Rich Ling’s research in Norway between 1997 and 2000 revealed the rise of mobile phones as symbols of identity among teenagers. Initially deemed vulgar and tied to yuppie culture, by 2000 nearly all teenagers owned them, with the model and manner of display reflecting individual and group identity. Ling’s findings suggest that phone choice and presentation is influenced by peer group pressures and are valuable for studying postmodern theories of identity.
Rich Ling conducted interview research on the meaning of mobile phone use among teenagers in Norway (Ling, 2000). (1)
Ling conducted interviews in 1997 and 1999-2000 to uncover attitudes to mobile phones as a fashion item. In 1997 hardly anyone owned a mobile phone, but by 2000 nearly everyone owned them. This is an interesting study showing how this change impacted the relationship between mobile phones and identity.
Ling saw mobile phone use as a source of group and individual identity. Mobile phones can be used to express both group membership and individual uniqueness.
Mobile phones are not just a functional device. They are also part of an individual’s personality kit, one of the tools they use to express their identity to others. Among teenagers, the ownership and display of mobile phones is an important part of their lifestyle.
Mobile Phones, Fashion and identity
Ling argues fashion is a way individuals communicate intention or status to others. Material objects such as phones help the individual to express group identities, such as those related to class or ethnicity.
Teenagers in particular are of an age where they need to establish a group identity. But they simultaneously need to collaborate with peers to include themselves in a group and exclude others.
Fashion is the main way this is achieved, and mobile phones are part of this. However the problem with fashion is that it is always changing. To successfully negotiate group membership, you have to get in on a fashion as it rises in popularity and then out before it declines.
In terms of mobile phones, you thus need the right mobile at the right time. Some groups subvert this by being anti-fashion, but there is still no escaping it!
Changing mobile phone fashions and identity
In 1997 in Norway most teenagers had pagers. At that time mobile phones were associated with yuppie culture and so were not necessarily cool. Those who owned mobile phones and constantly displayed them were seen as vulgar.
Some teenagers who owned Nokias (a popular phone at that time) and displayed them ostentatiously saw themselves as cool, and as having status. However most others saw them as pretentious, pompous and vulgar.
By 2000 mobiles were owned by most teenagers and simply owning or not owning one was not a significant source of identity any more.
By 2000, the age, price and style of mobile phones had become more important as a signifier of identity.
Having a mobile was no longer thought of as snobby. In just three years since 1997 having an old ‘brick phone’ was seen as embarrassing. Just having one of these marked you as someone who didn’t fit in.
The Brick Phone: already unfashionable by 2000!
The way a mobile was displayed was a source of identity. For example, carrying it around on your belt was seen as silly.
Evaluation
Ling’s work can be used to criticise postmodern theories of identity. With mobile phones, individuals are not entirely free to choose which ones they use, or how they display them. At least not if they wish to fit in with certain peer groups.
Peer groups exercise considerable power over the choice of phones individuals make.
Signposting and relevance to A-level sociology
This post is mainly relevant to the culture and identity module. This module is an option in the first year of the AQA’s A-level sociology specification.
Bivaraiate analysis methods include contigency tables + chi square, Pearson’s R, and Spearman’s Rho,
Bivariate analysis involves analysing two variables at a time in order to uncover whether the two variables are related.
Exploring relationships between variables means searching for evidence that the variation in one variable coincides with variation in another variable.
There are a variety of techniques you can use to conduct bivariate analysis but their use depends on the nature of the two variables being analysed.
