No relationships between self-reported Instagram use or type of use and mental well-being

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# No relationships between self-reported Instagram use or type of use and mental well-being
### Thomas Pollet Northumbria University (<a href="mailto:thomas.pollet@northumbria.ac.uk" class="email">thomas.pollet@northumbria.ac.uk</a>)
### 2023-07-23 | <a href="http://tvpollet.github.io/disclaimer">disclaimer</a>

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## Today

* Quick background, but warning: very, very low in theory.

* Focus on data.

* Findings from a larger study funded by [Facebook Research](https://research.facebook.com/blog/2018/12/announcing-the-winners-of-the-instagram-research-awards-for-social-technologies/).

* Together with Sam Roberts (LJMU), Connor Malcolm (Northumbria), Kris McCarty (Northumbria).

---
## Social media: Good or bad... .

Since their inception this has been a debate.

A lot of work focused on Facebook but more recently a focus on Instagram <a name=cite-Faelens2021></a>([Faelens, Hoorelbeke, Cambier, van
Put, Van
de
Putte, De Raedt, and Koster, 2021](https://doi.org/10.1016/j.chbr.2021.100121.)).

[Faelens, Hoorelbeke, Cambier, et al. (2021)](https://doi.org/10.1016/j.chbr.2021.100121.) : no strong evidence for a consistent association between Instagram use and anxiety. Little research specifically focused on whether type of use is associated with anxiety.

---
## Moral panics

Why does this matter? <a name=cite-Orben2020></a>([Orben, 2020](https://doi.org/10.1177/1745691620919372))

???
Even though Twenge also notes that there are positives.

---
## This study

* Previous work convenience samples/student samples (but Instagram has >500 million users who are >35)

* Little work comparing users to non-users. This work yielded mixed findings and was often from non-representative samples <a name=cite-Brailovskaia2018></a><a name=cite-Mackson2019></a>([Brailovskaia and Margraf, 2018](https://doi.org/10.1371/journal.pone.0191810); [Mackson, Brochu, and Schneider, 2019](https://doi.org/10.1177/1461444819840021))

* Limited work on how type of use relates to well-being. This is also a mixed bag <a name=cite-Valkenburg2022></a>([Valkenburg, van
Driel, and Beyens, 2022](https://doi.org/10.1177/14614448211065425)).

???
1.2 Billion users overall
Yang (2016) found that Instagram interaction and browsing were related to lower levels of loneliness, with broadcasting associated with higher levels of loneliness.

---
## Method: participants

* 498 participants via Prolific, nationally representative sample based on age/gender (residency). Just before 2020 COVID measures.

* 257 women, 236 men, 2 neither male or female, 3 non-disclosures.

* Ages ranged from 19 to 82 years (_M_ = 49.15, _SD_ = 15.53).

* 289 out of 498 participants indicated that they had completed at least a Bachelor level degree.

* 375 out of 498 participants indicated that they used Instagram.

???
58% Ba degree

75% use Insta.

---
## Method: Measures

* Anxiety (HADS, Cronbach’s `\(\alpha\)` = 0.87) and depression (HADS, Cronbach’s `\(\alpha\)` = 0.83) <a name=cite-Zigmond1983></a>([Zigmond and Snaith, 1983](https://doi.org/10.1111/j.1600-0447.1983.tb09716.x)).

* Loneliness (UCLA, Cronbach’s `\(\alpha\)` = 0.94) <a name=cite-Russell1980></a>([Russell, Peplau, and Cutrona, 1980](https://doi.org/10.1037/0022-3514.39.3.472))

* Do you use Instagram? (yes/no)

* Instagram use scale <a name=cite-Yang2016></a>([Yang, 2016](https://doi.org/10.1089/cyber.2016.0201)): active use (interaction and broadcast) and passive use (browsing).

--> Interaction (Cronbach’s `\(\alpha\)` = 0.75), Browsing (Cronbach’s `\(\alpha\)`  = .81) and Broadcasting (Cronbach’s `\(\alpha\)` = .83)

???
Unitary construct.
Modified from 1-5 to 1-10. Exploratory factor analysis. 
"The scale consists of two items measuring Interaction (Comment on or reply to other’s posts; Tag others in your posts or comments), two items measuring Broadcast (Post/upload on your profile without tagging anyone; Post something that is not directed to specific people), and two items measuring Browsing (Browse the homepage/newsfeed without leaving comments; Check out others profiles without leaving comments)."

