Query regarding control variables in PLS-SEM

[Kashish Madaan]

Dear group members

Please help me with the measurement of control variables in PLS-SEM. Control variables are Age and academic qualification. In age, there are five categories and in academic qualification, there are 4.  Can these be divided into two categories like 

In age:  younger respondents and elder respondents

In academic qualification: low education level and high education level

Kindly suggest, whether is this the right method to code these demographic variables. If not, then what is it, how the measurement will be done, and what results will be reported? 

Please share some references for better understanding.

Thank you

[Shahzada Irfan Bashir]

Dear Kashish,

I think this is absolutely fine to reduce the 4 categories in to 2. You can also go ahead with 4 categories also.

You just have to code the excel fine like 1= younger and 2= older (e.g.. 1= 5 to 20 years and 2= more than 20 years).

Hope the answer for the question.

Rest Prof Neeraj can explain it in more detail.

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[Kashish Madaan]

Ok dear

You mean just like we measure any construct on likert scale, similarly 4 or 5 categories can be coded as 1,2,3,4 and 5 and then measured the same way just like we measure those controls in which there are only 2 categories.Right?

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Regards
Kashish
Research Scholar
Haryana School of Business GJUS&T
Hisar, Haryana

[Shahzada Irfan Bashir]

Dear Kashish,

You may like to refer to the following links for more clarity:)

https://m.youtube.com/watch?v=AjUt5FsHZiw

You can also watch Prof Neeraj’s playlist on SmartPLS and you can find many useful videos and you will get clarity on smartpls 

Link is below:)

https://youtu.be/C_SGOKL6WBg

Happy learning

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[Shahzada Irfan Bashir]

You can also use the below resource for reporting smartpls results:)

https://scholar.google.com/citations?view_op=view_citation&hl=en&user=KnnmEP4AAAAJ&citation_for_view=KnnmEP4AAAAJ:crhHmGRhyBgC

https://cir.nii.ac.jp/crid/1130282271982573184

[Kashish Madaan]

Okay. Thank you so much 😊

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[Atul Shiva]

Dear Kashish,

I am sending two slides for handling Control Variables with more than two categories. In the main website of adanco software professor Jorg Henseler has explained about the treatment of control variables by taking one category as a reference category.

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Dr. Atul Shiva
Associate Professor of Management

University School of Business 
Chandigarh University, Mohali, India
atul...@gmail.com
+91-8427001687

[Kashish Madaan]

Thanks for sharing sir. I got my answer now 👍👍

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[Anubhuti Dwivedi]

Dear Kashish,

The treatment of the demographic variables like age and education also depends on the way you have collected them. 

These can be treated as nominal variables (categorical) if you want to club the data you collected as class intervals (like age taken in 18-25, 26-35 and so on..) into young and old or even young, middle aged and old and then the method recommended by Prof. Henseler of making dummy variables for each category taking one as reference should be used. 

But you can also treat age as a metric variable (interval or ratio scale). If you have collected data on actual age in years, i.e. it is a continuous variable, use it as it is by making it a single item construct and connecting it to the endogenous (dependent) variables in the model. If you have collected age on interval scale (like 18-25, 26-35, 36-45 and so on), you can use it as interval scale variable and code these categories as 18-25 = 1, 26-35 = 2, 36-45 = 3 and so on. Please remember to check whether your class intervals were fairly equal or not otherwise the interval scale may not be a good idea.

Same logic applies to any control variable that you want to take in your PLS-SEM or regression model. Code them the way you want to treat them as per the type of data you have collected (categorical or metric) and then proceed by connecting them to the dependent variable.

Hope this helps in better understanding the valuable resources shared by other members. 

Regards,

Anubhuti Dwivedi

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[Kashish Madaan]

Okay mam. Thanks for the information. 

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