Stata vs SPSS: Structural Equation Modeling

We have been working on comparing Stata vs SPSS for many aspects like ease of use, handling of data, allowing for running a specific statistical technique and community involvement to answer some problems not commonly arising. These comparisons help in selection of Stata or SPSS by students and researchers to conduct data analysis for academic or commercial research projects. In this tutorial, we will provide a simple comparison of how Stata and SPSS can be used for running Structural Equation Modeling and what are the simple differences that one has to note down before deciding for selection o f Stata vs Stata.

Downloading Stata and SPSS

The Stata has a built in SEM Builder which does not need to buy a separate package or software extension to add additional statistical feature like SPSS needs AMOS to be bought and installed as a separate software. The Stata can be purchased here and evaluation version/trial version with full features including the SEM builder as built in can be requested here. On the other hands, the AMOS should be purchased separately here, can be downloaded for trial here and it needs you to have SPSS installed before AMOS is installed. Download SPSS for download on trial here.

SEM Builder vs AMOS

The simple outlook of the two SEM builders look different but we can say they are same at the outset. Like both allows you to design you path diagram, develop the linkages between latents and items and hence design the covariance. We can see the two screenshots from AMOS and Stata here.

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The Stata SEM Builder Screenshot

stata vs spss sem builder

Picture: AnEc Center for Econometrics Research Online Course in Structural Equation Model using Stata

The AMOS Screenshot

stata vs spss amos

Picture: AnEc Center for Econometrics Research Online Course in Structural Equation Model using SPSS/AMOS

Process of SEM in Stata vs SPSS AMOS

To run SEM in Stata, we follow these simple steps:

  1. Open Stata and your data on items for the corresponding items.
  2. Click on Statistics
  3. Click  SEM (structural equation modeling)
  4. Then click on Model building and estimation

The SEM Builder window will open. Now we need to carefully read the menu items on this window, its side bars etc. We can see basic constructs like Ovals, single headed Arrows and two headed Arrows and Squares. Some icons might combine these. We will use these tools to design the path diagram for our theoretical framework and estimation of the model. Ovals are used to denote Latent variables, Square is used to show an item/observed variable and single headed arrow is used to define one way effect and two headed arrows are used for covariance/correlation between the variables.

We can see the path diagram in the above screenshot from Stata SEM Builder. Once this kind of path diagram has been created, variables names are given to the latent-ovals and variables added to the Squared observed items, we can click on Estimation and proceed with the menus to create the model output in Stata. It looks like a simple OLS regression table with coefficients, SEs, and P-values. We can interpret these results in a separate article later.

To run SEM in AMOS, we proceed to follow these steps:

In SPSS, once AMOS is installed separately, we can click estimate an SEM model following these steps:

  1. Open SPSS and data file
  2. Click on Analyse
  3. Click on AMOS at the bottom of the menu items if AMOS is installed.
  4. The AMOS Graphics will open the new Window of AMOS which looks like the above screenshot.

In AMOS, the menu bar are also very simple to follow and the left bar has direct options to create latent variables, observed variables and groups of measurements (latent with observed variables) and arrows. In AMOS, this side bar has many other important options to alter the path diagrams and finalize the path diagram. Once the model has been created, estimated and results are produced (we will write a separate article on interpretation of SEM results in coming days).

Community Support for Stata vs SPSS

We can easily find that Stata has a huge community compared to SPSS through social media, lists and forums while SPSS has a disadvantage at this despite having some great and privately managed tutorials websites. It is quite easy to find basic answers to many questions on the official FAQs pages from both Stata vs SPSS but the instant community based discussion gives Stata an edge over the SPSS.

Conclusion

This comparison is not for marketing purposes or technical evaluation of Stata and SPSS. We recommend you request a technical review of both the statistical softwares from our experts if you need more deeper analysis.

Stata verses SPSS: Multiple regression example

Comparing Stata verses SPSS based on multiple regression is a useful tool to determine the capabilities of the two softwares. Many researchers and data analysts in social sciences prefers SPSS over Stata for many reasons while some love Stata more than SPSS for other reasons. In this tutorial, we demonstrate the estimation of a Multiple Regression model for a given dataset and what we can do with Stata verses SPSS.

Stata verses SPSS: Multiple Regression

Generally we can see that Stata offers more options to estimate a multiple regression model than SPSS because we can handle many types of data which SPSS cannot handle. One example for such comparative preference of Stata over SPSS for many in the area of social sciences and healthcare is analysis of limited dependent variables and count data types with Poisson family regression models like Negative Binomial Regression models.

Multiple Regression using SPSS: Menu System

In the following screenshot, we can see the menu in SPSS to select running a multiple regression model.

stata verses spss menu 1

It can also be seen from the second screenshot that SPSS offers a submenu to produce addition post estimation results like measuring Autocorrelation through Durbin-Watson Statistic and testing for multicolinearity.

stata verses spss submenu spss

Also another submenu can be used to plot the residuals and predicted values from the multiple regression model. This gives us a slight perception that it is much easier to run a multiple regression model in SPSS verses Stata.

