Bayesian Structural Equation Modeling
Autor Sarah Depaolien Limba Engleză Hardback – 26 oct 2021
În volumul Bayesian Structural Equation Modeling, Sarah Depaoli propune o abordare sistematică a cadrului Bayesian aplicat modelării prin ecuații structurale (SEM), o metodologie din ce în ce mai solicitată în cercetarea avansată. Remarcăm faptul că lucrarea este concepută pentru a fi accesibilă, eliminând barierele matematice dense în favoarea unei implementări practice. Fiecare capitol este dedicat unui model specific, explicând logica Bayesiană și ghidând cititorul prin procesul de estimare și soluționare a eventualelor erori statistice.
Considerăm structura cărții ca fiind unul dintre punctele sale forte: progresia de la fundamentele SEM și notația LISREL către modele complexe este susținută de elemente ajutătoare constante, precum glosarele de notații și secțiunile „Major Take-Home Points”. Un aspect distinctiv față de manualele tehnice standard este includerea instrucțiunilor explicite pentru redactarea rezultatelor în vederea publicării, oferind modele de text pentru planul de analiză și interpretarea datelor. Experiența de lectură este una aplicată, fiind facilitată de codul sursă furnizat atât pentru R, cât și pentru Mplus.
Ca alternativă la Principles and Practice of Structural Equation Modeling, Fifth Edition de Rex B Kline pentru cursurile de metodologie psihologică, acest volum are avantajul specializării stricte pe inferența Bayesiană, oferind instrumente mai nuanțate pentru gestionarea datelor complexe decât abordările clasice bazate pe covarianță. În timp ce Confirmatory Factor Analysis for Applied Research, Second Edition de Timothy A. Brown se concentrează pe analiza factorială confirmatorie, lucrarea de față extinde orizontul către întregul spectru de modele SEM sub umbrela Bayesiană, fiind un instrument de lucru indispensabil pentru mediul academic și profesional.
Preț: 564.66 lei
Preț vechi: 613.75 lei
-8%
Puncte Express: 847
Carte disponibilă
Livrare economică 06-20 mai
Livrare express 21-25 aprilie pentru 57.55 lei
Specificații
ISBN-13: 9781462547746
ISBN-10: 1462547745
Pagini: 521
Dimensiuni: 178 x 254 x 32 mm
Greutate: 1.11 kg
Ediția:1
Editura: Guilford Publications
Colecția Guilford Press
ISBN-10: 1462547745
Pagini: 521
Dimensiuni: 178 x 254 x 32 mm
Greutate: 1.11 kg
Ediția:1
Editura: Guilford Publications
Colecția Guilford Press
Public țintă
Professional Practice & DevelopmentDe ce să citești această carte
Această carte este esențială pentru cercetătorii din științele sociale care doresc să treacă de la metodele frecventiste la flexibilitatea cadrului Bayesian în SEM. Cititorul câștigă nu doar cunoștințe teoretice, ci și instrumente practice imediate: cod R/Mplus și șabloane de redactare pentru jurnalele științifice. Este resursa ideală pentru a transforma analizele complexe în rezultate publicabile și riguroase.
Despre autor
Sarah Depaoli este o figură proeminentă în domeniul metodologiei cantitative, expertiza sa concentrându-se pe aplicarea și dezvoltarea statisticilor Bayesiene în științele sociale și comportamentale. Prin activitatea sa academică, aceasta a contribuit semnificativ la popularizarea modelelor de ecuații structurale (SEM) în rândul cercetătorilor, punând accent pe accesibilitatea tehnicilor de estimare complexe. Lucrarea de față reflectă experiența sa în predarea metodelor avansate, fiind recunoscută pentru claritatea cu care explică intersecția dintre teoria statistică și practica de cercetare.
