A 20-Year Review of Outcome Reporting Bias in Moderated Multiple Regression

Abstract Moderated multiple regression (MMR) remains the most popular method of testing interactions in management and applied psychology. Recent discussions of MMR have centered on their small effect sizes and typically being statistically underpowered (e.g., Murphy & Russell, Organizational Re...
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Autor*in:	O’Boyle, Ernest [verfasserIn] Banks, George C. [verfasserIn] Carter, Kameron [verfasserIn] Walter, Sheryl [verfasserIn] Yuan, Zhenyu [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2018

Schlagwörter:	Outcome reporting bias Publication bias Questionable reporting practices Moderated multiple regression Meta-analysis

Übergeordnetes Werk:	Enthalten in: Journal of business and psychology - Dordrecht [u.a.] : Springer Science + Business Media B.V, 1986, 34(2018), 1 vom: 20. Apr., Seite 19-37
Übergeordnetes Werk:	volume:34 ; year:2018 ; number:1 ; day:20 ; month:04 ; pages:19-37

Links:	Volltext

DOI / URN:	10.1007/s10869-018-9539-8

Katalog-ID:	SPR014200090

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245	1	2	\|a A 20-Year Review of Outcome Reporting Bias in Moderated Multiple Regression
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520			\|a Abstract Moderated multiple regression (MMR) remains the most popular method of testing interactions in management and applied psychology. Recent discussions of MMR have centered on their small effect sizes and typically being statistically underpowered (e.g., Murphy & Russell, Organizational Research Methods, 2016). Although many MMR tests are likely plagued by type II errors, they may also be particularly prone to outcome reporting bias (ORB) resulting in elevated false positives (type I errors). We tested the state of MMR through a 20-year review of six leading journals. Based on 1218 MMR tests nested within 343 studies, we found that despite low statistical power, most MMR tests (54%) were reported as statistically significant. Further, although sample size has remained relatively unchanged (r = − .002), statistically significant MMR tests have risen from 41% (1995–1999) to 49% (2000–2004), to 60% (2005–2009), and to 69% (2010–2014). This could indicate greater methodological and theoretical precision but leaves open the possibility of ORB. In our review, we found evidence that both increased rigor and theoretical precision play an important role in MMR effect size magnitudes, but also found evidence for ORB. Specifically, (a) smaller sample sizes are associated with larger effect sizes, (b) there is a substantial frequency spike in p values just below the .05 threshold, and (c) recalculated p values less than .05 always converged with authors’ conclusions of statistical significance but recalculated p values between .05 and .10 only converged with authors’ conclusions about half (54%) of the time. The findings of this research provide important implications for future application of MMR.
650		4	\|a Outcome reporting bias \|7 (dpeaa)DE-He213
650		4	\|a Publication bias \|7 (dpeaa)DE-He213
650		4	\|a Questionable reporting practices \|7 (dpeaa)DE-He213
650		4	\|a Moderated multiple regression \|7 (dpeaa)DE-He213
650		4	\|a Meta-analysis \|7 (dpeaa)DE-He213
700	1		\|a Banks, George C. \|e verfasserin \|4 aut
700	1		\|a Carter, Kameron \|e verfasserin \|4 aut
700	1		\|a Walter, Sheryl \|e verfasserin \|4 aut
700	1		\|a Yuan, Zhenyu \|e verfasserin \|4 aut
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912			\|a GBV_ILN_60
912			\|a GBV_ILN_62
912			\|a GBV_ILN_63
912			\|a GBV_ILN_69
912			\|a GBV_ILN_70
912			\|a GBV_ILN_73
912			\|a GBV_ILN_74
912			\|a GBV_ILN_90
912			\|a GBV_ILN_95
912			\|a GBV_ILN_100
912			\|a GBV_ILN_101
912			\|a GBV_ILN_105
912			\|a GBV_ILN_110
912			\|a GBV_ILN_120
912			\|a GBV_ILN_138
912			\|a GBV_ILN_150
912			\|a GBV_ILN_151
912			\|a GBV_ILN_152
912			\|a GBV_ILN_161
912			\|a GBV_ILN_170
912			\|a GBV_ILN_171
912			\|a GBV_ILN_187
912			\|a GBV_ILN_213
912			\|a GBV_ILN_224
912			\|a GBV_ILN_230
912			\|a GBV_ILN_250
912			\|a GBV_ILN_281
912			\|a GBV_ILN_285
912			\|a GBV_ILN_293
912			\|a GBV_ILN_370
912			\|a GBV_ILN_374
912			\|a GBV_ILN_602
912			\|a GBV_ILN_636
912			\|a GBV_ILN_702
912			\|a GBV_ILN_2001
912			\|a GBV_ILN_2003
912			\|a GBV_ILN_2004
912			\|a GBV_ILN_2005
912			\|a GBV_ILN_2006
912			\|a GBV_ILN_2007
912			\|a GBV_ILN_2008
912			\|a GBV_ILN_2009
