Hướng dẫn sử dụng AMOS toàn tập

Hướng dẫn sử dụng AMOS toàn tập

James L Arbuckle

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Amos 6.0 User’s Guide

Copyright © 1995–2005 by Amos Development Corporation

All rights reserved.

Printed in the United States of America.

No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without the prior written permission of the publisher.

Part I: Getting Started

Featured Methods 2

About the Tutorial 3

About the Examples 3

About the Documentation 4

Other Sources of Information 4

Acknowledgements 5

2 New Features 7 Bayesian Estimation 7

Data Imputation 7

Print Preview for Path Diagrams 8

Improved Zooming and Scrolling 9

Drawing Path Diagrams 10

Copying Path Diagrams 10

Multiple Path Diagrams 10

Incompatibilities with Amos 5.0 11

Other Changes between Amos 5.0 and Amos 6.0 11

Introduction 13

About the Data 14

Launching Amos Graphics 15

Creating a New Model 16

Specifying the Data File 17

Specifying the Model and Drawing Variables 17

Naming the Variables 18

Drawing Arrows 19

Constraining a Parameter 20

Altering the Appearance of a Path Diagram 21

Setting Up Optional Output 22

Performing the Analysis 23

Viewing Output 24

Printing the Path Diagram 26

Copying the Path Diagram 27

Copying Text Output 27

Part II: Examples 1 Estimating Variances and Covariances 29 Introduction 29

About the Data 29

Bringing In the Data 30

Analyzing the Data 31

Viewing Graphics Output 34

Viewing Text Output 35

Modeling in C# 45

Other Program Development Tools 46

2 Testing Hypotheses 47 Introduction 47

About the Data .47

Parameters Constraints .47

Moving and Formatting Objects 51

Data Input .52

Optional Output 54

Labeling Output 57

Hypothesis Testing 58

Displaying Chi-Square Statistics on the Path Diagram 59

Modeling in VB.NET .61

3 More Hypothesis Testing 65 Introduction 65

About the Data .65

Bringing In the Data .65

Testing a Hypothesis That Two Variables Are Uncorrelated .66

Specifying the Model 66

Viewing Text Output .68

Viewing Graphics Output 69

Modeling in VB.NET .71

About the Data 73

Analysis of the Data 74

Specifying the Model 75

Identification 76

Fixing Regression Weights 77

Viewing the Text Output 78

Viewing Graphics Output 81

Viewing Additional Text Output 82

Modeling in VB.NET 83

5 Unobserved Variables 87 Introduction 87

About the Data 87

Model A 89

Measurement Model 89

Structural Model 90

Identification 91

Specifying the Model 91

Results for Model A 96

Model B 99

Results for Model B 100

Testing Model B against Model A 102

Modeling in VB.NET 104

About the Data 107

Model A for the Wheaton Data 108

Model B for the Wheaton Data 113

Model C for the Wheaton Data 120

Multiple Models in a Single Analysis 122

Output from Multiple Models 125

Modeling in VB.NET 129

7 A Nonrecursive Model 135 Introduction 135

About the Data 135

Felson and Bohrnstedt’s Model 136

Model Identification 137

Results of the Analysis 137

Modeling in VB.NET 142

8 Factor Analysis 143 Introduction 143

About the Data 143

A Common Factor Model 144

Identification 145

Specifying the Model 146

Results of the Analysis 147

Modeling in VB.NET 150

Analysis of Covariance and Its Alternative 151

About the Data 152

Analysis of Covariance 153

Model A for the Olsson Data 153

Identification 154

Specifying Model A 155

Results for Model A 155

Searching for a Better Model 155

Model B for the Olsson Data 156

Results for Model B 157

Model C for the Olsson Data 159

Results for Model C 160

Fitting All Models At Once 160

Modeling in VB.NET 161

10 Simultaneous Analysis of Several Groups 165 Introduction 165

Analysis of Several Groups 165

About the Data 166

Model A 166

Model B 174

Modeling in VB.NET 177

Felson and Bohrnstedt’s Model 181

About the Data 181

Specifying Model A for Girls and Boys 182

Text Output for Model A 185

Graphics Output for Model A 187

Model B for Girls and Boys 188

Results for Model B 190

Fitting Models A and B in a Single Analysis 194

Model C for Girls and Boys 194

Results for Model C 197

Modeling in VB.NET 198

12 Simultaneous Factor Analysis for Several Groups 201 Introduction 201

About the Data 201

Model A for the Holzinger and Swineford Boys and Girls 202

Results for Model A 204

Model B for the Holzinger and Swineford Boys and Girls 206

Results for Model B 208

Modeling in VB.NET 212

Introduction 215

Means and Intercept Modeling 215

About the Data 216

Model A for Young and Old Subjects 216

Mean Structure Modeling in Amos Graphics 216