Cuprins
□P□r□e□f□a□c□e□<�□B□R□ □/□>□ □I□.□ □I□n□t□r□o□d□u□c□t□i□o□n□<�□B□R□ □/□>□ □1□.□ □B□a□c□k□g□r□o□u□n□d□<�□B□R□ □/□>□ □1□.□1□.□ □B□a□y□e□s□i□a□n□ □S□t□a□t□i□s□t□i□c□a□l□ □M□o□d□e□l□i□n□g□:□ □T□h□e□ □F□r□e□q□u□e□n□c□y□ □o□f□ □U□s□e□<�□B□R□ □/□>□ □1□.□2□.□ □T□h□e□ □K□e□y□ □I□m□p□e□d□i□m□e□n□t□s□ □w□i□t□h□i□n□ □B□a□y□e□s□i□a□n□ □S□t□a□t□i□s□t□i□c□s□<�□B□R□ □/□>□ □1□.□3□.□ □B□e□n□e□f□i□t□s□ □o□f□ □B□a□y□e□s□i□a□n□ □S□t□a□t□i□s□t□i□c□s□ □w□i□t□h□i□n□ □S□E□M□<�□B□R□ □/□>□ □1□.□3□.□1□.□ □A□ □R□e□c□a□p□:□ □W□h□y□ □B□a□y□e□s□i□a□n□ □S□E□M□?□<�□B□R□ □/□>□ □1□.□4□.□ □M□a□s□t□e□r□i□n□g□ □t□h□e□ □S□E□M□ □B□a□s□i□c□s□:□ □P□r□e□c□u□r□s□o□r□s□ □t□o□ □B□a□y□e□s□i□a□n□ □S□E□M□<�□B□R□ □/□>□ □1□.□4□.□1□.□ □T□h□e□ □F□u□n□d□a□m□e□n□t□a□l□s□ □o□f□ □S□E□M□ □D□i□a□g□r□a□m□s□ □a□n□d□ □T□e□r□m□i□n□o□l□o□g□y□<�□B□R□ □/□>□ □1□.□4□.□2□.□ □L□I□S□R□E□L□ □N□o□t□a□t□i□o□n□<�□B□R□ □/□>□ □1□.□4□.□3□.□ □A□d□d□i□t□i□o□n□a□l□ □C□o□m□m□e□n□t□s□ □a□b□o□u□t□ □N□o□t□a□t□i□o□n□<�□B□R□ □/□>□ □1□.□5□.□ □D□a□t□a□s□e□t□s□ □u□s□e□d□ □i□n□ □t□h□e□ □C□h□a□p□t□e□r□ □E□x□a□m□p□l□e□s□<�□B□R□ □/□>□ □1□.□5□.□1□.□ □C□y□n□i□c□i□s□m□ □D□a□t□a□<�□B□R□ □/□>□ □1□.□5□.□2□.□ □E□a□r□l□y□ □C□h□i□l□d□h□o□o□d□ □L□o□n□g□i□t□u□d□i□n□a□l□ □S□u□r□v□e□y□&□n□d□a□s□h□;□K□i□n□d□e□r□g□a□r□t□e□n□ □C□l□a□s□s□<�□B□R□ □/□>□ □1□.□5□.□3□.□ □H□o□l□z□i□n□g□e□r□ □a□n□d□ □S□w□i□n□e□f□o□r□d□ □(□1□9□3□9□)□<�□B□R□ □/□>□ □1□.□5□.□4□.□ □I□P□I□P□ □5□0□:□ □B□i□g□ □Q□u□e□s□t□i□o□n□n□a□i□r□e□<�□B□R□ □/□>□ □1□.□5□.□5□.□ □L□a□k□a□e□v□ □A□c□a□d□e□m□i□c□ □S□t□r□e□s□s□ □R□e□s□p□o□n□s□e□ □S□c□a□l□e□<�□B□R□ □/□>□ □1□.□5□.□6□.□ □P□o□l□i□t□i□c□a□l□ □D□e□m□o□c□r□a□c□y□<�□B□R□ □/□>□ □1□.□5□.□7□.□ □P□r□o□g□r□a□m□ □f□o□r□ □I□n□t□e□r□n□a□t□i□o□n□a□l□ □S□t□u□d□e□n□t□ □A□s□s□e□s□s□m□e□n□t□<�□B□R□ □/□>□ □1□.□5□.□8□.□ □Y□o□u□t□h□ □R□i□s□k□ □B□e□h□a□v□i□o□r□ □S□u□r□v□e□y□<�□B□R□ □/□>□ □2□.