---
## Procedure

* Full study protocol was preregistered on the [OSF](https://osf.io/twjup).

* Embedded in a Social Network Survey in [GENSI](http://www.tobiasstark.nl/GENSI/GENSI.htm). Not discussed here.

---
## Analysis: Matching

* Matching algorithm via [`MatchIt`](https://kosukeimai.github.io/MatchIt/) <a name=cite-Ho2011></a>([Ho, Imai, King, and Stuart, 2011](https://doi.org/10.18637/jss.v042.i08)).

* Matched on Gender, Age, Educational attainment and Nationality.

* Full results: pre: [here](pre_.html) and post: [here](post_.html).

This generates weights to be used in regression model (372 users vs 100 non-users).

???
genetic matching algorithm.

From Stuart 2010: "the matching on or controlling for the observed covariates also matches on or controls for the unobserved covariates, in so much as they are correlated with those that are observed. Thus, the only unobserved covariates of concern are those unrelated to the observed covariates"

---
## Analysis: Bayes Factors

* Allows us to quantify evidence for the null hypothesis <a name=cite-Dienes2016></a><a name=cite-Wagenmakers2007></a>([Dienes, 2016](https://doi.org/10.1016/j.jmp.2015.10.003); [Wagenmakers, 2007](https://doi.org/10.3758/BF03194105)).

* Qualifications for evidence by <a name=cite-Jeffreys1961></a>([Jeffreys, 1961](#bib-Jeffreys1961)): BF = 1 - No evidence, 1 < BF <= 3 - Anecdotal, 3 < BF <= 10 - Moderate, 10 < BF <= 30 - Strong, 30 < BF <= 100 - Very strong, BF > 100 - Extreme.

