Moving in Parallel Toward a Modern Modeling Epistemology: Bayes Factors and Frequentist Modeling Methods
The Bayesian-frequentist debate typically portrays these statistical perspectives as opposing views. However, both Bayesian and frequentist statisticians have expanded their epistemological basis away from a singular focus on the null hypothesis, to a broader perspective involving the development an...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Rodgers, Joseph Lee [verfasserIn] |
|---|
| Format: |
Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2016 |
|---|
| Rechteinformationen: |
Nutzungsrecht: © 2016 Taylor & Francis Group, LLC 2016 |
|---|
| Schlagwörter: |
|---|
| Übergeordnetes Werk: |
Enthalten in: Multivariate behavioral research - Philadelphia, Pa. : Taylor & Francis, 1966, 51(2016), 1, Seite 30 |
|---|---|
| Übergeordnetes Werk: |
volume:51 ; year:2016 ; number:1 ; pages:30 |
| Links: |
|---|
| DOI / URN: |
10.1080/00273171.2015.1093459 |
|---|
| Katalog-ID: |
OLC1973804999 |
|---|
| LEADER | 01000caa a2200265 4500 | ||
|---|---|---|---|
| 001 | OLC1973804999 | ||
| 003 | DE-627 | ||
| 005 | 20211208221930.0 | ||
| 007 | tu | ||
| 008 | 160430s2016 xx ||||| 00| ||eng c | ||
| 024 | 7 | |a 10.1080/00273171.2015.1093459 |2 doi | |
| 028 | 5 | 2 | |a PQ20160430 |
| 035 | |a (DE-627)OLC1973804999 | ||
| 035 | |a (DE-599)GBVOLC1973804999 | ||
| 035 | |a (PRQ)i1676-7ab3d0be3a5e90c6f3334c3496b074f061706d91d045305be06409ba7e79b12e0 | ||
| 035 | |a (KEY)0067469920160000051000100030movinginparalleltowardamodernmodelingepistemologyb | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 082 | 0 | 4 | |a 150 |q DNB |
| 100 | 1 | |a Rodgers, Joseph Lee |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Moving in Parallel Toward a Modern Modeling Epistemology: Bayes Factors and Frequentist Modeling Methods |
| 264 | 1 | |c 2016 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ohne Hilfsmittel zu benutzen |b n |2 rdamedia | ||
| 338 | |a Band |b nc |2 rdacarrier | ||
| 520 | |a The Bayesian-frequentist debate typically portrays these statistical perspectives as opposing views. However, both Bayesian and frequentist statisticians have expanded their epistemological basis away from a singular focus on the null hypothesis, to a broader perspective involving the development and comparison of competing statistical/mathematical models. For frequentists, statistical developments such as structural equation modeling and multilevel modeling have facilitated this transition. For Bayesians, the Bayes factor has facilitated this transition. The Bayes factor is treated in articles within this issue of Multivariate Behavioral Research. The current presentation provides brief commentary on those articles and more extended discussion of the transition toward a modern modeling epistemology. In certain respects, Bayesians and frequentists share common goals. | ||
| 540 | |a Nutzungsrecht: © 2016 Taylor & Francis Group, LLC 2016 | ||
| 650 | 4 | |a Bayesian methods | |
| 650 | 4 | |a frequentist methods | |
| 650 | 4 | |a statistical modeling | |
| 650 | 4 | |a Bayes factor | |
| 650 | 4 | |a Behavioral sciences | |
| 650 | 4 | |a Bayesian analysis | |
| 650 | 4 | |a Mathematical models | |
| 773 | 0 | 8 | |i Enthalten in |t Multivariate behavioral research |d Philadelphia, Pa. : Taylor & Francis, 1966 |g 51(2016), 1, Seite 30 |w (DE-627)129488682 |w (DE-600)205705-0 |w (DE-576)014881500 |x 0027-3171 |7 nnns |
| 773 | 1 | 8 | |g volume:51 |g year:2016 |g number:1 |g pages:30 |
| 856 | 4 | 1 | |u http://dx.doi.org/10.1080/00273171.2015.1093459 |3 Volltext |
| 856 | 4 | 2 | |u http://www.tandfonline.com/doi/abs/10.1080/00273171.2015.1093459 |
| 856 | 4 | 2 | |u http://www.ncbi.nlm.nih.gov/pubmed/26881955 |
| 856 | 4 | 2 | |u http://search.proquest.com/docview/1765581701 |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a SYSFLAG_A | ||
| 912 | |a GBV_OLC | ||
| 912 | |a SSG-OLC-PSY | ||
| 912 | |a SSG-OLC-SOW | ||
| 912 | |a GBV_ILN_11 | ||
| 912 | |a GBV_ILN_21 | ||
| 912 | |a GBV_ILN_31 | ||
| 912 | |a GBV_ILN_60 | ||
| 912 | |a GBV_ILN_100 | ||
| 912 | |a GBV_ILN_4012 | ||
| 912 | |a GBV_ILN_4037 | ||
| 912 | |a GBV_ILN_4046 | ||
| 912 | |a GBV_ILN_4311 | ||
| 912 | |a GBV_ILN_4314 | ||
| 912 | |a GBV_ILN_4700 | ||
| 951 | |a AR | ||
| 952 | |d 51 |j 2016 |e 1 |h 30 | ||
| author_variant |
j l r jl jlr |
|---|---|
| matchkey_str |
article:00273171:2016----::oignaalloadmdrmdlneitmlgbysatradr |
| hierarchy_sort_str |
2016 |
| publishDate |
2016 |
| allfields |
