Deletion Of Outliers Research Articles

Psychological hardiness, social support, and emotional labor among nurses in Iran during the COVID-19 pandemic: A cross-sectional survey study

BackgroundOur study of nurses in Tehran was conducted in June of 2020, when the lockdown from the pandemic had been implemented. Nurses had been faced with how to effectively manage their own emotion responses during patientcare. ObjectivesOur study aims to evaluate how psychological and social resources were jointly related to the use of emotional labor through surface acting and deep acting among nurses at public hospitals. DesignThe study design was a single-wave, cross-sectional self-report questionnaire survey containing validated measures where the nurses reported on their work experiences during the pandemic. SettingsThe participants came from five out of 50 public hospitals within Tehran. ParticipantsOf the 250 nurses chosen by using multi-stage randomly sampling, 224 were retained after listwise deletion of missing data and outliers. MethodThrough a survey questionnaire, participants responded to scale measures of psychological hardiness, social support, and emotional labor to investigate the joint impact of hardiness and social support on emotional labor. Their responses provided information on the (1) validity and reliability of all variables, and (2) the hypothesized structural relations, using SPSS-AMOS 22 software. ResultsChallenge and control were related to social support; coworker sympathy and supervisory support were related to surface acting; coworker sympathy was related to deep acting. Under strong support, high hardiness was most negatively related to surface acting and positively related to deep acting. ConclusionThrough coworker and supervisory support, hardiness became an effective means for nurses to regulate their own emotions during interactions to enhance patientcare.

Surviving and thriving through a crisis: a resiliency branding approach to transform airline industry

PurposeThere has been substantial research on branding globally. However, there is a dearth of studies empirically investigating branding strategies during the pandemic to mitigate COVID-19 effects on the airline industry. The paper considers three factors which are brand familiarity, brand communication and brand reputation to develop the brand trust of customers and that ultimately influence their brand preferences.Design/methodology/approachThe study is based on self-administrative surveys as 450 questionnaires were spread, received 339 responses and a total of 301 questionnaires were selected for data analysis by structural-based modeling after the deletion of outliers and partially filled questionnaires. The data was collected through purposive sampling from Malaysian airports.FindingsThe findings confirm the relationship of brand communication, brand familiarity and brand reputation to brand trust and brand preference through mediation and directly except direct relationship of brand familiarity to brand preference. The study is limited to the provided dataset of surveys. The present study couldn't interview respondents which can be done by future studies and also effects of COVID-19 can be examined on related industries or through comparative studies among countries.Originality/valueThe present study is the first to investigate the effects of COVID-19 on airline brands and explored the strategies to respond to crises. The study is one of the rare studies that consider branding strategies to the uplift airline industry and mitigate post-pandemic effects from the airline sector.

Impact of trade policy uncertainty on export products quality: new evidence by considering role of social capital

ABSTRACT An increase in trade policy uncertainty raises policymakers’ concerns, as it can be harmful to investments and growth globally. This study examines the impact of reducing trade policy uncertainty on export product quality. Based on the ASEAN–China Free Trade Area (ACFTA), the difference-in-difference, two-way fixed, and triple difference methods were used to conduct benchmark tests. The results show that reducing trade policy uncertainty improves export product quality. Social capital has strengthened the role of the changing trade environments. The results were robust after the PSM-DID, placebo test, and deletion of outliers. Furthermore, the role of social capital is incorporated into the regression model. From the perspective of informal internal systems, this study expands the theoretical view of regional trade integration research and answers the current trade strategy adjustment and export transformation policy concerns.

The Redescending M estimator For detection and deletion of Outliers in Regression analysis

Outliers in a statistical analysis strongly affect the performance of the ordinary least squares, such outliers need to be detected and extreme outliers deleted. Thisp is aimed at proposing a Redescending M-estimator which is more efficient and robust compared to other existing methods. The results show that the proposed method is effective in detection and deletion of extreme outliers compared to the other existing ones.

