Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/533
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dc.contributor.authorSakai, Tetsuya. ;en_US
dc.date.accessioned2013en_US
dc.date.accessioned2020-05-17T08:18:14Z-
dc.date.available2020-05-17T08:18:14Z-
dc.date.issued2018en_US
dc.identifier.isbn9789811311994 ;en_US
dc.identifier.isbn9789811311987 (print) ;en_US
dc.identifier.isbn9789811312007 (print) ;en_US
dc.identifier.urihttp://localhost/handle/Hannan/533-
dc.descriptionen_US
dc.descriptionQA75.5-76.95 ;en_US
dc.descriptionSpringerLink (Online service) ;en_US
dc.descriptionPrinted edition: ; 9789811311987. ;en_US
dc.descriptionen_US
dc.descriptionPrinted edition: ; 9789811312007. ;en_US
dc.descriptionen_US
dc.descriptionen_US
dc.description.abstractCovering aspects from principles and limitations of statistical significance tests to topic set size design and power analysis, this book guides readers to statistically well-designed experiments. Although classical statistical significance tests are to some extent useful in information retrieval (IR) evaluation, they can harm research unless they are used appropriately with the right sample sizes and statistical power and unless the test results are reported properly. The first half of the book is mainly targeted at undergraduate students, and the second half is suitable for graduate students and researchers who regularly conduct laboratory experiments in IR, natural language processing, recommendations, and related fields. Chapters 1ee5 review parametric significance tests for comparing system means, namely, t-tests and ANOVAs, and show how easily they can be conducted using Microsoft Excel or R. These chapters also discuss a few multiple comparison procedures for researchers who are interested in comparing every system pair, including a randomised version of Tukey's Honestly Significant Difference test. The chapters then deal with known limitations of classical significance testing and provide practical guidelines for reporting research results regarding comparison of means. Chapters 6 and 7 discuss statistical power. Chapter 6 introduces topic set size design to enable test collection builders to determine an appropriate number of topics to create. Readers can easily use the authorees Excel tools for topic set size design based on the paired and two-sample t-tests, one-way ANOVA, and confidence intervals. Chapter 7 describes power-analysis-based methods for determining an appropriate sample size for a new experiment based on a similar experiment done in the past, detailing how to utilize the authorees R tools for power analysis and how to interpret the results. Case studies from IR for both Excel-based topic set size design and R-based power analysis are also provided. ;en_US
dc.description.statementofresponsibilityby Tetsuya Sakai.en_US
dc.description.tableofcontents1 Preliminaries -- 2 t-tests -- 3 Analysis of Variance -- 4 Multiple Comparison Procedures -- 5 The Correct Ways to Use Significance Tests -- 6 Topic Set Size Design Using Excel -- 7 Power Analysis Using R -- 8 Conclusions. ;en_US
dc.format.extentIX, 150 p. 53 illus., 43 illus. in color. ; online resource. ;en_US
dc.publisherSpringer Singapore :en_US
dc.publisherImprint: Springer,en_US
dc.relation.ispartofseriesThe Information Retrieval Series, ; 1387-5264 ; ; 40. ;en_US
dc.relation.ispartofseriesThe Information Retrieval Series, ; 1387-5264 ; ; 40. ;en_US
dc.relation.haspart9789811311987.pdfen_US
dc.subjectInformation storage and retrieva. ;en_US
dc.subjectStatistics. ;en_US
dc.subjectInformation Storage and Retrieval. ; http://scigraph.springernature.com/things/product-market-codes/I18032. ;en_US
dc.subjectStatistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences. ; http://scigraph.springernature.com/things/product-market-codes/S17020. ;en_US
dc.subject.ddc025.04 ; 23 ;en_US
dc.titleLaboratory Experiments in Information Retrievalen_US
dc.title.alternativeSample Sizes, Effect Sizes, and Statistical Power /en_US
dc.typeBooken_US
dc.publisher.placeSingapore :en_US
Appears in Collections:مدیریت فناوری اطلاعات

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9789811311987.pdf5.17 MBAdobe PDFThumbnail
