The Gaussian Graphical Model in Cross-Sectional and Time-Series Data

We discuss the Gaussian graphical model (GGM; an undirected network of partial correlation coefficients) and detail its utility as an exploratory data analysis tool. The GGM shows which variables predict one-another, allows for sparse modeling of covariance structures, and may highlight potential ca...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	Multivariate behavioral research Ročník 53; číslo 4; s. 453 - 480
Hlavní autoři:	Epskamp, Sacha, Waldorp, Lourens J., Mõttus, René, Borsboom, Denny
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States Routledge 04.07.2018 Taylor & Francis Ltd
Témata:	Between-subjects design Computer Simulation Correlation coefficients Covariance Cross-Sectional Studies Data analysis Data Interpretation, Statistical Datasets Economic models Empirical analysis exploratory-data analysis Humans Mathematical models Models, Statistical multilevel modeling multivariate analysis network modeling Software Surveys and Questionnaires Time Factors Time series Time-series analysis multilevel modeling Time-series analysis exploratory-data analysis network modeling multivariate analysis
ISSN:	0027-3171, 1532-7906, 1532-7906
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Abstract	We discuss the Gaussian graphical model (GGM; an undirected network of partial correlation coefficients) and detail its utility as an exploratory data analysis tool. The GGM shows which variables predict one-another, allows for sparse modeling of covariance structures, and may highlight potential causal relationships between observed variables. We describe the utility in three kinds of psychological data sets: data sets in which consecutive cases are assumed independent (e.g., cross-sectional data), temporally ordered data sets (e.g., n = 1 time series), and a mixture of the 2 (e.g., n > 1 time series). In time-series analysis, the GGM can be used to model the residual structure of a vector-autoregression analysis (VAR), also termed graphical VAR. Two network models can then be obtained: a temporal network and a contemporaneous network. When analyzing data from multiple subjects, a GGM can also be formed on the covariance structure of stationary means-the between-subjects network. We discuss the interpretation of these models and propose estimation methods to obtain these networks, which we implement in the R packages graphicalVAR and mlVAR. The methods are showcased in two empirical examples, and simulation studies on these methods are included in the supplementary materials.
AbstractList	We discuss the Gaussian graphical model (GGM; an undirected network of partial correlation coefficients) and detail its utility as an exploratory data analysis tool. The GGM shows which variables predict one-another, allows for sparse modeling of covariance structures, and may highlight potential causal relationships between observed variables. We describe the utility in three kinds of psychological data sets: data sets in which consecutive cases are assumed independent (e.g., cross-sectional data), temporally ordered data sets (e.g., n = 1 time series), and a mixture of the 2 (e.g., n > 1 time series). In time-series analysis, the GGM can be used to model the residual structure of a vector-autoregression analysis (VAR), also termed graphical VAR. Two network models can then be obtained: a temporal network and a contemporaneous network. When analyzing data from multiple subjects, a GGM can also be formed on the covariance structure of stationary means-the between-subjects network. We discuss the interpretation of these models and propose estimation methods to obtain these networks, which we implement in the R packages graphicalVAR and mlVAR. The methods are showcased in two empirical examples, and simulation studies on these methods are included in the supplementary materials. We discuss the Gaussian graphical model (GGM; an undirected network of partial correlation coefficients) and detail its utility as an exploratory data analysis tool. The GGM shows which variables predict one-another, allows for sparse modeling of covariance structures, and may highlight potential causal relationships between observed variables. We describe the utility in three kinds of psychological data sets: data sets in which consecutive cases are assumed independent (e.g., cross-sectional data), temporally ordered data sets (e.g., n = 1 time series), and a mixture of the 2 (e.g., n > 1 time series). In time-series analysis, the GGM can be used to model the residual structure of a vector-autoregression analysis (VAR), also termed graphical VAR. Two network models can then be obtained: a temporal network and a contemporaneous network. When analyzing data from multiple subjects, a GGM can also be formed on the covariance structure of stationary means-the between-subjects network. We discuss the interpretation of these models and propose estimation methods to obtain these networks, which we implement in the R packages graphicalVAR and mlVAR. The methods are showcased in two empirical examples, and simulation studies on these methods are included in the supplementary materials.We discuss the Gaussian graphical model (GGM; an undirected network of partial correlation coefficients) and detail its utility as an exploratory data analysis tool. The GGM shows which variables predict one-another, allows for sparse modeling of covariance structures, and may highlight potential causal relationships between observed variables. We describe the utility in three kinds of psychological data sets: data sets in which consecutive cases are assumed independent (e.g., cross-sectional data), temporally ordered data sets (e.g., n = 1 time series), and a mixture of the 2 (e.g., n > 1 time series). In time-series analysis, the GGM can be used to model the residual structure of a vector-autoregression analysis (VAR), also termed graphical VAR. Two network models can then be obtained: a temporal network and a contemporaneous network. When analyzing data from multiple subjects, a GGM can also be formed on the covariance structure of stationary means-the between-subjects network. We discuss the interpretation of these models and propose estimation methods to obtain these networks, which we implement in the R packages graphicalVAR and mlVAR. The methods are showcased in two empirical examples, and simulation studies on these methods are included in the supplementary materials.
Author	Waldorp, Lourens J. Mõttus, René Epskamp, Sacha Borsboom, Denny
Author_xml	– sequence: 1 givenname: Sacha surname: Epskamp fullname: Epskamp, Sacha email: sacha.epskamp@gmail.com organization: Department of Psychological Methods, University of Amsterdam – sequence: 2 givenname: Lourens J. surname: Waldorp fullname: Waldorp, Lourens J. organization: Department of Psychological Methods, University of Amsterdam – sequence: 3 givenname: René surname: Mõttus fullname: Mõttus, René organization: Department of Psychology, University of Edinburgh – sequence: 4 givenname: Denny surname: Borsboom fullname: Borsboom, Denny organization: Department of Psychological Methods, University of Amsterdam
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/29658809$$D View this record in MEDLINE/PubMed
