Alternative Data Analysis Methods Research Articles

Model-Fitting Weighted Least Squares as an Alternative to Principal Component Analysis for Analyzing Energy-Dispersive X-ray Spectroscopy Spectrum Images.

Spectrum imaging with energy-dispersive X-ray spectroscopy (EDS) has become ubiquitous in material characterization using electron microscopy. Multivariate statistical methods, commonly principal component analysis (PCA), are often used to aid analysis of the resulting multidimensional datasets; PCA can provide denoising prior to further analysis or grouping of pixels into distinct phases with similar signals. However, it is well known that PCA can introduce artifacts at low signal-to-noise ratios. Unfortunately, when evaluating the benefits and risks with PCA, it is often compared only against raw data, where it tends to shine; alternative data analysis methods providing a fair point of comparison are often lacking. Here, we directly compare PCA with a strategy based on (the conceptually and computationally simpler) weighted least squares (WLS). We show that for four representative cases, model fitting of the sum spectrum followed by WLS (mfWLS) consistently outperforms PCA in terms of finding and accurately describing compositional gradients and inclusions and as a preprocessing step to clustering. Additionally, we demonstrate that some common artifacts and biases displayed by PCA are avoided with the mfWLS approach. In summary, mfWLS can provide a superior option to PCA for analysis of EDS spectrum images as the signal is simply and accurately modeled.

Використання великих даних для інформаційного забезпечення ринку праці

The challenges of collecting current labor market data amidst global changes and geopolitical fluctuations increasingly necessitate new approaches and alternative methods of data collection and analysis. This highlights the relevance of research aimed at developing and adapting labor market information provision methods to contemporary challenges. A promising approach in this context is the use of Big Data for labor market assessment, which involves collecting information from sources such as online job search and vacancy portals. This method allows for a deeper analysis of market trends and provides a more accurate and timely assessment of labor market needs and opportunities. The aim of this article is to discuss approaches to developing labor market information systems using Big Data, particularly online data from job vacancy websites. It examines the use of Big Data in labor market analysis based on a database containing over four million job vacancies posted on Ukrainian job search portals over the last five years, provided by the European Training Foundation (ETF). The effectiveness of these approaches in facilitating job search for all interested parties is evaluated, particularly through providing insights into the dynamics of supply and demand in the labor market based on data from these portals. The opportunities and limitations of using Big Data in this context are analyzed, including their impact on employment policy development and labor market planning. The potential benefits of Big Data in providing deeper and more accurate market condition analyses are outlined, along with technical aspects and challenges associated with their processing and interpretation. The article examines methodological approaches to data collection and analytical processing in the context of accelerated transformations, volatility, and limited access to traditional information resources. The scientific novelty of the article lies in the substantiation of the feasibility and appropriateness of using open data from online job portals for labor market information provision under current conditions. In conducting the research, methods of analysis, synthesis, and generalization were applied to identify the main contemporary issues of labor market information provision in Ukraine. The effectiveness of data collection methods based on web scraping and parsing techniques was evaluated, as well as the use of the integrated Snowflake platform to identify key trends and patterns in the labor market. The conclusions summarize the main points and substantiate directions for further research, highlighting the significance of Big Data in developing employment strategies and optimizing the labor market.

A reevaluation of selected mortality risks in the updated NCI/NIOSH acrylonitrile cohort study.

The study aimed to determine whether the National Cancer Institute's (NCI) recent suggestion of associations between acrylonitrile (AN) exposure and mortality in lung and bladder cancer and pneumonitis is robust to alternative methods of data analysis. We used the Richardson method to indirectly adjust risk ratios (RRs) in relation to AN exposure for potential confounding by smoking and asbestos. We repeated key analyses omitting workers from Plant 4 to account for possible local, historical shipyard-related asbestos exposures. The adjustment of lung cancer RRs for confounding by both smoking and asbestos and omitting Plant 4 workers yielded mostly decreased RRs and much less evidence of a positive association with cumulative AN exposure. Overall, our reanalysis provided little evidence to support NCI's suggestion of associations between AN exposure and mortality in lung and bladder cancer and pneumonitis.

