Selecting the right analyses for your data :: quantitative, qualitative, and mixed methods /
"What are the most effective methods to code and analyze data for a particular study? This thoughtful and engaging book reviews the selection criteria for coding and analyzing any set of data--whether qualitative, quantitative, mixed, or visual. The authors systematically explain when to use ve...
Gespeichert in:
| 1. Verfasser: | |
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| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
New York, NY :
Guilford Publications,
[2014]
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| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | "What are the most effective methods to code and analyze data for a particular study? This thoughtful and engaging book reviews the selection criteria for coding and analyzing any set of data--whether qualitative, quantitative, mixed, or visual. The authors systematically explain when to use verbal, numerical, graphic, or combined codes, and when to use qualitative, quantitative, graphic, or mixed-methods modes of analysis. Chapters on each topic are organized so that researchers can read them sequentially or can easily "flip and find" answers to specific questions. Nontechnical discussions of cutting-edge approaches--illustrated with real-world examples--emphasize how to choose (rather than how to implement) the various analyses. The book shows how using the right analysis methods leads to more justifiable conclusions and more persuasive presentations of research results. Useful features for teaching or self-study: *Chapter-opening preview boxes that highlight useful topics addressed. *End-of-chapter summary tables recapping the 'dos and don'ts' and advantages and disadvantages of each analytic technique. *Annotated suggestions for further reading and technical resources on each topic. Subject Areas/Keywords: analyses, coding, combined methods, data analysis, data collection, dissertation, graphical, interpretation, mixed methods, qualitative, quantitative, research analysis, research designs, research methods, social sciences, thesis, visual Audience: Researchers, instructors, and graduate students in a range of disciplines, including psychology, education, social work, sociology, health, and management; administrators and managers who need to make data-driven decisions"-- |
| Beschreibung: | 1 online resource (xix, 500 pages) |
| Bibliographie: | Includes bibliographical references and index. |
| ISBN: | 9781462516032 1462516033 |
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| 264 | 1 | |a New York, NY : |b Guilford Publications, |c [2014] | |
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| 520 | |a "What are the most effective methods to code and analyze data for a particular study? This thoughtful and engaging book reviews the selection criteria for coding and analyzing any set of data--whether qualitative, quantitative, mixed, or visual. The authors systematically explain when to use verbal, numerical, graphic, or combined codes, and when to use qualitative, quantitative, graphic, or mixed-methods modes of analysis. Chapters on each topic are organized so that researchers can read them sequentially or can easily "flip and find" answers to specific questions. Nontechnical discussions of cutting-edge approaches--illustrated with real-world examples--emphasize how to choose (rather than how to implement) the various analyses. The book shows how using the right analysis methods leads to more justifiable conclusions and more persuasive presentations of research results. Useful features for teaching or self-study: *Chapter-opening preview boxes that highlight useful topics addressed. *End-of-chapter summary tables recapping the 'dos and don'ts' and advantages and disadvantages of each analytic technique. *Annotated suggestions for further reading and technical resources on each topic. Subject Areas/Keywords: analyses, coding, combined methods, data analysis, data collection, dissertation, graphical, interpretation, mixed methods, qualitative, quantitative, research analysis, research designs, research methods, social sciences, thesis, visual Audience: Researchers, instructors, and graduate students in a range of disciplines, including psychology, education, social work, sociology, health, and management; administrators and managers who need to make data-driven decisions"-- |c Provided by publisher | ||
| 504 | |a Includes bibliographical references and index. | ||
| 588 | 0 | |a Print version record. | |
| 505 | 0 | |a Part I. Coding data-by design -- Part II. Analysis and interpretation of quantitative data -- Part III. Analysis and interpretation of qualitative and combined/mixed data. | |
| 505 | 0 | 0 | |t General introduction -- |t What are data? -- |t Two basic organizing questions -- |t Ranks or ordered coding (when to use ordinal data) -- |t Visual/graphic data, coding, and analyses -- |t At what point does coding occur in the course of your research project? -- |t Codes and the phenomena we study -- |t A graphic depiction of the relation of coding to analysis -- |t Examples of coding and analysis -- |t Looking ahead -- |g Part I. |t Coding data-by design -- |t Introduction to Part I -- |g 1. |t Coding survey data -- |t An example : pitfalls when constructing a survey -- |t What methods to use to construct an effective questionnaire -- |t Coding and measuring respondents' answers to the questions -- |t Conclusion : where to find analysis guidelines for surveys in this book -- |g 2. |t Coding interview data -- |t Goals : what do you seek when asking questions? -- |t Your role : what should your part be in the dialogue? -- |t Samples : how many interviews and with whom? -- |t Questions : when do you ask what kinds of questions? -- |t Modes : how do you communicate with interviewees? -- |t Observations : what Is important that isn't said? -- |t Records : what methods do you use to preserve the dialogue? -- |t Tools : when should you use computers to code your data? -- |t Getting help : when to use member checks and multiple coders -- |t Conclusion -- |g 3. |t Coding experimental data -- |t Coding and measurement issues for all experimental designs -- |t Coding and measurement issues that vary by type of experimental design -- |t Conclusion : where in this book to find guidelines for analyzing experimental data -- |g 4. |t Coding data from