10.1080/00273171.2015.1093459 doi PQ20160430 (DE-627)OLC1973804999 (DE-599)GBVOLC1973804999 (PRQ)i1676-7ab3d0be3a5e90c6f3334c3496b074f061706d91d045305be06409ba7e79b12e0 (KEY)0067469920160000051000100030movinginparalleltowardamodernmodelingepistemologyb DE-627 ger DE-627 rakwb eng 150 DNB Rodgers, Joseph Lee verfasserin aut Moving in Parallel Toward a Modern Modeling Epistemology: Bayes Factors and Frequentist Modeling Methods 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier The Bayesian-frequentist debate typically portrays these statistical perspectives as opposing views. However, both Bayesian and frequentist statisticians have expanded their epistemological basis away from a singular focus on the null hypothesis, to a broader perspective involving the development and comparison of competing statistical/mathematical models. For frequentists, statistical developments such as structural equation modeling and multilevel modeling have facilitated this transition. For Bayesians, the Bayes factor has facilitated this transition. The Bayes factor is treated in articles within this issue of Multivariate Behavioral Research. The current presentation provides brief commentary on those articles and more extended discussion of the transition toward a modern modeling epistemology. In certain respects, Bayesians and frequentists share common goals. Nutzungsrecht: © 2016 Taylor & Francis Group, LLC 2016 Bayesian methods frequentist methods statistical modeling Bayes factor Behavioral sciences Bayesian analysis Mathematical models Enthalten in Multivariate behavioral research Philadelphia, Pa. : Taylor & Francis, 1966 51(2016), 1, Seite 30 (DE-627)129488682 (DE-600)205705-0 (DE-576)014881500 0027-3171 nnns volume:51 year:2016 number:1 pages:30 http://dx.doi.org/10.1080/00273171.2015.1093459 Volltext http://www.tandfonline.com/doi/abs/10.1080/00273171.2015.1093459 http://www.ncbi.nlm.nih.gov/pubmed/26881955 http://search.proquest.com/docview/1765581701 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-PSY SSG-OLC-SOW GBV_ILN_11 GBV_ILN_21 GBV_ILN_31 GBV_ILN_60 GBV_ILN_100 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4311 GBV_ILN_4314 GBV_ILN_4700 AR 51 2016 1 30 |
| spelling |
10.1080/00273171.2015.1093459 doi PQ20160430 (DE-627)OLC1973804999 (DE-599)GBVOLC1973804999 (PRQ)i1676-7ab3d0be3a5e90c6f3334c3496b074f061706d91d045305be06409ba7e79b12e0 (KEY)0067469920160000051000100030movinginparalleltowardamodernmodelingepistemologyb DE-627 ger DE-627 rakwb eng 150 DNB Rodgers, Joseph Lee verfasserin aut Moving in Parallel Toward a Modern Modeling Epistemology: Bayes Factors and Frequentist Modeling Methods 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier The Bayesian-frequentist debate typically portrays these statistical perspectives as opposing views. However, both Bayesian and frequentist statisticians have expanded their epistemological basis away from a singular focus on the null hypothesis, to a broader perspective involving the development and comparison of competing statistical/mathematical models. For frequentists, statistical developments such as structural equation modeling and multilevel modeling have facilitated this transition. For Bayesians, the Bayes factor has facilitated this transition. The Bayes factor is treated in articles within this issue of Multivariate Behavioral Research. The current presentation provides brief commentary on those articles and more extended discussion of the transition toward a modern modeling epistemology. In certain respects, Bayesians and frequentists share common goals. Nutzungsrecht: © 2016 Taylor & Francis Group, LLC 2016 Bayesian methods frequentist methods statistical modeling Bayes factor Behavioral sciences Bayesian analysis Mathematical models Enthalten in Multivariate behavioral research Philadelphia, Pa. : Taylor & Francis, 1966 51(2016), 1, Seite 30 (DE-627)129488682 (DE-600)205705-0 (DE-576)014881500 0027-3171 nnns volume:51 year:2016 number:1 pages:30 http://dx.doi.org/10.1080/00273171.2015.1093459 Volltext http://www.tandfonline.com/doi/abs/10.1080/00273171.2015.1093459 http://www.ncbi.nlm.nih.gov/pubmed/26881955 http://search.proquest.com/docview/1765581701 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-PSY SSG-OLC-SOW GBV_ILN_11 GBV_ILN_21 GBV_ILN_31 GBV_ILN_60 GBV_ILN_100 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4311 GBV_ILN_4314 GBV_ILN_4700 AR 51 2016 1 30 |
| allfields_unstemmed |
