Hierarchical regression analysis using stata

[Urfat]

Hi everyone. I hope you all are doing well. I need help regarding carrying out hierarchical regression. While I was carrying out it in Stata. I included step wise variables firstly control variables particularly demographic variables such as age, gender education level, economic status and so on. second step i included the main variable in my study were gratitude, patience and third i included intervening variables in my study was social support. the results were as expected the main variables were contributing substantial amount of R 2 to the dependent variable. but i thought i also have another variable which was different district but as soon as i included this variable it added substantial amount of R2 to the original value. and it also reduced the earlier amount of variance contributed by the main variables. what should i do? Please help me why this is happening. what interpretations should i make out of it? 

[Neeraj Kaushik]

One-word answer is multicollinearity.

On Thu, Dec 11, 2025 at 6:37 PM Urfat <urfat...@gmail.com> wrote:

Hi everyone. I hope you all are doing well. I need help regarding carrying out hierarchical regression. While I was carrying out it in Stata. I included step wise variables firstly control variables particularly demographic variables such as age, gender education level, economic status and so on. second step i included the main variable in my study were gratitude, patience and third i included intervening variables in my study was social support. the results were as expected the main variables were contributing substantial amount of R 2 to the dependent variable. but i thought i also have another variable which was different district but as soon as i included this variable it added substantial amount of R2 to the original value. and it also reduced the earlier amount of variance contributed by the main variables. what should i do? Please help me why this is happening. what interpretations should i make out of it? 

--
The members of this group are expected to follow the following Protocols:
1. Please search previous posts in the group before posting the question.
2. Don't write the query in someone's post. Always use the option of New topic for the new question. You can do this by writing to dataanaly...@googlegroups.com
3. It’s better to give a proper subject to your post/query. It'll help others while searching.
4. Never write Open-ended queries. This group intends to help research scholars, NOT TO WORK FOR THEM.
5. Never write words like URGENT in your posts. People will help when they are free.
6. Never upload any information about National Seminars/Conferences. Send such information
in personal emails and feel free to share any RESEARCH-related information.
7. No Happy New Year, Happy Diwali, Happy Holi, Happy Birthday, Happy Anniversary, etc. allowed in this group.
8. Asking or sharing Research Papers is NOT ALLOWED.
9. You can share your questionnaire only once.
---
You received this message because you are subscribed to the Google Groups "DataAnalysis" group.
To unsubscribe from this group and stop receiving emails from it, send an email to dataanalysistrai...@googlegroups.com.
To view this discussion visit https://groups.google.com/d/msgid/dataanalysistraining/ef322885-9098-4034-a535-dee93ae905fdn%40googlegroups.com.

[Urfat]

Thanks for the response. I checked multicollinearity it is within below 10 for all varaibles. 

To view this discussion visit https://groups.google.com/d/msgid/dataanalysistraining/CAAd%3Dc8Nw%2BtN4_vHhXJ1ByjZOA24Aec%2BmX4TPnbvkXqt5CeA9Ug%40mail.gmail.com.

[Neeraj Kaushik]

For the Time series data, multicollinearity shd be <10

For the Cross-sectional data,  multicollinearity shd be <5 (preferable <3)

To view this discussion visit https://groups.google.com/d/msgid/dataanalysistraining/CALYpHBMypUXyFebUxXLFqv57wNa8f5mYxE%3DriXL5MuiHSyTgUw%40mail.gmail.com.

[Urfat]

Thank you sir, it is between 1.49-4.49

To view this discussion visit https://groups.google.com/d/msgid/dataanalysistraining/CAAd%3Dc8M%3DQetp-2MBYf0tkB9U4gz_vx_%2BaEitYVQEhcM-v2KgUw%40mail.gmail.com.

[Urfat]

For example, if there is multicollinearity what to do in that case. 

[Urfat]

Should we remove that variable if it shows high multicollinearity?

if two variables are conceptually similar and shows high multicollinearity. should we create separate model for each or keep them together