Classical Factor Analysis Research Articles

Systematic review of the Health Literacy Questionnaire (HLQ) for global health literacy development

Abstract Background The Health Literacy Questionnaire (HLQ) is used to inform programs and policies in more than 80 countries. It is a key needs assessment in the Ophelia (Optimising Health Literacy and Access) process, a codesign process to build effective interventions. Ophelia is applied in the European Commission Joint Action on Cardiovascular Diseases and Diabetes initiative (25 projects/13 countries). Given growing demand, an Ophelia digital platform is being developed, and confirmation of robust HLQ psychometric evidence is needed for user confidence in the interpretation and use of HLQ scores in their contexts. This study is a review of the HLQ. Methods Developed in Australia in 2013, the HLQ measures nine health literacy domains and is translated to 40 languages. A March 2024 PRISMA systematic search (PROSPERO CRD42022361966) identified 257 articles, which were screened for evidence about internal structure and reliability. Results Across 15 countries/languages, 22 articles were included. Study samples included diverse groups, settings and contexts. Out of the 18 studies that used factor analysis, three used the Bayesian approach. All 15 classical factor analysis studies reported satisfactory fit indices in restricted 9-factor models (range CFI=0.841 to 0.995, TLI=0.826 to 0.969, RMSEA=0.048 to 0.084), except two required correlated residuals for acceptable fit indices. Factor loadings were significant except for five items across five studies (range .20 to .99). Three studies used Rasch modelling and found all domains had unidimensionality except for Domain 9 in one study. Most studies reported Cronbach’s alpha (range .57 to .95) across all domains, with only three studies reporting α<.7. Conclusions The HLQ has exceptional robust psychometric properties across cultures and languages, paving the way for users to have confidence in HLQ psychometric validity evidence on the upcoming Digital Ophelia platform to support scaling up of health literacy development. Key messages • The HLQ has robust psychometric properties across countries and languages. • The HLQ 9-domain profiles can inform intervention development for equitable health outcomes.

Detailed Late Pliocene-Quaternary sedimentation history and diagenesis processes in the eastern part of the Norwegian-Greenland Basin based on mathematical statistical methods

As a result of univariate regression analysis, quantitative parameters on the distribution of Mn/Al and Mn/Fe ratios in the sections of the studied holes of ODP Leg 151 were obtained. On the basis of correlation analysis data with Pearson matrix construction and classical factor analysis with varimax rotation, a division of rather homogeneous Upper Pliocene-Quaternary sediments was carried out with the allocation of 3 main factors that characterize the history of sedimentation and diagenesis in the Norwegian-Greenland Basin. The boundaries between lithostratigraphic subdivisions are drawn on a strict mathematical basis. It is established that the nature and degree of intensity of diagenesis processes in this region were influenced by the following main factors: sedimentation environments in the Norwegian-Greenland Basin in the Late Pliocene-Quaternary time; grain-size and chemical composition of the studied sediments; total organic carbon content in the sediments; sedimentation rates.

СЛУЧАЙ НАСЛЕДСТВЕННОГО НАРУШЕНИЯ СВЕРТЫВАНИЯ КРОВИ В РАННЕМ НЕОНАТАЛЬНОМ ПЕРИОДЕ

The article describes a case of bleeding that occurred on the first day of life in an infant with severe hemophilia A, analyzes the directions of clinical and laboratory search for disorders of the primary and secondary blood coagulation, which made it possible to clarify the diagnosis and select appropriate hemostatic therapy. Hemorrhagic disorders in the neonatal period can be caused by a deficiency of vitamin K-dependent factors, intrauterine infection, hereditary coagulopathies, primary and secondary disorders of platelet hemostasis and a number of other reasons. Clinical manifestations of hemorrhagic syndrome in a newborn child are nonspecific; they allow one to suspect the type of bleeding, but not identify the cause of the bleeding. Additional studies reflecting the viscoelastic properties of the clot have made it possible to evaluate the effectiveness of hemostatic therapy. To clarify the true cause of bleeding, in addition to thorombeastography, it became necessary to supplement the examination of the child with factor analysis based on classical clotting tests. In terms of diagnosing blood coagulation disorders, thromboelastography had no advantages over classical factor analysis.