How to Classify, Detect, and Manage Univariate and Multivariate Outliers, With Emphasis on Pre-Registration

Researchers often lack knowledge about how to deal with outliers when analyzing their data. Even more frequently, researchers do not pre-specify how they plan to manage outliers. In this paper we aim to improve research practices by outlining what you need to know about outliers. We start by providing a functional definition of outliers. We then lay down an appropriate nomenclature/classification of outliers. This nomenclature is used to understand what kinds of outliers can be encountered and serves as a guideline to make appropriate decisions regarding the conservation, deletion, or recoding of outliers. These decisions might impact the validity of statistical inferences as well as the reproducibility of our experiments. To be able to make informed decisions about outliers you first need proper detection tools. We remind readers why the most common outlier detection methods are problematic and recommend the use of the median absolute deviation to detect univariate outliers, and of the Mahalanobis-MCD distance to detect multivariate outliers. An R package was created that can be used to easily perform these detection tests. Finally, we promote the use of pre-registration to avoid flexibility in data analysis when handling outliers.

Job analysis ratings and criterion‐related validity: Are they related and can validity be used as a measure of accuracy?

Job analysis data are largely judgements from subject matter experts (SMEs), judgements with unknown accuracy. To date, accuracy has been inferred largely based on inter‐rater reliability or agreement between SMEs and without reference to an external criterion. The current research examined job analysis rating accuracy by comparing SME importance ratings of knowledge, skills, abilities, and other requirements (KSAOs) with the validity of measures of these same KSAOs in predicting job performance. We tested hypotheses about whether SME judgement accuracy is moderated by SME job tenure, industry experience, role, self‐reported knowledge of the job, and data scrubbing. Four independent tests involving 48 separate validation studies were conducted. In three of the four samples, there was a large (r = .50 range) relationship between trait importance and trait validity, showing that job analysis ratings can be directly related to test validities and serve as a measure of job analysis accuracy. Moderator analyses showed that the best results may come from supervisors, rather than incumbents, and those who know the job extremely well (there were no differences due to SME job tenure, industry experience, or deletion of outliers). Showing a direct relationship between SME judgements and actual criterion‐related validity provides a new lens for operationalizing accuracy in job analysis research.Practitioner Points This study demonstrates that test validities can serve as a measure of accuracy, providing a new avenue for job analysis research. The most accurate job analysis ratings came from supervisors and those who reported knowing the job extremely well.

Confidence Distance Matrix for outlier identification: A new method to improve the characterizations of surfaces measured by confocal microscopy

This paper proposes a statistical method for outlier identification for surface measurement data obtained by confocal microscopy. The implemented statistical method is Confidence Distance Matrix (CDM) which were widely used in statistics and many engineering areas, such as signal processing, sensor data fusion, information problems, etc. However, no investigations on identifying outliers in measured surface data using CDM have been found. This paper introduces and simplifies the mathematical model of CDM method. Algorithms for identifying random outliers using Monte Carlo method for uncertainty evaluation and for identifying outliers in a unique measured surface are developed and validated. For validation of the algorithms, a synthetic data SG_3-3 provided by National Institute of Standards and Technology and a data of artificial stochastic surface generated by our own algorithms are implemented. The difference of Sq of the data with outliers is 2.3342% and after deletion of outliers is 0.0037% with reference to the certified value. A type C1 spacing standard with dust dropped is measured and processed using CDM. The difference of Sa decreases from 29.65% to 3.52% after processing outliers with reference to the certified value Ra. A steel plate is measured and processed. Surface slopes and curvatures of the data in the two validations and two experiments are compared. All those parameters, the surface reconstructions, histogram of heights, and QQ plot of the measured surface data versus the data after deletion of outliers indicate our proposed method working well.

Outliers/Most Influential Observationsin Variable Returns to Scale Data Envelopment Analysis