□ □B□a□s□i□c□ □E□l□e□m□e□n□t□s□ □o□f□ □B□a□y□e□s□i□a□n□ □S□t□a□t□i□s□t□i□c□s□<�□B□R□ □/□>□ □2□.□1□.□ □A□ □B□r□i□e□f□ □I□n□t□r□o□d□u□c□t□i□o□n□ □t□o□ □B□a□y□e□s□i□a□n□ □S□t□a□t□i□s□t□i□c□s□<�□B□R□ □/□>□ □2□.□2□.□ □S□e□t□t□i□n□g□ □t□h□e□ □S□t□a□g□e□<�□B□R□ □/□>□ □2□.□3□.□ □C□o□m□p□a□r□i□n□g□ □F□r□e□q□u□e□n□t□i□s□t□ □a□n□d□ □B□a□y□e□s□i□a□n□ □I□n□f□e□r□e□n□c□e□<�□B□R□ □/□>□ □2□.□4□.□ □T□h□e□ □B□a□y□e□s□i□a□n□ □R□e□s□e□a□r□c□h□ □C□i□r□c□l□e□<�□B□R□ □/□>□ □2□.□5□.□ □B□a□y□e□s□&□r□s□q□u□o□;□ □R□u□l□e□<�□B□R□ □/□>□ □2□.□6□.□ □P□r□i□o□r□ □D□i□s□t□r□i□b□u□t□i□o□n□s□<�□B□R□ □/□>□ □2□.□6□.□1□.□ □T□h□e□ □N□o□r□m□a□l□ □P□r□i□o□r□<�□B□R□ □/□>□ □2□.□6□.□2□.□ □T□h□e□ □U□n□i□f□o□r□m□ □P□r□i□o□r□<�□B□R□ □/□>□ □2□.□6□.□3□.□ □T□h□e□ □I□n□v□e□r□s□e□ □G□a□m□m□a□ □P□r□i□o□r□<�□B□R□ □/□>□ □2□.□6□.□4□.□ □T□h□e□ □G□a□m□m□a□ □P□r□i□o□r□<�□B□R□ □/□>□ □2□.□6□.□5□.□ □T□h□e□ □I□n□v□e□r□s□e□ □W□i□s□h□a□r□t□ □P□r□i□o□r□<�□B□R□ □/□>□ □2□.□6□.□6□.□ □T□h□e□ □W□i□s□h□a□r□t□ □P□r□i□o□r□<�□B□R□ □/□>□ □2□.□6□.□7□.□ □T□h□e□ □B□e□t□a□ □P□r□i□o□r□<�□B□R□ □/□>□ □2□.□6□.□8□.□ □T□h□e□ □D□i□r□i□c□h□l□e□t□ □P□r□i□o□r□<�□B□R□ □/□>□ □2□.□6□.□9□.□ □D□i□f□f□e□r□e□n□t□ □L□e□v□e□l□s□ □o□f□ □I□n□f□o□r□m□a□t□i□v□e□n□e□s□s□ □f□o□r□ □P□r□i□o□r□ □D□i□s□t□r□i□b□u□t□i□o□n□s□<�□B□R□ □/□>□ □2□.□6□.□1□0□.□ □P□r□i□o□r□ □E□l□i□c□i□t□a□t□i□o□n□<�□B□R□ □/□>□ □2□.□6□.□1□1□.□ □P□r□i□o□r□ □P□r□e□d□i□c□t□i□v□e□ □C□h□e□c□k□i□n□g□<�□B□R□ □/□>□ □2□.□7□.□ □T□h□e□ □L□i□k□e□l□i□h□o□o□d□ □(□F□r□e□q□u□e□n□t□i□s□t□ □a□n□d□ □B□a□y□e□s□i□a□n□ □P□e□r□s□p□e□c□t□i□v□e□s□)□<�□B□R□ □/□>□ □2□.□8□.□ □T□h□e□ □P□o□s□t□e□r□i□o□r□<�□B□R□ □/□>□ □2□.□8□.□1□.□ □A□n□ □I□n□t□r□o□d□u□c□t□i□o□n□ □t□o□ □M□a□r□k□o□v□ □C□h□a□i□n□ □M□o□n□t□e□ □C□a□r□l□o□ □M□e□t□h□o□d□s□<�□B□R□ □/□>□ □2□.□8□.□2□.□ □S□a□m□p□l□i□n□g□ □A□l□g□o□r□i□t□h□m□s□<�□B□R□ □/□>□ □2□.