912			\|a GBV_ILN_2010
912			\|a GBV_ILN_2011
912			\|a GBV_ILN_2014
912			\|a GBV_ILN_2015
912			\|a GBV_ILN_2018
912			\|a GBV_ILN_2020
912			\|a GBV_ILN_2021
912			\|a GBV_ILN_2025
912			\|a GBV_ILN_2026
912			\|a GBV_ILN_2027
912			\|a GBV_ILN_2031
912			\|a GBV_ILN_2034
912			\|a GBV_ILN_2037
912			\|a GBV_ILN_2038
912			\|a GBV_ILN_2039
912			\|a GBV_ILN_2044
912			\|a GBV_ILN_2048
912			\|a GBV_ILN_2049
912			\|a GBV_ILN_2050
912			\|a GBV_ILN_2055
912			\|a GBV_ILN_2056
912			\|a GBV_ILN_2057
912			\|a GBV_ILN_2059
912			\|a GBV_ILN_2061
912			\|a GBV_ILN_2064
912			\|a GBV_ILN_2065
912			\|a GBV_ILN_2068
912			\|a GBV_ILN_2070
912			\|a GBV_ILN_2086
912			\|a GBV_ILN_2088
912			\|a GBV_ILN_2093
912			\|a GBV_ILN_2106
912			\|a GBV_ILN_2107
912			\|a GBV_ILN_2108
912			\|a GBV_ILN_2110
912			\|a GBV_ILN_2111
912			\|a GBV_ILN_2112
912			\|a GBV_ILN_2113
912			\|a GBV_ILN_2116
912			\|a GBV_ILN_2118
912			\|a GBV_ILN_2119
912			\|a GBV_ILN_2122
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912			\|a GBV_ILN_2144
912			\|a GBV_ILN_2147
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912			\|a GBV_ILN_2152
912			\|a GBV_ILN_2153
912			\|a GBV_ILN_2188
912			\|a GBV_ILN_2190
912			\|a GBV_ILN_2232
912			\|a GBV_ILN_2336
912			\|a GBV_ILN_2446
912			\|a GBV_ILN_2470
912			\|a GBV_ILN_2472
912			\|a GBV_ILN_2507
912			\|a GBV_ILN_2522
912			\|a GBV_ILN_2548
912			\|a GBV_ILN_2936
912			\|a GBV_ILN_2941
912			\|a GBV_ILN_2949
912			\|a GBV_ILN_2950
912			\|a GBV_ILN_4012
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912			\|a GBV_ILN_4246
912			\|a GBV_ILN_4249
912			\|a GBV_ILN_4251
912			\|a GBV_ILN_4305
912			\|a GBV_ILN_4306
912			\|a GBV_ILN_4307
912			\|a GBV_ILN_4313
912			\|a GBV_ILN_4322
912			\|a GBV_ILN_4323
912			\|a GBV_ILN_4324
912			\|a GBV_ILN_4325
912			\|a GBV_ILN_4326
912			\|a GBV_ILN_4333
912			\|a GBV_ILN_4334
912			\|a GBV_ILN_4335
912			\|a GBV_ILN_4336
912			\|a GBV_ILN_4338
912			\|a GBV_ILN_4346
912			\|a GBV_ILN_4393
912			\|a GBV_ILN_4700
936	b	k	\|a 85.05 \|q ASE
951			\|a AR
952			\|d 34 \|j 2018 \|e 1 \|b 20 \|c 04 \|h 19-37

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bklnumber	85.05
publishDate	2018
allfields	10.1007/s10869-018-9539-8 doi (DE-627)SPR014200090 (SPR)s10869-018-9539-8-e DE-627 ger DE-627 rakwb eng 150 ASE 85.05 bkl O’Boyle, Ernest verfasserin aut A 20-Year Review of Outcome Reporting Bias in Moderated Multiple Regression 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Moderated multiple regression (MMR) remains the most popular method of testing interactions in management and applied psychology. Recent discussions of MMR have centered on their small effect sizes and typically being statistically underpowered (e.g., Murphy & Russell, Organizational Research Methods, 2016). Although many MMR tests are likely plagued by type II errors, they may also be particularly prone to outcome reporting bias (ORB) resulting in elevated false positives (type I errors). We tested the state of MMR through a 20-year review of six leading journals. Based on 1218 MMR tests nested within 343 studies, we found that despite low statistical power, most MMR tests (54%) were reported as statistically significant. Further, although sample size has remained relatively unchanged (r = − .002), statistically significant MMR tests have risen from 41% (1995–1999) to 49% (2000–2004), to 60% (2005–2009), and to 69% (2010–2014). This could indicate greater methodological and theoretical precision but leaves open the possibility of ORB. In our review, we found evidence that both increased rigor and theoretical precision play an important role in MMR effect size magnitudes, but also found evidence for ORB. Specifically, (a) smaller sample sizes are associated with larger effect sizes, (b) there is a substantial frequency spike in p values just below the .05 threshold, and (c) recalculated p values less than .05 always converged with authors’ conclusions of statistical significance but recalculated p values between .05 and .10 only converged with authors’ conclusions about half (54%) of the time. The findings of this research provide important implications for future application of MMR. Outcome reporting bias (dpeaa)DE-He213 Publication bias (dpeaa)DE-He213 Questionable reporting practices (dpeaa)DE-He213 Moderated multiple regression (dpeaa)DE-He213 Meta-analysis (dpeaa)DE-He213 Banks, George C. verfasserin aut Carter, Kameron verfasserin aut Walter, Sheryl