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Evidence-based software engineering and systematic reviews

Auteur : B A Kitchenham; D Budgen; Pearl Brereton; Taylor & Francis.
Éditeur: Boca Raton, FL : CRC Press, Taylor & Francis Group, [2016] ©2016
Collection: Chapman & Hall/CRC innovations in software engineering and software development.
Édition/format:   Livre imprimé : Anglais : First editionVoir toutes les éditions et tous les formats
Résumé:
In the decade since the idea of adapting the evidence-based paradigm for software engineering was first proposed, it has become a major tool of empirical software engineering. Evidence-Based Software Engineering and Systematic Reviews provides a clear introduction to the use of an evidence-based model for software engineering research and practice. The book explains the roles of primary studies (experiments,  Lire la suite...
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Format – détails additionnels: Print version
Type d’ouvrage: Ressource Internet
Type de document: Livre, Ressource Internet
Tous les auteurs / collaborateurs: B A Kitchenham; D Budgen; Pearl Brereton; Taylor & Francis.
ISBN: 9781482228656 1482228653
Numéro OCLC: 1058183109
Notes: "A Chapman & Hall book."
Description: xxxiv, 399 pages : illustrations (black and white) ; 25 cm
Contenu: Machine generated contents note: I. Evidence-Based Practices in Software Engineering --
1. The Evidence-Based Paradigm --
1.1. What do we mean by evidence? --
1.2. Emergence of the evidence-based movement --
1.3. The systematic review --
1.4. Some limitations of an evidence-based view of the world --
2. Evidence-Based Software Engineering (EBSE) --
2.1. Empirical knowledge before EBSE --
2.2. From opinion to evidence --
2.3.Organising evidence-based software engineering practices --
2.4. Software engineering characteristics --
2.5. Limitations of evidence-based practices in software engineering --
2.5.1. Constraints from software engineering --
2.5.2. Threats to validity --
3. Using Systematic Reviews in Software Engineering --
3.1. Systematic reviews --
3.2. Mapping studies --
3.3. Meta-analysis --
4. Planning a Systematic Review --
4.1. Establishing the need for a review --
4.2. Managing the review project --
4.3. Specifying the research questions --
4.4. Developing the protocol Note continued: 4.4.1. Background --
4.4.2. Research questions(s) --
4.4.3. Search strategy --
4.4.4. Study selection --
4.4.5. Assessing the quality of the primary studies --
4.4.6. Data extraction --
4.4.7. Data synthesis and aggregation strategy --
4.4.8. Limitations --
4.4.9. Reporting --
4.4.10. Review management --
4.5. Validating the protocol --
5. Searching for Primary Studies --
5.1.Completeness --
5.2. Validating the search strategy --
5.3. Methods of searching --
5.4. Examples of search strategies --
6. Study Selection --
6.1. Selection criteria --
6.2. Selection process --
6.3. The relationship between papers and studies --
6.4. Examples of selection criteria and process --
7. Assessing Study Quality --
7.1. Why assess quality? --
7.2. Quality assessment criteria --
7.2.1. Study quality checklists --
7.2.2. Dealing with multiple study types --
7.3. Procedures for assessing quality --
7.4. Examples of quality assessment criteria and procedures --
8. Extracting Study Data Note continued: 8.1. Overview of data extraction --
8.2. Examples of extracted data and extraction procedures --
9. Mapping Study Analysis --
9.1. Analysis of publication details --
9.2. Classification analysis --
9.3. Automated content analysis --
9.4. Clusters, gaps, and models --
10. Qualitative Synthesis --
10.1. Qualitative synthesis in software engineering research --
10.2. Qualitative analysis terminology and concepts --
10.3. Using qualitative synthesis methods in software engineering systematic reviews --
10.4. Description of qualitative synthesis methods --
10.4.1. Meta-ethnography --
10.4.2. Narrative synthesis --
10.4.3. Qualitative cross-case analysis --
10.4.4. Thematic analysis --
10.4.5. Meta-summary --
10.4.6. Vote counting --
10.5. General problems with qualitative meta-synthesis --
10.5.1. Primary study quality assessment --
10.5.2. Validation of meta-syntheses --
11. Meta-Analysis with Lech Madeyski --
11.1. Meta-analysis example --