Type of variable
Nominal
Ordinal
Interval/ Ratio
Dichotomous
Nominal
Contingency table + chi-square + Cramer’s V
Contingency table + chi-square +Cramer’s V
Contingency table + chi-square +Cramer’s V, compare means and eta
Contingency table + chi-square +Cramer’s V
Ordinal
Contingency table + chi-square + Cramer’s V
Spearmans’ rho
Spearmans’ rho
Spearmans’ rho
Interval/ ratio
Contingency table + chi-square +Cramer’s V, compare means and eta
Spearmans’ rho
Pearson’s R
Spearmans’ rho
Dichotomous
Contingency table + chi-square + Cramer’s V
Spearmans’ rho
Spearmans’ rho
phi
Bivariate analysis for different types of variable
Bivariate Analysis: Relationships, not causality
If there is a relationship between two variables, this does not necessarily mean one causes the other.
Even if there is a causal relationship, we need to take care to make sure the direction of causality is correct. Researchers must be careful not to let their assumptions influence the direction of causality.
For example, Sutton and Rafaeli (1998) conducted bivariate analysis on the relationship between the display of positive emotions by retail staff and levels of retail sales.
Common sense might tell you that positive staff sell more, however Sutton and Rafaeli found that the relationship was the other way around: higher levels of sales resulted in more positive emotions among staff. This was unexpected, but also makes sense.
Sometimes you can infer the direction of causality with 100% certainty. For example with the relationship between age and voting patterns. Younger people are less likely to vote, and thus age must be the independent variable. There is no way voting patterns can influence age.
Contingency Tables
A contingency table is like a frequency table but it allows two variables to be analysed simultaneously so that relationships between them can be examined.
They usually contain percentages since these make the relationships easier to see.
Male
Female
Number
Percent
Number
Percent
Sociology
60
30
120
40
Maths
20
10
60
20
English
20
10
60
20
Dance
100
50
60
20
200
100
300
100
Students studying subjects in one college, by gender.
The table above contains both the numbers of the variables and their percentages as a proportion of the total next to them.
The percentages are column percentages: they calculate the number in each cell as a percentage of the total number in that column. Hence why the percent columns add up to 100!
In the above table we can see that there are more female students than male students and females dominate in every subject other than dance, because dance is much more popular among male students. (It’s quite an unusual college!)
Contingency tables can be applied to all types of variable, but they are not always an efficient method.
Pearson’s R
Pearon’s R is a method for examining relationships between interval/ ratio variables. The main features of this method of analysis are:
The coefficient will lie between 0 and 1 which indicates the strength of a relationship. 0 means no relationship, 1 means a perfect relationship.
The closer the coefficient is to one, the stronger the relationship, the closer to 0, the weaker the relationship.
The coefficient will either be positive or negative which indicates the direction of the relationship.
Examples of Pearsons’ R correlations
The table below show the relationship between age and four other variables. (Note this data is hypothetical or made up and for illustrative purposes only!)
Age group
happiness score
wealth £
hours watching TV per week
ave no of friends
20
10
£10,000
15
5
30
8
£20,000
10
8
40
6
£30,000
33
11
50
4
£40,000
22
10
60-69
2
£50,000
9
16
Pearson’s R
-1
1
0
0.93
The correlations are as follows:
between age and happiness: perfect negative correlation.
between age and wealth: perfect positive correlation.
between age and watching TV: no correlation
between age and number of friends: strong positive correlation.
The scatter plots for the above data are as follows:
Age and happiness
Age and wealth
Age and TV
Age and friends
Spearman’s Rho
Spearmans’ Rho is often represented with Greek letter p and is designed for use with ordinal variables. It can also be used when one variable is ordinal and the other is interval/ ratio.
It is exactly the same as Pearson’s R in that the computed value will be between 0 and 1 and either positive or negative.
Pearson’s R can only be used when both variables are interval/ ratio. Spearman’s Rho can be used when on the the variables is ordinal.
Phi and Cramer’s V
The Phi coefficient is used for the analysis of the relationship between two dichotomous variables. Like Pearsons R it results in computed statistic which is either positive or negative and varies between 0 and 1.
Cramer’s V can be used with nominal variables. It can only show the strength of relation between two variables, not the direction.
Cramers’ V is usually reported along with a contingency table and chi-square test.
