It is better an approximate answer to the right question than the exact answer to the wrong question : the case of the psychometric analysis of the ASQ:SE

Exact Answer ◽

Wide Range ◽

Ages And Stages Questionnaire ◽

The Right

One of the main activities in psychometrics is to analyze the internal structure of a test. Multivariate statistical methods, including Exploratory Factor analysis (EFA) and Principal Component Analysis (PCA) are frequently used to do this, but the growth of Network Analysis (NA) places this method as a promising candidate. The results obtained by these methods are of valuable interest, as they not only produce evidence to explore if the test is measuring its intended construct, but also to deal with the substantive theory that motivated the test development. However, these different statistical methods come up with different answers, providing the basis for different analytical and theoretical strategies when one needs to choose a solution. In this study, we took advantage of a large volume of published data (n = 22,331) obtained by the Ages and Stages Questionnaire Social-Emotional (ASQ:SE), and formed a subset of 500 children to present and discuss alternative psychometric solutions to its internal structure, and also to its subjacent theory. The analyses were based on a polychoric matrix, the number of factors to retain followed several well-known rules of thumb, and a wide range of exploratory methods was fitted to the data, including EFA, PCA, and NA. The statistical outcomes were divergent, varying from 1 to 6 domains, allowing a flexible interpretation of the results. We argue that the use of statistical methods in the absence of a well-grounded psychological theory has limited applications, despite its appeal. All data and codes are available at https://osf.io/z6gwv/.

Multivariate Techniques of Use in Biological Research

Experimental Agriculture ◽

10.1017/s0014479700009996 ◽

1969 ◽

Vol 5 (1) ◽

pp. 67-77 ◽

Cited By ~ 10

Author(s):

S. C. Pearce

Keyword(s):

Principal Component Analysis ◽

Statistical Methods ◽

Matrix Algebra ◽

Principal Component ◽

Component Analysis ◽

Canonical Analysis ◽

Biological Research ◽

Multivariate Techniques ◽

Multivariate Statistical

SUMMARYMultivariate statistical methods are used increasingly in biological research to investigate the responses of organisms considered as a whole, whereas established statistical methods are usually concerned with measured characteristics considered one at a time. Multivariate techniques are mostly explained in terms of matrix algebra, which is a way of dealing with groups of numbers rather than individual ones. A brief description is given of some elementary results of matrix algebra and a method is presented whereby hypotheses can be generated about interrelations within an organism. Two techniques, principal component analysis and canonical analysis, are described in greater detail. It is emphasized that hypotheses need to be tested even though they have been generated by objective statistical means.

Evaluation of colon cancer elements contents in serum using statistical methods

Chemical Papers ◽

10.2478/s11696-007-0046-z ◽

2007 ◽

Vol 61 (5) ◽

Cited By ~ 1

Author(s):

D. Milde ◽

J. Macháček ◽

V. Stužka

Keyword(s):

Statistical Methods ◽

Tnm Classification ◽

Principal Component ◽

Absorption Spectrometry ◽

Acid Mixture ◽

Serum Samples ◽

Colorectal Cancer Patients

AbstractClassification of normal and different cancer groups (TNM classification) with univariate and multivariate statistical methods according to the contents of Cu, Fe, Mn, Se, and Zn in blood serum is discussed. All serum samples were digested by acid mixture in a microwave mineralization unit prior to the analysis by atomic absorption spectrometry. Results show that univariate methods can distinguish normal and cancer groups. Level of selenium evaluated as arithmetic mean with its standard deviation in colorectal cancer patients was (42.61 ± 23.76) µg L−1. Retransformed mean was used to evaluate levels of managanese (11.99 ± 1.71) µg L−1, copper (1.05 ± 0.06) mg L−1, zinc (2.14 ± 0.21) mg L−1, and iron (1.82 ± 0.22) mg L−1. Conclusions of multivariate statistical procedures (principal component analysis, hierarchical, and k-means clustering) do not correlate very well with the division of serum samples according to the TNM classification.