verfasserin aut Yuan, Zhenyu verfasserin aut Enthalten in Journal of business and psychology Dordrecht [u.a.] : Springer Science + Business Media B.V, 1986 34(2018), 1 vom: 20. Apr., Seite 19-37 (DE-627)320573206 (DE-600)2016738-6 1573-353X nnns volume:34 year:2018 number:1 day:20 month:04 pages:19-37 https://dx.doi.org/10.1007/s10869-018-9539-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 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_374 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_2018 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_2070 GBV_ILN_2086 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_2116 GBV_ILN_2118 GBV_ILN_2119 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_2936 GBV_ILN_2941 GBV_ILN_2949 GBV_ILN_2950 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_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_4336 GBV_ILN_4338 GBV_ILN_4346 GBV_ILN_4393 GBV_ILN_4700 85.05 ASE AR 34 2018 1 20 04 19-37
spelling	10.1007/s10869-018-9539-8 doi (DE-627)SPR014200090 (SPR)s10869-018-9539-8-e DE-627 ger DE-627 rakwb eng 150 ASE 85.05 bkl O’Boyle, Ernest verfasserin aut A 20-Year Review of Outcome Reporting Bias in Moderated Multiple Regression 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Moderated multiple regression (MMR) remains the most popular method of testing interactions in management and applied psychology. Recent discussions of MMR have centered on their small effect sizes and typically being statistically underpowered (e.g., Murphy & Russell, Organizational Research Methods, 2016). Although many MMR tests are likely plagued by type II errors, they may also be particularly prone to outcome reporting bias (ORB) resulting in elevated false positives (type I errors). We tested the state of MMR through a 20-year review of six leading journals. Based on 1218 MMR tests nested within 343 studies, we found that despite low statistical power, most MMR tests (54%) were reported as statistically significant. Further, although sample size has remained relatively unchanged (r = − .002), statistically significant MMR tests have risen from 41% (1995–1999) to 49% (2000–2004), to 60% (2005–2009), and to 69% (2010–2014). This could indicate greater methodological and theoretical precision but leaves open the possibility of ORB. In our review, we found evidence that both increased rigor and theoretical precision play an important role in MMR effect size magnitudes, but also found evidence for ORB. Specifically, (a) smaller sample sizes are associated with larger effect sizes, (b) there is a substantial frequency spike in p values just below the .05 threshold, and (c) recalculated p values less than .05 always converged with authors’ conclusions of statistical significance but recalculated p values between .05 and .10 only converged with authors’ conclusions about half (54%) of the time. The findings of this research provide important implications for future application of MMR. Outcome reporting bias (dpeaa)DE-He213 Publication bias (dpeaa)DE-He213 Questionable reporting practices (dpeaa)DE-He213 Moderated multiple regression (dpeaa)DE-He213 Meta-analysis (dpeaa)DE-He213 Banks, George C. verfasserin aut Carter, Kameron verfasserin aut Walter, Sheryl verfasserin aut Yuan, Zhenyu verfasserin aut Enthalten in Journal of business and psychology Dordrecht [u.a.] : Springer Science + Business Media B.V, 1986 34(2018), 1 vom: 20. Apr., Seite 19-37 (DE-627)320573206 (DE-600)2016738-6 1573-353X nnns volume:34 year:2018 number:1 day:20 month:04 pages:19-37 https://dx.doi.org/10.1007/s10869-018-9539-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 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_374 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_2018 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_2070 GBV_ILN_2086 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_2116 GBV_ILN_2118 GBV_ILN_2119 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_2936 GBV_ILN_2941 GBV_ILN_2949 GBV_ILN_2950 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_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_4336 GBV_ILN_4338 GBV_ILN_4346 GBV_ILN_4393 GBV_ILN_4700 85.05 ASE AR 34 2018 1 20 04 19-37
