Transcript
STRUCTURAL EQUATION MODELING IN AMOS
Balaji.P
Basic Concept Partial Correlation:Correlation between Y and X1 where effects ofX2 have been removed from X1 but not from Yis partial correlation
Interpretation of Partial correlation
Part correlation squared is the unique amount of total variance explained.
Sum of part correlations squared does NOT equal R square because of overlapping variance.
Multi collinearity: Existence of substantial correlation among a set of independent variables.
Latent variableLatent variables: representation of the variance shared among the variables
common variance without error or specific variance
Structural Equation Modeling Structural equation modeling (SEM), as a concept, is
a combination of statistical techniques such as exploratory factor analysis and multiple regression.
The purpose of SEM is to examine a set of relationships between one or more Independent Variables (IV) and one or more Dependent Variables (DV).
Goals of SEM To understand the patterns of
correlation/covariance among a set of variables.
To explain as much of their variance as possible with the model specified .
How SEM is different from traditional approach? Multiple equations can be estimated simultaneously Non-recursive models are possible Correlations among disturbances are possible Formal specification of a model is required Measurement and structural relations are
separated, with relations among latent variables rather than measured variables
Assessing of model fit is not as straightforward
Types of SEM models Path analysis Confirmatory factor analysis. Structural regression model Latent change model
Approach to SEM analysis Review the relevant theory and research literature to support
model specification Specify a model (e.g., diagram, equations) Determine model identification Collect data Conduct preliminary descriptive statistical analysis Estimate parameters in the model (Model Estimation) Assess model fit Model Respecification Interpret and present results.
Components of SEMLatent variables, factors, constructs
Observed variables, measures, indicators, manifest variables
Direction of influence, relationship from one variableto another
Association not explained within the model
Important Definition A measured variable (MV) is a variables that is
directly measured. A latent variable could be defined as whatever
its multiple indicators have in common with each other. It isn't measured directly.
Relationships between variables are of three types such as Association (Correlation, covariance), direct effect and indirect effect.
Path Analysis Extension of multiple regression allowing us to consider
more than one dependent variable at a time and more importantly, allowing variables to be both Dependent and Independent variables.
B is dependent as well as independent variable (mediating variable).
Path AnalysisOnce the data is available, conduction of path analysis is straightforward:
Draw a path diagram according to the theory. Conduct one or more regression analyses. Compare the regression estimates (B) to the theoretical
assumptions or (Beta) other studies. If needed, modify the model by removing or adding
connecting paths between the variables and redo stages 2 and 3.
Examples of Path Analysis
illness = p5p3 fitness + p5p4 stress + p5p1 exercise + p5p2 hardy + e5;
Examples of Path Analysis
fitness = p3p1 exercise + e3,stress = p4p2 hardy + e4,illness = p5p3 fitness + p5p4 stress + e5;
Software’s (SEM) LISREL AMOS EQS MPLUS SAS
AMOS (Analysis of Moment Structures) Starting AMOS Graphics
Reading Data in to AMOS
Filename Data.sav
AMOS can read Currently AMOS reads the following data
file formats: Access dBase 3 – 5 Microsft Excel 3, 4, 5, and 97 FoxPro 2.0, 2.5 and 2.6 Lotus wk1, wk3, and wk4 SPSS *.sav files,
Data File
List variable in data set
Drawing in AMOS (Draw observed variable)
Move the cursor to the place where you want to place an observed variable and click your mouse. Drag the box in order to adjust the size of the box.
Click Draw unobserved
Direct effectClick path icon of direct effect and click respective independent variable drag up to dependent variable
Touch up variable
It gives neat look to our model. Click the touch up variable and click respective observed or unobserved which you want to look neat.
Add unique variable
Click and then click a box or a circle to which you want to add errors or a unique variables. (When you use "Unique Variable" button, the path coefficient will be automatically constrained to 1.)
Naming the variables in AMOS
Click list all the variable. Drag the variable from list and put directly in to observed variable.
Naming the variables in AMOS double click on the objects in the path
diagram. The Object Properties dialog box appears. And
Click on the Text tab and enter the name of the variable in the Variable name field.
Regression Weight
Normally for error value, regression weight takes the value of 1.
Performing the analysis in AMOS For our example, check the Minimization
history, Standardized estimates, and Squared multiple correlations boxes.
To run AMOS, click on the Calculate estimates icon on the toolbar.
AMOS will want to save this problem to a file.
Results When AMOS has completed the
calculations, you have two options for viewing the output:
text output, graphics output.
For text output, click the View Text icon on the toolbar.
Text output
Viewing the graphics output in AMOS
To view the graphics output, click the View output icon next to the drawing area.
Chose to view either unstandardized or standardized estimates by click one or the other in the Parameter Formats panel next to your drawing area
Standardized vs. Unstandardized Standardized coefficients can be compared across variables
within a model. Standardized coefficients reflect not only the strength of the
relationship but also variances and covariance's of variables included in the model as well of variance of variables not included in the model and subsumed under the error term.