Results for Model A 218

Model B for Young and Old Subjects 220

Results for Model B 222

Comparison of Model B with Model A 222

Multiple Model Input 222

Mean Structure Modeling in VB.NET 223

14 Regression with an Explicit Intercept 227 Introduction 227

Assumptions Made by Amos 227

About the Data 228

Specifying the Model 228

Results of the Analysis 229

Modeling in VB.NET 231

15 Factor Analysis with Structured Means 235 Introduction 235

Factor Means 235

About the Data 236

Model B for Boys and Girls 241

Results for Model B 243

Comparing Models A and B 243

Modeling in VB.NET 244

16 Sörbom’s Alternative to Analysis of Covariance 247 Introduction 247

Assumptions 247

About the Data 248

Changing the Default Behavior 249

Model A 249

Results for Model A 251

Model B 253

Results for Model B 255

Model C 256

Results for Model C 257

Model D 258

Results for Model D 259

Model E 261

Results for Model E 261

Fitting Models A Through E in a Single Analysis 261

Comparison of Sörbom’s Method with the Method of Example 9 262

Model X 262

Modeling in Amos Graphics 262

Results for Model X 263

Results for Model Z 267

Modeling in VB.NET 268

17 Missing Data 275 Introduction 275

Incomplete Data 275

About the Data 276

Specifying the Model 277

Saturated and Independence Models 278

Results of the Analysis 279

Modeling in VB.NET 281

18 More about Missing Data 289 Introduction 289

Missing Data 289

About the Data 290

Model A 291

Results for Model A 293

Model B 296

Output from Models A and B 297

Modeling in VB.NET 298

The Bootstrap Method 301

About the Data 302

A Factor Analysis Model 302

Monitoring the Progress of the Bootstrap 303

Results of the Analysis 303

Modeling in VB.NET 307

20 Bootstrapping for Model Comparison 309 Introduction 309

Bootstrap Approach to Model Comparison 309

About the Data 310

Five Models 310

Summary 316

Modeling in VB.NET 316

21 Bootstrapping to Compare Estimation Methods 317 Introduction 317

Estimation Methods 317

About the Data 318

About the Model 318

Modeling in VB.NET 324

About the Data 325

About the Model 325

Specification Search with Few Optional Arrows 326

Specification Search with Many Optional Arrows 351

Limitations 355

23 Exploratory Factor Analysis by Specification Search 357 Introduction 357

About the Data 357

About the Model 357

Specifying the Model 358

Opening the Specification Search Window 358

Making All Regression Weights Optional 359

Setting Options to Their Defaults 359

Performing the Specification Search 361

Using BCC to Compare Models 362

Viewing the Scree Plot 365

Viewing the Short List of Models 365

Heuristic Specification Search 366

Performing a Stepwise Search 367

Viewing the Scree Plot 368

Limitations of Heuristic Specification Searches 369

About the Data 371

Model 24a: Modeling Without Means and Intercepts 371

Customizing the Analysis 377

Model 24b: Comparing Factor Means 378

25 Multiple-Group Analysis 385 Introduction 385

About the Data 385

About the Model 385

Specifying the Model 386

Constraining the Latent Variable Means and Intercepts 386

Generating Cross-Group Constraints 387

Fitting the Models 389

Viewing the Text Output 389

Examining the Modification Indices 390

26 Bayesian Estimation 393 Introduction 393

Bayesian Estimation 393

Results of Maximum Likelihood Analysis 397

Bayesian Analysis 398

Replicating Bayesian Analysis and Data Imputation Results 400

Assessing Convergence 404

Diagnostic Plots 406

Introduction 417

About the Example 417

More about Bayesian Estimation 417

Bayesian Analysis and Improper Solutions 418

About the Data 418

Fitting a Model by Maximum Likelihood 419

Bayesian Estimation with a Non-Informative (Diffuse) Prior 420

28 Bayesian Estimation of Values Other Than Model Parameters 431 Introduction 431

About the Example 431

The Wheaton Data Revisited 431

Indirect Effects 432

Bayesian Analysis of Model C 435

Additional Estimands 436

Inferences about Indirect Effects 439

Introduction 445

About the Example 445

The Stability of Alienation Model 445

Numeric Custom Estimands 451

Dichotomous Custom Estimands 465

30 Data Imputation 469 Introduction 469

About the Example 469

Multiple Imputation 470

Model-Based Imputation 470

Performing Multiple Data Imputation Using Amos Graphics 470

31 Analyzing Multiply Imputed Data Sets 477 Introduction 477

Analyzing the Imputed Data Files Using Amos Graphics 477

Step 2: Ten Separate Analyses 478

Step 3: Combining Results of Multiply Imputed Data Files 479