allfields_unstemmed	10.1007/s10869-018-9539-8 doi (DE-627)SPR014200090 (SPR)s10869-018-9539-8-e DE-627 ger DE-627 rakwb eng 150 ASE 85.05 bkl O’Boyle, Ernest verfasserin aut A 20-Year Review of Outcome Reporting Bias in Moderated Multiple Regression 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Moderated multiple regression (MMR) remains the most popular method of testing interactions in management and applied psychology. Recent discussions of MMR have centered on their small effect sizes and typically being statistically underpowered (e.g., Murphy & Russell, Organizational Research Methods, 2016). Although many MMR tests are likely plagued by type II errors, they may also be particularly prone to outcome reporting bias (ORB) resulting in elevated false positives (type I errors). We tested the state of MMR through a 20-year review of six leading journals. Based on 1218 MMR tests nested within 343 studies, we found that despite low statistical power, most MMR tests (54%) were reported as statistically significant. Further, although sample size has remained relatively unchanged (r = − .002), statistically significant MMR tests have risen from 41% (1995–1999) to 49% (2000–2004), to 60% (2005–2009), and to 69% (2010–2014). This could indicate greater methodological and theoretical precision but leaves open the possibility of ORB. In our review, we found evidence that both increased rigor and theoretical precision play an important role in MMR effect size magnitudes, but also found evidence for ORB. Specifically, (a) smaller sample sizes are associated with larger effect sizes, (b) there is a substantial frequency spike in p values just below the .05 threshold, and (c) recalculated p values less than .05 always converged with authors’ conclusions of statistical significance but recalculated p values between .05 and .10 only converged with authors’ conclusions about half (54%) of the time. The findings of this research provide important implications for future application of MMR. Outcome reporting bias (dpeaa)DE-He213 Publication bias (dpeaa)DE-He213 Questionable reporting practices (dpeaa)DE-He213 Moderated multiple regression (dpeaa)DE-He213 Meta-analysis (dpeaa)DE-He213 Banks, George C. verfasserin aut Carter, Kameron verfasserin aut Walter, Sheryl verfasserin aut Yuan, Zhenyu verfasserin aut Enthalten in Journal of business and psychology Dordrecht [u.a.] : Springer Science + Business Media B.V, 1986 34(2018), 1 vom: 20. Apr., Seite 19-37 (DE-627)320573206 (DE-600)2016738-6 1573-353X nnns volume:34 year:2018 number:1 day:20 month:04 pages:19-37 https://dx.doi.org/10.1007/s10869-018-9539-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 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_374 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_2018 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_2070 GBV_ILN_2086 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_2116 GBV_ILN_2118 GBV_ILN_2119 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_2936 GBV_ILN_2941 GBV_ILN_2949 GBV_ILN_2950 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_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_4336 GBV_ILN_4338 GBV_ILN_4346 GBV_ILN_4393 GBV_ILN_4700 85.05 ASE AR 34 2018 1 20 04 19-37
allfieldsGer	10.1007/s10869-018-9539-8 doi (DE-627)SPR014200090 (SPR)s10869-018-9539-8-e DE-627 ger DE-627 rakwb eng 150 ASE 85.05 bkl O’Boyle, Ernest verfasserin aut A 20-Year Review of Outcome Reporting Bias in Moderated Multiple Regression 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Moderated multiple regression (MMR) remains the most popular method of testing interactions in management and applied psychology. Recent discussions of MMR have centered on their small effect sizes and typically being statistically underpowered (e.g., Murphy & Russell, Organizational Research Methods, 2016). Although many MMR tests are likely plagued by type II errors, they may also be particularly prone to outcome reporting bias (ORB) resulting in elevated false positives (type I errors). We tested the state of MMR through a 20-year review of six leading journals. Based on 1218 MMR tests nested within 343 studies, we found that despite low statistical power, most MMR tests (54%) were reported as statistically significant. Further, although sample size has remained relatively unchanged (r = − .002), statistically significant MMR tests have risen from 41% (1995–1999) to 49% (2000–2004), to 60% (2005–2009), and to 69% (2010–2014). This could indicate greater methodological and theoretical precision but leaves open the possibility of ORB. In our review, we found evidence that both increased rigor and theoretical precision play an important role in MMR effect size magnitudes, but