Number of Subjects and Time Points Needed for Multilevel Time-Series Analysis: A Simulation Study of Dynamic Structural Equation Modeling
Dynamic structural equation modeling (DSEM) is a novel, intensive longitudinal data (ILD) analysis framework. DSEM models intraindividual changes over time on Level 1 and allows the parameters of these processes to vary across individuals on Level 2 using random effects. DSEM merges time series, str...
Saved in:
| Published in | Structural equation modeling Vol. 25; no. 4; pp. 495 - 515 |
|---|---|
| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
Hove
Psychology Press
04.07.2018
|
| Subjects | |
| Online Access | Get full text |
| ISSN | 1070-5511 1532-8007 1532-8007 |
| DOI | 10.1080/10705511.2017.1392862 |
Cover
| Abstract | Dynamic structural equation modeling (DSEM) is a novel, intensive longitudinal data (ILD) analysis framework. DSEM models intraindividual changes over time on Level 1 and allows the parameters of these processes to vary across individuals on Level 2 using random effects. DSEM merges time series, structural equation, multilevel, and time-varying effects models. Despite the well-known properties of these analysis areas by themselves, it is unclear how their sample size requirements and recommendations transfer to the DSEM framework. This article presents the results of a simulation study that examines the estimation quality of univariate 2-level autoregressive models of order 1, AR(1), using Bayesian analysis in Mplus Version 8. Three features are varied in the simulations: complexity of the model, number of subjects, and number of time points per subject. Samples with many subjects and few time points are shown to perform substantially better than samples with few subjects and many time points. |
|---|---|
| AbstractList | Dynamic structural equation modeling (DSEM) is a novel, intensive longitudinal data (ILD) analysis framework. DSEM models intraindividual changes over time on Level 1 and allows the parameters of these processes to vary across individuals on Level 2 using random effects. DSEM merges time series, structural equation, multilevel, and time-varying effects models. Despite the well-known properties of these analysis areas by themselves, it is unclear how their sample size requirements and recommendations transfer to the DSEM framework. This article presents the results of a simulation study that examines the estimation quality of univariate 2-level autoregressive models of order 1, AR(1), using Bayesian analysis in Mplus Version 8. Three features are varied in the simulations: complexity of the model, number of subjects, and number of time points per subject. Samples with many subjects and few time points are shown to perform substantially better than samples with few subjects and many time points. |
| Author | Muthén, Bengt Schultzberg, Mårten |
| Author_xml | – sequence: 1 givenname: Mårten orcidid: 0000-0002-1260-7737 surname: Schultzberg fullname: Schultzberg, Mårten organization: Uppsala University – sequence: 2 givenname: Bengt surname: Muthén fullname: Muthén, Bengt organization: Muthén and Muthén, Los Angeles, CA |
| BackLink | https://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-353189$$DView record from Swedish Publication Index |