The output in SPSS for multiple regression looks like this:

stata verses spss output in spss

Multiple Regression using Stata: Menu System

In Stata verses SPSS, the menu system in Stata is very similar to SPSS but the difference is in names of main menu bar and then the basic difference arises in specification menu to select the dependent and independent variables. Also, we saw that SPSS allows us to select the post estimation tests after multiple regression during the model specification and variables selection while in Stata, we have more useful and valuable options through his menu than post estimation tests like weights selection, comparing results across groups and conditioning the results through IF and in menu. Also, Stata offers us to select the reporting styles and conditions for our estimated model. See the menu in Stata here:

stata verses spss menu 2

and submenu for options (not post estimation tests as we did in SPSS) here:

stata verses spss postestimation in Stata

We can see that Stata offers a unique PostEstimation sub menu through the main menu bar. It allows us to test for many more tests than autocorrelation and multicollinearity like Heteroscedasticity and creating Marginals and Elasticities as well which SPSS lacks.

Finally, we can see that outputs in Stata verses SPSS is also different. SPSS provides split tables for multiple parts of the regression results like ANOVA and Diagnostics are produced in separate tables, R square and explanatory statistics are produced in another tables and coefficients are produced in another separate table. The results in Stata is somehow amazingly arranged into one condensed table. Explanatory part and R Square is produced at the left top of the table of table of coefficients estimates with ANOVA at the right top. This makes the reading of regression estimates easily. The SPSS output gives postestimation tests separately as done by Stata as well.

So the conclude on Stata verses SPSS, there is slight differences in the menu system as well as offered tests in Stata and SPSS. One can easily determine based on convenience to select a model very quickly both in Stata and SPSS.

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Note: This comparison is our private view. There is sponsoring content. Also, SPSS and Stata are copyrights of StataCorp and IBM SPSS.

Structural Equation Modeling Using SPSS AMOS

Structural Equation Modeling using SPSS AMOS

Learn structural equation modeling using SPSS AMOS in private and instructor led online courses at AnEc Center for Econometrics Research.

The course aims to develop a strong foundation of the theory and application of modeling relationships between observed and unobserved variables, path analysis and confirmatory factor analysis using structural equation modeling with SPSS and AMOS.

Enroll for SEM using SPSS and AMOS

Course Objectives

Structural Equation Modeling using SPSS AMOS is advanced level research course aiming to train PhD and MS students in Economics, Business, Finance and Social Sciences to develop modeling skills for analysis of latent variables and observed variables with multi-equation and endogenous covariates. The course also aims to use SPSS and AMOS at advanced level.

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Understanding the theory and practice of SEM
Developing the skills of modeling latent variables
Analysis of data using complex path analysis
Using SPSS and AMOS at advanced level
Writing effective research reports and thesis

Registration Fees

Structural Equation Modeling using SPSS AMOS is an online, private and instructor led course from AnEc Center for Econometrics Research led by Professor Anees Muhammad since 2010.

The course is designed to help the students in Economics, Finance, Business, Management, Psychology, Political Sciences, Social Sciences and Healthcare pursuing PhD and MS research. The course can be enrolled in private or in group. Private courses are recommended for students who need to use SEM during their writing of PhD thesis.

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STRUCTURAL EQUATION MODELING USING SPSS AMOS

Selected Course Contents for SEM using SPSS

These are only selected topics and we will cover the contents in more details so specific list of topics for each lessons and practical demonstration of SEM using SPSS AMOS

Introduction to SEM and Theory of SEM

Overview of linear structural equation modeling and fitting and evaluating structural equation models
Description of path diagrams, AMOS tools for SEM
, Building models using the GUI for AMOS
Details on specific types of structural equation models, Models for observed variables and Linear regression
Path analysis, Mediation and Moderation analysis, Exploratory and Confirmatory factor analysis

Validation of Estimated SEM Model and Tests

Full structural equation models, Latent growth curves
, Multiple group analysis and interpreting linear SEM results
Standardized results vs Unstandardized results, Direct, indirect, and total effects and Goodness-of-fit statistics
Modification indices, Score tests and Wald tests, Tests for multiple group analysis and fitting multilevel models
Generalized structural equation modeling vs simple structural equation modeling

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Partial Least Squares Regression Using SPSS

Partial Least Squares Regression Using SPSS

In this video, we demonstrate to run a partial least squares regression model using SPSS. Partial Least squares using SPSS is commonly believe not feasible in SPSS because of some technical reasons but when a user of SPSS can install a pre-required components including Python and its two libraries,

Watch Video TutorialRead The Steps Here

Video Tutorial on Partial Least Squares Regression

Step by Step Guide to run Partial Least Squares regression model in SPSS

In this video, we demonstrate to run a partial least squares regression model using SPSS. Partial Least squares using SPSS is commonly believe not feasible in SPSS because of some technical reasons but when a user of SPSS can install a pre-required components including Python and its two libraries, NumPy and SciPy and an SPSS plugin called PLS, the estimation of PLS in SPSS becomes more than simple. In this video, we have completely covered all these steps of downloading and installing NumPy, SciPy, Anaconda, PLS Plugin and then running a sample PLS model between a dependent variable and independent variable.