Fuzzy regression model with Bayesian approach and its application to public health data

The application of the Bayesian Linear Regression (BLR) and Fuzzy Bayesian Linear Regression method through the SAS algorithm is the focus of this paper. As an alternative method of data analysis in biostatistics, this modified method can be used. This modified method includes a bootstrapping technique, residual normality checking and some Bayesian Linear Regression Modeling (BLR) enhancement through Fuzzy Bayesian Linear Regression. We illustrated the application of the algorithm for Bayesian Linear Regression (BLR) and Fuzzy Bayesian Linear Regression in this paper.

Mitigating Bias in the Measurement of Heart Rate Variability in Physiological Studies of Spinal Manipulation: A Comparison Between Authentic and Sham Manipulation

ObjectiveThe purpose of this study was to identify sources and strategies for the mitigation of bias in studies of spinal manipulation and heart rate variability. MethodsA small-scale study compared the effects of a single session of sham and authentic cervical manipulation on heart rate variability as measured by power spectrum analysis. The participants were a sample of 31 healthy young students from the Canadian Memorial Chiropractic College, randomized into 2 study arms. The effectiveness of blinding was evaluated, and 2 alternative methods of data analysis were explored to mitigate risk of bias. Following execution of the study, the stages of implementation and data processing were scored against version 2 of the Cochrane risk-of-bias tool for randomized trials for risk of bias. ResultsThe risk of bias arising from (1) the randomization process, (2) missing outcome data, and (3) selection of reported results was judged to be low. Risk of bias in (1) deviations from intended interventions (particularly due to the failure of masking) and (2) the measurement of the outcome, for example, through cleaning of the data, were judged to be high. ConclusionThe use of power spectrum analysis of heart rate variability based on 5-minute recordings of echocardiogram pre-and post-intervention contained multiple sources of bias that were challenging to mitigate. Based upon these findings, power spectrum analysis of heart rate variability using these parameters may be ill-suited to the study of physiological effects of spinal manipulative therapy.

How narrow is the M87* ring? I. The choice of closure likelihood function

ABSTRACT Event Horizon Telescope (EHT) observations of the core of the galaxy M87 suggest an observational appearance dominated by a ring of approximately 40 $\mu$as in diameter. The thickness of the ring is less certain: imaging efforts constrained it to be less than half the diameter (consistent with an imaging resolution of 20 $\mu$as), while visibility-domain modelling suggested a variety of fractional widths, including as low as $10{{\ \rm per\ cent}}$ on some days. The fractional width is very interesting as it has the potential to discriminate between different astrophysical scenarios for the source; in fact, the 10–$20{{\ \rm per\ cent}}$ range is so narrow as to be in tension with theoretical expectations. In the first of a series of papers on the width of the observed ring, we reproduce a subset of EHT visibility-domain modelling results and we explore whether alternative data analysis methods might favour thicker rings. We point out that the closure phase (and closure amplitude) likelihood function is not independent of residual station gain amplitudes, even at high signal-to-noise, and explore two approximations of practical interest: one standard in the field (and employed by the EHT collaboration), and a new one that we propose. Analysing the public data, we find that the new likelihood approximation prefers somewhat thicker rings, more in line with theoretical expectations. Further analysis is needed, however, to determine which approximation is better for the EHT data.

Measuring Anxiety-Like Behaviors in Rodent Models of Traumatic Brain Injury.