---
## Correlations

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## Correlations II

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 4.Anxiety<o:p></o:p>
 </td>
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 1.11<o:p></o:p>
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  <o:p></o:p>
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 <td width=82 style='width:61.2pt;padding:0cm 5.0pt 0cm 5.0pt'>
  <o:p></o:p>
 </td>
 <td width=82 style='width:61.2pt;padding:0cm 5.0pt 0cm 5.0pt'>
  <o:p></o:p>
 </td>
 </tr>
 <tr style='mso-yfti-irow:11'>
 <td width=122 style='width:91.75pt;padding:0cm 5.0pt 0cm 5.0pt'>
  <o:p></o:p>
 </td>
 <td width=82 style='width:61.25pt;padding:0cm 5.0pt 0cm 5.0pt'>
  <o:p></o:p>
 </td>
 <td width=82 style='width:61.2pt;padding:0cm 5.0pt 0cm 5.0pt'>
  <o:p></o:p>
 </td>
 <td width=82 style='width:61.2pt;padding:0cm 5.0pt 0cm 5.0pt'>
 [-.01,
 .19]<o:p></o:p>
 </td>
 <td width=82 style='width:61.2pt;padding:0cm 5.0pt 0cm 5.0pt'>
 [.07,
 .26]<o:p></o:p>
 </td>
 <td width=82 style='width:61.2pt;padding:0cm 5.0pt 0cm 5.0pt'>
 [.03,
 .23]<o:p></o:p>
 </td>
 <td width=82 style='width:61.2pt;padding:0cm 5.0pt 0cm 5.0pt'>
  <o:p></o:p>
 </td>
 <td width=82 style='width:61.2pt;padding:0cm 5.0pt 0cm 5.0pt'>
  <o:p></o:p>
 </td>
 <td width=82 style='width:61.2pt;padding:0cm 5.0pt 0cm 5.0pt'>
  <o:p></o:p>
 </td>
 </tr>
 <tr style='mso-yfti-irow:12'>
 <td width=122 style='width:91.75pt;padding:0cm 5.0pt 0cm 5.0pt'>
  <o:p></o:p>
 </td>
 <td width=82 style='width:61.25pt;padding:0cm 5.0pt 0cm 5.0pt'>
  <o:p></o:p>
 </td>
 <td width=82 style='width:61.2pt;padding:0cm 5.0pt 0cm 5.0pt'>
  <o:p></o:p>
 </td>
 <td width=82 style='width:61.2pt;padding:0cm 5.0pt 0cm 5.0pt'>
  <o:p></o:p>
 </td>
 <td width=82 style='width:61.2pt;padding:0cm 5.0pt 0cm 5.0pt'>
  <o:p></o:p>
 </td>
 <td width=82 style='width:61.2pt;padding:0cm 5.0pt 0cm 5.0pt'>
  <o:p></o:p>
 </td>
 <td width=82 style='width:61.2pt;padding:0cm 5.0pt 0cm 5.0pt'>
  <o:p></o:p>
 </td>
 <td width=82 style='width:61.2pt;padding:0cm 5.0pt 0cm 5.0pt'>
  <o:p></o:p>
 </td>
 <td width=82 style='width:61.2pt;padding:0cm 5.0pt 0cm 5.0pt'>
  <o:p></o:p>
 </td>
 </tr>
 <tr style='mso-yfti-irow:13'>
 <td width=122 style='width:91.75pt;padding:0cm 5.0pt 0cm 5.0pt'>
 5.Depression<o:p></o:p>
 </td>
 <td width=82 style='width:61.25pt;padding:0cm 5.0pt 0cm 5.0pt'>
 0.78<o:p></o:p>
 </td>
 <td width=82 style='width:61.2pt;padding:0cm 5.0pt 0cm 5.0pt'>
 0.56<o:p></o:p>
 </td>
 <td width=82 style='width:61.2pt;padding:0cm 5.0pt 0cm 5.0pt'>
 .02<o:p></o:p>
 </td>
 <td width=82 style='width:61.2pt;padding:0cm 5.0pt 0cm 5.0pt'>
 .05<o:p></o:p>
 </td>
 <td width=82 style='width:61.2pt;padding:0cm 5.0pt 0cm 5.0pt'>
 .05<o:p></o:p>
 </td>
 <td width=82 style='width:61.2pt;padding:0cm 5.0pt 0cm 5.0pt'>
 .64**<o:p></o:p>
 </td>
 <td width=82 style='width:61.2pt;padding:0cm 5.0pt 0cm 5.0pt'>
  <o:p></o:p>
 </td>
 <td width=82 style='width:61.2pt;padding:0cm 5.0pt 0cm 5.0pt'>
  <o:p></o:p>
 </td>
 </tr>
 <tr style='mso-yfti-irow:14'>
 <td width=122 style='width:91.75pt;padding:0cm 5.0pt 0cm 5.0pt'>
  <o:p></o:p>
 </td>
 <td width=82 style='width:61.25pt;padding:0cm 5.0pt 0cm 5.0pt'>
  <o:p></o:p>
 </td>
 <td width=82 style='width:61.2pt;padding:0cm 5.0pt 0cm 5.0pt'>
  <o:p></o:p>
 </td>
 <td width=82 style='width:61.2pt;padding:0cm 5.0pt 0cm 5.0pt'>
 [-.08,
 .12]<o:p></o:p>
 </td>
 <td width=82 style='width:61.2pt;padding:0cm 5.0pt 0cm 5.0pt'>
 [-.06,
 .15]<o:p></o:p>
 </td>
 <td width=82 style='width:61.2pt;padding:0cm 5.0pt 0cm 5.0pt'>