BookMark	eNqFkUFP3DAQha0KVBban9AqUi9csh3bsR2LC2iBpRKIA9uzNUkcYZTYWzsR4t-TaHcvHOjJ0vh7bzTvnZIjH7wl5AeFJYUSfgMwxamiSwa0XNJCFCXjX8iCCs5ypUEekcXM5DN0Qk5TegEAKQr9lZwwLUVZgl6Q682zzdY4puTQZ-uI22dXY5c9hMZ2mfPZKoaU8idbDy746QN9k21cb6dRdDZl1zjgN3LcYpfs9_17Rv7e3mxWd_n94_rP6uo-rwWoIaeFotIi2rpRrAFohShpRXXVVoJrilzyVheiklKpRukGLEqsbFVBUXBpNT8j5zvfbQz_RpsG07tU265Db8OYDAMmSgBdign99QF9CWOcDpgoykAoJWE2_Lmnxqq3jdlG12N8M4d8JuBiB9RzDNG2pnYDzlEMEV1nKJi5DXNow8xtmH0bk1p8UB8W_E93udM534bY42uIXWMGfOtCbCP62iXDP7d4B7I8nVc
CitedBy_id	crossref_primary_10_1007_s10862_023_10047_7 crossref_primary_10_1007_s11031_022_09962_1 crossref_primary_10_1017_S0033291719003209 crossref_primary_10_1007_s10802_022_00933_1 crossref_primary_10_1007_s00406_024_01916_7 crossref_primary_10_1002_da_22976 crossref_primary_10_1002_eat_24040 crossref_primary_10_1177_20552076231158036 crossref_primary_10_1016_j_jpsychires_2025_08_007 crossref_primary_10_2478_amns_2023_2_00315 crossref_primary_10_1016_j_dcn_2024_101341 crossref_primary_10_1111_jcal_13089 crossref_primary_10_1111_sjop_12795 crossref_primary_10_1016_j_ejtd_2024_100447 crossref_primary_10_1016_j_jpsychires_2022_03_036 crossref_primary_10_3390_bs13060472 crossref_primary_10_1016_j_lindif_2025_102705 crossref_primary_10_1080_1068316X_2025_2555590 crossref_primary_10_1177_2167702620901744 crossref_primary_10_1007_s11229_022_04008_y crossref_primary_10_1038_s44277_024_00004_x crossref_primary_10_1007_s11482_023_10206_8 crossref_primary_10_1002_smi_3515 crossref_primary_10_1002_smi_3514 crossref_primary_10_1016_j_jmp_2020_102441 crossref_primary_10_1017_S0954579421000225 crossref_primary_10_1080_00273171_2020_1743630 crossref_primary_10_1186_s12888_021_03445_2 crossref_primary_10_1016_j_childyouth_2025_108227 crossref_primary_10_1177_01902725221123577 crossref_primary_10_1097_PHM_0000000000002229 crossref_primary_10_1186_s40359_023_01275_3 crossref_primary_10_2196_35206 crossref_primary_10_3389_fpsyg_2024_1465407 crossref_primary_10_1080_00273171_2024_2317928 crossref_primary_10_1002_jclp_23679 crossref_primary_10_1007_s10461_023_04069_1 crossref_primary_10_1080_00273171_2023_2254767 crossref_primary_10_1080_10503307_2022_2050830 crossref_primary_10_1109_ACCESS_2022_3230352 crossref_primary_10_3390_brainsci13081155 crossref_primary_10_1080_00273171_2023_2254769 crossref_primary_10_1061_JCEMD4_COENG_13567 crossref_primary_10_1002_jclp_23432 crossref_primary_10_1016_j_actpsy_2023_104078 crossref_primary_10_1038_s41598_018_34138_8 crossref_primary_10_1080_10705511_2021_1937177 crossref_primary_10_1016_j_janxdis_2020_102222 crossref_primary_10_1016_j_jrp_2024_104468 crossref_primary_10_1007_s10802_025_01319_9 crossref_primary_10_1007_s13253_023_00596_5 crossref_primary_10_1007_s10479_021_04446_w crossref_primary_10_1016_j_psychres_2024_116197 crossref_primary_10_3758_s13428_021_01760_w crossref_primary_10_1016_j_seizure_2020_06_019 crossref_primary_10_1097_PSY_0000000000001159 crossref_primary_10_2147_PRBM_S524256 crossref_primary_10_1080_03610926_2024_2348075 crossref_primary_10_3389_fpsyt_2022_959103 crossref_primary_10_1016_j_rser_2023_114029 crossref_primary_10_1038_s41467_022_28513_3 crossref_primary_10_3758_s13428_020_01500_6 crossref_primary_10_1038_s41562_025_02171_z crossref_primary_10_1080_09603123_2020_1803805 crossref_primary_10_1016_j_sleep_2022_12_005 crossref_primary_10_1007_s10802_020_00733_5 crossref_primary_10_1016_j_jad_2022_07_061 crossref_primary_10_3389_fpsyt_2022_917833 crossref_primary_10_1186_s12888_025_07144_0 crossref_primary_10_1177_00139165251356912 crossref_primary_10_1007_s11469_021_00590_3 crossref_primary_10_1080_10705511_2024_2429544 crossref_primary_10_3389_fpubh_2022_1024436 crossref_primary_10_3389_fpsyt_2020_580922 crossref_primary_10_3389_fpsyg_2019_01056 crossref_primary_10_1007_s11218_024_09917_z crossref_primary_10_1038_s41598_024_68334_6 crossref_primary_10_1093_jrsssb_qkaf032 crossref_primary_10_1007_s11336_021_09796_9 crossref_primary_10_1038_s41598_023_40648_x crossref_primary_10_1080_20008066_2022_2115635 crossref_primary_10_1016_j_seizure_2021_08_014 crossref_primary_10_1159_000534130 crossref_primary_10_1002_pits_23531 crossref_primary_10_3389_fpubh_2022_1034119 crossref_primary_10_1186_s12889_024_21201_5 crossref_primary_10_1016_j_jad_2021_12_137 crossref_primary_10_1017_S003329172000135X crossref_primary_10_3389_fpsyt_2022_993328 crossref_primary_10_3389_fpsyg_2020_563023 crossref_primary_10_3917_grh1_204_0115 crossref_primary_10_3758_s13428_024_02541_x crossref_primary_10_1016_j_janxdis_2018_05_007 crossref_primary_10_1017_S1366728925100540 crossref_primary_10_1186_s12916_020_01558_1 crossref_primary_10_1007_s12144_022_03196_7 crossref_primary_10_1111_aphw_12599 crossref_primary_10_3389_fpsyg_2024_1433609 crossref_primary_10_3389_fpsyt_2021_594340 crossref_primary_10_3390_ijerph18041840 crossref_primary_10_1080_00273171_2020_1767532 crossref_primary_10_1016_j_midw_2022_103529 crossref_primary_10_1017_S0033291720001002 crossref_primary_10_1111_bjc_12295 crossref_primary_10_1002_ejp_70059 crossref_primary_10_1080_00332747_2020_1762393 crossref_primary_10_1016_j_tvjl_2023_105954 crossref_primary_10_1186_s12889_025_21496_y crossref_primary_10_3846_tede_2020_13159 crossref_primary_10_1186_s12877_025_06017_1 crossref_primary_10_1080_0092623X_2020_1760979 crossref_primary_10_1093_gerona_glaf162 crossref_primary_10_1155_da_7589775 crossref_primary_10_1016_j_jad_2024_05_076 crossref_primary_10_1111_aphw_12488 crossref_primary_10_1016_j_intell_2020_101466 crossref_primary_10_1080_08964289_2025_2469911 crossref_primary_10_1186_s12888_024_05733_z crossref_primary_10_1002_jts_22433 crossref_primary_10_1007_s10940_020_09462_w crossref_primary_10_1016_j_jad_2024_03_119 crossref_primary_10_1093_schbul_sbaf026 crossref_primary_10_1111_aphw_12363 crossref_primary_10_7717_peerj_16356 crossref_primary_10_1016_j_spc_2023_07_008 crossref_primary_10_1186_s12909_024_05908_y crossref_primary_10_1016_j_chb_2020_106510 crossref_primary_10_1155_2019_9424605 crossref_primary_10_1016_j_agwat_2025_109306 crossref_primary_10_1093_schbul_sbaf023 crossref_primary_10_1177_00332941231213649 crossref_primary_10_1016_j_brat_2024_104574 crossref_primary_10_1080_1068316X_2022_2030336 crossref_primary_10_1080_00273171_2024_2432918 crossref_primary_10_1186_s12888_025_06793_5 crossref_primary_10_3389_fpsyt_2025_1567448 crossref_primary_10_1016_j_jclinepi_2022_11_010 crossref_primary_10_1007_s10919_024_00466_9 crossref_primary_10_1177_0004944120957477 crossref_primary_10_1016_j_socscimed_2024_117628 crossref_primary_10_3390_jrfm16030183 crossref_primary_10_1186_s12888_025_07091_w crossref_primary_10_3389_fpubh_2022_1014548 crossref_primary_10_3390_educsci15060728 crossref_primary_10_1016_j_jpsychores_2022_111139 crossref_primary_10_1002_wps_21241 crossref_primary_10_1016_j_janxdis_2020_102293 crossref_primary_10_1016_j_cobme_2018_12_003 crossref_primary_10_1016_j_jaacop_2023_11_001 crossref_primary_10_1080_17522439_2022_2162955 crossref_primary_10_1111_jopy_12472 crossref_primary_10_3758_s13428_022_01839_y crossref_primary_10_3390_app13106120 crossref_primary_10_3390_medicina60030437 crossref_primary_10_1016_j_learninstruc_2022_101649 crossref_primary_10_1007_s10212_023_00703_7 crossref_primary_10_1136_svn_2024_003623 crossref_primary_10_1016_j_jpsychires_2021_09_017 