How to perform Structural Equation Modeling (SEM) in R

Рет қаралды 11,930

Жыл бұрын

In this video tutorial by AGRON Info Tech, we dive into the topic of Understanding Structural Equation Modeling (SEM) in R. Learn how to clear the R environment, import data, specify the model, estimate parameters, interpret results, and visualize the model using a path diagram. Gain valuable insights into complex relationships between variables and make informed decisions based on your findings. Don't forget to like, subscribe, and leave your questions and suggestions in the comments!
Video contains:
1. Clear R environment
2. Importing data
3. Converting variables to factors
4. Performing SEM
5. Model specification
6. Model estimation and identification
7. Path diagram
8. Customizing path diagram
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Download Links
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Download R-script & data file - visit this blog:
www.agroninfo.com/how-to-perf...
R is free software and you can download it from the link given below
www.r-project.org/
Download link for R studio
www.rstudio.com/products/rstu...
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#SEM #StructuralEquationModeling #RProgramming #DataAnalysis #Statistics #ResearchMethods #AGRONInfoTech #Tutorial #DataScience #PathDiagram

Пікірлер: 22

@asadalisarkar4253 11 ай бұрын

Please make SEM with Likert based data.

@MalikG-pt3ie 3 ай бұрын

Very helpful for understanding SEM in R

@AGRONInfoTech 3 ай бұрын

Thank you

@NatashaMWeah 5 ай бұрын

Using semplot, what does those codes represent on the path diagram?

@vivikinda 10 ай бұрын

hii, what journal do you get the data in the video from?

@AGRONInfoTech 10 ай бұрын

Hi it's not published data. I have used it just as an example. You may say it's dummy dataset

@hassanarena 23 күн бұрын

can you provide the dataset file.

@AGRONInfoTech 23 күн бұрын

You can download the dataset from the link provided in the description of this video.

@biologicalstatistics3320 Жыл бұрын

How is lavaan different from other model-generating packages in R?

@AGRONInfoTech Жыл бұрын

Lavaan supports a wide range of models, including confirmatory factor analysis (CFA), path analysis, structural equation modeling, and growth curve modeling. It also allows for the estimation of complex models involving multiple latent variables and observed indicators.

@biologicalstatistics3320 Жыл бұрын

@@AGRONInfoTech thanks.

@ldsharma6546 9 күн бұрын

Sir, I have analysed 7 fractions of soil zinc and other soil chemical properties. How can I develop a SEM using my data. Could you please teach me.

@AGRONInfoTech 8 күн бұрын

You may use the following model for your data as I am not aware of the complete list of variables. Here is the model: # Example SEM model model

@ldsharma6546 8 күн бұрын

@@AGRONInfoTech Thank you Sir

@AGRONInfoTech 8 күн бұрын

@ldsharma6546 you are welcome

@redamed6379 4 ай бұрын

can we use Pls method ?

@AGRONInfoTech 4 ай бұрын

Yes, absolutely! Partial Least Squares (PLS) is a popular method for structural equation modeling (SEM) and can be effectively used.

@chiaras2274 2 ай бұрын

Can i use it also if one of my dependent variable is dummy?

@AGRONInfoTech 2 ай бұрын

Yes, you can use even if one of your dependent variables is a dummy variable (binary). SEM can handle both continuous and categorical (including binary) variables.

@AGRONInfoTech 2 ай бұрын

I shall soon upload one more video tutorial on SEM using likert scale data.

@chiaras2274 2 ай бұрын

@@AGRONInfoTech does this specification consider the correlation between errors? For instance in stata to get estimation coefficient equivalent to ivregress you must specify cov(e*e), is it the same in this package?

@AGRONInfoTech 2 ай бұрын

@@chiaras2274 In the lavaan package for R, you can specify correlations between the errors of different equations (residual covariances) directly in the model syntax. This is similar to specifying cov(e*e) in Stata to account for Correlationterms. For example: # Define the model with correlation term model