Introducing ν-CLEAR : a latent variable approach to measuring nuclear proficiency
The causes and consequences of nuclear proficiency are central to important questions in international relations. At present, researchers tend to use observable characteristics as a proxy. However, aggregation is a problem: existing measures implicitly assume that each indicator is equally informati...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Smith, Bradley C. [verfasserIn] Spaniel, William [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2020 |
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| Schlagwörter: |
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| Anmerkung: |
Literaturverzeichnis Seite 255-256 |
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| Umfang: |
1 Online-Ressource ; Diagramme, Karten |
| Übergeordnetes Werk: |
Enthalten in: Conflict management and peace science - Thousand Oaks, Calif. : Sage, 1980, 37(2020), 2 vom: März, Seite 232–256 |
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| Übergeordnetes Werk: |
volume:37 ; year:2020 ; number:2 ; month:03 ; pages:232–256 |
| Links: |
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| DOI / URN: |
10.1177/0738894217741619 |
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| Katalog-ID: |
1692911775 |
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| 245 | 1 | 0 | |a Introducing ν-CLEAR |b a latent variable approach to measuring nuclear proficiency |c Bradley C. Smith (Department of Political Science, Vanderbilt University, USA), William Spaniel (Department of Political Science, University of Pittsburgh, USA) |
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| 520 | |a The causes and consequences of nuclear proficiency are central to important questions in international relations. At present, researchers tend to use observable characteristics as a proxy. However, aggregation is a problem: existing measures implicitly assume that each indicator is equally informative and that measurement error is not a concern. We overcome these issues by applying a statistical measurement model to directly estimate nuclear proficiency from observed indicators. The resulting estimates form a new dataset on nuclear proficiency which we call ν-CLEAR. We demonstrate that these estimates are consistent with known patterns of nuclear proficiency while also uncovering more nuance than existing measures. Additionally, we demonstrate how scholars can use these estimates to account for measurement error by revisiting existing results with our measure. | ||
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10.1177/0738894217741619 doi (DE-627)1692911775 (DE-599)KXP1692911775 DE-627 ger DE-627 rda eng RS fivr SC06.02 fivs Smith, Bradley C. verfasserin (DE-588)1206664215 (DE-627)1692912003 aut Introducing ν-CLEAR a latent variable approach to measuring nuclear proficiency Bradley C. Smith (Department of Political Science, Vanderbilt University, USA), William Spaniel (Department of Political Science, University of Pittsburgh, USA) 2020 1 Online-Ressource Diagramme, Karten Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Literaturverzeichnis Seite 255-256 The causes and consequences of nuclear proficiency are central to important questions in international relations. At present, researchers tend to use observable characteristics as a proxy. However, aggregation is a problem: existing measures implicitly assume that each indicator is equally informative and that measurement error is not a concern. We overcome these issues by applying a statistical measurement model to directly estimate nuclear proficiency from observed indicators. The resulting estimates form a new dataset on nuclear proficiency which we call ν-CLEAR. We demonstrate that these estimates are consistent with known patterns of nuclear proficiency while also uncovering more nuance than existing measures. Additionally, we demonstrate how scholars can use these estimates to account for measurement error by revisiting existing results with our measure. 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10.1177/0738894217741619 doi (DE-627)1692911775 (DE-599)KXP1692911775 DE-627 ger DE-627 rda eng RS fivr SC06.02 fivs Smith, Bradley C. verfasserin (DE-588)1206664215 (DE-627)1692912003 aut Introducing ν-CLEAR a latent variable approach to measuring nuclear proficiency Bradley C. Smith (Department of Political Science, Vanderbilt University, USA), William Spaniel (Department of Political Science, University of Pittsburgh, USA) 2020 1 Online-Ressource Diagramme, Karten Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Literaturverzeichnis Seite 255-256 The causes and consequences of nuclear proficiency are central to important questions in international relations. At present, researchers tend to use observable characteristics as a proxy. However, aggregation is a problem: existing measures implicitly assume that each indicator is equally informative and that measurement error is not a concern. We overcome these issues by applying a statistical measurement model to directly