crossref_primary_10_1016_j_ymeth_2021_11_006 crossref_primary_10_1016_j_jcbs_2022_06_002 crossref_primary_10_1371_journal_pone_0280343 crossref_primary_10_1080_15366367_2024_2363718 crossref_primary_10_1007_s10608_025_10631_z crossref_primary_10_1080_0092623X_2025_2536094 crossref_primary_10_3389_fpsyt_2022_969164 crossref_primary_10_1002_cncr_33352 crossref_primary_10_1177_10790632231170823 crossref_primary_10_1016_j_biopsych_2022_12_012 crossref_primary_10_1007_s10551_022_05128_8 crossref_primary_10_1186_s12916_020_01740_5 crossref_primary_10_1177_01461672231203471 crossref_primary_10_1016_j_cpr_2020_101824 crossref_primary_10_1016_j_brat_2025_104788 crossref_primary_10_1080_00273171_2024_2335401 crossref_primary_10_1177_00220221251347966 crossref_primary_10_3389_fpos_2024_1401758 crossref_primary_10_1007_s11336_021_09764_3 crossref_primary_10_1186_s12966_020_01066_8 crossref_primary_10_1016_j_jamda_2020_02_005 crossref_primary_10_1007_s10862_022_10014_8 crossref_primary_10_1038_s41598_022_11092_0 crossref_primary_10_1080_15298868_2021_1944298 crossref_primary_10_1080_00273171_2024_2395941 crossref_primary_10_1177_0306624X241240697 crossref_primary_10_1080_10888691_2020_1766356 crossref_primary_10_3389_fpsyt_2024_1328857 crossref_primary_10_1007_s11136_024_03678_0 crossref_primary_10_2147_PRBM_S507074 crossref_primary_10_3389_fpsyg_2023_1129692 crossref_primary_10_1016_j_jad_2024_12_005 crossref_primary_10_1007_s12144_024_07001_5 crossref_primary_10_1186_s40359_025_03120_1 crossref_primary_10_3389_fpsyg_2019_02448 crossref_primary_10_1186_s40359_025_02826_6 crossref_primary_10_3390_brainsci15080878 crossref_primary_10_3389_fnsys_2022_817962 crossref_primary_10_3389_fpsyt_2022_916332 crossref_primary_10_1002_aur_3292 crossref_primary_10_1111_eip_13537 crossref_primary_10_1097_HTR_0000000000000601 crossref_primary_10_1007_s11336_021_09753_6 crossref_primary_10_1038_s41398_023_02659_0 crossref_primary_10_1016_j_socscimed_2025_118195 crossref_primary_10_1016_j_tra_2020_01_014 crossref_primary_10_1177_1754073920988787 crossref_primary_10_1214_20_AOS2019 crossref_primary_10_1016_j_ejpsy_2023_03_001 crossref_primary_10_1016_j_appet_2022_106019 crossref_primary_10_1177_21677026231208172 crossref_primary_10_1111_jcpp_13911 crossref_primary_10_1186_s40359_021_00670_y crossref_primary_10_1016_j_jcbs_2021_12_002 crossref_primary_10_3390_jrfm14020079 crossref_primary_10_1007_s11336_021_09765_2 crossref_primary_10_1080_00273171_2019_1677207 crossref_primary_10_3390_ijerph18105149 crossref_primary_10_3389_fphys_2021_801622 crossref_primary_10_3390_educsci15050555 crossref_primary_10_1007_s00181_022_02264_y crossref_primary_10_1186_s12912_024_02408_8 crossref_primary_10_1080_09515089_2025_2521436 crossref_primary_10_1038_s41598_025_94782_9 crossref_primary_10_1017_S003329171900045X crossref_primary_10_1002_ejsp_2505 crossref_primary_10_1002_per_2311 crossref_primary_10_1016_j_neucli_2023_102937 crossref_primary_10_1002_wics_70021 crossref_primary_10_1016_j_paid_2020_110154 crossref_primary_10_1017_S0033291722000095 crossref_primary_10_3389_fpsyt_2022_925187 crossref_primary_10_3758_s13428_022_01976_4 crossref_primary_10_3389_fpsyg_2021_635406 crossref_primary_10_1002_per_2263 crossref_primary_10_1007_s10964_023_01802_w crossref_primary_10_1111_jpr_12604 crossref_primary_10_1002_per_2265 crossref_primary_10_1080_21642850_2019_1682587 crossref_primary_10_1016_j_janxdis_2020_102312 crossref_primary_10_1002_mpr_2034 crossref_primary_10_1007_s11764_025_01773_w crossref_primary_10_1007_s11135_025_02182_y crossref_primary_10_1016_j_jad_2024_05_111 crossref_primary_10_1080_16506073_2023_2282374 crossref_primary_10_1007_s12144_023_05055_5 crossref_primary_10_2147_PRBM_S485555 crossref_primary_10_1007_s12144_022_04217_1 crossref_primary_10_3389_fpsyt_2025_1557351 crossref_primary_10_1007_s10803_024_06695_7 crossref_primary_10_1016_j_puhe_2024_12_020 crossref_primary_10_1016_j_psychres_2025_116562 crossref_primary_10_1080_20008198_2020_1736411 crossref_primary_10_3389_fpsyg_2025_1414563 crossref_primary_10_4081_ripppo_2023_659 crossref_primary_10_1016_j_lindif_2022_102233 crossref_primary_10_1017_S0272263122000407 crossref_primary_10_1080_00273171_2023_2229310 crossref_primary_10_1016_j_jpain_2025_105555 crossref_primary_10_1002_eat_23398 crossref_primary_10_3390_brainsci11050656 crossref_primary_10_1007_s10803_022_05723_8 crossref_primary_10_1177_00207640241277164 crossref_primary_10_1016_j_jad_2025_120082 crossref_primary_10_3389_fpubh_2021_636089 crossref_primary_10_1016_j_jad_2024_05_018 crossref_primary_10_1007_s00406_023_01664_0 crossref_primary_10_1007_s12671_021_01726_1 crossref_primary_10_1017_S0033291725000030 crossref_primary_10_1192_bjp_2024_19 crossref_primary_10_1371_journal_pone_0314102 crossref_primary_10_2147_AMEP_S495472 crossref_primary_10_1177_08902070241278020 crossref_primary_10_1177_20438087231178123 crossref_primary_10_7717_peerj_6119 crossref_primary_10_1016_j_physrep_2025_06_002 crossref_primary_10_2147_PRBM_S483231 crossref_primary_10_1177_1745691619895057 crossref_primary_10_1016_j_jad_2022_02_014 crossref_primary_10_1016_j_jpsychires_2025_09_024 crossref_primary_10_1017_S0033291723001368 crossref_primary_10_1017_S0033291720004444 crossref_primary_10_1017_S0033291724000023 crossref_primary_10_2196_32598 crossref_primary_10_1002_per_2237 crossref_primary_10_1017_S0033291725000261 crossref_primary_10_1080_08870446_2024_2395854 crossref_primary_10_1007_s00737_025_01570_5 crossref_primary_10_3389_frcha_2023_1045161 crossref_primary_10_1007_s12672_025_02837_0 crossref_primary_10_1007_s43076_025_00438_y crossref_primary_10_1080_17437199_2021_1987953 crossref_primary_10_3390_bs13080641 crossref_primary_10_1007_s12144_020_01321_y crossref_primary_10_3758_s13428_021_01637_y crossref_primary_10_1007_s11469_024_01408_8 crossref_primary_10_1093_schbul_sbaa187 crossref_primary_10_1007_s12144_023_04871_z crossref_primary_10_1016_j_jad_2019_11_011 crossref_primary_10_1111_acps_13528 crossref_primary_10_2147_PRBM_S417541 crossref_primary_10_1007_s10608_024_10530_9 crossref_primary_10_1111_bjet_13452 crossref_primary_10_1080_00273171_2025_2454901 crossref_primary_10_1108_JACPR_02_2022_0685 crossref_primary_10_3390_nu17101605 crossref_primary_10_1186_s12889_025_21357_8 crossref_primary_10_1038_s41598_024_58598_3 crossref_primary_10_1002_per_2295 crossref_primary_10_1016_j_jad_2020_07_050 crossref_primary_10_1111_add_16658 crossref_primary_10_1111_jpr_12538 crossref_primary_10_1080_10503307_2021_1916639 crossref_primary_10_1177_21677026211017839 crossref_primary_10_3389_fpsyt_2025_1502252 crossref_primary_10_1080_13607863_2022_2134294 crossref_primary_10_1080_13552600_2024_2423677 crossref_primary_10_1016_j_eplepsyres_2021_106696 crossref_primary_10_1080_00273171_2018_1516540 crossref_primary_10_2147_NSS_S517178 crossref_primary_10_1016_j_cpr_2021_102000 crossref_primary_10_1007_s10802_021_00856_3 crossref_primary_10_1080_08039488_2019_1692234 crossref_primary_10_1007_s11336_022_09861_x crossref_primary_10_1080_00273171_2025_2551370 crossref_primary_10_1080_08039488_2025_2451370 crossref_primary_10_1111_nhs_13089 crossref_primary_10_1080_10615806_2020_1772969 crossref_primary_10_3389_fpsyt_2023_1144413 crossref_primary_10_1017_S1366728925000318 crossref_primary_10_1111_jan_14742 