Multivariate data analysis of categorical data: taking advantage of the rhetorical power of numbers in qualitative research

There is a general understanding that quantitative methods are more trustworthy than methods based uniquely on words and discourse. In this paper, we depart from this thinking to explore how numbers can be used in qualitative research so as to take advantage of its expressive power. We present a tec...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Quality & quantity 2023-12, Vol.57 (6), p.5283-5312
Hauptverfasser: Donaires, Omar Sacilotto, Cezarino, Luciana Oranges, Liboni, Lara Bartocci, Ribeiro, Evandro Marcos Saidel, Martins, Flávio Pinheiro
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page 5312
container_issue 6
container_start_page 5283
container_title Quality & quantity
container_volume 57
creator Donaires, Omar Sacilotto
Cezarino, Luciana Oranges
Liboni, Lara Bartocci
Ribeiro, Evandro Marcos Saidel
Martins, Flávio Pinheiro
description There is a general understanding that quantitative methods are more trustworthy than methods based uniquely on words and discourse. In this paper, we depart from this thinking to explore how numbers can be used in qualitative research so as to take advantage of its expressive power. We present a technique that enables the application of multivariate data analysis—particularly of interdependence methods, which include principal components analysis, factor analysis, cluster analysis, and multidimensional scaling—in qualitative research. The technique consists in translating categorical data from qualitative research into a binary form that enables the calculation of correlations, similarity coefficients, and distances, thus enabling the application of the interdependence methods of multivariate data analysis. Results also include a brief taxonomy of literature review. It contributes by demonstrating how qualitative research can benefit from quantitative analysis.
doi_str_mv 10.1007/s11135-022-01589-1
format Article
fullrecord <record><control><sourceid>gale_proqu</sourceid><recordid>TN_cdi_proquest_journals_2880584236</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><galeid>A770145139</galeid><sourcerecordid>A770145139</sourcerecordid><originalsourceid>FETCH-LOGICAL-c2731-d3a7e9b3ab6cb0009447b22e313c9f74a2708dc92d0b9d7371aa0932eb84d24f3</originalsourceid><addsrcrecordid>eNp9kc1q3DAUhUVoIdMkL5CVIGtPr34c2d0NIf2BlG6StbiWrz1KPfaMJKeEvHw18UB3RQvB0ffpShzGrgWsBYD5HIUQqixAygJEWdWFOGMrURpVmEqXH9gKQKmiFMacs08xPgNkTZsVe_s5D8m_YPCYiLeYkOOIw2v0kU8ddzntp-AdDu-HX3jC337sObYvOCbs6UilLfGwpXQC99MfCsd8nHcNhcj9yA8zDj5hHpVRioTBbS_Zxw6HSFen_YI9fb1_vPtePPz69uNu81A4aZQoWoWG6kZhc-saAKi1No2UpIRydWc0SgNV62rZQlO3RhmBCLWS1FS6lbpTF-xmuXcfpsNMMdnnaQ75l9HKqoKy0lLdZmq9UD0OZP3YTSmgy6ulnXfTSJ3P-cYYELoUqs6CXAQXphgDdXYf_A7DqxVgj63YpRWbW7HvrViRJbVIMcNjT-HfW_5j_QVFppDU</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2880584236</pqid></control><display><type>article</type><title>Multivariate data analysis of categorical data: taking advantage of the rhetorical power of numbers in qualitative research</title><source>SpringerLink Journals</source><source>Sociological Abstracts</source><creator>Donaires, Omar Sacilotto ; Cezarino, Luciana Oranges ; Liboni, Lara Bartocci ; Ribeiro, Evandro Marcos Saidel ; Martins, Flávio Pinheiro</creator><creatorcontrib>Donaires, Omar Sacilotto ; Cezarino, Luciana Oranges ; Liboni, Lara Bartocci ; Ribeiro, Evandro Marcos Saidel ; Martins, Flávio Pinheiro</creatorcontrib><description>There is a general understanding that quantitative methods are more trustworthy than methods based uniquely on words and discourse. In this paper, we depart from this thinking to explore how numbers can be used in qualitative research so as to take advantage of its expressive power. We present a technique that enables the application of multivariate data analysis—particularly of interdependence methods, which include principal components analysis, factor analysis, cluster analysis, and multidimensional scaling—in qualitative research. The technique consists in translating categorical data from qualitative research into a binary form that enables the calculation of correlations, similarity coefficients, and distances, thus enabling the application of the interdependence methods of multivariate data analysis. Results also include a brief taxonomy of literature review. It contributes by demonstrating how qualitative research can benefit from quantitative analysis.