also found evidence for ORB. Specifically, (a) smaller sample sizes are associated with larger effect sizes, (b) there is a substantial frequency spike in p values just below the .05 threshold, and (c) recalculated p values less than .05 always converged with authors’ conclusions of statistical significance but recalculated p values between .05 and .10 only converged with authors’ conclusions about half (54%) of the time. The findings of this research provide important implications for future application of MMR. Outcome reporting bias (dpeaa)DE-He213 Publication bias (dpeaa)DE-He213 Questionable reporting practices (dpeaa)DE-He213 Moderated multiple regression (dpeaa)DE-He213 Meta-analysis (dpeaa)DE-He213 Banks, George C. verfasserin aut Carter, Kameron verfasserin aut Walter, Sheryl verfasserin aut Yuan, Zhenyu verfasserin aut Enthalten in Journal of business and psychology Dordrecht [u.a.] : Springer Science + Business Media B.V, 1986 34(2018), 1 vom: 20. Apr., Seite 19-37 (DE-627)320573206 (DE-600)2016738-6 1573-353X nnns volume:34 year:2018 number:1 day:20 month:04 pages:19-37 https://dx.doi.org/10.1007/s10869-018-9539-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 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_374 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_2018 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_2070 GBV_ILN_2086 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_2116 GBV_ILN_2118 GBV_ILN_2119 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_2936 GBV_ILN_2941 GBV_ILN_2949 GBV_ILN_2950 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_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_4336 GBV_ILN_4338 GBV_ILN_4346 GBV_ILN_4393 GBV_ILN_4700 85.05 ASE AR 34 2018 1 20 04 19-37
allfieldsSound	10.1007/s10869-018-9539-8 doi (DE-627)SPR014200090 (SPR)s10869-018-9539-8-e DE-627 ger DE-627 rakwb eng 150 ASE 85.05 bkl O’Boyle, Ernest verfasserin aut A 20-Year Review of Outcome Reporting Bias in Moderated Multiple Regression 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Moderated multiple regression (MMR) remains the most popular method of testing interactions in management and applied psychology. Recent discussions of MMR have centered on their small effect sizes and typically being statistically underpowered (e.g., Murphy & Russell, Organizational Research Methods, 2016). Although many MMR tests are likely plagued by type II errors, they may also be particularly prone to outcome reporting bias (ORB) resulting in elevated false positives (type I errors). We tested the state of MMR through a 20-year review of six leading journals. Based on 1218 MMR tests nested within 343 studies, we found that despite low statistical power, most MMR tests (54%) were reported as statistically significant. Further, although sample size has remained relatively unchanged (r = − .002), statistically significant MMR tests have risen from 41% (1995–1999) to 49% (2000–2004), to 60% (2005–2009), and to 69% (2010–2014). This could indicate greater methodological and theoretical precision but leaves open the possibility of ORB. In our review, we found evidence that both increased rigor and theoretical precision play an important role in MMR effect size magnitudes, but also found evidence for ORB. Specifically, (a) smaller sample sizes are associated with larger effect sizes, (b) there is a substantial frequency spike in p values just below the .05 threshold, and (c) recalculated p values less than .05 always converged with authors’ conclusions of statistical significance but recalculated p values between .05 and .10 only converged with authors’ conclusions about half (54%) of the time. The findings of this research provide important implications for future application of MMR. Outcome reporting bias (dpeaa)DE-He213 Publication bias (dpeaa)DE-He213 Questionable reporting practices (dpeaa)DE-He213 Moderated multiple regression (dpeaa)DE-He213 Meta-analysis (dpeaa)DE-He213 Banks, George C. verfasserin aut Carter, Kameron verfasserin aut Walter, Sheryl verfasserin aut Yuan, Zhenyu verfasserin aut Enthalten in Journal of business and psychology Dordrecht [u.a.] : Springer Science + Business Media B.V, 1986 34(2018), 1 vom: 20. Apr., Seite 19-37 (DE-627)320573206 (DE-600)2016738-6 1573-353X nnns volume:34 year:2018 number:1 day:20 month:04 pages:19-37 https://dx.doi.org/10.1007/s10869-018-9539-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 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_374 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_2018 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_2070 GBV_ILN_2086 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_2116 GBV_ILN_2118 GBV_ILN_2119 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_2936 GBV_ILN_2941 GBV_ILN_2949 GBV_ILN_2950 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_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_4336 GBV_ILN_4338 GBV_ILN_4346 GBV_ILN_4393 GBV_ILN_4700 85.05 ASE AR 34 2018 1 20 04 19-37