naturalistic and participant observations -- |t Introduction to observational research -- |t Phase 1 : observing -- |t Phase 2 : recording -- |t Phase 3 : coding -- |t Recommendations -- |t Conclusions and tips for completing an observational study -- |t Appendix 4.1. Example of a site visit protocol -- |g 5. |t Coding archival data : literature reviews, big data, and new media -- |t Reviews of the research literature -- |t Big data -- |t Coding data from the web, including new media -- |t Conclusion : coding data from archival, web, and new media sources -- |g Part II. |t Analysis and interpretation of quantitative data -- |t Introduction to Part II -- |t Conceptual and terminological housekeeping : theory, model, hypothesis, concept, variable -- |t And a note on software -- |g 6. |t Describing, exploring, and visualizing your data -- |t What is meant by descriptive statistics? -- |t Overview of the main types of descriptive statistics and their uses -- |t What descriptive statistics to use to prepare for further analyses -- |t When to use correlations as descriptive statistics -- |t When and why to make the normal curve your point of reference -- |t When can you use descriptive statistics substantively? -- |t When to use descriptive statistics preparatory to applying missing data procedures -- |t Conclusion -- |g 7. |t What methods of statistical inference to use when -- |t Null hypothesis significance testing -- |t Which statistical tests to use for what -- |t When to use confidence intervals -- |t When to report power and precision of your estimates -- |t When should you use distribution-free, nonparametric significance tests? -- |t When to use the bootstrap and other resampling methods -- |t When to use Bayesian methods -- |t Which approach to statistical inference should you take? -- |t The "silent killer" of valid inferences : missing data -- |t Conclusion -- |t Appendix 7.1. Examples of output of significance tests -- |g 8. |t What associational statistics to use when -- |t When to use correlations to analyze data -- |t When to use regression analysis -- |t What to do when your dependent variables are categorical -- |t Summary : which associational methods work best for what sorts of data and problems? -- |t The most important question : when to include which variables -- |t Conclusion : relations among variables to investigate using regression analysis -- |g 9. |t Advanced associational methods -- |t Multilevel modeling -- |t Path analysis -- |t Factor analysis-exploratory and confirmatory -- |t Structural equation modeling -- |t Conclusion -- |g 10. |t Model building and selection -- |t When can you benefit from building a model or constructing a theory? -- |t When to use a multimodel approach -- |t Conclusion : a research agenda -- |g Part III. |t Analysis and interpretation of qualitative and combined/mixed data -- |t Introduction to Part III -- |g 11. |t Inductive analysis of qualitative data : ethnographic approaches and grounded theory -- |t The foundations of inductive social research in ethnographic fieldwork -- |t Grounded theory : an inductive approach to theory building -- |t Conclusion -- |g 12. |t Deductive analyses of qualitative data : comparative case studies and qualitative comparative analysis -- |t Case studies and deductive analyses -- |t When to do a single-case analysis : discovering, describing, and explaining causal links -- |t When to conduct small-n comparative case studies -- |t When to conduct analyses with an intermediate n of cases -- |t Conclusions -- |g 13. |t Coding and analyzing data from combined and mixed designs -- |t Coding and analysis considerations for deductive and inductive designs -- |t Coding considerations for sequential analysis approaches -- |t Data transformation/data merging in combined designs -- |t Conclusions -- |g 14. |t Conclusion : common themes and diverse choices -- |t Common themes -- |t The choice problem -- |t Strategies and tactics. |
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| contents | Part I. Coding data-by design -- Part II. Analysis and interpretation of quantitative data -- Part III. Analysis and interpretation of qualitative and combined/mixed data. General introduction -- What are data? -- Two basic organizing questions -- Ranks or ordered coding (when to use ordinal data) -- Visual/graphic data, coding, and analyses -- At what point does coding occur in the course of your research project? -- Codes and the phenomena we study -- A graphic depiction of the relation of coding to analysis -- Examples of coding and analysis -- Looking ahead -- Coding data-by design -- Introduction to Part I -- Coding survey data -- An example : pitfalls when constructing a survey -- What methods to use to construct an effective questionnaire -- Coding and measuring respondents' answers to the questions -- Conclusion : where to find analysis guidelines for surveys in this book -- Coding interview data -- Goals : what do you seek when asking questions? -- Your role : what should your part be in the dialogue? -- Samples : how many interviews and with whom? -- Questions : when do you ask what kinds of questions? -- Modes : how do you communicate with interviewees? -- Observations : what Is important that isn't said? -- Records : what methods do you use to preserve the dialogue? -- Tools : when should you use computers to code your data? -- Getting help : when to use member checks and multiple coders -- Conclusion -- Coding experimental data -- Coding and measurement issues for all experimental designs -- Coding and measurement issues that vary by type of experimental design -- Conclusion : where in this book to find guidelines for analyzing experimental data -- Coding data from naturalistic and participant observations -- Introduction to observational research -- Phase 1 : observing -- Phase 2 : recording -- Phase 3 : coding -- Recommendations -- Conclusions and tips for completing an observational study -- Appendix 4.1. Example of a site visit protocol -- Coding archival data : literature reviews, big data, and new media -- Reviews of the research literature -- Big data -- Coding data from the web, including new media -- Conclusion : coding data from archival, web, and new media sources -- Analysis and interpretation of quantitative data -- Introduction to Part II -- Conceptual and terminological housekeeping : theory, model, hypothesis, concept, variable -- And a