Conducting a Path Analysis with SAS Proc Calis PDF

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Conducting a Path Analysis with SAS Proc Calis...

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Conducting a Path Analysis with SAS Proc Calis We shall use the data from the thesis published in this article: Ingram, K. L., Cope, J. G., Harju, B. L., & Wuensch, K. L. (2000). Applying to graduate school: A test of the theory of planned behavior. Journal of Social Behavior and Personality, 15, 215-226. The path model is:

Copy and paste the program boxed below into the SAS editor and then run it. 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 Proc Calis PRINT; LINEQS Intent = b1 Attitude + b2 SubNorm + b3 PBC + E1, Behavior = b4 Intent + b5 PBC + E2; STD E1-E2 = V1-V2; run;

Explanation of the program. Rather than input the raw data, I have used correlation matrix input. The PRINT option in Proc Calis adds to the default output the total effects matrix (and some other things). To learn more about what the output options are for Proc Calis, click the help icon in SAS, select the “Index” tab, enter keyword “calis,” and click “Display.” Click “Syntax” and then “Proc Calis.” On the command bar, click “Edit,” “Find in this topic.” Enter “Displayed Output Options” and click “Next” twice.

Following the LINEQS statement the model is defined. The first equation indicates that 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. The second equation indicates that Behavior has paths to it from Intent, PBC, and E2. SAS assumes that the exogenous variables (Attitude, SubNorm, and PBC) are correlated. The STD statement asks that the error terms be estimated as parameters V1 and V2. The Output The Standardized Path Coefficients Standardized Results for Linear Equations Intent = Behavior =

0.8061 * Attitude + 0.0939 * SubNorm + -0.1255 * PBC + 1.0000 0.3491 * Intent + 0.3361 * PBC + 1.0000 E2

The Standardized Error Coefficients Standardized Results for Variances of Exogenous Variables Variable Standard Variable Parameter Estimate Type Error t Value Error

E1

V1

0.40058

0.08075 4.96056

E2

V2

0.65770

0.10019 6.56441

Standardized Coefficients Among Exogenous Variables Standardized Results for Covariances Among Exogenous Variables Estimate

Standard Error

t Value

_Add4

0.47200

0.10118

4.66473

Attitude

_Add5

0.66500

0.07262

9.15775

SubNorm

_Add6

0.50500

0.09699

5.20686

Var1

Var2

Parameter

SubNorm

Attitude

PBC PBC

E1

Standardized Direct, Indirect, and Total Effects Standardized Direct Effects Effect / Std Error / t Value / p Value Intent Attitude Behavior

PBC SubNorm

0.3491 0.1137 3.0693 0.002145

0

0.3361 0.1142 2.9445 0.003234

0

0

0.8061 0.0919 8.7690 .05, you conclude that the fit is good. Incremental Indices. These compare your model to an independence model (a model where all of the path coefficients are zero). The bigger the better. Bentler Comparative Fit Index 1.0000

A value below .9 is considered poor, .9 is considered marginal, .95 is good.

Bentler-Bonett Normed Fit Index 0.9936

A value below .9 is considered poor, .9 is considered marginal, .95 is good. Bentler-Bonett Non-normed Index 1.0461

A value below .9 is considered poor, .9 is considered marginal, .95 is good. If the value exceeds one, reduce it to one.

Links • Wuensch’s Stats Lessons • •

An Introduction to Path Analysis Adventures in Path Analysis

•

David Kenney on Fit Indices

Karl L. Wuensch Dept. of Psychology, East Carolina University, Greenville, NC 27858 USA April, 2016...