</description><identifier>ISSN: 0033-5177</identifier><identifier>EISSN: 1573-7845</identifier><identifier>DOI: 10.1007/s11135-022-01589-1</identifier><language>eng</language><publisher>Dordrecht: Springer Netherlands</publisher><subject>Bibliometrics ; Classification ; Data ; Data analysis ; Datasets ; Factor analysis ; Information management ; Interdependence ; Literature reviews ; Methodology of the Social Sciences ; Nominal measurement ; Power ; Principal components analysis ; Qualitative research ; Quantitative analysis ; Researchers ; Social Sciences ; Statistics</subject><ispartof>Quality &amp; quantity, 2023-12, Vol.57 (6), p.5283-5312</ispartof><rights>The Author(s), under exclusive licence to Springer Nature B.V. 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</rights><rights>COPYRIGHT 2023 Springer</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c2731-d3a7e9b3ab6cb0009447b22e313c9f74a2708dc92d0b9d7371aa0932eb84d24f3</citedby><cites>FETCH-LOGICAL-c2731-d3a7e9b3ab6cb0009447b22e313c9f74a2708dc92d0b9d7371aa0932eb84d24f3</cites><orcidid>0000-0001-5556-8275</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s11135-022-01589-1$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s11135-022-01589-1$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,780,784,27344,27924,27925,33774,41488,42557,51319</link.rule.ids></links><search><creatorcontrib>Donaires, Omar Sacilotto</creatorcontrib><creatorcontrib>Cezarino, Luciana Oranges</creatorcontrib><creatorcontrib>Liboni, Lara Bartocci</creatorcontrib><creatorcontrib>Ribeiro, Evandro Marcos Saidel</creatorcontrib><creatorcontrib>Martins, Flávio Pinheiro</creatorcontrib><title>Multivariate data analysis of categorical data: taking advantage of the rhetorical power of numbers in qualitative research</title><title>Quality &amp; quantity</title><addtitle>Qual Quant</addtitle><description>There is a general understanding that quantitative methods are more trustworthy than methods based uniquely on words and discourse. In this paper, we depart from this thinking to explore how numbers can be used in qualitative research so as to take advantage of its expressive power. We present a technique that enables the application of multivariate data analysis—particularly of interdependence methods, which include principal components analysis, factor analysis, cluster analysis, and multidimensional scaling—in qualitative research. The technique consists in translating categorical data from qualitative research into a binary form that enables the calculation of correlations, similarity coefficients, and distances, thus enabling the application of the interdependence methods of multivariate data analysis. Results also include a brief taxonomy of literature review. It contributes by demonstrating how qualitative research can benefit from quantitative analysis.</description><subject>Bibliometrics</subject><subject>Classification</subject><subject>Data</subject><subject>Data analysis</subject><subject>Datasets</subject><subject>Factor analysis</subject><subject>Information management</subject><subject>Interdependence</subject><subject>Literature reviews</subject><subject>Methodology of the Social Sciences</subject><subject>Nominal measurement</subject><subject>Power</subject><subject>Principal components analysis</subject><subject>Qualitative research</subject><subject>Quantitative analysis</subject><subject>Researchers</subject><subject>Social Sciences</subject><subject>Statistics</subject><issn>0033-5177</issn><issn>1573-7845</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>8G5</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>BHHNA</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><sourceid>GUQSH</sourceid><sourceid>M2O</sourceid><recordid>eNp9kc1q3DAUhUVoIdMkL5CVIGtPr34c2d0NIf2BlG6StbiWrz1KPfaMJKeEvHw18UB3RQvB0ffpShzGrgWsBYD5HIUQqixAygJEWdWFOGMrURpVmEqXH9gKQKmiFMacs08xPgNkTZsVe_s5D8m_YPCYiLeYkOOIw2v0kU8ddzntp-AdDu-HX3jC337sObYvOCbs6UilLfGwpXQC99MfCsd8nHcNhcj9yA8zDj5hHpVRioTBbS_Zxw6HSFen_YI9fb1_vPtePPz69uNu81A4aZQoWoWG6kZhc-saAKi1No2UpIRydWc0SgNV62rZQlO3RhmBCLWS1FS6lbpTF-xmuXcfpsNMMdnnaQ75l9HKqoKy0lLdZmq9UD0OZP3YTSmgy6ulnXfTSJ3P-cYYELoUqs6CXAQXphgDdXYf_A7DqxVgj63YpRWbW7HvrViRJbVIMcNjT-HfW_5j_QVFppDU</recordid><startdate>20231201</startdate><enddate>20231201</enddate><creator>Donaires, Omar Sacilotto</creator><creator>Cezarino, Luciana Oranges</creator><creator>Liboni, Lara Bartocci</creator><creator>Ribeiro, Evandro Marcos Saidel</creator><creator>Martins, Flávio Pinheiro</creator><general>Springer Netherlands</general><general>Springer</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>0-V</scope><scope>3V.