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Monday, July 20, 2020 | History

2 edition of rigorous systematic approach to automatic data editing and its statistical basis found in the catalog.

rigorous systematic approach to automatic data editing and its statistical basis

G. E. Liepins

rigorous systematic approach to automatic data editing and its statistical basis

by G. E. Liepins

  • 280 Want to read
  • 15 Currently reading

Published by Dept. of Energy, Oak Ridge National Laboratory, for sale by the National Technical Information Service] in Oak Ridge, Tenn, [Springfield, Va .
Written in English

    Subjects:
  • Data editing -- Automation.,
  • Error analysis (Mathematics).

  • Edition Notes

    StatementG. E. Liepins, Regional and Urban Studies Section, Energy Divsion.
    SeriesORNL/TM ; 7126, ORNL/TM -- 7126.
    ContributionsOak Ridge National Laboratory. Energy Division. Regional and Urban Studies Section.
    The Physical Object
    Paginationiii, 31 p. ;
    Number of Pages31
    ID Numbers
    Open LibraryOL17650917M

    procedures, experimental studies, numerical schemes, statistical approaches, etc. Research methods help us collect samples, data and find a solution to a problem. Particularly, sci-entific research methods call for explanations based on collected facts, measurements and observations and not . A systemic approach to analysis that is a search for patterns in data as they are coded, sorted into categories, and examined in different contexts Theoretical sampling Decision making, while concurrently collecting and analyzing data, about the data and data sources that are needed further to .

    Statistical Data Editing (SDE) is the process of checking and correcting data for errors. Winkler () defines it the set of methods used to edit (clean-up) and impute (fill-in) missing or contradictory data. The result of SDE is data that can be used for analytic purposes. Editing literature goes Cited by: 2. 3. Statistical edit versus data validation 20 4. Imputation 21 5. Added capacity and flexibility 21 6. Ease of use 21 7. System attributes 21 C. Implementation 22 1. Broad aims 22 2. Impact of editing, contribution of nonresponse 22 3. Statistical edit versus data validation 22 4. Imputation 23 5. Added capacity and flexibility 23 6. Ease of.

    priate statistical methods or incorrect conclusions. We outline a framework for initial data analysis and illustrate the impact of initial data analysis on research studies. Examples of reporting of initial data analysis in publications are given. A systematic and careful approach to initial data analysis is needed as good research practice. Presenting Methodology and Research Approach OVERVIEW Chapter 3 of the dissertation presents the research design and the specific procedures used in conducting your study. A research design includes various interrelated elements that reflect its sequential nature. This chapter is intended to show the reader that you have an understanding of the.


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Rigorous systematic approach to automatic data editing and its statistical basis by G. E. Liepins Download PDF EPUB FB2

Get this from a library. A rigorous systematic approach to automatic data editing and its statistical basis. [G E Liepins; Oak Ridge National Laboratory. Regional and Urban Studies Section.]. The process of collecting accurate data through interviewing, questionnaires, and other methods has not always been clear.

However, data collection in field settings can be done in a structured, systematic and scientific way. These authors show us how. First, they focus on the importance of finding the right questions to ask.

The aim of this publication is to assist National. Statistical Offices in their efforts to improve and economize their data editing processes.

Different methods and techniques can be used in the various stages of the data editing. process; that is in survey management, data capture, data review and data adjustment. A β-profile stores a distribution model of certain entities or their content could range from simple statistical distributions to complex algorithms which abstract a certain behavior.

In this respect, representing a β-profile as a procedure in a (supporting) programming language will allow for maximum expressiveness when dealing with a variety of by: A research protocol should include a systematic plan and a budget for the initial data analysis.

Methods used for the initial data analysis and the decisions made in and on the basis of the initial data analysis should be included in medical scientific reports. Cited by: 8.

Vol. 1 () - Data editing methods and techniques may significantly influence the quality of statistical data as well as the cost efficiency of statistical aim of this publication is to assist National Statistical Offices in their efforts to improve and economize their data editing processes.

Different methods and techniques can be used in the various stages of the data. In contrast, systematic reviews are conducted using systematic and explicit methods to identify, select and critically appraise relevant research, and to collect and analyse data from the studies that are included in the review.