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Full metadata record
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dc.contributor.authorSakai, Tetsuya. ;en_US
dc.date.accessioned2013en_US
dc.date.accessioned2020-05-17T08:18:14Z-
dc.date.available2020-05-17T08:18:14Z-
dc.date.issued2018en_US
dc.identifier.isbn9789811311994 ;en_US
dc.identifier.isbn9789811311987 (print) ;en_US
dc.identifier.isbn9789811312007 (print) ;en_US
dc.identifier.urihttp://localhost/handle/Hannan/533-
dc.descriptionen_US
dc.descriptionQA75.5-76.95 ;en_US
dc.descriptionSpringerLink (Online service) ;en_US
dc.descriptionPrinted edition: ; 9789811311987. ;en_US
dc.descriptionen_US
dc.descriptionPrinted edition: ; 9789811312007. ;en_US
dc.descriptionen_US
dc.descriptionen_US
dc.description.abstractCovering aspects from principles and limitations of statistical significance tests to topic set size design and power analysis, this book guides readers to statistically well-designed experiments. Although classical statistical significance tests are to some extent useful in information retrieval (IR) evaluation, they can harm research unless they are used appropriately with the right sample sizes and statistical power and unless the test results are reported properly. The first half of the book is mainly targeted at undergraduate students, and the second half is suitable for graduate students and researchers who regularly conduct laboratory experiments in IR, natural language processing, recommendations, and related fields. Chapters 1ee5 review parametric significance tests for comparing system means, namely, t-tests and ANOVAs, and show how easily they can be conducted using Microsoft Excel or R. These chapters also discuss a few multiple comparison procedures for researchers who are interested in comparing every system pair, including a randomised version of Tukey's Honestly Significant Difference test. The chapters then deal with known limitations of classical significance testing and provide practical guidelines for reporting research results regarding comparison of means. Chapters 6 and 7 discuss statistical power. Chapter 6 introduces topic set size design to enable test collection builders to determine an appropriate number of topics to create. Readers can easily use the authorees Excel tools for topic set size design based on the paired and two-sample t-tests, one-way ANOVA, and confidence intervals. Chapter 7 describes power-analysis-based methods for determining an appropriate sample size for a new experiment based on a similar experiment done in the past, detailing how to utilize the authorees R tools for power analysis and how to interpret the results. Case studies from IR for both Excel-based topic set size design and R-based power analysis are also provided. ;en_US
dc.description.statementofresponsibilityby Tetsuya Sakai.en_US
dc.description.tableofcontents1 Preliminaries -- 2 t-tests -- 3 Analysis of Variance -- 4 Multiple Comparison Procedures -- 5 The Correct Ways to Use Significance Tests -- 6 Topic Set Size Design Using Excel -- 7 Power Analysis Using R -- 8 Conclusions. ;en_US
dc.format.extentIX, 150 p. 53 illus., 43 illus. in color. ; online resource. ;en_US
dc.publisherSpringer Singapore :en_US
dc.publisherImprint: Springer,en_US
dc.relation.ispartofseriesThe Information Retrieval Series, ; 1387-5264 ; ; 40. ;en_US
dc.relation.ispartofseriesThe Information Retrieval Series, ; 1387-5264 ; ; 40. ;en_US
dc.relation.haspart9789811311987.pdfen_US
dc.subjectInformation storage and retrieva. ;en_US
dc.subjectStatistics. ;en_US
dc.subjectInformation Storage and Retrieval. ; http://scigraph.springernature.com/things/product-market-codes/I18032. ;en_US
dc.subjectStatistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences. ; http://scigraph.springernature.com/things/product-market-codes/S17020. ;en_US
dc.subject.ddc025.04 ; 23 ;en_US
dc.titleLaboratory Experiments in Information Retrievalen_US
dc.title.alternativeSample Sizes, Effect Sizes, and Statistical Power /en_US
dc.typeBooken_US
dc.publisher.placeSingapore :en_US
Appears in Collections:مدیریت فناوری اطلاعات

Files in This Item:
File Description SizeFormat 
