How rare is rare? : how common is common? : empirical issues associated with binary dependent variables with rare or common event rates
Gespeichert in:
| Autor*in: |
Woo, Hyun-Soo [verfasserIn] Berns, John P. [verfasserIn] Solanelles, Pol [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: Organizational research methods - London [u.a.] : Sage, 1998, 26(2023), 4 vom: Okt., Seite 655-677 |
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| Übergeordnetes Werk: |
volume:26 ; year:2023 ; number:4 ; month:10 ; pages:655-677 |
| Links: |
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| DOI / URN: |
10.1177/10944281221083197 |
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| Katalog-ID: |
1871106028 |
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| 245 | 1 | 0 | |a How rare is rare? |b how common is common? : empirical issues associated with binary dependent variables with rare or common event rates |c Hyun-Soo Woo, John P. Berns, and Pol Solanelles |
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| 982 | |2 26 |1 00 |x DE-206 |b The use of logit and probit models when examining binary dependent variables including those in the form 0/1 (i.e., dummy variables), yes/no, and true/false (hereafter binary DVs) is commonplace. Yet, the appropriateness and effectiveness of such models are challenged when the event rate of a binary DV is rare or common. To better understand the impact on the field of strategy, we undertook a literature review and assessed recently published research in the Strategic Management Journal. We then utilized Monte Carlo simulations with results showing that as event rates become rarer or more common, issues including biased coefficients, standard error inflation, low statistical power to detect significant effects, and model convergence failure increasingly arise. In addition, small sample sizes amplified these empirical issues. Using a strategy example study, we also show how various analytic tools can lead to different findings when empirical models face an extreme event rate with small sample sizes. Based on our findings, we provide step-by-step guidance for strategy researchers going forward. | ||
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| allfields |
10.1177/10944281221083197 doi (DE-627)1871106028 (DE-599)KXP1871106028 DE-627 ger DE-627 rda eng Woo, Hyun-Soo verfasserin aut How rare is rare? how common is common? : empirical issues associated with binary dependent variables with rare or common event rates Hyun-Soo Woo, John P. Berns, and Pol Solanelles 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier logit model (dpeaa)DE-206 probit model (dpeaa)DE-206 rare event rates (dpeaa)DE-206 common event rates (dpeaa)DE-206 monte carlo simulation (dpeaa)DE-206 dummy variable (dpeaa)DE-206 Berns, John P. verfasserin aut Solanelles, Pol verfasserin aut Enthalten in Organizational research methods London [u.a.] : Sage, 1998 26(2023), 4 vom: Okt., Seite 655-677 Online-Ressource (DE-627)324657935 (DE-600)2029600-9 (DE-576)276556372 1552-7425 nnns volume:26 year:2023 number:4 month:10 pages:655-677 https://journals.sagepub.com/doi/pdf/10.1177/10944281221083197 Verlag lizenzpflichtig https://doi.org/10.1177/10944281221083197 Resolving-System lizenzpflichtig GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_20 GBV_ILN_22 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_69 GBV_ILN_70 GBV_ILN_100 GBV_ILN_110 GBV_ILN_120 GBV_ILN_152 GBV_ILN_161 GBV_ILN_165 GBV_ILN_171 GBV_ILN_187 GBV_ILN_293 GBV_ILN_370 GBV_ILN_374 GBV_ILN_636 GBV_ILN_702 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_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2035 GBV_ILN_2036 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2043 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_2070 GBV_ILN_2086 GBV_ILN_2093 GBV_ILN_2098 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2125 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2145 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2158 GBV_ILN_2190 GBV_ILN_2193 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_2705 GBV_ILN_2889 GBV_ILN_2890 GBV_ILN_4012 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_4277 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_4335 GBV_ILN_4338 GBV_ILN_4346 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 GBV_ILN_4753 AR 26 2023 4 10 655-677 26 01 0206 4417433399 x1z 24-11-23 26 00 DE-206 The use of logit and probit models when examining binary dependent variables including those in the form 0/1 (i.e., dummy variables), yes/no, and true/false (hereafter binary DVs) is commonplace. Yet, the appropriateness and effectiveness of such models are challenged when the event rate of a binary DV is