 [-.06,
 .15]<o:p></o:p>
 </td>
 <td width=82 style='width:61.2pt;padding:0cm 5.0pt 0cm 5.0pt'>
 [.57,
 .70]<o:p></o:p>
 </td>
 <td width=82 style='width:61.2pt;padding:0cm 5.0pt 0cm 5.0pt'>
  <o:p></o:p>
 </td>
 <td width=82 style='width:61.2pt;padding:0cm 5.0pt 0cm 5.0pt'>
  <o:p></o:p>
 </td>
 </tr>
 <tr style='mso-yfti-irow:15'>
 <td width=122 style='width:91.75pt;padding:0cm 5.0pt 0cm 5.0pt'>
  <o:p></o:p>
 </td>
 <td width=82 style='width:61.25pt;padding:0cm 5.0pt 0cm 5.0pt'>
  <o:p></o:p>
 </td>
 <td width=82 style='width:61.2pt;padding:0cm 5.0pt 0cm 5.0pt'>
  <o:p></o:p>
 </td>
 <td width=82 style='width:61.2pt;padding:0cm 5.0pt 0cm 5.0pt'>
  <o:p></o:p>
 </td>
 <td width=82 style='width:61.2pt;padding:0cm 5.0pt 0cm 5.0pt'>
  <o:p></o:p>
 </td>
 <td width=82 style='width:61.2pt;padding:0cm 5.0pt 0cm 5.0pt'>
  <o:p></o:p>
 </td>
 <td width=82 style='width:61.2pt;padding:0cm 5.0pt 0cm 5.0pt'>
  <o:p></o:p>
 </td>
 <td width=82 style='width:61.2pt;padding:0cm 5.0pt 0cm 5.0pt'>
  <o:p></o:p>
 </td>
 <td width=82 style='width:61.2pt;padding:0cm 5.0pt 0cm 5.0pt'>
  <o:p></o:p>
 </td>
 </tr>
 <tr style='mso-yfti-irow:16'>
 <td width=122 style='width:91.75pt;padding:0cm 5.0pt 0cm 5.0pt'>
 6. Loneliness<o:p></o:p>
 </td>
 <td width=82 style='width:61.25pt;padding:0cm 5.0pt 0cm 5.0pt'>
 2.26<o:p></o:p>
 </td>
 <td width=82 style='width:61.2pt;padding:0cm 5.0pt 0cm 5.0pt'>
 0.55<o:p></o:p>
 </td>
 <td width=82 style='width:61.2pt;padding:0cm 5.0pt 0cm 5.0pt'>
 -.01<o:p></o:p>
 </td>
 <td width=82 style='width:61.2pt;padding:0cm 5.0pt 0cm 5.0pt'>
 .05<o:p></o:p>
 </td>
 <td width=82 style='width:61.2pt;padding:0cm 5.0pt 0cm 5.0pt'>
 .06<o:p></o:p>
 </td>
 <td width=82 style='width:61.2pt;padding:0cm 5.0pt 0cm 5.0pt'>
 .52**<o:p></o:p>
 </td>
 <td width=82 style='width:61.2pt;padding:0cm 5.0pt 0cm 5.0pt'>
 .66**<o:p></o:p>
 </td>
 <td width=82 style='width:61.2pt;padding:0cm 5.0pt 0cm 5.0pt'>
  <o:p></o:p>
 </td>
 </tr>
 <tr style='mso-yfti-irow:17'>
 <td width=122 style='width:91.75pt;padding:0cm 5.0pt 0cm 5.0pt'>
  <o:p></o:p>
 </td>
 <td width=82 style='width:61.25pt;padding:0cm 5.0pt 0cm 5.0pt'>
  <o:p></o:p>
 </td>
 <td width=82 style='width:61.2pt;padding:0cm 5.0pt 0cm 5.0pt'>
  <o:p></o:p>
 </td>
 <td width=82 style='width:61.2pt;padding:0cm 5.0pt 0cm 5.0pt'>
 [-.11,
 .10]<o:p></o:p>
 </td>
 <td width=82 style='width:61.2pt;padding:0cm 5.0pt 0cm 5.0pt'>
 [-.05,
 .15]<o:p></o:p>
 </td>
 <td width=82 style='width:61.2pt;padding:0cm 5.0pt 0cm 5.0pt'>
 [-.05,
 .16]<o:p></o:p>
 </td>
 <td width=82 style='width:61.2pt;padding:0cm 5.0pt 0cm 5.0pt'>
 [.44,
 .59]<o:p></o:p>
 </td>
 <td width=82 style='width:61.2pt;padding:0cm 5.0pt 0cm 5.0pt'>
 [.60,
 .71]<o:p></o:p>
 </td>
 <td width=82 style='width:61.2pt;padding:0cm 5.0pt 0cm 5.0pt'>
  <o:p></o:p>
 </td>
 </tr>
 <tr style='mso-yfti-irow:18'>
 <td width=122 style='width:91.75pt;padding:0cm 5.0pt 0cm 5.0pt'>
  <o:p></o:p>
 </td>
 <td width=82 style='width:61.25pt;padding:0cm 5.0pt 0cm 5.0pt'>
  <o:p></o:p>
 </td>
 <td width=82 style='width:61.2pt;padding:0cm 5.0pt 0cm 5.0pt'>
  <o:p></o:p>
 </td>
 <td width=82 style='width:61.2pt;padding:0cm 5.0pt 0cm 5.0pt'>
  <o:p></o:p>
 </td>
 <td width=82 style='width:61.2pt;padding:0cm 5.0pt 0cm 5.0pt'>
  <o:p></o:p>
 </td>
 <td width=82 style='width:61.2pt;padding:0cm 5.0pt 0cm 5.0pt'>
  <o:p></o:p>
 </td>
 <td width=82 style='width:61.2pt;padding:0cm 5.0pt 0cm 5.0pt'>
  <o:p></o:p>
 </td>
 <td width=82 style='width:61.2pt;padding:0cm 5.0pt 0cm 5.0pt'>