The following steps should be following to run Partial Least Squares Regression Model in SPSS:

1. Open your data in SPSS.
2. Click on Utilities and click on Extension Bundles.
3. Click on Download and Add Extension Bundles.
4. Search for PLS in the search box on the new small window that opens when we follow step 3 in SPSS.
5. Select the PLS check box and click on OK.
6. PLS Library/Plugin will be downloaded and installed directly on to SPSS package.
7. Now open the Anaconda website to download Python version specific for your SPSS.
8. Install the Python and then make sure NumPy and SciPy libraries (default in Anaconda) are installed.
9. Click on Analyse, Regression, Partial Least Squares in order.
10. Select your dependent variables, independent variables and identification variables.
11. Get your Partial Least Squares Regression using SPSS done.

The Partial Least Square is now available in SPSS in SmartPLS directly and more conveniently through Path Analysis and SEM. We recommend using both SPSS and SmartPLS for running PLS.

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Stata vs SPSS : Which One Is Better?

As a regular lover of Stata for econometric analysis and quantitative analysis, I am happy to share my personal experience about few things that will help justify Stata vs SPSS. This does not mean we qualify or certify any of Stata vs SPSS is better than others. Yes, we personally believe, there are some advantages in Stata vs SPSS.
The Stata vs SPSS comparison can be After studying Stata for about half a year my department asked me to tell them some more about STATA. One of the things my coll qualified by answering a simple question that what makes Stata better than SPSS or what makes SPSS better than Stata? To answer this question about Stata vs SPSS and compare based on the econometric analysis or statistical techniques or even using Stata for Quantitative Analysis or Quantitative Analysis using SPSS, one can see the major differences between the Stata vs SPSS. So this personal evaluation of Stata vs SPSS can be easily qualified purely on an academic observation and personal views.

On Statalist, where I read about Stata frequently, Marion de Leeuw of Dept. of Methodology and Statistics at Maastricht University wrote that SPSS has two advantages vs many disadvantages. The advantages are user friendliness in complex graphics and charting and routine for logistic regression allowing for interactions. Marion also mention that SPSS's ANOVA commands are might also be considered as user friendly but he does not use it. Some statistical techniques like running Probit Models are nearly impossible to run with SPSS with stinking documentation for help.
Anees has mentioned on his social media pages on facebook (https://www.facebook.com/stata.help) in his satire style that Stata can do 95% of the econometrics and statistical analysis while SPSS can help you with 30% of them. This is though a silly view as he claims it, but if one looks into the Statistics menu in Stata and Analyse menu in SPSS, Stata vs SPSS can be answered by anyone with great confirmation.

An ideal option for commercial statistical software production is to help the user as much as possible. Stata vs SPSS comparison is thus in favour of Stata for its official or technical help compared to the help provision from IBM-SPSS. The documentation of Stata are mor rich with examples compared to documentation of SPSS on major statistical applications. Stata documentation is also to be considered more in academically viable with strong use of examples while SPSS documentation is little lacking in this regard. This further seeks our attention to favour Stata vs SPSS for personal reasons.

One can see easily that the most common techniques for econometric or statistical analysis is multivariate analysis in SPSS is limited to OLS, probit, and logit while Stata can be found to have more rich routines in multiple pooled cross sectional time series. One can easily see that Stata offers a lot more in Count Data Models based on Poisson, negative binomial and the zero inflated poisson routines and maximum likelihood based routines for Tobit, multinomial logit, ordinal logit or probit, and complementary log-log which are not found in SPSS. A regular academic user in many field with the application of these techniques is thus forced to favour Stata vs SPSS.

A well documented observation related to post estimation after many regression models is that SPSS does not offer much to see validity of the estimated models while Stata has a rich help sources from official routines and unofficial but regularly debated Statalist archives with personally contributed private routines. This makes Stata vs SPSS comparison and decision making even more easier. One can easily decide in favour of Stata based on is based on the estimation of complex surveys based models and covering the clustering options or weights. SPSS allows some but Stata offers much much more options to take clustering, aweights, iweights and pweignts better than SPSS. An example of post estimation results commonly required by researchers and analysts include AIC and BIC or Pseudo RSquared values. SPSS does not allow you to report these values easily until you write Visual Basic scripts for that. Stata on the other hands will ease this issue for you mostly through built in routines within a technique or small ado routines by contributors freely available.

Based on the above, one can develop a good sense to further detail down on Stata vs SPSS. Stay tune on this page as we will compare Stata vs SPSS for specific techniques in next few days.

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[youtube https://www.youtube.com/watch?v=ndpEhC6mysM]