11.2. Effect sizes Note continued: 11.2.1. Mean difference --
11.2.2. Standardised mean difference --
11.2.2.1. Standardised mean difference effect size --
11.2.2.2. Standardised difference effect size variance --
11.2.2.3. Adjustment for small sample sizes --
11.2.3. The correlation coefficient effect size --
11.2.4. Proportions and counts --
11.3. Conversion between different effect sizes --
11.3.1. Conversions between d and r --
11.3.2. Conversion between log odds and d --
11.4. Meta-analysis methods --
11.4.1. Meta-analysis models --
11.4.2. Meta-analysis calculations --
11.5. Heterogeneity --
11.6. Moderator analysis --
11.7. Additional analyses --
11.7.1. Publication bias --
11.7.2. Sensitivity analysis --
12. Reporting a Systematic Review --
12.1. Planning reports --
12.2. Writing reports --
12.3. Validating reports --
13. Tool Support for Systematic Reviews with Christopher Marshall --
13.1. Review tools in other disciplines --
13.2. Tools for software engineering reviews Note continued: 14. Evidence to Practice: Knowledge Translation and Diffusion --
14.1. What is knowledge translation? --
14.2. Knowledge translation in the context of software engineering --
14.3. Examples of knowledge translation in software engineering --
14.3.1. Assessing software cost uncertainty --
14.3.2. Effectiveness of pair programming --
14.3.3. Requirements elicitation techniques --
14.3.4. Presenting recommendations --
14.4. Diffusion of software engineering knowledge --
14.5. Systematic reviews for software engineering education --
14.5.1. Selecting the studies --
14.5.2. Topic coverage --
Further Reading for Part I --
II. The Systematic Reviewer's Perspective of Primary Studies --
15. Primary Studies and Their Role in EBSE --
15.1. Some characteristics of primary studies --
15.2. Forms of primary study used in software engineering --
15.3. Ethical issues --
15.4. Reporting primary studies --
15.4.1. Meeting the needs of a secondary study Note continued: 15.4.2. What needs to be reported? --
15.5. Replicated studies --
Further reading --
16. Controlled Experiments and Quasi-Experiments --
16.1. Characteristics of controlled experiments and quasi-experiments --
16.1.1. Controlled experiments --
16.1.2. Quasi-experiments --
16.1.3. Problems with experiments in software engineering --
16.2. Conducting experiments and quasi-experiments --
16.2.1. Dependent variables, independent variables and confounding factors --
16.2.2. Hypothesis testing --
16.2.3. The design of formal experiments --
16.2.4. The design of quasi-experiments --
16.2.5. Threats to validity --
16.3. Research questions that can be answered by using experiments and quasi-experiments --
16.3.1. Pair designing --
16.3.2.Comparison of diagrammatical forms --
16.3.3. Effort estimation --
16.4. Examples from the software engineering literature --
16.4.1. Randomised experiment: Between subjects --
16.4.2. Quasi-experiment: Within-subjects before --
after study Note continued: 16.4.3. Quasi-experiment: Within-subjects cross-over study --
16.4.4. Quasi-experiment: Interrupted time series --
16.5. Reporting experiments and quasi-experiments --
Further reading --
17. Surveys --
17.1. Characteristics of surveys --
17.2. Conducting surveys --
17.3. Research questions that can be answered by using surveys --
17.4. Examples of surveys from the software engineering literature --
17.4.1. Software development risk --
17.4.2. Software design patterns --
17.4.3. Use of the UML --
17.5. Reporting surveys --
Further reading --
18. Case Studies --
18.1. Characteristics of case studies --
18.2. Conducting case study research --
18.2.1. Single-case versus multiple-case --
18.2.2. Choice of the units of analysis --
18.2.3.Organising a case study --
18.3. Research questions that can be answered by using case studies --
18.4. Example of a case study from the software engineering literature --
18.4.1. Why use a case study? --
18.4.2. Case study parameters Note continued: 18.5. Reporting case studies --
Further reading --
19. Qualitative Studies --
19.1. Characteristics of a qualitative study --
19.2. Conducting qualitative research --
19.3. Research questions that can be answered using qualitative studies --
19.4. Examples of qualitative studies in software engineering --
19.4.1. Mixed qualitative and quantitative studies --