note on software -- Describing, exploring, and visualizing your data -- What is meant by descriptive statistics? -- Overview of the main types of descriptive statistics and their uses -- What descriptive statistics to use to prepare for further analyses -- When to use correlations as descriptive statistics -- When and why to make the normal curve your point of reference -- When can you use descriptive statistics substantively? -- When to use descriptive statistics preparatory to applying missing data procedures -- What methods of statistical inference to use when -- Null hypothesis significance testing -- Which statistical tests to use for what -- When to use confidence intervals -- When to report power and precision of your estimates -- When should you use distribution-free, nonparametric significance tests? -- When to use the bootstrap and other resampling methods -- When to use Bayesian methods -- Which approach to statistical inference should you take? -- The "silent killer" of valid inferences : missing data -- Appendix 7.1. Examples of output of significance tests -- What associational statistics to use when -- When to use correlations to analyze data -- When to use regression analysis -- What to do when your dependent variables are categorical -- Summary : which associational methods work best for what sorts of data and problems? -- The most important question : when to include which variables -- Conclusion : relations among variables to investigate using regression analysis -- Advanced associational methods -- Multilevel modeling -- Path analysis -- Factor analysis-exploratory and confirmatory -- Structural equation modeling -- Model building and selection -- When can you benefit from building a model or constructing a theory? -- When to use a multimodel approach -- Conclusion : a research agenda -- Analysis and interpretation of qualitative and combined/mixed data -- Introduction to Part III -- Inductive analysis of qualitative data : ethnographic approaches and grounded theory -- The foundations of inductive social research in ethnographic fieldwork -- Grounded theory : an inductive approach to theory building -- Deductive analyses of qualitative data : comparative case studies and qualitative comparative analysis -- Case studies and deductive analyses -- When to do a single-case analysis : discovering, describing, and explaining causal links -- When to conduct small-n comparative case studies -- When to conduct analyses with an intermediate n of cases -- Conclusions -- Coding and analyzing data from combined and mixed designs -- Coding and analysis considerations for deductive and inductive designs -- Coding considerations for sequential analysis approaches -- Data transformation/data merging in combined designs -- Conclusion : common themes and diverse choices -- Common themes -- The choice problem -- Strategies and tactics. |
| ctrlnum | (OCoLC)879074384 |
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| discipline | Allgemeines |
| format | Electronic eBook |
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Paul Vogt [and others].</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">New York, NY :</subfield><subfield code="b">Guilford Publications,</subfield><subfield code="c">[2014]</subfield></datafield><datafield tag="264" ind1=" " ind2="4"><subfield code="c">©2014</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 online resource (xix, 500 pages)</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">computer</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">online resource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="546" ind1=" " ind2=" "><subfield code="a">Text in English.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">"What are the most effective methods to code and analyze data for a particular study? This thoughtful and engaging book reviews the selection criteria for coding and analyzing any set of data--whether qualitative, quantitative, mixed, or visual. The authors systematically explain when to use verbal, numerical, graphic, or combined codes, and when to use qualitative, quantitative, graphic, or mixed-methods modes of analysis. Chapters on each topic are organized so that researchers can read them sequentially or can easily "flip and find" answers to specific questions. Nontechnical discussions of cutting-edge approaches--illustrated with real-world examples--emphasize how to choose (rather than how to implement) the various analyses. The book shows how using the right analysis methods leads to more justifiable conclusions and more persuasive presentations of research results. Useful features for teaching or self-study: *Chapter-opening preview boxes that highlight useful topics addressed. *End-of-chapter summary tables recapping the 'dos and don'ts' and advantages and disadvantages of each analytic technique. *Annotated suggestions for further reading and technical resources on each topic. Subject Areas/Keywords: analyses, coding, combined methods, data analysis, data collection, dissertation, graphical, interpretation, mixed methods, qualitative, quantitative, research analysis, research designs, research methods, social sciences, thesis, visual Audience: Researchers, instructors, and graduate students in a range of disciplines, including psychology, education, social work, sociology, health, and management; administrators and managers who need to make data-driven decisions"--</subfield><subfield code="c">Provided by publisher</subfield></datafield><datafield tag="504" ind1=" " ind2=" "><subfield code="a">Includes bibliographical references and index.</subfield></datafield><datafield tag="588" ind1="0" ind2=" "><subfield code="a">Print version record.</subfield></datafield><datafield tag="505" ind1="0" ind2=" "><subfield code="a">Part I. Coding data-by design -- Part II. Analysis and interpretation of quantitative data -- Part III. Analysis and interpretation of qualitative and combined/mixed data.</subfield></datafield><datafield tag="505" ind1="0" ind2="0"><subfield code="t">General introduction --</subfield><subfield code="t">What are data? --</subfield><subfield code="t">Two basic organizing questions --</subfield><subfield code="t">Ranks or ordered coding (when to use ordinal data) --</subfield><subfield code="t">Visual/graphic data, coding, and analyses --</subfield><subfield code="t">At what point does coding occur in the course of your research project? --</subfield><subfield code="t">Codes and the phenomena we study --</subfield><subfield code="t">A graphic depiction of the relation of coding to analysis --</subfield><subfield code="t">Examples of coding and analysis --</subfield><subfield code="t">Looking ahead --</subfield><subfield code="g">Part I.