Multiblock discriminant correspondence analysis: Exploring group differences with structured categorical data

Psychological research often involves complex datasets that cannot easily be analyzed using traditional statistical methods. Multiblock Discriminant Correspondence Analysis (multiblock dica, also called mudica) examines group differences in large, structured categorical datasets and identifies block...
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Autor*in:	Anjali Krishnan [verfasserIn] Ju-Chi Yu [verfasserIn] Rona Miles [verfasserIn] Derek Beaton [verfasserIn] Laura A. Rabin [verfasserIn] Hervé Abdi [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Discriminant correspondence analysis DICA MUDICA Mental health literacy

Übergeordnetes Werk:	In: Methods in Psychology - Elsevier, 2021, 7(2022), Seite 100100-
Übergeordnetes Werk:	volume:7 ; year:2022 ; pages:100100-

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1016/j.metip.2022.100100

Katalog-ID:	DOAJ005565162

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spelling	10.1016/j.metip.2022.100100 doi (DE-627)DOAJ005565162 (DE-599)DOAJ7aaee1159cac403b9368734bdb5a163f DE-627 ger DE-627 rakwb eng BF1-990 Anjali Krishnan verfasserin aut Multiblock discriminant correspondence analysis: Exploring group differences with structured categorical data 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Psychological research often involves complex datasets that cannot easily be analyzed using traditional statistical methods. Multiblock Discriminant Correspondence Analysis (multiblock dica, also called mudica) examines group differences in large, structured categorical datasets and identifies blocks of variables that contribute to these differences. Data for this illustration were obtained from a study on mental health literacy (N = 648) that included 33 questions that were arranged into four blocks: etiology, symptoms, treatment, and general knowledge of psychological disorders. With non-parametric inference tests and results displayed as intuitive maps, mudica revealed differences in performance across groups not readily detectable using standard methods. Discriminant correspondence analysis DICA MUDICA Mental health literacy Psychology Ju-Chi Yu verfasserin aut Rona Miles verfasserin aut Derek Beaton verfasserin aut Laura A. Rabin verfasserin aut Hervé Abdi verfasserin aut In Methods in Psychology Elsevier, 2021 7(2022), Seite 100100- (DE-627)1689632550 25902601 nnns volume:7 year:2022 pages:100100- https://doi.org/10.1016/j.metip.2022.100100 kostenfrei https://doaj.org/article/7aaee1159cac403b9368734bdb5a163f kostenfrei http://www.sciencedirect.com/science/article/pii/S259026012200011X kostenfrei https://doaj.org/toc/2590-2601 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 7 2022 100100-
allfields_unstemmed	10.1016/j.metip.2022.100100 doi (DE-627)DOAJ005565162 (DE-599)DOAJ7aaee1159cac403b9368734bdb5a163f DE-627 ger DE-627 rakwb eng BF1-990 Anjali Krishnan verfasserin aut Multiblock discriminant correspondence analysis: Exploring group differences with structured categorical data 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Psychological research often involves complex datasets that cannot easily be analyzed using traditional statistical methods. Multiblock Discriminant Correspondence Analysis (multiblock dica, also called mudica) examines group differences in large, structured categorical datasets and identifies blocks of variables that contribute to these differences. Data for this illustration were obtained from a study on mental health literacy (N = 648) that included 33 questions that were arranged into four blocks: etiology, symptoms, treatment, and general knowledge of psychological disorders. With non-parametric inference tests and results displayed as intuitive maps, mudica revealed differences in performance across groups not readily detectable using standard methods. Discriminant correspondence analysis DICA MUDICA Mental health literacy Psychology Ju-Chi Yu verfasserin aut Rona Miles verfasserin aut Derek Beaton verfasserin aut Laura A. Rabin verfasserin aut Hervé Abdi verfasserin aut In Methods in Psychology Elsevier, 2021 7(2022), Seite 100100- (DE-627)1689632550 25902601 nnns volume:7 year:2022 pages:100100- https://doi.org/10.1016/j.metip.2022.100100 kostenfrei https://doaj.org/article/7aaee1159cac403b9368734bdb5a163f kostenfrei http://www.sciencedirect.com/science/article/pii/S259026012200011X kostenfrei https://doaj.org/toc/2590-2601 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 7 2022 100100-