10.1080/00273171.2015.1093459 doi PQ20160430 (DE-627)OLC1973804999 (DE-599)GBVOLC1973804999 (PRQ)i1676-7ab3d0be3a5e90c6f3334c3496b074f061706d91d045305be06409ba7e79b12e0 (KEY)0067469920160000051000100030movinginparalleltowardamodernmodelingepistemologyb DE-627 ger DE-627 rakwb eng 150 DNB Rodgers, Joseph Lee verfasserin aut Moving in Parallel Toward a Modern Modeling Epistemology: Bayes Factors and Frequentist Modeling Methods 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier The Bayesian-frequentist debate typically portrays these statistical perspectives as opposing views. However, both Bayesian and frequentist statisticians have expanded their epistemological basis away from a singular focus on the null hypothesis, to a broader perspective involving the development and comparison of competing statistical/mathematical models. For frequentists, statistical developments such as structural equation modeling and multilevel modeling have facilitated this transition. For Bayesians, the Bayes factor has facilitated this transition. The Bayes factor is treated in articles within this issue of Multivariate Behavioral Research. The current presentation provides brief commentary on those articles and more extended discussion of the transition toward a modern modeling epistemology. In certain respects, Bayesians and frequentists share common goals. Nutzungsrecht: © 2016 Taylor & Francis Group, LLC 2016 Bayesian methods frequentist methods statistical modeling Bayes factor Behavioral sciences Bayesian analysis Mathematical models Enthalten in Multivariate behavioral research Philadelphia, Pa. : Taylor & Francis, 1966 51(2016), 1, Seite 30 (DE-627)129488682 (DE-600)205705-0 (DE-576)014881500 0027-3171 nnns volume:51 year:2016 number:1 pages:30 http://dx.doi.org/10.1080/00273171.2015.1093459 Volltext http://www.tandfonline.com/doi/abs/10.1080/00273171.2015.1093459 http://www.ncbi.nlm.nih.gov/pubmed/26881955 http://search.proquest.com/docview/1765581701 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-PSY SSG-OLC-SOW GBV_ILN_11 GBV_ILN_21 GBV_ILN_31 GBV_ILN_60 GBV_ILN_100 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4311 GBV_ILN_4314 GBV_ILN_4700 AR 51 2016 1 30 |
| allfieldsGer |
10.1080/00273171.2015.1093459 doi PQ20160430 (DE-627)OLC1973804999 (DE-599)GBVOLC1973804999 (PRQ)i1676-7ab3d0be3a5e90c6f3334c3496b074f061706d91d045305be06409ba7e79b12e0 (KEY)0067469920160000051000100030movinginparalleltowardamodernmodelingepistemologyb DE-627 ger DE-627 rakwb eng 150 DNB Rodgers, Joseph Lee verfasserin aut Moving in Parallel Toward a Modern Modeling Epistemology: Bayes Factors and Frequentist Modeling Methods 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier The Bayesian-frequentist debate typically portrays these statistical perspectives as opposing views. However, both Bayesian and frequentist statisticians have expanded their epistemological basis away from a singular focus on the null hypothesis, to a broader perspective involving the development and comparison of competing statistical/mathematical models. For frequentists, statistical developments such as structural equation modeling and multilevel modeling have facilitated this transition. For Bayesians, the Bayes factor has facilitated this transition. The Bayes factor is treated in articles within this issue of Multivariate Behavioral Research. The current presentation provides brief commentary on those articles and more extended discussion of the transition toward a modern modeling epistemology. In certain respects, Bayesians and frequentists share common goals. Nutzungsrecht: © 2016 Taylor & Francis Group, LLC 2016 Bayesian methods frequentist methods statistical modeling Bayes factor Behavioral sciences Bayesian analysis Mathematical models Enthalten in Multivariate behavioral research Philadelphia, Pa. : Taylor & Francis, 1966 51(2016), 1, Seite 30 (DE-627)129488682 (DE-600)205705-0 (DE-576)014881500 0027-3171 nnns volume:51 year:2016 number:1 pages:30 http://dx.doi.org/10.1080/00273171.2015.1093459 Volltext http://www.tandfonline.com/doi/abs/10.1080/00273171.2015.1093459 http://www.ncbi.nlm.nih.gov/pubmed/26881955 http://search.proquest.com/docview/1765581701 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-PSY SSG-OLC-SOW GBV_ILN_11 GBV_ILN_21 GBV_ILN_31 GBV_ILN_60 GBV_ILN_100 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4311 GBV_ILN_4314 GBV_ILN_4700 AR 51 2016 1 30 |
| allfieldsSound |