crossref_primary_10_1176_appi_ajp_20220868 crossref_primary_10_3390_ijerph19159376 crossref_primary_10_1016_j_addbeh_2022_107462 crossref_primary_10_1186_s12888_024_05698_z crossref_primary_10_1186_s12877_025_06130_1 crossref_primary_10_1186_s12888_021_03276_1 crossref_primary_10_1371_journal_pone_0276341 crossref_primary_10_1016_j_bodyim_2025_101884 crossref_primary_10_1016_j_ejon_2025_102944 crossref_primary_10_1007_s12144_025_07989_4 crossref_primary_10_1080_00273171_2021_1994848 crossref_primary_10_3389_fneur_2024_1285744 crossref_primary_10_1007_s10803_024_06674_y crossref_primary_10_1007_s11469_023_01019_9 crossref_primary_10_1080_00273171_2023_2289058 crossref_primary_10_1080_00273171_2023_2283632 crossref_primary_10_1002_per_2278 crossref_primary_10_1155_da_1885004 crossref_primary_10_1002_per_2277 crossref_primary_10_1080_10503307_2022_2088313 crossref_primary_10_1080_10705511_2021_1911657 crossref_primary_10_1038_s41380_025_02896_3 crossref_primary_10_1080_10615806_2025_2554809 crossref_primary_10_1016_j_mhp_2025_200446 crossref_primary_10_1016_j_sleep_2023_01_007 crossref_primary_10_1017_S0954579422000463 crossref_primary_10_1002_mpr_1951 crossref_primary_10_1007_s11136_019_02298_3 crossref_primary_10_3389_fpsyt_2022_921781 crossref_primary_10_1038_s41598_025_09746_w crossref_primary_10_1080_10705511_2025_2452884 crossref_primary_10_1080_20008198_2019_1618134 crossref_primary_10_1146_annurev_clinpsy_081423_020831 crossref_primary_10_1016_j_chb_2023_107920 crossref_primary_10_1146_annurev_clinpsy_081423_022947 crossref_primary_10_1177_09717218241265004 crossref_primary_10_1007_s11469_023_01212_w crossref_primary_10_1111_bjop_12486 crossref_primary_10_1016_j_jlp_2023_105169 crossref_primary_10_3389_fpubh_2022_981136 crossref_primary_10_1016_j_addbeh_2025_108498 crossref_primary_10_3390_s22062225 crossref_primary_10_1186_s12991_023_00461_2 crossref_primary_10_1038_s41380_020_00963_5 crossref_primary_10_1080_00273171_2022_2128020 crossref_primary_10_1111_jsr_13776 crossref_primary_10_1371_journal_pone_0256902 crossref_primary_10_1016_j_drugalcdep_2023_109828 crossref_primary_10_1016_j_brat_2022_104221 crossref_primary_10_3389_fpsyg_2021_709805 crossref_primary_10_1016_j_ejon_2025_102924 crossref_primary_10_1080_09588221_2023_2192762 crossref_primary_10_1186_s40479_023_00228_x crossref_primary_10_1016_j_jpsychores_2021_110428 crossref_primary_10_2196_76210 crossref_primary_10_3390_jcm13051452 crossref_primary_10_1038_s44184_023_00022_1 crossref_primary_10_1007_s10578_021_01273_9 crossref_primary_10_1007_s11336_024_09985_2 crossref_primary_10_1017_S003329172300185X crossref_primary_10_1016_j_jnc_2022_126329 crossref_primary_10_1186_s13012_021_01081_8 crossref_primary_10_1016_j_jad_2021_10_030 crossref_primary_10_1186_s13063_021_05712_9 crossref_primary_10_1371_journal_pone_0263574 crossref_primary_10_1016_j_jad_2021_10_034 crossref_primary_10_1017_SJP_2024_24 crossref_primary_10_1177_20438087231151505 crossref_primary_10_1159_000536056 crossref_primary_10_1080_00273171_2024_2354232 crossref_primary_10_3389_fpubh_2024_1368752 crossref_primary_10_3389_fpsyg_2021_752564 crossref_primary_10_1186_s40359_024_01964_7 crossref_primary_10_1038_s44184_025_00148_4 crossref_primary_10_1159_000497425 crossref_primary_10_1016_j_cortex_2021_01_002 crossref_primary_10_1002_alz_14286 crossref_primary_10_1371_journal_pone_0186695 crossref_primary_10_1080_10705511_2022_2056039 crossref_primary_10_1155_2023_8469620 crossref_primary_10_1016_j_apjon_2025_100731 crossref_primary_10_3389_fpsyg_2022_976675 crossref_primary_10_3390_bs13080662 crossref_primary_10_1002_jcv2_70037 crossref_primary_10_1038_s44220_023_00192_z crossref_primary_10_1007_s11336_021_09820_y crossref_primary_10_1007_s12671_024_02394_7 crossref_primary_10_1177_01461672251333472 crossref_primary_10_3389_fpsyg_2024_1388539 crossref_primary_10_3390_medicina60010032 crossref_primary_10_1177_18344909221117257 crossref_primary_10_1016_j_janxdis_2022_102536 crossref_primary_10_1080_01621459_2024_2395504 crossref_primary_10_1007_s11222_021_10049_z crossref_primary_10_3389_fpubh_2023_1079873 crossref_primary_10_1016_j_janxdis_2022_102658 crossref_primary_10_3389_fpsyt_2022_993814 crossref_primary_10_1007_s11336_021_09767_0 crossref_primary_10_1016_j_jagp_2025_07_010 crossref_primary_10_1111_famp_12879 crossref_primary_10_1007_s12671_020_01335_4 crossref_primary_10_1016_j_jcomdis_2024_106476 crossref_primary_10_1186_s12888_023_04700_4 crossref_primary_10_1186_s12888_025_07207_2 crossref_primary_10_1371_journal_pone_0242670 crossref_primary_10_1016_j_beth_2019_02_002 crossref_primary_10_1093_schbul_sbab002 crossref_primary_10_1016_j_brat_2018_10_005 crossref_primary_10_1016_j_brat_2021_104011 crossref_primary_10_1186_s12888_024_06443_2 crossref_primary_10_1080_20008066_2023_2179799 crossref_primary_10_1016_j_janxdis_2023_102822 crossref_primary_10_1186_s12889_025_23877_9 crossref_primary_10_1016_j_jpsychires_2023_10_038 crossref_primary_10_1016_j_neubiorev_2021_07_027 crossref_primary_10_1080_01639625_2025_2562117 crossref_primary_10_3389_fpsyg_2020_544565 crossref_primary_10_1016_j_jad_2021_07_043 crossref_primary_10_1017_S0033291725100779 crossref_primary_10_1111_aphw_12555 crossref_primary_10_31083_AP43496 crossref_primary_10_1007_s12144_021_01765_w crossref_primary_10_3390_ijms232214398 crossref_primary_10_1016_j_comppsych_2024_152501 crossref_primary_10_1111_bmsp_12394 crossref_primary_10_1016_j_gerinurse_2025_103446 crossref_primary_10_1186_s12889_023_17435_4 crossref_primary_10_1007_s11136_023_03471_5 crossref_primary_10_1007_s10608_025_10575_4 crossref_primary_10_1016_j_jad_2023_06_006 crossref_primary_10_1080_00273171_2024_2336178 crossref_primary_10_1007_s12144_024_06997_0 crossref_primary_10_1016_j_paid_2025_113290 crossref_primary_10_1186_s13063_024_08235_1 crossref_primary_10_1186_s12966_022_01293_1 crossref_primary_10_1038_s41598_022_17283_z crossref_primary_10_1080_00224499_2024_2307441 crossref_primary_10_1016_j_jad_2023_07_036 crossref_primary_10_1111_aphw_12442 crossref_primary_10_1016_j_chb_2023_107991 crossref_primary_10_1080_07481187_2025_2537981 crossref_primary_10_1017_S0033291722002604 crossref_primary_10_1016_j_apjon_2024_100648 crossref_primary_10_3389_fpsyt_2024_1359932 crossref_primary_10_1080_00223891_2025_2509496 crossref_primary_10_1080_20008198_2019_1700614 crossref_primary_10_1038_s43856_025_01105_0 crossref_primary_10_1097_MD_0000000000044762 crossref_primary_10_1016_j_actpsy_2024_104296 crossref_primary_10_1016_j_ejon_2025_102799 crossref_primary_10_1016_j_jad_2025_03_016 crossref_primary_10_1007_s11336_021_09825_7 crossref_primary_10_1016_j_schres_2024_06_055 crossref_primary_10_3390_bs11050077 crossref_primary_10_1186_s12916_021_02179_y crossref_primary_10_1111_aphw_12531 crossref_primary_10_1136_bmjopen_2021_050953 crossref_primary_10_3389_fncom_2023_1192876 crossref_primary_10_2478_picbe_2024_0228 crossref_primary_10_1007_s11336_020_09697_3 crossref_primary_10_1017_S0033291724000734 crossref_primary_10_1111_bmsp_12173 crossref_primary_10_1007_s10578_025_01831_5 crossref_primary_10_1080_20008198_2022_2057674 crossref_primary_10_1038_s41537_020_00129_w crossref_primary_10_1016_j_addbeh_2020_106689 crossref_primary_10_3389_fpsyt_2025_1581827 crossref_primary_10_1177_13591053241274472 crossref_primary_10_1186_s12888_020_02849_w