</subfield><subfield code="t">Coding data-by design --</subfield><subfield code="t">Introduction to Part I --</subfield><subfield code="g">1.</subfield><subfield code="t">Coding survey data --</subfield><subfield code="t">An example : pitfalls when constructing a survey --</subfield><subfield code="t">What methods to use to construct an effective questionnaire --</subfield><subfield code="t">Coding and measuring respondents' answers to the questions --</subfield><subfield code="t">Conclusion : where to find analysis guidelines for surveys in this book --</subfield><subfield code="g">2.</subfield><subfield code="t">Coding interview data --</subfield><subfield code="t">Goals : what do you seek when asking questions? --</subfield><subfield code="t">Your role : what should your part be in the dialogue? --</subfield><subfield code="t">Samples : how many interviews and with whom? --</subfield><subfield code="t">Questions : when do you ask what kinds of questions? --</subfield><subfield code="t">Modes : how do you communicate with interviewees? --</subfield><subfield code="t">Observations : what Is important that isn't said? --</subfield><subfield code="t">Records : what methods do you use to preserve the dialogue? --</subfield><subfield code="t">Tools : when should you use computers to code your data? --</subfield><subfield code="t">Getting help : when to use member checks and multiple coders --</subfield><subfield code="t">Conclusion --</subfield><subfield code="g">3.</subfield><subfield code="t">Coding experimental data --</subfield><subfield code="t">Coding and measurement issues for all experimental designs --</subfield><subfield code="t">Coding and measurement issues that vary by type of experimental design --</subfield><subfield code="t">Conclusion : where in this book to find guidelines for analyzing experimental data --</subfield><subfield code="g">4.</subfield><subfield code="t">Coding data from naturalistic and participant observations --</subfield><subfield code="t">Introduction to observational research --</subfield><subfield code="t">Phase 1 : observing --</subfield><subfield code="t">Phase 2 : recording --</subfield><subfield code="t">Phase 3 : coding --</subfield><subfield code="t">Recommendations --</subfield><subfield code="t">Conclusions and tips for completing an observational study --</subfield><subfield code="t">Appendix 4.1. Example of a site visit protocol --</subfield><subfield code="g">5.</subfield><subfield code="t">Coding archival data : literature reviews, big data, and new media --</subfield><subfield code="t">Reviews of the research literature --</subfield><subfield code="t">Big data --</subfield><subfield code="t">Coding data from the web, including new media --</subfield><subfield code="t">Conclusion : coding data from archival, web, and new media sources --</subfield><subfield code="g">Part II.</subfield><subfield code="t">Analysis and interpretation of quantitative data --</subfield><subfield code="t">Introduction to Part II --</subfield><subfield code="t">Conceptual and terminological housekeeping : theory, model, hypothesis, concept, variable --</subfield><subfield code="t">And a note on software --</subfield><subfield code="g">6.</subfield><subfield code="t">Describing, exploring, and visualizing your data --</subfield><subfield code="t">What is meant by descriptive statistics? --</subfield><subfield code="t">Overview of the main types of descriptive statistics and their uses --</subfield><subfield code="t">What descriptive statistics to use to prepare for further analyses --</subfield><subfield code="t">When to use correlations as descriptive statistics --</subfield><subfield code="t">When and why to make the normal curve your point of reference --</subfield><subfield code="t">When can you use descriptive statistics substantively? --</subfield><subfield code="t">When to use descriptive statistics preparatory to applying missing data procedures --</subfield><subfield code="t">Conclusion --</subfield><subfield code="g">7.</subfield><subfield code="t">What methods of statistical inference to use when --</subfield><subfield code="t">Null hypothesis significance testing --</subfield><subfield code="t">Which statistical tests to use for what --</subfield><subfield code="t">When to use confidence intervals --</subfield><subfield code="t">When to report power and precision of your estimates --</subfield><subfield code="t">When should you use distribution-free, nonparametric significance tests? --</subfield><subfield code="t">When to use the bootstrap and other resampling methods --</subfield><subfield code="t">When to use Bayesian methods --</subfield><subfield code="t">Which approach to statistical inference should you take? --</subfield><subfield code="t">The "silent killer" of valid inferences : missing data --</subfield><subfield code="t">Conclusion --</subfield><subfield code="t">Appendix 7.1. 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--</subfield><subfield code="t">When to use a multimodel approach --</subfield><subfield code="t">Conclusion : a research agenda --</subfield><subfield code="g">Part III.</subfield><subfield code="t">Analysis and interpretation of qualitative and combined/mixed data --</subfield><subfield code="t">Introduction to Part III --</subfield><subfield code="g">11.</subfield><subfield code="t">Inductive analysis of qualitative data : ethnographic approaches and grounded theory --</subfield><subfield code="t">The foundations of inductive social research in ethnographic fieldwork --</subfield><subfield code="t">Grounded theory : an inductive approach to theory building --</subfield><subfield code="t">Conclusion --</subfield><subfield code="g">12.</subfield><subfield code="t">Deductive analyses of qualitative data : comparative case studies and qualitative comparative analysis --</subfield><subfield code="t">Case studies and deductive analyses --</subfield><subfield code="t">When to do a single-case analysis : discovering, describing, and explaining causal links --</subfield><subfield code="t">When to conduct small-n comparative case studies --</subfield><subfield code="t">When to conduct analyses with an intermediate n of cases --</subfield><subfield code="t">Conclusions --</subfield><subfield code="g">13.