<p>I wrote a paper that focuses on this question (Anglim &amp; Grant, 2014; pre-print is <a href="https://osf.io/g8kbj/download" rel="noreferrer">https://osf.io/g8kbj/download</a>).</p>

<p>In short, if you're interested in estimating how well facets predict an outcome, then you should include all facets as predictors. Likewise, if you want to estimate the incremental prediction of facets over domains (e.g., 30 facets over the Big 5), then you should include all domains in the domain regression model and all facets in the facet regression model.</p>

<p>If you're doing research, then presumably, you don't know which facets predict the outcome. Therefore, it's an empirical question which do predict.</p>

<p>An important point is that you use an estimator that corrects for biases when including many predictors. A reasonable approach is to use adjusted r-squared, although when you have 30 facets, large samples help a lot.</p>

<p>I also have more recent primer that is designed to be fairly accessible (Anglim &amp; O'Connor, 2018, see preprint <a href="https://psyarxiv.com/a78g2/download" rel="noreferrer">https://psyarxiv.com/a78g2/download</a> )</p>

<p>If you want more detail, post further questions in the comments.</p>

<h3>References</h3>

<ul>
<li>Anglim, J., &amp; Grant, S. L. (2014). Incremental criterion prediction of personality facets over factors: Obtaining unbiased estimates and confidence intervals. Journal of Research in Personality, 53, 148-157. <a href="https://osf.io/g8kbj/download" rel="noreferrer">https://osf.io/g8kbj/download</a></li>
<li>Anglim, J., &amp; O'Connor, P. (2018). Measurement and Research Using the Big Five, HEXACO, and Narrow Traits: A Primer for Researchers and Practitioners. Australian Journal of Psychology. <a href="https://psyarxiv.com/a78g2/download" rel="noreferrer">https://psyarxiv.com/a78g2/download</a></li>
</ul>

CC BY-SA 3.0
2018-04-09T16:17:30.417Z
Thank you very much for your response; your 2014 paper greatly influenced my research!  
I am currently writing my thesis about how personality research can be used in economics. Indeed, one of my main concern is addressing the biases that arise when many predictors are included. To my knowledge, most psychologists mitigate the multiple comparison bias by correcting the significance level. However, none address the problem of overfitting. I hope to mitigate this issue by using the holdout method. Any thoughts on this would be greatly appreciated, otherwise thank you for your time!
RedStapler
CC BY-SA 3.0
2018-04-09T05:22:56.207Z
2018-04-09T05:22:56.207Z
Jeromy Anglim
<p>What are the advantages/disadvantages of using all 30 facets (<a href="https://en.wikipedia.org/wiki/Revised_NEO_Personality_Inventory" rel="nofollow noreferrer">NEO PI-R; Costa &amp; McCrae, 1992</a>) in a multiple regression versus only using the facets that you have a hypothesis for?</p>

<p>I see that the vast majority of research always includes all 30 facets, but I don't quite understand the added value of this.</p>

<p><strong>References</strong></p>

<p>Costa, P. T., Jr., &amp; McCrae, R. R. (1992). <em>Revised NEO personality inventory (NEO PI-R™) and NEO five-factor inventory (NEO-FFI): Professional manual</em>. Odessa, FL: Psychological Assessment Resources.</p>

CC BY-SA 3.0
2018-04-07T11:26:05.393Z
Welcome to Psychology.SE  It is required that references are provided for statements made unless commonly known.  To help, I have provided a couple for you.
Chris Rogers
<personality><statistics>
What are the benefits of using all 30 personality facets in multiple regressions compared to just including hypothesised facets?
RedStapler
# What are the benefits of using all 30 personality facets in multiple regressions compared to just including hypothesised facets?

(Asked by: username_0 on 2018-04-07)

What are the advantages/disadvantages of using all 30 facets ([NEO PI-R; Costa & McCrae, 1992](https://en.wikipedia.org/wiki/Revised_NEO_Personality_Inventory)) in a multiple regression versus only using the facets that you have a hypothesis for?

I see that the vast majority of research always includes all 30 facets, but I don't quite understand the added value of this.

**References**

Costa, P. T., Jr., & McCrae, R. R. (1992). *Revised NEO personality inventory (NEO PI-R™) and NEO five-factor inventory (NEO-FFI): Professional manual*. Odessa, FL: Psychological Assessment Resources.

**Tags:** personality, statistics

> **Comments:**
> **username_1**: Welcome to Psychology.SE  It is required that references are provided for statements made unless commonly known.  To help, I have provided a couple for you.  

---

## 1 answers

### Answer by username_2 on 2018-04-09. Score: 5 | Accepted answer

I wrote a paper that focuses on this question (Anglim & Grant, 2014; pre-print is <https://osf.io/g8kbj/download>).

In short, if you're interested in estimating how well facets predict an outcome, then you should include all facets as predictors. Likewise, if you want to estimate the incremental prediction of facets over domains (e.g., 30 facets over the Big 5), then you should include all domains in the domain regression model and all facets in the facet regression model.

If you're doing research, then presumably, you don't know which facets predict the outcome. Therefore, it's an empirical question which do predict.

An important point is that you use an estimator that corrects for biases when including many predictors. A reasonable approach is to use adjusted r-squared, although when you have 30 facets, large samples help a lot.

I also have more recent primer that is designed to be fairly accessible (Anglim & O'Connor, 2018, see preprint <https://psyarxiv.com/a78g2/download> )

If you want more detail, post further questions in the comments.

### References

* Anglim, J., & Grant, S. L. (2014). Incremental criterion prediction of personality facets over factors: Obtaining unbiased estimates and confidence intervals. Journal of Research in Personality, 53, 148-157. <https://osf.io/g8kbj/download>
* Anglim, J., & O'Connor, P. (2018). Measurement and Research Using the Big Five, HEXACO, and Narrow Traits: A Primer for Researchers and Practitioners. Australian Journal of Psychology. <https://psyarxiv.com/a78g2/download>

> **Comments:**
> **username_0**: Thank you very much for your response; your 2014 paper greatly influenced my research!   I am currently writing my thesis about how personality research can be used in economics. Indeed, one of my main concern is addressing the biases that arise when many predictors are included. To my knowledge, most psychologists mitigate the multiple comparison bias by correcting the significance level. However, none address the problem of overfitting. I hope to mitigate this issue by using the holdout method. Any thoughts on this would be greatly appreciated, otherwise thank you for your time!  

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