Query Regarding Insignificant Effects of Demographic Control Variables in PLS-SEM

[Yashi Kapil]

Respected group members and Neeraj Sir

I hope this mail finds you in good spirits. 

I am writing this email for your guidance regarding an issue I am encountering in my PLS-SEM analysis.

My study is based on a reflective-reflective higher-order model, and my dependent variable is a Higher-Order Construct (HOC). I have included several demographic variables as control variables, namely age, gender, and years of work experience. However, after running the structural model, I found that all of these control variables show non-significant effects on the HOC.

I would like to seek your advice on the following:

1. Does the non-significance of all demographic control variables indicate a problem with my model or analysis approach?
2. Is it methodologically acceptable for control variables to be non-significant while the main hypothesized relationships remain significant?
3. Should these control variables be retained in the final model despite their non-significant effects?
4. Does the interpretation differ when the dependent variable is a higher-order construct rather than a first-order construct?
5. If the current approach is correct, how should these findings be interpreted and reported in a research paper?

For example, would it be appropriate to state that:

"The effects of age, gender, and years of work experience on the dependent variable were found to be statistically non-significant, suggesting that the outcome variable is relatively independent of these demographic characteristics. Therefore, the observed relationships among the focal study variables are unlikely to be explained by demographic differences among respondents."

Or would there be a more appropriate way to interpret and discuss these findings?

I would greatly appreciate any methodological guidance, references, or examples from prior studies that could help me report these results accurately.

Thanks, and regards,

Yashi Kapil

Research Scholar, School of Management

Bennett University.

[Ajit Pal Singh]

• Dear Yashi Kapil  

• Non‑significance does not indicate a model problem

  • It is common for demographic controls (age, gender, tenure) to show non‑significant effects in SEM.

  • Their role is to rule out alternative explanations, not necessarily to contribute significant variance.

  • As long as your hypothesized paths remain significant and the model fit indices are acceptable, the non‑significance of controls is not problematic.

• Methodological acceptability

  • Yes, it is methodologically acceptable for control variables to be non‑significant while focal relationships are significant.

  • Many published SEM studies retain controls for transparency, even when they are not statistically significant.

• Retention in the final model

  • Best practice is to retain control variables in the reported model, even if non‑significant, to demonstrate that you tested for demographic confounds.

  • Dropping them may raise reviewer concerns about omitted variable bias.

• Higher‑order constructs vs. first‑order constructs

  • The interpretation does not fundamentally change because the dependent variable is a higher‑order construct.

  • Controls are still tested against the latent construct score; the logic of non‑significance remains the same.

• Interpretation and reporting

  • Your suggested phrasing is appropriate:

    “The effects of age, gender, and years of work experience on the dependent variable were found to be statistically non‑significant, suggesting that the outcome variable is relatively independent of these demographic characteristics. Therefore, the observed relationships among the focal study variables are unlikely to be explained by demographic differences among respondents.”

  • Alternatively, you can frame it more neutrally:

    “Control variables (age, gender, work experience) were included in the model to account for demographic differences. None showed significant effects on the higher‑order construct, indicating that demographic characteristics did not confound the hypothesized relationships.”

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[Neeraj Kaushik]

Dear Yashi Kapil,

Here are my inputs:

1.  Non-significance of control variables does not indicate a problem with your model or analysis. 
2.  It is perfectly acceptable for control variables to be non-significant. Their role is to ensure that the effects of your main independent variables are not confounded by these demographic factors.
3.  Generally, it is recommended to retain and report them in the final model to demonstrate that you have controlled for these factors. 

4.  The interpretation does not fundamentally change. Whether the dependent variable is a first-order or higher-order construct, the goal remains to assess if the demographics impact the variance explained in that construct.
5.  Your proposed statement is appropriate. It correctly identifies that the outcome variable is independent of those demographic characteristics, thereby increasing the robustness of your main findings.

Best wishes

Neeraj

On Tue, Jun 2, 2026 at 5:31 PM 'Yashi Kapil' via DataAnalysis <dataanalys...@googlegroups.com> wrote:

[Yashi Kapil]

Thank you very much, sir, for your insights. Your comments have really helped me clear my doubts. 

Regards

Yashi Kapil

Research Scholar, Bennett University. 

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From: dataanalys...@googlegroups.com <dataanalys...@googlegroups.com> on behalf of Neeraj Kaushik <kaushi...@gmail.com>
Sent: 03 June 2026 14:20
To: dataanalys...@googlegroups.com <dataanalys...@googlegroups.com>
Subject: Re: Query Regarding Insignificant Effects of Demographic Control Variables in PLS-SEM

 

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