| BookMark | eNqFkctuFDEQRS0UJJLAJyBZYksPLnd67IbVKAkPKQlIE9hatrs68sjdnvgRNJ_AX-PJBBZsWFWVde4tue4JOZrDjIS8BrYAJtk7YIJ1HcCCMxALaHsul_wZOYau5Y1kTBzVvjLNHnpBTlLaMAYSuDwmv27KZDDSMNJ1MRu0OVE9D_TWTUi_BTfX-QZxwIGOIdLr4rPz-ID-kWjWGB0mupq13yWX3tMVXbupeJ1dmOk6l2G3t77YzXpytj7EYnOJ2tPL-3KArsOA3s13L8nzUfuEr57qKfn-8fL2_HNz9fXTl_PVVWNbkLkxoPWZsBaMxFGw5SBb0wOeaYGCSbs0I_IeRG8EtozpgQvbSRjE2JkBesvaU_L24Jt-4rYYtY1u0nGngnbqwv1YqRDvVCmq7eq-vuJvDvg2hvuCKatNKLH-NynOuOhkdReV6g6UjSGliONfW2BqH5L6E5Lah6SeQqq6D__orMuPd8lRO_8f9W9iSpoY |
| CitedBy_id | crossref_primary_10_1002_smi_3229 crossref_primary_10_1080_10705511_2018_1555692 crossref_primary_10_2308_JFR_2019_0021 crossref_primary_10_1080_08870446_2020_1800010 crossref_primary_10_1177_10731911241235467 crossref_primary_10_1007_s10212_023_00693_6 crossref_primary_10_1177_20501579231193941 crossref_primary_10_3389_feduc_2021_714495 crossref_primary_10_1146_annurev_orgpsych_110622_054803 crossref_primary_10_1177_25152459241267912 crossref_primary_10_1080_10503307_2024_2325509 crossref_primary_10_1016_j_addbeh_2020_106788 crossref_primary_10_1177_21677026221129279 crossref_primary_10_1146_annurev_orgpsych_041621_031401 crossref_primary_10_1111_ijal_12595 crossref_primary_10_1136_bmjopen_2023_072641 crossref_primary_10_3389_fpsyt_2020_00049 crossref_primary_10_1093_joc_jqaa039 crossref_primary_10_1016_j_cedpsych_2021_101987 crossref_primary_10_1111_cdev_13447 crossref_primary_10_1177_21677026221147262 crossref_primary_10_3389_fpsyg_2021_693576 crossref_primary_10_1007_s10802_023_01070_z crossref_primary_10_1177_09567976221116816 crossref_primary_10_1016_j_jpain_2019_11_003 crossref_primary_10_1016_j_learninstruc_2022_101664 crossref_primary_10_4236_jss_2024_1211045 crossref_primary_10_1007_s10802_023_01077_6 crossref_primary_10_1016_j_lindif_2025_102658 crossref_primary_10_1007_s12144_023_04400_y crossref_primary_10_1097_j_pain_0000000000001626 crossref_primary_10_3389_fpsyg_2023_1168020 crossref_primary_10_1007_s10802_023_01110_8 crossref_primary_10_1016_j_dcn_2024_101353 crossref_primary_10_1080_10705511_2023_2169698 crossref_primary_10_1186_s12916_020_01738_z crossref_primary_10_1080_10705511_2023_2192889 crossref_primary_10_1016_j_brat_2023_104398 crossref_primary_10_1080_02699931_2024_2427886 crossref_primary_10_1016_j_jcbs_2024_100781 crossref_primary_10_1093_jpepsy_jsae001 crossref_primary_10_1177_00936502211038196 crossref_primary_10_1007_s11336_021_09803_z crossref_primary_10_1080_00273171_2024_2350236 crossref_primary_10_1080_10705511_2020_1779069 crossref_primary_10_1177_17456916211008407 crossref_primary_10_1186_s40359_022_00841_5 crossref_primary_10_1093_joc_jqae010 crossref_primary_10_1111_bjep_12575 crossref_primary_10_1080_10705511_2024_2415420 crossref_primary_10_1002_eat_24254 crossref_primary_10_1016_j_brat_2025_104685 crossref_primary_10_1080_02673843_2025_2467112 crossref_primary_10_1016_j_janxdis_2022_102571 crossref_primary_10_1080_10705511_2023_2287967 crossref_primary_10_1080_00273171_2023_2201824 crossref_primary_10_1080_00273171_2025_2462033 crossref_primary_10_1371_journal_pone_0240730 crossref_primary_10_1111_cdev_13502 crossref_primary_10_1016_j_learninstruc_2021_101494 crossref_primary_10_1080_1359432X_2023_2219002 crossref_primary_10_1111_jcpp_14161 crossref_primary_10_1007_s11336_022_09858_6 crossref_primary_10_1177_10731911241234220 crossref_primary_10_1080_10705511_2022_2130331 crossref_primary_10_1080_10503307_2024_2350681 crossref_primary_10_1177_1094428120913083 crossref_primary_10_1080_00273171_2023_2261224 crossref_primary_10_1177_2515245920978738 