Comparison of Classical and Robust Factor Analyses Methods

Factor analysis is a multivariate statistical analysis technique that has become very popular in recent years. In the factor analysis model, the error covariance matrix is assumed to be the multivariate normal distribution, and outliers are likely to be accounted for. Various estimation methods were compared with Monte Carlo simulation for the factor analysis model. The performances of the estimation methods were evaluated based on the ratio of the total variance explained and the criterion fit values. Considering the MLE, PCA, WLS, and GLS methods for classical factor analysis and the MCD, M, and S methods for robust factor analysis, the ratio of total variance explained, and fit values decreased as the sample size increased. When the number of variables increases, the ratio of total variance explained, and fit values increase at different sample sizes. It can be said that the WLS and GLS methods are better than others for classical factor analysis and the MCD and M methods are better than others for robust factor analysis.

How many dimensions does posttraumatic stress disorder have? A study in 127 representative samples of a population exposed by an earthquake.

Since its first definition, the number of dimensions of the posttraumatic stress disorder (PTSD) has increased (e.g., 1 to 7). This could be caused by 2 methodological reasons. First, the studies do not select representative samples of the population and do not consider the magnitude of exposure to the same event. Second, they use nonrobust data analysis, which increases the number of dimensions artificially. The aim is to determine how many dimensions PTSD has, when both sources of error are controlled. From 25,400 representative participants of the same population exposed to an earthquake and evaluated with the Davidson Trauma Scale, 127 smaller samples of 200 participants were obtained and classified according to the Mercalli magnitude. Parallel factor analyses (PFA), exploratory structural equations models (ESEM), and classic exploratory factor analysis (EFA) were applied to each sample. There were significant differences in fit and number of dimensions between the high and low Mercalli magnitude. In high exposure conditions: There were 1 dimension according to PFA and 3 according to EFA; ESEM and CFA indicated good fit. The main finding was that PTSD has a unique dimension that includes all of its symptoms. The need to use robust methods of analysis to evaluate data from samples homogeneously exposed to the same potentially traumatic event is argued. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Measurement of uncertainty tolerance revisited.

Uncertainty tolerance (UT) is attracting increasing attention in medical education due to the numerous challenges associated with uncertainty in professional life. Inconsistencies in analysing the relationship between UT and moderators may arise from inadequate measurement methods. Most instruments were formulated before the most widely accepted framework was published. Our aim was to investigate the validity of an UT scale using an actual framework to corroborate with better and accurate instruments. A total of 1052 students were invited. Various psychometric methods were used to explore validity of the TAMSAD scale in light of actual framework. Classic exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) were performed. Secondly, content item classification was triangulated with exploratory graph analysis (EGA), and the new EFA, CFA, and cognitive diagnostic modelling (CDM) analysis were conducted. The reliability was calculated using Cronbach's alpha and McDonald's omega. A total of 694 students (65.9%) responded to the questionnaire. The reliability of the TAMSAD scale was 0.782. The initial EFA revealed no clear interpretable dimensions. The TAMSAD scale items can be classified into sources of uncertainty. The EGA has three dimensions, and the new EFA led to a 17-item TAMSAD scale with the following three dimensions: ambiguity, complexity, and probability. These dimensions lead to better adjustment fit indices in the new CFA and CDM analyses. We found evidence that the TAMSAD scale can be considered a multidimensional scale, organised in terms of sources of uncertainty.

Analisis Motivasi Berwirausaha pada Mahasiswa Universitas Medika Suherman

This research aims to analyze how much motivation students have towards entrepreneurship and find out what factors are related to and influence motivation towards entrepreneurship among students. The method used in this study is a quantitative analysis method using primary data. The primary data was obtained from data collection by distributing questionnaires with a total sample of 120 students. Data analysis techniques in this study used instrument and classic assumption test, descriptive analysis, factor analysis, and multivariate analysis. The results of the descriptive analysis show that the empirical mean value is greater than the theoretical mean value for each variable (50.60 > 39), so descriptive analysis can interpret that the entrepreneurial motivation of Suherman Medika University students tends to be good. Furthermore, the results of the factor analysis in this study obtained a Kaiser Meyer Olkin Measure of Sampling Adequacy (KMO MSA) value of 0.661 > 0.50 and Bartlett's Test of Sphericity value of 0.000 <0.05, which means that the variables referred to in this study did not correlate with one another in the population. In addition, based on factor analysis found that self-efficacy, attitudes and social support are factors that influence entrepreneurial motivation. Then, the results of the multivariate test show that the significance value for Pillai's trace, Wilk's lambda, Hotelling trace, and Rpy's largest root is less than 0, which means that there is a partial effect on self-efficacy, attitudes, and social support on entrepreneurial motivation. Based on multivariate testing, it shows that the value of the self-efficacy determination coefficient is 0.740, Attitude determination coefficient value of 0.811, and the value of the Social Support determination coefficient is 0.536. Based on the results of this study, it is hoped that later both institutions, student organizations and the campus environment will be able to facilitate student needs better.

