Data:
FILE = 
longitudinal_dyadic wide2.csv;

VARIABLE:
NAMES = id dyadid person idp lengthymean index
deps audits cudits sats trusts commits csts cons
micros isms psss vics sods 
ismp microp pssp vicp sodp satp trustp commitp
cstp conp depp auditp cuditp
depsl auditsl cuditsl 
satsl trustsl commitsl cstsl consl
satpl trustpl commitpl 
cstpl conpl deppl auditpl cuditpl
noncis pover29;

Missing = ALL(-999);

UseVariables are psss
isms micros vics sods 
deps audits cudits;

cluster= dyadid;

within are psss
isms micros vics sods 
deps audits cudits;
between =  ;

ANALYSIS:
ESTIMATOR = MLR;
Type = twolevel ;
MODEL:
%WITHIN%
psss WITH
isms micros vics sods 
deps audits cudits;
isms WITH micros vics sods 
deps audits cudits;
micros WITH vics sods 
deps audits cudits;
vics WITH sods 
deps audits cudits;
sods WITH
deps audits cudits;
deps WITH audits cudits;
audits WITH cudits;
%BETWEEN%



!see within-person level variances and standard deviations

Plot:
Type = plot3;
OUTPUT: svalues residual STAND SAMPstat CINT Modindices(2) tech1 tech2 tech3;