□8□.□3□.□ □C□o□n□v□e□r□g□e□n□c□e□<�□B□R□ □/□>□ □2□.□8□.□4□.□ □M□C□M□C□ □B□u□r□n□-□i□n□ □P□h□a□s□e□<�□B□R□ □/□>□ □2□.□8□.□5□.□ □T□h□e□ □N□u□m□b□e□r□ □o□f□ □M□a□r□k□o□v□ □C□h□a□i□n□s□<�□B□R□ □/□>□ □2□.□8□.□6□.□ □A□ □N□o□t□e□ □a□b□o□u□t□ □S□t□a□r□t□i□n□g□ □V□a□l□u□e□s□<�□B□R□ □/□>□ □2□.□8□.□7□.□ □T□h□i□n□n□i□n□g□ □a□ □C□h□a□i□n□<�□B□R□ □/□>□ □2□.□9□.□ □P□o□s□t□e□r□i□o□r□ □I□n□f□e□r□e□n□c□e□<�□B□R□ □/□>□ □2□.□9□.□1□.□ □P□o□s□t□e□r□i□o□r□ □S□u□m□m□a□r□y□ □S□t□a□t□i□s□t□i□c□s□<�□B□R□ □/□>□ □2□.□9□.□2□.□ □I□n□t□e□r□v□a□l□s□<�□B□R□ □/□>□ □2□.□9□.□3□.□ □E□f□f□e□c□t□i□v□e□ □S□a□m□p□l□e□ □S□i□z□e□<�□B□R□ □/□>□ □2□.□9□.□4□.□ □T□r□a□c□e□-□p□l□o□t□s□<�□B□R□ □/□>□ □2□.□9□.□5□.□ □A□u□t□o□c□o□r□r□e□l□a□t□i□o□n□ □P□l□o□t□s□<�□B□R□ □/□>□ □2□.□9□.□6□.□ □P□o□s□t□e□r□i□o□r□ □H□i□s□t□o□g□r□a□m□ □a□n□d□ □D□e□n□s□i□t□y□ □P□l□o□t□s□<�□B□R□ □/□>□ □2□.□9□.□7□.□ □H□D□I□ □H□i□s□t□o□g□r□a□m□ □a□n□d□ □D□e□n□s□i□t□y□ □P□l□o□t□s□<�□B□R□ □/□>□ □2□.□9□.□8□.□ □M□o□d□e□l□ □A□s□s□e□s□s□m□e□n□t□<�□B□R□ □/□>□ □2□.□9□.□9□.□ □S□e□n□s□i□t□i□v□i□t□y□ □A□n□a□l□y□s□i□s□<�□B□R□ □/□>□ □2□.□1□0□.□ □A□ □S□i□m□p□l□e□ □E□x□a□m□p□l□e□<�□B□R□ □/□>□ □2□.□1□1□.□ □C□h□a□p□t□e□r□ □S□u□m□m□a□r□y□<�□B□R□ □/□>□ □2□.□1□1□.□1□.□ □M□a□j□o□r□ □T□a□k□e□ □H□o□m□e□ □P□o□i□n□t□s□<�□B□R□ □/□>□ □2□.□1□1□.□2□.□ □N□o□t□a□t□i□o□n□ □R□e□f□e□r□e□n□c□e□d□<�□B□R□ □/□>□ □2□.□1□1□.□3□.□ □A□n□n□o□t□a□t□e□d□ □B□i□b□l□i□o□g□r□a□p□h□y□ □o□f□ □S□e□l□e□c□t□ □R□e□s□o□u□r□c□e□s□<�□B□R□ □/□>□ □A□p□p□e□n□d□i□x□ □A□:□ □G□e□t□t□i□n□g□ □S□t□a□r□t□e□d□ □w□i□t□h□ □R□<�□B□R□ □/□>□ □I□I□.□ □M□e□a□s□u□r□e□m□e□n□t□ □M□o□d□e□l□s□ □a□n□d□ □R□e□l□a□t□e□d□ □I□s□s□u□e□s□<�□B□R□ □/□>□ □3□.□ □T□h□e□ □C□o□n□f□i□r□m□a□t□o□r□y□ □F□a□c□t□o□r□ □A□n□a□l□y□s□i□s□ □M□o□d□e□l□<�□B□R□ □/□>□ □3□.□1□.□ □I□n□t□r□o□d□u□c□t□i□o□n□ □t□o□ □B□a□y□e□s□i□a□n□ □C□F□A□<�□B□R□ □/□>□ □3□.□2□.