Understanding of Multiple Effects of Low Molecular Weight Compounds with Factor Analysis

The effects of drugs and other low-molecular-weight compounds are complex and may be unintended by the developer. These compounds and drugs should be avoided if these unintended effects are harmful; however, unintended effects are not always as harmful as suggested by drug repositioning. Therefore, a comprehensive understanding of complex drug actions is essential. Omics data can be regarded as the nonarbitrary transformation of biological information about a sample into comprehensive numerical information comprising multivariate data with a large number of variables. However, the changes are often based on a small number of elements in different dimensions (i.e., latent variables). The omics data of compound-treated samples comprehensively capture the complex effects of compounds, including their unrecognized aspects. Therefore, finding latent variables in these data is expected to contribute to the understanding of multiple effects. In particular, it can be interpreted as decomposing multiple effects into a smaller number of easily understandable effects. Although latent variable models of omics data have been used to understand the mechanisms of diseases, no approach has considered the multiple effects of compounds and their decomposition. Therefore, we propose to decompose and understand the multiple effects of low-molecular-weight compounds without arbitrariness and have been developing analytical methods and verifying their usefulness. In particular, we focused on classical factor analysis among latent variable models and have been examining the biological validity of the estimates obtained under linear assumptions.

A Tool for Assessing the Quality of Life of Adolescents in Youth Care: Psychometric Properties of the QOLYSS.

Quality of life (QOL) has gained increased interest as a critical pathway to better understanding the lives and circumstances of children and adolescents in both the general population and among specific populations. Yet, QOL assessment among youngsters in youth care services remains a highly under-researched topic. This study examines the suitability and psychometric properties of a new QOL self-report scale for adolescents between 12 and 18 years old in youth care: the Quality of Life in Youth Services Scale (QOLYSS). The provisional version of the QOLYSS was pre-tested in a sample of 28 adolescents in youth care to examine its applicability and feasibility. Next, a comprehensive evaluation of the psychometric properties of the field-test version was conducted in a sample of 271 adolescents in youth care in Flanders, Belgium (M = 15.43, SD = 1.73). Classical item and factor analyses were carried out per subscale, (test-retest) reliability and item-discriminant validity of the subscales were examined, convergent validity was explored, and confirmatory factor analysis was used to examine the goodness-of-fit of different measurement models. Reliability measures of the scale are satisfactory, results are indicative of convergent validity, and confirmatory factor analysis provides evidence for the eight correlated factors model. Future lines of research concerning the ongoing development and application of the QOLYSS are discussed.

Simulation of the calculation of deviations in the system "standard-cost" on the basis of factor analysis

The subject of the research is methodical approaches used in the framework of management accounting to determine the deviations of actual indicators from the planned ones during the implementation of plan-actual analysis in the "standard-cost(ing)" system. The purpose of the work is to substantiate the expediency of using the universal tool of classical factor analysis in the process of plan-fact analysis - the method of relative differences, which simplifies mastering the methodology of calculating deviations. The methodology of the conducted research is based on the use of general scientific and specialized methodological techniques. A bibliographic review on the topic of the study made it possible to formulate the hypothesis that currently "ready-made solutions" are used to calculate deviations, without interpreting the sources of the methodology. As a result, a universal approach is proposed, which is based on the methods of analysis and synthesis, which were used to decompose the differences between the obtained actual indicators and the planned ones into components that logically reflect economic regularities and dependencies within the framework of the built economic models, therefore, the modeling method was used. Research results, their use and conclusions.This study raises the issue of the methodology of plan-fact analysis within the "standard-cost(ing)" cost management system. "Standard cost(ing) is considered an effective way of managing production costs and controlling expenses, but it requires a thorough understanding of the reasons for deviations in profit-forming factors - sales and expenses. This understanding is provided through variance analysis, which is based on establishing discrepancies between planned and actual indicators. A literature review was conducted and the peculiarities of the calculation of deviations for various components of production costs were determined. A universal approach to the analysis of deviations based on the method of absolute differences is proposed. An additive-multiplicative model of profit determination is proposed, which can be analyzed using the method of absolute differences. This approach allows you to master the principle of calculating the influence of factors regardless of specific indicators in the model, which simplifies the analysis and ensures clarity for all participants. The proposed methodological aspects of the analysis of deviations can find application in modern technologies of business analytics and contribute to the improvement of the processes of cost control and cost control. These results open perspectives for further research and improvement of the "standard-cost(ing)" system in the context of modern requirements of economic analytics.