Fractionation of muscle activity in rapid responses to startling cues

Journal of Neurophysiology ◽

10.1152/jn.01009.2015 ◽

2017 ◽

Vol 117 (4) ◽

pp. 1713-1719 ◽

Cited By ~ 12

Author(s):

Lauren R. Dean ◽

Stuart N. Baker

Keyword(s):

Muscle Activity ◽

Human Subjects ◽

Activity Patterns ◽

Reaction Times ◽

Principal Component ◽

Similar Proportion ◽

Descending Pathways ◽

Healthy Human ◽

Wide Range ◽

The Right

Movements in response to acoustically startling cues have shorter reaction times than those following less intense sounds; this is known as the StartReact effect. The neural underpinnings for StartReact are unclear. One possibility is that startling cues preferentially invoke the reticulospinal tract to convey motor commands to spinal motoneurons. Reticulospinal outputs are highly divergent, controlling large groups of muscles in synergistic patterns. By contrast the dominant pathway in primate voluntary movement is the corticospinal tract, which can access small groups of muscles selectively. We therefore hypothesized that StartReact responses would be less fractionated than standard voluntary reactions. Electromyogram recordings were made from 15 muscles in 10 healthy human subjects as they carried out 32 varied movements with the right forelimb in response to startling and nonstartling auditory cues. Movements were chosen to elicit a wide range of muscle activations. Multidimensional muscle activity patterns were calculated at delays from 0 to 100 ms after the onset of muscle activity and subjected to principal component analysis to assess fractionation. In all cases, a similar proportion of the total variance could be explained by a reduced number of principal components for the startling and the nonstartling cue. Muscle activity patterns for a given task were very similar in response to startling and nonstartling cues. This suggests that movements produced in the StartReact paradigm rely on similar contributions from different descending pathways as those following voluntary responses to nonstartling cues. NEW & NOTEWORTHY We demonstrate that the ability to activate muscles selectively is preserved during the very rapid reactions produced following a startling cue. This suggests that the contributions from different descending pathways are comparable between these rapid reactions and more typical voluntary movements.

Geochemical classification of groundwater using multivariate statistical analysis in Latvia

Hydrology Research ◽

10.2166/nh.2016.020 ◽

2016 ◽

Vol 47 (4) ◽

pp. 799-813 ◽

Cited By ~ 7

Author(s):

Inga Retike ◽

Andis Kalvans ◽

Konrads Popovs ◽

Janis Bikse ◽

Alise Babre ◽

...

Keyword(s):

Trace Elements ◽

Statistical Methods ◽

Principal Component ◽

Hierarchical Cluster ◽

Ion Chemistry ◽

Gypsum Dissolution ◽

Major Ion

Multivariate statistical methods – principal component analysis (PCA) and hierarchical cluster analysis (HCA) – are applied to identify geochemically distinct groundwater groups in the territory of Latvia. The main processes observed to be responsible for groundwater chemical composition are carbonate and gypsum dissolution, fresh and saltwater mixing and ion exchange. On the basis of major ion concentrations, eight clusters (C1–C8) are identified. C6 is interpreted as recharge water not in equilibrium with most sediment forming minerals. Water table aquifers affected by diffuse agricultural influences are found in C3. Groundwater in C4 reflects brine or seawater admixture and gypsum dissolution in C5. C7 and C2 belong to typical bicarbonate groundwater resulting from calcite and dolomite weathering. Extremely low Cl− and SO42− are observed in C8 and described as pre-industrial groundwater or a solely carbonate weathering result. Finally, C1 seems to be a poorly defined subgroup resulting from mixing between other groups. This research demonstrates the validity of applying multivariate statistical methods (PCA and HCA) on major ion chemistry to distribute characteristic trace elements in each cluster even when incomplete records of trace elements are present.

Use of multivariate statistical methods for classification of olive oil

Journal of Environmental Analysis and Progress ◽

10.24221/jeap.6.1.2021.2815.035-043 ◽

2021 ◽

Vol 6 (1) ◽

pp. 035-043

Author(s):

Moacyr Cunha Filho ◽

Renisson Neponuceno Araujo Filho ◽

Ana Luiza Xavier Cunha ◽

Victor Casimiro Piscoya ◽

Guilherme Rocha Moreira ◽

...

Keyword(s):

Olive Oil ◽

Statistical Methods ◽

Virgin Olive Oil ◽

Principal Component ◽

Extra Virgin Olive Oil ◽

Fuzzy Cluster ◽

Fuzzy Clustering Method ◽

Silhouette Index

Multivariate statistical methods can contribute significantly to classification studies of extra virgin and common olive oil groups. Therefore, nuclear magnetic resonance (NMR) was used to discriminate olive oil samples, multivariate statistical techniques Principal Component Analysis - PCA, Fuzzy Cluster, Silhouette Validation Method to describe and classify. The groups' distinction into organic and common was observed by applying the non-hierarchical Fuzzy grouping with a distinction between the two groups with a 65% confidence interval. The validation was performed by the silhouette index that presented S (i) of 0.73, which showed that the adopted grouping presented adequate strength and distinction criterion. However, PCA only analyzed the behaviors of data from extra virgin olive oil. Thus, the Fuzzy clustering method was the most suitable for classifying extra virgin olive oil.

