Longitudinal modeling in developmental neuroimaging research: Common challenges, and solutions from developmental psychology

Hypotheses about change over time are central to informing our understanding of development. Developmental neuroscience is at critical juncture: although the majority of longitudinal imaging studies have observations with two time points, researchers are increasingly obtaining three or more observat...
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Autor*in:	King, Kevin M. [verfasserIn] Littlefield, Andrew K. [verfasserIn] McCabe, Connor J. [verfasserIn] Mills, Kathryn L. [verfasserIn] Flournoy, John [verfasserIn] Chassin, Laurie [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2017

Schlagwörter:	Longitudinal methods Growth curve models Change over time

Übergeordnetes Werk:	Enthalten in: Developmental cognitive neuroscience - Amsterdam [u.a.] : Elsevier, 2011, 33, Seite 54-72
Übergeordnetes Werk:	volume:33 ; pages:54-72

DOI / URN:	10.1016/j.dcn.2017.11.009

Katalog-ID:	ELV001001361

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520			\|a Hypotheses about change over time are central to informing our understanding of development. Developmental neuroscience is at critical juncture: although the majority of longitudinal imaging studies have observations with two time points, researchers are increasingly obtaining three or more observations of the same individuals. The goals of the proposed manuscript are to draw upon the long history of methodological and applied literature on longitudinal statistical models to summarize common problems and issues that arise in their use. We also provide suggestions and solutions to improve the design, analysis and interpretation of longitudinal data, and discuss the importance of matching the theory of change with the appropriate statistical model used to test the theory. Researchers should articulate a clear theory of change and to design studies to capture that change and use appropriately sensitive measures to assess that change during development. Simulated data are used to demonstrate several common analytic approaches to longitudinal analyses. We provide the code for our simulations and figures in an online supplement to aid researchers in exploring and plotting their data. We provide brief examples of best practices for reporting such models. Finally, we clarify common misunderstandings in the application and interpretation of these analytic approaches.
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700	1		\|a Chassin, Laurie \|e verfasserin \|4 aut
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publishDate	2017
allfields	10.1016/j.dcn.2017.11.009 doi (DE-627)ELV001001361 (ELSEVIER)S1878-9293(17)30030-0 DE-627 ger DE-627 rda eng 610 DE-600 King, Kevin M. verfasserin aut Longitudinal modeling in developmental neuroimaging research: Common challenges, and solutions from developmental psychology 2017 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Hypotheses about change over time are central to informing our understanding of development. Developmental neuroscience is at critical juncture: although the majority of longitudinal imaging studies have observations with two time points, researchers are increasingly obtaining three or more observations of the same individuals. The goals of the proposed manuscript are to draw upon the long history of methodological and applied literature on longitudinal statistical models to summarize common problems and issues that arise in their use. We also provide suggestions and solutions to improve the design, analysis and interpretation of longitudinal data, and discuss the importance of matching the theory of change with the appropriate statistical model used to test the theory. Researchers should articulate a clear theory of change and to design studies to capture that change and use appropriately sensitive measures to assess that change during development. Simulated data are used to demonstrate several common analytic approaches to longitudinal analyses. We provide the code for our simulations and figures in an online supplement to aid researchers in exploring and plotting their data. We provide brief examples of best practices for reporting such models. Finally, we clarify common misunderstandings in the application and interpretation of these analytic approaches. Longitudinal methods Growth curve models Change over time Littlefield, Andrew K. verfasserin aut McCabe, Connor J. verfasserin aut Mills, Kathryn L. verfasserin aut Flournoy, John verfasserin aut Chassin, Laurie verfasserin aut Enthalten in Developmental cognitive neuroscience Amsterdam [u.a.] : Elsevier, 2011 33, Seite 54-72 Online-Ressource (DE-627)635133784 (DE-600)2572271-2 (DE-576)331556308 1878-9307 nnns volume:33 pages:54-72 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 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_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 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_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 33 54-72