crossref_primary_10_1007_s10803_021_05094_6 crossref_primary_10_3390_geriatrics9050111 crossref_primary_10_1016_j_janxdis_2024_102896 crossref_primary_10_4103_indianjpsychiatry_indianjpsychiatry_377_21 crossref_primary_10_1186_s12889_021_11553_7 crossref_primary_10_1136_bmjopen_2022_066577 crossref_primary_10_3389_frsus_2024_1455335 crossref_primary_10_3390_cancers15133496 crossref_primary_10_3389_fpsyg_2025_1533687 crossref_primary_10_1002_smi_3215 crossref_primary_10_7120_09627286_30_3_001 crossref_primary_10_1007_s10608_023_10400_w crossref_primary_10_1111_add_16231 crossref_primary_10_1016_j_jad_2025_119981 crossref_primary_10_3390_brainsci13040583 crossref_primary_10_3390_ijms23063348 crossref_primary_10_1007_s10639_024_13001_6 crossref_primary_10_1177_21677026231217316 crossref_primary_10_2196_70483 crossref_primary_10_1007_s11469_025_01539_6 crossref_primary_10_3389_fpsyt_2024_1321207 crossref_primary_10_3389_fpsyg_2025_1483099 crossref_primary_10_1080_03610926_2020_1811338 crossref_primary_10_2196_64806 crossref_primary_10_1038_s44159_022_00062_y crossref_primary_10_1016_j_paid_2025_113160 crossref_primary_10_1111_aphw_12631 crossref_primary_10_1016_j_addbeh_2025_108412 crossref_primary_10_1016_j_heliyon_2023_e22816 crossref_primary_10_3389_fpsyg_2023_1102624 crossref_primary_10_1002_jts_23135 crossref_primary_10_1111_brv_70001 crossref_primary_10_1016_j_comppsych_2024_152543 crossref_primary_10_1016_j_jad_2023_08_048 crossref_primary_10_1007_s12144_023_05567_0 crossref_primary_10_1080_10705511_2023_2207749 crossref_primary_10_1016_j_chiabu_2025_107301 crossref_primary_10_1080_20008198_2020_1759279 crossref_primary_10_1016_j_yebeh_2024_109748 crossref_primary_10_1080_02664763_2022_2116746 crossref_primary_10_1080_00273171_2021_1894412 crossref_primary_10_1186_s12888_025_06992_0 crossref_primary_10_1002_sim_10143 crossref_primary_10_1016_j_jrp_2021_104166 crossref_primary_10_1186_s40359_024_01543_w crossref_primary_10_1080_10447318_2024_2356358 crossref_primary_10_1027_1015_5759_a000612 crossref_primary_10_1186_s13034_025_00936_y crossref_primary_10_1080_00273171_2019_1624147 crossref_primary_10_1186_s13034_023_00620_z crossref_primary_10_2147_PPA_S409802 crossref_primary_10_1111_jan_16830 crossref_primary_10_3390_bs14080709 crossref_primary_10_1016_j_landurbplan_2024_105247 crossref_primary_10_1016_j_copsyc_2021_03_004 crossref_primary_10_1186_s12912_021_00670_8 crossref_primary_10_1177_00207640221141784 crossref_primary_10_1038_s41398_023_02444_z crossref_primary_10_3389_fpsyt_2023_1082549 crossref_primary_10_1177_2167702619842466 crossref_primary_10_3389_fpsyt_2022_899757 crossref_primary_10_1007_s12144_024_07249_x crossref_primary_10_1080_00273171_2019_1672515 crossref_primary_10_1111_jcpp_13556 crossref_primary_10_1111_jcpp_13794 crossref_primary_10_1186_s13034_025_00947_9 crossref_primary_10_1186_s12888_021_03455_0 crossref_primary_10_1111_jopy_12987 crossref_primary_10_1177_0963721420915860 crossref_primary_10_1017_S0954579423000020 crossref_primary_10_1007_s12671_022_02066_4 crossref_primary_10_1162_imag_a_00542 crossref_primary_10_1016_j_paid_2020_109917 crossref_primary_10_3389_fpsyt_2022_816298 crossref_primary_10_1007_s00520_024_08560_w crossref_primary_10_1038_s41380_021_01310_y crossref_primary_10_1016_j_csda_2019_106880 crossref_primary_10_1097_HRP_0000000000000351 crossref_primary_10_3389_fpsyg_2023_1218747 crossref_primary_10_1002_jad_12140 crossref_primary_10_2147_PRBM_S398503 crossref_primary_10_1007_s00170_024_13565_0 crossref_primary_10_1186_s12888_025_06532_w crossref_primary_10_3224_diskurs_v14i3_06 crossref_primary_10_1002_jclp_22957 crossref_primary_10_1016_j_jad_2025_119786 crossref_primary_10_1016_j_ajp_2022_103062 crossref_primary_10_1080_00273171_2024_2436413 crossref_primary_10_1016_j_brat_2023_104456 crossref_primary_10_1016_j_eneco_2021_105395 crossref_primary_10_1080_00273171_2023_2283837 crossref_primary_10_1080_00273171_2025_2522733 crossref_primary_10_1007_s11205_024_03352_5 crossref_primary_10_1093_schbul_sbab084 crossref_primary_10_1111_jan_16862 crossref_primary_10_1177_10731911221101367 crossref_primary_10_1007_s10964_019_01178_w crossref_primary_10_1080_00273171_2025_2450648 crossref_primary_10_2147_PPA_S452773 crossref_primary_10_1080_00273171_2023_2235685
Cites_doi	10.1214/009053606000000281 10.1007/BF02294374 10.1037/a0024595 10.1126/science.103.2684.677 10.1080/00273171.2015.1065398 10.1207/s15327906mbr3003_4 10.1017/S0140525X09991567 10.4135/9781412985215 10.1201/b18401 10.2307/1912791 10.1007/978-3-319-52452-8 10.18637/jss.v035.i03 10.7551/mitpress/6444.001.0001 10.1146/annurev-statistics-060116-053803 10.1080/10705510903206030 10.17605/OSF.IO/54XRS 10.1214/11-STS358 10.1037/a0039802 10.1001/jamapsychiatry.2015.2079 10.3389/fpsyg.2014.01492 10.1016/j.jad.2015.09.005 10.1017/CBO9780511790942 10.1017/S0033291708004947 10.1186/1471-2105-10-384 10.1016/j.neuroimage.2012.06.026 10.1038/srep46523 10.1093/oso/9780198522195.001.0001 10.1093/biostatistics/kxt005 10.1016/j.jrp.2014.07.003 10.1037/met0000062 10.1207/s15366359mea0204_1 10.1177/2167702617744325 10.1214/aoms/1177732287 10.1177/2167702614553230 10.1007/s41237-017-0024-x 10.18637/jss.v048.i04 10.2466/pr0.1990.66.1.195 10.17505/jpor.2017.01 10.1093/schbul/sbw055 10.1177/1754073915590619 10.1371/journal.pone.0027407 10.1198/jcgs.2011.11051a 10.1093/biostatistics/kxm045 10.1080/00273171.2017.1379379 10.1214/aos/1176350174 10.1093/schbul/sbx037 10.1016/j.jeconom.2005.06.032 10.1016/j.neuroimage.2009.12.117 10.1037/0033-2909.114.1.185 10.1037/abn0000028 10.1007/BF02980577 10.1037/met0000085 10.1017/S0033291714001809 10.1038/srep01898 10.1111/j.2517-6161.1951.tb00088.x 10.1017/S0033291715000331 10.1038/srep09050 10.1016/j.jrp.2016.06.017 10.1097/PSY.0b013e3182545d47 10.1146/annurev-clinpsy-050212-185608 10.1038/srep34175 10.1111/j.1467-6494.1992.tb00970.x 10.1037/met0000112 10.1093/schbul/sbw049 10.1001/jamapsychiatry.2015.3103 10.1177/0963721416666518 10.1080/10705511.2017.1406803 10.1371/journal.pone.0167490 10.3389/fnagi.2010.00027 10.1111/j.2517-6161.1996.tb02080.x 10.1146/annurev.psych.093008.100356 10.3758/s13428-017-0862-1 10.1093/biomet/asn034 10.1515/9780691218632 10.1037/a0032401 10.1037/0033-295X.113.4.842 10.18637/jss.v047.i11 10.18637/jss.v077.i05 10.1016/j.drugalcdep.2016.02.005 10.1016/j.compbiomed.2011.09.004 10.1016/j.newideapsych.2011.02.007 10.1159/000453583 10.1080/00273171.2016.1151333 10.1186/1471-2288-10-28 10.1080/15427600902911189 10.1371/journal.pone.0179891 10.1177/2167702614540645 10.1007/s11136-015-1127-z 10.3389/fpsyg.2017.00262 10.1371/journal.pone.0060188 10.1093/biomet/asm018 10.3389/fpsyg.2015.01038 10.1080/00273171.2016.1150151 10.4324/9781315744094 10.1198/jcgs.2010.09188 10.1017/CBO9781107049994 10.1007/s11336-008-9106-8 10.1371/journal.pone.0155205 10.1007/978-3-540-27752-1 10.1037/met0000167 10.1371/journal.pone.0174035 10.1002/per.1866 10.1038/srep05918 10.1007/s11336-017-9557-x
ContentType	Journal Article
Copyright	2018 Taylor & Francis Group, LLC 2018 2018 Taylor & Francis Group, LLC
Copyright_xml	– notice: 2018 Taylor & Francis Group, LLC 2018 – notice: 2018 Taylor & Francis Group, LLC
DBID	0YH AAYXX CITATION CGR CUY CVF ECM EIF NPM 7X8
DOI	10.1080/00273171.2018.1454823