</scope><scope>7U4</scope><scope>7WY</scope><scope>7WZ</scope><scope>7XB</scope><scope>87Z</scope><scope>88G</scope><scope>88J</scope><scope>8BJ</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>8FL</scope><scope>8G5</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ALSLI</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BEZIV</scope><scope>BHHNA</scope><scope>CCPQU</scope><scope>DWI</scope><scope>DWQXO</scope><scope>FQK</scope><scope>FRNLG</scope><scope>FYUFA</scope><scope>F~G</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>GUQSH</scope><scope>HEHIP</scope><scope>JBE</scope><scope>K60</scope><scope>K6~</scope><scope>L.-</scope><scope>M0C</scope><scope>M2M</scope><scope>M2O</scope><scope>M2R</scope><scope>M2S</scope><scope>MBDVC</scope><scope>PQBIZ</scope><scope>PQBZA</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PSYQQ</scope><scope>Q9U</scope><scope>WZK</scope><orcidid>https://orcid.org/0000-0001-5556-8275</orcidid></search><sort><creationdate>20231201</creationdate><title>Multivariate data analysis of categorical data: taking advantage of the rhetorical power of numbers in qualitative research</title><author>Donaires, Omar Sacilotto ; Cezarino, Luciana Oranges ; Liboni, Lara Bartocci ; Ribeiro, Evandro Marcos Saidel ; Martins, Flávio Pinheiro</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c2731-d3a7e9b3ab6cb0009447b22e313c9f74a2708dc92d0b9d7371aa0932eb84d24f3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Bibliometrics</topic><topic>Classification</topic><topic>Data</topic><topic>Data analysis</topic><topic>Datasets</topic><topic>Factor analysis</topic><topic>Information management</topic><topic>Interdependence</topic><topic>Literature reviews</topic><topic>Methodology of the Social Sciences</topic><topic>Nominal measurement</topic><topic>Power</topic><topic>Principal components analysis</topic><topic>Qualitative research</topic><topic>Quantitative analysis</topic><topic>Researchers</topic><topic>Social Sciences</topic><topic>Statistics</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Donaires, Omar Sacilotto</creatorcontrib><creatorcontrib>Cezarino, Luciana Oranges</creatorcontrib><creatorcontrib>Liboni, Lara Bartocci</creatorcontrib><creatorcontrib>Ribeiro, Evandro Marcos Saidel</creatorcontrib><creatorcontrib>Martins, Flávio Pinheiro</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Social Sciences Premium Collection</collection><collection>ProQuest Central (Corporate)</collection><collection>Sociological Abstracts (pre-2017)</collection><collection>Access via ABI/INFORM (ProQuest)</collection><collection>ABI/INFORM Global (PDF only)</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>ABI/INFORM Global (Alumni Edition)</collection><collection>Psychology Database (Alumni)</collection><collection>Social Science Database (Alumni Edition)</collection><collection>International Bibliography of the Social Sciences (IBSS)</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ABI/INFORM Collection (Alumni Edition)</collection><collection>Research Library (Alumni Edition)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Social Science Premium Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Business Premium Collection</collection><collection>Sociological Abstracts</collection><collection>ProQuest One Community College</collection><collection>Sociological Abstracts</collection><collection>ProQuest Central Korea</collection><collection>International Bibliography of the Social Sciences</collection><collection>Business Premium Collection (Alumni)</collection><collection>Health Research Premium Collection</collection><collection>ABI/INFORM