Background/Objectives: Outliers are extreme observations which deviate significantly from the normal observations. Their presence distorts the end results of scientific study. In Data Envelopment Analysis (DEA), most influential observations are outliers. Identification of outliers and their deletion is important in DEA problems. Deletion of an extreme observation results in contraction of the production possibility set. Methods/Analysis: This study identifies outliers as these extreme observations whose absence results in significant contraction of the production possibility set. Data Envelopment Analysis is based on solving linear programming problems to assess the efficiency scores of Decision Making Units (DMUs). The present study can estimate efficiency scores of DMUs with and without outliers. Deletion of outliers provides smaller targets to the inefficient DMUs. Findings: For identification of outliers super efficiency, problems can be solved for extremely efficient DMUs, which results in the largest dimension of the contracted Production Possibility Set (PPS). The present study provides many dimensions of the contracted PPset, solving Banker, Charnes and Cooper (BCC 1984) linear programming problems. Sum of these dimensions can be viewed as histogram that closely envelops the contracted Production Possibility Set. Larger is the sum of all dimensions, more influential is the decision making unit. To identify outliers in DEA in this study a statistical test is outlined. Application/Improvements: The methodology outlined can be used for DMUs competing with each other in a competitive environment employing similar inputs to produce similar outputs.

The Mediating Effects of Green Trust and Perceived Behavioral Control on the Direct Determinants of Intention to Purchase Green Products in Nigeria

Global awareness towards the environment and green marketing in recent years is yet to promote Nigerians predisposition towards green practices. Being faced with environmental degradation in all facets, there is the need to spur green consciousness and pro-environmental behavioral intentions among the Nigerian populaces. Therefore this study examines the determinants of green purchase intention (green availability, green perceived value, green perceived knowledge) and secondly, to test the extent of mediating effects of green trust and perceived behavioral control on the determinants earlier mentioned. This survey uses quantitative method of research; data was collected through personal administration of questionnaires to lecturers in three universities in Nigerian. 750 questionnaires were distributed and 502 datasets were returned representing 68% response rate. 440 datasets were left for further analysis after deletion of multivariate outliers; Structural Equation Modeling is used to analyze data .The findings indicate that out of the eleven direct hypotheses, seven were found to be significant while four were not supported. Similarly, six mediation effects were proposed, the result shows five paths were mediated with two being full mediation and three partial. In conclusion, knowledge of green products, perceived behavioral control and availability of green products will heighten green purchase intention. DOI: 10.5901/mjss.2015.v6n4p256

Optimizing the k-Nearest Neighbors technique for estimating forest aboveground biomass using airborne laser scanning data

Nearest neighbors techniques calculate predictions as linear combinations of observations for a selected number of population units in a sample that are most similar, or nearest, in a space of auxiliary variables to the population unit requiring the prediction. Nearest neighbors techniques have been shown to be particularly effective when used with forest inventory and remotely sensed data. Recent attention has focused on developing an underlying foundation consisting of diagnostic tools, inferential extensions, and techniques for optimization.For a study area in Norway, forest inventory and airborne laser scanning data were used with the k-Nearest Neighbors technique to estimate mean aboveground biomass per unit area. Optimization entailed reduction of the dimension of feature space, deletion of influential outliers, and selection of optimal weights for the weighted Euclidean distance metric. These optimization steps increased the proportion of variability explained in the reference set by as much as 20%, reduced confidence interval widths by as much as 35%, and produced standard errors that were as small as 3% of the estimate of the mean.

Doubtful Outliers with Robust Regression of an M-estimator In Cluster Analysis

Doubtful outlier between clusters may show some meaningful data. In some cases for example it may explain the potential or the unique pattern within the data. However, there is still no further analysis to show how this data (doubtful) connected to one another. In the simulation, we use different threshold values to detect how many doubtful outliers exist between clusters. For these cases we will use 1%, 5%, 10%, 15% and 20% of threshold values. For real data, we fit a linear model using an M estimator with the existences of doubtful data with 10% threshold value. The objective is to determine if doubtful data affect the parameter of M estimator. By comparing using linear model with the deletion of outliers we can conclude that doubtful outlier affect the parameter of M estimator make it less robust towards doubtful outliers in the present of 10% of threshold value. Keywords :Doubtful outlier, Cluster Analysis, Robust Regression, M estimator. 2010 Mathematics Subject Classification: 46N60, 92B99.

