How Data Analysis Can Dominate Interpretations of Dominant General Factors

2015 ◽  
Vol 8 (3) ◽  
pp. 438-445 ◽  
Author(s):  
Brenton M. Wiernik ◽  
Michael P. Wilmot ◽  
Jack W. Kostal
Keyword(s):  
Data Analysis ◽  
Substantial Proportion ◽  
Specific Factor ◽  
General Factor ◽  
Data Sets ◽  
Single Factor ◽  
Traditional Methods ◽  
External Variables ◽  
Specific Factors ◽  
Data Analytic

A dominant general factor (DGF) is present when a single factor accounts for the majority of reliable variance across a set of measures (Ree, Carretta, & Teachout, 2015). In the presence of a DGF, dimension scores necessarily reflect a blend of both general and specific factors. For some constructs, specific factors contain little unique reliable variance after controlling for the general factor (Reise, 2012), whereas for others, specific factors contribute a more substantial proportion of variance (e.g., Kinicki, McKee-Ryan, Schriesheim, & Carson, 2002). We agree with Ree et al. that the presence of a DGF has implications for interpreting scores. However, we argue that the conflation of general and specific factor variances has the strongest implications for understanding how constructs relate to external variables. When dimension scales contain substantial general and specific factor variance, traditional methods of data analysis will produce ambiguous or even misleading results. In this commentary, we show how several common data analytic methods, when used with data sets containing a DGF, will substantively alter conclusions.

Domain-Specificity of Need for Cognition Among High School Students

2019 ◽  
Vol 35 (5) ◽  
pp. 607-616 ◽  
Author(s):  
Ulrich Keller ◽  
Anja Strobel ◽  
Romain Martin ◽  
Franzis Preckel
Keyword(s):  
High School Students ◽  
Structural Equation ◽  
Factor Model ◽  
Need For Cognition ◽  
Self Concept ◽  
Equation Modeling ◽  
Specific Factor ◽  
General Factor ◽  
Domain Specific ◽  
Specific Factors

Abstract. Need for Cognition (NFC) is increasingly investigated in educational research. In contrast to other noncognitive constructs in this area, such as academic self-concept and interest, NFC has consistently been conceptualized as domain-general. We employed structural equation modeling to address the question of whether NFC can be meaningfully and gainfully conceptualized as domain-specific. To this end, we developed a domain-specific 20-item NFC scale with parallel items for Science, Mathematics, German, and French. Additionally, domain-general NFC was assessed with five domain-general items. Using a cross-sectional sample of more than 4,500 Luxembourgish 9th graders, we found that a nested-factor model incorporating both a general factor and domain-specific factors better accounted for the data than a single-factor or a correlated-factor model. However, the influence of the general factor was markedly stronger than in corresponding models for academic self-concept and interest. When controlling for the domain-specific factors, only Mathematics achievement was significantly predicted by the domain-general factor, while all achievement measures (Mathematics, French, and German) were predicted by the corresponding domain-specific factor. The nested domain-specific NFC factors were clearly empirically distinguishable from first-order domain-specific interest factors.

scholarly journals Factor structure of Raven's Coloured Progressive Matrices

Psico-USF ◽  
2016 ◽  
Vol 21 (2) ◽  
pp. 259-272 ◽  
Author(s):  
Monalisa Muniz ◽  
Cristiano Mauro Assis Gomes ◽  
Sonia Regina Pasian
Keyword(s):  
Standard Deviation ◽  
Factor Structure ◽  
Factor Model ◽  
Specific Factor ◽  
General Factor ◽  
Factor Analyses ◽  
Confirmatory Factor Analyses ◽  
Confirmatory Factor ◽  
Specific Factors ◽  
Three Factor