Standardized parameter estimates are transformations of unstandardized estimates that remove scaling and can be used for informal comparisons of parameters throughout the model.
Standardized vs. Unstandardized Unstandardized parameter estimates retain
scaling information of variables and can only be interpreted with reference to the scales of the variables.
A correlation matrix standardizes values and loses the metric of the scales.
Therefore for correlation matrix, both standardizes and unstandardized are same.
Graphics output
Improving the appearance of the path diagram
You can change the appearance of your path diagram by moving objects around
To move an object, click on the Move icon on the toolbar. You will notice that the picture of a little moving truck appears below your mouse pointer when you move into the drawing area. This lets you know the Move function is active.
Then click and hold down your left mouse button on the object you wish to move. With the mouse button still depressed, move the object to where you want it, and let go of your mouse button. Amos Graphics will automatically redraw all connecting arrows.
Improving the appearance of the path diagram
If you make a mistake, there are always three icons on the toolbar to quickly bail you out: the Erase and Undo functions.
To erase an object, simply click on the Erase icon and then click on the object you wish to erase.
To undo your last drawing activity, click on the Undo icon and your last activity disappears.
Each time you click Undo, your previous activity will be removed.
If you change your mind, click on Redo to restore a change.
SEM could impacted by the requirement of sufficient sample size. A desirable goal is to
have a 20:1 ratio for the number of subjects to the number of model parameters . However, a 10:1 may be a realistic target. If the ratio is less than 5:1, the estimates may be unstable.
measurement instruments multivariate normality parameter identification outliers missing data interpretation of model fit indices
Model Identification A model is identified if:
It is theoretically possible to derive a unique estimate of each parameter
The number of equations is equal to the number of parameters to be estimated
It is fully recursive (No feedback loop)
Model identification A model is over identified if:
A model has fewer parameters than observations.
There are more equations than are necessary for the purpose of estimating parameters
Model identification A model is under identified or not
identified if: It is not theoretically possible to derive a unique
estimate of each parameter There is insufficient information for the purpose of
obtaining a determinate solution of parameters. There are an infinite number of solutions may be
obtained
Model identification Determine the # of parameters you have. Formula: (v(v+1) / 2), where v= # of observed variables Use of this formula, allows to see if trying to guess more
than the number of parameters the existing data allows. Do not want to be JUST identified (cause lack of fit
indices) or UNDER identified, therefore looking to be OVER-identified.
Being OVER identified essentially means that there are more available parameters than trying to estimate.
Example
Example
Example
Example
Model Estimation Maximum Likelihood Generalized and UnGeneralized least
square 2 stage and 3 stage least square
Model fit Model fit = sample data are consistent with
the implied model The smaller the discrepancy between the
implied model and the sample data, the better the fit.
Many fit indexes None are fallible (though some are better than others)
Fit indexes
Fit indexes
Model RespecificationWhat if the model does NOT fit? Model trimming and building
LaGrange Multiplier test (add parameters) Wald test (drop parameters)
Empirical vs. theoretical respecification What justification do you have to respecify?
Consider equivalent models
Confirmatory factor analysis How it differs from the more commonly
encountered forms of factor analysis. What is factor analysis (FA)?
have many variables and want to examine if they can be explained by a smaller number of factors.
No a priori hypothesis (impossible to even indicate a hunch to the program) as to which variables will cluster together on which factor.
Confirmatory factor analysis The major difference is that an a priori
hypothesis is essential: which variables grouped together as
manifestations of an underlying construct and fits the model.
Like with path analysis, it can be helpful to draw hypothesized relations in a diagram.
CFA is not model building With CFA, you stipulate where you think the variables
should load. Then, the program simply tells you whether your model fits the data.
If no fit, then there are few clues to guide you how to shuffle the variables around to make the model better fit the data.
Note: Even if the model does fit, it does not guarantee that a new arrangement of variables would be an even better fit.
Therefore, one must really use theory, knowledge, or previous research to guide your model, rather than rely on statistical criteria.
Scaling Scaling factor: constrain one of the
factor loadings to 1 ( that variables called – reference variable, the factor has a scale related to the explained variance of the reference variable).
fix factor variance to a constant ( ex. 1), so all factor loadings are free parameters
CFA
Duplicate objectSelect object which you want to duplicate. Click wherever you wanted same variable.
Unobserved
Click unobserved icon and draw like observed icon
Correlation or covariance
Unstandardized and Standardized estimates
Unstandardized solution Factor loadings =unstandardized regression coefficient Unanalyzed association between factors or errors= covariances
Standardized solution Unanalyzed association between factors or errors= correlations Factor loadings =standardized regression coefficient ( structure
coefficient) The square of the factor loadings = the proportion of the
explained ( common) indicator variance, R2(squared multiple correlation)
Standardized regression model Inclusion of observed and latent
variables Assessment both of relationship between
observed and latent variables.
Latent growth Analysis Can change in responses be tracked over
time? Latent Growth Curve Analysis
Thank you
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