rare or common. To better understand the impact on the field of strategy, we undertook a literature review and assessed recently published research in the Strategic Management Journal. We then utilized Monte Carlo simulations with results showing that as event rates become rarer or more common, issues including biased coefficients, standard error inflation, low statistical power to detect significant effects, and model convergence failure increasingly arise. In addition, small sample sizes amplified these empirical issues. Using a strategy example study, we also show how various analytic tools can lead to different findings when empirical models face an extreme event rate with small sample sizes. Based on our findings, we provide step-by-step guidance for strategy researchers going forward. |
| spelling |
10.1177/10944281221083197 doi (DE-627)1871106028 (DE-599)KXP1871106028 DE-627 ger DE-627 rda eng Woo, Hyun-Soo verfasserin aut How rare is rare? how common is common? : empirical issues associated with binary dependent variables with rare or common event rates Hyun-Soo Woo, John P. Berns, and Pol Solanelles 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier logit model (dpeaa)DE-206 probit model (dpeaa)DE-206 rare event rates (dpeaa)DE-206 common event rates (dpeaa)DE-206 monte carlo simulation (dpeaa)DE-206 dummy variable (dpeaa)DE-206 Berns, John P. verfasserin aut Solanelles, Pol verfasserin aut Enthalten in Organizational research methods London [u.a.] : Sage, 1998 26(2023), 4 vom: Okt., Seite 655-677 Online-Ressource (DE-627)324657935 (DE-600)2029600-9 (DE-576)276556372 1552-7425 nnns volume:26 year:2023 number:4 month:10 pages:655-677 https://journals.sagepub.com/doi/pdf/10.1177/10944281221083197 Verlag lizenzpflichtig https://doi.org/10.1177/10944281221083197 Resolving-System lizenzpflichtig GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_20 GBV_ILN_22 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_69 GBV_ILN_70 GBV_ILN_100 GBV_ILN_110 GBV_ILN_120 GBV_ILN_152 GBV_ILN_161 GBV_ILN_165 GBV_ILN_171 GBV_ILN_187 GBV_ILN_293 GBV_ILN_370 GBV_ILN_374 GBV_ILN_636 GBV_ILN_702 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_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2035 GBV_ILN_2036 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2043 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_2070 GBV_ILN_2086 GBV_ILN_2093 GBV_ILN_2098 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2125 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2145 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2158 GBV_ILN_2190 GBV_ILN_2193 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_2705 GBV_ILN_2889 GBV_ILN_2890 GBV_ILN_4012 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_4277 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_4335 GBV_ILN_4338 GBV_ILN_4346 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 GBV_ILN_4753 AR 26 2023 4 10 655-677 26 01 0206 4417433399 x1z 24-11-23 26 00 DE-206 The use of logit and probit models when examining binary dependent variables including those in the form 0/1 (i.e., dummy variables), yes/no, and true/false (hereafter binary DVs) is commonplace. Yet, the appropriateness and effectiveness of such models are challenged when the event rate of a binary DV is rare or common. To better understand the impact on the field of strategy, we undertook a literature review and assessed recently published research in the Strategic Management Journal. We then utilized Monte Carlo simulations with results showing that as event rates become rarer or more common, issues including biased coefficients, standard error inflation, low statistical power to detect significant effects, and model convergence failure increasingly arise. In addition, small sample sizes amplified these empirical issues. Using a strategy example study, we also show how various analytic tools can lead to different findings when empirical models face an extreme event rate with small sample sizes. Based on our findings, we provide step-by-step guidance for strategy researchers going forward. |