Further Reading 481

Measures of Parsimony 490

Minimum Sample Discrepancy Function 491

Measures Based On the Population Discrepancy 494

Information-Theoretic Measures 497

Comparisons to a Baseline Model 500

Parsimony Adjusted Measures 504

GFI and Related Measures 505

Miscellaneous Measures 507

Selected List of Fit Measures 509

includes, as special cases, many well-known conventional techniques, including the general linear model and common factor analysis.

Amos (Analysis of Moment Structures) is an easy-to-use program for visual SEM

With Amos, you can quickly specify, view, and modify your model graphically

using simple drawing tools Then you can assess your model’s fit, make any

modifications, and print out a publication-quality graphic of your final model

Simply specify the model graphically (left) Amos quickly performs the

computations and displays the results (right)

.70 65

.74

.88 83

.84 49

Chi-square = 7.853 (8 df)

p = 448

Output:

Structural equation modeling (SEM) is sometimes thought of as esoteric and difficult

to learn and use This is incorrect Indeed, the growing importance of SEM in data analysis is largely due to its ease of use SEM opens the door for nonstatisticians to solve estimation and hypothesis testing problems that once would have required the services of a specialist

Amos was originally designed as a tool for teaching this powerful and

fundamentally simple method For this reason, every effort was made to see that it is easy to use Amos integrates an easy-to-use graphical interface with an advanced computing engine for SEM The publication-quality path diagrams of Amos provide a clear representation of models for students and fellow researchers The numeric methods implemented in Amos are among the most effective and reliable available

Featured Methods

Amos provides the following methods for estimating structural equation models:

„ Unweighted least squares

„ Generalized least squares

„ Browne’s asymptotically distribution-free criterion

„ Scale-free least squares

Amos goes well beyond the usual capabilities found in other structural equation modeling programs When confronted with missing data, Amos performs

state-of-the-art estimation by full information maximum likelihood instead of relying

on ad-hoc methods like listwise or pairwise deletion, or mean imputation The program can analyze data from several populations at once It can also estimate means for exogenous variables and intercepts in regression equations

The program makes bootstrapped standard errors and confidence intervals available for all parameter estimates, effect estimates, sample means, variances, covariances, and correlations It also implements percentile intervals and bias-corrected percentile intervals (Stine, 1989), as well as Bollen and Stine’s (1992) bootstrap approach to model testing

Multiple models can be fitted in a single analysis Amos examines every pair of models in which one model can be obtained by placing restrictions on the parameters

of the other The program reports several statistics appropriate for comparing such

models It provides a test of univariate normality for each observed variable as well as

a test of multivariate normality and attempts to detect outliers

Amos accepts a path diagram as a model specification and displays parameter estimates graphically on a path diagram Path diagrams used for model specification and those that display parameter estimates are of presentation quality They can be printed directly or imported into other applications such as word processors, desktop publishing programs, and general-purpose graphics programs

About the Tutorial

The tutorial is designed to get you up and running with Amos Graphics It covers some

of the basic functions and features and guides you through your first Amos analysis Once you have worked through the tutorial, you can learn about more advanced functions using the online Help, or you can continue working through the examples to get a more extended introduction to structural modeling with Amos

About the Examples

Many people like to learn by doing Knowing this, we have developed 31 examples that quickly demonstrate practical ways to use Amos The initial examples introduce the basic capabilities of Amos as applied to simple problems You learn which buttons to click, how to access the several supported data formats, and how to maneuver through the output Later examples tackle more advanced modeling problems and are less concerned with program interface issues