spelling	10.1016/j.dcn.2017.11.009 doi (DE-627)ELV001001361 (ELSEVIER)S1878-9293(17)30030-0 DE-627 ger DE-627 rda eng 610 DE-600 King, Kevin M. verfasserin aut Longitudinal modeling in developmental neuroimaging research: Common challenges, and solutions from developmental psychology 2017 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Hypotheses about change over time are central to informing our understanding of development. Developmental neuroscience is at critical juncture: although the majority of longitudinal imaging studies have observations with two time points, researchers are increasingly obtaining three or more observations of the same individuals. The goals of the proposed manuscript are to draw upon the long history of methodological and applied literature on longitudinal statistical models to summarize common problems and issues that arise in their use. We also provide suggestions and solutions to improve the design, analysis and interpretation of longitudinal data, and discuss the importance of matching the theory of change with the appropriate statistical model used to test the theory. Researchers should articulate a clear theory of change and to design studies to capture that change and use appropriately sensitive measures to assess that change during development. Simulated data are used to demonstrate several common analytic approaches to longitudinal analyses. We provide the code for our simulations and figures in an online supplement to aid researchers in exploring and plotting their data. We provide brief examples of best practices for reporting such models. Finally, we clarify common misunderstandings in the application and interpretation of these analytic approaches. Longitudinal methods Growth curve models Change over time Littlefield, Andrew K. verfasserin aut McCabe, Connor J. verfasserin aut Mills, Kathryn L. verfasserin aut Flournoy, John verfasserin aut Chassin, Laurie verfasserin aut Enthalten in Developmental cognitive neuroscience Amsterdam [u.a.] : Elsevier, 2011 33, Seite 54-72 Online-Ressource (DE-627)635133784 (DE-600)2572271-2 (DE-576)331556308 1878-9307 nnns volume:33 pages:54-72 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 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_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 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_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 33 54-72
allfields_unstemmed	10.1016/j.dcn.2017.11.009 doi (DE-627)ELV001001361 (ELSEVIER)S1878-9293(17)30030-0 DE-627 ger DE-627 rda eng 610 DE-600 King, Kevin M. verfasserin aut Longitudinal modeling in developmental neuroimaging research: Common challenges, and solutions from developmental psychology 2017 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Hypotheses about change over time are central to informing our understanding of development. Developmental neuroscience is at critical juncture: although the majority of longitudinal imaging studies have observations with two time points, researchers are increasingly obtaining three or more observations of the same individuals. The goals of the proposed manuscript are to draw upon the long history of methodological and applied literature on longitudinal statistical models to summarize common problems and issues that arise in their use. We also provide suggestions and solutions to improve the design, analysis and interpretation of longitudinal data, and discuss the importance of matching the theory of change with the appropriate statistical model used to test the theory. Researchers should articulate a clear theory of change and to design studies to capture that change and use appropriately sensitive measures to assess that change during development. Simulated data are used to demonstrate several common analytic approaches to longitudinal analyses. We provide the code for our simulations and figures in an online supplement to aid researchers in exploring and plotting their data. We provide brief examples of best practices for reporting such models. Finally, we clarify common misunderstandings in the application and interpretation of these analytic approaches. Longitudinal methods Growth curve models Change over time Littlefield, Andrew K. verfasserin aut McCabe, Connor J. verfasserin aut Mills, Kathryn L. verfasserin aut Flournoy, John verfasserin aut Chassin, Laurie verfasserin aut Enthalten in Developmental cognitive neuroscience Amsterdam [u.a.] : Elsevier, 2011 33, Seite 54-72 Online-Ressource (DE-627)635133784 (DE-600)2572271-2 (DE-576)331556308 1878-9307 nnns volume:33 pages:54-72 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 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_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 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_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 33 54-72
