class: center, middle, inverse, title-slide # Correlation ## Practical 05 ### Jennifer Mankin ### 25 - 26 February 2021 --- <script type="text/javascript"> setup() </script> ## Today's Tasks - Defining the Problem: Mental Health and Social Media - First Half: Data Exploration and Analysis - Second Half: Interpretation and Write-Up --- ## Mental Health and Social Media <center> <blockquote class="twitter-tweet"><p lang="en" dir="ltr">A new study shows a rise in depression and stress among young people parallels the growth in smartphone and social media use.<a href="https://t.co/AxyseUyBxn">https://t.co/AxyseUyBxn</a></p>&mdash; NPR (@NPR) <a href="https://twitter.com/NPR/status/1106264229210775552?ref_src=twsrc%5Etfw">March 14, 2019</a></blockquote> <script async src="https://platform.twitter.com/widgets.js" charset="utf-8"></script> </center> --- ## Mental Health in Young People - How does social media use impact mental health? - In everyone? - In young people in particular? - Is the moral panic over "kids these days" and technology substantiated? --- ## Mental Health in Social Media - This is (to some degree) a scientific question - So, let's form a hypothesis, look at the evidence, and critically evaluate the results -- - First: clearly define our research question - Social skills, personality traits, attention, mental health etc. are all different *constructs* - Let's focus today on wellbeing - Wellbeing is mental-health-adjacent, but a bit easier to measure in young people! -- - Conceptual hypothesis: Social media use has an impact on wellbeing in young people --- ## Today's Data: The Millennium Cohort - Real data about young people born 2000 - 2002 - Began with almost 19,000 participants! - [Read more about the Millennium Cohort](https://cls.ucl.ac.uk/cls-studies/millennium-cohort-study/) - Subset of variables: - Demographics: age (all around 10 - 11 years), sex - Social media/Internet use - Happiness/wellbeing --- ## Part 1 - Work through the worksheet in your teams - **Choose** two variables to use in your analysis - Read the codebook carefully! - Run the analysis and report the results - Post your results on Padlet and read through other groups' - Keep in mind: What is the answer to our research question? --- <br><br><br><br><br> # Attendance and Break --- ## Part 2 - Take a closer look at your result - Think about what your correlation does and doesn't tell you - Make a scatterplot of your variables - OPTIONAL: Explore significant effects in large samples - Why are small/weak effects still significant? - How can we interpret these effects? --- class: last-slide <br><br><br><br><br> # And that's it!