estimate nuclear proficiency from observed indicators. The resulting estimates form a new dataset on nuclear proficiency which we call ν-CLEAR. We demonstrate that these estimates are consistent with known patterns of nuclear proficiency while also uncovering more nuance than existing measures. Additionally, we demonstrate how scholars can use these estimates to account for measurement error by revisiting existing results with our measure. (DE-588)4178671-3 (DE-627)105339695 (DE-576)209985984 Rüstungspolitik gnd (DE-588)4284936-6 (DE-627)104319569 (DE-576)210795735 Potenzial gnd (DE-588)4064228-8 (DE-627)104152125 (DE-576)209152141 Waffe gnd (DE-588)4226353-0 (DE-627)104972300 (DE-576)210313374 Kampfkraft gnd (DE-588)4046514-7 (DE-627)104152206 (DE-576)209069945 Politik gnd (DE-588)4117573-6 (DE-627)105798142 (DE-576)209511117 Waffensystem gnd (DE-588)4309759-5 (DE-627)123828147 (DE-576)211118567 Proliferation Militär gnd (DE-588)4003434-3 (DE-627)106389599 (DE-576)208851577 Kernwaffe gnd Statistische Analyse Statistical analysis Spaniel, William verfasserin (DE-588)1154972194 (DE-627)1016288549 (DE-576)501327266 aut Enthalten in Conflict management and peace science Thousand Oaks, Calif. : Sage, 1980 37(2020), 2 vom: März, Seite 232–256 Online-Ressource (DE-627)387481516 (DE-600)2145757-8 (DE-576)112775640 1549-9219 nnns volume:37 year:2020 number:2 month:03 pages:232–256 https://doi.org/10.1177/0738894217741619 Resolving-System GBV_USEFLAG_U GBV_ILN_2470 ISIL_DE-615 ISIL_DE-F197 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_121 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_165 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 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_647 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_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_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_2705 GBV_ILN_2889 GBV_ILN_2890 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_4238 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 (DE-627)1756134723 RS Ohne direkten Regionalbezug Ohne direkten Regionalbezug fivr (DE-627)1756135452 SC06.02 Rüstung Internationale Sicherheit/Verteidigung Wehrpotential Rüstung fivs (DE-627)1756243549 (DE-615)6606687 Rüstungspolitik fivt (DE-627)1756240469 (DE-615)6604801 Potential fivt (DE-627)1756159114 (DE-615)6602543 Waffenpotential fivt (DE-627)1756237263 (DE-615)6605943 Militärische Schlagkraft fivt (DE-627)1756178976 (DE-615)6602937 Politische Bedeutung von Waffensystemen fivt (DE-627)1756171971 (DE-615)6609690 Proliferation nuklearer Waffen fivt (DE-627)1756166986 (DE-615)6603060 Statistische Analyse fiva AR 37 2020 2 3 232–256 2470 01 DE-615 3610456655 00 --%%-- --%%-- --%%-- --%%-- l01 18-03-20 2470 02 DE-F197 3610843950 00 --%%-- --%%-- --%%-- l l02 19-03-20 2470 01 DE-615 00 DEIBHSFK |
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10.1177/0738894217741619 doi (DE-627)1692911775 (DE-599)KXP1692911775 DE-627 ger DE-627 rda eng RS fivr SC06.02 fivs Smith, Bradley C. verfasserin (DE-588)1206664215 (DE-627)1692912003 aut Introducing ν-CLEAR a latent variable approach to measuring nuclear proficiency Bradley C. Smith (Department of Political Science, Vanderbilt University, USA), William Spaniel (Department of Political Science, University of Pittsburgh, USA) 2020 1 Online-Ressource Diagramme, Karten Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Literaturverzeichnis Seite 255-256 The causes and consequences of nuclear proficiency are central to important questions in international relations. At present, researchers tend to use observable characteristics as a proxy. However, aggregation is a problem: existing measures implicitly assume that each indicator is equally informative and that measurement error is not a concern. We overcome these issues by applying a statistical measurement model to directly estimate nuclear proficiency from observed indicators. The resulting estimates form a new dataset on nuclear proficiency which we call ν-CLEAR. We demonstrate that these estimates are consistent with known patterns of nuclear proficiency while also uncovering more nuance than existing measures. Additionally, we demonstrate how scholars can use these estimates to account for measurement error by revisiting existing results with our measure. (DE-588)4178671-3 (DE-627)105339695 (DE-576)209985984 Rüstungspolitik gnd (DE-588)4284936-6 (DE-627)104319569 (DE-576)210795735 Potenzial gnd (DE-588)4064228-8 (DE-627)104152125 (DE-576)209152141 Waffe gnd (DE-588)4226353-0 (DE-627)104972300 (DE-576)210313374 Kampfkraft gnd (DE-588)4046514-7 (DE-627)104152206 (DE-576)209069945 Politik gnd (DE-588)4117573-6 (DE-627)105798142 (DE-576)209511117 Waffensystem gnd (DE-588)4309759-5 (DE-627)123828147 (DE-576)211118567 Proliferation Militär gnd (DE-588)4003434-3 (DE-627)106389599 (DE-576)208851577 Kernwaffe gnd Statistische Analyse Statistical analysis Spaniel, William verfasserin (DE-588)1154972194 (DE-627)1016288549 (DE-576)501327266 aut Enthalten in Conflict management and peace science Thousand Oaks, Calif. : Sage, 