</subfield><subfield code="t">Coding and analyzing data from combined and mixed designs --</subfield><subfield code="t">Coding and analysis considerations for deductive and inductive designs --</subfield><subfield code="t">Coding considerations for sequential analysis approaches --</subfield><subfield code="t">Data transformation/data merging in combined designs --</subfield><subfield code="t">Conclusions --</subfield><subfield code="g">14.</subfield><subfield code="t">Conclusion : common themes and diverse choices --</subfield><subfield code="t">Common themes --</subfield><subfield code="t">The choice problem --</subfield><subfield code="t">Strategies and tactics.</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">Social sciences</subfield><subfield code="x">Research</subfield><subfield code="x">Methodology.</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">Quantitative research.</subfield><subfield code="0">http://id.loc.gov/authorities/subjects/sh2007000909</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">Qualitative research.</subfield><subfield code="0">http://id.loc.gov/authorities/subjects/sh99004969</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">Research.</subfield><subfield code="0">http://id.loc.gov/authorities/subjects/sh85113021</subfield></datafield><datafield tag="650" ind1=" " ind2="2"><subfield code="a">Social Sciences</subfield><subfield code="x">methods</subfield></datafield><datafield tag="650" ind1=" " ind2="2"><subfield code="a">Research</subfield></datafield><datafield tag="650" ind1=" " ind2="6"><subfield code="a">Sciences sociales</subfield><subfield code="x">Recherche</subfield><subfield code="x">Méthodologie.</subfield></datafield><datafield tag="650" ind1=" " ind2="6"><subfield code="a">Recherche quantitative.</subfield></datafield><datafield tag="650" ind1=" " ind2="6"><subfield code="a">Recherche qualitative.</subfield></datafield><datafield tag="650" ind1=" " ind2="6"><subfield code="a">Recherche.</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">research (function)</subfield><subfield code="2">aat</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">REFERENCE</subfield><subfield code="x">Questions & Answers.</subfield><subfield code="2">bisacsh</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Research</subfield><subfield code="2">fast</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Qualitative research</subfield><subfield code="2">fast</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Quantitative research</subfield><subfield code="2">fast</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Social sciences</subfield><subfield code="x">Research</subfield><subfield code="x">Methodology</subfield><subfield code="2">fast</subfield></datafield><datafield tag="758" ind1=" " ind2=" "><subfield code="i">has work:</subfield><subfield code="a">Selecting the right analyses for your data (Text)</subfield><subfield code="1">https://id.oclc.org/worldcat/entity/E39PCFMj3VKwybyxD4c3x6J6jC</subfield><subfield code="4">https://id.oclc.org/worldcat/ontology/hasWork</subfield></datafield><datafield tag="776" ind1="0" ind2="8"><subfield code="i">Print version:</subfield><subfield code="a">Vogt, W. 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| id | ZDB-4-EBA-ocn879074384 |
| illustrated | Not Illustrated |
| indexdate | 2024-11-27T13:25:57Z |
| institution | BVB |
| isbn | 9781462516032 1462516033 |
| language | English |
| oclc_num | 879074384 |
| open_access_boolean | |
| owner | MAIN DE-863 DE-BY-FWS |
| owner_facet | MAIN DE-863 DE-BY-FWS |
| physical | 1 online resource (xix, 500 pages) |
| psigel | ZDB-4-EBA |
| publishDate | 2014 |
| publishDateSearch | 2014 |
| publishDateSort | 2014 |
| publisher | Guilford Publications, |
| record_format | marc |
| spelling | Vogt, W. Paul, author. http://id.loc.gov/authorities/names/n85313590 Selecting the right analyses for your data : quantitative, qualitative, and mixed methods / W. Paul Vogt [and others]. New York, NY : Guilford Publications, [2014] ©2014 1 online resource (xix, 500 pages) text txt rdacontent computer c rdamedia online resource cr rdacarrier Text in English. "What are the most effective methods to code and analyze data for a particular study? This thoughtful and engaging book reviews the selection criteria for coding and analyzing any set of data--whether qualitative, quantitative, mixed, or visual. The authors systematically explain when to use verbal, numerical, graphic, or combined codes, and when to use qualitative, quantitative, graphic, or mixed-methods modes of analysis. Chapters on each topic are organized so that researchers can read them sequentially or can easily "flip and find" answers to specific questions. Nontechnical discussions of cutting-edge approaches--illustrated with real-world examples--emphasize how to choose (rather than how to implement) the various analyses. The book shows how using the right analysis methods leads to more justifiable conclusions and more persuasive presentations of research results. Useful features for teaching or self-study: *Chapter-opening preview boxes that highlight useful topics addressed. *End-of-chapter summary tables recapping the 'dos and don'ts' and advantages and disadvantages of each analytic technique. *Annotated suggestions for further reading and technical resources on each topic. Subject Areas/Keywords: analyses, coding, combined methods, data analysis, data collection, dissertation, graphical, interpretation, mixed methods, qualitative, quantitative, research analysis, research designs, research methods, social sciences, thesis, visual Audience: Researchers, instructors, and graduate students in a range of disciplines, including psychology, education, social work, sociology, health, and management; administrators and managers who need to make data-driven decisions"-- Provided by publisher Includes bibliographical references and index. Print version record. Part I. Coding data-by design -- Part II. Analysis and interpretation of quantitative data -- Part III. Analysis and interpretation of qualitative and combined/mixed data. General introduction -- What are data? -- Two basic organizing questions -- Ranks or ordered coding (when to use ordinal data) -- Visual/graphic data, coding, and analyses -- At what point