  <o:p></o:p>
 </td>
 <td width=82 style='width:61.2pt;padding:0cm 5.0pt 0cm 5.0pt'>
  <o:p></o:p>
 </td>
 </tr>
 <tr style='mso-yfti-irow:19'>
 <td width=122 style='width:91.75pt;padding:0cm 5.0pt 0cm 5.0pt'>
 7. Age<o:p></o:p>
 </td>
 <td width=82 style='width:61.25pt;padding:0cm 5.0pt 0cm 5.0pt'>
 42.77<o:p></o:p>
 </td>
 <td width=82 style='width:61.2pt;padding:0cm 5.0pt 0cm 5.0pt'>
 15.35<o:p></o:p>
 </td>
 <td width=82 style='width:61.2pt;padding:0cm 5.0pt 0cm 5.0pt'>
 -.12*<o:p></o:p>
 </td>
 <td width=82 style='width:61.2pt;padding:0cm 5.0pt 0cm 5.0pt'>
 -.42**<o:p></o:p>
 </td>
 <td width=82 style='width:61.2pt;padding:0cm 5.0pt 0cm 5.0pt'>
 -.14**<o:p></o:p>
 </td>
 <td width=82 style='width:61.2pt;padding:0cm 5.0pt 0cm 5.0pt'>
 -.21**<o:p></o:p>
 </td>
 <td width=82 style='width:61.2pt;padding:0cm 5.0pt 0cm 5.0pt'>
 -.08<o:p></o:p>
 </td>
 <td width=82 style='width:61.2pt;padding:0cm 5.0pt 0cm 5.0pt'>
 -.12*<o:p></o:p>
 </td>
 </tr>
 <tr style='mso-yfti-irow:20'>
 <td width=122 style='width:91.75pt;padding:0cm 5.0pt 0cm 5.0pt'>
  <o:p></o:p>
 </td>
 <td width=82 style='width:61.25pt;padding:0cm 5.0pt 0cm 5.0pt'>
  <o:p></o:p>
 </td>
 <td width=82 style='width:61.2pt;padding:0cm 5.0pt 0cm 5.0pt'>
  <o:p></o:p>
 </td>
 <td width=82 style='width:61.2pt;padding:0cm 5.0pt 0cm 5.0pt'>
 [-.22,
 -.01]<o:p></o:p>
 </td>
 <td width=82 style='width:61.2pt;padding:0cm 5.0pt 0cm 5.0pt'>
 [-.50,
 -.33]<o:p></o:p>
 </td>
 <td width=82 style='width:61.2pt;padding:0cm 5.0pt 0cm 5.0pt'>
 [-.23,
 -.03]<o:p></o:p>
 </td>
 <td width=82 style='width:61.2pt;padding:0cm 5.0pt 0cm 5.0pt'>
 [-.31,
 -.12]<o:p></o:p>
 </td>
 <td width=82 style='width:61.2pt;padding:0cm 5.0pt 0cm 5.0pt'>
 [-.18,
 .02]<o:p></o:p>
 </td>
 <td width=82 style='width:61.2pt;padding:0cm 5.0pt 0cm 5.0pt'>
 [-.22,
 -.02]<o:p></o:p>
 </td>
 </tr>
 <tr style='mso-yfti-irow:21;mso-yfti-lastrow:yes'>
 <td width=122 style='width:91.75pt;border:none;border-bottom:solid windowtext 1.0pt;
 mso-border-bottom-alt:solid windowtext .75pt;padding:0cm 5.0pt 0cm 5.0pt'>
  <o:p></o:p>
 </td>
 <td width=82 style='width:61.25pt;border:none;border-bottom:solid windowtext 1.0pt;
 mso-border-bottom-alt:solid windowtext .75pt;padding:0cm 5.0pt 0cm 5.0pt'>
  <o:p></o:p>
 </td>
 <td width=82 style='width:61.2pt;border:none;border-bottom:solid windowtext 1.0pt;
 mso-border-bottom-alt:solid windowtext .75pt;padding:0cm 5.0pt 0cm 5.0pt'>
  <o:p></o:p>
 </td>
 <td width=82 style='width:61.2pt;border:none;border-bottom:solid windowtext 1.0pt;
 mso-border-bottom-alt:solid windowtext .75pt;padding:0cm 5.0pt 0cm 5.0pt'>
  <o:p></o:p>
 </td>
 <td width=82 style='width:61.2pt;border:none;border-bottom:solid windowtext 1.0pt;
 mso-border-bottom-alt:solid windowtext .75pt;padding:0cm 5.0pt 0cm 5.0pt'>
  <o:p></o:p>
 </td>
 <td width=82 style='width:61.2pt;border:none;border-bottom:solid windowtext 1.0pt;
 mso-border-bottom-alt:solid windowtext .75pt;padding:0cm 5.0pt 0cm 5.0pt'>
  <o:p></o:p>
 </td>
 <td width=82 style='width:61.2pt;border:none;border-bottom:solid windowtext 1.0pt;
 mso-border-bottom-alt:solid windowtext .75pt;padding:0cm 5.0pt 0cm 5.0pt'>
  <o:p></o:p>
 </td>
 <td width=82 style='width:61.2pt;border:none;border-bottom:solid windowtext 1.0pt;
 mso-border-bottom-alt:solid windowtext .75pt;padding:0cm 5.0pt 0cm 5.0pt'>
  <o:p></o:p>
 </td>
 <td width=82 style='width:61.2pt;border:none;border-bottom:solid windowtext 1.0pt;
 mso-border-bottom-alt:solid windowtext .75pt;padding:0cm 5.0pt 0cm 5.0pt'>
  <o:p></o:p>
 </td>
 </tr>
</table>