Bayesian semiparametric approach to quantile nonlinear dynamic factor analysis models with mixed ordered and nonignorable missing data

In classical nonlinear dynamic factor analysis models (NDFAMs), we assume manifest variables follow the normal distribution. However, in some applications, the normality assumption may be unreasonable or questionable. This paper proposes a quantile NDFAM in which manifest variables may be observed or unobserved but associated with the observed ordinal indicators or subject to missingness, and develops a Bayesian semiparametric method to estimate unknown parameters and latent variables by utilizing the Dirichlet process prior to approximate unknown distribution of random effects. Logistic and probit models are adopted to specify propensity scores for missing manifests and covariates, respectively. An exponential family distribution is assumed for missing covariates. Bayesian local influence is presented to assess the effect of minor perturbations to data, priors, and sampling distributions. Several simulation studies and a real example are illustrated by the proposed methodologies.

The network of the subjective experience in embodiment phenomena

IntroductionBody illusions are designed to temporarily alter body representation by embodying fake bodies or part of them. Despite their large use, the embodiment questionnaires have been validated only for the embodiment of fake hands in the rubber hand illusion (RHI).MethodsWith the current study, we aimed at (1) extending the validation of embodiment questionnaires to a different illusory situation e.g., the full-body illusion (FBI); (2) comparing two methods to explore the questionnaires structures: a classic exploratory factor analysis (EFA) and a modern exploratory graph analysis (EGA). 118 healthy participants completed an FBI procedure where the subjective experience of embodiment was measured with a standard questionnaire. ResultsThe EFA results in two-factor structures. However, the confirmatory factor analysis (CFA) fit indices do not show a good fit with the data. Conversely, the EGA identified four communities: ownership, agency, co-location and disembodiment; the solution was confirmed by a CFA.ConlcusionsOverall, the EGA seems to be the best fitting method for the present data. Our results confirm the EGA as a suitable substitute for a more classical EFA. Moreover, the emerged structure suggests that the FBI induces similar effects to the RHI, implying that the embodiment sensations are common to different illusory methods.

Conditional Dynamic Dependence and Risk Spillover between Crude Oil Prices and Foreign Exchange Rates: New Evidence from a Dynamic Factor Copula Model

This paper proposes a dynamic factor model to accurately characterize the dynamic dependence and risk spillovers between the crude oil and exchange rate returns of oil-trading countries from 2000–2020, conditional on the common factors. To this end, we first identify the common factors related to returns of crude oil prices and exchange rates from 14 typical oil-trading countries. Then, we use the AR-GARCH model to filter the respective idiosyncratic factors and conduct a comparative study of conditional dynamic dependence between the crude oil and exchange rate returns of oil importers and exporters. Finally, we combine the dynamic factor copula model with the CoVaR method to measure the conditional risk spillover effect between crude oil and exchange rate markets. The empirical study indicates that the classical factor analysis can be used to precisely identify the common factors related to both financial markets, with the similar trend of macro-economic indicators. Furthermore, the factor copula model can capture the dynamic structure between crude oil and exchange rate markets more accurately than the traditional Copula–GARCH model. Specifically, the idiosyncratic factors related to each return series still have a significant impact on the dependence between the crude oil and exchange rate returns of oil importers, while the common factors have played an important role in the relationship of oil exporters’ exchange rates with crude oil prices. Finally, the crude oil market has enjoyed a relative risk premium to the exchange rate markets of oil-trading countries. However, there is almost no conditional risk spillover from the corresponding exchange rates to the crude oil prices. Finally, we discuss the implications for investors, policymakers and respective exchange rate regulators from oil-trading countries with further insights from the macro-economic perspective.