does coding occur in the course of your research project? -- Codes and the phenomena we study -- A graphic depiction of the relation of coding to analysis -- Examples of coding and analysis -- Looking ahead -- Part I. Coding data-by design -- Introduction to Part I -- 1. Coding survey data -- An example : pitfalls when constructing a survey -- What methods to use to construct an effective questionnaire -- Coding and measuring respondents' answers to the questions -- Conclusion : where to find analysis guidelines for surveys in this book -- 2. Coding interview data -- Goals : what do you seek when asking questions? -- Your role : what should your part be in the dialogue? -- Samples : how many interviews and with whom? -- Questions : when do you ask what kinds of questions? -- Modes : how do you communicate with interviewees? -- Observations : what Is important that isn't said? -- Records : what methods do you use to preserve the dialogue? -- Tools : when should you use computers to code your data? -- Getting help : when to use member checks and multiple coders -- Conclusion -- 3. Coding experimental data -- Coding and measurement issues for all experimental designs -- Coding and measurement issues that vary by type of experimental design -- Conclusion : where in this book to find guidelines for analyzing experimental data -- 4. Coding data from naturalistic and participant observations -- Introduction to observational research -- Phase 1 : observing -- Phase 2 : recording -- Phase 3 : coding -- Recommendations -- Conclusions and tips for completing an observational study -- Appendix 4.1. Example of a site visit protocol -- 5. Coding archival data : literature reviews, big data, and new media -- Reviews of the research literature -- Big data -- Coding data from the web, including new media -- Conclusion : coding data from archival, web, and new media sources -- Part II. Analysis and interpretation of quantitative data -- Introduction to Part II -- Conceptual and terminological housekeeping : theory, model, hypothesis, concept, variable -- And a note on software -- 6. Describing, exploring, and visualizing your data -- What is meant by descriptive statistics? -- Overview of the main types of descriptive statistics and their uses -- What descriptive statistics to use to prepare for further analyses -- When to use correlations as descriptive statistics -- When and why to make the normal curve your point of reference -- When can you use descriptive statistics substantively? -- When to use descriptive statistics preparatory to applying missing data procedures -- Conclusion -- 7. What methods of statistical inference to use when -- Null hypothesis significance testing -- Which statistical tests to use for what -- When to use confidence intervals -- When to report power and precision of your estimates -- When should you use distribution-free, nonparametric significance tests? -- When to use the bootstrap and other resampling methods -- When to use Bayesian methods -- Which approach to statistical inference should you take? -- The "silent killer" of valid inferences : missing data -- Conclusion -- Appendix 7.1. Examples of output of significance tests -- 8. What associational statistics to use when -- When to use correlations to analyze data -- When to use regression analysis -- What to do when your dependent variables are categorical -- Summary : which associational methods work best for what sorts of data and problems? -- The most important question : when to include which variables -- Conclusion : relations among variables to investigate using regression analysis -- 9. Advanced associational methods -- Multilevel modeling -- Path analysis -- Factor analysis-exploratory and confirmatory -- Structural equation modeling -- Conclusion -- 10. Model building and selection -- When can you benefit from building a model or constructing a theory? -- When to use a multimodel approach -- Conclusion : a research agenda -- Part III. Analysis and interpretation of qualitative and combined/mixed data -- Introduction to Part III -- 11. Inductive analysis of qualitative data : ethnographic approaches and grounded theory -- The foundations of inductive social research in ethnographic fieldwork -- Grounded theory : an inductive approach to theory building -- Conclusion -- 12. Deductive analyses of qualitative data : comparative case studies and qualitative comparative analysis -- Case studies and deductive analyses -- When to do a single-case analysis : discovering, describing, and explaining causal links -- When to conduct small-n comparative case studies -- When to conduct analyses with an intermediate n of cases -- Conclusions -- 13. Coding and analyzing data from combined and mixed designs -- Coding and analysis considerations for deductive and inductive designs -- Coding considerations for sequential analysis approaches -- Data transformation/data merging in combined designs -- Conclusions -- 14. Conclusion : common themes and diverse choices -- Common themes -- The choice problem -- Strategies and tactics. Social sciences Research Methodology. Quantitative research. http://id.loc.gov/authorities/subjects/sh2007000909 Qualitative research. http://id.loc.gov/authorities/subjects/sh99004969 Research. http://id.loc.gov/authorities/subjects/sh85113021 Social Sciences methods Research Sciences sociales Recherche Méthodologie. Recherche quantitative. Recherche qualitative. Recherche. research (function) aat REFERENCE Questions & Answers. bisacsh Research fast Qualitative research fast Quantitative research fast Social sciences Research Methodology fast has work: Selecting the right analyses for your data (Text) https://id.oclc.org/worldcat/entity/E39PCFMj3VKwybyxD4c3x6J6jC https://id.oclc.org/worldcat/ontology/hasWork Print version: Vogt, W. Paul. Selecting the right analyses for your data. New York : Guilford Publications, 2014 9781462515769 FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=771500 Volltext |