Comparing means and eta
If you need to examine the relationship between an interval/ ratio variable and a nominal variable if the latter can be relatively unambiguously identified as the independent variable, then it might be useful to compare the means of the interval/ratio variable for each subgroup of the nominal variable.
This procedure is often accompanied by a test of association between variables called eta. The statistic expresses the level of association between the two variables will always be positive.
Eta-squared expresses the amount of variation in the interval/ ratio variable that is due to the nominal variable.
Signposting and sources
This material should be of interest to anyone studying quantitative social research methods.
Postmodern theories underline leisure and consumer activities as key sources of identity in postmodern society, contrary to identity formed by work or social class in the modern era. In his book, “Decentring Leisure,” Chris Rojek argues that leisure, often blurred with work, isn’t defined separately but in relation to other experiences. Rojek and later theorists like Scraton and Bramham agree that leisure has transformed with the advent of postmodernity – shifting from a concept of escape to self-indulgence and expression, primarily driven by consumerism and personal lifestyle.
Postmodern theories of identity stress the importance of leisure and consumerism as sources of identity.
In postmodern society people no longer construct identities from their work, social class background or religion. Rather they construct identities through the products they choose to consume.
Decentering Leisure
Chris Rojek explored the changing nature of leisure in his 1995 book ‘Decentring Leisure’. He argued that if we want to understand leisure we must decentralise the concept. By this he meant that we cannot understand leisure by looking at it on its own. We must look at the experience of leisure in relation to other experiences.
Rojek argued that in postmodern society the meaning of leisure had become less clear.
Modern societies had a relatively clear idea of what leisure was. Leisure was associated with freedom and meant escape from the constraints of limited social roles such as those from work.
Thus in modern society leisure was not an important part of identity. Identity came from adopting social roles, mainly at work, and then leisure was a time to escape this.
With postmodernity, the distinction between work and leisure becomes much more confused. For example:
Increasing numbers of people work in the leisure industry.
It is easier to find enjoyment in work.
Some people even see work as a leisure pursuit.
Modern societies tended to contrast the authentic with the inauthentic. They saw the authentic as superior to the inauthentic. There was also a tendency to plan leisure activities so they provided a sense of purposes for those involved.
In postmodern societies people are less likely to seek out authentic activities. They are just as content to play computer games or hang out in virtual worlds as they are to do in real life activities. Leisure also tends to be less planned. People are more likely to just hang out and do activities for the sake of doing them.
With postmodernisation in postmodern society leisure becomes an end in itself rather than a planned escape from working life.
Postmodernity, Leisure and Identity
These changes to the nature of leisure change the way people think about themselves, they change their identities.
With postmodernity the sense of the integrated self disappears. Postmodern societies become more pluralistic in their lifestyles and identities become less rigid and more fragmented.
For example in modern societies people saw themselves as passing through distinct stages in a lifecycle. They went from children to teenagers to young adults and middle aged. Each age group had certain leisure pursuits appropriate to it. For example, night clubs were for younger people, knitting at home was for older people.
However in postmodern society these barriers break down. Older people are more likely to go to nightclubs, younger people are more likely to stay in and knit.
Identity politics and leisure
Identity politics becomes more important: the ability to choose an identity unconstrained by your background.
Leisure plays a central role in identity politics. In postmodern society you become who you are through the leisure activities you pursue. This is different to modern societies where your leisure activities reflected who you were based on your social position.
Evaluations of Rojak
Rojak exaggerates and simplifies the changes in leisure he claims to have taken place. Leisure in modern and postmodern societies may not be as different and clear cut as he claims.
In postmodern society people’s ethnicities and jobs are still important sources of identity for some.
Leisure, postmodernity and identity
Sheila Scraton and Peter Bramham Drew on the work of E.P. Thompson to argue that Leisure was a product of modernity. With the onset of postmodernity the nature of leisure has changed.
