Matrix
Notes
Output Created
08-APR-2021 23:23:08
Comments
Input
Data
S:\Users\Arlish
Kahf\Downloads\cfa_C_1.sav
Active Dataset
DataSet1
Filter
<none>
Weight
<none>
Split File
<none>
N of Rows in Working Data
File
202
Syntax
MATRIX.
compute wnames='xxxxx'.
compute znames='xxxxx'.
compute mcerpt=0.
compute wiscov=0.
compute ziscov=0.
compute tooman=0.
compute errcode=make(100,1,0).
compute notecode=make(100,1,0).
compute model = trunc( 7 ).
compute iterate = abs(trunc( 100 )).
compute converge = abs( 0.00001 ).
compute itprobtg=0.
compute v2tag=0.
compute ydich=0.
compute maxwwarn=0.
compute minwwarn=0.
compute maxzwarn=0.
compute minzwarn=0.
compute toomany=0.
compute wdich=0.
compute zdich=0.
compute wnotev=0.
compute znotev=0.
compute nxpval=1.
compute nwpval=1.
compute nzpval=1.
compute errs=1.
compute notes=1.
compute criterr=0.
compute novar=0.
compute adjust=0.
compute ncs=0.
compute serial=0.
compute sobelok=0.
compute hasw=0.
compute hasz=0.
compute printw=0.
compute printz=0.
compute counterf=0.
compute wmodcust=0.
compute zmodcust=0.
compute booting=0.
compute bootiter=0.
compute iterrmod=0.
Resources
Processor Time
00:00:13.29
Elapsed Time
00:00:13.31
Run MATRIX procedure:
**************** PROCESS Procedure for SPSS Version 3.5.3 ****************
Written by Andrew F. Hayes, Ph.D.
www.afhayes.com
Documentation available in Hayes (2018). www.guilford.com/p/hayes3
**************************************************************************
Model : 7
Y : NVPF
X : SOF
M : NRAF
W : SIF
Sample
Size: 202
**************************************************************************
OUTCOME VARIABLE:
NRAF
Model Summary
R
.2853
R-sq
.0814
MSE
.5471
F
5.8491
df1
3.0000
df2
198.0000
p
.0008
Model
constant
SOF
SIF
Int_1
coeff
1.1820
.7858
1.8834
-.3924
se
1.0653
.2782
.5772
.1496
Product terms key:
Int_1
:
SOF
x
Test(s) of highest
R2-chng
X*W
.0319
---------Focal predict:
Mod var:
(X)
(W)
t
1.1096
2.8250
3.2631
-2.6234
p
.2685
.0052
.0013
.0094
LLCI
-.9188
.2373
.7452
-.6874
ULCI
3.2829
1.3343
3.0216
-.0974
SIF
order unconditional interaction(s):
F
df1
df2
p
6.8821
1.0000
198.0000
.0094
SOF
SIF
Conditional effects of the focal predictor at values of the moderator(s):
SIF
1.3376
1.8745
2.1946
Effect
.2609
.0501
-.0755
se
.0940
.0620
.0837
t
2.7760
.8091
-.9017
p
.0060
.4194
.3683
LLCI
.0755
-.0721
-.2405
ULCI
.4462
.1723
.0896
**************************************************************************
OUTCOME VARIABLE:
NVPF
Model Summary
R
.2906
R-sq
.0844
MSE
.0212
F
9.1768
df1
2.0000
df2
199.0000
p
.0002
Model
constant
SOF
NRAF
coeff
.5913
.0141
.0538
se
.0772
.0121
.0135
t
7.6627
1.1681
3.9848
p
.0000
.2442
.0001
LLCI
.4392
-.0097
.0272
ULCI
.7435
.0380
.0804
****************** DIRECT AND INDIRECT EFFECTS OF X ON Y *****************
Direct effect of X on Y
Effect
se
.0141
.0121
t
1.1681
p
.2442
LLCI
-.0097
ULCI
.0380
Conditional indirect effects of X on Y:
INDIRECT EFFECT:
SOF
->
SIF
1.3376
1.8745
2.1946
SIF
---
NRAF
Effect
.0140
.0027
-.0041
->
BootSE
.0096
.0033
.0046
NVPF
BootLLCI
-.0009
-.0030
-.0146
Index of moderated mediation:
Index
BootSE
BootLLCI
-.0211
.0146
-.0552
BootULCI
.0360
.0100
.0035
BootULCI
.0009
*********************** ANALYSIS NOTES AND ERRORS ************************
Level of confidence for all confidence intervals in output:
95.0000
Number of bootstrap samples for percentile bootstrap confidence intervals:
5000
W values in conditional tables are the 16th, 50th, and 84th percentiles.