DatabaseName	Taylor & Francis Open Access CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed MEDLINE - Academic
DatabaseTitle	CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) MEDLINE - Academic
DatabaseTitleList	MEDLINE MEDLINE - Academic
Database_xml	– sequence: 1 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 2 dbid: 0YH name: Taylor & Francis Open Access url: https://www.tandfonline.com sourceTypes: Publisher – sequence: 3 dbid: 7X8 name: MEDLINE - Academic url: https://search.proquest.com/medline sourceTypes: Aggregation Database
DeliveryMethod	fulltext_linktorsrc
Discipline	Psychology
EISSN	1532-7906
EndPage	480
ExternalDocumentID	29658809 10_1080_00273171_2018_1454823 1454823
Genre	Article Journal Article
GrantInformation_xml	– fundername: NWO grantid: 406-11-066
GroupedDBID	--Z -~X .7I .QK 0BK 0R~ 0YH 123 4.4 5VS 8VB AAGDL AAGZJ AAHIA AAMFJ AAMIU AAPUL AATTQ AAZMC ABCCY ABFIM ABIVO ABJNI ABLIJ ABLJU ABPEM ABPPZ ABRYG ABTAI ABXUL ABXYU ABZLS ACGFS ACHQT ACIWK ACNCT ACTIO ACTOA ADAHI ADCVX ADKVQ AECIN AEFOU AEISY AEKEX AENEX AEOZL AEPSL AEYOC AEZRU AFHDM AFRVT AGDLA AGMYJ AGRBW AHDZW AIJEM AIYEW AJWEG AKBVH ALMA_UNASSIGNED_HOLDINGS ALQZU AQTUD AVBZW AWYRJ BEJHT BLEHA BMOTO BOHLJ CCCUG CQ1 CS3 DGFLZ DKSSO DU5 EBS EJD EMOBN E~B E~C F5P FEDTE G-F GTTXZ H13 HF~ HZ~ J.O KYCEM M4Z MS~ NA5 NW- O9- P2P PQQKQ QWB RNANH ROSJB RSYQP S-F STATR TASJS TBQAZ TDBHL TEH TFH TFL TFW TN5 TNTFI TRJHH TUROJ TWZ UT5 UT9 VAE WH7 YNT YQT ZL0 ~01 ~S~ AAYXX CITATION .GJ 07M 53G AANPH ABRLO ABVXC ABWZE ACPKE ACRBO ADEWX ADIUE ADXAZ ADYSH AEXSR AFFNX AIXGP ALEEW ALLRG C5A CAG CBZAQ CGR CKOZC COF CUY CVF C~T DGXZK ECM EFRLQ EGDCR EIF FXNIP HVGLF H~9 IPNFZ JLMOS L7Y LJTGL LPU NEJ NPM OHT P-O QZZOY RBICI RIG ROL UA1 UAP VXZ XOL ZCG ZXP 7X8
ID	FETCH-LOGICAL-c507t-14716eaaecd72d00f5581b19bfb5391a363f945b6677d79d0ea6abebb04436e93
IEDL.DBID	0YH
ISICitedReferencesCount	714
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000449304000001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	0027-3171 1532-7906
IngestDate	Sun Nov 09 10:51:43 EST 2025 Mon Nov 10 02:56:34 EST 2025 Wed Feb 19 02:34:37 EST 2025 Sat Nov 29 06:40:29 EST 2025 Tue Nov 18 22:13:33 EST 2025 Mon Oct 20 23:50:03 EDT 2025
IsDoiOpenAccess	true
IsOpenAccess	true
IsPeerReviewed	true
IsScholarly	true
Issue	4
Keywords	multilevel modeling Time-series analysis exploratory-data analysis network modeling multivariate analysis
Language	English
License	open-access: http://creativecommons.org/licenses/by-nc-nd/4.0/: This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way.
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c507t-14716eaaecd72d00f5581b19bfb5391a363f945b6677d79d0ea6abebb04436e93
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
OpenAccessLink	https://www.tandfonline.com/doi/abs/10.1080/00273171.2018.1454823
PMID	29658809
PQID	2120577609
PQPubID	47318
PageCount	28
ParticipantIDs	proquest_journals_2120577609 pubmed_primary_29658809 crossref_primary_10_1080_00273171_2018_1454823 proquest_miscellaneous_2025800985 crossref_citationtrail_10_1080_00273171_2018_1454823 informaworld_taylorfrancis_310_1080_00273171_2018_1454823
PublicationCentury	2000
PublicationDate	2018-07-04
PublicationDateYYYYMMDD	2018-07-04
PublicationDate_xml	– month: 07 year: 2018 text: 2018-07-04 day: 04
PublicationDecade	2010
PublicationPlace	United States
PublicationPlace_xml	– name: United States – name: Mahwah
PublicationTitle	Multivariate behavioral research
PublicationTitleAlternate	Multivariate Behav Res
PublicationYear	2018
Publisher	Routledge Taylor & Francis Ltd
Publisher_xml	– name: Routledge – name: Taylor & Francis Ltd
References	cit0110 Bolger N. (cit0005) 2013 cit0078 cit0111 cit0074 cit0071 cit0072 cit0070 Kalisch M. (cit0073) 2007; 8 Hamaker E. L (cit0056) 2012 cit0119 cit0116 cit0117 cit0114 cit0115 cit0113 cit0067 cit0100 cit0064 cit0065 Hallquist M. (cit0054) 2017 Schuurman N. K (cit0121) 2016 cit0061 Haslbeck J. M. B. (cit0063) 2016 Epskamp S (cit0032) 2017 cit0107 cit0108 cit0105 cit0106 cit0104 cit0068 cit0101 cit0069 cit0011 cit0012 cit0133 cit0097 cit0130 cit0010 cit0098 cit0131 cit0095 cit0096 Epskamp S (cit0031) 2017 cit0093 cit0094 cit0092 cit0090 Chatfield C (cit0017) 2016 Muthén L. K. (cit0103) 2017 cit0019 cit0138 cit0018 cit0136 cit0016 cit0137 cit0013 cit0134 cit0014 cit0135 cit0088 cit0001 cit0089 cit0122 cit0120 Heiser W. J (cit0066) 2017 cit0082 Abegaz F. (cit0002) 2015 cit0083 cit0080 cit0081 Epskamp S (cit0030) 2017 Hamaker E. L (cit0057) 2017 cit0008 cit0129 cit0009 Kim C.-J. (cit0077) 1999 cit0006 cit0127 cit0007 cit0128 cit0004 cit0125 cit0126 cit0123 cit0003 Hamilton J. D (cit0060) 1994; 2 cit0124 cit0033 cit0034 Epskamp S. (cit0036) 2017 Mohammadi A (cit0099) 2015 Lane S. (cit0084) 2016 Schafer J (cit0118) 2017 cit0037 Marchetti G. M. (cit0091) 2015 cit0038 cit0035 Zhao T (cit0142) 2015 cit0022 cit0023 cit0020 cit0141 cit0021 Liu H. (cit0086) 2009; 10 Kalisch M. (cit0075) 2017 R Core Team (cit0112) 2017 Lauritzen S. L (cit0085) 1996 van Bork R. (cit0132) 2016 cit0028 Pearl J (cit0109) 2000 cit0029 Epskamp S. (cit0039) cit0026 cit0027 cit0024 cit0025 cit0055 cit0053 cit0051 cit0052 cit0050 Koller D. (cit0079) 2009 cit0059 cit0058 cit0044 cit0045 cit0043 Murphy K. P (cit0102) 2012 cit0040 Woodward J (cit0139) 2005 cit0041 Brown T. A (cit0015) 2014 Friedman J. H. (cit0046) 2014 Foygel R. (cit0042) 2010; 23 Kaplan D (cit0076) 2000 Haslbeck J. M. B. (cit0062) 2016 Lord F. M. (cit0087) cit0048 cit0049 Wright S (cit0140) 1921; 20 cit0047
References_xml	– ident: cit0098 doi: 10.1214/009053606000000281 – ident: cit0106 doi: 10.1007/BF02294374 – year: 2014 ident: cit0046 publication-title: glasso: Graphical lasso-estimation of Gaussian graphical models (R package version 1.8) – ident: cit0050 doi: 10.1037/a0024595 – volume-title: R: A Language and Environment for Statistical Computing year: 2017 ident: cit0112 – start-page: 139 year: 2016 ident: cit0121 publication-title: Multilevel autoregressive modeling in psychology: Snags and solutions – ident: cit0129 doi: 10.1126/science.103.2684.677 – ident: cit0123 doi: 10.1080/00273171.2015.1065398 – ident: cit0114 doi: 10.1207/s15327906mbr3003_4 – ident: cit0023 doi: 10.1017/S0140525X09991567 – ident: cit0010 doi: 10.4135/9781412985215 – ident: cit0064 doi: 10.1201/b18401 – ident: cit0029 – ident: cit0052 doi: 10.2307/1912791 – ident: cit0126 doi: 10.1007/978-3-319-52452-8 – ident: cit0125 doi: 10.18637/jss.v035.i03 – volume-title: State-space models with regime switching: Classical and Gibbs-sampling approaches with applications year: 1999 ident: cit0077 doi: 10.7551/mitpress/6444.001.0001 – ident: cit0027 doi: 10.1146/annurev-statistics-060116-053803 – volume-title: Making things happen: A theory of causal explanation year: 2005 ident: cit0139 – ident: cit0065 doi: 10.1080/10705510903206030 – year: 2015 ident: cit0142 publication-title: huge: High-dimensional undirected graph estimation (R package version 1.2.7) – ident: cit0033 doi: 10.17605/OSF.IO/54XRS – year: 2017 ident: cit0057 publication-title: A brief history of dynamic modeling in psychology – ident: cit0110 doi: 10.1214/11-STS358 – ident: cit0025 doi: 10.1037/a0039802 – ident: cit0134 doi: 10.1001/jamapsychiatry.2015.2079 – volume-title: The analysis of time series: An introduction year: 2016 ident: cit0017 – volume-title: Probabilistic graphical