19.4.2. Fully qualitative studies --
19.5. Reporting qualitative studies --
Further reading --
20. Data Mining Studies --
20.1. Characteristics of data mining studies --
20.2. Conducting data mining research in software engineering --
20.3. Research questions that can be answered by data mining --
20.4. Examples of data mining studies --
20.5. Problems with data mining studies in software engineering --
20.6. Reporting data mining studies --
Further reading --
21. Replicated and Distributed Studies --
21.1. What is a replication study? --
21.2. Replications in software engineering Note continued: 21.2.1. Categorising replication forms --
21.2.2. How widely are replications performed? --
21.2.3. Reporting replicated studies --
21.3. Including replications in systematic reviews --
21.4. Distributed studies --
Further reading --
III. Guidelines for Systematic Reviews --
22. Systematic Review and Mapping Study Procedures --
22.1. Introduction --
22.2. Preliminaries --
22.3. Review management --
22.4. Planning a systematic review --
22.4.1. The need for a systematic review or mapping study --
22.4.2. Specifying research questions --
22.4.2.1. Research questions for systematic reviews --
22.4.2.2. Research questions for mapping studies --
22.4.3. Developing the protocol --
22.4.4. Validating the protocol --
22.5. The search process --
22.5.1. The search strategy --
22.5.1.1. Is completeness critical? --
22.5.1.2. Validating the search strategy --
22.5.1.3. Deciding which search methods to use --
22.5.2. Automated searches Note continued: 22.5.2.1. Sources to search for an automated search --
22.5.2.2. Constructing search strings --
22.5.3. Selecting sources for a manual search --
22.5.4. Problems with the search process --
22.6. Primary study selection process --
22.6.1.A team-based selection process --
22.6.2. Selection processes for lone researchers --
22.6.3. Selection process problems --
22.6.4. Papers versus studies --
22.6.5. The interaction between the search and selection processes --
22.7. Validating the search and selection process --
22.8. Quality assessment --
22.8.1. Is quality assessment necessary? --
22.8.2. Quality assessment criteria --
22.8.2.1. Primary study quality --
22.8.2.2. Strength of evidence supporting review findings --
22.8.3. Using quality assessment results --
22.8.4. Managing the quality assessment process --
22.8.4.1.A team-based quality assessment process --
22.8.4.2. Quality assessment for lone researchers --
22.9. Data extraction Note continued: 22.9.1. Data extraction for quantitative systematic reviews --
22.9.1.1. Data extraction planning for quantitative systematic reviews --
22.9.1.2. Data extraction team process for quantitative systematic reviews --
22.9.1.3. Quantitative systematic reviews data extraction process for lone researchers --
22.9.2. Data extraction for qualitative systematic reviews --
22.9.2.1. Planning data extraction for qualitative systematic reviews --
22.9.2.2. Data extraction process for qualitative systematic reviews --
22.9.3. Data extraction for mapping studies --
22.9.3.1. Planning data extraction for mapping studies --
22.9.3.2. Data extraction process for mapping studies --
22.9.4. Validating the data extraction process --
22.9.5. General data extraction issues --
22.10. Data aggregation and synthesis --
22.10.1. Data synthesis for quantitative systematic reviews --
22.10.1.1. Data synthesis using meta-analysis --
22.10.1.2. Reporting meta-analysis results Note continued: 22.10.1.3. Vote counting for quantitative systematic reviews --
22.10.2. Data synthesis for qualitative systematic reviews --
22.10.3. Data aggregation for mapping studies --
22.10.3.1. Tables versus graphics --
22.10.4. Data synthesis validation --
22.11. Reporting the systematic review --
22.11.1. Systematic review readership --
22.11.2. Report structure --
22.11.3. Validating the report --
Appendix: Catalogue of Systematic Reviews Relevant to Education and Practice with Sarah Drummond and Nikki Williams --
A.1. Professional Practice (PRF) --
A.2. Modelling and Analysis (MAA) --
A.3. Software Design (DES) --
A.4. Validation and Verification (VAV) --
A.5. Software Evolution (EVO) --
A.6. Software Process (PRO) --
A.7. Software Quality (QUA) --
A.8. Software Management (MGT).