Global (Corporate)</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>Research Library Prep</collection><collection>Sociology Collection</collection><collection>International Bibliography of the Social Sciences</collection><collection>ProQuest Business Collection (Alumni Edition)</collection><collection>ProQuest Business Collection</collection><collection>ABI/INFORM Professional Advanced</collection><collection>ABI/INFORM Global</collection><collection>Psychology Database</collection><collection>Research Library</collection><collection>Social Science Database</collection><collection>Sociology Database (ProQuest)</collection><collection>Research Library (Corporate)</collection><collection>ProQuest One Business</collection><collection>ProQuest One Business (Alumni)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest One Psychology</collection><collection>ProQuest Central Basic</collection><collection>Sociological Abstracts (Ovid)</collection><jtitle>Quality &amp; quantity</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Donaires, Omar Sacilotto</au><au>Cezarino, Luciana Oranges</au><au>Liboni, Lara Bartocci</au><au>Ribeiro, Evandro Marcos Saidel</au><au>Martins, Flávio Pinheiro</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Multivariate data analysis of categorical data: taking advantage of the rhetorical power of numbers in qualitative research</atitle><jtitle>Quality &amp; quantity</jtitle><stitle>Qual Quant</stitle><date>2023-12-01</date><risdate>2023</risdate><volume>57</volume><issue>6</issue><spage>5283</spage><epage>5312</epage><pages>5283-5312</pages><issn>0033-5177</issn><eissn>1573-7845</eissn><abstract>There is a general understanding that quantitative methods are more trustworthy than methods based uniquely on words and discourse. In this paper, we depart from this thinking to explore how numbers can be used in qualitative research so as to take advantage of its expressive power. We present a technique that enables the application of multivariate data analysis—particularly of interdependence methods, which include principal components analysis, factor analysis, cluster analysis, and multidimensional scaling—in qualitative research. The technique consists in translating categorical data from qualitative research into a binary form that enables the calculation of correlations, similarity coefficients, and distances, thus enabling the application of the interdependence methods of multivariate data analysis. Results also include a brief taxonomy of literature review. It contributes by demonstrating how qualitative research can benefit from quantitative analysis.</abstract><cop>Dordrecht</cop><pub>Springer Netherlands</pub><doi>10.1007/s11135-022-01589-1</doi><tpages>30</tpages><orcidid>https://orcid.org/0000-0001-5556-8275</orcidid></addata></record>
fulltext fulltext
identifier ISSN: 0033-5177
ispartof Quality & quantity, 2023-12, Vol.57 (6), p.5283-5312
issn 0033-5177
1573-7845
language eng
recordid cdi_proquest_journals_2880584236
source SpringerLink Journals; Sociological Abstracts
subjects Bibliometrics
Classification
Data
Data analysis
Datasets
Factor analysis
Information management
Interdependence
Literature reviews
Methodology of the Social Sciences
Nominal measurement
Power
Principal components analysis
Qualitative research
Quantitative analysis
Researchers
Social Sciences
Statistics
title Multivariate data analysis of categorical data: taking advantage of the rhetorical power of numbers in qualitative research
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-01T02%3A14%3A56IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-gale_proqu&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Multivariate%20data%20analysis%20of%20categorical%20data:%20taking%20advantage%20of%20the%20rhetorical%20power%20of%20numbers%20in%20qualitative%20research&rft.jtitle=Quality%20&%20quantity&rft.au=Donaires,%20Omar%20Sacilotto&rft.date=2023-12-01&rft.volume=57&rft.issue=6&rft.spage=5283&rft.epage=5312&rft.pages=5283-5312&rft.issn=0033-5177&rft.eissn=1573-7845&rft_id=info:doi/10.1007/s11135-022-01589-1&rft_dat=%3Cgale_proqu%3EA770145139%3C/gale_proqu%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2880584236&rft_id=info:pmid/&rft_galeid=A770145139&rfr_iscdi=true