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Depression leads to more social media usage, not the other way around!

Recent longitudinal research from Brock University in Canada suggests that depression leads to people spending more time on social media, rather than those who spend more time of social media being more likely to develop depression.

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This study contradicts many of the ‘moral panic’ type headlines which suggests a link between heavy social media use and depression. Such headlines tend to be based on studies which look at correlations between indicators of depression and indicators of social media use at the same point in time, which cannot tell us which comes first: the depression or the heavy social media use.

This Canadian study followed a sample of teenagers from 2015 (and university students for 6 years) and surveyed them at intervals using a set of questions designed to measure depression levels and another set designed to measure social media usage and other aspects of screen time.

What they found was that teenage girls who showed signs of depression early on in the study were more likely to have higher rates of social media usage later on, leading to the theory that teenage girls who are depressed may well turn to social media to make themselves feel better.

The study found no relationship between boys or adults of both sexes and depression and social media.

This is an interesting research study which really goes to show the advantages of the longitudinal method (researching the same sample at intervals over time) in possibly busting a few myths about the harmful effects of social media!

 

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Longitudinal Studies

Longitudinal Studies are studies in which data is collected at specific intervals over a long period of time in order to measure changes over time. This post provides one example of a longitudinal study and explores some the strengths and limitations of this research method.

With a longitudinal study you might start with an original sample of respondents in one particular year (say the year 2000) and then go back to them every year, every five years, or every ten years, aiming to collect data from the same people. One of the biggest problems with Longitudinal Studies is the attrition rate, or the subject dropout rate over time.

The Millennium Cohort Study

One recent example of a Longitudinal study is the Millennium Cohort Study, which stretched from 2000 to 2011, with an initial sample of 19 000 children.

The study tracked children until the age of 11 and has provide an insight into how differences in early socialisation affect child development in terms of health and educational outcomes.

The study also allowed researchers to make comparisons in rates of development between children of different sexes and from different economic backgrounds.

Led by the Centre for Longitudinal Studies at the Institute of Education, it was funded by the Economic and Social Research Council and government departments. The results below come from between 2006 and 2007, when the children were aged five.

Selected Findings

  • The survey found that children whose parents read to them every day at the age of three were more likely to flourish in their first year in primary school, getting more than two months ahead not just in language and literacy but also in maths
  • Children who were read to on a daily basis were 2.4 months ahead of those whose parents never read to them in maths, and 2.8 months ahead in communication, language and literacy.
  • Girls were consistently outperforming boys at the age of five, when they were nine months ahead in creative development – activities like drama, singing and dancing, and 4.2 months ahead in literacy.
  • Children from lower-income families with parents who were less highly educated were less advanced in their development at age five. Living in social housing put them 3.2 months behind in maths and 3.5 months behind in literacy.

The strengths of longitudinal studies

  • They allow researchers to trace developments over time, rather than just taking a one-off ‘snapshot’ of one moment.
  • By making comparisons over time, they can identify causes. The Millennium Cohort study, for example suggests a clear correlation between poverty and its early impact on low educational achievement

The limitations of longitudinal studies

  • Sample attrition – people dropping out of the study, and the people who remain in the study may not end up being representative of the starting sample.
  • People may start to act differently because they know they are part of the study
  • Because they take a long time, they are costly and time consuming.
  • Continuity over many years may be a problem – if a lead researcher retires, for example, her replacement might not have the same rapport with respondents.

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Explaining Social Class Differences in Educational Through Longitudinal Studies

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Explaining Social Class Differences in Education Using Longitudinal Studies

Why do working class children do worse than middle class children in education? This post looks at some quantitative, longitudinal data to explore why.

A recent report by the Joseph Rowntree Foundation argues that early intervention is not enough to tackle the persistent differences in class inequalities in educational achievement – The report is a follow up to earlier research published March last year which is summarised below

This four page summary (and the longer document which you can get if you follow the links) is an excellent example of a quantitative approach to social research – in the tradition of Positivism (although strictly speaking, not purely Positivist). NB IF THE IMAGES AREN’T CLEAR JUST CLICK ON THEM! I’ve spent way too long faffing about with them already.

This study uses statistical data from four longitudinal studies  to uncover the main ‘causal factors’ behind why children from low income backgrounds do so badly in education.

Before we get onto the ’causes’ please note that ‘educational achievement gap’ between the social classes widens as children get older. The study notes that –

The research showed that educational deficits emerge early in children’s lives, even before entry into school, and widen throughout childhood. Even by the age of three there is a considerable gap in cognitive test scores between children in the poorest fifth of the population compared with those from better-off backgrounds. This gap widens as children enter and move through the schooling system, especially during primary school years.

The report demonstrates this graphically as follows –

 

Differences in 'cognitive ability' by income and age
Differences in ‘cognitive ability’ by income and age

 

And you can see from the table below how the differences are greater by ages 7 and 11…

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According to the study The main ’causes’ of class differences in educational achievement are –

  • Children from poorer backgrounds are much less likely to experience a rich home learning environment than children from better-off backgrounds. At age three, for example, reading to the child is less likely to happen in poorer households.

Reasons for the widening gap between children from richer and poorer backgrounds are:

  • lower parental aspirations for higher education – (81% of the richest mothers hope their child at age 9 will go to university, compared to only 39% of the poorest mothers)
  • how far parents and children believe their own actions can affecttheir lives;
  • children’s behavioural problems.

• It becomes harder to reverse patterns of under-achievement by the teenage years, but disadvantage and poor school results continue to be linked, including through:

  • – teenagers’ and parents’ expectations for higher education
  • material resources such as access to a computer and the internet at home;
  •  engagement in anti-social behaviour;
  • and young people’s belief in their own ability at school.

What’s interesting is the way the stats visually display the multiple disadvantages people from low incomes face – for example –

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Probably my favourite graphic of all is this – which is hopefully at least partially self explanatory
 
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If it’s not clear from the graphic – this is saying that family background is correlated with  two thirds of the difference in cognitive ability between the richest and poorest children aged three.

Overall, the main message of this study – that home background and parental aspiration matter a lot when it comes to explaining class differences in educational achievement.

The study also mentions that there are certain policy implications that need to be followed through if the government wishes to address these issues, but of course just because some research suggest certain courses of action, it doesn’t necessarily mean the government will adopt those courses of action, because of funding constraints, or ideological biases.

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Sociology and Social Policy