May-happen-in-parallel analysis with static vector clocks

2018 ◽  
Author(s):  
Qing Zhou ◽  
Lian Li ◽  
Lei Wang ◽  
Jingling Xue ◽  
Xiaobing Feng
Keyword(s):  
Parallel Analysis ◽  
Vector Clocks

Number of Factors Decision: Parallel Analysis Is Not the Panacea

2006 ◽  
Author(s):  
Jinyan Fan ◽  
Felix James Lopez ◽  
Jennifer Nieman ◽  
Robert C. Litchfield ◽  
Robert S. Billings
Keyword(s):  
Parallel Analysis ◽  
Number Of Factors

scholarly journals Adaptation and validation of the Nigerian Igbo Multidimensional Scale of Perceived Social Support in patients with chronic low back pain

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Chinonso Nwamaka Igwesi-Chidobe ◽  
Sheila Kitchen ◽  
Isaac Olubunmi Sorinola ◽  
Emma Louise Godfrey
Keyword(s):  
Social Support ◽  
Low Back Pain ◽  
Back Pain ◽  
Perceived Social Support ◽  
Parallel Analysis ◽  
Dimensional Structure ◽  
Low Back ◽  
Validity And Reliability ◽  
Rural Nigeria ◽  
Kaiser Criterion

Abstract Introduction Social support may be important in the perpetuation of symptoms in chronic low back pain (CLBP). The multidimensional scale of perceived social support (MSPSS) is one of the best measures of social support with applicability in Africa. Aims The aims of this study were to translate, culturally adapt, test–retest, and assess cross-sectional psychometric properties of the Igbo-MSPSS. Methods Forward and backward translation of the MSPSS was done by clinicians and non-clinician translators and evaluated by a specialist review committee. The adapted measure was piloted amongst twelve adults with CLBP in rural Nigeria. Cronbach’s alpha and McDonald’s omega coefficient were used for investigating internal consistency. Intra-class correlation coefficient (ICC: two-way random effects model, average of raters’ measurements, absolute definition of agreement) reflecting both the degree of correlation and agreement between measurements was used for the statistical investigation of test–retest reliability. Criterion validity of the adapted measure was investigated with the eleven-point box scale, back performance scale, Roland Morris Disability Questionnaire, and World Health Organisation Disability Assessment Schedule amongst 200 people with CLBP in rural Nigeria using Spearman’s correlation analyses. Exploratory factor analyses conducted using Kaiser criterion and parallel analysis as methods for determining dimensionality were used to determine the structural validity of the adapted measure amongst the same sample of 200 rural dwellers. Results Igbo-MSPSS had excellent internal consistency (0.88) and ICC of 0.82. There were moderate correlations with measures associated with the social support construct. The same item–factor pattern in the three-dimensional structure (with Kaiser criterion) as in the original measure and a two-dimensional structure (with parallel analysis) were produced. Conclusions Igbo-MSPSS is a measure of social support with some evidence of validity and reliability and can be used clinically or for research. Future studies are required to confirm its validity and reliability.

scholarly journals An FDA-Based Approach for Clustering Elicited Expert Knowledge

Stats ◽  
2021 ◽  
Vol 4 (1) ◽  
pp. 184-204
Author(s):  
Carlos Barrera-Causil ◽  
Juan Carlos Correa ◽  
Andrew Zamecnik ◽  
Francisco Torres-Avilés ◽  
Fernando Marmolejo-Ramos
Keyword(s):  
Functional Data ◽  
Expert Knowledge ◽  
Probability Distributions ◽  
Knowledge Elicitation ◽  
Parallel Analysis ◽  
Data Sets ◽  
Dimensional Problem ◽  
Prior Distributions ◽  
Infinite Dimensional ◽  
Finite Dimensional

Expert knowledge elicitation (EKE) aims at obtaining individual representations of experts’ beliefs and render them in the form of probability distributions or functions. In many cases the elicited distributions differ and the challenge in Bayesian inference is then to find ways to reconcile discrepant elicited prior distributions. This paper proposes the parallel analysis of clusters of prior distributions through a hierarchical method for clustering distributions and that can be readily extended to functional data. The proposed method consists of (i) transforming the infinite-dimensional problem into a finite-dimensional one, (ii) using the Hellinger distance to compute the distances between curves and thus (iii) obtaining a hierarchical clustering structure. In a simulation study the proposed method was compared to k-means and agglomerative nesting algorithms and the results showed that the proposed method outperformed those algorithms. Finally, the proposed method is illustrated through an EKE experiment and other functional data sets.

scholarly journals Parallel analysis of RNA ends enhances global investigation of microRNAs and target RNAs of Brachypodium distachyon

2013 ◽  
Vol 14 (12) ◽  
pp. R145 ◽  
Author(s):  
Dong-Hoon Jeong ◽  
Skye A Schmidt ◽  
Linda A Rymarquis ◽  
Sunhee Park ◽  
Matthias Ganssmann ◽  
...  
Keyword(s):  
Brachypodium Distachyon ◽  
Parallel Analysis

A clinical research framework for fast parallel analysis of functional magnetic resonance imaging (fMRI)

NeuroImage ◽  
2000 ◽  
Vol 11 (5) ◽  
pp. S560 ◽  
Author(s):  
Stephan G. Erberich ◽  
Martin Hoppe ◽  
Manou Liebert ◽  
Thomas Schmidt ◽  
Christian Jansen ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Functional Magnetic Resonance Imaging ◽  
Clinical Research ◽  
Parallel Analysis ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Research Framework

Determining the Number of Factors When Population Models Can Be Closely Approximated by Parsimonious Models

2021 ◽  
pp. 001316442199283
Author(s):  
Yan Xia
Keyword(s):  
Factor Model ◽  
Population Level ◽  
Parallel Analysis ◽  
Model Data ◽  
Behavioral Studies ◽  
Number Of Factors ◽  
Parsimonious Models ◽  
Parsimonious Model ◽  
Fit Index ◽  
Tucker Lewis Index

Despite the existence of many methods for determining the number of factors, none outperforms the others under every condition. This study compares traditional parallel analysis (TPA), revised parallel analysis (RPA), Kaiser’s rule, minimum average partial, sequential χ2, and sequential root mean square error of approximation, comparative fit index, and Tucker–Lewis index under a realistic scenario in behavioral studies, where researchers employ a closing–fitting parsimonious model with K factors to approximate a population model, leading to a trivial model-data misfit. Results show that while traditional and RPA both stand out when zero population-level misfits exist, the accuracy of RPA substantially deteriorates when a K-factor model can closely approximate the population. TPA is the least sensitive to trivial misfits and results in the highest accuracy across most simulation conditions. This study suggests the use of TPA for the investigated models. Results also imply that RPA requires further revision to accommodate a degree of model–data misfit that can be tolerated.

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