Anxiety is a common complaint following acquired traumatic brain injury (TBI). However, the measurement of dysfunctional anxiety behavioral states following experimental TBI in rodents is complex. Some studies report increased anxiety after TBI, whereas others find a decreased anxiety-like state, often described as increased risk-taking behavior or impulsivity. These inconsistencies may reflect a lack of standardization of experimental injury models or of behavioral testing techniques. Here, we review the most commonly employed unconditioned tests of anxiety and discuss them in a context of experimental TBI. Special attention is given to the effects of repeated testing, and consideration of potential sensory and motor confounds in injured rodents. The use of multiple tests and alternative data analysis methods are discussed, as well as the potential for the application of common data elements (CDEs) as a means of providing a format for documentation of experimental details and procedures of each published research report. CDEs may improve the rigor, reproducibility, as well as endpoint for better relating findings with clinical TBI phenotypes and the final goal of translation. While this may not resolve all incongruities in findings across laboratories, it is seen as a way forward for standardized and universal data collection for improvement of data quality and sharing, and advance therapies for neuropsychiatric symptoms that often present for decades following TBI.

Geographic Information System (GIS) in Evaluating the Accessibility of Healthcare Facility for Patients with Colon and Rectal Carcinoma in the State of Kelantan and Sabah

Malaysian National Cancer Registry (MNCR) 2007-2011, has ranked colorectal cancer (CRC) as the most common cancer for men and the second most common cancer for the women. We investigated the distribution of CRC patients and their accessibility to government healthcare facility using spatial analysis in both states. This is a retrospective cross-sectional study of patients who were diagnosed with CRC in the state of Kelantan and Sabah from the year 2016 till 2018. A total of 363 and 589 patients from Kelantan and Sabah respectively were recruited into the study. QGIS was used to create a distribution map and spatial analysis (average nearest neighbour and hotspot analysis) was performed with ArcGIS. The average age of patients with CRC in the state of Sabah was lower than that of Kelantan. The results from the average nearest neighbour suggest clustered pattern for both states. Hotspot analysis showed focused area on cluster in Kelantan over the northeast region. Spatial analysis shows both states to have patients with CRC who travelled more than 10km to reach the nearest hospital. GIS provides an alternative method of data analysis in comparison to conventional tabular form for CRC cases in Kelantan and Sabah.

Exploratory factor analysis for defect identification with active thermography

Non-destructive testing (NDT) methods are commonly used to disclose defective inner structures of materials, where active infrared thermography is a popular NDT technique because of its easy operation, low cost, and the capability of rapid scanning of large areas. However, the thermographic signals often suffer from noise and non-uniform backgrounds, making defect identification difficult. Therefore, an additional processing step of thermographic data is often required to enhance the contrast between defects and their surroundings. In recent years, multivariate statistical analysis methods have been widely adopted to achieve this aim, among which principal component thermography (PCT) is a typical example. Sparse PCT (SPCT) further improves PCT by adding sparsity constraints to the optimization problem. Nevertheless, the performance of SPCT depends on the subjective selection of the tuning parameters. The optimal parameter values are case-dependent and unknown. In this work, an alternative thermographic data analysis method is proposed based on exploratory factor analysis (EFA). By means of factor rotation, EFA minimizes the complexity of factor loadings and makes the results more interpretable. In doing this, EFA provides results similar to those of SPCT, while there is no need for parameter selection. Experimental results illustrate the feasibility of the proposed method.

Anatomical region identification in medical X-ray computed tomography (CT) scans: development and comparison of alternative data analysis and vision-based methods

Many medical image processing applications rely on targeted regions of interest within a larger volumetric image. Whole-body scans represent an extreme case in which large volumes must be broken into smaller sub-volumes for regional analysis. In this work, we sought automatic solutions to divide medical X-ray computed tomography (CT) images into six main anatomical regions: head, neck, chest, abdomen, pelvis and legs. We implemented and compared three methods: (1) an analytical approach which does not require training and solely relies on utilizing critical points in image intensity profiles to derive cut-planes that divide the scan into the mentioned regions, (2) a classical convolutional neural network (CNN) approach, which classifies each transaxial 2D plane independently and then concatenates classification results, and (3) CNN followed by a context-based correction algorithm (CBCA) which improves the CNN classification using positional relationships between all CT slices. The analytical approach achieved acceptable accuracy for anatomical region segmentation without the need for explicit data labeling and was effective for batch labeling whole-body CTs, greatly reducing manual labeling efforts. CNNs achieved superior accuracy and allowed for rapid development and training, but required labeled data and were susceptible to produce discontinuous anatomical regions and therefore ambiguous anatomical boundaries. Post hoc correction of CNN results using CBCA overcame these limitations, achieving nearly perfect CT slice labeling and anatomical region segmentation.

