Why to use Poisson regression for count data analysis in consumer behavior research
Gespeichert in:
| Autor*in: |
Xia, Feihong [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2023 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Journal of marketing analytics - Houndmills : Palgrave Macmillan, 2013, 11(2023), 3 vom: Sept., Seite 379-384 |
|---|---|
| Übergeordnetes Werk: |
volume:11 ; year:2023 ; number:3 ; month:09 ; pages:379-384 |
| Links: |
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| DOI / URN: |
10.1057/s41270-022-00166-7 |
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| Katalog-ID: |
186378781X |
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| 982 | |2 26 |1 00 |x DE-206 |b Count data are often encountered in consumer behavior research. Normal regression, or ordinary least squares, has been used predominantly to analyze count data in experimental studies, while the appropriate models for count data analysis such as Poisson regression have not been fully embraced in consumer behavior research. The fact that only a small fraction of published papers in consumer behavior research with count data have used Poisson regression calls for a push to rethink the common approach of using normal regression for count data analysis. To demonstrate the importance and value of using Poisson regression for count data, we first discuss the parametric forms and properties of both normal regression and Poisson regression, and then show readers through large-scale simulated experiments that Poisson regression is the appropriate model to use for count data, not only because of better model fit but also because of lower error rates in hypothesis testing in various experimental settings, which is critical for consumer behavior researchers. | ||
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10.1057/s41270-022-00166-7 doi (DE-627)186378781X (DE-599)KXP186378781X DE-627 ger DE-627 rda eng Xia, Feihong verfasserin (DE-588)119494664X (DE-627)1677132906 aut Why to use Poisson regression for count data analysis in consumer behavior research Feihong Xia 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Consumer behavior research (dpeaa)DE-206 Count data (dpeaa)DE-206 False negative (dpeaa)DE-206 False positive (dpeaa)DE-206 Hypothesis testing (dpeaa)DE-206 Poisson regression (dpeaa)DE-206 Enthalten in Journal of marketing analytics Houndmills : Palgrave Macmillan, 2013 11(2023), 3 vom: Sept., Seite 379-384 Online-Ressource (DE-627)769570925 (DE-600)2735891-4 (DE-576)394190548 2050-3326 nnns volume:11 year:2023 number:3 month:09 pages:379-384 https://link.springer.com/content/pdf/10.1057/s41270-022-00166-7.pdf Verlag lizenzpflichtig https://doi.org/10.1057/s41270-022-00166-7 Resolving-System lizenzpflichtig GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_184 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 11 2023 3 9 379-384 26 01 0206 4387036087 x1z 13-10-23 26 00 DE-206 Count data are often encountered in consumer behavior research. Normal regression, or ordinary least squares, has been used predominantly to analyze count data in experimental studies, while the appropriate models for count data analysis such as Poisson regression have not been fully embraced in consumer behavior research. The fact that only a small fraction of published papers in consumer behavior research with count data have used Poisson regression calls for a push to rethink the common approach of using normal regression for count data analysis. To demonstrate the importance and value of using Poisson regression for count data, we first discuss the parametric forms and properties of both normal regression and Poisson regression, and then show readers through large-scale simulated experiments that Poisson regression is the appropriate model to use for count data, not only because of better model fit but also because of lower error rates in hypothesis testing in various experimental settings, which is critical for consumer behavior researchers. |
| spelling |
10.1057/s41270-022-00166-7 doi (DE-627)186378781X (DE-599)KXP186378781X DE-627 ger DE-627 rda eng Xia, Feihong verfasserin (DE-588)119494664X (DE-627)1677132906 aut Why to use Poisson regression for count data analysis in consumer behavior research Feihong Xia 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Consumer behavior research (dpeaa)DE-206 Count data (dpeaa)DE-206 False negative (dpeaa)DE-206 False positive (dpeaa)DE-206 Hypothesis testing (dpeaa)DE-206 Poisson regression (dpeaa)DE-206 Enthalten in Journal of marketing analytics Houndmills : Palgrave Macmillan, 2013 11(2023), 3 vom: Sept., Seite 379-384 Online-Ressource (DE-627)769570925 (DE-600)2735891-4 (DE-576)394190548 2050-3326 nnns volume:11 year:2023 number:3 month:09 pages:379-384 https://link.springer.com/content/pdf/10.1057/s41270-022-00166-7.pdf Verlag lizenzpflichtig https://doi.org/10.1057/s41270-022-00166-7 Resolving-System lizenzpflichtig GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_184 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 11 2023 3 9 379-384 26 01 0206 4387036087 x1z 13-10-23 26 00 DE-206 Count data are often encountered in consumer behavior research. Normal regression, or ordinary least squares, has been used predominantly to analyze count data in experimental studies, while the appropriate models for count data analysis such as Poisson regression have not been fully embraced in consumer behavior research. The fact that only a small fraction of published papers in consumer behavior research with count data have used Poisson regression calls for a push to rethink the common approach of using normal regression for count data analysis. To demonstrate the importance and value of using Poisson regression for count data, we first discuss the parametric forms and properties of both normal regression and Poisson regression, and then show readers through large-scale simulated experiments that Poisson regression is the appropriate model to use for count data, not only because of better model fit but also because of lower error rates in hypothesis testing in various experimental settings, which is critical for consumer behavior researchers. |