10.1080/00273171.2015.1093459 doi PQ20160430 (DE-627)OLC1973804999 (DE-599)GBVOLC1973804999 (PRQ)i1676-7ab3d0be3a5e90c6f3334c3496b074f061706d91d045305be06409ba7e79b12e0 (KEY)0067469920160000051000100030movinginparalleltowardamodernmodelingepistemologyb DE-627 ger DE-627 rakwb eng 150 DNB Rodgers, Joseph Lee verfasserin aut Moving in Parallel Toward a Modern Modeling Epistemology: Bayes Factors and Frequentist Modeling Methods 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier The Bayesian-frequentist debate typically portrays these statistical perspectives as opposing views. However, both Bayesian and frequentist statisticians have expanded their epistemological basis away from a singular focus on the null hypothesis, to a broader perspective involving the development and comparison of competing statistical/mathematical models. For frequentists, statistical developments such as structural equation modeling and multilevel modeling have facilitated this transition. For Bayesians, the Bayes factor has facilitated this transition. The Bayes factor is treated in articles within this issue of Multivariate Behavioral Research. The current presentation provides brief commentary on those articles and more extended discussion of the transition toward a modern modeling epistemology. In certain respects, Bayesians and frequentists share common goals. Nutzungsrecht: © 2016 Taylor & Francis Group, LLC 2016 Bayesian methods frequentist methods statistical modeling Bayes factor Behavioral sciences Bayesian analysis Mathematical models Enthalten in Multivariate behavioral research Philadelphia, Pa. : Taylor & Francis, 1966 51(2016), 1, Seite 30 (DE-627)129488682 (DE-600)205705-0 (DE-576)014881500 0027-3171 nnns volume:51 year:2016 number:1 pages:30 http://dx.doi.org/10.1080/00273171.2015.1093459 Volltext http://www.tandfonline.com/doi/abs/10.1080/00273171.2015.1093459 http://www.ncbi.nlm.nih.gov/pubmed/26881955 http://search.proquest.com/docview/1765581701 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-PSY SSG-OLC-SOW GBV_ILN_11 GBV_ILN_21 GBV_ILN_31 GBV_ILN_60 GBV_ILN_100 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4311 GBV_ILN_4314 GBV_ILN_4700 AR 51 2016 1 30 |
| language |
English |
| source |
Enthalten in Multivariate behavioral research 51(2016), 1, Seite 30 volume:51 year:2016 number:1 pages:30 |
| sourceStr |
Enthalten in Multivariate behavioral research 51(2016), 1, Seite 30 volume:51 year:2016 number:1 pages:30 |
| format_phy_str_mv |
Article |
| institution |
findex.gbv.de |
| topic_facet |
Bayesian methods frequentist methods statistical modeling Bayes factor Behavioral sciences Bayesian analysis Mathematical models |
| dewey-raw |
150 |
| isfreeaccess_bool |
false |
| container_title |
Multivariate behavioral research |
| authorswithroles_txt_mv |
Rodgers, Joseph Lee @@aut@@ |
| publishDateDaySort_date |
2016-01-01T00:00:00Z |
| hierarchy_top_id |
129488682 |
| dewey-sort |
3150 |
| id |
OLC1973804999 |
| language_de |
englisch |
| fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a2200265 4500</leader><controlfield tag="001">OLC1973804999</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20211208221930.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">160430s2016 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1080/00273171.2015.1093459</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">PQ20160430</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC1973804999</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)GBVOLC1973804999</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(PRQ)i1676-7ab3d0be3a5e90c6f3334c3496b074f061706d91d045305be06409ba7e79b12e0</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(KEY)0067469920160000051000100030movinginparalleltowardamodernmodelingepistemologyb</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">150</subfield><subfield code="q">DNB</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Rodgers, Joseph Lee</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Moving in Parallel Toward a Modern Modeling Epistemology: Bayes Factors and Frequentist Modeling Methods</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2016</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">The Bayesian-frequentist debate typically portrays these statistical perspectives as opposing views. However, both Bayesian and frequentist statisticians have expanded their epistemological basis away from a singular focus on the null hypothesis, to a broader perspective involving the development and comparison of competing statistical/mathematical models. For frequentists, statistical developments such as structural equation modeling and multilevel modeling have facilitated this transition. For Bayesians, the Bayes factor has facilitated this transition. The Bayes factor is treated in articles within this issue of Multivariate Behavioral Research. The current presentation provides brief commentary on those articles and more extended discussion of the transition toward a modern modeling epistemology. In certain respects, Bayesians and frequentists share common goals.