crossref_primary_10_1016_j_psychsport_2022_102161 crossref_primary_10_1007_s12646_023_00727_5 crossref_primary_10_1080_10705511_2022_2103703 crossref_primary_10_1080_19312458_2022_2109608 crossref_primary_10_1111_jan_15173 crossref_primary_10_1186_s40359_024_01904_5 crossref_primary_10_3724_SP_J_1042_2021_01948 crossref_primary_10_1111_acer_14881 crossref_primary_10_1016_j_envres_2024_120283 crossref_primary_10_1111_cdev_13733 crossref_primary_10_1007_s10802_023_01045_0 crossref_primary_10_1016_j_learninstruc_2022_101595 crossref_primary_10_1007_s10802_023_01101_9 crossref_primary_10_3389_fpsyg_2019_02350 crossref_primary_10_1002_brb3_3134 crossref_primary_10_1016_j_paid_2021_110808 crossref_primary_10_1038_s41598_022_16877_x crossref_primary_10_3758_s13428_022_01995_1 crossref_primary_10_1080_10705511_2021_1961587 crossref_primary_10_1080_00273171_2024_2347959 crossref_primary_10_1080_00273171_2022_2128020 crossref_primary_10_2196_30901 crossref_primary_10_1080_10503307_2021_1998700 crossref_primary_10_1016_j_cpr_2024_102435 crossref_primary_10_1016_j_sleh_2019_08_002 crossref_primary_10_1177_00936502241276793 crossref_primary_10_1038_s41598_023_47912_0 crossref_primary_10_1186_s40359_023_01209_z crossref_primary_10_1177_1094428119833164 crossref_primary_10_1093_jcmc_zmad008 crossref_primary_10_2139_ssrn_2614085 crossref_primary_10_1080_00273171_2022_2029339 crossref_primary_10_1080_10503307_2020_1769875 crossref_primary_10_1111_jora_12471 crossref_primary_10_1016_j_learninstruc_2022_101626 crossref_primary_10_1177_10731911221137541 crossref_primary_10_1287_orsc_2023_1673 crossref_primary_10_1080_10705511_2023_2268293 crossref_primary_10_1080_00273171_2019_1627660 crossref_primary_10_1177_21677026231196121 crossref_primary_10_1080_15213269_2021_1959350 crossref_primary_10_1002_eat_23398 crossref_primary_10_1080_1743727X_2020_1793939 crossref_primary_10_1080_10705511_2023_2224515 crossref_primary_10_2196_68667 crossref_primary_10_1080_10503307_2021_1930242 crossref_primary_10_1016_j_jrp_2023_104445 crossref_primary_10_1016_j_jvb_2022_103837 crossref_primary_10_1080_10705511_2020_1753517 crossref_primary_10_1136_bmjopen_2023_077222 crossref_primary_10_1080_00273171_2021_1957663 crossref_primary_10_3758_s13428_024_02589_9 crossref_primary_10_2196_11845 crossref_primary_10_1080_00273171_2023_2205392 crossref_primary_10_1016_j_brat_2024_104518 crossref_primary_10_1016_j_brat_2024_104645 crossref_primary_10_1080_10705511_2019_1578657 crossref_primary_10_1007_s42761_024_00291_9 crossref_primary_10_1177_2515245920924686 crossref_primary_10_1080_10705511_2019_1626733 crossref_primary_10_1111_add_15344 crossref_primary_10_1177_08902070221124705 crossref_primary_10_1016_j_psychsport_2023_102384 crossref_primary_10_3224_diskurs_v14i3_06 crossref_primary_10_1080_00273171_2018_1446819 crossref_primary_10_1111_jopy_12911 crossref_primary_10_1016_j_system_2024_103493 crossref_primary_10_1016_j_cpr_2019_01_002 crossref_primary_10_1080_00273171_2024_2436420 crossref_primary_10_1007_s12144_022_03844_y crossref_primary_10_1080_10705511_2021_1915788 crossref_primary_10_1016_j_jad_2024_01_269 crossref_primary_10_1177_19485506241234093 crossref_primary_10_2196_38582 crossref_primary_10_1016_j_janxdis_2025_102983 crossref_primary_10_1080_17437199_2021_1987953 crossref_primary_10_1080_1359432X_2023_2276534 crossref_primary_10_2337_dc22_2008 |