Abstract This study's objective was to verify the factor structure of Raven's Coloured Progressive Matrices (CPM). The database used included the responses of 1,279 children, 50.2% of which were males with an average age of 8.48 years old and a standard deviation of 1.49 yrs. Confirmatory factor analyses were run to test seven models based on CPM theory and on a Brazilian study addressing the test's structure. The results did not confirm the CPM theoretical proposition concerning the scales but indicated that the test can be interpreted by one general factor and one specific factor or one general factor and three specific factors; both are bi-dimensional models. The three-factor model is, however, more interpretable, suggesting that the factors can be used as a means of screening children's cognitive developmental stage.

scholarly journals Criterion Validity and Relationships between Alternative Hierarchical Dimensional Models of General and Specific Psychopathology

2020 ◽  
Author(s):  
Tyler M. Moore ◽  
Antonia N. Kaczkurkin ◽  
E. Leighton Durham ◽  
Hee Jung Jeong ◽  
Malerie G. McDowell ◽  
...  
Keyword(s):  
Lower Order ◽  
Statistical Models ◽  
Child Psychopathology ◽  
Criterion Validity ◽  
Second Order ◽  
Specific Factor ◽  
General Factor ◽  
External Criterion ◽  
Bifactor Models ◽  
External Variables

ABSTRACTPsychopathology can be viewed as a hierarchy of correlated dimensions. Many studies have supported this conceptualization, but they have used alternative statistical models with differing interpretations. In bifactor models, every symptom loads on both the general factor and one specific factor (e.g., internalizing), which partitions the total explained variance in each symptom between these orthogonal factors. In second-order models, symptoms load on one of several correlated lower-order factors. These lower-order factors load on a second-order general factor, which is defined by the variance shared by the lower-order factors. Thus, the factors in second-order models are not orthogonal. Choosing between these valid statistical models depends on the hypothesis being tested. Because bifactor models define orthogonal phenotypes with distinct sources of variance, they are optimal for studies of shared and unique associations of the dimensions of psychopathology with external variables putatively relevant to etiology and mechanisms. Concerns have been raised, however, about the reliability of the orthogonal specific factors in bifactor models. We evaluated this concern using parent symptom ratings of 9-10 year olds in the ABCD Study. Psychometric indices indicated that all factors in both bifactor and second-order models exhibited at least adequate construct reliability and estimated replicability. The factors defined in bifactor and second-order models were highly to moderately correlated across models, but have different interpretations. All factors in both models demonstrated significant associations with external criterion variables of theoretical and clinical importance, but the interpretation of such associations in second-order models was ambiguous due to shared variance among factors.General Scientific SummarySome investigators have proposed that viewing the correlated symptoms of psychopathology as a hierarchy in which all symptoms are related to both a general (p) factor of psychopathology and a more specific factor will make it easier to distinguish potential risk factors and mechanisms that are nonspecifically related to all forms of psychopathology versus those that are associated with specific dimensions of psychopathology. Parent ratings of child psychopathology items from the Adolescent Brain Cognitive Development (ABCD) Study were analyzed using two alternative statistical models of the proposed hierarchy. All factors of psychopathology defined in both bifactor and second-order models demonstrated adequate psychometric properties and criterion validity, but associations of psychopathology factors with external variables were more easily interpreted in bifactor than in second-order models.

scholarly journals Searching for G: A New Evaluation of SPM-LS Dimensionality

2019 ◽  
Vol 7 (3) ◽  
pp. 14 ◽  
Author(s):  
Garcia-Garzon ◽  
Abad ◽  
Garrido
Keyword(s):  
Specific Factor ◽  
General Factor ◽  
Graph Analysis ◽  
Reliable Measure ◽  
Initial Validation ◽  
Specific Factors ◽  
Raven's Progressive Matrices ◽  
Essential Unidimensionality ◽  
Future Work ◽  
Short Versions

There has been increased interest in assessing the quality and usefulness of short versions of the Raven’s Progressive Matrices. A recent proposal, composed of the last twelve matrices of the Standard Progressive Matrices (SPM-LS), has been depicted as a valid measure of g. Nonetheless, the results provided in the initial validation questioned the assumption of essential unidimensionality for SPM-LS scores. We tested this hypothesis through two different statistical techniques. Firstly, we applied exploratory graph analysis to assess SPM-LS dimensionality. Secondly, exploratory bi-factor modelling was employed to understand the extent that potential specific factors represent significant sources of variance after a general factor has been considered. Results evidenced that if modelled appropriately, SPM-LS scores are essentially unidimensional, and that constitute a reliable measure of g. However, an additional specific factor was systematically identified for the last six items of the test. The implications of such findings for future work on the SPM-LS are discussed.