</subfield></datafield><datafield tag="540" ind1=" " ind2=" "><subfield code="a">Nutzungsrecht: © 2016 Taylor & Francis Group, LLC 2016</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Bayesian methods</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">frequentist methods</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">statistical modeling</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Bayes factor</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Behavioral sciences</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Bayesian analysis</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Mathematical models</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Multivariate behavioral research</subfield><subfield code="d">Philadelphia, Pa. : Taylor & Francis, 1966</subfield><subfield code="g">51(2016), 1, Seite 30</subfield><subfield code="w">(DE-627)129488682</subfield><subfield code="w">(DE-600)205705-0</subfield><subfield code="w">(DE-576)014881500</subfield><subfield code="x">0027-3171</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:51</subfield><subfield code="g">year:2016</subfield><subfield code="g">number:1</subfield><subfield code="g">pages:30</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">http://dx.doi.org/10.1080/00273171.2015.1093459</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">http://www.tandfonline.com/doi/abs/10.1080/00273171.2015.1093459</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">http://www.ncbi.nlm.nih.gov/pubmed/26881955</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">http://search.proquest.com/docview/1765581701</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PSY</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-SOW</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_11</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_21</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_100</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4046</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4311</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4314</subfield></datafield><datafield tag="912" 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">51</subfield><subfield code="j">2016</subfield><subfield code="e">1</subfield><subfield code="h">30</subfield></datafield></record></collection>
|
| author |
Rodgers, Joseph Lee |
| spellingShingle |
Rodgers, Joseph Lee ddc 150 misc Bayesian methods misc frequentist methods misc statistical modeling misc Bayes factor misc Behavioral sciences misc Bayesian analysis misc Mathematical models Moving in Parallel Toward a Modern Modeling Epistemology: Bayes Factors and Frequentist Modeling Methods |
| authorStr |
Rodgers, Joseph Lee |
| ppnlink_with_tag_str_mv |
@@773@@(DE-627)129488682 |
| format |
Article |
| dewey-ones |
150 - Psychology |
| delete_txt_mv |
keep |
| author_role |
aut |
| collection |
OLC |
| remote_str |
false |
| illustrated |
Not Illustrated |
| issn |
0027-3171 |
| topic_title |
150 DNB Moving in Parallel Toward a Modern Modeling Epistemology: Bayes Factors and Frequentist Modeling Methods Bayesian methods frequentist methods statistical modeling Bayes factor Behavioral sciences Bayesian analysis Mathematical models |
| topic |
ddc 150 misc Bayesian methods misc frequentist methods misc statistical modeling misc Bayes factor misc Behavioral sciences misc Bayesian analysis misc Mathematical models |
| topic_unstemmed |
ddc 150 misc Bayesian methods misc frequentist methods misc statistical modeling misc Bayes factor misc Behavioral sciences misc Bayesian analysis misc Mathematical models |
| topic_browse |
ddc 150 misc Bayesian methods misc frequentist methods misc statistical modeling misc Bayes factor misc Behavioral sciences misc Bayesian analysis misc Mathematical models |
| format_facet |
Aufsätze Gedruckte Aufsätze |
| format_main_str_mv |
Text Zeitschrift/Artikel |
| carriertype_str_mv |
nc |
| hierarchy_parent_title |
Multivariate behavioral research |
| hierarchy_parent_id |
129488682 |
| dewey-tens |
150 - Psychology |
| hierarchy_top_title |
Multivariate behavioral research |
| isfreeaccess_txt |
false |
| familylinks_str_mv |
(DE-627)129488682 (DE-600)205705-0 (DE-576)014881500 |
| title |
Moving in Parallel Toward a Modern Modeling Epistemology: Bayes Factors and Frequentist Modeling Methods |