□ □T□h□e□ □M□o□d□e□l□ □a□n□d□ □N□o□t□a□t□i□o□n□<�□B□R□ □/□>□ □3□.□2□.□1□.□ □H□a□n□d□l□i□n□g□ □I□n□d□e□t□e□r□m□i□n□a□c□i□e□s□ □i□n□ □C□F□A□<�□B□R□ □/□>□ □3□.□3□.□ □T□h□e□ □B□a□y□e□s□i□a□n□ □F□o□r□m□ □o□f□ □t□h□e□ □C□F□A□ □M□o□d□e□l□<�□B□R□ □/□>□ □3□.□3□.□1□.□ □A□d□d□i□t□i□o□n□a□l□ □I□n□f□o□r□m□a□t□i□o□n□ □a□b□o□u□t□ □t□h□e□ □(□I□n□v□e□r□s□e□)□ □W□i□s□h□a□r□t□ □P□r□i□o□r□<�□B□R□ □/□>□ □3□.□3□.□2□.□ □A□l□t□e□r□n□a□t□i□v□e□ □P□r□i□o□r□s□ □f□o□r□ □C□o□v□a□r□i□a□n□c□e□ □M□a□t□r□i□c□e□s□<�□B□R□ □/□>□ □3□.□3□.□3□.□ □A□l□t□e□r□n□a□t□i□v□e□ □P□r□i□o□r□s□ □f□o□r□ □V□a□r□i□a□n□c□e□s□<�□B□R□ □/□>□ □3□.□3□.□4□.□ □A□l□t□e□r□n□a□t□i□v□e□ □P□r□i□o□r□s□ □f□o□r□ □F□a□c□t□o□r□ □L□o□a□d□i□n□g□s□<�□B□R□ □/□>□ □3□.□4□.□ □E□x□a□m□p□l□e□:□ □B□a□s□i□c□ □C□o□n□f□i□r□m□a□t□o□r□y□ □F□a□c□t□o□r□ □A□n□a□l□y□s□i□s□ □M□o□d□e□l□<�□B□R□ □/□>□ □3□.□5□.□ □E□x□a□m□p□l□e□:□ □I□m□p□l□e□m□e□n□t□i□n□g□ □N□e□a□r□-□Z□e□r□o□ □P□r□i□o□r□s□ □f□o□r□ □C□r□o□s□s□-□L□o□a□d□i□n□g□s□<�□B□R□ □/□>□ □3□.□6□.□ □H□o□w□ □t□o□ □W□r□i□t□e□ □u□p□ □B□a□y□e□s□i□a□n□ □C□F□A□ □R□e□s□u□l□t□s□<�□B□R□ □/□>□ □3□.□6□.□1□.□ □H□y□p□o□t□h□e□t□i□c□a□l□ □D□a□t□a□ □A□n□a□l□y□s□i□s□ □P□l□a□n□<�□B□R□ □/□>□ □3□.□6□.□2□.□ □H□y□p□o□t□h□e□t□i□c□a□l□ □R□e□s□u□l□t□s□ □S□e□c□t□i□o□n□<�□B□R□ □/□>□ □3□.□6□.□3□.□ □D□i□s□c□u□s□s□i□o□n□ □P□o□i□n□t□s□ □R□e□l□e□v□a□n□t□ □t□o□ □t□h□e□ □A□n□a□l□y□s□i□s□<�□B□R□ □/□>□ □3□.□7□.□ □C□h□a□p□t□e□r□ □S□u□m□m□a□r□y□<�□B□R□ □/□>□ □3□.□7□.□1□.□ □M□a□j□o□r□ □T□a□k□e□ □H□o□m□e□ □P□o□i□n□t□s□<�□B□R□ □/□>□ □3□.□7□.□2□.□ □N□o□t□a□t□i□o□n□ □R□e□f□e□r□e□n□c□e□d□<�□B□R□ □/□>□ □3□.□7□.□3□.□ □A□n□n□o□t□a□t□e□d□ □B□i□b□l□i□o□g□r□a□p□h□y□ □o□f□ □S□e□l□e□c□t□ □R□e□s□o□u□r□c□e□s□<�□B□R□ □/□>□ □3□.□7□.□4□.□ □E□x□a□m□p□l□e□ □C□o□d□e□ □f□o□r□ □M□<�□I□>□p□l□u□s□<�□/□I□>□<�□B□R□ □/□>□ □3□.□7□.□5□.□ □E□x□a□m□p□l□e□ □C□o□d□e□ □f□o□r□ □R□<�□B□R□ □/□>□ □4□.