| Cites_doi | 10.1111/j.1467-6494.1991.tb00253.x 10.1080/10705511.2014.935256 10.1515/9780691218632 10.1080/10705511.2016.1253479 10.1037/0022-3514.45.4.851 10.1037/0022-006X.72.2.192 10.3389/fpsyg.2016.00486 10.1037/a0016972 10.1037/a0014131 10.1037/a0012532 10.1201/b16018 10.1080/00273171.2014.1003772 10.1080/10705511.2017.1406803 |
| ContentType | Journal Article |
| Copyright | Copyright © Taylor & Francis Group, LLC |
| Copyright_xml | – notice: Copyright © Taylor & Francis Group, LLC |
| DBID | AAYXX CITATION AHOVV ADTPV AOWAS DF2 |
| DOI | 10.1080/10705511.2017.1392862 |
| DatabaseName | CrossRef Education Research Index SwePub SwePub Articles SWEPUB Uppsala universitet |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Mathematics |
| EISSN | 1532-8007 |
| EndPage | 515 |
| ExternalDocumentID | oai_DiVA_org_uu_353189 10_1080_10705511_2017_1392862 |
| GroupedDBID | .7I .QK 0BK 0R~ 123 4.4 5VS AAGDL AAGZJ AAHIA AAMFJ AAMIU AAPUL AATTQ AAYXX AAZMC ABCCY ABFIM ABIVO ABJNI ABLIJ ABLJU ABPEM ABRYG ABTAI ABXUL ABXYU ABZLS ACGFS ACTIO ACTOA ADAHI ADCVX ADKVQ AECIN AEFOU AEISY AEKEX AEMXT AEOZL AEPSL AEYOC AEZRU AFHDM AFRVT AGDLA AGMYJ AGRBW AIJEM AIYEW AJWEG AKBVH ALMA_UNASSIGNED_HOLDINGS ALQZU AVBZW AWYRJ BEJHT BLEHA BMOTO BOHLJ CCCUG CITATION CJ0 CQ1 DGFLZ DKSSO EBS EJD E~B E~C F5P G-F GTTXZ H13 HF~ HZ~ IPNFZ J.O KYCEM M4Z NA5 NW- O9- P2P PQQKQ RIG RNANH ROSJB RSYQP S-F STATR TASJS TBQAZ TDBHL TEH TFH TFL TFW TNTFI TRJHH TUROJ UT5 UT9 VAE XSW ~01 ~S~ AHOVV 07M 4B3 AAAVZ AANPH ABBZI ABVXC ABWZE ACIKQ ACPKE ACRBO ADEWX ADIUE ADLFI ADTPV ADXAZ AEXSR AIXGP ALLRG AMVHM AOWAS C5A CAG CBZAQ CKOZC COF C~T DF2 DGXZK EFRLQ EGDCR FXNIP JLMOS L7Y LJTGL OHT QZZOY RBICI ROL TBH UA1 YHZ |
| ID | FETCH-LOGICAL-c318t-b1aa47cc1b8ef706d83b91e4a7e708c6bfe29179b7e300ad27c581d7f5bd19c03 |
| ISSN | 1070-5511 1532-8007 |
| IngestDate | Thu Aug 21 07:01:56 EDT 2025 Thu Aug 14 00:04:50 EDT 2025 Wed Jul 30 11:47:47 EDT 2025 Thu Apr 24 23:00:47 EDT 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 4 |
| Language | English |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c318t-b1aa47cc1b8ef706d83b91e4a7e708c6bfe29179b7e300ad27c581d7f5bd19c03 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| ORCID | 0000-0002-1260-7737 |
| PQID | 2027585817 |
| PQPubID | 46559 |
| PageCount | 21 |
| ParticipantIDs | swepub_primary_oai_DiVA_org_uu_353189 proquest_journals_2027585817 crossref_primary_10_1080_10705511_2017_1392862 crossref_citationtrail_10_1080_10705511_2017_1392862 |
| 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 | Hove |
| PublicationPlace_xml | – name: Hove |
| PublicationTitle | Structural equation modeling |
| PublicationYear | 2018 |
| Publisher | Psychology Press |
| Publisher_xml | – name: Psychology Press |
| References | Muthén L. K. (CIT0018) 2017 CIT0010 CIT0021 CIT0020 CIT0001 Cohen J. (CIT0008) 1992; 112 CIT0011 CIT0022 Hamilton J. D. (CIT0013) 1994 Hamaker E. (CIT0012) 2017 Asparouhov T. (CIT0004) 2010 CIT0003 CIT0014 CIT0002 CIT0016 CIT0015 Bolger N. (CIT0005) 2013 CIT0006 CIT0017 Chatfield C. (CIT0007) 2016 CIT0009 CIT0019 |