Titre de collection: Chapman & Hall/CRC innovations in software engineering and software development.
Responsabilité: Barbara Ann Kitchenham, David Budgen, Pearl Brereton.

Résumé:

In the decade since the idea of adapting the evidence-based paradigm for software engineering was first proposed, it has become a major tool of empirical software engineering. Evidence-Based Software Engineering and Systematic Reviews provides a clear introduction to the use of an evidence-based model for software engineering research and practice. The book explains the roles of primary studies (experiments, surveys, case studies) as elements of an over-arching evidence model, rather than as disjointed elements in the empirical spectrum. Supplying readers with a clear understanding of empirical software engineering best practices, it provides up-to-date guidance on how to conduct secondary studies in software engineering--replacing the existing 2004 and 2007 technical reports. The book is divided into three parts. The first part discusses the nature of evidence and the evidence-based practices centered on a systematic review, both in general and as applying to software engineering. The second part examines the different elements that provide inputs to a systematic review (usually considered as forming a secondary study), especially the main forms of primary empirical study currently used in software engineering. The final part provides practical guidance on how to conduct systematic reviews (the guidelines), drawing together accumulated experiences to guide researchers and students in planning and conducting their own studies. The book includes an extensive glossary and an appendix that provides a catalogue of reviews that may be useful for practice and teaching.

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    schema:description "Note continued: 15.4.2. What needs to be reported? -- 15.5. Replicated studies -- Further reading -- 16. Controlled Experiments and Quasi-Experiments -- 16.1. Characteristics of controlled experiments and quasi-experiments -- 16.1.1. Controlled experiments -- 16.1.2. Quasi-experiments -- 16.1.3. Problems with experiments in software engineering -- 16.2. Conducting experiments and quasi-experiments -- 16.2.1. Dependent variables, independent variables and confounding factors -- 16.2.2. Hypothesis testing -- 16.2.3. The design of formal experiments -- 16.2.4. The design of quasi-experiments -- 16.2.5. Threats to validity -- 16.3. Research questions that can be answered by using experiments and quasi-experiments -- 16.3.1. Pair designing -- 16.3.2.Comparison of diagrammatical forms -- 16.3.3. Effort estimation -- 16.4. Examples from the software engineering literature -- 16.4.1. Randomised experiment: Between subjects -- 16.4.2. Quasi-experiment: Within-subjects before -- after study"@en ;
    schema:description "Note continued: 18.5. Reporting case studies -- Further reading -- 19. Qualitative Studies -- 19.1. Characteristics of a qualitative study -- 19.2. Conducting qualitative research -- 19.3. Research questions that can be answered using qualitative studies -- 19.4. Examples of qualitative studies in software engineering -- 19.4.1. Mixed qualitative and quantitative studies -- 19.4.2. Fully qualitative studies -- 19.5. Reporting qualitative studies -- Further reading -- 20. Data Mining Studies -- 20.1. Characteristics of data mining studies -- 20.2. Conducting data mining research in software engineering -- 20.3. Research questions that can be answered by data mining -- 20.4. Examples of data mining studies -- 20.5. Problems with data mining studies in software engineering -- 20.6. Reporting data mining studies -- Further reading -- 21. Replicated and Distributed Studies -- 21.1. What is a replication study? -- 21.2. Replications in software engineering"@en ;