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10.1177/10944281221083197 doi (DE-627)1871106028 (DE-599)KXP1871106028 DE-627 ger DE-627 rda eng Woo, Hyun-Soo verfasserin aut How rare is rare? how common is common? : empirical issues associated with binary dependent variables with rare or common event rates Hyun-Soo Woo, John P. Berns, and Pol Solanelles 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier logit model (dpeaa)DE-206 probit model (dpeaa)DE-206 rare event rates (dpeaa)DE-206 common event rates (dpeaa)DE-206 monte carlo simulation (dpeaa)DE-206 dummy variable (dpeaa)DE-206 Berns, John P. verfasserin aut Solanelles, Pol verfasserin aut Enthalten in Organizational research methods London [u.a.] : Sage, 1998 26(2023), 4 vom: Okt., Seite 655-677 Online-Ressource (DE-627)324657935 (DE-600)2029600-9 (DE-576)276556372 1552-7425 nnns volume:26 year:2023 number:4 month:10 pages:655-677 https://journals.sagepub.com/doi/pdf/10.1177/10944281221083197 Verlag lizenzpflichtig https://doi.org/10.1177/10944281221083197 Resolving-System lizenzpflichtig GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_20 GBV_ILN_22 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_69 GBV_ILN_70 GBV_ILN_100 GBV_ILN_110 GBV_ILN_120 GBV_ILN_152 GBV_ILN_161 GBV_ILN_165 GBV_ILN_171 GBV_ILN_187 GBV_ILN_293 GBV_ILN_370 GBV_ILN_374 GBV_ILN_636 GBV_ILN_702 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_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2035 GBV_ILN_2036 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2043 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_2070 GBV_ILN_2086 GBV_ILN_2093 GBV_ILN_2098 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2125 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2145 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2158 GBV_ILN_2190 GBV_ILN_2193 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_2705 GBV_ILN_2889 GBV_ILN_2890 GBV_ILN_4012 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_4277 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_4335 GBV_ILN_4338 GBV_ILN_4346 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 GBV_ILN_4753 AR 26 2023 4 10 655-677 26 01 0206 4417433399 x1z 24-11-23 26 00 DE-206 The use of logit and probit models when examining binary dependent variables including those in the form 0/1 (i.e., dummy variables), yes/no, and true/false (hereafter binary DVs) is commonplace. Yet, the appropriateness and effectiveness of such models are challenged when the event rate of a binary DV is rare or common. To better understand the impact on the field of strategy, we undertook a literature review and assessed recently published research in the Strategic Management Journal. We then utilized Monte Carlo simulations with results showing that as event rates become rarer or more common, issues including biased coefficients, standard error inflation, low statistical power to detect significant effects, and model convergence failure increasingly arise. In addition, small sample sizes amplified these empirical issues. Using a strategy example study, we also show how various analytic tools can lead to different findings when empirical models face an extreme event rate with small sample sizes. Based on our findings, we provide step-by-step guidance for strategy researchers going forward. |
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10.1177/10944281221083197 doi (DE-627)1871106028 (DE-599)KXP1871106028 DE-627 ger DE-627 rda eng Woo, Hyun-Soo verfasserin aut How rare is rare? how common is common? : empirical issues associated with binary dependent variables with rare or common event rates Hyun-Soo Woo, John P. Berns, and Pol Solanelles 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier logit model (dpeaa)DE-206 probit model (dpeaa)DE-206 rare event rates (dpeaa)DE-206 common event rates (dpeaa)DE-206 monte carlo simulation (dpeaa)DE-206 dummy variable (dpeaa)DE-206 Berns, John P. verfasserin aut Solanelles, Pol verfasserin aut Enthalten in Organizational research methods London [u.a.] : Sage, 1998 26(2023), 4 vom: Okt., Seite 655-677 Online-Ressource (DE-627)324657935 (DE-600)2029600-9 (DE-576)276556372 1552-7425 nnns volume:26 year:2023 number:4 month:10 pages:655-677 https://journals.sagepub.com/doi/pdf/10.1177/10944281221083197 Verlag lizenzpflichtig https://doi.org/10.1177/10944281221083197 Resolving-System lizenzpflichtig GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_20 GBV_ILN_22 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_69 GBV_ILN_70 GBV_ILN_100 GBV_ILN_110 GBV_ILN_120 GBV_ILN_152 GBV_ILN_161 GBV_ILN_165 GBV_ILN_171 GBV_ILN_187 GBV_ILN_293 GBV_ILN_370 GBV_ILN_374 GBV_ILN_636 GBV_ILN_702 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_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2035 GBV_ILN_2036 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2043 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_2070 GBV_ILN_2086 GBV_ILN_2093 GBV_ILN_2098 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2125 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2145 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2158 GBV_ILN_2190 GBV_ILN_2193 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_2705 GBV_ILN_2889 GBV_ILN_2890 GBV_ILN_4012 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_4277 