□ □M□u□l□t□i□p□l□e□ □G□r□o□u□p□ □M□o□d□e□l□s□<�□B□R□ □/□>□ □4□.□1□.□ □A□ □B□r□i□e□f□ □I□n□t□r□o□d□u□c□t□i□o□n□ □t□o□ □M□u□l□t□i□-□G□r□o□u□p□ □M□o□d□e□l□s□<�□B□R□ □/□>□ □4□.□2□.□ □I□n□t□r□o□d□u□c□t□i□o□n□ □t□o□ □t□h□e□ □M□u□l□t□i□p□l□e□-□G□r□o□u□p□ □C□F□A□ □M□o□d□e□l□ □(□w□i□t□h□ □M□e□a□n□ □D□i□f□f□e□r□e□n□c□e□s□)□<�□B□R□ □/□>□ □4□.□3□.□ □T□h□e□ □M□o□d□e□l□ □a□n□d□ □N□o□t□a□t□i□o□n□<�□B□R□ □/□>□ □4□.□4□.□ □T□h□e□ □B□a□y□e□s□i□a□n□ □F□o□r□m□ □o□f□ □t□h□e□ □M□u□l□t□i□p□l□e□-□G□r□o□u□p□ □C□F□A□ □M□o□d□e□l□<�□B□R□ □/□>□ □4□.□5□.□ □E□x□a□m□p□l□e□:□ □U□s□i□n□g□ □a□ □M□e□a□n□ □D□i□f□f□e□r□e□n□c□e□s□,□ □M□u□l□t□i□p□l□e□-□G□r□o□u□p□ □C□F□A□ □M□o□d□e□l□ □t□o□ □A□s□s□e□s□s□ □f□o□r□ □S□c□h□o□o□l□ □D□i□f□f□e□r□e□n□c□e□s□<�□B□R□ □/□>□ □4□.□6□.□ □I□n□t□r□o□d□u□c□t□i□o□n□ □t□o□ □t□h□e□ □M□I□M□I□C□ □M□o□d□e□l□<�□B□R□ □/□>□ □4□.□7□.□ □T□h□e□ □M□o□d□e□l□ □a□n□d□ □N□o□t□a□t□i□o□n□<�□B□R□ □/□>□ □4□.□8□.□ □T□h□e□ □B
Notă biografică
Sarah Depaoli, PhD, is Associate Professor of Quantitative Methods, Measurement, and Statistics in the Department of Psychological Sciences at the University of California, Merced, where she teaches undergraduate statistics and a variety of graduate courses in quantitative methods. Her research interests include examining different facets of Bayesian estimation for latent variable, growth, and finite mixture models. She has a continued interest in the influence of prior distributions and robustness of results under different prior specifications, as well as issues tied to latent class separation. Her recent research has focused on using Bayesian semi- and non-parametric methods for obtaining proper class enumeration and assignment, examining parameterization issues within Bayesian SEM, and studying the impact of priors on longitudinal models.