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10.1057/s41270-022-00166-7 doi (DE-627)186378781X (DE-599)KXP186378781X DE-627 ger DE-627 rda eng Xia, Feihong verfasserin (DE-588)119494664X (DE-627)1677132906 aut Why to use Poisson regression for count data analysis in consumer behavior research Feihong Xia 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Consumer behavior research (dpeaa)DE-206 Count data (dpeaa)DE-206 False negative (dpeaa)DE-206 False positive (dpeaa)DE-206 Hypothesis testing (dpeaa)DE-206 Poisson regression (dpeaa)DE-206 Enthalten in Journal of marketing analytics Houndmills : Palgrave Macmillan, 2013 11(2023), 3 vom: Sept., Seite 379-384 Online-Ressource (DE-627)769570925 (DE-600)2735891-4 (DE-576)394190548 2050-3326 nnns volume:11 year:2023 number:3 month:09 pages:379-384 https://link.springer.com/content/pdf/10.1057/s41270-022-00166-7.pdf Verlag lizenzpflichtig https://doi.org/10.1057/s41270-022-00166-7 Resolving-System lizenzpflichtig GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_184 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 11 2023 3 9 379-384 26 01 0206 4387036087 x1z 13-10-23 26 00 DE-206 Count data are often encountered in consumer behavior research. Normal regression, or ordinary least squares, has been used predominantly to analyze count data in experimental studies, while the appropriate models for count data analysis such as Poisson regression have not been fully embraced in consumer behavior research. The fact that only a small fraction of published papers in consumer behavior research with count data have used Poisson regression calls for a push to rethink the common approach of using normal regression for count data analysis. To demonstrate the importance and value of using Poisson regression for count data, we first discuss the parametric forms and properties of both normal regression and Poisson regression, and then show readers through large-scale simulated experiments that Poisson regression is the appropriate model to use for count data, not only because of better model fit but also because of lower error rates in hypothesis testing in various experimental settings, which is critical for consumer behavior researchers. |
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ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">11</subfield><subfield code="j">2023</subfield><subfield code="e">3</subfield><subfield code="c">9</subfield><subfield code="h">379-384</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">26</subfield><subfield code="1">01</subfield><subfield code="x">0206</subfield><subfield code="b">4387036087</subfield><subfield code="y">x1z</subfield><subfield code="z">13-10-23</subfield></datafield><datafield tag="982" ind1=" " ind2=" "><subfield code="2">26</subfield><subfield code="1">00</subfield><subfield code="x">DE-206</subfield><subfield code="b">Count data are often encountered in consumer behavior research. Normal regression, or ordinary least squares, has been used predominantly to analyze count data in experimental studies, while the appropriate models for count data analysis such as Poisson regression have not been fully embraced in consumer behavior research. The fact that only a small fraction of published papers in consumer behavior research with count data have used Poisson regression calls for a push to rethink the common approach of using normal regression for count data analysis. To demonstrate the importance and value of using Poisson regression for count data, we first discuss the parametric forms and properties of both normal regression and Poisson regression, and then show readers through large-scale simulated experiments that Poisson regression is the appropriate model to use for count data, not only because of better model fit but also because of lower error rates in hypothesis testing in various experimental settings, which is critical for consumer behavior researchers.</subfield></datafield></record></collection>
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26 00 DE-206 Count data are often encountered in consumer behavior research. Normal regression, or ordinary least squares, has been used predominantly to analyze count data in experimental studies, while the appropriate models for count data analysis such as Poisson regression have not been fully embraced in consumer behavior research. The fact that only a small fraction of published papers in consumer behavior research with count data have used Poisson regression calls for a push to rethink the common approach of using normal regression for count data analysis. To demonstrate the importance and value of using Poisson regression for count data, we first discuss the parametric forms and properties of both normal regression and Poisson regression, and then show readers through large-scale simulated experiments that Poisson regression is the appropriate model to use for count data, not only because of better model fit but also because of lower error rates in hypothesis testing in various experimental settings, which is critical for consumer behavior researchers Why to use Poisson regression for count data analysis in consumer behavior research Feihong Xia Consumer behavior research (dpeaa)DE-206 Count data (dpeaa)DE-206 False negative (dpeaa)DE-206 False positive (dpeaa)DE-206 Hypothesis testing (dpeaa)DE-206 Poisson regression (dpeaa)DE-206 |
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| title_short |
Why to use Poisson regression for count data analysis in consumer behavior research |
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https://link.springer.com/content/pdf/10.1057/s41270-022-00166-7.pdf https://doi.org/10.1057/s41270-022-00166-7 |
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10.1057/s41270-022-00166-7 |
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2024-07-04T12:09:07.200Z |
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ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">11</subfield><subfield code="j">2023</subfield><subfield code="e">3</subfield><subfield code="c">9</subfield><subfield code="h">379-384</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">26</subfield><subfield code="1">01</subfield><subfield code="x">0206</subfield><subfield code="b">4387036087</subfield><subfield code="y">x1z</subfield><subfield code="z">13-10-23</subfield></datafield><datafield tag="982" ind1=" " ind2=" "><subfield code="2">26</subfield><subfield code="1">00</subfield><subfield code="x">DE-206</subfield><subfield code="b">Count data are often encountered in consumer behavior research. Normal regression, or ordinary least squares, has been used predominantly to analyze count data in experimental studies, while the appropriate models for count data analysis such as Poisson regression have not been fully embraced in consumer behavior research. The fact that only a small fraction of published papers in consumer behavior research with count data have used Poisson regression calls for a push to rethink the common approach of using normal regression for count data analysis. To demonstrate the importance and value of using Poisson regression for count data, we first discuss the parametric forms and properties of both normal regression and Poisson regression, and then show readers through large-scale simulated experiments that Poisson regression is the appropriate model to use for count data, not only because of better model fit but also because of lower error rates in hypothesis testing in various experimental settings, which is critical for consumer behavior researchers.</subfield></datafield></record></collection>
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7.399951 |