1980 37(2020), 2 vom: März, Seite 232–256 Online-Ressource (DE-627)387481516 (DE-600)2145757-8 (DE-576)112775640 1549-9219 nnns volume:37 year:2020 number:2 month:03 pages:232–256 https://doi.org/10.1177/0738894217741619 Resolving-System GBV_USEFLAG_U GBV_ILN_2470 ISIL_DE-615 ISIL_DE-F197 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_121 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_165 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 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_647 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_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_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_2705 GBV_ILN_2889 GBV_ILN_2890 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_4238 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 (DE-627)1756134723 RS Ohne direkten Regionalbezug Ohne direkten Regionalbezug fivr (DE-627)1756135452 SC06.02 Rüstung Internationale Sicherheit/Verteidigung Wehrpotential Rüstung fivs (DE-627)1756243549 (DE-615)6606687 Rüstungspolitik fivt (DE-627)1756240469 (DE-615)6604801 Potential fivt (DE-627)1756159114 (DE-615)6602543 Waffenpotential fivt (DE-627)1756237263 (DE-615)6605943 Militärische Schlagkraft fivt (DE-627)1756178976 (DE-615)6602937 Politische Bedeutung von Waffensystemen fivt (DE-627)1756171971 (DE-615)6609690 Proliferation nuklearer Waffen fivt (DE-627)1756166986 (DE-615)6603060 Statistische Analyse fiva AR 37 2020 2 3 232–256 2470 01 DE-615 3610456655 00 --%%-- --%%-- --%%-- --%%-- l01 18-03-20 2470 02 DE-F197 3610843950 00 --%%-- --%%-- --%%-- l l02 19-03-20 2470 01 DE-615 00 DEIBHSFK |
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10.1177/0738894217741619 doi (DE-627)1692911775 (DE-599)KXP1692911775 DE-627 ger DE-627 rda eng RS fivr SC06.02 fivs Smith, Bradley C. verfasserin (DE-588)1206664215 (DE-627)1692912003 aut Introducing ν-CLEAR a latent variable approach to measuring nuclear proficiency Bradley C. Smith (Department of Political Science, Vanderbilt University, USA), William Spaniel (Department of Political Science, University of Pittsburgh, USA) 2020 1 Online-Ressource Diagramme, Karten Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Literaturverzeichnis Seite 255-256 The causes and consequences of nuclear proficiency are central to important questions in international relations. At present, researchers tend to use observable characteristics as a proxy. However, aggregation is a problem: existing measures implicitly assume that each indicator is equally informative and that measurement error is not a concern. We overcome these issues by applying a statistical measurement model to directly estimate nuclear proficiency from observed indicators. The resulting estimates form a new dataset on nuclear proficiency which we call ν-CLEAR. We demonstrate that these estimates are consistent with known patterns of nuclear proficiency while also uncovering more nuance than existing measures. Additionally, we demonstrate how scholars can use these estimates to account for measurement error by revisiting existing results with our measure. (DE-588)4178671-3 (DE-627)105339695 (DE-576)209985984 Rüstungspolitik gnd (DE-588)4284936-6 (DE-627)104319569 (DE-576)210795735 Potenzial gnd (DE-588)4064228-8 (DE-627)104152125 (DE-576)209152141 Waffe gnd (DE-588)4226353-0 (DE-627)104972300 (DE-576)210313374 Kampfkraft gnd (DE-588)4046514-7 (DE-627)104152206 (DE-576)209069945 Politik gnd (DE-588)4117573-6 (DE-627)105798142 (DE-576)209511117 Waffensystem gnd (DE-588)4309759-5 (DE-627)123828147 (DE-576)211118567 Proliferation Militär gnd (DE-588)4003434-3 (DE-627)106389599 (DE-576)208851577 Kernwaffe gnd Statistische Analyse Statistical analysis Spaniel, William verfasserin (DE-588)1154972194 (DE-627)1016288549 (DE-576)501327266 aut Enthalten in Conflict management and peace science Thousand Oaks, Calif. : Sage, 1980 37(2020), 2 vom: März, Seite 232–256 Online-Ressource (DE-627)387481516 (DE-600)2145757-8 (DE-576)112775640 1549-9219 nnns volume:37 year:2020 number:2 month:03 pages:232–256 https://doi.org/10.1177/0738894217741619 Resolving-System GBV_USEFLAG_U GBV_ILN_2470 ISIL_DE-615 ISIL_DE-F197 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_121 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_165 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 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_647 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_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_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_2705 GBV_ILN_2889 GBV_ILN_2890 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_4238 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 (DE-627)1756134723 RS Ohne direkten Regionalbezug Ohne direkten Regionalbezug fivr (DE-627)1756135452 SC06.02 Rüstung Internationale Sicherheit/Verteidigung Wehrpotential Rüstung fivs (DE-627)1756243549 (DE-615)6606687 Rüstungspolitik fivt (DE-627)1756240469 (DE-615)6604801 Potential fivt (DE-627)1756159114 (DE-615)6602543 Waffenpotential fivt (DE-627)1756237263 (DE-615)6605943 Militärische Schlagkraft fivt (DE-627)1756178976 (DE-615)6602937 Politische Bedeutung von Waffensystemen fivt (DE-627)1756171971 (DE-615)6609690 Proliferation nuklearer Waffen fivt (DE-627)1756166986 (DE-615)6603060 Statistische Analyse fiva AR 37 2020 2 3 232–256 2470 01 DE-615 3610456655 00 --%%-- --%%-- --%%-- --%%-- l01 18-03-20 2470 02 DE-F197 3610843950 00 --%%-- --%%-- --%%-- l l02 19-03-20 2470 01 DE-615 00 DEIBHSFK |