models: Principles and techniques year: 2009 ident: cit0079 – ident: cit0058 doi: 10.3389/fpsyg.2014.01492 – volume-title: Handbook of psychometrics ident: cit0039 – ident: cit0044 doi: 10.1016/j.jad.2015.09.005 – ident: cit0049 doi: 10.1017/CBO9780511790942 – ident: cit0104 doi: 10.1017/S0033291708004947 – ident: cit0081 doi: 10.1186/1471-2105-10-384 – volume-title: Intensive longitudinal methods year: 2013 ident: cit0005 – ident: cit0047 doi: 10.1016/j.neuroimage.2012.06.026 – ident: cit0128 doi: 10.1038/srep46523 – volume-title: Graphical models year: 1996 ident: cit0085 doi: 10.1093/oso/9780198522195.001.0001 – ident: cit0001 doi: 10.1093/biostatistics/kxt005 – year: 2015 ident: cit0002 publication-title: SparseTSCGM: Sparse time series chain graphical models (R package version 2.2) – ident: cit0020 doi: 10.1016/j.jrp.2014.07.003 – ident: cit0122 doi: 10.1037/met0000062 – ident: cit0100 doi: 10.1207/s15366359mea0204_1 – ident: cit0041 doi: 10.1177/2167702617744325 – ident: cit0053 doi: 10.1214/aoms/1177732287 – ident: cit0096 doi: 10.1177/2167702614553230 – ident: cit0094 doi: 10.1007/s41237-017-0024-x – ident: cit0035 doi: 10.18637/jss.v048.i04 – ident: cit0097 doi: 10.2466/pr0.1990.66.1.195 – ident: cit0082 doi: 10.17505/jpor.2017.01 – year: 2016 ident: cit0062 publication-title: arXiv preprint – year: 2015 ident: cit0091 publication-title: ggm: Functions for graphical Markov models (R package version 2.3) – ident: cit0072 doi: 10.1093/schbul/sbw055 – ident: cit0055 doi: 10.1177/1754073915590619 – year: 2017 ident: cit0118 publication-title: corpcor: Efficient estimation of covariance and (partial) correlation (R package version 1.6.9) – ident: cit0007 doi: 10.1371/journal.pone.0027407 – ident: cit0138 doi: 10.1198/jcgs.2011.11051a – ident: cit0045 doi: 10.1093/biostatistics/kxm045 – ident: cit0092 doi: 10.1080/00273171.2017.1379379 – ident: cit0069 doi: 10.1214/aos/1176350174 – ident: cit0078 doi: 10.1093/schbul/sbx037 – ident: cit0028 doi: 10.1016/j.jeconom.2005.06.032 – ident: cit0048 doi: 10.1016/j.neuroimage.2009.12.117 – ident: cit0089 doi: 10.1037/0033-2909.114.1.185 – ident: cit0043 doi: 10.1037/abn0000028 – ident: cit0070 doi: 10.1007/BF02980577 – ident: cit0011 doi: 10.1037/met0000085 – ident: cit0012 doi: 10.1017/S0033291714001809 – ident: cit0111 doi: 10.1038/srep01898 – ident: cit0127 doi: 10.1111/j.2517-6161.1951.tb00088.x – ident: cit0136 doi: 10.1017/S0033291715000331 – year: 2016 ident: cit0084 publication-title: Gimme: Group iterative multiple model estimation. (R package version 0.1-7) – ident: cit0093 doi: 10.1038/srep09050 – ident: cit0101 doi: 10.1016/j.jrp.2016.06.017 – ident: cit0115 doi: 10.1097/PSY.0b013e3182545d47 – ident: cit0006 doi: 10.1146/annurev-clinpsy-050212-185608 – year: 2016 ident: cit0063 publication-title: arXiv preprint – ident: cit0083 doi: 10.1038/srep34175 – ident: cit0095 doi: 10.1111/j.1467-6494.1992.tb00970.x – ident: cit0107 doi: 10.1037/met0000112 – ident: cit0071 doi: 10.1093/schbul/sbw049 – year: 2017 ident: cit0036 publication-title: mlVAR: Multi-level vector autoregression (R package version 0.4) – year: 2015 ident: cit0099 publication-title: arXiv preprint – volume-title: Confirmatory factor analysis for applied research year: 2014 ident: cit0015 – ident: cit0008 doi: 10.1001/jamapsychiatry.2015.3103 – ident: cit0059 doi: 10.1177/0963721416666518 – ident: cit0003 doi: 10.1080/10705511.2017.1406803 – ident: cit0021 doi: 10.1371/journal.pone.0167490 – ident: cit0119 doi: 10.3389/fnagi.2010.00027 – ident: cit0131 doi: 10.1111/j.2517-6161.1996.tb02080.x – ident: cit0024 doi: 10.1146/annurev.psych.093008.100356 – volume: 23 start-page: 2020 year: 2010 ident: cit0042 publication-title: Advances in Neural Information Processing Systems – volume: 20 start-page: 557 issue: 7 year: 1921 ident: cit0140 publication-title: Journal of Agricultural Research – ident: cit0034 doi: 10.3758/s13428-017-0862-1 – ident: cit0019 doi: 10.1093/biomet/asn034 – volume: 2 year: 1994 ident: cit0060 publication-title: Time series analysis doi: 10.1515/9780691218632 – ident: cit0014 doi: 10.1037/a0032401 – year: 2017 ident: cit0066 publication-title: Early psychometric contributions to Gaussian graphical modeling: A tribute to Louis Guttman (1916-1987) – volume-title: Statistical theories of mental test scores. ident: cit0087 – volume-title: Structural equation modeling: Foundations and extensions year: 2000 ident: cit0076 – ident: cit0135 doi: 10.1037/0033-295X.113.4.842 – ident: cit0074 doi: 10.18637/jss.v047.i11 – ident: cit0026 doi: 10.18637/jss.v077.i05 – start-page: 85 year: 2017 ident: cit0031 publication-title: Network psychometrics – ident: cit0113 doi: 10.1016/j.drugalcdep.2016.02.005 – ident: cit0018 doi: 10.1016/j.compbiomed.2011.09.004 – ident: cit0120 doi: 10.1016/j.newideapsych.2011.02.007 – ident: cit0009 doi: 10.1159/000453583 – ident: cit0004 doi: 10.1080/00273171.2016.1151333 – start-page: 237 year: 2017 ident: cit0030 publication-title: Network psychometrics – ident: cit0090 – ident: cit0137 doi: 10.1186/1471-2288-10-28 – ident: cit0068 doi: 10.1080/15427600902911189 – year: 2017 ident: cit0103 publication-title: Statistical analysis with latent variables – volume-title: Machine learning: A probabilistic perspective year: 2012 ident: cit0102 – year: 2017 ident: cit0032 publication-title: graphicalVAR: Graphical VAR for experience sampling data (R package version 0.1.6) – ident: cit0038 doi: 10.1371/journal.pone.0179891 – start-page: 43 volume-title: Handbook of research methods for studying daily life year: 2012 ident: cit0056 – ident: cit0108 doi: 10.1177/2167702614540645 – ident: cit0080 doi: 10.1007/s11136-015-1127-z – ident: cit0117 doi: 10.3389/fpsyg.2017.00262 – ident: cit0013 doi: 10.1371/journal.pone.0060188 – ident: cit0141 doi: 10.1093/biomet/asm018 – ident: cit0124 doi: 10.3389/fpsyg.2015.01038 – volume: 8 start-page: 613 year: 2007 ident: cit0073 publication-title: Journal of Machine Learning Research – ident: cit0016 doi: 10.1080/00273171.2016.1150151 – ident: cit0067 doi: 10.4324/9781315744094 – ident: cit0116 doi: 10.1198/jcgs.2010.09188 – volume-title: Causality: Models, reasoning, and inference year: 2000 ident: cit0109 – ident: cit0061 doi: 10.1017/CBO9781107049994 – ident: cit0105 doi: 10.1007/s11336-008-9106-8 – ident: cit0130 doi: 10.1371/journal.pone.0155205 – ident: cit0088 doi: 10.1007/978-3-540-27752-1 – year: 2016 ident: cit0132 publication-title: arXiv preprint – ident: cit0037 doi: 10.1037/met0000167 – year: 2017 ident: cit0075 publication-title: pcalg: Estimation of CPDAG/PAG and causal inference using the IDA algorithm. (R package version 2.5-0) – volume: 10 start-page: 2295 year: 2009 ident: cit0086 publication-title: The Journal of Machine Learning Research – ident: cit0051 doi: 10.1371/journal.pone.0174035 – year: 2017 ident: cit0054 publication-title: MplusAutomation: Automating Mplus model estimation and interpretation [Computer software manual] (R package version 0.7) – ident: cit0022 doi: 10.1002/per.1866 – ident: cit0133 doi: 10.1038/srep05918 – ident: cit0040 doi: 10.1007/s11336-017-9557-x
SSID	ssj0006549
Score	2.6665916
Snippet	We discuss the Gaussian graphical model (GGM; an undirected network of partial correlation coefficients) and detail its utility as an exploratory data analysis...