The Hamburg Rutting Test (HWTT) alternative data analysis methods and HMA screening criteria

The Hamburg Wheel Tracking Test (HWTT) is a widely used routine laboratory test for identifying and screening hot-mix asphalt (HMA) mixes that are prone to rutting. The standard HMA pass-fail screening criterion under the current HWTT protocol is 12.5 mm rutting at 50°C. However, Texas has experienced record high summer temperatures in the recent years (i.e., over 122°F), and several rutting failures have occurred in the field with some surface HMA mixes that had passed the HWTT in the laboratory. These failures occurred mostly in high shear-stress locations, in particular with slow moving (accelerating/decelerating) traffic at controlled highway intersections, stop-go sections, in areas of elevated temperatures, heavy/high traffic loading, and/or where lower performance grade (PG) of asphalt binders have been used. This laboratory hybrid study was thus initiated to explore new data analysis methods and introduce new alternative rutting parameters to supplement the traditional HWTT pass-fail screening criteria (≤ 12.5 mm rut depth at 50°C) for surface HMA mixes. Several HMA mixes commonly used in Texas were evaluated in the laboratory and new HWTT analysis parameters, such as the rutting area (ΔA), the normalized rutting area (RutΔ), and the shape factor (SF) with the potential to capture the HMA rutting path-history, were formulated. In addition, a comparison between the newly formulated and traditional rutting parameters with field performance observations was conducted and yielded plausible results in terms of predicting the early-life rutting performance of HMA mixes.

Matching Golfers’ Movement Patterns during a Golf Swing

The golf swing is a multidimensional movement requiring alternative data analysis methods to interpret non-linear relationships in biomechanics data related to golf shot outcomes. The purpose of this study was to use a combined principal component analysis (PCA), fuzzy coding, and multiple correspondence analysis (MCA) data analysis approach to visualise associations within key biomechanics movement patterns and impact parameters in a group of low handicap golfers. Biomechanics data was captured and analysed for 22 golfers when hitting shots with their own driver. Relationships between biomechanics variables were firstly achieved by quantifying principal components, followed by fuzzy coding and finally MCA. Clubhead velocity and ball velocity were included as supplementary data in MCA. A total of 35.9% of inertia was explained by the first factor plane of MCA. Dimension one and two, and subsequent visualisation of MCA results, showed a separation of golfers’ biomechanics (i.e., swing techniques). The MCA plot can be used to simply and quickly identify movement patterns of a group of similar handicap golfers if supported with appropriate descriptive interpretation of the data. This technique also has the potential to highlight mismatched golfer biomechanics variables which could be contributing to weaker impact parameters.

The challenge of assessing financial literacy: alternative data analysis methods within the Italian context