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_4335 GBV_ILN_4338 GBV_ILN_4346 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 GBV_ILN_4753 AR 26 2023 4 10 655-677 26 01 0206 4417433399 x1z 24-11-23 26 00 DE-206 The use of logit and probit models when examining binary dependent variables including those in the form 0/1 (i.e., dummy variables), yes/no, and true/false (hereafter binary DVs) is commonplace. Yet, the appropriateness and effectiveness of such models are challenged when the event rate of a binary DV is rare or common. To better understand the impact on the field of strategy, we undertook a literature review and assessed recently published research in the Strategic Management Journal. We then utilized Monte Carlo simulations with results showing that as event rates become rarer or more common, issues including biased coefficients, standard error inflation, low statistical power to detect significant effects, and model convergence failure increasingly arise. In addition, small sample sizes amplified these empirical issues. Using a strategy example study, we also show how various analytic tools can lead to different findings when empirical models face an extreme event rate with small sample sizes. Based on our findings, we provide step-by-step guidance for strategy researchers going forward. |
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26 00 DE-206 The use of logit and probit models when examining binary dependent variables including those in the form 0/1 (i.e., dummy variables), yes/no, and true/false (hereafter binary DVs) is commonplace. Yet, the appropriateness and effectiveness of such models are challenged when the event rate of a binary DV is rare or common. To better understand the impact on the field of strategy, we undertook a literature review and assessed recently published research in the Strategic Management Journal. We then utilized Monte Carlo simulations with results showing that as event rates become rarer or more common, issues including biased coefficients, standard error inflation, low statistical power to detect significant effects, and model convergence failure increasingly arise. In addition, small sample sizes amplified these empirical issues. Using a strategy example study, we also show how various analytic tools can lead to different findings when empirical models face an extreme event rate with small sample sizes. Based on our findings, we provide step-by-step guidance for strategy researchers going forward How rare is rare? how common is common? : empirical issues associated with binary dependent variables with rare or common event rates Hyun-Soo Woo, John P. Berns, and Pol Solanelles logit model (dpeaa)DE-206 probit model (dpeaa)DE-206 rare event rates (dpeaa)DE-206 common event rates (dpeaa)DE-206 monte carlo simulation (dpeaa)DE-206 dummy variable (dpeaa)DE-206 |
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How rare is rare? how common is common? : empirical issues associated with binary dependent variables with rare or common event rates |
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| title_short |
How rare is rare? |
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https://journals.sagepub.com/doi/pdf/10.1177/10944281221083197 https://doi.org/10.1177/10944281221083197 |
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Berns, John P. Solanelles, Pol |
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Berns, John P. Solanelles, Pol |
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| doi_str |
10.1177/10944281221083197 |
| up_date |
2024-07-04T14:31:18.566Z |
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1803659225142919168 |
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Yet, the appropriateness and effectiveness of such models are challenged when the event rate of a binary DV is rare or common. To better understand the impact on the field of strategy, we undertook a literature review and assessed recently published research in the Strategic Management Journal. We then utilized Monte Carlo simulations with results showing that as event rates become rarer or more common, issues including biased coefficients, standard error inflation, low statistical power to detect significant effects, and model convergence failure increasingly arise. In addition, small sample sizes amplified these empirical issues. Using a strategy example study, we also show how various analytic tools can lead to different findings when empirical models face an extreme event rate with small sample sizes. Based on our findings, we provide step-by-step guidance for strategy researchers going forward.</subfield></datafield></record></collection>
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