DTI data analysis: application of fiber tracking to group averaged data sets

2010 ◽  
Vol 41 (01) ◽  
Author(s):  
HP Müller ◽  
A Unrath ◽  
A Riecker ◽  
AC Ludolph ◽  
J Kassubek
Keyword(s):  
Data Analysis ◽  
Fiber Tracking ◽  
Data Sets ◽  
Analysis Application

Comparing Hierarchical Models of Personality Pathology

2018 ◽  
Author(s):  
Whitney R. Ringwald ◽  
Aidan G.C. Wright ◽  
Joseph E. Beeney ◽  
Paul A. Pilkonis
Keyword(s):  
Hierarchical Models ◽  
Empirical Work ◽  
Hierarchical Classification ◽  
Personality Pathology ◽  
General Factor ◽  
Factor Analyses ◽  
Conceptual Structures ◽  
Individual Personality ◽  
Exploratory Factor Analyses ◽  
Specific Factors

Two dimensional, hierarchical classification models of personality pathology have emerged as alternatives to traditional categorical systems: multi-tiered models with increasing numbers of factors and models that distinguish between a general factor of severity and specific factors reflecting style. Using a large sample (N=840) with a range of psychopathology, we conducted exploratory factor analyses of individual personality disorder criteria to evaluate the validity of these conceptual structures. We estimated an oblique, “unfolding” hierarchy and a bifactor model, then examined correlations between these and multi-method functioning measures to enrich interpretation. Four-factor solutions for each model, reflecting rotations of each other, fit well and equivalently. The resulting structures are consistent with previous empirical work and provide support for each theoretical model.

The Relationship Between Career Adaptability and Job-Search Self-Efficacy of Graduates: The Bifactor Approach

2021 ◽  
pp. 106907272110022
Author(s):  
Marijana Matijaš ◽  
Darja Maslić Seršić
Keyword(s):  
Job Search ◽  
Self Efficacy ◽  
Cross Sectional Study ◽  
Sem Analysis ◽  
Bifactor Model ◽  
Specific Factor ◽  
General Factor ◽  
Career Adaptability ◽  
Cross Sectional ◽  
Interview Performance

Career adaptability is an important resource for dealing with career transitions such as the transition from university to work. Previous research emphasized the importance of focusing on career adapt-abilities instead only on general career adaptability. The aim of this research was to investigate whether career adaptability can be conceptualized as a bifactor model and whether general and specific dimensions of career adaptability have a relationship with job-search self-efficacy of graduates. In an online cross-sectional study, 667 graduates completed the Career Adapt-Abilities Scale and Job Search Skill and Confidence Scale. The CFA analysis showed that the bifactor model of career adaptability had a good fit where general factor explained most of the items’ variance. The SEM analysis revealed that general career adaptability and the specific factor of confidence positively correlated with job-search and interview performance self-efficacy. Control only correlated with interview performance self-efficacy. Neither concern nor curiosity showed a significant relationship with job-search and interview performance self-efficacy.

Manifest Anxiety: Unifactorial or Multifactorial Composition?