BackgroundAssessing individuals’ financial literacy levels is currently widely recognized as being necessary to design effective financial education programs and also to evaluate their actual impact. To address the lack of a consensus regarding an appropriate instrument to measure financial literacy, the OECD and its International Network on Financial Education (INFE) developed a core questionnaire in 2011, to be administered across a wide range of countries. Italy participated in the study with a survey promoted by the financial consortium ABI–PattiChiari. A tailored version of the OECD/INFE questionnaire was used in the survey, with three indicators of financial literacy taken from the OECD survey (financial behavior index, financial attitude index, financial knowledge index) and two new indicators (financial familiarity index and financial planning).PurposeThe present paper focuses on data analysis methods used to evaluate financial literacy among the Italian adult population. It reviews data analysis approaches used to evaluate financial literacy and proposes a new method to gauge this latent construct in order to obtain a valid and reliable index that is able to capture educational needs in a manner that is as accurate and targeted as possible.MethodsThe sample used for the survey consisted of 1247 Italian residents of at least 18 years of age who were reached via CATI. The sample was obtained by appropriate stratification across several dimensions (gender, age, geographical area, and municipality size). We propose alternative data analysis methods to treat the survey data: item response theory (IRT) and classification and regression tree analysis.ResultsThe analysis highlighted the crucial role that data analysis methods play in assessing financial literacy. Comparing the results for classical test theory and IRT, this paper suggests that financial literacy research should be open to alternative and multiple approaches to obtain reliable measures of financial literacy that are able to capture the educational needs of different population groups and can help to design effective financial education programs.

Enhanced mortar approach to characterize the effect of reclaimed asphalt pavement on virgin binder true grade

The asphalt community seeks a solvent-free method to determine the properties of RAP binder and those of its blends with virgin binders. A promising approach is to test mortars composed of fine fractions of RAP and a virgin binder, and to calculate grade change rate (GCR) to predict blended binder true grade at any binder replacement rate (BRR). However, the existing mortar approach underestimates the effect of RAP on binder grade, particularly at high temperatures. This study identified the use of a shift factor in the existing method as the source of underestimations. An alternative data analysis method was developed, which eliminates the shift factor by using the relationship between binder and mortar properties. Dynamic shear rheometer tests were conducted on a total of 12 mortar combinations, including 4 virgin binders and 3 RAP sources at a BRR of 15%, and then, RAP GCR values were determined by following both the existing and the alternative methods. Satisfactory comparisons were only observed between grades predicted with the alternative method and measured values of manual RAP binder blends: the average difference was lower than 1 °C for BRR of 15 and 30%, and < 4 °C for BRR of 100%. This not only validated the alternative method but also substantiated that RAP GCR is constant, i.e., the grade of RAP blends linearly increased with BRR. Further evaluation of the enhanced mortar approach is recommended at intermediate and low temperatures as well as with blends including recycled asphalt shingles.

Sync or separate? No compelling evidence for unintentional interpersonal coordination between Usain Bolt and Tyson Gay on the 100-meter world record race.

In a recent observation article in Journal of Experimental Psychology: Human Perception and Performance (JEP:HPP; Varlet & Richardson, 2015) the 100-m sprint final of the World Championship in Athletics in Berlin of 2009 (i.e., the current world record race) was analyzed. That study reported occurrence of spontaneous, unintentional interpersonal synchronization between Usain Bolt and Tyson Gay, the respective winner and runner-up of that race. In the present commentary article, however, we argue that the results and conclusion of that study cannot be warranted because of methodological shortcomings. We addressed the same research question and reassessed the same race using an alternative data analysis method. These results revealed that as yet there is no sufficient ground to conclude that in the 100-m world record race synchronization occurred between Bolt and Gay. Yet, our reanalysis suggested that even at this very elite level the individual movement frequencies did seem to vary to such an extent that synchronization would theoretically still be possible, thereby providing incentives for further examination of potential unintentional synchronization in coactive sports. (PsycINFO Database Record

Optimizing a custom tiling microarray for low input detection and identification of unamplified virus targets

Viruses are major pathogens causing foodborne illnesses and are often present at low levels in foods, thus requiring sensitive techniques for their detection in contaminated foods. The lack of efficient culture methods for many foodborne viruses and the potential for multi-species viral contamination have driven investigation toward non-amplification based methods for virus detection and identification. A custom DNA microarray (FDA_EVIR) was assessed for its sensitivity in the detection and identification of low-input virus targets, human hepatitis A virus, norovirus, and coxsackievirus, individually and in combination. Modifications to sample processing were made to accommodate low input levels of unamplified virus targets, which included addition of carrier cDNA, RNase treatment, and optimization of DNase I-mediated target fragmentation. Amplification-free detection and identification of foodborne viruses were achieved in the range of 250–500 copies of virus RNA. Alternative data analysis methods were employed to distinguish the genotypes of the viruses particularly at lower levels of target input and the single probe-based analysis approach made it possible to identify a minority species in a multi-virus complex. The oligonucleotide array is shown to be a promising platform to detect foodborne viruses at low levels close to what are anticipated in food or environmental samples.