The Predictors of Corporate Image in Direct selling Companies in Thailand

The image of the direct selling company seems to be tarnished by frauds and internet scams in recent years. Thus, the purpose of this study is to examine the relationships of several predictors of corporate image in direct selling companies in Thailand. Six identified predictor variables are measured using past developed instruments (41 items) such as: brand architecture (6 items), Corporate social responsibility (7 items), internet marketing (7 items), trust (7 items), perceived value (7 items), and attitude (7 items) and corporate image (7-items) as the dependent variable. A total of 200 questionnaires were distributed and 180 was collected representing 90% response rate. After deletion of outliers, the final usable questionnaire is 165 which were analyzed using regression analysis. This study has established one significant predictor of corporate image (attitude) (β = 0.750, t = 14.262, and p = 0.000 < 0.001). It shows that attitude has significant positive relationship on corporate image. The model could explain 56.6 percent of variance in corporate image (F=36.665, p=0.000<0.001). This study concludes the findings with suggestion towards direct selling company.

Re: Microalbuminuria in Normal Korean Children

Re: Microalbuminuria in Normal Korean Children

Robust analysis of the central tendency, simple and multiple regression and ANOVA: a step by step tutorial.

After much exertion and care to run an experiment in social science, the analysis of data should not be ruined by an improper analysis. Often, classical methods, like the mean, the usual simple and multiple linear regressions, and the ANOVA require normality and absence of outliers, which rarely occurs in data coming from experiments. To palliate to this problem, researchers often use some ad-hoc methods like the detection and deletion of outliers. In this tutorial, we will show the shortcomings of such an approach. In particular, we will show that outliers can sometimes be very difficult to detect and that the full inferential procedure is somewhat distorted by such a procedure. A more appropriate and modern approach is to use a robust procedure that provides estimation, inference and testing that are not influenced by outlying observations but describes correctly the structure for the bulk of the data. It can also give diagnostic of the distance of any point or subject relative to the central tendency. Robust procedures can also be viewed as methods to check the appropriateness of the classical methods. To provide a step-by-step tutorial, we present descriptive analyses that allow researchers to make an initial check on the conditions of application of the data. Next, we compare classical and robust alternatives to ANOVA and regression and discuss their advantages and disadvantages. Finally, we present indices and plots that are based on the residuals of the analysis and can be used to determine if the conditions of applications of the analyses are respected. Examples on data from psychological research illustrate each of these points and for each analysis and plot, R code is provided to allow the readers to apply the techniques presented throughout the article.

Chronology of the Obi-Rakhmat Grotto (Uzbekistan): First Results on the Dating and Problems of the Paleolithic Key Site in Central Asia

The Obi-Rakhmat Grotto is one of the key Paleolithic sites in Central Asia. Archaeological excavations have revealed 22 strata containing archaeological materials. Lithic assemblages from all cultural layers display features similar to both late Middle Paleolithic blade industries and early Upper Paleolithic complexes in Southwest Asia and the Siberian Altai Mountains; this suggests a gradual Middle-to-Upper Paleolithic transition occurred in western Central Asia. Hominid remains found at Obi-Rakhmat (layer 16) show a mixture of archaic and modern traits. Different chronometric methods (radiocarbon, optically stimulated luminescence [OSL], U-series, and electron spin resonance [ESR]) were applied to the site's deposits. It appears that 14C dates are more reliable in terms of correspondence to the general framework of the Paleolithic of Central Asia and neighboring regions, and after critical analysis and the deletion of outliers, the upper part of the site's cultural sequence can be dated between 36,000–41,400 BP (layer 7) and ∼48,800 BP (layer 14.1). The U-series dating results are less secure due to the high uranium content and the presence of detritus, which contaminates dated sediments (travertine). The OSL dating gave uniform ages for all cultural succession (∼8 m of deposits), and confirms a very rapid sedimentation rate. Results of ESR dating depend greatly on the choice of uptake model. Dates calculated for the early uptake to some extent correspond to 14C data. The linear uptake chosen by Skinner et al. (2007) makes sediments very old (about 55,000–90,000 yr ago), which contradicts 14C dates and does not correspond well to the regional archaeological context.

Outliers Detection, Correction of Financial Time-Series Anomalies and Distributional Timing for Robust Efficient Higher-Order Moment Asset Allocations

We propose a new methodology for abnormal return detection and correction, and evaluate the economic impacts of outliers on asset allocations with higher-order moments (Cf. Jurczenko et al., 2008). Indeed, extreme returns and outliers greatly affect empirical higher-order moment estimations (Cf. Kim and White, 2004). We thus extend the outlier detection procedures of Franses and Ghijsels (1999) and Charles and Darne (2005) with an Artificial Neural Network - GARCH model (Cf. Donaldson and Kamstra, 1997). The proposed method for deletion and correction of outliers, coupled with the use of a robust approach based on higher-order L-moments, clearly show some improvements of the portfolio allocation performance in the French stock market.

