Statistical Explanation Research Articles

Exploring the Validity Based on Internal Structure of the Oldenburg Burnout Inventory – Medical Student (OLBI-MS)

Construct The study gathers validity evidence for the use of the Oldenburg Burnout Inventory – Medical Student (OLBI-MS), a 16-item scale used to measure medical student burnout. The 16 items on the OLBI-MS are split to form two subscales, disengagement and exhaustion. Background Medical student burnout has been empirically linked to several detrimental professional and personal consequences. In recognition of the high prevalence of medical student burnout, one recommendation has been to regularly measure burnout using standardized measures that have strong validity evidence for their intended use. The OLBI-MS, a frequently used measure of medical student burnout, was adapted from the Oldenburg Burnout Inventory (OLBI). The OLBI has been studied in many occupational settings and been found to have a two-factor solution in majority of these populations, but there is limited validity evidence available that supports the use of the OLBI-MS subscales in a medical student population. Approach Two years of Association of American Medical College Year 2 Questionnaire data (n = 24,008) were used in the study for a series of exploratory and confirmatory factor analyses. The data from the first year (n = 11,586) was randomly split into a confirmatory and exploratory sample, with the data from the second year (n = 12,422) used as a secondary confirmatory sample. Because the questionnaire is administered to medical students during their second year of undergraduate medical education, we consider this a study as providing validity evidence specifically for the measure’s use with that population. Findings The two-factor structure of the OLBI-MS was not empirically supported in the second year medical-student population. Several of the items had low inter-item correlations and/or moderate correlations with unexpected items. Three modified versions of the OLBI-MS were tested using subsets of the original items. Two of the modified versions were adequate statistical explanations of the relationships in the data. However, it is unclear if these revised scales appropriately measure all aspects of the construct of burnout and additional validity evidence is needed prior to their use. Conclusions The use of the OLBI-MS is not recommended for measuring second-year medical student burnout. It is unclear if the OLBI-MS is appropriate for medical students at all, or if different measures are necessary at different stages in a medical student’s professional development. Additional research is needed to either improve the OLBI-MS or use it as a foundation for a new measure. Supplemental data for this article is available online at at www.tandfonline.com/htlm .

Unilateral Alignment: An interpretable machine learning method for geophysical logs calibration

Most of the existing machine learning studies in logs interpretation do not consider the data distribution discrepancy issue, so the trained model cannot well generalize to the unseen data without calibrating the logs. In this paper, we formulated the geophysical logs calibration problem and give its statistical explanation, and then exhibited an interpretable machine learning method, i.e., Unilateral Alignment, which could align the logs from one well to another without losing the physical meanings. The involved UA method is an unsupervised feature domain adaptation method, so it does not rely on any labels from cores. The experiments in 3 wells and 6 tasks showed the effectiveness and interpretability from multiple views.

Automated Machine-Learning Framework Integrating Histopathological and Radiological Information for Predicting IDH1 Mutation Status in Glioma.

Diffuse gliomas are the most common malignant primary brain tumors. Identification of isocitrate dehydrogenase 1 (IDH1) mutations aids the diagnostic classification of these tumors and the prediction of their clinical outcomes. While histology continues to play a key role in frozen section diagnosis, as a diagnostic reference and as a method for monitoring disease progression, recent research has demonstrated the ability of multi-parametric magnetic resonance imaging (MRI) sequences for predicting IDH genotypes. In this paper, we aim to improve the prediction accuracy of IDH1 genotypes by integrating multi-modal imaging information from digitized histopathological data derived from routine histological slide scans and the MRI sequences including T1-contrast (T1) and Fluid-attenuated inversion recovery imaging (T2-FLAIR). In this research, we have established an automated framework to process, analyze and integrate the histopathological and radiological information from high-resolution pathology slides and multi-sequence MRI scans. Our machine-learning framework comprehensively computed multi-level information including molecular level, cellular level, and texture level information to reflect predictive IDH genotypes. Firstly, an automated pre-processing was developed to select the regions of interest (ROIs) from pathology slides. Secondly, to interactively fuse the multimodal complementary information, comprehensive feature information was extracted from the pathology ROIs and segmented tumor regions (enhanced tumor, edema and non-enhanced tumor) from MRI sequences. Thirdly, a Random Forest (RF)-based algorithm was employed to identify and quantitatively characterize histopathological and radiological imaging origins, respectively. Finally, we integrated multi-modal imaging features with a machine-learning algorithm and tested the performance of the framework for IDH1 genotyping, we also provided visual and statistical explanation to support the understanding on prediction outcomes. The training and testing experiments on 217 pathologically verified IDH1 genotyped glioma cases from multi-resource validated that our fully automated machine-learning model predicted IDH1 genotypes with greater accuracy and reliability than models that were based on radiological imaging data only. The accuracy of IDH1 genotype prediction was 0.90 compared to 0.82 for radiomic result. Thus, the integration of multi-parametric imaging features for automated analysis of cross-modal biomedical data improved the prediction accuracy of glioma IDH1 genotypes.