Detection and quantitative determination of diethylene glycol in ethyl alcohol using gamma- ray spectroscopy.

Determination of the toxic diethylene glycol contamination in ethyl alcohol demands a rapid, accurate and reliable method. Diethylene glycol (DEG) ingestion, accidental or intentional, can lead to death. Clinical and analytical methods used to detect diethylene glycol in alcohol require several hours to days due to tedious instrument handling and measurements. Enzymatic assays face difficulty due to analytic problems. As an alternative method of data analysis, we have used γ-ray spectroscopic method to estimate the diethylene glycol contamination in alcohol by monitoring the variation in the linear and mass attenuation coefficients. This method is simple, robust, portable and can provide reliable and quantitative information about the ethyl alcohol adulterated with diethylene glycol which is of broader interest to society.

Differences Observed in Data Analysis Techniques: An Example Using Natural Fibers’ Diameters and Absorption Times

<abstract> <bold><sc>Abstract. </sc></bold>The properties of natural fibers are often evaluated across species, with the average value of the property of interest being compared via analysis of variance (ANOVA). Researchers also frequently adjust property values to account for the natural variation existing between some characteristic of the fibers. Weighting the property to correct for the variation, then using ANOVA, or applying analysis of covariance (ANCOVA) are two alternative data analysis methods. While studying the surface characteristics of bamboo, jute, ramie, and hemp fibers, observations were made on the differences in absorption time of the natural fibers. These differences were then compared using three data analysis methods- ANOVA using the average and an adjusted average based on the diameter of the fiber for multiple comparisons, and an adjusted average following an analysis of covariance using fiber diameter as the covariate. Only hemp’s mean absorption time remained in the same t grouping regardless of the method employed. The other species’ average absorption times changed t groupings depending on the method chosen. This simplified example highlights the value of the method chosen for data analysis.

A Comparison of TIMSS Items Using Cognitive Domains

The results of international assessments such as the Trends in International Mathematics and Science Study (TIMSS) are often reported as rankings of nations. Focusing solely on national rank can result in invalid inferences about the relative quality of educational systems that can, in turn, lead to negative consequences for teachers and students. This study seeks an alternative data analysis method that allows for improved inferences about international performance on the TIMSS. In this study, four classroom teachers categorized a sample of TIMSS items by the cognitive domains of knowing and applying using the definitions provided by the TIMSS 2011 Assessment Frameworks. Items of different cognitive domains were analyzed separately. This disaggregation allowed for more valid inferences to be made about student performance. Results showed almost no significant difference between the performance of U.S. students and the students of five other nations. Additionally, no differences were observed in U.S. students' performance on knowing items and applying items, although students from some sample nations performed significantly better on knowing items. These results suggest that policy makers, educators, and citizens should be cautious when interpreting the results of TIMSS rank tables.

Application of the discrete distribution in Bayes analYsis of nature area coverage data

Classical statistical methods do not always provide desired results for every situation. Therefore, new alternative methods of data analysis are in demand. As the computational power becomes more modern, Bayes statistical methods are increasingly applied for statistical data analysis. This article describes several discrete models for analyzing nature area coverage. These models can be applied for analysis of such areas as forests, water ponds, soil, etc. when data is provided in integer data in percent. Poisson and negative binomial distributions are used in this article. Unknown parameters of the models were estimated using Bayes statistical methods in OpenBUGS modeling environment. The models of nature area coverage analysis were implemented using the data of Baltic Sea bottom algae coverage. This article analyzes coverage dependence of abiotic and physical factors.