Subfossil Chironomidae (Insecta: Diptera) along a latitudinal gradient in Finland: development of a new temperature inference model

AbstractNumerical techniques were used to study chironomid distribution and abundance in lakes from a 1000 km transect in Finland, with special interest on the effect of local summer air temperatures on chironomid assemblages. The final aim of the study was to develop a chironomid‐based palaeotemperature inference model. The dataset consisted of 82 lakes (of which 77 were used in the model after deletion of outliers), with catchments spanning from boreal coniferous forests to mountain birch woodland and tundra vegetation. Numerical analysis showed that the mean July air temperature was the most significant variable explaining the distribution and abundance of chironomids in Finnish lakes. Weighted‐averaging partial least squares techniques were used to develop a palaeotemperature inference model for mean July air temperature reconstructions. The model performance statistics were favourable, with cross‐validated coefficient of determination (r2) of 0.78, root mean squared error of prediction of 0.721°C and maximum bias of 0.794°C. Based on these values, the transfer function is a valid means of performing quantitative palaeotemperature estimates in downcore studies. Copyright © 2008 John Wiley &amp; Sons, Ltd.

Robust and traditional statistical methods in the establishment of immunoglobulin E target values in external quality assessment program

BackgroundIn an EQA scheme, peer group mean is conventionally regarded as the target after removing values exceeding the mean±3SD. However, this computation fails if there are some outliers. MethodsThe outliers in each original data set were separately deleted by traditional and robust statistical methods. The data distributions of original and trimmed data sets were separately tested by Shapiro–Wilks or Kolmogorov–Smirnov Z test. The means of peer groups were used to set IgE targets. The difference between targets set by both methods was tested by 1-sample t test. ResultsThe original data sets were not all Gaussian distributed. After deletion of outliers using robust statistical method, the modified data sets of all tested groups were normally distributed. IgE targets established by robust and traditional methods were significantly different in some groups. The incompatible IgE targets derived all from the original and trimmed data sets which were not Gaussian distributed. ConclusionsThe premise for choosing a traditional method to delete outliers and using peer group means as targets was that the data sets must be Gaussian distributed. The reasons that caused the targets to be incompatible possibly were the 2 rules to be broken. Robust statistical method was used for deletion of outliers due to the distorted distribution.

Comparing Student Satisfaction With Distance Education to Traditional Classrooms in Higher Education: A Meta-Analysis

Meta-analysis provides a method of quantitatively summarizing and comparing empirical literature to reduce Type I and Type II error. The meta-analysis described here indicates a slight student preference for a traditional educational format over a distance education format (average r = .031, after the deletion of outliers), and little difference in satisfaction levels. A comparison of distance education methods that include direct interactive links with those that do not include interactive links demonstrates no difference in satisfaction levels. However, student satisfaction levels diminish as additional information is added to the available channel of instruction (e.g., written to audio to video). The findings support those of researchers arguing that distance education does not diminish the level of student satisfaction when compared to traditional face-to-face methods of instruction.

Quantitative Structure-Activity Relationships Based on Functional and Structural Characteristics of Organic Compounds

In the present quantitative structure-activity relationship (QSAR) modeling, organic compounds, including priority pollutants, have been considered and classified based on their functional and structural characteristics. Five physico-chemical characteristics have been used to develop a QSAR model for Pimephales promelas, by means of multiple regression analysis. Collinearity diagnostics was carried out using two different approaches based on condition index and K correlation index. The outlier analysis was carried out using the variable subsets obtained through both the approaches. An attempt has been made to justify the deletion of outliers in each group referring to their physicochemical characteristics. The expressions obtained by using both approaches provide almost the same prediction accuracy, however, the latter approach resulted in expressions with reduced number of molecular descriptors. The QSARs obtained through this exercise would certainly assist in designing environment-friendly molecules with lower toxicity.