Before industrialisation and modernity there was no clear distinction between work and leisure. Natural cycles governed time and work and leisure activities were intermingled.
The advent of modernity and industrialisation changed this. In the factory system workers were paid for their time. This created a strong distinction between work-time and leisure-time.
In modern societies Fordist production techniques produced standardised goods for mass consumption. Systematic planning was also part of modernist production. These norms of work all influenced the development of leisure.
Modernity and leisure
Organised leisure was part of the modernist project and was organised primarily around social class.
Leisure was time left over from work which could be filled with free-time activities which supported the existing economic and political structure.
The state and voluntary sector were involved in organising leisure activities which were supposed to benefit both the individual and society.
However the idea of rational, planned and organised leisure began to lose influence after World War Two.
The influence of American culture through rock and roll, the women’s movement and immigration all raised questions about homogenised leisure.
These heralded changes to leisure in postmodern society, when leisure became more diverse and fragmented.
Postmodern Leisure
Scraton and Bramham identify three key features of postwar leisure that are postmodern:
Postmodern leisure is based on consumption
Leisure is an expression of lifestyle
Leisure is about the body.
Postmodern leisure is based on consumption
Postmodern leisure is based on individuals buying goods and services.
Modern leisure was discipline, postmodern leisure was more about self indulgence. You do what you want rather than doing what others determine is good for you. Postmodern leisure is like shopping: you indulge yourself in exploration and choice.
Postmodern Leisure is an expression of lifestyle
Leisure becomes an expression of a lifestyle rather than a search for self-improvement or relaxation. It becomes a playful means to express who you are. Individualism, privatisation and commercialism undermine rational recreation, games, team spirit, fair play and traditional sporting values. People’s identities become wrapped up in the goods they buy rather than being rooted in their jobs, families or communities.
Postmodern leisure is about the body
Postmodern leisure involves an increasing concern with the body. In modernity rational leisure was concerned with health and fitness, postmodern leisure is about achieving the desired body shape as an expression of the self.
Evaluating of postmodern theories of leisure
Scraton and Bamham argue that these changes affect some groups more than others.
Many people do not have the money to engage in consumption to construct an identity. For those on low incomes shopping is still just a means to buy food and clothes to survive.
Leisure also remains gendered. Video games and sex tourism.
Racism may also prevent some ethnic minority groups from accesses certain types of leisure activity.
For the over 50s clearly enjoying, but for many leisure is limited by resources and still remains linked to work!
Sources and Signposting
This material is mainly relevant to the culture and identity option.
In October 2023, Bernie Ecclestone, who evolved Formula One into a global brand, was found guilty of tax evasion amounting to £400 million. Originating from a 2015 case, the 92-year-old was sentenced to 17 months jail, suspended due to his age, and fined over £650 million. Critics argue this punishment is lenient, underlining the £400 million impact on UK tax revenue and upholding the Marxist perspective that justice is softer on the affluent.
Bernie Ecclestone was found guilty of tax evasion in October 2023. The total amount of tax he evaded paying was around £400 million (1)
The case dates back to 2015 when he had a meeting with UK tax officers from the HMRC. He failed to declare that he was paying into one particular trust in Singapore, money which he should have been paying tax on.
He must have paid in HUGE sums to this trust and made huge profits to have run up a £400M tax bill (which he evaded). The profits would have been at least double that amount!
He plead guilty to this charge and was given a 17th month jail sentence and a fine, meaning he will have to pay just over £650 million to the UK tax authority, and £74 000 in costs to the prosecution.
His sentence was suspended so he won’t spend any time in jail, the judge saying this is because of his age. He is 92.
A massive crime with a weak punishment
This is a clear case of tax evasion (2). Ecclestone knowingly concealed information about his finances from the HMRC to not pay tax. This is illegal and carries a maximum penalty of seven years in jail and a 200% max fine.
Bernie received 17 months suspended and his penalty seems to be around 60%.