<o:p>&nbsp;</o:p>

</div>

</body>

</html>

---
## Anxiety

???
372 users, 100 non-users

---
## Depression

---
## Loneliness

---
## Users vs. non users

<table style="text-align:center"><tr><td colspan="4" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left"></td><td>Anxiety</td><td>Depression</td><td>Loneliness</td></tr>
<tr><td style="text-align:left"></td><td>Model 1</td><td>Model 2</td><td>Model 3</td></tr>
<tr><td colspan="4" style="border-bottom: 1px solid black"></td></tr><tr><td style="text-align:left">Instagram User</td><td>-0.020</td><td>-0.087</td><td>-0.095</td></tr>
<tr><td style="text-align:left"></td><td>(0.074)</td><td>(0.064)</td><td>(0.062)</td></tr>
<tr><td style="text-align:left">Constant</td><td>1.129***</td><td>0.862***</td><td>2.357***</td></tr>
<tr><td style="text-align:left"></td><td>(0.066)</td><td>(0.057)</td><td>(0.055)</td></tr>
<tr><td style="text-align:left">N</td><td>472</td><td>472</td><td>472</td></tr>
<tr><td style="text-align:left">R2</td><td>0.0002</td><td>0.004</td><td>0.005</td></tr>
<tr><td style="text-align:left">Adjusted R2</td><td>-0.002</td><td>0.002</td><td>0.003</td></tr>
<tr><td style="text-align:left">Residual Std. Error (df = 470)</td><td>0.655</td><td>0.567</td><td>0.548</td></tr>
<tr><td style="text-align:left">F Statistic (df = 1; 470)</td><td>0.073</td><td>1.848</td><td>2.393</td></tr>
<tr><td colspan="4" style="border-bottom: 1px solid black"></td></tr><tr><td colspan="4" style="text-align:left">*p < .05; **p < .01; ***p < .001</td></tr>
</table>

???
If anything there users were less anxious, depressed, lonely than non-users.

