Introduction to Statistical Methods for Outlier Detection and Sample Homogeneity Assessment of Reference Materials and Proficiency Test Items

Mapping Intimacies ◽

10.51843/wsproceedings.2021.15 ◽

2021 ◽

Author(s):

Yi-Ting Chen ◽

Keyword(s):

Data Analysis ◽

Outlier Detection ◽

Reference Materials ◽

Proficiency Test ◽

Test Items ◽

Homogeneity Assessment ◽

Iso 13528 ◽

Scope Of Application ◽

Iso 5725 ◽

Sample Homogeneity

Due to the homogeneity of the product or sample, it will affect whether it meets the scope of application and purpose. For example, the reference materials(RM) produced by the reference material producer(RMP), and the proficiency test items selected by the proficiency testing provider(PTP), in order to ensure the reference materials or proficiency test items have consistent characteristics or comparability, they should be proved to have certain homogeneity. However, before performing homogeneity assessment, it is necessary to measure the characteristic parameters of the reference materials or proficiency test items to obtain a sufficient number of measured values for data analysis, but there may be outliers in the measured values that may affect data analysis and interpretation of the results. Therefore, this article will refer to ASTM E178-16a:2016[1], ISO 5725-2:1994[2], ISO 13528:2015[3], etc., to introduce several outlier detection and homogeneity assessment methods, supplemented by case studies. Finally, this article will remind the precautions for the use of the method, so that readers can choose the appropriate method for use in the actual analysis.

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A critical view at the ISO 13528 and IUPAC's harmonized protocol approach for proficiency testing for homogeneity assessment for quantitative variables

The Analyst ◽

10.1039/d0an01607a ◽

2020 ◽

Vol 145 (23) ◽

pp. 7630-7635

Author(s):

Wim Coucke ◽

Mohamed Rida Soumali

Keyword(s):

High Risk ◽

Proficiency Testing ◽

Homogeneity Test ◽

Critical View ◽

Homogeneity Assessment ◽

Iso 13528 ◽

Sample Homogeneity

Current PT sample homogeneity test has high risk of accepting heterogeneous.

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Detection of Anomalies in Water Networks by Functional Data Analysis

Mathematical Problems in Engineering ◽

10.1155/2018/5129735 ◽

2018 ◽

Vol 2018 ◽

pp. 1-13 ◽

Cited By ~ 8

Author(s):

Laura Millán-Roures ◽

Irene Epifanio ◽

Vicente Martínez

Keyword(s):

Data Analysis ◽

Outlier Detection ◽

Functional Data Analysis ◽

Functional Data ◽

Real Data ◽

Water Networks ◽

Archetypal Analysis ◽

Detection Techniques ◽

Second Stage ◽

Two Phases

A functional data analysis (FDA) based methodology for detecting anomalous flows in urban water networks is introduced. Primary hydraulic variables are recorded in real-time by telecontrol systems, so they are functional data (FD). In the first stage, the data are validated (false data are detected) and reconstructed, since there could be not only false data, but also missing and noisy data. FDA tools are used such as tolerance bands for FD and smoothing for dense and sparse FD. In the second stage, functional outlier detection tools are used in two phases. In Phase I, the data are cleared of anomalies to ensure that data are representative of the in-control system. The objective of Phase II is system monitoring. A new functional outlier detection method is also proposed based on archetypal analysis. The methodology is applied and illustrated with real data. A simulated study is also carried out to assess the performance of the outlier detection techniques, including our proposal. The results are very promising.

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KEMAMPUAN PENALARAN BERDASARKAN KEYAKINAN MATEMATIKA DALAM PEMBELAJARAN PBL MELALUI PENDEKATAN INTERAKSI DYADIC

Jurnal Ilmiah Matematika dan Pendidikan Matematika ◽

10.20884/1.jmp.2018.10.2.2842 ◽

2018 ◽

Vol 10 (2) ◽

pp. 31

Author(s):

Dyah Retno Kusumawardani

Keyword(s):

Data Analysis ◽

Quantitative Research ◽

Mathematical Reasoning ◽

T Test ◽

Qualitative Description ◽

Dyadic Interaction ◽

Reasoning Ability ◽

Test Items ◽

The Difference ◽

Interaction Approach

The purposes of this research were (1) to describe the learning quality of PBL through dyadic interaction approach to mathematical reasoning ability of grade X students of IT Al Irsyad Purwokerto High School (2) to find pattern of students reasoning ability based on mathematical belief. This study uses a mixed method. Data analysis started from the analysis of test items. The analysis uses the prerequisite test and then hypothesis testing uses rara average (t-test), then the proportion of comparative tests (test-z) is to calculate the classical completeness. Further testing of determining the difference between the two classes uses different test average (t-test right side). Qualitative data analysis uses qualitative description. The results of quantitative research shows that learning class with PBL and dyadic interaction approach reached classical total 80%. The average difference test showed class’ results with PBL dyadic interaction approach better than a class activity with PBL. Subjects with very low belief can only fulfill 1 reasoning indicator well. Subjects with low belief can fulfill 3 reasoning indicators well and have not been able to fulfill 1 other indicator. Subjects with high belief can fulfill 4 indicators where 1 indicator is imperfect and subject with very high belief can fulfill all the indicators of reasoning well and complete.