I understand suspending the sentence because he is 92, kind of fair enough. But as a symbolic message this hardly seems an appropriate penalty.
The harm Ecclestone caused to British society is £400 million lost tax revenue. That is 1/10th of the entire annual tax gap for tax evasion in the UK (3).
You might remember that a number of schools closed because of crumbling concrete earlier this year. The total estimated cost of repairing all of them is £150 million. That’s just one of the things Bernie’s tax could have prevented, had he paid it.
But no, he preferred to squirrel it away in a trust fund so he could pass it on to his undeserving children. And received no real punishment.
Who is Bernie Ecclestone?
He ran Formula one from the late 1970s until 2017, during which time he grew it into a global brand. Ecclestone essentially made F1 into one of the most valuable global media assets. It sits between the motor industry and a global audience.
According to the The Forbes Billionaires rich list his net worth is around $3 billion. (2). So yes the £600 million fine will hurt, but he’ll still have over $2 billion left.
Sociological analysis
This case study is an excellent example which supports the Marxist perspective on crime. According to Marxists the criminal justice system punishes the rich less than the poor. This is precisely what is happening here.
There’s no real debate about it, it’s just very strong supporting evidence for the continued relevance of Marxism today!
Univariate analysis reviews one variable at a time and typically uses frequency tables and diagrams like bar charts and pie charts. Measures of central tendency and dispersion are tools for analyzing data, with central tendency often involving the mean, median, or mode while dispersion relies on range and standard deviation. Understanding these statistical methods aids in the comprehension of data distribution in areas of interest such as wealth statistics.
Univariate analysis refers to the analysis of one variable at a time.
The most common approaches are:
Frequency tables.
Diagrams: bar charts, histograms, pie charts.
Measures of central tendency: mean, median, mode.
Measures of dispersion: range and standard deviation.
Frequency Tables
A frequency table provides the number of cases and the percentages belonging to each of the categories for a variable. Frequency tables can be used for all the different types of variable.
Below is a simple example of a frequency table showing the number of schools in three different categories of the ‘type of school’ variable for the 2022-2023 academic year. I rounded the percentages below.
Type of school
Number of schools
Percent
Local Authority
11858
48
Academy
10176
42
Independent
2408
10
Total
24442
100
Number of schools by type of school, England and Wales 2022-23.
Analysts usually clean from raw data to make frequency tables so people can understand and visualise them more easily.
Frequency tables are the starting point for generating diagrams which put the data into visual form making trends stand out.
Diagrams
Diagrams representing quantitative data in visual form to make data easier to understand and interpret. Bar charts and pie charts are two of the most commonly used visual representations of quantitative data.
Bar charts
The chart below shows the same data as in the frequency table above. Each bar represents one of the three school types.
The bar chart below shows the largest category is Local Authority (LA) maintained schools with academies the second largest category. You can also see there are relatively few independent schools.
Pie Charts
The main advantaged of a pie chart is that you can see the proportion of each category in relation to the total. A pie chart shows this sense of relation to the whole more clearly than a bar chart.
For example you can clearly see below that LA Maintained schools make up nearly 50% of the total. This doesn’t stand out as much in the bar chart.
You can also see that Independent schools represent around 10% of schools from the pie chart.
Frequency tables and diagrams: final thoughts
Diagrams are useful to make frequency tables easer to understand.
Bar charts are more useful when you want to look at proportions in relation to each other. Pie charts are more useful when you want to look at proportions in relation to the whole.
Keep in mind however that charts are only as useful as the data. For example, one limitation with the above data is that it tells you nothing about pupil numbers, only school numbers!
Measures of central tendency encapsulate in one figure a value which is typical for a distribution of values. In effect, we are seeking out an average for a distribution.
Quantitative social research analysts recognise three different forms of average:
mean
median
mode
Diagram 1: Mean, median and mode for a random distribution of ages.