---
## Bayes Factors

* **Anxiety : 9.41**

* **Depression : 4.01**

* **Loneliness: 3.08**

This suggests moderate support **against** the hypothesis that Instagram use is related to mental well-being.

---
## Anxiety

* Effect for _Browsing_ at _p_ <.05, but drops out of significance once age is included in the model (_p_ jumps to >.36).

* [Full results table](anx_table.html)

* Model 1 - Bayes Factor  = 2.78 in favour of effect, but note not longer supported after inclusion of age.

???
Further exploration with a decision tree model from machine learning only showed age as relevant predictor.

Reference categories are female (Gender), Other (Nationality) and A-Level (Education)

---
## Depression

* no sig. effect at _p_ < .05. (all 3 _p_'s > .5)

* [Full results table](depression_table.html)

* Model 1 - Bayes Factor  = 127.52 in favour of null.

---
## Loneliness

* no sig. effect at _p_ < .05. (all 3 _p_'s > .3)

* [Full results table](loneliness_table.html)

* Model 1 - Bayes Factor  = 79.68 in favour of null.

---
## Summary

* No support for a difference between users and non-users in mental well-being.

* No relationships between type of use and our outcomes. With the exception of anxiety, but this is weak evidence at best, and no longer supported after inclusion of age.

In line with some published work finding no relationships,... .

Null findings are important,... .

---
## Limitations

* Representativeness / survivorship bias

---
## Limitations II

* Measures / Self-reports... .

---
## Limitations III

* Cross-sectional data: No causality.

---
## Further research

* Starting to use or leaving Instagram. (Wave 2 = + 3 months, Wave 3 = + 6 months)

* Very preliminary analysis for Wave 2 (316 responses). 280 no change, 21 left, 15 joined -- **No matching on sociodemographics.**

* **Bayesian t-tests**: leaving vs. no change: Anxiety: 1.70; Depression: 3.07; Loneliness: 3.82.

* **Bayesian t-tests**: joining vs. no change: Anxiety: 2.91; Depression: 2.64; Loneliness: 3.17.

* **Bayesian t-tests**: joining vs. leaving: Anxiety: 1.61; Depression: 3.06; Loneliness: 3.05.

???
All in favour of null. But note Covid19 fully grasped hold... .

---
## Further research 2

* Actual usage patterns from Instagram account.

---
## Any Questions?

[http://tvpollet.github.io](http://tvpollet.github.io)

Twitter: @tvpollet; Mastodon: @tvpollet@sciences.social

---
## Acknowledgments

* I am greatly indebted to my collaborators. (Any mistakes are my own!). Preprint is [here](https://psyarxiv.com/2583s/).

* This project was funded by [Facebook(/Meta)](https://research.facebook.com/blog/2018/12/announcing-the-winners-of-the-instagram-research-awards-for-social-technologies/). The funder was not involved in the study design, collection, analysis, interpretation of data, the writing of this presentation, or the decision to submit it for this conference.

* slides are built in [Xaringan](https://github.com/yihui/xaringan) and [XaringanExtra](https://pkg.garrickadenbuie.com/xaringanExtra/#/)

* You for listening!

---
## References (errors = blame RefManageR)

<a name=bib-Brailovskaia2018></a>[Brailovskaia, J. and J.
Margraf](#cite-Brailovskaia2018) (2018). "What Does Media Use Reveal
about Personality and Mental Health? An Exploratory Investigation among
German Students". In: _PloS one_ 13.1, p. e0191810. ISSN: 1932-6203.
DOI:
[10.1371/journal.pone.0191810](https://doi.org/10.1371%2Fjournal.pone.0191810).

<a name=bib-Dienes2016></a>[Dienes, Z.](#cite-Dienes2016) (2016). "How
Bayes Factors Change Scientific Practice". In: _Journal of Mathematical
Psychology_ 72, pp. 78-89. ISSN: 00222496. DOI:
[10.1016/j.jmp.2015.10.003](https://doi.org/10.1016%2Fj.jmp.2015.10.003).