| spellingShingle | Vogt, W. Paul Selecting the right analyses for your data : quantitative, qualitative, and mixed methods / Part I. Coding data-by design -- Part II. Analysis and interpretation of quantitative data -- Part III. Analysis and interpretation of qualitative and combined/mixed data. General introduction -- What are data? -- Two basic organizing questions -- Ranks or ordered coding (when to use ordinal data) -- Visual/graphic data, coding, and analyses -- At what point does coding occur in the course of your research project? -- Codes and the phenomena we study -- A graphic depiction of the relation of coding to analysis -- Examples of coding and analysis -- Looking ahead -- Coding data-by design -- Introduction to Part I -- Coding survey data -- An example : pitfalls when constructing a survey -- What methods to use to construct an effective questionnaire -- Coding and measuring respondents' answers to the questions -- Conclusion : where to find analysis guidelines for surveys in this book -- Coding interview data -- Goals : what do you seek when asking questions? -- Your role : what should your part be in the dialogue? -- Samples : how many interviews and with whom? -- Questions : when do you ask what kinds of questions? -- Modes : how do you communicate with interviewees? -- Observations : what Is important that isn't said? -- Records : what methods do you use to preserve the dialogue? -- Tools : when should you use computers to code your data? -- Getting help : when to use member checks and multiple coders -- Conclusion -- Coding experimental data -- Coding and measurement issues for all experimental designs -- Coding and measurement issues that vary by type of experimental design -- Conclusion : where in this book to find guidelines for analyzing experimental data -- Coding data from naturalistic and participant observations -- Introduction to observational research -- Phase 1 : observing -- Phase 2 : recording -- Phase 3 : coding -- Recommendations -- Conclusions and tips for completing an observational study -- Appendix 4.1. Example of a site visit protocol -- Coding archival data : literature reviews, big data, and new media -- Reviews of the research literature -- Big data -- Coding data from the web, including new media -- Conclusion : coding data from archival, web, and new media sources -- Analysis and interpretation of quantitative data -- Introduction to Part II -- Conceptual and terminological housekeeping : theory, model, hypothesis, concept, variable -- And a note on software -- Describing, exploring, and visualizing your data -- What is meant by descriptive statistics? -- Overview of the main types of descriptive statistics and their uses -- What descriptive statistics to use to prepare for further analyses -- When to use correlations as descriptive statistics -- When and why to make the normal curve your point of reference -- When can you use descriptive statistics substantively? -- When to use descriptive statistics preparatory to applying missing data procedures -- What methods of statistical inference to use when -- Null hypothesis significance testing -- Which statistical tests to use for what -- When to use confidence intervals -- When to report power and precision of your estimates -- When should you use distribution-free, nonparametric significance tests? -- When to use the bootstrap and other resampling methods -- When to use Bayesian methods -- Which approach to statistical inference should you take? -- The "silent killer" of valid inferences : missing data -- Appendix 7.1. Examples of output of significance tests -- What associational statistics to use when -- When to use correlations to analyze data -- When to use regression analysis -- What to do when your dependent variables are categorical -- Summary : which associational methods work best for what sorts of data and problems? -- The most important question : when to include which variables -- Conclusion : relations among variables to investigate using regression analysis -- Advanced associational methods -- Multilevel modeling -- Path analysis -- Factor analysis-exploratory and confirmatory -- Structural equation modeling -- Model building and selection -- When can you benefit from building a model or constructing a theory? -- When to use a multimodel approach -- Conclusion : a research agenda -- Analysis and interpretation of qualitative and combined/mixed data -- Introduction to Part III -- Inductive analysis of qualitative data : ethnographic approaches and grounded theory -- The foundations of inductive social research in ethnographic fieldwork -- Grounded theory : an inductive approach to theory building -- Deductive analyses of qualitative data : comparative case studies and qualitative comparative analysis -- Case studies and deductive analyses -- When to do a single-case analysis : discovering, describing, and explaining causal links -- When to conduct small-n comparative case studies -- When to conduct analyses with an intermediate n of cases -- Conclusions -- Coding and analyzing data from combined and mixed designs -- Coding and analysis considerations for deductive and inductive designs -- Coding considerations for sequential analysis approaches -- Data transformation/data merging in combined designs -- Conclusion : common themes and diverse choices -- Common themes -- The choice problem -- Strategies and tactics. Social sciences Research Methodology. Quantitative research. http://id.loc.gov/authorities/subjects/sh2007000909 Qualitative research. http://id.loc.gov/authorities/subjects/sh99004969 Research. http://id.loc.gov/authorities/subjects/sh85113021 Social Sciences methods Research Sciences sociales Recherche Méthodologie. Recherche quantitative. Recherche qualitative. Recherche. research (function) aat REFERENCE Questions & Answers. bisacsh Research fast Qualitative research fast Quantitative research fast Social sciences Research Methodology fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh2007000909 http://id.loc.gov/authorities/subjects/sh99004969 http://id.loc.gov/authorities/subjects/sh85113021 |
| title | Selecting the right analyses for your data : quantitative, qualitative, and mixed methods / |