Arithmetic mean
The mean is the sum of all values in a distribution divided by the number of values.
In diagram one above, we add ALL the ages together and divide by 20 which is the total number of ages in the sample. This gives us a mean of 51.6.
The mean should be applied to interval/ ratio variables. It can also be applied to ordinal variables too.
Median
The median is the mid-point in a distribution of values. We arrive at the median by lining up all the values smallest to largest and then finding the middle value.
Whereas the mean is vulnerable to outliers which are extreme values at either end of the distribution. Outliers can greatly increase or decrease the mean, but they have much less of an affect on the median.
We see this in diagram one above, where the median point is 45.5, considerably lower than the mean of 51.6. In the case above the mean is higher because the oldest four people skew the mean average upwards. The four oldest are a lot older than the people in the middle, compared to the average ages of the rest of population.
The median can be used in relation to both interval/ratio and ordinal variables.
Mode
The mode is simply the value that occurs most frequently in a distribution. The mode can be applied to all types of variable.
In the diagram above, the mode is 28, because that is the only age which occurs twice.
Median more useful than the Mean?
With social data it is often more useful to know the median rather than the mean. This is especially true with wealth statistics in the UK.
Wealth and income distribution are of special interest to sociologists, because there is a lot of variation in distribution. Neither wealth nor income are equally distributed. Understanding how they are distributed has significant implications for life chances and social policy.
Table showing household wealth distribution in the UK by decile, 2018 to 2020.
Visualising the total wealth in a bar chart looks like this:
Here you can clearly see a skew towards the top two deciles, especially the first decile. The richest 10% of households have an average of almost £2 million in wealth, which 8 times more than even the 4th decile.
In cases where there is a lot of variation in data, in terms of a large skew showing up at one end, as above, then get the mean and median being very different.
in the chart above the mean is £489 000, pulled up by the huge relative wealth of the top 20%.
The median wealth is only £280 000 and 50% of people have less than this.
Mean wealth in the UK gives you a misleading picture of the amount of wealth most people in the UK have!
Measures of dispersion show the variation in a distribution.
Two measures of dispersion include:
the range (the simplest)
the standard variation.
Range
The range of data is the distance between minimum and maximum values in a distribution. Like the mean, outliers can greatly affect the range.
The range of household wealth (grouped by decile) in the UK is £1.9 Million (see chart below).
This is a very simple measure which doesn’t tell us vary much about how much wealth ordinary people.
For example it doesn’t tell us that the top decile of households are almost twice as wealthy as the next decile down.
Standard Deviation
We calculate the standard deviation by taking the difference in each value in a distribution from the mean and then dividing the total of the differences by the number of values.
The standard deviation is the average amount of deviation around the mean.
For example, the standard deviation of wealth in the UK (grouped by decile) is £575 211.
Outliers don’t affect the standard deviation as much as the range. The impact of outliers on the standard deviation is offset by dividing by the number of values.
Box Plots
Box plots are popular for showing dispersion for interval/ratio variables.
The box plot provides an indication of both the central tendency (median) and dispersion (outliers).
The box plot of wealth below treats the top richest decile as an outlier. It clearly shows you the skew is the top.
The box shows you where the middle 50% of households sit: between £800 000 and £50 000.
The line in the box shows you the median value of household wealth: £280 000.
Box plot of wealth, UK 2018-2020
The shape of a box plot will vary depending on whether cases tend to be high or low in relation to the median. They show us whether there is more or less variation above or below the median.
This material is most relevant to the Research Methods module. It might be a little advanced for A-level sociology. You are more likely to need this during a first year university statistical methods course.
The Icon of the High seas is the largest cruise ship in the world. It is 1198 feet long and weighs 250 800 tons. The ship has berths for 5610 passengers and 2350 crew. It cost more than $2 billion to build.
The Icon of the Seas.
