<p>When I embarked on using bootstrapping in my research I found this chapter/lecture notes by Cosma Shalizi very useful:</p>

<ul>
<li>The Bootstrap, 36-402, Advanced Data Analysis, 3 February 2011
<a href="http://www.stat.cmu.edu/~cshalizi/402/lectures/08-bootstrap/lecture-08.pdf" rel="nofollow">http://www.stat.cmu.edu/~cshalizi/402/lectures/08-bootstrap/lecture-08.pdf</a></li>
</ul>

<p>I see now that he has also written a short introduction for American Scientist</p>

<ul>
<li>Shalizi, C. (2010). The bootstrap. American Scientist, 98(3),
186-190. <a href="http://www.americanscientist.org/libraries/documents/201048159157846-2010-05Shalizi.pdf" rel="nofollow">http://www.americanscientist.org/libraries/documents/201048159157846-2010-05Shalizi.pdf</a></li>
</ul>

<p>We published this paper, which uses bootstrapping in the third analysis (and so may directly answer your question). </p>

<ul>
<li>Stafford, T., &amp; Dewar, M. (2014). Tracing the trajectory of skill learning with a very large sample of online game players. Psychological science, 25(2), 511-518. <a href="http://pss.sagepub.com/content/25/2/511.full" rel="nofollow">http://pss.sagepub.com/content/25/2/511.full</a></li>
</ul>

<p>Aside from mere egotism on my part, the analysis code for the paper is available, so you can see mechanically how I did it for my analysis (in Python)</p>

<ul>
<li><a href="https://github.com/tomstafford/axongame" rel="nofollow">https://github.com/tomstafford/axongame</a></li>
</ul>

CC BY-SA 3.0
2018-03-05T08:53:47.557Z
I'm unsure why you deleted this, but I took the liberty to undelete it. Ping me if you disagree please.
AliceD
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2018-03-05T15:46:36.607Z
@AliceD Who deleted? Me or Tom? I may have done this only by accident (so thx).
Lil'Lobster
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2018-03-05T18:50:35.793Z
@Lili,  it was Tom's doing and I don't understand why they deleted their post.
AliceD
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2018-03-06T11:38:16.023Z
I don't know why I deleted this either, most likely it was an accident, so happy for you to undelete
Idiot Tom
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2015-12-31T08:56:02.530Z
2015-12-31T08:56:02.530Z
Idiot Tom
<p>I am interested in bootstrap, especially how it's entering the psychologist's world.</p>

<p>Boostrapping is used to 'omit' classical assumptions to get statistics. Bootstrapping starts with creating new samples from an original sample (that is considered as a population). Those new samples are generated via resampling with replacement (so that in a new sample observations can be repeated). Doing this (for example) 1000 times, you can get 1000 new samples and then calculate statistics of interest on each of a new sample. Thus you get 1000 of means (as an example). In the end you can calculate (bootstrapped) confidence interval.</p>

<p>I am looking for papers on bootstrap in social sciences (like this one Wright, D.B, London, K., Field, A.P. (2011) Using Bootstrap Estimation and the Plug-in Principle for Clinical Psychology Data, <em>Journal of Experimental Psychology</em>, DOI: 10.5127/jep.013611), where the authors explain bootstrap technique to psychologists in their subfield or those papers (in social science) where bootstrap is used extensively.</p>

<p>Has anyone has seen, read or known such papers?</p>

CC BY-SA 3.0
2015-12-29T17:34:45.333Z
Would you mind explaining in greater depth what bootstrap is and how it's typically used? The reference to the paper is useful, but a quick paragraph explaining the principle would be greatly appreciated.
Seanny123
CC BY-SA 3.0
2015-12-29T17:48:45.613Z
... in greater depth... I'd like to address you to wikipedia, but it's used to 'omit' classical assumptions to get statistics.
Bootstrap starts with creating new samples from an original sample (that is considered as a population). Those new samples are generated via resampling with replacement (so that in a new sample observations can be repeated). Doing this (f.e.) 1000 times, you can get 1000 new samples and then calculate statistics of interest on each of a new sample. Thus you get 1000 of means (as an example). In the end you can calculate (bootstrapped) confidence interval.
Lil'Lobster
<reference-request><experimental-psychology><statistics>
Bootstrapping in Psychology
Lil'Lobster
# Bootstrapping in Psychology

(Asked by: username_0 on 2015-12-29)

I am interested in bootstrap, especially how it's entering the psychologist's world.

Boostrapping is used to 'omit' classical assumptions to get statistics. Bootstrapping starts with creating new samples from an original sample (that is considered as a population). Those new samples are generated via resampling with replacement (so that in a new sample observations can be repeated). Doing this (for example) 1000 times, you can get 1000 new samples and then calculate statistics of interest on each of a new sample. Thus you get 1000 of means (as an example). In the end you can calculate (bootstrapped) confidence interval.

I am looking for papers on bootstrap in social sciences (like this one Wright, D.B, London, K., Field, A.P. (2011) Using Bootstrap Estimation and the Plug-in Principle for Clinical Psychology Data, *Journal of Experimental Psychology*, DOI: 10.5127/jep.013611), where the authors explain bootstrap technique to psychologists in their subfield or those papers (in social science) where bootstrap is used extensively.

Has anyone has seen, read or known such papers?

**Tags:** reference-request, experimental-psychology, statistics

> **Comments:**
> **username_1**: Would you mind explaining in greater depth what bootstrap is and how it's typically used? The reference to the paper is useful, but a quick paragraph explaining the principle would be greatly appreciated.  
> **username_0**: ... in greater depth... I'd like to address you to wikipedia, but it's used to 'omit' classical assumptions to get statistics. Bootstrap starts with creating new samples from an original sample (that is considered as a population). Those new samples are generated via resampling with replacement (so that in a new sample observations can be repeated). Doing this (f.e.) 1000 times, you can get 1000 new samples and then calculate statistics of interest on each of a new sample. Thus you get 1000 of means (as an example). In the end you can calculate (bootstrapped) confidence interval.  

---

## 1 answers

### Answer by username_2 on 2015-12-31. Score: 2 | Accepted answer

When I embarked on using bootstrapping in my research I found this chapter/lecture notes by Cosma Shalizi very useful:

* The Bootstrap, 36-402, Advanced Data Analysis, 3 February 2011
  <http://www.stat.cmu.edu/~cshalizi/402/lectures/08-bootstrap/lecture-08.pdf>

I see now that he has also written a short introduction for American Scientist

* Shalizi, C. (2010). The bootstrap. American Scientist, 98(3),
  186-190. <http://www.americanscientist.org/libraries/documents/201048159157846-2010-05Shalizi.pdf>

We published this paper, which uses bootstrapping in the third analysis (and so may directly answer your question).

* Stafford, T., & Dewar, M. (2014). Tracing the trajectory of skill learning with a very large sample of online game players. Psychological science, 25(2), 511-518. <http://pss.sagepub.com/content/25/2/511.full>

Aside from mere egotism on my part, the analysis code for the paper is available, so you can see mechanically how I did it for my analysis (in Python)

* <https://github.com/tomstafford/axongame>

> **Comments:**
> **username_3**: I'm unsure why you deleted this, but I took the liberty to undelete it. Ping me if you disagree please.  
> **username_0**: @username_3 Who deleted? Me or Tom? I may have done this only by accident (so thx).  
> **username_3**: @Lili,  it was Tom's doing and I don't understand why they deleted their post.  
> **username_2**: I don't know why I deleted this either, most likely it was an accident, so happy for you to undelete  

---

