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Bridging the gap: Qualitative & Quantitative methodologies. Jo Ferrie Interim Director of Graduate Training School of Social & Political Sciences. Venues and moodle codes. SSS1 Lecture 12-3pm Mondays (lecture 12-2.15ish (mandatory), then a revision session (optional))

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bridging the gap qualitative quantitative methodologies

Bridging the gap:Qualitative & Quantitative methodologies

Jo Ferrie Interim Director of Graduate Training

School of Social & Political Sciences

venues and moodle codes
Venues and moodle codes
  • SSS1
    • Lecture 12-3pm Mondays (lecture 12-2.15ish (mandatory), then a revision session (optional))
    • Macgregor Building on the Western Infirmary Site(Main lecture theatre)
    • Tutorials will be assigned to you at the first lecture
    • Moodle code – sss1
  • Qualitative Methods
    • Lecture 5:30-6:30 Tuesdays
    • Adam Smith Lecture Theatre Room 1115
    • Tutorials begin the week after (week beginning: 30th Sept), groups will be assigned to you at the first lecture
    • Moodle code – qual2012
  • Qualitative & Quantitative approaches to social science research – general reflections
  • Quantitative research methods
    • Use of statistical data in the media
    • Mixed methods
why so important
Why so important?
  • Unsettling the way in which you go about ‘doing’ your research;
  • Challenging disciplinary conventions;
  • Potential value of mixed method approaches
  • Appreciation of other types of research /approaches
search for truth
Search for truth …

“Each society has its regime of truth, its ‘general politics’ of truth: that is, the types of discourse which it accepts and makes function as true; the mechanisms and instances which enable one to distinguish true and false statements, the means by which each is sanctioned; the techniques and procedures accorded value in the acquisition of truth; the status of those who are charged with saying what counts as true” (Foucault 1980, 131).

  • Foucault M. 1980. Power/knowledge: Selected interviews and other writings 1972–77, ed. C Gordon. New York: Pantheon.
qualitative or quantitative approaches to research
Qualitative or Quantitative approaches to research?
  • “Lies, damned lies, and statistics”
  • ‘Physics envy’ – purported prestige of the ‘hard’ sciences
  • Rigour/reliability – qualitative/subjective (?) etc.
  • “Scientists are not and cannot be concerned with the individual case. They seek laws, systematic relations, explanations of phenomena. And their results are always statistical.” (Kerlinger 1979: 270)
  • “The degree to which the observations can be quantified (translated into numbers) is often a good index of the maturity of a science” (Mussen, Conger & Kagan, 1977: 13)
  • Understanding human nature vs. understanding the physical world(?)
  • Mixed methods – best of both worlds?
5 minute discussion
5 minute discussion
  • Watch the excerpt from Moneyball
    • Reflect on the different styles that are brought to play – subjective judgement vs. statistics etc. and associated claims to ‘truth’
    • Reflect on the way in which individuals react to different claims to insight, ‘truth’ etc.
  • 2 minute scene
  • The baseball team has limited funds
  • The team’s general manager (Billy Beane) is trying to approach the issue of which players to buy from a different angle – one based on statistics/number-crunching.
  • Challenges the usual way in which things have been done i.e. judgement based on subjective factors, experience, ‘feel’ etc.

With your neighbour discuss:

    • Your preference for Qualitative and/or Quantitative research methodologies
      • Indeed, do you think about social science research methodologies in this way?
    • Relative strengths of Qualitative & Quantitative research methodologies
let s dwell on statistics can we find truth in statistics
Let’s dwell on statistics...can we find truth in statistics?
  • Yes
  • But … we need to know how to read them
  • Drinking beer reduces risk of coronary heart disease
  • Eating oily fish reduces risk of stroke
  • Eating five pieces of fruit and vegetables increases your health rating
    • Really?
    • How can we be sure who to trust?
    • Which truth is the real truth?
making the case for quantitative methods
Making the case for quantitative methods
  • Ontological
    • Assumption that the social world is in fact a mathematical problem that can be solved to produce universal laws
  • Epistemological
    • Research data should be quantitative in order to afford comparisons across studies and theories
  • Technical
    • Statistical techniques are power tools for handling large amounts of data
  • Rhetorical (informing/influencing the audience)
    • Hard quantified facts are more convincing to the public – more trustworthy


  • Application of the methods of the natural sciences to the study of social reality
    • Only knowledge confirmed by the senses is knowledge
    • Positivism aimed to provide the laws of society and the possibility of socially engineering society
    • Laws are assessed via hypothesis testing
    • ‘Value free’ and ‘objective’ conduct
    • “The real power of man to modify phenomena at will can only result from a knowledge of their natural laws” (Comte, 1974: 142).

Comte, A. (1974) The Essential Comte: Selections from Course de Philosophie Positive (S. Andreski, Ed.) New York: Barnes & Noble

main requirements of quantitative methods
Main ‘requirements’ of quantitative methods
  • Requires a greater degree of objectivity compared with qualitative methods
    • Findings will be scrutinised
    • Findings should be replicable
  • Usually deductive as it relies on relationship between theory and research in order to develop a hypothesis that is then proved or disproved by data
  • Figure: The process of reduction, Bryman, 2008: 10


2. Hypothesis

3. Data Collection

4. Findings

5. Hypotheses confirmed

or rejected


How statistics are used daily?Examples from the world of sport

  • Descriptive statistics often used to record sporting achievement
  • Tables include
    • simple counts
    • basic calculations e.g. goal difference
  • Replicated across all walks of life
    • exam league tables
    • unemployment counts
  • Not scary!

Do statistics always tell the whole story? (thanks to Cat Nixon MRC)Using the London 2012 Olympic medal table

  • Olympic medal table example of “count” variable
    • Records number of medals won by colour
    • Tells us that the USA was most successful at the Olympics
  • But what if the medal table isn’t telling the whole story?
    • Is it fair to just “count” medals?
    • What if we compared how countries did based upon population size and GDP?
    • Do you think the USA would still have come first in the table?
  • Let’s have a look shall we….

Olympic table normally ordered by medals won and then sorted by Gold Medals

  • For simplicity let’s order by total number of medals won
  • Can see that USA is most successful team

Medals per 100,000 population = (no. medals won/population size)100000

Medals per $1bn GDP = no. medals won/GDP


Logarithmic adjustment for GDP and weighted by medal colour

Logarithmic adjustment for population size and weighted by medal colour


So what does it mean?

  • How data are recorded and analysed affect how results are interpreted
  • “Counting” shows us that the USA were most successful at London 2012
  • But when you consider other factors a different picture emerges:
    • adjusting for GDP means that the USA would have come 66th
    • adjusting for population size means that the USA would have come 47th
    • Grenada could be considered most successful country at Olympics!
  • Why does this matter? As social scientists we try to explain differences in behaviours, outcomes etc by looking at the differences between and within groups, not just by counting the number of times an outcome occurs.

Do you always believe what you read?Yet another example from the world of sport

  • Statistics are often our main source of information on a topic
  • Data are easily manipulated to form arguments
  • Data are often misreported within the wider press

- Creates “knowledge-base” that becomes “fact-base”

  • Having a basic understanding of both qualitative and quantitative methods allows us to move beyond merely accepting data and ask questions about the validity of evidence

118 medals won by 113 athletes

  • If we know what schools the athletes attended then we can calculate %age of medals won by each group
  • Is there evidence to support the claims made by the press?

Number of athletes who won a medal at Olympic Games by education type


Is it really that simple?

  • There appears to b e evidence to support the statement that 4 in 10 medallists educated at private schools
  • BUT there are approx. 9,500,000 school children in the UK and only 6.5% (617500) of which attend private schools
  • What if we scaled the results to reflect this by creating a “medals won per 10000 children” figure?
  • Do you think you would be more likely to win a medal at a private or state school?

Medals won per 10000 children = (medals won/no. educated in 2012)10000

  • Results show that the number of medals won per 10000 children were higher for those in private education
  • Dividing 0.7 by 0.08 we can conclude that based upon population size, being educated in a private school makes you nearly 9 times more likely to win a medal!

A real world example of mixed methods

  • PhD study funded by Chief Scientist Office for Scotland
  • Aims of study are to:
    • explore why sexual health of young people looked after by the state is worse than that of general population
    • identify how looked after young people learn about sexual health and use services
    • establish what role carers can play in improving outcomes for looked after young people
  • Mixed methods approach
    • Secondary data analysis of nationally representative sexual health dataset
    • Systematic literature review
    • In-depth and semi-structured interviews with looked after young people, carers and social workers
  • Sexual health dataset and interviews mainly focused on sexual health, but data exists on wider health risk behaviours
  • Going to present data on substance use

Who are looked after children? (Cat Nixon again, MRC)

  • Young people aged 0-18 who are looked after by the state due to difficulties within the family
  • Reasons for being looked after include:
    • physical, emotional and sexual abuse
    • parental ill-health
    • child requires specialist care for medical reasons
    • parental substance use
    • persistent truanting from school
    • involvement in youth justice system
    • Small population but associated problems more likely
    • 1.4% of 0-18 year olds in Scotland are looked after each year by local authorities
    • significantly more likely to be excluded from school,
    • higher rate of mental health problems
    • care history associated with homelessness, early parenthood and involvement in sex-working

Prevalence of health risk behaviours in children looked after by state and those living with at least one biological parent


Looked after young people are significantly more likely to:

    • get drunk at least once a week
    • smoke cigarettes
    • smoke cannabis once a week
  • Literature suggests that the adoption of these behaviours is associated with:
    • deprivation
    • low levels of parental supervision
    • disengagement from education
    • having ‘risky’ peers
  • All of which are seen in higher rates amongst looked after young people
  • Adjusting for the effects of these in statistical models explains difference in behaviour between looked after young people and rest of population
  • But what it doesn’t tell us is why they use substances at a higher rate

Qualitative interviews

  • Interviews conducted with 33 young people aged 14-23 between August and December 2011
  • Combination of in-depth and semi-structured interviewing used to accommodate differences in educational, emotional and communication differences
  • Thematic analysis

Reasons for drug and alcohol use

  • Young people often exposed to drugs and alcohol by other young people they were living with
  • Drugs and alcohol used to:
    • protect against “bullying” from other young people
    • “fit in” with other young people
    • to escape from the past
    • to block out being a looked after child

at first it was cannabis, poppers, aerosols, all the kind of stupit stuff but then I started going out with a few of the people when I moved into (the unit) and you started going out at the weekends and there was eccies flying aboot and there was cocaine flying aboot and speed, I mean I was only 12 year old and I was taking them all just to show I wasnae a daft wee lassie… I was just trying to act the big hard brass Andrea which I was at one point but that’s what everyone thought but I was a vulnerable wee lassie on the inside who just wanted to go out and drink and dae drugs tae just try to block it all oot”.

Andrea, aged 16


“I’d just take anything, anything we could get

our hands on. We’d smoke it all and snort it all or

whatever and you used to feel really good, but then

the next day you’d just think ‘why am I doing this?’,

but it was really, I just think it was getting away

from everything that made it worthwhile.”

Sophie aged 14