| ctrlnum |
(DE-627)OLC1973804999 (DE-599)GBVOLC1973804999 (PRQ)i1676-7ab3d0be3a5e90c6f3334c3496b074f061706d91d045305be06409ba7e79b12e0 (KEY)0067469920160000051000100030movinginparalleltowardamodernmodelingepistemologyb |
| title_full |
Moving in Parallel Toward a Modern Modeling Epistemology: Bayes Factors and Frequentist Modeling Methods |
| author_sort |
Rodgers, Joseph Lee |
| journal |
Multivariate behavioral research |
| journalStr |
Multivariate behavioral research |
| lang_code |
eng |
| isOA_bool |
false |
| dewey-hundreds |
100 - Philosophy & psychology |
| recordtype |
marc |
| publishDateSort |
2016 |
| contenttype_str_mv |
txt |
| container_start_page |
30 |
| author_browse |
Rodgers, Joseph Lee |
| container_volume |
51 |
| class |
150 DNB |
| format_se |
Aufsätze |
| author-letter |
Rodgers, Joseph Lee |
| doi_str_mv |
10.1080/00273171.2015.1093459 |
| dewey-full |
150 |
| title_sort |
moving in parallel toward a modern modeling epistemology: bayes factors and frequentist modeling methods |
| title_auth |
Moving in Parallel Toward a Modern Modeling Epistemology: Bayes Factors and Frequentist Modeling Methods |
| abstract |
The Bayesian-frequentist debate typically portrays these statistical perspectives as opposing views. However, both Bayesian and frequentist statisticians have expanded their epistemological basis away from a singular focus on the null hypothesis, to a broader perspective involving the development and comparison of competing statistical/mathematical models. For frequentists, statistical developments such as structural equation modeling and multilevel modeling have facilitated this transition. For Bayesians, the Bayes factor has facilitated this transition. The Bayes factor is treated in articles within this issue of Multivariate Behavioral Research. The current presentation provides brief commentary on those articles and more extended discussion of the transition toward a modern modeling epistemology. In certain respects, Bayesians and frequentists share common goals. |
| abstractGer |
The Bayesian-frequentist debate typically portrays these statistical perspectives as opposing views. However, both Bayesian and frequentist statisticians have expanded their epistemological basis away from a singular focus on the null hypothesis, to a broader perspective involving the development and comparison of competing statistical/mathematical models. For frequentists, statistical developments such as structural equation modeling and multilevel modeling have facilitated this transition. For Bayesians, the Bayes factor has facilitated this transition. The Bayes factor is treated in articles within this issue of Multivariate Behavioral Research. The current presentation provides brief commentary on those articles and more extended discussion of the transition toward a modern modeling epistemology. In certain respects, Bayesians and frequentists share common goals. |
| abstract_unstemmed |
The Bayesian-frequentist debate typically portrays these statistical perspectives as opposing views. However, both Bayesian and frequentist statisticians have expanded their epistemological basis away from a singular focus on the null hypothesis, to a broader perspective involving the development and comparison of competing statistical/mathematical models. For frequentists, statistical developments such as structural equation modeling and multilevel modeling have facilitated this transition. For Bayesians, the Bayes factor has facilitated this transition. The Bayes factor is treated in articles within this issue of Multivariate Behavioral Research. The current presentation provides brief commentary on those articles and more extended discussion of the transition toward a modern modeling epistemology. In certain respects, Bayesians and frequentists share common goals. |
| collection_details |
GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-PSY SSG-OLC-SOW GBV_ILN_11 GBV_ILN_21 GBV_ILN_31 GBV_ILN_60 GBV_ILN_100 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4311 GBV_ILN_4314 GBV_ILN_4700 |
| container_issue |
1 |
| title_short |
Moving in Parallel Toward a Modern Modeling Epistemology: Bayes Factors and Frequentist Modeling Methods |
| url |
http://dx.doi.org/10.1080/00273171.2015.1093459 http://www.tandfonline.com/doi/abs/10.1080/00273171.2015.1093459 http://www.ncbi.nlm.nih.gov/pubmed/26881955 http://search.proquest.com/docview/1765581701 |
| remote_bool |
false |
| ppnlink |
129488682 |
| mediatype_str_mv |
n |
| isOA_txt |
false |
| hochschulschrift_bool |
false |
| doi_str |
