Data Analysis with Mplus (Methodology in the Social by Christian Geiser

By Christian Geiser

a pragmatic advent to utilizing Mplus for the research of multivariate information, this quantity offers step by step tips, entire with actual facts examples, quite a few reveal photographs, and output excerpts. the writer indicates the best way to arrange an information set for import in Mplus utilizing SPSS. He explains the right way to specify sorts of types in Mplus syntax and tackle average caveats--for instance, assessing dimension invariance in longitudinal SEMs. insurance comprises course and issue analytic types in addition to mediational, longitudinal, multilevel, and latent type types. particular programming tips and resolution ideas are offered in packing containers in every one bankruptcy. The significant other web site (www.guilford.com/geiser-materials) positive factors facts units, annotated syntax documents, and output for the entire examples. Of distinctive software to teachers and scholars, a number of the examples should be run with the unfastened demo model of Mplus.

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Example text

8ll KFT_N3 Corr e l a t i ons KFT_V3 KFT_Vl KFT_Ql KFT_Vl 1 . 000 KFT_V3 0 . 54 7 1 . 000 KFT_Ql 0 . 388 0 . 373 KFT_Q3 0 . 385 0 . 401 KFT_Nl 0 . 44 0 0 . 508 0 . 399 KFT_N3 1 . oco 0 . 4E2 1. 0 00 0 . 372 0. 383 0 . 496 K F T_N KFT_Q3 1. 0 0 . 4 Corre l a t i ons KFT_N3 1 . 000 KFT_N3 tit le : 1'<. e aci Check dat a : citu... a t ha t � c: t. {f T . d-� " dat a s e t K F T . d at ; l i s t tJ i s e •. _ r e ad c o r r e c t l y opt ion de l e t i o n command kf t_v3 a l l ( -99) ; of Mp l us cases r.

Then one can slowly increase the complexity of the model. In this way, poss ible errors and problems can he more e a s i l y traced back to a spe c i fic part o f the model or con stell ation of variables . As a consequence, troub leshooting will be easier than for a complex model that already contains m a ny variables and pa ram­ eters. 4. We can see that a new subcom mand has b een added to the var i able com m a nd . The relevant com m a nd is the so-called u s eva r subcomma n d , w h i c h a l lows us to speci fy which variable s in the data set w i l l actua l l y be use d i n the model .

I r : H:. � -' t u r a t :.. o:-. o ! t v l kf t v3 k f t -q l IC' f t. l l. l"lp 1: 2_$ill'ple_regesSlllfl_Wltli_c Save as type I ·I Detnadlled I •I rr!! l l/ 1 1/20 1 1 tO: t . . l l/l'l/20t l 6: l� . . 1 11es r�J 3 I· u-a> F'e INP Fle s... 3. Saving the Mplus input fi le kom Figure 2 . 1 under a new name to generate a new input fi le for the bivariate regression model . 3. Tips for the First Model Specification A fter checking t he correct data i m p ort into Mplu s using the BAS I C option (see C hapter 2), one can begin with the specification of the first actual model.

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