the results under alternative factorial procedures is explored. The advantages of the higher-order factorial approach compared with previously suggested methodologies are documented.

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.

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.

Validity of Higher-Order Ability Constructs in Structure-of-Intellect Tests All Involving Semantic Content and Operations of Cognition or Evaluation: A Confirmatory Maximum Likelihood Factor Analysis

1985 ◽  
Vol 45 (2) ◽  
pp. 353-359 ◽  
Author(s):  
David E. Mace ◽  
William B. Michael ◽  
Dennis Hocevar
Keyword(s):  
Factor Analysis ◽  
Maximum Likelihood ◽  
Semantic Content ◽  
Higher Order ◽  
Second Order ◽  
Third Order ◽  
Maximum Likelihood Factor Analysis ◽  
Practical Standpoint ◽  
Order Factor ◽  
Structure Of Intellect

Through use of confirmatory maximum likelihood factor analysis in conjunction with the LISREL V computer program devised by Jöreskog and Sörbom, an evaluation was made of the validity of higher-order ability constructs in structure-of-intellect tests all containing semantic content and operations of cognition or evaluation. The hypothesized first-, second-, and third-order factors were all reproducible with every one of the estimated factor loadings being significant beyond the .01 level. Although the first- and second-order factors were shown to be reproducible and statistically separable, the high intercorrelations among the six first-order product factors and the two second-order operations factors would suggest from a practical standpoint that the single third-order factor of semantic content would constitute a plausible alternative for accounting for much of the covariance among the test variables.

Helping Orientations: Four Dimensions,

1989 ◽  
Vol 69 (3-2) ◽  
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.

Exploratory Higher Order Analysis of the Luria Interpretive Model on the Kaufman Assessment Battery for Children–Second Edition (KABC-II) School-Age Battery

Assessment ◽  
2015 ◽  
Vol 24 (4) ◽  
pp. 540-552 ◽  
Author(s):  
Ryan J. McGill ◽  
Angelia R. Spurgin
Keyword(s):  
Factor Analysis ◽  
Exploratory Factor Analysis ◽  
Factor Structure ◽  
Age Groups ◽  
Higher Order ◽  
General Factor ◽  
Normative Sample ◽  
Assessment Battery ◽  
Order Factor ◽  
Interpretive Scheme

Higher order factor structure of the Luria interpretive scheme on the Kaufman Assessment Battery for Children–Second Edition (KABC-II) for the 7- to 12-year and the 13- to 18-year age groups in the KABC-II normative sample ( N = 2,025) is reported. Using exploratory factor analysis, multiple factor extraction criteria, and hierarchical exploratory factor analysis not included in the KABC-II manual, two-, three-, and four-factor extractions were analyzed to assess the hierarchical factor structure by sequentially partitioning variance appropriately to higher order and lower order dimensions as recommended by Carroll. No evidence for a four-factor solution was found. Results showed that the largest portions of total and common variance were accounted for by the second-order general factor and that interpretation should focus primarily, if not exclusively, at that level of measurement.

scholarly journals Neuro-transcriptomic signatures for mood disorder morbidity and suicide mortality

2019 ◽  
Author(s):  
Mbemba Jabbi ◽  
Dhivya Arasappan ◽  
Simon B. Eickhoff ◽  
Stephen M. Strakowski ◽  
Charles B. Nemeroff ◽  
...  
Keyword(s):  
Gene Expression ◽  
Factor Analysis ◽  
Mood Disorders ◽  
Rna Sequencing ◽  
Meta Analysis ◽  
Psychiatric Morbidity ◽  
Higher Order ◽  
Suicide Mortality ◽  
High Morbidity ◽  
Order Factor

ABSTRACTSuicidal behaviors are strongly linked with mood disorders, but the specific neurobiological and functional gene-expression correlates for this linkage remain elusive. We performed neuroimaging-guided RNA-sequencing in two studies to test the hypothesis that imaging-localized gray matter volume (GMV) loss in mood disorders, harbors gene-expression changes associated with disease morbidity and related suicide mortality in an independent postmortem cohort. To do so, first, we conducted study 1 using an anatomical likelihood estimation (ALE) MRI meta-analysis including a total of 47 voxel-based morphometry (VBM) publications (i.e. 26 control>major depressive disorder (MDD) studies, and 21 control>bipolar disorder (BD) studies) in 2387 (living) participants. Study 1 meta-analysis identified a selective anterior insula cortex (AIC) GMV loss in mood disorders. We then used this results to guide study 2 postmortem tissue dissection and RNA-Sequencing of 100 independent donor brain samples with a life-time history of MDD (N=30), BD (N=37) and control (N=33). In study 2, exploratory factor-analysis identified a higher-order factor representing number of Axis-1 diagnoses (e.g. substance use disorders/psychosis/anxiety, etc.), referred to here as morbidity and suicide-completion referred to as mortality. Comparisons of case-vs-control, and factor-analysis defined higher-order-factor contrast variables revealed that the imaging-identified AIC GMV loss sub-region harbors differential gene-expression changes in high morbidity-&-mortality versus low morbidity-&-mortality cohorts in immune, inflammasome, and neurodevelopmental pathways. Weighted gene co-expression network analysis further identified co-activated gene modules for psychiatric morbidity and mortality outcomes. These results provide evidence that AIC anatomical signature for mood disorders are possible correlates for gene-expression abnormalities in mood morbidity and suicide mortality.

Higher-Order Factors in a Five-Factor Personality Inventory and its Relation to Social Desirability

2007 ◽  
Vol 23 (2) ◽  
pp. 63-70 ◽  
Author(s):  
Martin Bäckström
Keyword(s):  
Factor Analysis ◽  
Social Desirability ◽  
Factor Model ◽  
Factor Models ◽  
Higher Order ◽  
Personality Inventory ◽  
Personality Factors ◽  
The Internet ◽  
Confirmatory Factor ◽  
Order Factor

Abstract. This paper compared two higher-order factor models using a 100-item, five-factor personality inventory originating from the IPIP database. The sample consisted of 2,019 subjects tested on the Internet. The two models were compared using confirmatory factor analysis. The two-factor model showed a similar fit to the data. The criteria for parsimony favored a hierarchical model with one higher-order factor at the top and five personality factors beneath. The single higher-order factor was found to be related to social desirability in a subsample of 196 subjects.

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