    schema:description "Machine generated contents note: I. Evidence-Based Practices in Software Engineering -- 1. The Evidence-Based Paradigm -- 1.1. What do we mean by evidence? -- 1.2. Emergence of the evidence-based movement -- 1.3. The systematic review -- 1.4. Some limitations of an evidence-based view of the world -- 2. Evidence-Based Software Engineering (EBSE) -- 2.1. Empirical knowledge before EBSE -- 2.2. From opinion to evidence -- 2.3.Organising evidence-based software engineering practices -- 2.4. Software engineering characteristics -- 2.5. Limitations of evidence-based practices in software engineering -- 2.5.1. Constraints from software engineering -- 2.5.2. Threats to validity -- 3. Using Systematic Reviews in Software Engineering -- 3.1. Systematic reviews -- 3.2. Mapping studies -- 3.3. Meta-analysis -- 4. Planning a Systematic Review -- 4.1. Establishing the need for a review -- 4.2. Managing the review project -- 4.3. Specifying the research questions -- 4.4. Developing the protocol"@en ;
    schema:description "Note continued: 16.4.3. Quasi-experiment: Within-subjects cross-over study -- 16.4.4. Quasi-experiment: Interrupted time series -- 16.5. Reporting experiments and quasi-experiments -- Further reading -- 17. Surveys -- 17.1. Characteristics of surveys -- 17.2. Conducting surveys -- 17.3. Research questions that can be answered by using surveys -- 17.4. Examples of surveys from the software engineering literature -- 17.4.1. Software development risk -- 17.4.2. Software design patterns -- 17.4.3. Use of the UML -- 17.5. Reporting surveys -- Further reading -- 18. Case Studies -- 18.1. Characteristics of case studies -- 18.2. Conducting case study research -- 18.2.1. Single-case versus multiple-case -- 18.2.2. Choice of the units of analysis -- 18.2.3.Organising a case study -- 18.3. Research questions that can be answered by using case studies -- 18.4. Example of a case study from the software engineering literature -- 18.4.1. Why use a case study? -- 18.4.2. Case study parameters"@en ;
    schema:description "Note continued: 22.5.2.1. Sources to search for an automated search -- 22.5.2.2. Constructing search strings -- 22.5.3. Selecting sources for a manual search -- 22.5.4. Problems with the search process -- 22.6. Primary study selection process -- 22.6.1.A team-based selection process -- 22.6.2. Selection processes for lone researchers -- 22.6.3. Selection process problems -- 22.6.4. Papers versus studies -- 22.6.5. The interaction between the search and selection processes -- 22.7. Validating the search and selection process -- 22.8. Quality assessment -- 22.8.1. Is quality assessment necessary? -- 22.8.2. Quality assessment criteria -- 22.8.2.1. Primary study quality -- 22.8.2.2. Strength of evidence supporting review findings -- 22.8.3. Using quality assessment results -- 22.8.4. Managing the quality assessment process -- 22.8.4.1.A team-based quality assessment process -- 22.8.4.2. Quality assessment for lone researchers -- 22.9. Data extraction"@en ;
    schema:description "Note continued: 14. Evidence to Practice: Knowledge Translation and Diffusion -- 14.1. What is knowledge translation? -- 14.2. Knowledge translation in the context of software engineering -- 14.3. Examples of knowledge translation in software engineering -- 14.3.1. Assessing software cost uncertainty -- 14.3.2. Effectiveness of pair programming -- 14.3.3. Requirements elicitation techniques -- 14.3.4. Presenting recommendations -- 14.4. Diffusion of software engineering knowledge -- 14.5. Systematic reviews for software engineering education -- 14.5.1. Selecting the studies -- 14.5.2. Topic coverage -- Further Reading for Part I -- II. The Systematic Reviewer's Perspective of Primary Studies -- 15. Primary Studies and Their Role in EBSE -- 15.1. Some characteristics of primary studies -- 15.2. Forms of primary study used in software engineering -- 15.3. Ethical issues -- 15.4. Reporting primary studies -- 15.4.1. Meeting the needs of a secondary study"@en ;