language	English
source	Enthalten in Journal of business and psychology 34(2018), 1 vom: 20. Apr., Seite 19-37 volume:34 year:2018 number:1 day:20 month:04 pages:19-37
sourceStr	Enthalten in Journal of business and psychology 34(2018), 1 vom: 20. Apr., Seite 19-37 volume:34 year:2018 number:1 day:20 month:04 pages:19-37
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Outcome reporting bias Publication bias Questionable reporting practices Moderated multiple regression Meta-analysis
dewey-raw	150
isfreeaccess_bool	false
container_title	Journal of business and psychology
authorswithroles_txt_mv	O’Boyle, Ernest @@aut@@ Banks, George C. @@aut@@ Carter, Kameron @@aut@@ Walter, Sheryl @@aut@@ Yuan, Zhenyu @@aut@@
publishDateDaySort_date	2018-04-20T00:00:00Z
hierarchy_top_id	320573206
dewey-sort	3150
id	SPR014200090
language_de	englisch
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author	O’Boyle, Ernest
spellingShingle	O’Boyle, Ernest ddc 150 bkl 85.05 misc Outcome reporting bias misc Publication bias misc Questionable reporting practices misc Moderated multiple regression misc Meta-analysis A 20-Year Review of Outcome Reporting Bias in Moderated Multiple Regression
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topic_title	150 ASE 85.05 bkl A 20-Year Review of Outcome Reporting Bias in Moderated Multiple Regression Outcome reporting bias (dpeaa)DE-He213 Publication bias (dpeaa)DE-He213 Questionable reporting practices (dpeaa)DE-He213 Moderated multiple regression (dpeaa)DE-He213 Meta-analysis (dpeaa)DE-He213
topic	ddc 150 bkl 85.05 misc Outcome reporting bias misc Publication bias misc Questionable reporting practices misc Moderated multiple regression misc Meta-analysis
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title	A 20-Year Review of Outcome Reporting Bias in Moderated Multiple Regression
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title_full	A 20-Year Review of Outcome Reporting Bias in Moderated Multiple Regression
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title_sort	20-year review of outcome reporting bias in moderated multiple regression
title_auth	A 20-Year Review of Outcome Reporting Bias in Moderated Multiple Regression
abstract	Abstract Moderated multiple regression (MMR) remains the most popular method of testing interactions in management and applied psychology. Recent discussions of MMR have centered on their small effect sizes and typically being statistically underpowered (e.g., Murphy & Russell, Organizational Research Methods, 2016). Although many MMR tests are likely plagued by type II errors, they may also be particularly prone to outcome reporting bias (ORB) resulting in elevated false positives (type I errors). We tested the state of MMR through a 20-year review of six leading journals. Based on 1218 MMR tests nested within 343 studies, we found that despite low statistical power, most MMR tests (54%) were reported as statistically significant. Further, although sample size has remained relatively unchanged (r = − .002), statistically significant MMR tests have risen from 41% (1995–1999) to 49% (2000–2004), to 60% (2005–2009), and to 69% (2010–2014). This could indicate greater methodological and theoretical precision but leaves open the possibility of ORB. In our review, we found evidence that both increased rigor and theoretical precision play an important role in MMR effect size magnitudes, but also found evidence for ORB. Specifically, (a) smaller sample sizes are associated with larger effect sizes, (b) there is a substantial frequency spike in p values just below the .05 threshold, and (c) recalculated p values less than .05 always converged with authors’ conclusions of statistical significance but recalculated p values between .05 and .10 only converged with authors’ conclusions about half (54%) of the time. The findings of this research provide important implications for future application of MMR.