Adaptation and validation of the Preferences of Learning Styles (Index of Learning Styles, ILS, 1997) questionnaire to the cultural and cognitive characteristics of Spanish students aged 11-15 years

Introduction. The Index of Learning Styles (ILS) is a questionnaire created to assess students’ preferences regarding how they learn. It was originally designed in English for university students. The objective of the present work is to adapt the ILS questionnaire to Spanish and validate this adapted version, taking into account the cultural characteristics and cognitive level of the recipients, namely Spanish students aged 11–15. Method. We used the Delphi method involving expert opinions for content validation and the Brown and Maydeu-Olivares model, an approach from the Item Response Theory, for construct validation (this model is recommended for forced-choice items). Results. We located new items in the data scrubbing process, which in previous research were missed using classical factor analysis. The results for the global fit of the model show that 32 of the 44 items in the original questionnaire, display optimal values in various indices (RMSEA, CFI, X2, among others), and so this instrument can be used to identify learning style preferences in Spanish adolescents. Discussion. The Brown and Maydeu-Olivares model takes into account the nature of forced-choice items and this is the first the first time it has been applied to the ILS questionnaire. This validation procedure is novel and proves to be an optimal validation method for questionnaires with this type of items. The interest in the ILS questionnaire is due to the dimensions it assesses, which provide teachers and students with information about learning preferences for better psycho-pedagogical orientation and for the design of more personalized educational interventions.

Detecting Correlated Residuals in Exploratory Factor Analysis: New Proposals and a Comparison of Procedures

ABSTRACT In the classical exploratory factor analysis (EFA) model, residuals are constrained to be uncorrelated. However, since the 1960s, extensions of the classical model that allow correlated residuals to be modeled exist. Furthermore, in many EFA applications (especially those intended for item analysis) it is highly relevant to decide whether an extended solution is more appropriate than the simpler classical solution. This decision, in turn, requires effective and powerful methods for detecting correlated residuals (doublets) when they are really present to be available. This paper discusses two existing detection approaches in the EFA context, and proposes a third, new procedure. Reference values, based on the concept of parallel analysis, are proposed for deciding the relevance of the flagged doublets in all the considered procedures. The functioning of the three procedures is assessed by using simulation, and illustrated with an illustrative example. The proposal, finally, has been implemented in a well-known noncommercial EFA program, and an implementation in R is being developed.

Does non-linear factorization of financial returns help build better and stabler portfolios?

A portfolio allocation method based on linear and non-linear latent constrained conditional factors is presented. The factor loadings are constrained to always be positive in order to obtain long-only portfolios, which is not guaranteed by classical factor analysis or PCA. In addition, the factors are to be uncorrelated among clusters in order to build long-only portfolios. Our approach is based on modern machine learning tools: convex Non-negative Matrix Factorization (NMF) and autoencoder neural networks, designed in a specific manner to enforce the learning of useful hidden data structure such as correlation between the assets' returns. Our technique finds lowly correlated linear and non-linear conditional latent factors which are used to build outperforming global portfolios consisting of cryptocurrencies and traditional assets, similar to hierarchical clustering method. We study the dynamics of the derived non-linear factors in order to forecast tail losses of the portfolios and thus build more stable ones.

Differentiation of 2 Obsessive-Compulsive Disorder Subgroups with Regard to Demographic and Phenomenological Characteristics Combining Multiple Correspondence and Latent Class Analysis

Introduction: Classic factor analysis of obsessive-compulsive disorder (OCD) dimensions and attempts to identify valid subgroups have not yet produced definitive conclusions. Objective and Methods: This study aims to examine possible homogeneous subgrouping of demographic and phenomenological characteristics in 134 treatment-seeking OCD patients. A combination of multiple correspondence analysis (MCA) and latent class analysis (LCA) was used. Results: MCA showed 2 distinct subgroups of OCD patients and LCA confirmed this result by a two-class solution. Both analyses demonstrated (a) a clear subgroup of female patients with washing compulsions, obsessions related to contamination, and late age of onset and (b) a subgroup comprised mostly of male patients with earlier onset of OCD, checking rituals, and doubts or aggressive obsessions. Mental, ordering, hoarding compulsions, religious, or sexual obsessions and images appeared exclusively in this subgroup. Conclusions: Using 2 different analytic methods, we confirmed at least 2 subgroups in a clinical sample of Greek OCD patients. Future research combining dimensional and latent approaches could facilitate our understanding of the heterogeneous phenotype of OCD.