Examples 1 through 4 show how you can use Amos to do some conventional analyses—analyses that could be done using a standard statistics package These examples show a new approach to some familiar problems while also demonstrating all of the basic features of Amos There are sometimes good reasons for using Amos

to do something simple, like estimating a mean or correlation or testing the hypothesis that two means are equal For one thing, you might want to take advantage of the ability

of Amos to handle missing data Or maybe you want to use the bootstrapping capability

of Amos, particularly to obtain confidence intervals

Examples 5 through 8 illustrate the basic techniques that are commonly used nowadays in structural modeling

Example 9 and those that follow demonstrate advanced techniques that have so far not been used as much as they deserve These techniques include:

„ Simultaneous analysis of data from several different populations

„ Estimation of means and intercepts in regression equations

„ Maximum likelihood estimation in the presence of missing data

„ Bootstrapping to obtain estimated standard errors Amos makes these techniques especially easy to use, and we hope that they will become more commonplace

Tip: If you have questions about a particular Amos feature, you can always refer to the extensive online Help provided by the program

About the Documentation

Amos 6.0 comes with extensive documentation, including an online Help system, this user’s guide, and advanced reference material for Amos Basic and the Amos API (Application Programming Interface) If you performed a typical installation, you can

find the Amos 6.0 Programming Reference Guide in the following location:

C:\Program Files\Amos 6\Documentation\Programming Reference.pdf.

Other Sources of Information

Although this user’s guide contains a good bit of expository material, it is not by any means a complete guide to the correct and effective use of structural modeling Many excellent SEM textbooks are available

„ Structural Equation Modeling: A Multidisciplinary Journal contains

methodological articles as well as applications of structural modeling It is published by:

Lawrence Erlbaum Associates, Inc

Journal Subscription Department

10 Industrial Avenue

Mahwah, NJ 07430-2262 USA

www.erlbaum.com

„ Carl Ferguson and Edward Rigdon established an electronic mailing list called

Semnet to provide a forum for discussions related to structural modeling You can

find information about subscribing to Semnet at

www.gsu.edu/~mkteer/semnet.html.

„ Edward Rigdon also maintains a list of frequently asked questions about structural

equation modeling That FAQ is located at www.gsu.edu/~mkteer/semfaq.html.

Acknowledgements

Tor Neilands wrote the new material for this edition of the user’s guide and provided suggestions and bug reports as he did for previous Amos versions Joseph Schafer reviewed portions of the manuscript and added significant passages, as well as providing bug reports John Raz performed testing Pat O’Neil edited this book.Numerous users of preliminary versions of the program provided valuable

feedback, including Stephen J Aragon, Chris Burant, David Burns, Mark A

Davenport, Kristen diNovi, Akihiro Inoue, Yutaka Kano, Kyle Kercher, Morton Kleban, Sik-Yum Lee, Michelle Little, Sheela Pandey, Rachel Pruchno, and Shu Zou

A last word of warning: While Amos Development Corporation and SPSS have engaged in extensive program testing to ensure that Amos operates correctly, all complicated software, Amos included, is bound to contain some undetected bugs We are committed to correcting any program errors If you believe you have encountered one, please report it to the SPSS technical support staff

James L ArbuckleAmbler, Pennsylvania

„ Explicit incorporation of any available prior information or insight about model parameters.

„ Superior performance in small samples (Lee and Song, 2004)

„ Avoidance of inadmissible model parameter values (for example, negative variances) through the choice of an appropriate prior distribution

„ Estimation and hypothesis testing for any user-specified function of the model parameters

Data Imputation

Amos 6.0 provides three methods of data imputation

„ In regression imputation, the model is initially fitted using maximum likelihood

After setting the model parameters equal to their maximum likelihood estimates, linear regression is used to predict the unobserved values for each case as a linear combination of the observed values for that same case Predicted values from the regression equations are then plugged in for the missing values

„ Stochastic regression imputation imputes values for each case by drawing, at

random, from the conditional distribution of the missing values given the observed values, with the unknown model parameters fixed at their maximum-likelihood estimates

