Path AnalysisSAS/Calis

Theory of Planned Behavior

Note that this model is not saturated.

Zero-Order Correlations 

Attitude SubNorm PBC Intent Behavior

Attitude 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 Dataoptions 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 60MEAN . 32.02 45.71 40.25 16.92 43.92STD . 6.96 12.32 7.62 3.83 16.66CORR Attitude 1 .472 .665 .767 .525CORR Subnorm .472 1 .505 .411 .379CORR PBC .665 .505 1 .458 .496CORR Intent .767 .411 .458 1 .503CORR Behavior .525 .379 .496 .503 1

Conduct the AnalysisProc Calis PRINT;

• PRINT adds to the default output the total effects matrix (and some other things)

Linear EquationsLINEQSIntent = 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 EquationsBehavior = 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 EquationsSTD 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 * SubNorm + -0.1255 * PBC

Behavior = 0.3491 * Intent + 0.3361 * PBC        

Standardized Error Coefficients

Variable Parameter EstimateE1 V1 0.40058E2 V2 0.65770

Standardized Coefficients Among Exogenous Variables

Var1 Var2 EstimateSubNorm Attitude 0.47200PBC Attitude 0.66500PBC SubNorm 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