PRI Workshop Introduction to AMOS

Transcription

1 PRI Workshop Introduction to AMOS Krissy Zeiser Pennsylvania State University 2-pm /3/2008

2 Setting up the Dataset Missing values should be recoded in another program (preferably with SPSS since AMOS uses SPSS datasets) Dummy variables and recoding of ordinal/continuous variables should be done before working in AMOS Along with SPSS datasets (.sav), AMOS also reads.dbf,.xls,.txt, and.csv files Missing Data AMOS prefers complete datasets, though it can perform FIML It is common to replace missing values with the variable s mean or mode May perform listwise deletion first if you know which variables you will be using Benefits of AMOS You can specify when variables or error terms should be correlated You can set error terms equal to each other You can assign values to correlations, factor loadings, etc. Easy to conceptualize models and provide illustrations to you structural equation model Compares different models that use different constraints (nested models) Allows you to perform the same model on different subgroups of the sample Can perform latent growth curve modeling Can use visual basic code to run AMOS Drawbacks to AMOS Must have a continuous (or ordinal) dependent variable Normally doesn t use syntax-though the model is saved if you need to replicate the results Amos does not allow you to weight your data to correct for sample design and attrition Today s Example Today, we will be using variables from the National Education Longitudinal Study of 988 (NELS88) I will be demonstrating SEM using FIML, so missing values have already been recoded into. For more information, the.pdf file of the Amos manual can be found at:

3 Analyzing in AMOS Simple and Multiple Linear Regression Example : two observed independent variables (math and reading test scores) predicting an observed dependent variable (educational expectations) Observed variables are placed in boxes You can use the copy machine to copy, drag, and paste your boxes rather than create new ones The endogenous (dependent) variable requires an error term-just click on the icon and then click on the dependent variable Right-click the error term and click Object properties to name it so that AMOS knows how to identify the error term in the output. When you re done typing, exit the window by clicking the red x in the upper right-hand corner. o When you open a window to change preferences or label variables, Amos will remember your changes when you close the window. There is not an option to save changes. Arrows represent a regression with the arrows pointing toward the dependent variable Covariances are identified with a 2-sided arrow o If two exogenous variables are not connected with a covariance arrow, an error message will appear. You are allowed to apply the constraint that two exogenous variables are not correlated, but you need to tell AMOS it s ok

4 e After drawing you structural equation model, you must select which dataset you will use Next, you must open the dataset and drag the variables into the appropriate boxes e reading_score educ_ex math_score Before running analyses, you must save your program To perform Full Information Maximum Likelihood (FIML) to account for missing data, go into View Analysis Properties and select the box next to Estimate Means and Intercepts If you want to see standardized regression coefficients, under the output tab of analysis properties, select the box next to standardized estimates. When you close the menu, what you have selected will be remembered

5 You re finally ready to run your model Once analysis is run, you may look at the estimated unstandardized or standardized coefficients directly on your model The output will show you the estimates as well as the model fit statistics. This output will be saved as a separate file in the same location as your input file. You can copy your model to a word document by clicking the button with the clipboard and pasting it right into your document

6 Multivariate Linear Regression Example 2: four observed independent variables (racial categories for Asian, Hispanic black, and Indian) predicting two observed dependent variables (educational expectations and reading test scores) You can have more than one dependent variable Each dependent variable requires an error term with a variable name To make your model better-looking and easier to read, by clicking the error term icon multiple times, the error term and regression arrow will rotate around your endogenous variable. Simply stop clicking when your error term is where you want it to be positioned. As a shortcut to drawing covariances to and from various exogenous variables, select which variables you wish to correlate using the icon with the single pointing finger and then click on your exogenous variables Once the variables are selected (they turn blue), go to Plugins draw covariances and Amos will draw the covariance arrows. To un-select variables and covariances, click on the icon with the closed fist You cannot correlate endogenous variables To make the covariances look neater, you can use the moving truck to move the arrows or the variables themselves

7 e asian educ_ex hispanic reading_score black e2 indian

8 Linear Regression with Latent Variables Example 3: three observed independent variables (family structure variables for two parents (non-biological, single parent, and other ) predicting an (unobserved) latent dependent variable (emotional support) composed of five observed variables Use the red X to delete variables you no longer need-just click on the icon and then click on the variable(s) you wish to delete Unobserved variables are placed in ovals To make a latent construct click on the latent construct icon. Then, click on your latent variable oval once for every observed variable that comprises the latent construct. o Notice that error terms are automatically attached to these boxes and that one of the factor loadings is already constrained to equal o You have to create an error term for the latent oval as well o Right-click the oval and select object properties to name your latent construct. Don t forget to name all of the error terms, too. o To make the latent construct look neater, click on the rotate indicators icon and click on the unobserved variable oval to rotate the observed variables around the latent construct Drag the observed variables into their respective boxes Save your model and run it AMOS only conducts confirmatory factor analysis and provides factor loadings. To perform exploratory factor analysis (to determine whether these variables should indeed be combined to make a scale or latent construct), you should use another package such as SPSS.

9 e5 e4 e3 e2 e emo_nolike emo_disap emo_und emo_getalo emo_fair twopar 0 Emotional Support single other e6

10 Running Regression with Multiple subgroups Example 3: three observed independent variables (family structure variables for two parents (non-biological, single parent, and other ) predicting an (unobserved) latent dependent variable (emotional support) composed of five observed variables, models run separately by gender You may also filter the dataset so that you are only looking at men or women, respondents of different races, etc. Double-click Group number. You may now rename the first group (i.e. males) and click the new button to create and name a new group (i.e. females) Where you select the dataset, you now must select a dataset (it may be the same one) for both groups For each of your groups, click on the button grouping variable, select which variable identifies your groups (i.e. female), and click ok. Click on the group you choose to identify, and then click the group value button and select which value identifies your group (i.e. 0=male). Do this for each of your groups, and then click ok to leave the data files window, Save your model and run it A box on the left-hand side of your output will allow you to select each group. While the model fit applies to all of the groups together, the estimates will change by group.