Fundamentals of Common Factor Analysis

Mulaik, Stanley A.

doi:10.1002/9781118489772.ch8

Cited by 9 publications

(9 citation statements)

References 48 publications

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“…The theory of this assessment suggests that in order to get the model fit, the X 2 / df must be smaller than 2, the RMSEA value should be less than 0.08, the CFI values should be 0.095 and above and the value of SRMR should be less than 0.10 (for details on these criteria, see Hair et al , 2006; Kim & Zhao, 2018). The criteria used to judge the convergent validity of the construct are explained by Hair et al (2006) and Mulaik (2018) as follows: (1) the factorial loading estimates must be significant; (2) the factorial loading estimate should be 0.50 and above to be considered a good item; and (3) the average variance extracted (AVE) should be equal to or exceed the cutoff level of 50 per cent. The construct discriminant validity was examined based on the comparison of the square root of AVE estimates for each of the constructs with the inter‐construct correlations of that factor.…”

Section: Analysis and Resultsmentioning

confidence: 99%

“…Dropping these items, however, did not affect the fit of the model, as illustrated in Table 1, because other items in each construct loaded significantly; thus, the CFA model fit indices resulted in acceptable statistical measurements: X 2 / df = 1.884, CFI = 0.935, SRMR = 0.042 and RMSEA = 0.062. The factor loading ranged between 0.658 and 0.911, indicating good loading patterns (Mulaik, 2018). Within each factor, the measurement indicators show an appropriate level of efficacy for measuring the coherence of each construct with an AVE above 0.50 and a reliability level ranging between 0.81 and 0.88.…”

Section: Analysis and Resultsmentioning

confidence: 99%

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The role of supervisor and peer support in training transfer in institutions of higher education

Yaghi¹,

Bates²

2020

Int J Training Development

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Purpose: This study focuses on leadership transfer by academic managers in public universities. Motivation to transfer is expected to mediate the relationship between peer and supervisor support (independent variables) and the transfer of training (dependent variable). Methodology: The study sample comprises 263 academic managers who completed leadership training programs in public universities. Structural equation modeling is used to test the study model for four hypotheses. Findings: In line with previous findings from different contexts, the study shows that (a) the motivation of academic managers to transfer training mediates between the actual transfer and the two types of organizational support, from peers and supervisors; (b) peer support has a stronger impact than supervisor support on motivation to transfer; (c) training transfer in public universities has a pattern similar to that in other organizations; and (d) the country context does not seem to affect the dynamics of training transfer. Implications: To remain competitive with successful policies, universities need to foster learning environments by effectively engaging those responsible for managing university policies. Applying new leadership knowledge, skills and abilities is a sophisticated process in which academic managers are not the only stakeholders. Given the nature of the organizational phenomenon, work environments are similar across countries and sectors; therefore, emphasizing the role of national cultural norms and values over the objective needs of the workplace seems problematic. Limitations: Structural equation modeling may not capture all psychological and personal aspects of transfer; therefore, triangulation methods can be useful. The competition in higher❒ Abdulfattah Yaghi, United Arab Emirates University, UAE.

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Section: Analysis and Resultsmentioning

confidence: 99%

Section: Analysis and Resultsmentioning

confidence: 99%

The role of supervisor and peer support in training transfer in institutions of higher education

Yaghi¹,

Bates²

2020

Int J Training Development

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“…In reality, the probability that the measurement model of a test obtains a good fit increases when the structural model applied to the scales also shows a correct fit (see Brown, 2015). This is due to mathematical reasons (see Mulaik, 2018) and because the structural model validates not only the quality of the measurements -whose test scales would be incorporated into the structural equations as observable variables -, it also validates the underlying theoretical construct. It is a top-down methodological process: valid constructs that form valid theories must offer guarantees that prove the validity of the respective measurements (see Gorsuch, 1983).…”

Section: Criticisms and Limitationsmentioning

confidence: 87%

“…Three confirmatory factor analyses (CFAs) were applied by the maximum likelihood estimation method and were based on: (1) the original Spanish version, (2) second-order factors extracted from a previous exploratory factorial analysis (hereafter EFA), and (3) the predictive value of second-order factors on the anomalous phenomena themselves (in this way, the underlying empirical-statistical model could be tested). In the EFA, the criterion based on the minimum unweighted residuals was used as the extraction method, since it does not require the a priori calculation of the communalities of items (see Mulaik, 2018). The parallel analysis technique was used to determine the number of factors to be extracted (e.g., Reise et al, 2000) because it is a more precise and effective method than the traditional Kaiser criterion (see Kline, 1999).…”

Section: Statistical Analysis Appliedmentioning

confidence: 99%

The Multivariable Multiaxial Suggestibility Inventory-2 (MMSI-2): A Psychometric Alternative to Measure and Explain Supernatural Experiences

2021

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This paper presents the English adaptation of the Multivariable Multiaxial Suggestibility Inventory-2 (MMSI-2), a questionnaire developed specifically for psychological assessment and prediction of anomalous phenomena. The sample consisted of 613 respondents from England (47.6% were women and 52.4% men). All of them were of legal age (mean = 34.5; standard deviation = 8.15). An exploratory factor analysis was applied, and three confirmatory factor models were adjusted. Omega coefficients and test-retest designs were used for reliability analysis. The MMSI-2 has a valid internal structure consisting of five macrofactors: Clinical Personality Tendencies (CPT), Anomalous Perceived Phenomena (APP), Incoherent Manipulations (IMA), Altered States of Consciousness (ASC), and Openness (OP). Omega coefficients for CPT and OP factors were low but acceptable. Furthermore, test-retest trials were excellent for all scales and factors. The psychological factors CPT, IMA, and ASC predicted 18.3% of the variance of anomalous experiences (APP). The authors concluded the English MMSI-2 was a valid and reliable test for the evaluation of anomalous phenomena but recommend that subsequent research reviews the predictive quality of the underlying model.

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“…Problem 2 arises in a remarkable variety of applications such as design, 28,29 studying a vibrating string, 30 nuclear spectroscopy, the educational testing problem, 14 the graph partitioning problem, the design of control systems, 23 and factor analysis 31 . The problem has also many applications in mathematical and numerical analysis, such as Sturm–Liouville problems and preconditioning 32 .…”

Section: Introductionmentioning

confidence: 99%

Newton‐like and inexact Newton‐like methods for a parameterized generalized inverse eigenvalue problem

Dalvand

Hajarian

2020

Math Methods in App Sciences

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In this paper, we establish the Newton‐like and inexact Newton‐like based methods for solving a type of parameterized generalized inverse eigenvalue problem. This type of parameterized generalized inverse eigenvalue problem, including multiplicative and additive inverse eigenvalue problems, appears in many applications. We show that the direction produced by the Newton‐like method does not depend explicitly on the eigenvalues. Also, the inexact version can minimize the oversolving problem of Newton‐like methods and hence improve efficiency. We discuss the convergence properties of the presented methods. Finally, the performance and effectiveness of the algorithms are tested on three numerical examples and compared to the Newton algorithm.

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Fundamentals of Common Factor Analysis

Cited by 9 publications

References 48 publications

The role of supervisor and peer support in training transfer in institutions of higher education

The role of supervisor and peer support in training transfer in institutions of higher education

The Multivariable Multiaxial Suggestibility Inventory-2 (MMSI-2): A Psychometric Alternative to Measure and Explain Supernatural Experiences

Newton‐like and inexact Newton‐like methods for a parameterized generalized inverse eigenvalue problem

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