People Judge Discrimination Against Women More Harshly Than Discrimination Against Men - Does Statistical Fairness Discrimination Explain Why?

Previous research has shown that people care less about men than about women who are left behind. We show that this finding extends to the domain of labor market discrimination: In identical scenarios, people judge discrimination against women more morally bad than discrimination against men. This result holds in a representative sample of the US population and in a larger but not representative sample of Amazon Mechanical Turk (Mturk) respondents. We test if this gender gap is driven by statistical fairness discrimination, a process in which people use the gender of the victim to draw inferences about other characteristics which matter for their fairness judgments. We test this explanation with a survey experiment in which we explicitly hold information about the victim of discrimination constant. Our results provide only mixed support for the statistical fairness discrimination explanation. In our representative sample, we see no meaningful or significant effect of the information treatments. By contrast, in our Mturk sample, we see that providing additional information partly reduces the effect of the victim’s gender on judgment of the discriminator. While people may engage in statistical fairness discrimination, this process is unlikely to be an exhaustive explanation for why discrimination against women is judged as worse.

Expansive Growth vs. pH Reflects a Poisson Point Process of Binding/Unbinding Events in Plant Cell Walls

The paramount role of mathrm{pH} and temperature left(Tright) in the expansive growth of a plant coleoptile/hypocotyl non-meristematic zone or plant and fungal cells was examined within the framework of the underlying chemical bond statistics in order to reproduce an experimental plot of growth vs. mathrm{pH}. Here, according to the definition, mathrm{pH}=mathrm{pH}left({mu }_{{mathrm{H}}^{+}}left(Tright), Tright) is considered as a function of the chemical potential of the H+ (hydronium) ions ({mu }_{{mathrm{H}}^{+}}), as well as an implicit and explicit function of T. The derivation of the mathrm{pH} and T dependent expansive growth distribution from the Poisson statistics of the “tethers” that reproduce the chemical bonds between microfibrils was determined. The probability distribution for the attachment/detachment/reattachment events of the tethers that are connected to the microfibrils in the elongation zone was obtained. The two distinct but interrelated modes of the expansive growth, which are known as “acid growth” and “auxin growth” were distinguished in the analytic model, while the acid growth hypothesis was verified and confirmed at the semi-empirical and microscopic levels for the first time. Moreover, further perspectives, in which the macroscopic variables left(P, V, Tright) with P standing for the turgor pressure and V for the cell volume, and the microscopic variables, {E}^{{varvec{d}},{varvec{r}}}, which represent the binding energies of the detachment/reattachment events at the expense of ATP energy, and {mu }_{{mathrm{H}}^{+}} can occur simultaneously, were identified. With a few assumptions that are partly based on experimental data it was possible to synthesise a link between the microscopic, explicit statistical explanation of bond dynamics and the macroscopic rheological properties of the cell wall at a given mathrm{pH} and temperature. A statistical description that predicted the importance of mathrm{pH} and temperature-dependent chemical potential of the H+ ions in microscopic events that result in growth would be supposedly applicable across scales.

The impacts of interaction of audit litigation and ownership structure on audit quality

This study examines the impact of the interactions of audit litigation and ownership structure on audit quality by Big 4 and non-Big 4 audit firms in Oman. This study uses modified audit opinion as proxies for audit quality, binary variable for audit litigation and percentage of shares owned by large shareholders and minority shareholders (consisting of Arab [non-GCC] shareholders) for ownership structure. The study uses size, risk, types of activity and ages of the firms as control variables. For the analysis and explanation of results descriptive statistics, correlation, regression techniques and T-test are used. Based on a sample of 107 listed companies on Muscat Securities Market (MSM) for 2013–2017, we find that audit litigation has a significant impact on audit quality for Big 4 audit firms, but not for non-Big 4 audit firms. Also, the results indicate that there is no difference between Big 4 and non-Big 4 audit firms as far as litigation risk is concerned.