allfieldsGer	10.1016/j.dcn.2017.11.009 doi (DE-627)ELV001001361 (ELSEVIER)S1878-9293(17)30030-0 DE-627 ger DE-627 rda eng 610 DE-600 King, Kevin M. verfasserin aut Longitudinal modeling in developmental neuroimaging research: Common challenges, and solutions from developmental psychology 2017 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Hypotheses about change over time are central to informing our understanding of development. Developmental neuroscience is at critical juncture: although the majority of longitudinal imaging studies have observations with two time points, researchers are increasingly obtaining three or more observations of the same individuals. The goals of the proposed manuscript are to draw upon the long history of methodological and applied literature on longitudinal statistical models to summarize common problems and issues that arise in their use. We also provide suggestions and solutions to improve the design, analysis and interpretation of longitudinal data, and discuss the importance of matching the theory of change with the appropriate statistical model used to test the theory. Researchers should articulate a clear theory of change and to design studies to capture that change and use appropriately sensitive measures to assess that change during development. Simulated data are used to demonstrate several common analytic approaches to longitudinal analyses. We provide the code for our simulations and figures in an online supplement to aid researchers in exploring and plotting their data. We provide brief examples of best practices for reporting such models. Finally, we clarify common misunderstandings in the application and interpretation of these analytic approaches. Longitudinal methods Growth curve models Change over time Littlefield, Andrew K. verfasserin aut McCabe, Connor J. verfasserin aut Mills, Kathryn L. verfasserin aut Flournoy, John verfasserin aut Chassin, Laurie verfasserin aut Enthalten in Developmental cognitive neuroscience Amsterdam [u.a.] : Elsevier, 2011 33, Seite 54-72 Online-Ressource (DE-627)635133784 (DE-600)2572271-2 (DE-576)331556308 1878-9307 nnns volume:33 pages:54-72 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 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_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 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_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 33 54-72
allfieldsSound	10.1016/j.dcn.2017.11.009 doi (DE-627)ELV001001361 (ELSEVIER)S1878-9293(17)30030-0 DE-627 ger DE-627 rda eng 610 DE-600 King, Kevin M. verfasserin aut Longitudinal modeling in developmental neuroimaging research: Common challenges, and solutions from developmental psychology 2017 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Hypotheses about change over time are central to informing our understanding of development. Developmental neuroscience is at critical juncture: although the majority of longitudinal imaging studies have observations with two time points, researchers are increasingly obtaining three or more observations of the same individuals. The goals of the proposed manuscript are to draw upon the long history of methodological and applied literature on longitudinal statistical models to summarize common problems and issues that arise in their use. We also provide suggestions and solutions to improve the design, analysis and interpretation of longitudinal data, and discuss the importance of matching the theory of change with the appropriate statistical model used to test the theory. Researchers should articulate a clear theory of change and to design studies to capture that change and use appropriately sensitive measures to assess that change during development. Simulated data are used to demonstrate several common analytic approaches to longitudinal analyses. We provide the code for our simulations and figures in an online supplement to aid researchers in exploring and plotting their data. We provide brief examples of best practices for reporting such models. Finally, we clarify common misunderstandings in the application and interpretation of these analytic approaches. Longitudinal methods Growth curve models Change over time Littlefield, Andrew K. verfasserin aut McCabe, Connor J. verfasserin aut Mills, Kathryn L. verfasserin aut Flournoy, John verfasserin aut Chassin, Laurie verfasserin aut Enthalten in Developmental cognitive neuroscience Amsterdam [u.a.] : Elsevier, 2011 33, Seite 54-72 Online-Ressource (DE-627)635133784 (DE-600)2572271-2 (DE-576)331556308 1878-9307 nnns volume:33 pages:54-72 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 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_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 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_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 33 54-72
language	English
source	Enthalten in Developmental cognitive neuroscience 33, Seite 54-72 volume:33 pages:54-72
sourceStr	Enthalten in Developmental cognitive neuroscience 33, Seite 54-72 volume:33 pages:54-72
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Longitudinal methods Growth curve models Change over time
dewey-raw	610
isfreeaccess_bool	false
container_title	Developmental cognitive neuroscience
authorswithroles_txt_mv	King, Kevin M. @@aut@@ Littlefield, Andrew K. @@aut@@ McCabe, Connor J. @@aut@@ Mills, Kathryn L. @@aut@@ Flournoy, John @@aut@@ Chassin, Laurie @@aut@@
publishDateDaySort_date	2017-01-01T00:00:00Z
hierarchy_top_id	635133784
dewey-sort	3610
id	ELV001001361
language_de	englisch
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author	King, Kevin M.
spellingShingle	King, Kevin M. ddc 610 misc Longitudinal methods misc Growth curve models misc Change over time Longitudinal modeling in developmental neuroimaging research: Common challenges, and solutions from developmental psychology
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topic_title	610 DE-600 Longitudinal modeling in developmental neuroimaging research: Common challenges, and solutions from developmental psychology Longitudinal methods Growth curve models Change over time