„ Bayesian imputation is like stochastic regression imputation except that it takes

into account the fact that the parameter values are only estimated and not known.Latent variables do not have a special status in any of the three imputation methods A latent variable is treated as an extreme case of missing data in which every observation

on the variable is missing Data files containing imputed values may be saved for subsequent analyses by Amos or any other statistical analysis programs

Print Preview for Path Diagrams

 To display the Print dialog box, choose File → Print

 Click the Preview button to see how the path diagram will appear when printed

Here we show the Print preview window for Example 26

Across the top of the Print preview window are, from left to right, icons to:

„ Print the document

„ Zoom in on a section of the output

„ Display from one to six pages of output

Improved Zooming and Scrolling

Amos 6.0 allows you to use the mouse wheel to zoom in on objects in a path diagram Besides the old method for scrolling the path diagram, you can now use the scrollbars that appear when the path diagram extends beyond the Amos Graphics window.The loupe (magnifying glass) tool has been improved You can now use the mouse wheel to adjust the magnification of the loupe

Drawing Path Diagrams

It is now possible to create variables in path diagrams with a single mouse click One click draws a rectangle or ellipse that is identical to the one most recently drawn There are also enhanced pop-up menus for drawing path diagrams

Copying Path Diagrams

In Amos 6.0, it is possible to copy selected portions of a path diagram to the Clipboard The Amos default is to copy the entire path diagram to the Clipboard

To Copy the Entire Path Diagram

To copy the entire path diagram, do one of the following without making any

selections:

„ From the menus, choose Edit → Copy

„ Press Ctrl+C on the keyboard

To Copy Selected Path Diagram Objects Only

 Select the objects that you want to copy, using the Select one object at a time toolbar button

 From the menus, choose Edit → Copy or press Ctrl+C on the keyboard

Multiple Path Diagrams

In previous versions of Amos Graphics, you could view only one path diagram at a time Now you can open multiple Amos Graphics windows, each one displaying a different path diagram

Incompatibilities with Amos 5.0

„ By default, path diagram files (*.amw files) that are saved by Amos 6.0 cannot be read by Amos 5.0 To save a *.amw file that can be read by Amos 5.0, from the

menus choose File → Save As In the Save As dialog box, select Amos 5 Input filefrom the Save as type dropdown list

„ Amos Basic programs and Visual Basic 6 programs require upgrading to VB.NET

or C#

„ Two members of the AmosEngine class were renamed in order to avoid conflicts with VB.NET keywords

„ The Structure method was renamed AStructure

„ The Dir property was renamed AmosDir

„ Members of the TMatrixID enum and the TMatrixContents enum were changed by dropping the ma prefix For example:

„ maAllImpliedMoments was renamed AllImpliedMoments

„ maImpliedMoments was renamed ImpliedMoments

„ maTotalEffects was renamed TotalEffects

Other Changes between Amos 5.0 and Amos 6.0

„ On the Amos Graphics menus, View/Set has been renamed View, and Model-Fit has been renamed Analyze

„ The key combination Ctrl+B is now a shortcut for Analyze → Bayesian In Amos 5.0, Ctrl+B was a shortcut for Tools → Outline

„ In Amos 5.0, you could open the Object Properties dialog box by double-clicking

an object in a path diagram In Amos 6.0, double-clicking an object does not open the Object Properties dialog box Instead, right-click the object and choose Object Properties from the pop-up menu In Amos 6.0, Object Properties is the first item on the pop-up menu (In Amos 5.0, it was the last item.)

For most Amos 6.0 windows, context-sensitive online Help can be displayed with the F1 key or with Shift+F1 For some windows, context-sensitive online Help is obtained,

as in Amos 5.0, by right-clicking a window element such as a label or check box and choosing What’s This or Help from the pop-up menu

a split second.

This tutorial is a little like that early statistics class There are many shortcuts to drawing and labeling path diagrams in Amos Graphics that you will discover as you work through the examples in this user’s guide or as you refer to the online Help The intent of this tutorial is to simply get you started using Amos Graphics It will cover

some of the basic functions and features of Amos and guide you through your first

Amos analysis

Once you have worked through the tutorial, you can learn about more advanced functions from the online Help, or you can continue to learn incrementally by working your way through the examples