Review: Proximities: Art, Education, Activism, by Linda Brooks

Review: <i>Proximities: Art, Education, Activism</i>, by Linda Brooks

Wisdom of crowds: much ado about nothing

The puzzling idea that the combination of independent estimates of the magnitude of a quantity results in a very accurate prediction, which is superior to any or, at least, to most of the individual estimates is known as the wisdom of crowds. Here we use the federal reserve bank of Philadelphia’s survey of professional forecasters database to confront the statistical and psychophysical explanations of this phenomenon. Overall we find that the data do not support any of the proposed explanations of the wisdom of crowds. In particular, we find a positive correlation between the variance (or diversity) of the estimates and the crowd error in disagreement with some interpretations of the diversity prediction theorem. In addition, in opposition to the predictions of the psychophysical augmented quincunx model, we find that the skew of the estimates offers no information about the crowd error. More importantly, we find that the crowd beats all individuals in less than 2% of the forecasts and beats most individuals in less than 70% of the forecasts, which means that there is fair chance that an individual selected at random will perform better than the crowd. These results contrast starkly with the performance of non-natural crowds composed of unbiased forecasters which beat most individuals in practically all forecasts. The moderate statistical advantage of a real-world crowd over its members does not justify the ado about its wisdom, which is most likely a product of the selective attention fallacy.

Exploration of the form factors of turbulence kinetic energy transfer for shear exfoliation of graphene

Mass production of defect-free and large-lateral-size 2D materials via cost-effective methods is very important. Recently, shear exfoliation has shown great promise for large-scale production due to its simple operation, environmental-benignity and wide adaptability. However, a long-standing challenge is that with the production of more nanosheets, a ceiling yield and shattered products are encountered, which significantly limits their wider application. The method and efficiency of energy transfer in fluid is undoubtedly the key point in determining exfoliation efficiency, yet its in-depth mechanism has not yet been described. Thus, a thorough investigation of turbulence energy transfer is critically necessary. Herein, we identify two main factors that critically determine the exfoliation yield and provide a statistical analysis of the relationship between these factors and the exfoliation yield. In the initial shearing process, the coexistence of the 2D nanosheets and raw particles is the dominant factor; as time passes, the dimensional change of raw materials gradually has a greater influence on the energy transfer. These factors together lead to attenuated efficiency and a power function relationship between yield and exfoliation time. This investigation gives a statistical explanation of shear exfoliation technology for 2D material preparation and provides valuable insights for mechanical exfoliating high-quality 2D materials.

Swarm-inspired data-driven approach for housing market segmentation: a case study of Taipei city

Data-driven housing-market segmentation has been given increasing prominence for its objectiveness in identifying submarkets based on the housing data’s underlying structures. However, when handling high-dimensionality housing dataset, traditional statistical-clustering methods have been found to tend to lose low-variance information of the dataset and be deficient in deriving the globally optimal number of submarkets. Accordingly, with the intention of achieving more rigorous high-dimensionality housing market segmentation, a swarm-inspired projection (SIP) algorithm is introduced by this study. Using a high-dimensionality Taipei city’s housing dataset in a case study, a comparison of the proposed SIP algorithm and a statistical-clustering method using the combination of principal component analysis (PCA) and K-means clustering is conducted in evaluating the predictive accuracy of hedonic price models of the housing submarkets. The results show that, as compared to the original single market, the segmented submarkets resulting from SIP algorithm are more homogenous and distinctive, where the resulted hedonic price models have high-level statistical explanation and disparate sets of hedonic prices for different submarkets. In addition, as compared to the use of a statistical-clustering method, SIP algorithm is found to obtain a more optimal number of submarkets, where the resulted hedonic price models are found to achieve greater improvement of statistical explanation and more stable reduction of prediction error. These findings highlight the advantages of our proposed SIP algorithm in high-dimensionality housing market segmentation, and thus it is hoped that the present research will serve as a practical tool to better inform further studies aimed at market-segmentation-related problems.

Cancer classification and biomarker selection via a penalized logsum network-based logistic regression model.

BACKGROUND: In genome research, it is particularly important to identify molecular biomarkers or signaling pathways related to phenotypes. Logistic regression model is a powerful discrimination method that can offer a clear statistical explanation and obtain the classification probability of classification label information. However, it is unable to fulfill biomarker selection.OBJECTIVE: The aim of this paper is to give the model efficient gene selection capability.METHODS: In this paper, we propose a new penalized logsum network-based regularization logistic regression model for gene selection and cancer classification.RESULTS: Experimental results on simulated data sets show that our method is effective in the analysis of high-dimensional data. For a large data set, the proposed method has achieved 89.66% (training) and 90.02% (testing) AUC performances, which are, on average, 5.17% (training) and 4.49% (testing) better than mainstream methods.CONCLUSIONS: The proposed method can be considered a promising tool for gene selection and cancer classification of high-dimensional biological data.