    schema:description "In the decade since the idea of adapting the evidence-based paradigm for software engineering was first proposed, it has become a major tool of empirical software engineering. Evidence-Based Software Engineering and Systematic Reviews provides a clear introduction to the use of an evidence-based model for software engineering research and practice. The book explains the roles of primary studies (experiments, surveys, case studies) as elements of an over-arching evidence model, rather than as disjointed elements in the empirical spectrum. Supplying readers with a clear understanding of empirical software engineering best practices, it provides up-to-date guidance on how to conduct secondary studies in software engineering--replacing the existing 2004 and 2007 technical reports. The book is divided into three parts. The first part discusses the nature of evidence and the evidence-based practices centered on a systematic review, both in general and as applying to software engineering. The second part examines the different elements that provide inputs to a systematic review (usually considered as forming a secondary study), especially the main forms of primary empirical study currently used in software engineering. The final part provides practical guidance on how to conduct systematic reviews (the guidelines), drawing together accumulated experiences to guide researchers and students in planning and conducting their own studies. The book includes an extensive glossary and an appendix that provides a catalogue of reviews that may be useful for practice and teaching."@en ;
    schema:description "Note continued: 11.2.1. Mean difference -- 11.2.2. Standardised mean difference -- 11.2.2.1. Standardised mean difference effect size -- 11.2.2.2. Standardised difference effect size variance -- 11.2.2.3. Adjustment for small sample sizes -- 11.2.3. The correlation coefficient effect size -- 11.2.4. Proportions and counts -- 11.3. Conversion between different effect sizes -- 11.3.1. Conversions between d and r -- 11.3.2. Conversion between log odds and d -- 11.4. Meta-analysis methods -- 11.4.1. Meta-analysis models -- 11.4.2. Meta-analysis calculations -- 11.5. Heterogeneity -- 11.6. Moderator analysis -- 11.7. Additional analyses -- 11.7.1. Publication bias -- 11.7.2. Sensitivity analysis -- 12. Reporting a Systematic Review -- 12.1. Planning reports -- 12.2. Writing reports -- 12.3. Validating reports -- 13. Tool Support for Systematic Reviews with Christopher Marshall -- 13.1. Review tools in other disciplines -- 13.2. Tools for software engineering reviews"@en ;
    schema:description "Note continued: 21.2.1. Categorising replication forms -- 21.2.2. How widely are replications performed? -- 21.2.3. Reporting replicated studies -- 21.3. Including replications in systematic reviews -- 21.4. Distributed studies -- Further reading -- III. Guidelines for Systematic Reviews -- 22. Systematic Review and Mapping Study Procedures -- 22.1. Introduction -- 22.2. Preliminaries -- 22.3. Review management -- 22.4. Planning a systematic review -- 22.4.1. The need for a systematic review or mapping study -- 22.4.2. Specifying research questions -- 22.4.2.1. Research questions for systematic reviews -- 22.4.2.2. Research questions for mapping studies -- 22.4.3. Developing the protocol -- 22.4.4. Validating the protocol -- 22.5. The search process -- 22.5.1. The search strategy -- 22.5.1.1. Is completeness critical? -- 22.5.1.2. Validating the search strategy -- 22.5.1.3. Deciding which search methods to use -- 22.5.2. Automated searches"@en ;