Heterogeneity of borderline personality disorder symptoms in help-seeking adolescents

BackgroundThe heterogeneous presentation of borderline personality disorder (BPD) represents a clinical challenge. There is an ongoing scientific debate whether the heterogeneity can best be understood in terms of qualitative (categorical) or quantitative (dimensional) differences between individuals. The present study examined the latent structure of BPD in adolescents.MethodsFive-hundred and six outpatients aged 12 to 17 years with risk-taking and/or self-harming behavior were assessed at baseline and one-year follow-up. Latent class analysis (corresponding with the categorical approach), factor analysis (corresponding with the dimensional approach), and factor mixture models (allowing for both categorical and dimensional aspects) were applied to the DSM-IV BPD criteria.ResultsThe best fitting model distinguished between a majority class with high probabilities for all BPD criteria (“borderline group”) and a minority class with high probabilities for the impulsivity and anger criteria only (“impulsive group”). Sex significantly affected latent class membership, and both a latent factor and age explained within-class variability. The borderline group primarily consisted of females, frequently reported adverse childhood experiences, scored high on the emotion dysregulation and inhibitedness personality traits, and was associated with internalizing psychopathology. In contrast, the impulsive group primarily consisted of males, scored high on the dissocial behavior personality trait, and was associated with externalizing psychopathology. After one year, the two groups showed similar clinical improvement.ConclusionsThe study provides evidence for two distinct subgroups of adolescents with BPD features that resemble the subtypes of the ICD-10 emotionally unstable personality disorder. More research is needed to further investigate the diagnostic stability of the two groups over time and potential differential treatment indications.

Psychometric Properties of the Persian Pittsburgh Sleep Quality Index for Adolescents.

Background: Both cross-sectional and longitudinal studies show that poor sleep is a health concern related to further psychological and physiological issues during adolescence. To assess subjective sleep quality and sleep patterns among adults, the Pittsburgh Sleep Quality Index (PSQI) is a well and internationally established tool. Here, we established the psychometric properties of the Persian version of the PSQI for adolescents. Method: A total of 1477 adolescents (mean age: 15.47 years; 53.2% females) took part in the study. They completed a booklet on sociodemographic information, the Persian version of the PSQI for adolescents, and the Adolescent Sleep Hygiene Scale (ASHS). We relied on classical test reliability approaches of exploratory and confirmatory factor analyses. Results: Classical exploratory factor analysis yielded the seven-factor solution, with concurrent confirmation and overlap with the dimensions of the ASHS, although correlation coefficients were small to medium. A further factor analysis yielded a four-factor solution, explaining 72% of the variance of the PSQI. Further, three out of these four factors predicted the ASHS overall score. Conclusions: The Persian version of the PSQI for adolescents showed satisfactory psychometric properties. It follows that the Persian PSQI is a suitable tool to assess sleep quality and sleep patterns among adolescents.

A Decision-Making Tool Based on Exploratory Visualization for the Automotive Industry

In recent years, the digital transformation has been advancing in industrial companies, supported by the Key Enabling Technologies (Big Data, IoT, etc.) of Industry 4.0. As a consequence, companies have large volumes of data and information that must be analyzed to give them competitive advantages. This is of the utmost importance in fields such as Failure Detection (FD) and Predictive Maintenance (PdM). Finding patterns in such data is not easy, but cutting-edge technologies, such as Machine Learning (ML), can make great contributions. As a solution, this study extends Hybrid Unsupervised Exploratory Plots (HUEPs), as a visualization technique that combines Exploratory Projection Pursuit (EPP) and Clustering methods. An extended formulation of HUEPs is proposed, adding for the first time the following EPP methods: Classical Multidimensional Scaling, Sammon Mapping and Factor Analysis. Extended HUEPs are validated in a case study associated with a multinational company in the automotive industry sector. Two real-life datasets containing data gathered from a Waterjet Cutting tool are visualized in an intuitive and informative way. The obtained results show that HUEPs is a technique that supports the continuous monitoring of machines in order to anticipate failures. This contribution to visual data analytics can help companies in decision-making, regarding FD and PdM projects.