allfieldsGer	10.1016/j.metip.2022.100100 doi (DE-627)DOAJ005565162 (DE-599)DOAJ7aaee1159cac403b9368734bdb5a163f DE-627 ger DE-627 rakwb eng BF1-990 Anjali Krishnan verfasserin aut Multiblock discriminant correspondence analysis: Exploring group differences with structured categorical data 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Psychological research often involves complex datasets that cannot easily be analyzed using traditional statistical methods. Multiblock Discriminant Correspondence Analysis (multiblock dica, also called mudica) examines group differences in large, structured categorical datasets and identifies blocks of variables that contribute to these differences. Data for this illustration were obtained from a study on mental health literacy (N = 648) that included 33 questions that were arranged into four blocks: etiology, symptoms, treatment, and general knowledge of psychological disorders. With non-parametric inference tests and results displayed as intuitive maps, mudica revealed differences in performance across groups not readily detectable using standard methods. Discriminant correspondence analysis DICA MUDICA Mental health literacy Psychology Ju-Chi Yu verfasserin aut Rona Miles verfasserin aut Derek Beaton verfasserin aut Laura A. Rabin verfasserin aut Hervé Abdi verfasserin aut In Methods in Psychology Elsevier, 2021 7(2022), Seite 100100- (DE-627)1689632550 25902601 nnns volume:7 year:2022 pages:100100- https://doi.org/10.1016/j.metip.2022.100100 kostenfrei https://doaj.org/article/7aaee1159cac403b9368734bdb5a163f kostenfrei http://www.sciencedirect.com/science/article/pii/S259026012200011X kostenfrei https://doaj.org/toc/2590-2601 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 7 2022 100100-
allfieldsSound	10.1016/j.metip.2022.100100 doi (DE-627)DOAJ005565162 (DE-599)DOAJ7aaee1159cac403b9368734bdb5a163f DE-627 ger DE-627 rakwb eng BF1-990 Anjali Krishnan verfasserin aut Multiblock discriminant correspondence analysis: Exploring group differences with structured categorical data 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Psychological research often involves complex datasets that cannot easily be analyzed using traditional statistical methods. Multiblock Discriminant Correspondence Analysis (multiblock dica, also called mudica) examines group differences in large, structured categorical datasets and identifies blocks of variables that contribute to these differences. Data for this illustration were obtained from a study on mental health literacy (N = 648) that included 33 questions that were arranged into four blocks: etiology, symptoms, treatment, and general knowledge of psychological disorders. With non-parametric inference tests and results displayed as intuitive maps, mudica revealed differences in performance across groups not readily detectable using standard methods. Discriminant correspondence analysis DICA MUDICA Mental health literacy Psychology Ju-Chi Yu verfasserin aut Rona Miles verfasserin aut Derek Beaton verfasserin aut Laura A. Rabin verfasserin aut Hervé Abdi verfasserin aut In Methods in Psychology Elsevier, 2021 7(2022), Seite 100100- (DE-627)1689632550 25902601 nnns volume:7 year:2022 pages:100100- https://doi.org/10.1016/j.metip.2022.100100 kostenfrei https://doaj.org/article/7aaee1159cac403b9368734bdb5a163f kostenfrei http://www.sciencedirect.com/science/article/pii/S259026012200011X kostenfrei https://doaj.org/toc/2590-2601 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 7 2022 100100-
language	English
source	In Methods in Psychology 7(2022), Seite 100100- volume:7 year:2022 pages:100100-
sourceStr	In Methods in Psychology 7(2022), Seite 100100- volume:7 year:2022 pages:100100-
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Discriminant correspondence analysis DICA MUDICA Mental health literacy Psychology
isfreeaccess_bool	true
container_title	Methods in Psychology
authorswithroles_txt_mv	Anjali Krishnan @@aut@@ Ju-Chi Yu @@aut@@ Rona Miles @@aut@@ Derek Beaton @@aut@@ Laura A. Rabin @@aut@@ Hervé Abdi @@aut@@
publishDateDaySort_date	2022-01-01T00:00:00Z
hierarchy_top_id	1689632550
id	DOAJ005565162
language_de	englisch