SourceID	proquest pubmed crossref informaworld
SourceType	Aggregation Database Index Database Enrichment Source Publisher
StartPage	453
SubjectTerms	Between-subjects design Computer Simulation Correlation coefficients Covariance Cross-Sectional Studies Data analysis Data Interpretation, Statistical Datasets Economic models Empirical analysis exploratory-data analysis Humans Mathematical models Models, Statistical multilevel modeling multivariate analysis network modeling Software Surveys and Questionnaires Time Factors Time series Time-series analysis
Title	The Gaussian Graphical Model in Cross-Sectional and Time-Series Data
URI	https://www.tandfonline.com/doi/abs/10.1080/00273171.2018.1454823 https://www.ncbi.nlm.nih.gov/pubmed/29658809 https://www.proquest.com/docview/2120577609 https://www.proquest.com/docview/2025800985
Volume	53
WOSCitedRecordID	wos000449304000001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText	1
inHoldings	1
isFullTextHit
isPrint
journalDatabaseRights	– providerCode: PRVAWR databaseName: Taylor & Francis Journals Complete customDbUrl: eissn: 1532-7906 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0006549 issn: 0027-3171 databaseCode: TFW dateStart: 19660101 isFulltext: true titleUrlDefault: https://www.tandfonline.com providerName: Taylor & Francis
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1La9wwEB7atIdckvSZzaOo0KtaPWzJOoaku3sKhaZ0ezKSLEMgOCX2BvLvM2N7TXMIOaRgBEaWkEYa6Rt59A3AF49jnmEel8rUPNM-cIQhiasqeR2LWNe-6INN2PPzYrVyP0ZvwnZ0qyQbuh6IIvq1mpTbh3bjEfet52CRlqw7WaCqI-hW-iW8UmiakP0l_iynxdjkIwJWdBxn5eYSz2PVPNieHpCXPg5B-61ovvsfOrEHOyMOZSfDxHkDL1LzFran5fDuHZzhDGILv27pmiVbELE1DSij6GlX7LJhp9QD_rP35aK6sCmMbpRwOnFLLTvznX8Pv-bfL06XfIy5wCMiw45L3KxM8j7FyqpKiDrPEdhKF-qQaye9Nrp2WR6MsbayrhLJGx9SCCLLtElOf4Ct5rpJ-8ByEbTOoxKGOL9U5h0-RqaIFo7zsZhBthF1GUdCcoqLcVXKibd0kFFJMipHGc3g61Ts78DI8VQB9-84ll1_FFIPcUtK_UTZo82gl6NytyXu9ohyrRFuBp-nbFRL-tfim3S9xm8QSxZE1prP4OMwWabWKiLcKYQ7eEbDDmGbXnvH4ewItrqbdTqG1_G2u2xvPvU6gKldFZhezH_fAyfQ_94
linkProvider	Taylor & Francis
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1LT9wwEB7xkuACtLR0y6OuxNU0jmMnPlbAAgL2wiK4WY7jSCuhULFZpP57ZpJsBAfEAaTcHFvOjMfzeeL5BuDAoc4TbOMi1iVPpMs5wpDA4yI46TNfli5rik2ko1F2d2de5sLQtUo6Q5ctUUSzV5NxUzB6fiXuT0PCIlI63okMbR1RdywXYVmhryX-_PHwtt-NteogcEzxuFTMs3jeGuaVf3rFXvo2Bm180XDjM75iE9Y7JMr-tkvnCyyE6ius9Rvi_y04xjXETt1sSomW7JSorUmljOqn3bNJxY7oE_h1c5uLxsK5MMop4RRzC1N27Gr3DW6GJ-OjM95VXeAesWHNBborHZwLvkjjIopKpRDaCpOXuZJGOKllaRKVa52mRWqKKDjt8pDnUZJIHYz8DkvVQxV-AFNRLqXycaSJ9StOnMFHi-DxjGOczwaQzGVtfUdJTpUx7q3omUtbGVmSke1kNIDDvtu_lpPjvQ7mpSJt3QRDyrZyiZXv9N2da9125j216O8R56Y6MgP43TejYdLfFleFhxm-g2gyI7pWNYDtdrX0s42JcieLzM8PTOwXrJ6Nry7t5fnoYgfWqKm5RpzswlL9OAt7sOKf6sn0cb8xiGfb2QId
linkToPdf	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1LT9wwEB5RqCouvFro8ihG6tVtHCdOfEQsCxVohQRVuVmOY0tIKCCSReq_ZybJRnBAHEDKzbHlzMPz2Rl_A_DTos4TbOMiVoEn0hYcYYjncemtdLkLweZtsYlsOs2vr_VFn01Y92mVtIcOHVFEu1aTc9-XYZ4R97vlYBEZ7e5Ejq6OoDuWn2AJobMiI7-a_BsWY5X2CDim47hMzC_xvDbMi_D0grz0dQjahqLJ6gd8xBqs9DiUHXaGsw4LvtqA5WE5_P8VxmhB7MTOarpmyU6I2JoUyqh62i27qdgRfQG_bHO5aCycCqMbJZxO3HzNxrax3-Dv5Pjq6JT3NRe4Q2TYcIHBSnlrvSuzuIyikKYIbIUuQpFKLaxUMugkLZTKsjLTZeStsoUviihJpPJabsJidVf578DSqJAydXGkiPMrTqzGRwnvcIejrctHkMxFbVxPSE51MW6NGHhLOxkZkpHpZTSCX0O3-46R460O-rkeTdMehYSubomRb_TdnSvd9M5dG4z2iHIzFekRHAzN6Jb0r8VW_m6G7yCWzImsNR3BVmcsw2xjItzJI739jontw5eL8cSc_5me7cAytbQ5xMkuLDYPM78Hn91jc1M__Gjd4Qm9bQDP
openUrl	ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=The+Gaussian+Graphical+Model+in+Cross-Sectional+and+Time-Series+Data&rft.jtitle=Multivariate+behavioral+research&rft.au=Epskamp%2C+Sacha&rft.au=Waldorp%2C+Lourens+J&rft.au=M%C3%B5ttus%2C+Ren%C3%A9&rft.au=Borsboom%2C+Denny&rft.date=2018-07-04&rft.issn=1532-7906&rft.eissn=1532-7906&rft.volume=53&rft.issue=4&rft.spage=453&rft_id=info:doi/10.1080%2F00273171.2018.1454823&rft.externalDBID=NO_FULL_TEXT
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0027-3171&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0027-3171&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0027-3171&client=summon