MTP2 and Partial Correlations in Monotone Higher-Order Factor Models

2015 ◽  
pp. 261-272
Author(s):  
Jules L. Ellis
Keyword(s):  
Factor Models ◽  
Higher Order ◽  
Partial Correlations ◽  
Order Factor

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.

Higher-order models with reflective indicators

2016 ◽  
Vol 11 (1) ◽  
pp. 180-188 ◽  
Author(s):  
Dirk Temme ◽  
Adamantios Diamantopoulos
Keyword(s):  
Design Methodology ◽  
Factor Models ◽  
Higher Order ◽  
Content Type ◽  
Modeling Approach ◽  
Order Factor ◽  
Higher Order Models

Purpose – Higher-order factor models have recently been dismissed as a ‘misleading’, ‘meaningless’, and ‘needless’ approach for modeling multidimensional constructs (Lee and Cadogan, 2013; L & C, 2013 hereafter). The purpose of this paper is to show that – in contrast to L & C’s (2013) verdict – higher-order factor models are still a legitimate operationalization option for multidimensional constructs. Design/methodology/approach – Basic conceptual and statistical premises of L & C’s (2013) arguments against higher-order factor models are scrutinized both conceptually and statistically as to their logic and validity. Findings – A thorough analysis of L & C’s (2013) arguments shows that they are fundamentally flawed both conceptually and statistically, rendering their conclusions invalid. Research limitations/implications – Researchers should not remove the well-established higher-order factor models from their methodological toolkit. Furthermore, empirical findings should not automatically be considered suspect simply because higher-factor models have been used to model multidimensional constructs. Originality/value – So far, L & C’s (2013) arguments against higher-order factor models have gone unchallenged in the literature. This rejoinder is a first, much needed attempt to protect applied researchers from getting the false impression that by using higher-factor models, they rely on a “misleading” or “meaningless” modeling approach.

When a Truly Positive Correlation Turns Negative: How Different Approaches to Model Hierarchically Structured Constructs Affect Estimated Correlations to Covariates

2021 ◽  
pp. 089020702110501 ◽  
Author(s):  
Morten Moshagen
Keyword(s):  
Measurement Model ◽  
Factor Models ◽  
Higher Order ◽  
General Factor ◽  
Single Factor ◽  
Personality Psychology ◽  
Order Factor ◽  
The Mean ◽  
Biased Estimates ◽  
True Correlation

Many constructs in personality psychology assume a hierarchical structure positing a general factor along with several narrower subdimensions or facets. Different approaches are commonly used to model such a structure, including higher-order factor models, bifactor models, single-factor models based on the responses on the observed items, and single-factor models based on parcels computed from the mean observed scores on the subdimensions. The present article investigates the consequences of adopting a certain approach for the validity of conclusions derived from the thereby obtained correlation of the most general factor to a covariate. Any of the considered approaches may closely approximate the true correlation when its underlying assumptions are met or when model misspecifications only pertain to the measurement model of the hierarchical construct. However, when misspecifications involve nonmodeled covariances between parts of the hierarchically structured construct and the covariate, higher-order models, single-factor representations, and facet-parcel approaches can yield severely biased estimates sometimes grossly misrepresenting the true correlation and even incurring sign changes. In contrast, a bifactor approach proved to be most robust and to provide rather unbiased results under all conditions. The implications are discussed and recommendations are provided.

Challenging the Multidimensional Conception of Perceived Person-Environment Fit

2020 ◽  
pp. 1-9
Author(s):  
Julian M. Etzel ◽  
Gabriel Nagy
Keyword(s):  
Higher Education ◽  
University Students ◽  
Educational Outcomes ◽  
Factor Model ◽  
Higher Order ◽  
Substantial Proportion ◽  
Unique Variance ◽  
First Order ◽  
Person Environment Fit ◽  
Order Factor

Abstract. In the current study, we examined the viability of a multidimensional conception of perceived person-environment (P-E) fit in higher education. We introduce an optimized 12-item measure that distinguishes between four content dimensions of perceived P-E fit: interest-contents (I-C) fit, needs-supplies (N-S) fit, demands-abilities (D-A) fit, and values-culture (V-C) fit. The central aim of our study was to examine whether the relationships between different P-E fit dimensions and educational outcomes can be accounted for by a higher-order factor that captures the shared features of the four fit dimensions. Relying on a large sample of university students in Germany, we found that students distinguish between the proposed fit dimensions. The respective first-order factors shared a substantial proportion of variance and conformed to a higher-order factor model. Using a newly developed factor extension procedure, we found that the relationships between the first-order factors and most outcomes were not fully accounted for by the higher-order factor. Rather, with the exception of V-C fit, all specific P-E fit factors that represent the first-order factors’ unique variance showed reliable and theoretically plausible relationships with different outcomes. These findings support the viability of a multidimensional conceptualization of P-E fit and the validity of our adapted instrument.

Speeded Paper-Pencil Sustained Attention and Mental Speed Tests

2008 ◽  
Vol 29 (4) ◽  
pp. 205-216 ◽  
Author(s):  
Stefan Krumm ◽  
Lothar Schmidt-Atzert ◽  
Kurt Michalczyk ◽  
Vanessa Danthiir
Keyword(s):  
Sustained Attention ◽  
Time Pressure ◽  
Factor Model ◽  
Higher Order ◽  
Future Research ◽  
Perceptual Speed ◽  
Speed Factor ◽  
Close Relationship ◽  
Task Processing ◽  
Order Factor

Mental speed (MS) and sustained attention (SA) are theoretically distinct constructs. However, tests of MS are very similar to SA tests that use time pressure as an impeding condition. The performance in such tasks largely relies on the participants’ speed of task processing (i.e., how quickly and correctly one can perform the simple cognitive tasks). The present study examined whether SA and MS are empirically the same or different constructs. To this end, 24 paper-pencil and computerized tests were administered to 199 students. SA turned out to be highly related to MS task classes: substitution and perceptual speed. Furthermore, SA showed a very close relationship with the paper-pencil MS factor. The correlation between SA and computerized speed was considerably lower but still high. In a higher-order general speed factor model, SA had the highest loading on the higher-order factor; the higher-order factor explained 88% of SA variance. It is argued that SA (as operationalized with tests using time pressure as an impeding condition) and MS cannot be differentiated, at the level of broad constructs. Implications for neuropsychological assessment and future research are discussed.

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.

Sign in / Sign up

Export Citation Format

Share Document