A Bayesian Multilevel Modeling Approach to Time-Series Cross-Sectional Data
The analysis of time-series cross-sectional (TSCS) data has become increasingly popular in political science. Meanwhile, political scientists are also becoming more interested in the use of multilevel models (MLM). However, little work exists to understand the benefits of multilevel modeling when ap...
Gespeichert in:
| Veröffentlicht in: | Political analysis 2007-04, Vol.15 (2), p.165-181 |
|---|---|
| Hauptverfasser: | , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 181 |
|---|---|
| container_issue | 2 |
| container_start_page | 165 |
| container_title | Political analysis |
| container_volume | 15 |
| creator | Shor, Boris Bafumi, Joseph Keele, Luke Park, David |
| description | The analysis of time-series cross-sectional (TSCS) data has become increasingly popular in political science. Meanwhile, political scientists are also becoming more interested in the use of multilevel models (MLM). However, little work exists to understand the benefits of multilevel modeling when applied to TSCS data. We employ Monte Carlo simulations to benchmark the performance of a Bayesian multilevel model for TSCS data. We find that the MLM performs as well or better than other common estimators for such data. Most importantly, the MLM is more general and offers researchers additional advantages. |
| doi_str_mv | 10.1093/pan/mpm006 |
| format | Article |
| fullrecord | <record><control><sourceid>jstor_proqu</sourceid><recordid>TN_cdi_proquest_miscellaneous_59774172</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><cupid>10_1093_pan_mpm006</cupid><jstor_id>25791887</jstor_id><oup_id>10.1093/pan/mpm006</oup_id><sourcerecordid>25791887</sourcerecordid><originalsourceid>FETCH-LOGICAL-c416t-6f7e6425b2ddb31300d5eb47ccc30a709c792f15788aa61bbbc5cafbb902daeb3</originalsourceid><addsrcrecordid>eNp9kM1r3DAQxU1oIWnaS-8FU0gOpW4kW9ZYx81um29KaBJKLkKSx4k2tuVKdkn--yg4hNJCTzPD-zHz5iXJe0q-UCKKvUH1e93QEcI3ki3KgGdMVOJV7AmDjIoKNpM3IawJoQBCbCUni3RfPWCwqk_Ppna0Lf7GNj1zNba2v0kXw-CdMrfp6NIL22H2A73FkC69CyEOZrSuV226UqN6m7xuVBvw3XPdTi6_fb1YHman3w-OlovTzDDKx4w3gJzlpc7rWhe0IKQuUTMwxhREAREGRN7QEqpKKU611qY0qtFakLxWqIvtZHfeG639mjCMsrPBYNuqHt0UZCkAGIU8gh__Atdu8tFukFQwXlHOqgh9miHz9JLHRg7edso_SErkU6gyhirnUCO8M8NuGv7PfZi5dRidfyHzEgStKoh6Nus2jHj_oit_JzkUUMrDn9dydXJwfny1upbnkf_8bFJ12tv6Bv945d_zj5kPn_Q</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>194681648</pqid></control><display><type>article</type><title>A Bayesian Multilevel Modeling Approach to Time-Series Cross-Sectional Data</title><source>Jstor Complete Legacy</source><source>Political Science Complete</source><source>Worldwide Political Science Abstracts</source><source>Cambridge University Press Journals Complete</source><creator>Shor, Boris ; Bafumi, Joseph ; Keele, Luke ; Park, David</creator><creatorcontrib>Shor, Boris ; Bafumi, Joseph ; Keele, Luke ; Park, David</creatorcontrib><description>The analysis of time-series cross-sectional (TSCS) data has become increasingly popular in political science. Meanwhile, political scientists are also becoming more interested in the use of multilevel models (MLM). However, little work exists to understand the benefits of multilevel modeling when applied to TSCS data. We employ Monte Carlo simulations to benchmark the performance of a Bayesian multilevel model for TSCS data. We find that the MLM performs as well or better than other common estimators for such data. Most importantly, the MLM is more general and offers researchers additional advantages.</description><identifier>ISSN: 1047-1987</identifier><identifier>EISSN: 1476-4989</identifier><identifier>DOI: 10.1093/pan/mpm006</identifier><language>eng</language><publisher>New York, US: Cambridge University Press</publisher><subject>Bayesian analysis ; Correlations ; Data models ; Estimation bias ; Estimators ; Methodology (Data Analysis) ; Modeling ; Monte Carlo simulation ; Multilevel models ; Optimism ; Political science ; Statistical discrepancies ; Statistical variance ; Time series</subject><ispartof>Political analysis, 2007-04, Vol.15 (2), p.165-181</ispartof><rights>Copyright © The Author 2007. Published by Oxford University Press on behalf of the Society for Political Methodology</rights><rights>Copyright © 2007 Society for Political Methodology</rights><rights>The Author 2007. Published by Oxford University Press on behalf of the Society for Political Methodology. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org 2007</rights><rights>Copyright Oxford University Press(England) Spring 2007</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c416t-6f7e6425b2ddb31300d5eb47ccc30a709c792f15788aa61bbbc5cafbb902daeb3</citedby><cites>FETCH-LOGICAL-c416t-6f7e6425b2ddb31300d5eb47ccc30a709c792f15788aa61bbbc5cafbb902daeb3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.jstor.org/stable/pdf/25791887$$EPDF$$P50$$Gjstor$$H</linktopdf><linktohtml>$$Uhttps://www.cambridge.org/core/product/identifier/S1047198700006446/type/journal_article$$EHTML$$P50$$Gcambridge$$H</linktohtml><link.rule.ids>164,314,776,780,799,27901,27902,55603,57992,58225</link.rule.ids></links><search><creatorcontrib>Shor, Boris</creatorcontrib><creatorcontrib>Bafumi, Joseph</creatorcontrib><creatorcontrib>Keele, Luke</creatorcontrib><creatorcontrib>Park, David</creatorcontrib><title>A Bayesian Multilevel Modeling Approach to Time-Series Cross-Sectional Data</title><title>Political analysis</title><addtitle>Polit. anal</addtitle><description>The analysis of time-series cross-sectional (TSCS) data has become increasingly popular in political science. Meanwhile, political scientists are also becoming more interested in the use of multilevel models (MLM). However, little work exists to understand the benefits of multilevel modeling when applied to TSCS data. We employ Monte Carlo simulations to benchmark the performance of a Bayesian multilevel model for TSCS data. We find that the MLM performs as well or better than other common estimators for such data. Most importantly, the MLM is more general and offers researchers additional advantages.</description><subject>Bayesian analysis</subject><subject>Correlations</subject><subject>Data models</subject><subject>Estimation bias</subject><subject>Estimators</subject><subject>Methodology (Data Analysis)</subject><subject>Modeling</subject><subject>Monte Carlo simulation</subject><subject>Multilevel models</subject><subject>Optimism</subject><subject>Political science</subject><subject>Statistical discrepancies</subject><subject>Statistical variance</subject><subject>Time series</subject><issn>1047-1987</issn><issn>1476-4989</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2007</creationdate><recordtype>article</recordtype><sourceid>7UB</sourceid><recordid>eNp9kM1r3DAQxU1oIWnaS-8FU0gOpW4kW9ZYx81um29KaBJKLkKSx4k2tuVKdkn--yg4hNJCTzPD-zHz5iXJe0q-UCKKvUH1e93QEcI3ki3KgGdMVOJV7AmDjIoKNpM3IawJoQBCbCUni3RfPWCwqk_Ppna0Lf7GNj1zNba2v0kXw-CdMrfp6NIL22H2A73FkC69CyEOZrSuV226UqN6m7xuVBvw3XPdTi6_fb1YHman3w-OlovTzDDKx4w3gJzlpc7rWhe0IKQuUTMwxhREAREGRN7QEqpKKU611qY0qtFakLxWqIvtZHfeG639mjCMsrPBYNuqHt0UZCkAGIU8gh__Atdu8tFukFQwXlHOqgh9miHz9JLHRg7edso_SErkU6gyhirnUCO8M8NuGv7PfZi5dRidfyHzEgStKoh6Nus2jHj_oit_JzkUUMrDn9dydXJwfny1upbnkf_8bFJ12tv6Bv945d_zj5kPn_Q</recordid><startdate>20070401</startdate><enddate>20070401</enddate><creator>Shor, Boris</creator><creator>Bafumi, Joseph</creator><creator>Keele, Luke</creator><creator>Park, David</creator><general>Cambridge University Press</general><general>Oxford University Press</general><scope>BSCLL</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7UB</scope></search><sort><creationdate>20070401</creationdate><title>A Bayesian Multilevel Modeling Approach to Time-Series Cross-Sectional Data</title><author>Shor, Boris ; Bafumi, Joseph ; Keele, Luke ; Park, David</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c416t-6f7e6425b2ddb31300d5eb47ccc30a709c792f15788aa61bbbc5cafbb902daeb3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2007</creationdate><topic>Bayesian analysis</topic><topic>Correlations</topic><topic>Data models</topic><topic>Estimation bias</topic><topic>Estimators</topic><topic>Methodology (Data Analysis)</topic><topic>Modeling</topic><topic>Monte Carlo simulation</topic><topic>Multilevel models</topic><topic>Optimism</topic><topic>Political science</topic><topic>Statistical discrepancies</topic><topic>Statistical variance</topic><topic>Time series</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Shor, Boris</creatorcontrib><creatorcontrib>Bafumi, Joseph</creatorcontrib><creatorcontrib>Keele, Luke</creatorcontrib><creatorcontrib>Park, David</creatorcontrib><collection>Istex</collection><collection>CrossRef</collection><collection>Worldwide Political Science Abstracts</collection><jtitle>Political analysis</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Shor, Boris</au><au>Bafumi, Joseph</au><au>Keele, Luke</au><au>Park, David</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A Bayesian Multilevel Modeling Approach to Time-Series Cross-Sectional Data</atitle><jtitle>Political analysis</jtitle><addtitle>Polit. anal</addtitle><date>2007-04-01</date><risdate>2007</risdate><volume>15</volume><issue>2</issue><spage>165</spage><epage>181</epage><pages>165-181</pages><issn>1047-1987</issn><eissn>1476-4989</eissn><abstract>The analysis of time-series cross-sectional (TSCS) data has become increasingly popular in political science. Meanwhile, political scientists are also becoming more interested in the use of multilevel models (MLM). However, little work exists to understand the benefits of multilevel modeling when applied to TSCS data. We employ Monte Carlo simulations to benchmark the performance of a Bayesian multilevel model for TSCS data. We find that the MLM performs as well or better than other common estimators for such data. Most importantly, the MLM is more general and offers researchers additional advantages.</abstract><cop>New York, US</cop><pub>Cambridge University Press</pub><doi>10.1093/pan/mpm006</doi><tpages>17</tpages></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1047-1987 |
| ispartof | Political analysis, 2007-04, Vol.15 (2), p.165-181 |
| issn | 1047-1987 1476-4989 |
| language | eng |
| recordid | cdi_proquest_miscellaneous_59774172 |
| source | Jstor Complete Legacy; Political Science Complete; Worldwide Political Science Abstracts; Cambridge University Press Journals Complete |
| subjects | Bayesian analysis Correlations Data models Estimation bias Estimators Methodology (Data Analysis) Modeling Monte Carlo simulation Multilevel models Optimism Political science Statistical discrepancies Statistical variance Time series |
| title | A Bayesian Multilevel Modeling Approach to Time-Series Cross-Sectional Data |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-09T13%3A45%3A06IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-jstor_proqu&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=A%20Bayesian%20Multilevel%20Modeling%20Approach%20to%20Time-Series%20Cross-Sectional%20Data&rft.jtitle=Political%20analysis&rft.au=Shor,%20Boris&rft.date=2007-04-01&rft.volume=15&rft.issue=2&rft.spage=165&rft.epage=181&rft.pages=165-181&rft.issn=1047-1987&rft.eissn=1476-4989&rft_id=info:doi/10.1093/pan/mpm006&rft_dat=%3Cjstor_proqu%3E25791887%3C/jstor_proqu%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=194681648&rft_id=info:pmid/&rft_cupid=10_1093_pan_mpm006&rft_jstor_id=25791887&rft_oup_id=10.1093/pan/mpm006&rfr_iscdi=true |