Recenzii
"The structure of each chapter is extremely well thought-out and facilitates understanding. A brief introduction to each topic is followed by an in-depth discussion, an example, and hypothetical results and discussion. The section about how to write up findings for each SEM analysis will be extremely helpful to readers; this is something that instructors are typically left to try to come up with on their own. I would absolutely consider using this book for a class on Bayesian SEM--or a lecture on the topic in a broader SEM course--as well as for my own professional use as a reference guide."--Katerina Marcoulides, PhD, Department of Psychology, University of Minnesota Twin Cities
"Depaoli has created a book that will quickly have a positive impact on researchers and students looking to expand their analytic capabilities. The text's design and writing style will engage readers with different levels of familiarity with Bayesian analysis and SEM. Instructors can flexibly change the level and amount of technical and mathematical information for different courses. I will add this text to my course to replace the hodgepodge of documents, website links, and articles needed for comprehension and usage of Bayesian SEM."--James B. Schreiber, PhD, School of Nursing, Duquesne University
"Researchers interested in applying Bayesian SEM in the social sciences will benefit from reading this book or taking a course based on it. Each chapter is well organized; the introduction sections are particularly useful. All methods are illustrated by code, which is an important step toward implementing the methods and applying them to real problems."--Peng Ding, PhD, Department of Statistics, University of California, Berkeley
"This book is a 'must read' for anyone who wants to do or review Bayesian SEM. It is structured well for the advanced graduate student and moderately versed researcher. The chapters are highly readable, and I really appreciate the annotated bibliography of select resources, which will be a great help to students and faculty."--Michael D. Toland, PhD, Executive Director, The Herb Innovation Center, Judith Herb College of Education, University of Toledo-
"Depaoli has created a book that will quickly have a positive impact on researchers and students looking to expand their analytic capabilities. The text's design and writing style will engage readers with different levels of familiarity with Bayesian analysis and SEM. Instructors can flexibly change the level and amount of technical and mathematical information for different courses. I will add this text to my course to replace the hodgepodge of documents, website links, and articles needed for comprehension and usage of Bayesian SEM."--James B. Schreiber, PhD, School of Nursing, Duquesne University
"Researchers interested in applying Bayesian SEM in the social sciences will benefit from reading this book or taking a course based on it. Each chapter is well organized; the introduction sections are particularly useful. All methods are illustrated by code, which is an important step toward implementing the methods and applying them to real problems."--Peng Ding, PhD, Department of Statistics, University of California, Berkeley
"This book is a 'must read' for anyone who wants to do or review Bayesian SEM. It is structured well for the advanced graduate student and moderately versed researcher. The chapters are highly readable, and I really appreciate the annotated bibliography of select resources, which will be a great help to students and faculty."--Michael D. Toland, PhD, Executive Director, The Herb Innovation Center, Judith Herb College of Education, University of Toledo-
Descriere scurtă
This book offers researchers a systematic and accessible introduction to using a Bayesian framework in structural equation modeling (SEM). Stand-alone chapters on each SEM model clearly explain the Bayesian form of the model and walk the reader through implementation. Engaging worked-through examples from diverse social science subfields illustrate the various modeling techniques, highlighting statistical or estimation problems that are likely to arise and describing potential solutions. For each model, instructions are provided for writing up findings for publication, including annotated sample data analysis plans and results sections. Other user-friendly features in every chapter include "Major Take-Home Points," notation glossaries, annotated suggestions for further reading, and sample code in both Mplus and R. The companion website (www.guilford.com/depaoli-materials) supplies data sets; annotated code for implementation in both Mplus and R, so that users can work within their preferred platform; and output for all of the book’s examples.