    schema:description "Note continued: 8.1. Overview of data extraction -- 8.2. Examples of extracted data and extraction procedures -- 9. Mapping Study Analysis -- 9.1. Analysis of publication details -- 9.2. Classification analysis -- 9.3. Automated content analysis -- 9.4. Clusters, gaps, and models -- 10. Qualitative Synthesis -- 10.1. Qualitative synthesis in software engineering research -- 10.2. Qualitative analysis terminology and concepts -- 10.3. Using qualitative synthesis methods in software engineering systematic reviews -- 10.4. Description of qualitative synthesis methods -- 10.4.1. Meta-ethnography -- 10.4.2. Narrative synthesis -- 10.4.3. Qualitative cross-case analysis -- 10.4.4. Thematic analysis -- 10.4.5. Meta-summary -- 10.4.6. Vote counting -- 10.5. General problems with qualitative meta-synthesis -- 10.5.1. Primary study quality assessment -- 10.5.2. Validation of meta-syntheses -- 11. Meta-Analysis with Lech Madeyski -- 11.1. Meta-analysis example -- 11.2. Effect sizes"@en ;
    schema:description "Note continued: 4.4.1. Background -- 4.4.2. Research questions(s) -- 4.4.3. Search strategy -- 4.4.4. Study selection -- 4.4.5. Assessing the quality of the primary studies -- 4.4.6. Data extraction -- 4.4.7. Data synthesis and aggregation strategy -- 4.4.8. Limitations -- 4.4.9. Reporting -- 4.4.10. Review management -- 4.5. Validating the protocol -- 5. Searching for Primary Studies -- 5.1.Completeness -- 5.2. Validating the search strategy -- 5.3. Methods of searching -- 5.4. Examples of search strategies -- 6. Study Selection -- 6.1. Selection criteria -- 6.2. Selection process -- 6.3. The relationship between papers and studies -- 6.4. Examples of selection criteria and process -- 7. Assessing Study Quality -- 7.1. Why assess quality? -- 7.2. Quality assessment criteria -- 7.2.1. Study quality checklists -- 7.2.2. Dealing with multiple study types -- 7.3. Procedures for assessing quality -- 7.4. Examples of quality assessment criteria and procedures -- 8. Extracting Study Data"@en ;
    schema:description "Note continued: 22.9.1. Data extraction for quantitative systematic reviews -- 22.9.1.1. Data extraction planning for quantitative systematic reviews -- 22.9.1.2. Data extraction team process for quantitative systematic reviews -- 22.9.1.3. Quantitative systematic reviews data extraction process for lone researchers -- 22.9.2. Data extraction for qualitative systematic reviews -- 22.9.2.1. Planning data extraction for qualitative systematic reviews -- 22.9.2.2. Data extraction process for qualitative systematic reviews -- 22.9.3. Data extraction for mapping studies -- 22.9.3.1. Planning data extraction for mapping studies -- 22.9.3.2. Data extraction process for mapping studies -- 22.9.4. Validating the data extraction process -- 22.9.5. General data extraction issues -- 22.10. Data aggregation and synthesis -- 22.10.1. Data synthesis for quantitative systematic reviews -- 22.10.1.1. Data synthesis using meta-analysis -- 22.10.1.2. Reporting meta-analysis results"@en ;
    schema:description "Note continued: 22.10.1.3. Vote counting for quantitative systematic reviews -- 22.10.2. Data synthesis for qualitative systematic reviews -- 22.10.3. Data aggregation for mapping studies -- 22.10.3.1. Tables versus graphics -- 22.10.4. Data synthesis validation -- 22.11. Reporting the systematic review -- 22.11.1. Systematic review readership -- 22.11.2. Report structure -- 22.11.3. Validating the report -- Appendix: Catalogue of Systematic Reviews Relevant to Education and Practice with Sarah Drummond and Nikki Williams -- A.1. Professional Practice (PRF) -- A.2. Modelling and Analysis (MAA) -- A.3. Software Design (DES) -- A.4. Validation and Verification (VAV) -- A.5. Software Evolution (EVO) -- A.6. Software Process (PRO) -- A.7. Software Quality (QUA) -- A.8. Software Management (MGT)."@en ;
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