------ END MATRIX -----
/* School of Communication */.
/* The Ohio State University */.
/* See Preacher, KJ, & Hayes, AF (2004). SPSS and SAS Procedures for Estimating */.
/* Indirect Effects in Simple Mediation Models, in Behavior Research Methods, Instruments
*/.
/* and Computers, 36, 717-731 */.
/* Version 3.6 uploaded April 13, 2011 */.
/* This version implements a new sorting routine to greatly decrease time to output */.
/* Since original publication, procedures have been added to SOBEL for estimating models
with */.
/* dichotomous outcomes and the computation of five different effect sizes for indirect
effects */.
DEFINE SOBEL (y = !charend('/')/x = !charend('/')/m = !charend('/')/varord = !charend('/')
!default(2)/iterate = !charend('/') !default(10000)
/converge = !charend('/') !default(0.0000001)/boot = !charend('/') !default(0)/effsize
=
!charend('/') !default(0)).
SET MXLOOPS = 10000001.
SET PRINTBACK = OFF.
MATRIX.
/* READ ACTIVE SPSS DATA FILE */.
get dd/variables = !y !x !m/names = nms/MISSING = OMIT.
compute n = nrow(dd).
/* DO SOME CHECKS FOR DATA ERRORS */.
compute converrb = 0.
compute converre = 0.
compute daterr = 0.
do if (n 5).
compute daterr = 1.
end if.
compute ones = make(n,1,1).
compute sigma = (t(dd)*(ident(n)-(1/n)*ones*t(ones))*dd)*(1/(n-1)).
compute var = diag(sigma).
do if csum ((abs(var 0.000000001)) > 0).
compute daterr = 2.
end if.
do if (rank(dd(:,2:3)) 2).
compute daterr = 3.
end if.
compute mvals = ncol(design(dd(:,3))).
do if (mvals 3).
compute daterr = 4.
end if.
compute bdbp = 0.
compute corrxtt = 0.
compute sdchkt = 0.
/* START THE COMPUTATIONS */.
do if (daterr = 0).
compute mndd = csum(dd)/n.
compute std = sqrt(var).
compute vr = sqrt(var).
compute corrx = sigma&/(std*t(std)).
compute cpath = corrx(2,1)*vr(1,1)/vr(2,1).
/* DEFINE NUMBER OF BOOTSTRAP SAMPLES */.
do if (!boot > 999).
compute btn = trunc(!boot/1000)*1000.
compute btnp = btn+1.
else.
compute btn = 1000.
compute btnp = btn+1.
end if.
compute res=make(btnp,1,0).
compute varord = !varord.
do if (varord 1).
compute varord = 2.
end if.
compute ovals = ncol(design(dd(:,1))).
do if (ovals = 2).
compute omx = cmax(dd(:,1)).
compute omn = cmin(dd(:,1)).
compute dd(:,1) = (dd(:,1) = omx).
compute rcd = {omn, 0; omx, 1}.
end if.
compute dat = dd.
/* START OF THE LOOP FOR BOOTSTRAPPING */.
loop #j = 1 to btnp.
do if (#j = 2 and !boot 1000).
BREAK.
end if.
/* DO THE RESAMPLING OF THE DATA */.
do if (#j > 1).
loop.
compute v = trunc(uniform(n,1)*n)+1.
compute dat(:,1:3)=dd(v,1:3).
compute sig = (t(dat)*(ident(n)-(1/n)*ones*t(ones))*dat)*(1/(n-1)).
compute vr = sqrt(diag(sig)).
compute sdchk = (csum(vr .00000001) > 0).
compute corrxt = sig&/(vr*t(vr)).
compute rsq = t(ryi)*bi.
do if (!effsize = 1).
compute r2my = corrxt(3,1)*corrxt(3,1).
compute r2xy = corrxt(2,1)*corrxt(2,1).
compute ryi = corrxt(2:3,1).
compute rii = corrxt(2:3,2:3).
compute bi=inv(rii)*ryi.
compute rsq = t(ryi)*bi.
compute r245 = r2my-(rsq-r2xy).
compute cpath = corrxt(2,1)*vr(1,1)/vr(2,1).
end if.
compute corrxt = (abs(corrxt(3,2)) > .99999999).
compute bdbp = bdbp+((sdchk = 1) or (corrxt = 1)).
compute corrxtt = corrxtt + corrxt.
compute sdchkt = sdchkt + sdchk.
end loop if (corrxt = 0 and sdchk = 0).
end if.
/* SET UP THE DATA COLUMNS FOR PROCESSING */.
compute y = dat(:,1).
compute x = dat(:,2).
compute z = dat(:,3).
compute xz = dat(:,2:3).
/* CALCULATE REGRESSION STATISTICS NEEDED TO COMPUTE indirect effect */
/* product of a and b is held as variable 'ind' */.
compute con = make(n,1,1).
compute xo = {con,x}.
compute bzx = inv(t(xo)*xo)*t(xo)*z.
compute bzx = bzx(2,1).
compute xzo = {con,xz}.
/* If Y is not dichotomous */.
do if (ovals 2).
compute byzx2 = inv(t(xzo)*xzo)*t(xzo)*y.
compute byzx = byzx2(3,1).
compute byxz = byzx2(2,1).
compute converrb = 0.
end if.
/* if Y is dichotomous */.
do if (ovals = 2).
compute pt1 = make(n,1,0.5).
compute bt1 = make(ncol(xzo),1,0).
compute LL1 = 0.
loop jjj = 1 to !iterate.
compute vt1 = mdiag(pt1&*(1-pt1)).
compute byzx2 = bt1+inv(t(xzo)*vt1*xzo)*t(xzo)*(y-pt1).
compute pt1 = 1/(1+exp(-(xzo*byzx2))).
compute LL = y&*ln(pt1)+(1-y)&*ln(1-pt1).
compute LL2 = -2*csum(ll).
do if (abs(LL1-LL2) !converge).
compute vt1 = mdiag(pt1&*(1-pt1)).
compute byzx = byzx2(3,1).
compute byxz = byzx2(2,1).
compute covb = inv(t(xzo)*vt1*xzo).
compute selgb = sqrt(diag(covb)).
break.
end if.
compute bt1 = byzx2.
compute LL1 = LL2.
end loop.
do if (jjj > !iterate).
do if (#j > 1).
compute converrb = 1.
else if (#j = 1).
compute converre = 1.
end if.
compute vt1 = mdiag(pt1&*(1-pt1)).
compute byzx = byzx2(3,1).
compute byxz = byzx2(2,1).
compute covb = inv(t(xzo)*vt1*xzo).
compute selgb = sqrt(diag(covb)).
end if.
end if.
compute ind = bzx*byzx.
compute res(#j,1) = ind.
/* GENERATE STATISTICS FOR BARON AND KENNY AND NORMAL SOBEL SECTION OF OUTPUT */.
do if (#j = 1).
compute sd = sqrt(((n*cssq(dat))-(csum(dat)&**2))/((n-1)*n)).
compute num = (n*sscp(dat)-(transpos(csum(dat))*(csum(dat)))).
compute den = sqrt(transpos((n*cssq(dat))-(csum(dat)&**2))*((n*cssq(dat))(csum(dat)&**2))).
compute r = num&/den.
compute sdbzx = (sd(1,3)/sd(1,2))*sqrt((1-(r(3,2)*r(3,2)))/(n-2)).
compute ryi = r(2:3,1).
compute rii = r(2:3,2:3).
compute bi=inv(rii)*ryi.
compute rsq = t(ryi)*bi.
compute sec=sqrt((1-rsq)/(n-3))*sqrt(1/(1-(r(3,2)*r(3,2)))).
compute sdyzx = (sd(1,1)/sd(1,3))*sec.
compute sdyxz = (sd(1,1)/sd(1,2))*sec.
compute byx = r(2,1)*sd(1,1)/sd(1,2).
compute sebyx = (sd(1,1)/sd(1,2))*sqrt((1-(r(2,1)*r(2,1)))/(n-2)).
compute amx = mndd(1,3)-(bzx*mndd(1,2)).
compute msres = (z-(amx+bzx*x))&*(z-(amx+bzx*x)).
compute msres = csum(msres)/(n-2).
do if (!effsize = 1).
compute eff = make(btnp,5,-999).
compute r2my = r(3,1)*r(3,1).
compute r2xy = r(2,1)*r(2,1).
compute r245 = r2my-(rsq-r2xy).
compute r245o = r245.
compute pm = ind/byx.
compute rm = ind/byxz.
compute abps = ind/sd(1,1).
compute abcs = abps*sd(1,2).
end if.
do if (ovals = 2).
compute sdyzx = selgb(3,1).
compute sdyxz = selgb(2,1).
compute xo = {con,x}.
compute pt1 = make(n,1,0.5).
compute bt1 = make(ncol(xo),1,0).
compute ll1 = 0.
loop jjj = 1 to !iterate.
compute vt1 = mdiag(pt1&*(1-pt1)).
compute byx = bt1+inv(t(xo)*vt1*xo)*t(xo)*(y-pt1).
compute pt1 = 1/(1+exp(-(xo*byx))).
compute LL = y&*ln(pt1)+(1-y)&*ln(1-pt1).
compute LL2 = -2*csum(ll).
do if (abs(LL1-LL2) !converge).
compute vt1 = mdiag(pt1&*(1-pt1)).
compute byx = byx(2,1).
compute covb = inv(t(xo)*vt1*xo).
compute sebyx = sqrt(diag(covb)).
compute sebyx = sebyx(2,1).
break.
end if.
compute bt1 = byx.
compute LL1 = LL2.
end loop.
do if (jjj > !iterate).
compute converre = 1.
compute vt1 = mdiag(pt1&*(1-pt1)).
compute byx = byx(2,1).
compute covb = inv(t(xo)*vt1*xo).
compute sebyx = sqrt(diag(covb)).
compute sebyx = sebyx(2,1).
end if.
end if.
compute seind = sqrt(((byzx*byzx)*(sdbzx*sdbzx))+((bzx*bzx)*(sdyzx*sdyzx))+
((sdbzx*sdbzx)*(sdyzx*sdyzx))).
do if (varord = 1).
compute seind = sqrt(((byzx*byzx)*(sdbzx*sdbzx))+((bzx*bzx)*(sdyzx*sdyzx))).
end if.
compute se = {sebyx; sdbzx; sdyzx; sdyxz}.
compute bb = {byx; bzx; byzx; byxz}.
compute tt = bb&/se.
compute p =2*(1-tcdf(abs(tt),n-2)).
compute p(3,1)=2*(1-tcdf(abs(tt(3,1)),n-3)).
compute p(4,1)=2*(1-tcdf(abs(tt(4,1)),n-3)).
compute tst = ind/seind.
compute bw = {bb,se,tt,p}.
compute p2=2*(1-cdfnorm(abs(tst))).
compute LL95 = ind-1.96*seind.
compute UL95=ind+1.96*seind.
compute byxstd = byx*sqrt(1+(((byzx*byzx)*msres)/((3.14159265*3.14159265)/3))).
compute op={ind, seind, LL95,UL95, tst, p2}.
end if.
do if (!effsize = 1 and !boot > 999 and ovals 2).
compute eff(#j,3) = r245.
compute eff(#j,2) = ind/byxz.
compute eff(#j,1) = ind/cpath.
compute eff(#j,4) = ind/vr(1,1).
compute eff(#j,5) = ind*vr(2,1)/vr(1,1).
end if.
end loop.
/* END OF BOOTSTRAPPING LOOP */.
do if (!boot > 999).
compute res10 = res((2:nrow(res)),1).
save res10/outfile = bootstrp.sav/variables = bootstrp.
end if.
/* COMPUTE MEAN AND STANDARD DEV OF INDIRECT EFFECT ACROSS BOOTSTRAP SAMPLES */.
compute res = res(2:btnp,1).
do if (ovals 2 and !effsize = 1 and !boot > 999).
compute res={res, eff(2:btnp, :)}.
end if.
compute mnbt = csum(res)/btn.
compute se = (sqrt(((btn*cssq(res))-(csum(res)&**2))/((btn-1)*btn))).
/* SORT THE BOOTSTRAP ESTIMATES */.
do if (!boot > 999).
loop #j = 1 to ncol(res).
compute res2 = res(:,#j).
compute res2(GRADE(res(:,#j))) = res(:,#j).
compute res(:,#j) = res2.
end loop.
/* GENERATE BOOTSTRAP CONFIDENCE INTERVAL FOR INDIRECT EFFECT */.
compute lower99 = res(.005*btn,1).
compute lower95 = res(.025*btn,1).
compute upper95 = res(1+.975*btn,1).
compute upper99 = res(1+.995*btn,1).
compute bt = {op(1,1),mnbt(1,1), se(1,1), lower99, lower95, upper95, upper99}.
end if.
/* GENERATE OUTPUT */.
print/title =
"*************************************************************************".
print/title = "Preacher and Hayes (2004) SPSS Macro for Simple Mediation".
print/title = "Written by Andrew F. Hayes, The Ohio State University".
print/title = "http://www.comm.ohio-state.edu/ahayes/".
print/title = "For details, see Preacher, K. J., & Hayes, A. F. (2004). SPSS and SAS".
print/title = "procedures for estimating indirect effects in simple mediation models".
print/title = "Behavior Research Methods, Instruments, and Computers, 36, 717-731.".
compute temp = {"Y"; "X"; "M"}.
compute temp = {temp, t(nms)}.
print temp/title = "VARIABLES IN SIMPLE MEDIATION MODEL"/format = A8.
compute corrx = {t(mndd), std, corrx}.
compute nmsc = {"Mean", "SD", nms}.
print corrx/title = "DESCRIPTIVES STATISTICS AND PEARSON CORRELATIONS"/rnames = nms/cnames
=
nmsc/format = F9.4.
print n/title = "SAMPLE SIZE"/format F8.0.
do if (converre = 0).
print bw/title = "DIRECT AND TOTAL EFFECTS"/clabels = "Coeff" "s.e." "t "
"Sig(two)"/rlabels = "b(YX)" "b(MX)" "b(YM.X)" "b(YX.M)"/format f9.4.
do if (ovals = 2).
print byxstd/title = "TOTAL EFFECT, b(YX), STANDARDIZED TO THE METRIC OF THE DIRECT EFFECT,
"+
"b(YX.M)"/
clabels = "Coeff"/rlabels = "b(YX)"/format = F9.4.
end if.
print op/title = "INDIRECT EFFECT AND SIGNIFICANCE USING NORMAL DISTRIBUTION"/rlabels
= "Effect"/clabels = "Value" "s.e." "LL95CI" "UL95CI" "Z" "Sig(two)"/format f9.4.
do if (!boot > 999 and converrb = 0).
print bt/title = "BOOTSTRAP RESULTS FOR INDIRECT EFFECT"/rlabels ="Effect"/clabels
"Data" "Mean" "s.e." "LL99 CI" "LL95CI" "UL95CI" "UL99CI"/format f9.4.
print btn/title = "NUMBER OF BOOTSTRAP RESAMPLES"/format F8.0.
end if.
do if (ovals 2 and !effsize = 1).
compute effsz = {op(1,1); pm; rm; r245o; abps; abcs}.
do if (!boot > 999).
compute effsz = {effsz, t(mnbt), t(se)}.
compute cimat = make(6,4,-999).
loop #j = 1 to 6.
compute cimat(#j,1) = res((.005*btn),#j).
compute cimat(#j,2) = res((.025*btn),#j).
compute cimat(#j,3) = res((1+.975*btn),#j).
compute cimat(#j,4) = res((1+.995*btn),#j).
end loop.
compute effsz = {effsz, cimat}.
end if.
print effsz/title = "POINT AND INTERVAL ESTIMATES OF EFFECT SIZE FOR INDIRECT
EFFECT"/rlabels =
"ab" "P_m" "R_m" "R2_45" "ab_ps" "ab_cs"
/clabels = "Data" "Mean" "s.e." "LL99CI" "LL95CI" "UL95CI" "UL99CI"/format F9.4.
end if.
end if.
print/title = "********************************* NOTES
**********************************".
do if (ovals = 2 and converre = 0).
print/title = "Model coefficients involving the binary outcome are logistic regression "+
"coefficients.".
compute nm = {nms(1,1), "Analysis"}.
print rcd/title = "Coding of binary Y for analysis:"/cnames = nm/format = F9.2.
do if (ovals = 2 and !effsize = 1).
print /title = "Effect size estimates not available for models with dichotomous
outcomes.".
end if.
end if.
do if (converre = 1).
print/title = "Convergence error in estimation of binary logistic model. Try adjusting
"+
"iteration criteria.".
end if.
do if (converrb = 1 and converre 1 and !boot > 0).
print/title = "At least one convergence failure while bootstrapping, so bootstrap output
is "+
"suppressed.".
end if.
do if (bdbp > 0).
print bdbp/title = "Bootstrap samples replaced due to singularity or constants after
resampling:".
end if.
end if.
do if (daterr = 1).
print/title = "ERROR: Insufficient data for analysis. There are too few cases in your data
file.".
else if (daterr = 2).
print/title = "ERROR: One of the variables in the model is constant.".
else if (daterr = 3).
print/title = "ERROR: M is a perfect function of X".
else if (daterr = 4).
print/title = "ERROR: There is no variance in M, or M is dichotomous".
end if.
do if (!boot 1000 and daterr = 0).
print/title = "Bootstrap confidence intervals are preferred to the Sobel test for inference
"+
"about indirect effects.".
print/title = "See Hayes, A.F.(2009). Beyond Baron and Kenny: Statistical mediation analysis
in "+
"the new millennium.".
print/title = "Communication Monographs, 76, 408-420.".
end if.
END MATRIX.
!ENDDEFINE.
sobel y = NVPF/x = SOF/m = NRAF/effsize = 0/boot = 0/varord = 2.
Matrix
Notes
Output Created
08-APR-2021 23:23:22
Comments
Input
Data
S:\Users\Arlish
Kahf\Downloads\cfa_C_1.sav
Active Dataset
DataSet1
Filter
<none>
Weight
<none>
Split File
<none>
N of Rows in Working Data
File
202
Syntax
MATRIX.
get dd/variables = NVPF SOF NRAF
/names = nms/MISSING = OMIT.
compute n = nrow(dd).
compute converrb = 0.
compute converre = 0.
compute daterr = 0.
do if (n < 5).
compute daterr = 1.
end if.
compute ones = make(n,1,1).
compute sigma =
(t(dd)*(ident(n)-(1/n)*ones*t(ones))*dd)*
(1/(n-1)).
compute var = diag(sigma).
do if csum ((abs(var < 0.000000001)) >
0).
compute daterr = 2.
end if.
do if (rank(dd(:,2:3)) < 2).
compute daterr = 3.
end if.
compute mvals = ncol(design(dd(:,3))).
do if (mvals < 3).
compute daterr = 4.
end if.
compute bdbp = 0.
compute corrxtt = 0.
compute sdchkt = 0.
do if (daterr = 0).
compute mndd = csum(dd)/n.
compute std = sqrt(var).
compute vr = sqrt(var).
compute corrx = sigma&/(std*t(std)).
compute cpath =
corrx(2,1)*vr(1,1)/vr(2,1).
do if ( 0 > 999).
compute btn = trunc( 0 /1000)*1000.
compute btnp = btn+1.
else.
compute btn = 1000.
compute btnp = btn+1.
end if.
compute res=make(btnp,1,0).
compute varord = 2.
Resources
Processor Time
00:00:00.23
Elapsed Time
00:00:00.23
Run MATRIX procedure:
Error # 34 in column 22. Text: bootstrp.sav
SPSS Statistics cannot access a file with the given file specification. The
file specification is either syntactically invalid, specifies an invalid
drive, specifies a protected directory, specifies a protected file, or
specifies a non-sharable file.
Execution of this command stops.
*************************************************************************
Preacher and Hayes (2004) SPSS Macro for Simple Mediation
Written by Andrew F. Hayes, The Ohio State University
http://www.comm.ohio-state.edu/ahayes/
For details, see Preacher, K. J., & Hayes, A. F. (2004). SPSS and SAS
procedures for estimating indirect effects in simple mediation models
Behavior Research Methods, Instruments, and Computers, 36, 717-731.
VARIABLES
Y
X
M
IN SIMPLE MEDIATION MODEL
NVPF
SOF
NRAF
DESCRIPTIVES STATISTICS AND PEARSON CORRELATIONS
Mean
SD
NVPF
SOF
NRAF
NVPF
.9072
.1516
1.0000
.1067
.2796
SOF
3.8243
.8542
.1067
1.0000
.0997
NRAF
4.8696
.7659
.2796
.0997
1.0000
SAMPLE SIZE
202
DIRECT AND TOTAL EFFECTS
Coeff
s.e.
b(YX)
.0189
.0125
b(MX)
.0894
.0631
b(YM.X)
.0538
.0135
b(YX.M)
.0141
.0121
t
1.5176
1.4165
3.9848
1.1681
Sig(two)
.1307
.1582
.0001
.2442
INDIRECT EFFECT AND SIGNIFICANCE USING NORMAL DISTRIBUTION
Value
s.e.
LL95CI
UL95CI
Z Sig(two)
Effect
.0048
.0037
-.0024
.0121
1.2988
.1940
********************************* NOTES **********************************
Bootstrap confidence intervals are preferred to the Sobel test for inference about
indirect effects.
See Hayes, A.F.(2009). Beyond Baron and Kenny: Statistical mediation analysis in
the new millennium.
Communication Monographs, 76, 408-420.
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