The final pathway for systematic review is a statistical summary of the results of primary studies, or : LP Wong. Introduction to statistical data analysis with R 4 Contents Contents Preface9 1 Statistical Software R 10 R and its development history 10 Structure of R 12 Installation of R 13 Working with R 14 Exercises 17 2 Descriptive Statistics 18 Basics 18 Excursus: Data Import and Export with R 22File Size: 6MB.

A systematic review is a protocol driven comprehensive review and synthesis of data focusing on a topic or on related key questions. It is typically performed by experienced methodologists with the input of domain experts.

The first step to conduct a systematic review is to formulate specific key by: 2. Thematic analysis in qualitative research is the main approach to analyze the data. Research requires rigorous methods for the data analysis, this requires a methodology that can help facilitate objectivity.

Rigorous thematic analysis can bring objectivity to the data analysis in qualitative research. Some researchers believe that thematic analysis is not a separate method of data analysis but rather it can help every qualitative research in the analysis.

If you are a data analyst in search of a systematic work process that will increase your efficiency, adequately document your results, and ensure that your work can be replicated, then this book is for you. The approach outlined in this book is straight forward yet comprehensive in scope, flexible in how it can be used, practical, and filled Author: Daniel Bretheim.

The use of Big Data for statistical purposes is still in its infancy, particularly in the development of efficient editing techniques.

One of the big challenges for Big Data is monitoring the quality of the data without the need to inspect the data in its most granular by: 9. Data Analysis and Graphics Using R: An Example-Based Approach (Cambridge Series in Statistical and Probabilistic Mathematics Book 10) - Kindle edition by Maindonald, John, Braun, W.

John. Download it once and read it on your Kindle device, PC, phones or tablets. Use features like bookmarks, note taking and highlighting while reading Data Analysis and Graphics Using R: An Example-Based Approach /5(9).

A systematic and rigorous statistical approach for establishing the accuracy of analytical results and its application to a comparison of alternative d: Authors: Abstract Previously established optimum operating conditions for simultaneous multi-element trace.

Data analysis and graphics using R: an example-based approach / John Maindonald, W. John Braun. – 3rd ed. – (Cambridge series in statistical and probabilistic mathematics ; 10) Includes bibliographical references and indexes.

ISBN 1. Statistics – Data processing. Statistics – Graphic methods – Data File Size: KB. General Approaches to Designing and Analysing Data of data in order to apply a frame pertinent to your discipline in the process of pre-senting results to a particular audience, e.g.

the frame ‘locus of control’ will have The particular approach which you have chosen may have its own set of guidelinesFile Size: KB. Steps in Systematic Data Analysis. Stepping Your Way through Effective Systematic Data Analysis.

Quantitative studies generally attempt to use statistical methods to explore differences between studies and combine their effects (see meta analysis below). If divergences are found, the source of the divergence is analysed. Research is the process of collecting, analyzing, and interpreting data in order to understand a phenomenon (Leedy & Ormrod).

Research is a logical and systematic search for new and useful information on a particular topic. According to English dictionary research is defined as The systematic investigation and study. Editing is the process of checking andadjusting data for omissions, consistency,and legibility So, the editor’s task is to check for errors andomissions on questionnaires or other datacollection forms.

When the editor discovers a problem, he orshe adjusts the data to make them morecomplete, consistent, or readable. R is a very powerful statistical software package that will enable you to analyse more or less any dataset.

This concise guide is designed to help you quickly to become familiar with R and to explore its potential as a powerful tool for analysing your data, whatever your field of research/5(5). Educational research refers to the systematic collection and analysis of data related to the field of education.

Research may involve a variety of methods and various aspects of education including student learning, teaching methods, teacher training, and classroom dynamics. Educational researchers generally agree that research should be rigorous and systematic. However, there is less agreement about .STANDARD All NCES data must be edited.

Data editing is an iterative and interactive process that includes procedures for detecting and correcting errors in the data. Data editing is first done prior to imputation. Data editing must be repeated after the data are imputed, and again after the data are altered during disclosure risk analysisFile Size: KB.Statistical inference is the process of using data analysis to deduce properties of an underlying distribution of probability.

Inferential statistical analysis infers properties of a population, for example by testing hypotheses and deriving is assumed that the observed data set is sampled from a larger population. Inferential statistics can be contrasted with descriptive statistics.