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title	Longitudinal modeling in developmental neuroimaging research: Common challenges, and solutions from developmental psychology
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title_full	Longitudinal modeling in developmental neuroimaging research: Common challenges, and solutions from developmental psychology
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title_sort	longitudinal modeling in developmental neuroimaging research: common challenges, and solutions from developmental psychology
title_auth	Longitudinal modeling in developmental neuroimaging research: Common challenges, and solutions from developmental psychology
abstract	Hypotheses about change over time are central to informing our understanding of development. Developmental neuroscience is at critical juncture: although the majority of longitudinal imaging studies have observations with two time points, researchers are increasingly obtaining three or more observations of the same individuals. The goals of the proposed manuscript are to draw upon the long history of methodological and applied literature on longitudinal statistical models to summarize common problems and issues that arise in their use. We also provide suggestions and solutions to improve the design, analysis and interpretation of longitudinal data, and discuss the importance of matching the theory of change with the appropriate statistical model used to test the theory. Researchers should articulate a clear theory of change and to design studies to capture that change and use appropriately sensitive measures to assess that change during development. Simulated data are used to demonstrate several common analytic approaches to longitudinal analyses. We provide the code for our simulations and figures in an online supplement to aid researchers in exploring and plotting their data. We provide brief examples of best practices for reporting such models. Finally, we clarify common misunderstandings in the application and interpretation of these analytic approaches.
abstractGer	Hypotheses about change over time are central to informing our understanding of development. Developmental neuroscience is at critical juncture: although the majority of longitudinal imaging studies have observations with two time points, researchers are increasingly obtaining three or more observations of the same individuals. The goals of the proposed manuscript are to draw upon the long history of methodological and applied literature on longitudinal statistical models to summarize common problems and issues that arise in their use. We also provide suggestions and solutions to improve the design, analysis and interpretation of longitudinal data, and discuss the importance of matching the theory of change with the appropriate statistical model used to test the theory. Researchers should articulate a clear theory of change and to design studies to capture that change and use appropriately sensitive measures to assess that change during development. Simulated data are used to demonstrate several common analytic approaches to longitudinal analyses. We provide the code for our simulations and figures in an online supplement to aid researchers in exploring and plotting their data. We provide brief examples of best practices for reporting such models. Finally, we clarify common misunderstandings in the application and interpretation of these analytic approaches.
abstract_unstemmed	Hypotheses about change over time are central to informing our understanding of development. Developmental neuroscience is at critical juncture: although the majority of longitudinal imaging studies have observations with two time points, researchers are increasingly obtaining three or more observations of the same individuals. The goals of the proposed manuscript are to draw upon the long history of methodological and applied literature on longitudinal statistical models to summarize common problems and issues that arise in their use. We also provide suggestions and solutions to improve the design, analysis and interpretation of longitudinal data, and discuss the importance of matching the theory of change with the appropriate statistical model used to test the theory. Researchers should articulate a clear theory of change and to design studies to capture that change and use appropriately sensitive measures to assess that change during development. Simulated data are used to demonstrate several common analytic approaches to longitudinal analyses. We provide the code for our simulations and figures in an online supplement to aid researchers in exploring and plotting their data. We provide brief examples of best practices for reporting such models. Finally, we clarify common misunderstandings in the application and interpretation of these analytic approaches.
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title_short	Longitudinal modeling in developmental neuroimaging research: Common challenges, and solutions from developmental psychology
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Longitudinal modeling in developmental neuroimaging research: Common challenges, and solutions from developmental psychology

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