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callnumber-first	B - Philosophy, Psychology, Religion
author	Anjali Krishnan
spellingShingle	Anjali Krishnan misc BF1-990 misc Discriminant correspondence analysis misc DICA misc MUDICA misc Mental health literacy misc Psychology Multiblock discriminant correspondence analysis: Exploring group differences with structured categorical data
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topic_title	BF1-990 Multiblock discriminant correspondence analysis: Exploring group differences with structured categorical data Discriminant correspondence analysis DICA MUDICA Mental health literacy
topic	misc BF1-990 misc Discriminant correspondence analysis misc DICA misc MUDICA misc Mental health literacy misc Psychology
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title	Multiblock discriminant correspondence analysis: Exploring group differences with structured categorical data
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title_full	Multiblock discriminant correspondence analysis: Exploring group differences with structured categorical data
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title_sort	multiblock discriminant correspondence analysis: exploring group differences with structured categorical data
callnumber	BF1-990
title_auth	Multiblock discriminant correspondence analysis: Exploring group differences with structured categorical data
abstract	Psychological research often involves complex datasets that cannot easily be analyzed using traditional statistical methods. Multiblock Discriminant Correspondence Analysis (multiblock dica, also called mudica) examines group differences in large, structured categorical datasets and identifies blocks of variables that contribute to these differences. Data for this illustration were obtained from a study on mental health literacy (N = 648) that included 33 questions that were arranged into four blocks: etiology, symptoms, treatment, and general knowledge of psychological disorders. With non-parametric inference tests and results displayed as intuitive maps, mudica revealed differences in performance across groups not readily detectable using standard methods.
abstractGer	Psychological research often involves complex datasets that cannot easily be analyzed using traditional statistical methods. Multiblock Discriminant Correspondence Analysis (multiblock dica, also called mudica) examines group differences in large, structured categorical datasets and identifies blocks of variables that contribute to these differences. Data for this illustration were obtained from a study on mental health literacy (N = 648) that included 33 questions that were arranged into four blocks: etiology, symptoms, treatment, and general knowledge of psychological disorders. With non-parametric inference tests and results displayed as intuitive maps, mudica revealed differences in performance across groups not readily detectable using standard methods.
abstract_unstemmed	Psychological research often involves complex datasets that cannot easily be analyzed using traditional statistical methods. Multiblock Discriminant Correspondence Analysis (multiblock dica, also called mudica) examines group differences in large, structured categorical datasets and identifies blocks of variables that contribute to these differences. Data for this illustration were obtained from a study on mental health literacy (N = 648) that included 33 questions that were arranged into four blocks: etiology, symptoms, treatment, and general knowledge of psychological disorders. With non-parametric inference tests and results displayed as intuitive maps, mudica revealed differences in performance across groups not readily detectable using standard methods.
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title_short	Multiblock discriminant correspondence analysis: Exploring group differences with structured categorical data
url	https://doi.org/10.1016/j.metip.2022.100100 https://doaj.org/article/7aaee1159cac403b9368734bdb5a163f http://www.sciencedirect.com/science/article/pii/S259026012200011X https://doaj.org/toc/2590-2601
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Multiblock discriminant correspondence analysis: Exploring group differences with structured categorical data

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