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Path Analysis Path Analysis

Path Analysis - PowerPoint Presentation

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Path Analysis - PPT Presentation

SPSSAMOS Theory of Planned Behavior ZeroOrder Correlations PATH INGRAMsav data file from SPSS data page   Attitude SubNorm PBC Intent Behavior Attitude 1000 472 665 ID: 533720

path click icon 000 click path 000 icon draw model variable mouse diagram amos attitude object analysis behavior button

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Slide1

Path AnalysisSPSS/AMOSSlide2

Theory of Planned BehaviorSlide3

Zero-Order CorrelationsPATH-INGRAM.sav

data file from

SPSS

data

page.

 

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

1.000Slide4

SPSS RegThe path coefficients can be obtained by a series of multiple regressions.

Behavior = Intention, PBC

Intention = Attitude,

SubNorm

, PBCSlide5

Predicting Behavior

 

Beta

t

Sig.

(Constant)

 

-

1.089

.281

Intent

.350

2.894

.005

PBC

.336

2.781

.007Slide6

Predicting Intention

 

Beta

t

Sig.

(Constant)

 

2.137

.037

Attitude

.807

6.966

.000

SubNorm

.095

.946

.348

PBC

-.126

-1.069

.290Slide7

Path DiagramSlide8

AMOS GraphicsClick Analyze, IBM SPSS AMOS. In the AMOS window which will open click File, New:Slide9

AMOSThe following slides illustrate doing path analysis with AMOS.But students at ECU do not have access to AMOS.

So I am going

to stop here.Slide10
Slide11

Draw That Path DiagramClick on the “Draw observed variables” icon which I have circled on the image two slides above.

Move the cursor over into the drawing space on the right

.

Hold down the left mouse button while you move the cursor to draw a

rectangle. Release the mouse button.Slide12

IconsSlide13

Duplicate IconsDraw one rectangle.

Now

click the Duplicate Objects icon, boxed in black

on the slide above

Point

at that rectangle, hold down the left mouse button while you move to the desired location for the second rectangle, and release the mouse button.Slide14

Altering/Moving ObjectsChange the Shape of Objects

Click the icon

Click the object and move the mouse.

Move Objects

Click the icon Click the object and move the mouseSlide15

Set Object PropertiesClick on the “List variables in data set” icon.

Drag

and drop variable names to the

boxes.

To view/edit object properties, right-click the object and select

Object Properties Slide16
Slide17

Draw PathsClick on the “Draw paths” icon.

Draw

a path from Attitude to Intent (hold down the left mouse button at the point you wish to start the path and then drag it to the ending point and release the mouse button

).

The borders of the objects being connected will change color when selected.Slide18

Draw CovariancesClick on the “Draw

Covariances

icon.

Draw

a covariance from SubNorm

to

Attitude.

Use

the “Change the shape of objects” tool

to

increase or decrease the arc of these

covariances

. Slide19

Adding An Unique VariableClick on the “Add a unique variable to an existing variable” icon.

Move

the cursor over the Intent variable and click the left mouse button to add the error variable

.

Right-click the error circle leading to Intent, select Object Properties, and name the variable “e1.”Slide20

Analysis Properties Click the “Analysis properties” icon -- to display the Analysis Properties window. Select the Output tab and ask for the output shown below.Slide21
Slide22
Slide23

Conduct the AnalysisFinish drawing the path diagram (illustrated earlier) and then Click on the “Calculate estimates”

icon.

In the “Save As” window browse to the desired folder and give the file a name

.

Click

Save.Slide24

One or More Variables Not NamedYou may get this error even when every variable in the model is named.

In my experience, you might as well start over from scratch at this point.

Suggested curses can be found at

http://www.vnutz.com/curse_and_swearSlide25

OK, Stop CussingIn BlackBoard, go to Documents, Structural Equation Modeling & Path

Analysis, Path Analysis Files.

Download the files.

Open Path-

Ingram.sav

in SPSS.Analyze

,

AMOS

File, Open, Path-

Ingram.amw

Calculate EstimatesSlide26

View the Output Path DiagramClick the icon outlined in red below.The one to the left will display the input path diagram.Slide27

Standardize the CoefficientsClick “Standardized estimates.”Slide28
Slide29

Export the Path DiagramClick the “Copy the path diagram to the clipboard icon. Open a Word document or photo editor and paste in the path diagram

.Slide30

View the Output DetailsClick the “View text” icon.Slide31

Export the DetailsThe Copy to Clipboard icon (green dot, above) can be used to copy the output to another document via the clipboard

.Slide32

2 Output

Chi-square = .847

Degrees of freedom = 2

Probability level = .655

The null here is that our model fits the data just as well as a saturated model (one with every variable connected to every other variable).Slide33

R2

Variable

 

 

 

Estimate

Intent

 

 

.600

Behavior

 

 

.343

These are for Intention predicted from Attitude, Subjective Norms, and Perceived Behavioral Control, and

Behavior predicted from Intention and Perceived Behavioral Control.Slide34

Standardized Direct Effects

 

SubNorm

PBC

Attitude

Intent

Intent

.095

-.126

.807

.000

Behavior

.000

.336

.000

.350

These are all shown in the path diagram.Slide35

Standardized Indirect Effects

 

SubNorm

PBC

Attitude

Intent

Intent

.000

.000

.000

.000

Behavior

.033

-.044

.282

.000

These are products of coefficients. For example, Attitude to Behavior is .81(.35) = .28.Slide36

Goodness of Fit IndicesGFI = .994. This tells

you what proportion of the variance in the sample variance-covariance matrix is accounted for by the

model.

This

should exceed .9 for a good model

.For the saturated model it will be a perfect 1.

Slide37

The Normed Fit Index (NFI)NFI = .994. .9 or higher is good.Compares our model to the independence model (a model with no paths or covariances

)

The Comparative Fit Index (CFI

) is similar, and good with smaller samples.

CFI = 1.000 Slide38

Root Mean Square Error of ApproximationEstimates lack of fit compared to the saturated model.

RMSEA

of .05 or less indicates good fit, and .08 or less adequate fit

.

RMSEA here is .000.