Construction of systems for remote monitoring of measurements of medical signal parameters

Some non conventional methods of model identification based on data normalization are presented in the article. The method of Data Normalization can be effectively used in those fields of industry optical engineers and technology processing of optical signals, which suffer from factual difficulties while gathering experimental data. The named method helps to receive information about some tendencies and regularities in process and objects dynamics, identify similarities and differences of their structure, their quantitative estimations, to provide the statistical explanation of experimental data and real-time analysis.

Francis Galton’s regression towards mediocrity and the stability of types

A prevalent narrative locates the discovery of the statistical phenomenon of regression to the mean in the work of Francis Galton. It is claimed that after 1885, Galton came to explain the fact that offspring deviated less from the mean value of the population than their parents did as a population-level statistical phenomenon and not as the result of the processes of inheritance. Arguing against this claim, we show that Galton did not explain regression towards mediocrity statistically, and did not give up on his ideas regarding an inheritance process that caused offspring to revert to the mean. While the common narrative focuses almost exclusively on Galton’s statistics, our arguments emphasize the anthropological and biological questions that Galton addressed. Galton used regression towards mediocrity to support the claim that some biological types were more stable than others and hence were resistant to evolutionary change. This view had implications concerning both natural selection and eugenics. The statistical explanation attributed to Galton appeared later, during the biometrician-mutationist debate in the early 1900s. It was in the context of this debate and specifically by the biometricians, that the development of the statistical explanation was originally attributed to Galton.

Factorial explanation of the integration of curriculum design, professional training and job training

This article tries to give a statistical explanation that links elements of curriculum design, professional training and job training through quantitative research. An instrument was elaborated expressly containing the three axes described above, with 10 simple variables per axis, obtaining a total of 30 variables of decimal ratio measurement. For this study, a statistical analysis of Cronbach's alpha was performed for the reliability of the instrument; frequency and percentage to describe the sample and a factor analysis for the integrational explanation of the phenomenon under study.

Not gendered… but different from each other? A structural equation model for explaining risky road behaviors of female and male pedestrians

As alternative transportation is getting more and more fashionable, and more people worldwide are "shifting" to walking trips, even for their daily commuting, traffic crashes suffered by pedestrians are still a great concern for road safety and public health researchers and practitioners. In this regard, risky or "aberrant" road behaviors have emerged, during the last few years, as a key issue to be considered for crash prevention. Nevertheless, the idea of a "generic pedestrian" is getting re-evaluated, and analyzing key features, such as gender, seems to be crucial for understanding pedestrians' performance and safety outcomes. ObjectiveThe objective of this study was to examine the effect of gender on pedestrians' both deliberate (traffic violations) and undeliberate (errors) risky walking behaviors, considering a set of theoretically based demographic and psychosocial variables as their potential predictors. MethodFor this cross-sectional study, data from 1070 Spanish pedestrians (60 % females and 40 % males, aged between 16 and 79) from the 17 regions of Spain, responding to an electronic questionnaire, were analyzed through a multi-group structural equation modeling (MGSEM) approach. ResultsAlthough age, handheld device-interaction, and sensation-seeking seem to have a similar effect on the errors and violations reported by both genders (similarities), factors such as risk perception, educational level and the misbehaviors observed in other road users are significant predictors only in the case of male pedestrians. On the other hand, road distractions have been shown to play a significant role in females' errors and violations, while males' road distractions seem to only affect their involuntary risky behaviors. ConclusionThe findings of this study support the influence of gender in the statistical explanation of both deliberate and undeliberate walking risky road behaviors, also depicting the differential role of certain demographic and psychosocial factors when we compare male and female pedestrians.

STATISTICAL EQUILIBRIUM METHODS IN ANALYTICAL POLITICAL ECONOMY

AbstractEconomic systems produce robust statistical patterns in key sate variables including prices and incomes. Statistical equilibrium methods explain the distributional properties of state variables as arising from specific institutional, environmental, and behavioral postulates. Two broad traditions have developed in political economy with the complementary aim of conceptualizing economic processes as irreducibly statistical phenomena but differ in their methodologies and interpretations of statistical explanation. These conceptual differences broadly mirror the methodological divisions in statistical mechanics, but also emerge in distinct ways when considered in the context of social sciences. This paper surveys the use of statistical equilibrium methods in analytical political economy and identifies the leading methodological and philosophical questions in this growing field of research.

