{"id":12628,"date":"2018-12-14T22:50:00","date_gmt":"2018-12-15T06:50:00","guid":{"rendered":"https:\/\/oslc.nineplanetsllc.com\/blog\/publication\/time-varying-effect-sizes-for-quadratic-growth-models-in-multilevel-and-latent-growth-modeling\/"},"modified":"2018-12-14T22:50:00","modified_gmt":"2018-12-15T06:50:00","slug":"time-varying-effect-sizes-for-quadratic-growth-models-in-multilevel-and-latent-growth-modeling","status":"publish","type":"publication","link":"https:\/\/www.oslc.org\/es\/blog\/publication\/time-varying-effect-sizes-for-quadratic-growth-models-in-multilevel-and-latent-growth-modeling\/","title":{"rendered":"Time-varying effect sizes for quadratic growth models in multilevel and latent growth modeling"},"content":{"rendered":"<p>Multilevel and latent growth modeling analysis (GMA) is often used to compare independent groups in linear random slopes of outcomes over time, particularly in randomized controlled trials. The unstandardized coef\ufb01cient for the effect of group on the slope from a linear GMA can be transformed into a model-estimated effect size for the group difference at the end of a study. Because effect sizes vary nonlinearly in quadratic GMA, the effect size at the end of a study using quadratic GMA cannot be derived from a single coef\ufb01cient, and cannot be used to estimate effect sizes at intermediate time points with backward extrapolation. This article formulates equations and associated input commands in Mplus for time-varying effect sizes for quadratic GMA. Illustrative analyses that produced these time-varying effect sizes were presented, and a Monte Carlo study found that bias in the effect sizes and their con\ufb01dence intervals was ignorable.<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"_acf_changed":false,"_seopress_robots_primary_cat":"","_seopress_titles_title":"","_seopress_titles_desc":"","_seopress_robots_index":"","_seopress_analysis_target_kw":"","site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"default","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","ast-disable-related-posts":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}}},"publication_keyword":[1422,154,1134,155],"research_type":[10],"class_list":["post-12628","publication","type-publication","status-publish","hentry","publication_keyword-condence-intervals","publication_keyword-effect-sizes","publication_keyword-latent-growth-models","publication_keyword-multilevel-analysis","research_type-basic"],"acf":{"citation":"Feingold, A. (2019). Time-varying effect sizes for quadratic growth models in multilevel and latent growth modeling. <em>Structural Equation Modeling, 26<\/em>(3), 418-429. doi:10.1080\/10705511.2018.1547110","publication_year":"2019","scientists":[10990]},"_links":{"self":[{"href":"https:\/\/www.oslc.org\/es\/wp-json\/wp\/v2\/publication\/12628","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.oslc.org\/es\/wp-json\/wp\/v2\/publication"}],"about":[{"href":"https:\/\/www.oslc.org\/es\/wp-json\/wp\/v2\/types\/publication"}],"acf:post":[{"embeddable":true,"href":"https:\/\/www.oslc.org\/es\/wp-json\/wp\/v2\/scientist\/10990"}],"wp:attachment":[{"href":"https:\/\/www.oslc.org\/es\/wp-json\/wp\/v2\/media?parent=12628"}],"wp:term":[{"taxonomy":"publication_keyword","embeddable":true,"href":"https:\/\/www.oslc.org\/es\/wp-json\/wp\/v2\/publication_keyword?post=12628"},{"taxonomy":"research_type","embeddable":true,"href":"https:\/\/www.oslc.org\/es\/wp-json\/wp\/v2\/research_type?post=12628"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}