<a name=bib-Faelens2021></a>[Faelens, L, K. Hoorelbeke, R. Cambier, et
al.](#cite-Faelens2021) (2021). "The Relationship between Instagram Use
and Indicators of Mental Health: A Systematic Review". In: _Computers
in Human Behavior Reports_ 4, p. 100121. ISSN: 2451-9588. DOI:
[10.1016/j.chbr.2021.100121.](https://doi.org/10.1016%2Fj.chbr.2021.100121.).

<a name=bib-Ho2011></a>[Ho, D. E, K. Imai, G. King, et
al.](#cite-Ho2011) (2011). "MatchIt : Nonparametric Preprocessing for
Parametric Causal Inference". In: _Journal of Statistical Software_
42.8. ISSN: 1548-7660. DOI:
[10.18637/jss.v042.i08](https://doi.org/10.18637%2Fjss.v042.i08).

<a name=bib-Jeffreys1961></a>[Jeffreys, H.](#cite-Jeffreys1961) (1961).
_The Theory of Probability_. 3rd Edition. Oxford, UK: Oxford University
Press.

---
## References II (errors = blame RefManageR)

<a name=bib-Mackson2019></a>[Mackson, S. B, P. M. Brochu, and B. A.
Schneider](#cite-Mackson2019) (2019). "Instagram: Friend or Foe? The
Application's Association with Psychological Well-Being". In: _New
Media & Society_ 21.10, pp. 2160-2182. DOI:
[10.1177/1461444819840021](https://doi.org/10.1177%2F1461444819840021).

<a name=bib-Orben2020></a>[Orben, A.](#cite-Orben2020) (2020). "The
Sisyphean Cycle of Technology Panics". In: _Perspectives on
Psychological Science_ 15.5, pp. 1143-1157. DOI:
[10.1177/1745691620919372](https://doi.org/10.1177%2F1745691620919372).

<a name=bib-Russell1980></a>[Russell, D. W, L. A. Peplau, and C. E.
Cutrona](#cite-Russell1980) (1980). "The Revised UCLA Loneliness Scale:
Concurrent and Discriminant Validity Evidence." In: _Journal of
Personality and Social Psychology_ 39.3, pp. 472-480. ISSN: 1939-1315.
DOI:
[10.1037/0022-3514.39.3.472](https://doi.org/10.1037%2F0022-3514.39.3.472).

<a name=bib-Valkenburg2022></a>[Valkenburg, P. M, I. I. van Driel, and
I. Beyens](#cite-Valkenburg2022) (2022). "The Associations of Active
and Passive Social Media Use with Well-Being: A Critical Scoping
Review". In: _New Media & Society_ 24.2, pp. 530-549. ISSN: 1461-4448.
DOI:
[10.1177/14614448211065425](https://doi.org/10.1177%2F14614448211065425).
(Visited on Jul. 21, 2023).

<a name=bib-Wagenmakers2007></a>[Wagenmakers,
E.](#cite-Wagenmakers2007) (2007). "A Practical Solution to the
Pervasive Problems of p Values". In: _Psychonomic Bulletin & Review_
14.5, pp. 779-804. ISSN: 1531-5320. DOI:
[10.3758/BF03194105](https://doi.org/10.3758%2FBF03194105).

---
## References III (errors = blame RefManageR)

<a name=bib-Yang2016></a>[Yang, C.](#cite-Yang2016) (2016). "Instagram
Use, Loneliness, and Social Comparison Orientation: Interact and Browse
on Social Media, But Don't Compare". In: _Cyberpsychology, Behavior,
and Social Networking_ 19.12, pp. 703-708. ISSN: 2152-2715. DOI:
[10.1089/cyber.2016.0201](https://doi.org/10.1089%2Fcyber.2016.0201).

<a name=bib-Zigmond1983></a>[Zigmond, A. S. and R. P.
Snaith](#cite-Zigmond1983) (1983). "The Hospital Anxiety and Depression
Scale". In: _Acta Psychiatrica Scandinavica_ 67.6, pp. 361-370. ISSN:
0001-690X. DOI:
[10.1111/j.1600-0447.1983.tb09716.x](https://doi.org/10.1111%2Fj.1600-0447.1983.tb09716.x).