Bayesian structural equation modeling

著者

• DePaoli, Sarah

書誌事項

Bayesian structural equation modeling

Sarah DePaoli

（Methodology in the social sciences）

Guilford Press, c2021

• : cloth

注記

Includes bibliographical references and indexes

内容説明・目次

内容説明

*First Bayesian SEM book specifically for social science researchers, not advanced statisticians; a strong background in calculus is not needed. *Engaging, worked-through examples help highlight problems that can arise during estimation, explore solutions, and provide guidance for writing up findings. *User-friendly features include take-home points, tips, warnings, sample data analysis plans, and more. *Each chapter contains excerpts of annotated code in both Mplus and R; the companion website supplies datasets, code, and output for all of the book's examples.

目次

• P r e f a c e I . I n t r o d u c t i o n 1 . B a c k g r o u n d 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 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 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 1 . 3 . 1 . A R e c a p : W h y B a y e s i a n S E M ? 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 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 1 . 4 . 2 . L I S R E L N o t a t i o n 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 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 1 . 5 . 1 . C y n i c i s m D a t a 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 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 ) 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 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 1 . 5 . 6 . P o l i t i c a l D e m o c r a c y 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 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 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 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 2 . 2 . S e t t i n g t h e S t a g e 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 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 2 . 5 . B a y e s & r s q u o
• R u l e 2 . 6 . P r i o r D i s t r i b u t i o n s 2 . 6 . 1 . T h e N o r m a l P r i o r 2 . 6 . 2 . T h e U n i f o r m P r i o r 2 . 6 . 3 . T h e I n v e r s e G a m m a P r i o r 2 . 6 . 4 . T h e G a m m a P r i o 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 2 . 6 . 6 . T h e W i s h a r t P r i o r 2 . 6 . 7 . T h e B e t a P r i o r 2 . 6 . 8 . T h e D i r i c h l e t P r i o 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 2 . 6 . 1 0 . P r i o r E l i c i t a t i o n 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 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 ) 2 . 8 . T h e P o s t e r i o 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 2 . 8 . 2 . S a m p l i n g A l g o r i t h m s 2 . 8 . 3 . C o n v e r g e n c e 2 . 8 . 4 . M C M C B u r n - i n P h a s e 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 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 2 . 8 . 7 . T h i n n i n g a C h a i n 2 . 9 . P o s t e r i o r I n f e r e n c e 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 2 . 9 . 2 . I n t e r v a l s 2 . 9 . 3 . E f f e c t i v e S a m p l e S i z e 2 . 9 . 4 . T r a c e - p l o t s 2 . 9 . 5 . A u t o c o r r e l a t i o n P l o t s 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 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 2 . 9 . 8 . M o d e l A s s e s s m e n t 2 . 9 . 9 . S e n s i t i v i t y A n a l y s i s 2 . 1 0 . A S i m p l e E x a m p l e 2 . 1 1 . C h a p t e r S u m m a r y 2 . 1 1 . 1 . M a j o r T a k e H o m e P o i n t s 2 . 1 1 . 2 . N o t a t i o n R e f e r e n c e d 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 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 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 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 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 3 . 2 . T h e M o d e l a n d N o t a t i o n 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 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 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 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 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 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 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 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 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 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 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 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 3 . 7 . C h a p t e r S u m m a r y 3 . 7 . 1 . M a j o r T a k e H o m e P o i n t s 3 . 7 . 2 . N o t a t i o n R e f e r e n c e d 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 3 . 7 . 4 . E x a m p l e C o d e f o r M p l u s 3 . 7 . 5 . E x a m p l e C o d e f o r R 4 . M u l t i p l e G r o u p M o d e l s 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 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 ) 4 . 3 . T h e M o d e l a n d N o t a t i o n 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 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 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 4 . 7 . T h e M o d e l a n d N o t a t i o n 4 . 8 . T h e B

「Nielsen BookData」 より

• NII書誌ID(NCID): BC06834804
• ISBN:
  • 9781462547746
• LCCN: 2021011543
• 出版国コード: us
• タイトル言語コード: eng
• 本文言語コード: eng
• 出版地: New York
• ページ数/冊数: xxvi, 521 p.
• 大きさ: 26 cm
• 分類:
  • LCC : BF39.2.B39
  • DC23 : 150.1/519542
  • DC23 : 150.1519542
• 件名:
  • LCSH : Bayesian statistical decision theory
  • LCSH : Social sciences -- Statistical methods
• 親書誌ID:
  • BA3787031X