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10.1177/0738894217741619 doi (DE-627)1692911775 (DE-599)KXP1692911775 DE-627 ger DE-627 rda eng RS fivr SC06.02 fivs Smith, Bradley C. verfasserin (DE-588)1206664215 (DE-627)1692912003 aut Introducing ν-CLEAR a latent variable approach to measuring nuclear proficiency Bradley C. Smith (Department of Political Science, Vanderbilt University, USA), William Spaniel (Department of Political Science, University of Pittsburgh, USA) 2020 1 Online-Ressource Diagramme, Karten Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Literaturverzeichnis Seite 255-256 The causes and consequences of nuclear proficiency are central to important questions in international relations. At present, researchers tend to use observable characteristics as a proxy. However, aggregation is a problem: existing measures implicitly assume that each indicator is equally informative and that measurement error is not a concern. We overcome these issues by applying a statistical measurement model to directly estimate nuclear proficiency from observed indicators. The resulting estimates form a new dataset on nuclear proficiency which we call ν-CLEAR. We demonstrate that these estimates are consistent with known patterns of nuclear proficiency while also uncovering more nuance than existing measures. Additionally, we demonstrate how scholars can use these estimates to account for measurement error by revisiting existing results with our measure. 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introducing ν-cleara latent variable approach to measuring nuclear proficiency |
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Introducing ν-CLEAR a latent variable approach to measuring nuclear proficiency |
| abstract |
The causes and consequences of nuclear proficiency are central to important questions in international relations. At present, researchers tend to use observable characteristics as a proxy. However, aggregation is a problem: existing measures implicitly assume that each indicator is equally informative and that measurement error is not a concern. We overcome these issues by applying a statistical measurement model to directly estimate nuclear proficiency from observed indicators. The resulting estimates form a new dataset on nuclear proficiency which we call ν-CLEAR. We demonstrate that these estimates are consistent with known patterns of nuclear proficiency while also uncovering more nuance than existing measures. Additionally, we demonstrate how scholars can use these estimates to account for measurement error by revisiting existing results with our measure. Literaturverzeichnis Seite 255-256 |
| abstractGer |
The causes and consequences of nuclear proficiency are central to important questions in international relations. At present, researchers tend to use observable characteristics as a proxy. However, aggregation is a problem: existing measures implicitly assume that each indicator is equally informative and that measurement error is not a concern. We overcome these issues by applying a statistical measurement model to directly estimate nuclear proficiency from observed indicators. The resulting estimates form a new dataset on nuclear proficiency which we call ν-CLEAR. We demonstrate that these estimates are consistent with known patterns of nuclear proficiency while also uncovering more nuance than existing measures. Additionally, we demonstrate how scholars can use these estimates to account for measurement error by revisiting existing results with our measure. Literaturverzeichnis Seite 255-256 |
| abstract_unstemmed |
The causes and consequences of nuclear proficiency are central to important questions in international relations. At present, researchers tend to use observable characteristics as a proxy. However, aggregation is a problem: existing measures implicitly assume that each indicator is equally informative and that measurement error is not a concern. We overcome these issues by applying a statistical measurement model to directly estimate nuclear proficiency from observed indicators. The resulting estimates form a new dataset on nuclear proficiency which we call ν-CLEAR. We demonstrate that these estimates are consistent with known patterns of nuclear proficiency while also uncovering more nuance than existing measures. Additionally, we demonstrate how scholars can use these estimates to account for measurement error by revisiting existing results with our measure. Literaturverzeichnis Seite 255-256 |
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Introducing ν-CLEAR |
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