Integrated Hydrochemistry and Statistical Methods to Investigate Groundwater Origin and Salinization Process in Arid Regions (Central-eastern Tunisia)

10.21203/rs.3.rs-276349/v1 ◽

2021 ◽

Author(s):

Ali Souei ◽

Fadoua Hamzaoui-Azaza ◽

Taher Zouaghi ◽

Chafik Oueslati

Keyword(s):

Statistical Methods ◽

Principal Component ◽

Hierarchical Cluster ◽

Exchange Reactions ◽

Groundwater Origin ◽

Geochemical Study ◽

Hydrogeological Study ◽

Scatter Plots ◽

Recharge Rates

Abstract Hydrochemistry is a discipline widely used given the groundwater quantitative and qualitative reliability in the hydrogeological study. The geochemical study of groundwater in the Nadhour-Sisseb-El Alem basin aimed to characterize the water chemistry, determination of the physicochemical parameters and chemical facies well as and the mineralization processes. The Piper and Durov diagrams and scatter plots, conventional classification techniques, are applied to evaluate the geochemical processes. Samples are classified using two multivariate statistical methods, Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA). Waters compositions are affected by cation exchange reactions in the intercalated clay, resulting in a Na+ increase, and peaks of K+, Ca2+ and Mg2+. PCA analyses show that the water samples have been classified into 8 groups. The waters quality deterioration is caused essentially by; overexploitation, decreased in freshwater recharge rates, climate condition; height evaporation low precipitation, artificial recharge by dam water, and irrigation return water.

Analysis of sweet corn nutritional values using multivariate statistical methods

Acta Agraria Debreceniensis ◽

10.34101/actaagrar/1/8587 ◽

2021 ◽

pp. 103-108

Author(s):

László Huzsvai ◽

Péter Fejér ◽

Árpád Illés ◽

Csaba Bojtor ◽

Csilla Bojté ◽

...

Keyword(s):

Statistical Methods ◽

Sweet Corn ◽

Correlation Coefficients ◽

Principal Component ◽

Nutritional Values ◽

Linear Effect ◽

Non Linear ◽

Correlation System ◽

Expert Models

Processing large amounts of data provided by automated analytical equipment requires carefulness. Most mathematical and statistical methods have strict application conditions. Most of these methods are based on eigenvalue calculations and require variables to be correlated in groups. If this condition is not met, the most popular multivariate methods cannot be used. The best procedure for such testing is the Kaiser-Meyer-Olkin test for Sampling Adequacy. Two databases were examined using the KMO test. One of them resulted from the sweet corn measured in the scone of the study, while the other from the 1979 book of János Sváb. For both databases, MSA (measures sampling adequacy) was well below the critical value, thus they are not suitable e.g. for principal component analysis. In both databases, the values of the partial correlation coefficients were much higher than Pearson’s correlation coefficients. Often the signs of partial coefficients did not match the signs of linear correlation coefficients. One of the main reasons for this is that the correlation between the variables is non-linear. Another reason is that control variables have a non-linear effect on a given variable. In such cases, classical methods should be disregarded and expert models better suited to the problem should be chosen in order to analyse the correlation system.

Métodos estadísticos multivariantes aplicados en el estudio del rendimiento académico: Una revisión de la literatura

Revista Cognosis. ISSN 2588-0578 ◽

10.33936/cognosis.v6i1.3347 ◽

2021 ◽

Vol 6 (1) ◽

pp. 103

Author(s):

María Elena López Zamora ◽

Lelly María Useche Castro

Keyword(s):