10.1080/00273171.2015.1093459 |
| up_date |
2024-07-04T03:10:35.851Z |
| _version_ |
1803616398466875392 |
| fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a2200265 4500</leader><controlfield tag="001">OLC1973804999</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20211208221930.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">160430s2016 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1080/00273171.2015.1093459</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">PQ20160430</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC1973804999</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)GBVOLC1973804999</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(PRQ)i1676-7ab3d0be3a5e90c6f3334c3496b074f061706d91d045305be06409ba7e79b12e0</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(KEY)0067469920160000051000100030movinginparalleltowardamodernmodelingepistemologyb</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">150</subfield><subfield code="q">DNB</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Rodgers, Joseph Lee</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Moving in Parallel Toward a Modern Modeling Epistemology: Bayes Factors and Frequentist Modeling Methods</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2016</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">The Bayesian-frequentist debate typically portrays these statistical perspectives as opposing views. However, both Bayesian and frequentist statisticians have expanded their epistemological basis away from a singular focus on the null hypothesis, to a broader perspective involving the development and comparison of competing statistical/mathematical models. For frequentists, statistical developments such as structural equation modeling and multilevel modeling have facilitated this transition. For Bayesians, the Bayes factor has facilitated this transition. The Bayes factor is treated in articles within this issue of Multivariate Behavioral Research. The current presentation provides brief commentary on those articles and more extended discussion of the transition toward a modern modeling epistemology. In certain respects, Bayesians and frequentists share common goals.</subfield></datafield><datafield tag="540" ind1=" " ind2=" "><subfield code="a">Nutzungsrecht: © 2016 Taylor & Francis Group, LLC 2016</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Bayesian methods</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">frequentist methods</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">statistical modeling</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Bayes factor</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Behavioral sciences</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Bayesian analysis</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Mathematical models</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Multivariate behavioral research</subfield><subfield code="d">Philadelphia, Pa. : Taylor & Francis, 1966</subfield><subfield code="g">51(2016), 1, Seite 30</subfield><subfield code="w">(DE-627)129488682</subfield><subfield code="w">(DE-600)205705-0</subfield><subfield code="w">(DE-576)014881500</subfield><subfield code="x">0027-3171</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:51</subfield><subfield code="g">year:2016</subfield><subfield code="g">number:1</subfield><subfield code="g">pages:30</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">http://dx.doi.org/10.1080/00273171.2015.1093459</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">http://www.tandfonline.com/doi/abs/10.1080/00273171.2015.1093459</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">http://www.ncbi.nlm.nih.gov/pubmed/26881955</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">http://search.proquest.com/docview/1765581701</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PSY</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-SOW</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_11</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_21</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_100</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4046</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4311</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4314</subfield></datafield><datafield tag="912" 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">51</subfield><subfield code="j">2016</subfield><subfield code="e">1</subfield><subfield code="h">30</subfield></datafield></record></collection>
|
| score |
7.3972797 |