It is set to have its maiden voyage in January 2024 with tickets costing from $1000 to $75 000. It represents the size-pinnacle of the modern cruise industry. 31.5 million people are expected to go on a cruise in 2023.
The ship has 20 decks and eight ‘neighbourhoods’ aimed at different types of passenger: from families to older adults. It has 40 bars, mini golf, rock climbing and a water park with seven swimming pools.
Cruise ships can pose challenges to the areas they visit as thousands of passengers suddenly disembark for only a couple of hours at a time. Amsterdam recently closed its cruise ship terminal for just this reason. However this is less of an option the Caribbean which absorbs about a third of the cruise industry’s capacity. That area is more reliant on cruise ship income.
The wrong kind of globalisation?
Cruise ships are a mobile example of globalisation benefitting the very wealthiest. Those who can afford it, typically older middle class people, can afford to go on a week or month long jaunt to foreign countries on such vessels.
You could also argue these benefit locals in the places they visit because they bring money and jobs to local areas, often in poorer parts of the world.
However the downside is that locals have to put up with a massive influx of tourists all at once in a short space of time, which can’t be pleasant.
There is also something quite detestable about the fact that it’s only the very rich that can afford to go on a cruise.
Cruise ships are also polluting: passengers on a seven day Antarctic cruise can produce as much CO2 as the average European in a year!
They are also great for transmitting infectious diseases around the world!
Structured interviews are a standardised way of collecting data typically using closed, pre-coded surveys.
A structured interview is where interviewers ask pre-written questions to candidates in a standardised way, following an interview schedule. As far as possible the interviewer asks the same questions in the same order and the same way to all candidates.
(An exception to this is filter questions in which case the interviewer may skip sub-questions if a negative response is provided).
Answers to structured interviews are usually closed, or pre-coded, and the interviewer ticks the appropriate box according to the respondents’ answers. However some structured interviews may be open ended in which case the interviewer writes in the answers for the respondent.
Social surveys are the main context in which researchers will conduct structured interviews.
This post covers:
the advantages of structured interviews
the different contexts in which they take place (phone and computer assisted).
the stages of conducting them: from knowing the schedule to leaving!
their limitations.
Advantages of Structured Interviews
The main advantage of structured interviews is that they promote standardisation in both the processes of asking questions and recording answers.
This reduces bias and error in the asking of questions and makes it easier to process respondents’ answers.
The two main advantages of structured interviews are thus:
Reducing error due to interviewer variability.
Increasing the accuracy and ease of data processing.
Reducing error due to interviewer variability
Structured interviews help to reduce the amount of error in data collection because they are standardised.
Variability and thus error can occur in two ways:
Intra-interviewer variability: occurs when an interviewer is not consistent with the way they ask the questions or record the answers.
Inter-interviewer variability: when there are more than two interviewers who are not consistent with each other in the way they ask questions or record answers.
These two sources of variability can occur together and compound the the problem of reduced validity.
The common sources of error in survey research include:
A poorly worded question.
The way the question is asked by the interviewer.
Misunderstanding on the part of the respondent being interviewed.
Memory problems on the part of the respondent.
The way the information is recorded by the interviewer.
The way the information is processed: coding of answers or data entry.
Because the asking of questions and recording of answers are standardised, this means any variation in answers from respondents should be due to true or real variation in the respondents answers, rather than variation arising because of differences in the interview context.
Accuracy and Ease of Data Processing
Structured interviews consist of mainly closed, pre-coded questions or fixed choice questions.
With closed-questions the respondent is given a limited choice of possible answers and is asked to select which response or responses apply to them.
The interviewer then simply ticks the appropriate box.
This limit box ticking procedure limits the scope for interviewer bias to introduce error. There is no scope for the interviewer to omit or modify anything the respondent says because they are not writing down their answer.
Another advantage with pre-coded data gained from the structured interview is that it allows for ‘automatic’ data processing.
If answers had been written down or transcribed from a recording, a researcher would have to examine this qualitative data, sort and assign the various answers to categories.