| References_xml | – ident: CIT0006 doi: 10.1111/j.1467-6494.1991.tb00253.x – ident: CIT0017 – ident: CIT0021 doi: 10.1080/10705511.2014.935256 – ident: CIT0011 – volume-title: Time series analysis year: 1994 ident: CIT0013 doi: 10.1515/9780691218632 – ident: CIT0001 doi: 10.1080/10705511.2016.1253479 – volume-title: Intensive longitudinal methods: An introduction to diary and experience sampling research year: 2013 ident: CIT0005 – volume: 112 start-page: 155 year: 1992 ident: CIT0008 publication-title: Bulletin – year: 2017 ident: CIT0012 publication-title: Multivariate Behavior Research – ident: CIT0016 doi: 10.1037/0022-3514.45.4.851 – ident: CIT0019 doi: 10.1037/0022-006X.72.2.192 – ident: CIT0015 doi: 10.3389/fpsyg.2016.00486 – ident: CIT0003 – volume-title: Mplus user’s guide year: 2017 ident: CIT0018 – ident: CIT0022 doi: 10.1037/a0016972 – volume-title: Bayesian analysis using Mplus: Technical implementation year: 2010 ident: CIT0004 – volume-title: The analysis of time series: An introduction (6th ed year: 2016 ident: CIT0007 – ident: CIT0009 doi: 10.1037/a0014131 – ident: CIT0020 doi: 10.1037/a0012532 – ident: CIT0010 doi: 10.1201/b16018 – ident: CIT0014 doi: 10.1080/00273171.2014.1003772 – ident: CIT0002 doi: 10.1080/10705511.2017.1406803 |
| SSID | ssj0018128 |
| Score | 2.5938537 |
| Snippet | Dynamic structural equation modeling (DSEM) is a novel, intensive longitudinal data (ILD) analysis framework. DSEM models intraindividual changes over time on... |
| SourceID | swepub proquest crossref |
| SourceType | Open Access Repository Aggregation Database Enrichment Source Index Database |
| StartPage | 495 |
| SubjectTerms | Bayesian analysis Bayesian Statistics between-level mediation model DSEM Mathematical models power sample size Simulation Structural equation modeling Structural Equation Models Time Time series |
| Title | Number of Subjects and Time Points Needed for Multilevel Time-Series Analysis: A Simulation Study of Dynamic Structural Equation Modeling |
| URI | https://www.proquest.com/docview/2027585817 https://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-353189 |
| Volume | 25 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV3Nb9MwFLdKd4ED4lMUBvIBTijFSZzY4VaxoWpSKwQd2i2qHXsr6lpYk8v-A_4T_kyev5IMJmBcojRynpO8X9979vtC6CUReRVXnEaKER3RpNJRIamMUioJFRosVNuLYDbPp8f06CQ7GQx-9KKWmlqM5eW1eSX_w1W4Bnw1WbI34GxLFC7AOfAXjsBhOP4Tj-e2nYfNO2nEFxuXYfbBTVrH6w_blYlwmYN2ApvSBBPaXNu1CRKyIyKzMaZ2bVkSl6P-aXXuG3rZEEPrfz9wXeuNB7uRrk7H4TdXItz2UlsH9eeN3N44Fcad98dZx88ZPM5liC6bWYd9dlF3mWmzpj5zbnzXUV5tTuv-HkXMbTwr7YlVECwR2GYORSqIWpDFxPW8DbLYJUF7zNGeYKWuFafX0ZlLAf1N_Lt4STObmcwE7rExmLgJ9xL_SrntX9RgG5wY-6qpgUxpyJSezC20lzCw0oZob_HxaDptPVZgKLm0S_-mIVuMkzfXPs9VO6i3uOkXrLVGzuIeuutXJ3jioHYfDdTmAboza0v77h6i7w50eKtxAB0G0GEDKexAhx3oMIAOd6DDPdDhALq3eII7yGELOUPaQw53UMIBcjhA7hE6fn-4eDeNfD-PSILmqCMRL5eUSRkLrjQjecVTUcSKLhkICi5zoVVSgIIQTKWELKuEyQyWU0xnoooLSdLHaLjZbtQThOGH0FKkqeYpzYkQuiJMVkpkRKVMixGi4euW0he7Nz1X1uUfuTtC4_a2r67ay99u2A-sK71g2JVmPxFW4TxmI_TKsbOlZqq5H6w-T8rtxWnZNGUKOpAXT2867zN0u_uf7aMhMEM9B-u4Fi88MH8C46m1Aw |
| linkProvider | Taylor & Francis |
| 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=Number+of+Subjects+and+Time+Points+Needed+for+Multilevel+Time-Series+Analysis%3A+A+Simulation+Study+of+Dynamic+Structural+Equation+Modeling&rft.jtitle=Structural+equation+modeling&rft.au=Schultzberg%2C+M%C3%A5rten&rft.au=Muth%C3%A9n%2C+Bengt&rft.date=2018-07-04&rft.issn=1070-5511&rft.eissn=1532-8007&rft.volume=25&rft.issue=4&rft.spage=495&rft.epage=515&rft_id=info:doi/10.1080%2F10705511.2017.1392862&rft.externalDBID=n%2Fa&rft.externalDocID=10_1080_10705511_2017_1392862 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1070-5511&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1070-5511&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1070-5511&client=summon |