Academic Performance ◽

Student Performance ◽

Statistical Methods ◽

Principal Component ◽

Higher Education System ◽

Revisión Sistemática ◽

Selection Of Variables ◽

El Sistema ◽

Toma De Decisiones

El rendimiento académico es sinónimo de calidad dentro de las instituciones de educación. Se plateó como objetivo de la investigación, el análisis mediante revisiones bibliográficas de los métodos estadísticos aplicados para el estudio del rendimiento académico, haciendo énfasis en las técnicas estadísticas multivariantes de reducción, clasificación y su relevancia para el estudio del rendimiento académico. Se analizaron investigaciones relevantes de ámbito educativo, utilizando argumentos de varios autores sobre el rendimiento académico. Se empleó una metodología de revisión sistemática de literatura para un conjunto de 49 artículos relacionados con palabras claves como, factores, rendimiento académico, método, estadística, multivariante, entre otros. Dentro de los resultados relevantes se destacan las metodologías como, Análisis de Correspondencia, Análisis de Componentes Principales, Análisis de Clúster, Análisis Discriminante, para evaluar y optimizar el proceso de selección de variables y construcción de índices, y así, mejora la comprensión de las características del rendimiento académico de las instituciones, y constituyen un aporte para la calidad en el sistema educativo superior. El análisis teórico de los trabajos realizados hasta la actualidad interesará para continuar con investigaciones orientadas a la mejora de los métodos estadísticos utilizados para el análisis del rendimiento académico, sirviendo para la toma de decisiones y reformas en el sector educativo a fin de obtener una tasa menor de deserción y un mejor desempeño de los estudiantes, entre otras problemáticas. PALABRAS CLAVE: rendimiento académico; componentes principales; correspondencias; clúster; discriminante. Multivariate statistical methods applied in the study of academic performance: a review of the literature ABSTRACT Academic performance is synonymous with quality within educational institutions. The objective of the research was the analysis through bibliographic reviews of the statistical methods applied for the study of academic performance, emphasizing the multivariate statistical techniques of reduction, classification, and their relevance for the study of academic performance. Relevant research in the educational field was analyzed, using arguments from various authors about academic performance. A systematic literature review methodology was used for a set of 49 articles related to keywords such as factors, academic performance, method, statistics, multivariate, among others. Among the relevant results, methodologies such as Correspondence Analysis, Principal Component Analysis, Cluster Analysis, Discriminant Analysis stand out, to evaluate and optimize the process of selection of variables and construction of indices, and thus, improves the understanding of the characteristics of the academic performance of institutions, and constitute a contribution to quality in the higher education system. The theoretical analysis of the work carried out to date will be of interest to continue with research aimed at improving the statistical methods used for the analysis of academic performance, serving for decision-making and reforms in the education sector to obtain a rate lower dropout rates and better student performance, among other problems. KEYWORDS: academic performance; main components; correspondences; cluster; discriminating.

The usefulness of robust multivariate methods: A case study with the menu items of a fast food restaurant chain

Ciência e Natura ◽

10.5902/2179460x39892 ◽

2020 ◽

Vol 42 ◽

pp. e17

Author(s):

Paulo Jorge Canas Rodrigues ◽

Rafael Almeida ◽

Kézia Mustafa

Keyword(s):

Principal Component Analysis ◽

Cluster Analysis ◽

Statistical Methods ◽

Fast Food ◽

Principal Component ◽

Component Analysis ◽

Fast Food Restaurant ◽

Robust Statistical Methods

Multivariate statistical methods have been playing an important role in statistics and data analysis for a very long time. Nowadays, with the increase in the amounts of data collected every day in many disciplines, and with the raise of data science, machine learning and applied statistics, that role is even more important. Two of the most widely used multivariate statistical methods are cluster analysis and principal component analysis. These, similarly to many other models and algorithms, are adequate when the data satisfies certain assumptions. However, when the distribution of the data is not normal and/or it shows heavy tails and outlying observations, the classic models and algorithms might produce erroneous conclusions. Robust statistical methods such as algorithms for robust cluster analysis and for robust principal component analysis are of great usefulness when analyzing contaminated data with outlying observations. In this paper we consider a data set containing the products available in a fast food restaurant chain together with their respective nutritional information, and discuss the usefulness of robust statistical methods for classification, clustering and data visualization.

Analysing microbiome intervention studies: Does choice of a statistical method affect biological interpretation?

10.21203/rs.3.rs-30540/v1 ◽

2020 ◽

Author(s):

Maryia Khomich ◽

Ingrid Måge ◽

Ingunn Berget ◽

Ida Rud

Keyword(s):

Statistical Methods ◽

Intervention Studies ◽

Dietary Intervention ◽

Statistical Significance ◽

Microbiome Composition ◽

Wide Range ◽

Biological Interpretation ◽

Intervention Trials ◽

And Function

Abstract Background The diet plays a major role in shaping gut microbiome composition and function in both humans and animals, and dietary intervention trials are often used to investigate and understand these effects. A plethora of statistical methods for analysing differential abundance of microbial taxa exists, and new methods are constantly being developed, but there is a lack of benchmarking studies and clear consensus on the best multivariate statistical practices. This makes it hard for a biologist to decide which method to use. Results We compared the outcomes of a wide range of ANOVA-like statistical methods to explore to what extent the choice of method affects the biological inferences made. The comparison is based on five published dietary intervention studies representing different subjects and study designs. We found that the methods producing outputs at the community level were in agreement regarding both effect size and statistical significance. However, the methods that provided ranking of operational taxonomic units (OTUs) gave incongruent results, implying that the choice of method is likely to influence the biological interpretations. Conclusions We concluded that inferences at the OTU level should be made with caution, and that a combination of several statistical methods allows to interpret the outcomes with higher confidence and simultaneously account for the limitations of each method.