abstractGer	Abstract Moderated multiple regression (MMR) remains the most popular method of testing interactions in management and applied psychology. Recent discussions of MMR have centered on their small effect sizes and typically being statistically underpowered (e.g., Murphy & Russell, Organizational Research Methods, 2016). Although many MMR tests are likely plagued by type II errors, they may also be particularly prone to outcome reporting bias (ORB) resulting in elevated false positives (type I errors). We tested the state of MMR through a 20-year review of six leading journals. Based on 1218 MMR tests nested within 343 studies, we found that despite low statistical power, most MMR tests (54%) were reported as statistically significant. Further, although sample size has remained relatively unchanged (r = − .002), statistically significant MMR tests have risen from 41% (1995–1999) to 49% (2000–2004), to 60% (2005–2009), and to 69% (2010–2014). This could indicate greater methodological and theoretical precision but leaves open the possibility of ORB. In our review, we found evidence that both increased rigor and theoretical precision play an important role in MMR effect size magnitudes, but also found evidence for ORB. Specifically, (a) smaller sample sizes are associated with larger effect sizes, (b) there is a substantial frequency spike in p values just below the .05 threshold, and (c) recalculated p values less than .05 always converged with authors’ conclusions of statistical significance but recalculated p values between .05 and .10 only converged with authors’ conclusions about half (54%) of the time. The findings of this research provide important implications for future application of MMR.
abstract_unstemmed	Abstract Moderated multiple regression (MMR) remains the most popular method of testing interactions in management and applied psychology. Recent discussions of MMR have centered on their small effect sizes and typically being statistically underpowered (e.g., Murphy & Russell, Organizational Research Methods, 2016). Although many MMR tests are likely plagued by type II errors, they may also be particularly prone to outcome reporting bias (ORB) resulting in elevated false positives (type I errors). We tested the state of MMR through a 20-year review of six leading journals. Based on 1218 MMR tests nested within 343 studies, we found that despite low statistical power, most MMR tests (54%) were reported as statistically significant. Further, although sample size has remained relatively unchanged (r = − .002), statistically significant MMR tests have risen from 41% (1995–1999) to 49% (2000–2004), to 60% (2005–2009), and to 69% (2010–2014). This could indicate greater methodological and theoretical precision but leaves open the possibility of ORB. In our review, we found evidence that both increased rigor and theoretical precision play an important role in MMR effect size magnitudes, but also found evidence for ORB. Specifically, (a) smaller sample sizes are associated with larger effect sizes, (b) there is a substantial frequency spike in p values just below the .05 threshold, and (c) recalculated p values less than .05 always converged with authors’ conclusions of statistical significance but recalculated p values between .05 and .10 only converged with authors’ conclusions about half (54%) of the time. The findings of this research provide important implications for future application of MMR.
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title_short	A 20-Year Review of Outcome Reporting Bias in Moderated Multiple Regression
url	https://dx.doi.org/10.1007/s10869-018-9539-8
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author2	Banks, George C. Carter, Kameron Walter, Sheryl Yuan, Zhenyu
author2Str	Banks, George C. Carter, Kameron Walter, Sheryl Yuan, Zhenyu
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doi_str	10.1007/s10869-018-9539-8
up_date	2024-07-04T00:35:43.362Z
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A 20-Year Review of Outcome Reporting Bias in Moderated Multiple Regression

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