| title_alt | General introduction -- What are data? -- Two basic organizing questions -- Ranks or ordered coding (when to use ordinal data) -- Visual/graphic data, coding, and analyses -- At what point does coding occur in the course of your research project? -- Codes and the phenomena we study -- A graphic depiction of the relation of coding to analysis -- Examples of coding and analysis -- Looking ahead -- Coding data-by design -- Introduction to Part I -- Coding survey data -- An example : pitfalls when constructing a survey -- What methods to use to construct an effective questionnaire -- Coding and measuring respondents' answers to the questions -- Conclusion : where to find analysis guidelines for surveys in this book -- Coding interview data -- Goals : what do you seek when asking questions? -- Your role : what should your part be in the dialogue? -- Samples : how many interviews and with whom? -- Questions : when do you ask what kinds of questions? -- Modes : how do you communicate with interviewees? -- Observations : what Is important that isn't said? -- Records : what methods do you use to preserve the dialogue? -- Tools : when should you use computers to code your data? -- Getting help : when to use member checks and multiple coders -- Conclusion -- Coding experimental data -- Coding and measurement issues for all experimental designs -- Coding and measurement issues that vary by type of experimental design -- Conclusion : where in this book to find guidelines for analyzing experimental data -- Coding data from naturalistic and participant observations -- Introduction to observational research -- Phase 1 : observing -- Phase 2 : recording -- Phase 3 : coding -- Recommendations -- Conclusions and tips for completing an observational study -- Appendix 4.1. Example of a site visit protocol -- Coding archival data : literature reviews, big data, and new media -- Reviews of the research literature -- Big data -- Coding data from the web, including new media -- Conclusion : coding data from archival, web, and new media sources -- Analysis and interpretation of quantitative data -- Introduction to Part II -- Conceptual and terminological housekeeping : theory, model, hypothesis, concept, variable -- And a note on software -- Describing, exploring, and visualizing your data -- What is meant by descriptive statistics? -- Overview of the main types of descriptive statistics and their uses -- What descriptive statistics to use to prepare for further analyses -- When to use correlations as descriptive statistics -- When and why to make the normal curve your point of reference -- When can you use descriptive statistics substantively? -- When to use descriptive statistics preparatory to applying missing data procedures -- What methods of statistical inference to use when -- Null hypothesis significance testing -- Which statistical tests to use for what -- When to use confidence intervals -- When to report power and precision of your estimates -- When should you use distribution-free, nonparametric significance tests? -- When to use the bootstrap and other resampling methods -- When to use Bayesian methods -- Which approach to statistical inference should you take? -- The "silent killer" of valid inferences : missing data -- Appendix 7.1. Examples of output of significance tests -- What associational statistics to use when -- When to use correlations to analyze data -- When to use regression analysis -- What to do when your dependent variables are categorical -- Summary : which associational methods work best for what sorts of data and problems? -- The most important question : when to include which variables -- Conclusion : relations among variables to investigate using regression analysis -- Advanced associational methods -- Multilevel modeling -- Path analysis -- Factor analysis-exploratory and confirmatory -- Structural equation modeling -- Model building and selection -- When can you benefit from building a model or constructing a theory? -- When to use a multimodel approach -- Conclusion : a research agenda -- Analysis and interpretation of qualitative and combined/mixed data -- Introduction to Part III -- Inductive analysis of qualitative data : ethnographic approaches and grounded theory -- The foundations of inductive social research in ethnographic fieldwork -- Grounded theory : an inductive approach to theory building -- Deductive analyses of qualitative data : comparative case studies and qualitative comparative analysis -- Case studies and deductive analyses -- When to do a single-case analysis : discovering, describing, and explaining causal links -- When to conduct small-n comparative case studies -- When to conduct analyses with an intermediate n of cases -- Conclusions -- Coding and analyzing data from combined and mixed designs -- Coding and analysis considerations for deductive and inductive designs -- Coding considerations for sequential analysis approaches -- Data transformation/data merging in combined designs -- Conclusion : common themes and diverse choices -- Common themes -- The choice problem -- Strategies and tactics. |
| title_auth | Selecting the right analyses for your data : quantitative, qualitative, and mixed methods / |
| title_exact_search | Selecting the right analyses for your data : quantitative, qualitative, and mixed methods / |
| title_full | Selecting the right analyses for your data : quantitative, qualitative, and mixed methods / W. Paul Vogt [and others]. |
| title_fullStr | Selecting the right analyses for your data : quantitative, qualitative, and mixed methods / W. Paul Vogt [and others]. |
| title_full_unstemmed | Selecting the right analyses for your data : quantitative, qualitative, and mixed methods / W. Paul Vogt [and others]. |
| title_short | Selecting the right analyses for your data : |
| title_sort | selecting the right analyses for your data quantitative qualitative and mixed methods |
| title_sub | quantitative, qualitative, and mixed methods / |
| topic | Social sciences Research Methodology. Quantitative research. http://id.loc.gov/authorities/subjects/sh2007000909 Qualitative research. http://id.loc.gov/authorities/subjects/sh99004969 Research. http://id.loc.gov/authorities/subjects/sh85113021 Social Sciences methods Research Sciences sociales Recherche Méthodologie. Recherche quantitative. Recherche qualitative. Recherche. research (function) aat REFERENCE Questions & Answers. bisacsh Research fast Qualitative research fast Quantitative research fast Social sciences Research Methodology fast |
| topic_facet | Social sciences Research Methodology. Quantitative research. Qualitative research. Research. Social Sciences methods Research Sciences sociales Recherche Méthodologie. Recherche quantitative. Recherche qualitative. Recherche. research (function) REFERENCE Questions & Answers. Qualitative research Quantitative research Social sciences Research Methodology |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=771500 |
| work_keys_str_mv | AT vogtwpaul selectingtherightanalysesforyourdataquantitativequalitativeandmixedmethods |