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New certified reference materials and proficiency test for environmental radioactivity measurements

Accreditation and Quality Assurance ◽

10.1007/s00769-016-1227-7 ◽

2016 ◽

Vol 21 (5) ◽

pp. 351-360 ◽

Cited By ~ 1

Author(s):

Abdulghani Shakhashiro ◽

Paul Doherty ◽

Jasmina Kožar Logar ◽

Branko Vodenik ◽

Leen Verheyen ◽

...

Keyword(s):

Reference Materials ◽

Proficiency Test ◽

Certified Reference Materials ◽

Environmental Radioactivity ◽

Radioactivity Measurements

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On matrix reference materials characterised by proficiency test

Analytical Methods ◽

10.1039/c6ay01215a ◽

2016 ◽

Vol 8 (24) ◽

pp. 4908-4911 ◽

Cited By ~ 2

Author(s):

Michael Thompson

Keyword(s):

Chemical Composition ◽

Reference Materials ◽

Proficiency Test

A properly-determined consensus from a proficiency test is a metrologically-sound indication of chemical composition.

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Multivariate Outlier Detection in Applied Data Analysis: Global, Local, Compositional and Cellwise Outliers

Mathematical Geosciences ◽

10.1007/s11004-020-09861-6 ◽

2020 ◽

Vol 52 (8) ◽

pp. 1049-1066

Author(s):

Peter Filzmoser ◽

Mariella Gregorich

Keyword(s):

Data Analysis ◽

Outlier Detection ◽

Spatial Information ◽

Compositional Data ◽

Subject Area ◽

Data Matrix ◽

Multivariate Outlier Detection ◽

Data Formats ◽

Atypical Cells ◽

The Subject

AbstractOutliers are encountered in all practical situations of data analysis, regardless of the discipline of application. However, the term outlier is not uniformly defined across all these fields since the differentiation between regular and irregular behaviour is naturally embedded in the subject area under consideration. Generalized approaches for outlier identification have to be modified to allow the diligent search for potential outliers. Therefore, an overview of different techniques for multivariate outlier detection is presented within the scope of selected kinds of data frequently found in the field of geosciences. In particular, three common types of data in geological studies are explored: spatial, compositional and flat data. All of these formats motivate new outlier concepts, such as local outlyingness, where the spatial information of the data is used to define a neighbourhood structure. Another type are compositional data, which nicely illustrate the fact that some kinds of data require not only adaptations to standard outlier approaches, but also transformations of the data itself before conducting the outlier search. Finally, the very recently developed concept of cellwise outlyingness, typically used for high-dimensional data, allows one to identify atypical cells in a data matrix. In practice, the different data formats can be mixed, and it is demonstrated in various examples how to proceed in such situations.

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Evaluation of sample homogeneity for geochemical standard reference materials used in microanalysis

Journal of Radioanalytical and Nuclear Chemistry ◽

10.1007/bf02055017 ◽

1987 ◽

Vol 110 (1) ◽

pp. 147-158 ◽

Cited By ~ 8

Author(s):

R. H. Filby ◽

S. Nguyen ◽

S. Campbell ◽

A. Bragg ◽

C. A. Grimm

Keyword(s):

Reference Materials ◽

Standard Reference Materials ◽

Materials Used ◽

Sample Homogeneity

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A Review on the Statistical Methods and Implementation to Homogeneity Assessment of Certified Reference Materials in Relation to Uncertainty

MAPAN ◽

10.1007/s12647-020-00383-4 ◽

2020 ◽

Vol 35 (3) ◽

pp. 457-470 ◽

Cited By ~ 2

Author(s):

Amit Kumar ◽

D. K. Misra

Keyword(s):

Statistical Methods ◽

Reference Materials ◽

Certified Reference Materials ◽

Homogeneity Assessment

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Structuring a Team-Based Approach to Coding Qualitative Data

International Journal of Qualitative Methods ◽

10.1177/1609406920968700 ◽

2020 ◽

Vol 19 ◽

pp. 160940692096870

Author(s):

Lindsay Giesen ◽

Allison Roeser

Keyword(s):

Qualitative Research ◽

Data Analysis ◽

Reference Materials ◽

Qualitative Data ◽

Research Process ◽

Lessons Learned ◽

Analysis Software ◽

Management Structure ◽

Qualitative Data Analysis Software ◽

Supportive Management

Improvements to qualitative data analysis software (QDAS) have both facilitated and complicated the qualitative research process. This technology allows us to work with a greater volume of data than ever before, but the increased volume of data frequently requires a large team to process and code. This paper presents insights on how to successfully structure and manage a team of staff in coding qualitative data. We draw on our experience in team-based coding of 154 interview transcripts for a study of school meal programs. The team consisted of four coders, three senior reviewers, and a lead analyst and external qualitative methodologist who shepherded the coding process together. Lessons learned from this study include: 1) establish a strong and supportive management structure; 2) build skills gradually by breaking training and coding into “bite-sized” pieces; and 3) develop detailed reference materials to guide your coding team.

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