People as Intuitive Scientists: Reconsidering Statistical Explanations of Decision Making.

A persistent metaphor in decision-making research casts people as intuitive statisticians. Popular explanations based on this metaphor assume that the way in which people represent the environment is specified and fixed a priori. A major flaw in this account is that it is not clear how people know what aspects of an environment are important, how to interpret those aspects, and how to make decisions based on them. We suggest a theoretical reorientation away from assuming people's representations towards a focus on explaining how people themselves specify what is important to represent. This perspective casts decision makers as intuitive scientists able to flexibly construct, modify, and replace the representations of the decision problems they face.

Target toxicity design for phase I dose-finding

We propose a new two-/three-stage dose-finding design called Target Toxicity (TT) for phase I clinical trials, where we link the decision rules in the dose-finding process with the conclusions from a hypothesis test. The power to detect excessive toxicity is also given. This solves the problem of why the minimal number of patients is needed for the selected dose level. Our method provides a statistical explanation of traditional ‘3+3’ design using frequentist framework. The proposed method is very flexible and it incorporates other interval-based decision rules through different parameter settings. We provide the decision tables to guide investigators when to decrease, increase or repeat a dose for next cohort of subjects. Simulation experiments were conducted to compare the performance of the proposed method with other dose-finding designs. A free open source R package tsdf is available on CRAN. It is dedicated to deriving two-/three-stage design decision tables and perform dose-finding simulations.

Effect of Transfusion on the Extension of IVH in Preterm Neonates

Background: Today, preterm birth is well known as the major risk factor for intraventricular hemorrhage (IVH). In the first week of life, some preterm infants may have grade 1 IVH extending to severe (grade 3 or 4) IVH by transfusion one or more units. Several previous studies have found that blood and blood product transfusions lead to adverse clinical outcomes in neonates. Therefore, this study aimed to explore the relationship between Red blood cell (RBC) transfusion and extension of IVH in preterm infants.Methods: For the purposes of the study, an observational retrospective case-control design was utilized. Moreover, all the neonates with grade 1 IVH in our referral hospital were identified in the past 5 years. Afterward, the subjects with extended IVH were compared with those who had resolved IVH.Results: In total, 1050, 36, and 24 neonates were diagnosed with grade 1, grade 3, and grade 4 IVH, respectively. The mean values of the birth weight of extended IVH and resolved IVH groups were 1285±615 g and 1361±348 g, respectively (P=0.05). Moreover, extended IVH and resolved IVH groups were 29±3 weeks and 30±2 weeks premature, respectively (P=0.36). The low 5-minute Apgar scores of the extended IVH and resolved IVH groups were 5±2 and 7±2, respectively (P=0.000). In addition, the low cord pH of the extended IVH and resolved IVH groups were 7.29±0.1 and 7.37±0.1, respectively (P=0.005). Administration of packed RBC transfusion before and on the day of the diagnosis of grade 1 IVH had the most significant relationship with the extension of IVH (IR, 10.602; 95% CI, 2.81-39.92). The obtained results confirmed that criteria to order the transfusions were similar in both groups, based on which they did not have any proportion of the transfusions of compliance with the guidelines.Conclusion: Based on the results, there was a great association between restrictive RBC transfusion and extension of a low-grade IVH into a higher grade (3 or 4) IVH. However, the statistical explanation is unclear and more studies are needed to discover the causality of this relationship.

Experimental control of the degree of non-classicality via quantum coherence

The origin of non-classicality in physical systems and its connection to distinctly quantum features such as entanglement and coherence is a central question in quantum physics. This work analyses this question theoretically and experimentally, linking quantitatively non-classicality with quantum coherence. On the theoretical front, we show when the coherence of an observable is linearly related to the degree of violation of the Kolmogorov condition, which quantifies the deviation from any classical (non-invasive) explanation of the multi-time statistics. Experimentally, we probe this connection between coherence and non-classicality in a time-multiplexed optical quantum walk. We demonstrate exquisite control of quantum coherence of the walker by varying the degree of coherent superposition effected by the coin, and we show a concomitant variation in the degree of non-classicality of the walker statistics, which can be accessed directly by virtue of the unprecedented control on the measurement-induced effects obtained via fast programmable electro-optic modulators.