If you performed a typical installation, you can find the path diagram constructed

in this tutorial in the following location: C:\Program Files\Amos 6\Tutorial The file Startsps.amw uses an SPSS data file Getstart.amw is the same path diagram but uses

data from a Microsoft Excel file

Tip: Amos 6.0 provides more than one way to accomplish most tasks For all menu commands except Tools → Macro, there is a toolbar button that performs the same task For many tasks, Amos also provides keyboard shortcuts The user’s guide

demonstrates the menu path For information about the toolbar buttons and keyboard shortcuts, see the online Help

About the Data

Hamilton (1990) provided several measurements on each of 21 states Three of the measurements will be used in this tutorial:

„ Average SAT score

„ Per capita income expressed in $1,000 units

„ Median education for residents 25 years of age or older

You can find the data in the Tutorial directory within the Excel 8.0 workbook

Hamilton.xls in the worksheet named Hamilton The data are as follows:

The following path diagram shows a model for these data:

This is a simple regression model where one observed variable, SAT, is predicted as a linear combination of the other two observed variables, Education and Income As with nearly all empirical data, the prediction will not be perfect The variable Other represents variables other than Education and Income that affect SAT.

Each single-headed arrow represents a regression weight The number 1 in the

figure specifies that Other must have a weight of 1 in the prediction of SAT Some such

constraint must be imposed in order to make the model identified, and it is one of the

features of the model that must be communicated to Amos

Launching Amos Graphics

You can launch Amos Graphics in any of the following ways:

„ Click Start on the Windows task bar, and choose Programs → Amos 6 → Amos Graphics

„ Double-click any path diagram (*.amw).

„ Drag a path diagram (*.amw) file from Windows Explorer to the Amos Graphics

Creating a New Model

 From the menus, choose File → New

Your work area appears The large area on the right is where you draw path diagrams The toolbar on the left provides one-click access to the most frequently used buttons You can use either the toolbar or menu commands for most operations

Specifying the Data File

The next step is to specify the file that contains the Hamilton data This tutorial uses a

Microsoft Excel 8.0 (*.xls) file, but Amos supports several common database formats, including SPSS *.sav files If you launch Amos from the Analyze menu in SPSS,

Amos automatically uses the file that is open in SPSS

 From the menus, choose File → Data Files

 In the Data Files dialog box, click File Name

 Browse to the Tutorial folder If you performed a typical installation, the path is C:\Program Files\Amos 6\Tutorial.

 In the Files of type list, select Excel 8.0 (*.xls)

 Select Hamilton.xls, and then click Open

 In the Data Files dialog box, click OK

Specifying the Model and Drawing Variables

The next step is to draw the variables in your model First, you’ll draw three rectangles

to represent the observed variables, and then you’ll draw an ellipse to represent the unobserved variable

 From the menus, choose Diagram → Draw Observed

 In the drawing area, move your mouse pointer to where you want the Education

rectangle to appear Click and drag to draw the rectangle Don’t worry about the exact size or placement of the rectangle because you can change it later

 Use the same method to draw two more rectangles for Income and SAT

 From the menus, choose Diagram → Draw Unobserved

 In the drawing area, move your mouse pointer to the right of the three rectangles and click and drag to draw the ellipse

The model in your drawing area should now look similar to the following:

Naming the Variables

 In the drawing area, right-click the top left rectangle and choose Object Properties from the pop-up menu

 Click the Text tab

 In the Variable name text box, type Education

 Use the same method to name the remaining variables Then close the Object Properties dialog box

Your path diagram should now look like this:

Drawing Arrows

Now you will add arrows to the path diagram, using the following model as your guide:

 From the menus, choose Diagram → Draw Path

 Click and drag to draw an arrow between Education and SAT

 Use this method to add each of the remaining single-headed arrows

 From the menus, choose Diagram → Draw Covariances

 Click and drag to draw a double-headed arrow between Income and Education Don’t worry about the curve of the arrow because you can adjust it later

Constraining a Parameter

To identify the regression model, you must define the scale of the latent variable Other

You can do this by fixing either the variance of Other or the path coefficient from Other to SAT at some positive value The following shows you how to fix the path

coefficient at unity (1)

 In the drawing area, right-click the arrow between Other and SAT and choose Object Properties from the pop-up menu

 Click the Parameters tab

 In the Regression weight text box, type 1

 Close the Object Properties dialog box

There is now a 1 above the arrow between Other and SAT Your path diagram is now complete, other than any changes you may wish to make to its appearance It should look something like this: