Path Analysis
SAS/Calis
Theory of Planned Behavior
Note that this model is not
saturated.
Zero-Order Correlations
Attitude SubNorm
Attitude
PBC
Intent
Behavior
1.000
.472
.665
.767
.525
SubNorm
.472
1.000
.505
.411
.379
PBC
.665
.505
1.000
.458
.496
Intent
.767
.411
.458
1.000
.503
Behavior
.525
.379
.496
.503
1.000
Read in the Data
options formdlim='-' nodate pagno=min;
TITLE 'Path Analysis, Ingram Data' ;
data Ingram(type=corr);
INPUT _TYPE_ $ _NAME_ $ Attitude
SubNorm PBC Intent Behavior;
CARDS;
N . 60 60 60 60 60
MEAN . 32.02 45.71 40.25 16.92 43.92
STD . 6.96 12.32 7.62 3.83 16.66
CORR Attitude
1 .472 .665 .767 .525
CORR Subnorm .472 1 .505 .411 .379
CORR PBC
.665 .505 1 .458 .496
CORR Intent
.767 .411 .458 1 .503
CORR Behavior
.525 .379 .496 .503 1
Conduct the Analysis
Proc Calis PRINT;
• PRINT adds to the default output the total
effects matrix (and some other things)
Linear Equations
LINEQS
Intent = b1 Attitude + b2 SubNorm + b3
PBC + E1,
• Intent has paths to it from Attitude,
SubNorm, PBC, and E1 (the error term)
• b1, b2, and b3 are the path coefficients
that we want SAS to estimate for us
Linear Equations
Behavior = b4 Intent + b5 PBC + E2;
• Behavior has paths to it from Intent, PBC,
and E2.
• SAS assumes that the exogenous
variables (Attitude, SubNorm, and PBC)
are correlated.
Linear Equations
STD E1-E2 = V1-V2;
run;
• The error terms be estimated as
parameters V1 and V2.
The Output
• The complete output is available online.
• Here I shall present the key parts of the
output.
The Path Coefficients
Standardized Results for Linear Equations
Intent
= 0.8061 * Attitude + 0.0939
Behavior = 0.3491 * Intent
+ 0.3361
* SubNorm + -0.1255 * PBC
* PBC
Standardized Error Coefficients
Variable
E1
E2
Parameter
V1
V2
Estimate
0.40058
0.65770
Standardized Coefficients
Among Exogenous Variables
Var1
SubNorm
PBC
PBC
Var2
Attitude
Attitude
SubNorm
Estimate
0.47200
0.66500
0.50500
Fit Summary
Chi-Square
0.8564
Chi-Square DF
2
above .05 is good
Pr > Chi-Square
0.6517
below .08 is good
Standardized RMSR (SRMSR) 0.0191
above .95 is good
Goodness of Fit Index (GFI)
0.9943
below .01 is excellent
RMSEA Estimate
0.0000
above .05 is good
Probability of Close Fit
0.6905
above .95 is good
Bentler Comparative Fit Index
1.0000
above .95 is good
Bentler-Bonett NFI
0.9936