9789811311987.pdf5.17 MBAdobe PDFThumbnail
Preview File
Full metadata record
DC FieldValueLanguage
dc.contributor.authorSakai, Tetsuya. ;en_US
dc.date.accessioned2013en_US
dc.date.accessioned2020-05-17T08:18:14Z-
dc.date.available2020-05-17T08:18:14Z-
dc.date.issued2018en_US
dc.identifier.isbn9789811311994 ;en_US
dc.identifier.isbn9789811311987 (print) ;en_US
dc.identifier.isbn9789811312007 (print) ;en_US
dc.identifier.urihttp://localhost/handle/Hannan/533-
dc.descriptionen_US
dc.descriptionQA75.5-76.95 ;en_US
dc.descriptionSpringerLink (Online service) ;en_US
dc.descriptionPrinted edition: ; 9789811311987. ;en_US
dc.descriptionen_US
dc.descriptionPrinted edition: ; 9789811312007. ;en_US
dc.descriptionen_US
dc.descriptionen_US
dc.description.abstractCovering aspects from principles and limitations of statistical significance tests to topic set size design and power analysis, this book guides readers to statistically well-designed experiments. Although classical statistical significance tests are to some extent useful in information retrieval (IR) evaluation, they can harm research unless they are used appropriately with the right sample sizes and statistical power and unless the test results are reported properly. The first half of the book is mainly targeted at undergraduate students, and the second half is suitable for graduate students and researchers who regularly conduct laboratory experiments in IR, natural language processing, recommendations, and related fields. Chapters 1ee5 review parametric significance tests for comparing system means, namely, t-tests and ANOVAs, and show how easily they can be conducted using Microsoft Excel or R. These chapters also discuss a few multiple comparison procedures for researchers who are interested in comparing every system pair, including a randomised version of Tukey's Honestly Significant Difference test. The chapters then deal with known limitations of classical significance testing and provide practical guidelines for reporting research results regarding comparison of means. Chapters 6 and 7 discuss statistical power. Chapter 6 introduces topic set size design to enable test collection builders to determine an appropriate number of topics to create. Readers can easily use the authorees Excel tools for topic set size design based on the paired and two-sample t-tests, one-way ANOVA, and confidence intervals. Chapter 7 describes power-analysis-based methods for determining an appropriate sample size for a new experiment based on a similar experiment done in the past, detailing how to utilize the authorees R tools for power analysis and how to interpret the results. Case studies from IR for both Excel-based topic set size design and R-based power analysis are also provided. ;en_US
dc.description.statementofresponsibilityby Tetsuya Sakai.en_US
dc.description.tableofcontents1 Preliminaries -- 2 t-tests -- 3 Analysis of Variance -- 4 Multiple Comparison Procedures -- 5 The Correct Ways to Use Significance Tests -- 6 Topic Set Size Design Using Excel -- 7 Power Analysis Using R -- 8 Conclusions. ;en_US
dc.format.extentIX, 150 p. 53 illus., 43 illus. in color. ; online resource. ;en_US
dc.publisherSpringer Singapore :en_US
dc.publisherImprint: Springer,en_US
dc.relation.ispartofseriesThe Information Retrieval Series, ; 1387-5264 ; ; 40. ;en_US
dc.relation.ispartofseriesThe Information Retrieval Series, ; 1387-5264 ; ; 40. ;en_US
dc.relation.haspart9789811311987.pdfen_US
dc.subjectInformation storage and retrieva. ;en_US
dc.subjectStatistics. ;en_US
dc.subjectInformation Storage and Retrieval. ; http://scigraph.springernature.com/things/product-market-codes/I18032. ;en_US
dc.subjectStatistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences. ; http://scigraph.springernature.com/things/product-market-codes/S17020. ;en_US
dc.subject.ddc025.04 ; 23 ;en_US
dc.titleLaboratory Experiments in Information Retrievalen_US
dc.title.alternativeSample Sizes, Effect Sizes, and Statistical Power /en_US
dc.typeBooken_US
dc.publisher.placeSingapore :en_US
Appears in Collections:مدیریت فناوری اطلاعات

Files in This Item:
File Description SizeFormat 
9789811311987.pdf5.17 MBAdobe PDFThumbnail
Preview File