1965 ◽  
Vol 20 (3) ◽  
pp. 773-780 ◽  
Author(s):  
Walter D. Fenz ◽  
Seymour Epstein
Keyword(s):  
Striated Muscle ◽  
Muscle Tension ◽  
Autonomic Arousal ◽  
Specific Factor ◽  
General Factor ◽  
Personality Variable ◽  
Specific Nature ◽  
Taylor Manifest Anxiety Scale ◽  
Manifest Anxiety ◽  
Differential Contribution

The study investigates three subscales of manifest anxiety, consisting of symptoms of striated muscle tension, symptoms of autonomic arousal, and feelings of fear and insecurity. There was both a general factor of anxiety and a specific factor associated with striated muscle tension. Further evidence for the specific nature of striated muscle tension was indicated by its positive relationship to feelings of hostility, its failure to relate to a personality variable of inhibition, and the relatively high score obtained by males. It was hypothesized that striated muscle tension is more closely associated with overt activity than autonomic symptoms, which represent a deeper level of inhibition. Discrepant results of studies using the Taylor Manifest Anxiety Scale may be due to a failure to take into account the differential contribution of items relating to different kinds of anxiety.

scholarly journals Modeling General, Specific, and Method Variance in Personality Measures: Results for ZKA-PQ and NEO-PI-R

Assessment ◽  
2016 ◽  
Vol 25 (8) ◽  
pp. 959-977 ◽  
Author(s):  
Francisco J. Abad ◽  
Miguel A. Sorrel ◽  
Luis Francisco Garcia ◽  
Anton Aluja
Keyword(s):  
Personality Inventory ◽  
Response Styles ◽  
General Factor ◽  
Personality Questionnaire ◽  
Neo Personality Inventory ◽  
Personality Inventories ◽  
Method Factor ◽  
Neo Personality Inventory Revised ◽  
Specific Factors ◽  
Using Data

Contemporary models of personality assume a hierarchical structure in which broader traits contain narrower traits. Individual differences in response styles also constitute a source of score variance. In this study, the bifactor model is applied to separate these sources of variance for personality subscores. The procedure is illustrated using data for two personality inventories—NEO Personality Inventory–Revised and Zuckerman–Kuhlman–Aluja Personality Questionnaire. The inclusion of the acquiescence method factor generally improved the fit to acceptable levels for the Zuckerman–Kuhlman–Aluja Personality Questionnaire, but not for the NEO Personality Inventory–Revised. This effect was higher in subscales where the number of direct and reverse items is not balanced. Loadings on the specific factors were usually smaller than the loadings on the general factor. In some cases, part of the variance was due to domains being different from the main one. This information is of particular interest to researchers as they can identify which subscale scores have more potential to increase predictive validity.

scholarly journals A multilevel approach to big data analysis using analytic tools and actor network theory

2018 ◽  
Vol 20 (1) ◽  
Author(s):  
Tiko Iyamu
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Network Theory ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Actor Network Theory ◽  
Big Data Analysis ◽  
Data Sets ◽  
Multilevel Approach ◽  
Actor Network

Background: Over the years, big data analytics has been statically carried out in a programmed way, which does not allow for translation of data sets from a subjective perspective. This approach affects an understanding of why and how data sets manifest themselves into various forms in the way that they do. This has a negative impact on the accuracy, redundancy and usefulness of data sets, which in turn affects the value of operations and the competitive effectiveness of an organisation. Also, the current single approach lacks a detailed examination of data sets, which big data deserve in order to improve purposefulness and usefulness.Objective: The purpose of this study was to propose a multilevel approach to big data analysis. This includes examining how a sociotechnical theory, the actor network theory (ANT), can be complementarily used with analytic tools for big data analysis.Method: In the study, the qualitative methods were employed from the interpretivist approach perspective.Results: From the findings, a framework that offers big data analytics at two levels, micro- (strategic) and macro- (operational) levels, was developed. Based on the framework, a model was developed, which can be used to guide the analysis of heterogeneous data sets that exist within networks.Conclusion: The multilevel approach ensures a fully detailed analysis, which is intended to increase accuracy, reduce redundancy and put the manipulation and manifestation of data sets into perspectives for improved organisations’ competitiveness.

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