For example if a survey had produced qualitative data on what respondents thought about Brexit, the researcher might categories the range of answers into ‘for Brexit’, ‘neutral’, and ‘against Brexit’.
This process of reducing more complex and varied data into fewer and simpler ‘higher level’ categories is known as coding data, or establishing a coding frame and is necessary for quantitive analysis to take place.
Coding (whether done before or after a structured interview takes place) introduces another source or potential error. Answers may be categorised incorrectly by the researchers. The researchers may categorise answers differently to how the respondents themselves would have categorised their answers.
There are two sources of error in recording data:
Intra-rater-variability: where the person applying the coding is inconsistent in the way they apply the rules of assigning answers to categories.
Inter-rater-variability: where two different raters apply the rules of assigning answers to categories differently.
If either or both of the above occur then variability in responses will be due to error rather than true variability in the responses.
The closed question survey/ interview avoids the above problem because respondents assign themselves to categories, simply by picking an option and the interviewer ticking a box.
There is very little opportunity with pre-coded interviews for interviewers or analysers to misinterpret or miss-assign respondents’ answers to the wrong categories.
Structured Interview Contexts
Structured interviews tend to be done when there is only one respondent. Group interviews are usually more qualitative because they dynamics of having two ore more respondents present mean answers tend to be more complex, and so tick-box answers are not usually sufficient to get valid data.
Besides the face to face interview, there are two particular contexts which are common with structured interviewing: telephone interviewing and computer assisted interviewing. (These are not mutually exclusive).
Telephone interviewing
Telephone interviews are very common with market research companies, and opinion polling companies such as YouGov. They are used less often by academic researchers but an exception to this was during the Covid-19 Pandemic when many studies which would usually rely on in-person interviews had to be carried out over the phone.
The advantages of telephone interviews
The advantages of telephone interviews compared to face to face interviews the advantages of telephone interviews are:
Telephone interviews are cheaper and quicker to administer because there is no travel time or costs involved in accessing the respondents. The more dispersed the research sample is geographically the larger the advantage.
Telephone interviews are easier to supervise than face to face interviews. You can have one supervisor in a room with several phone interviewers. Interviewers can be recorded and monitored, although care has to be taken with GDPR.
Telephone interviews reduce bias due to the personal characteristics of the interviewers. It is much more difficult to tell what the class background or ethnicity or the interviewer is over the phone, for example.
The limitations of phone interviews
People without phones cannot be part of the sample.
Call screening with mobile phones has greatly reduced the response rate of phone surveys.
Respondents with hearing impediments will find phone interviews more difficult.
The length of a phone interview generally can’t be sustained over 20-25 minutes.
There is a general belief that telephone interviews achieve lower response rates than face to face interviews.
There is some evidence that phone interviews are less useful when dealing with sensitive topics but the data is not clear cut.
There may. be validity problems because telephone interviews do not allow for observation. For example an interviewer cannot observe if a respondent is confused by a question.
In cases where researchers need specific types of people, telephone interviews do not allow us to check if the correct types of people are actually those being interviewed.
Computer assisted Interviewing
With computer assisted interviewing interviews questions are pre-written and appear on the computer screen. Interviewers follow the instructions and read out questions in order and key in the respondents’ answers, either as open or closed responses.
There are two main types of Computer Assisted Interviewing:
CAPI – Computer Assisted Personal Interviewing.
CATI – Computer Assisted Telephone Interviewing.
Most telephone interviews today are Computer Assisted. There are several survey software packages that allow for the construction of effective surveys with analytics tools for data analysis.
They are less popular for personal interviews but have been growing in popularity.
CATI and CAPI are more common among commercial survey organisations such as IPSOS but are used less in academic research conducted by universities.
The advantages of computer assisted interviewing
CAPI are very useful for filter questions as the software can skip to the next question if the previous one isn’t relevant. This reduces the likelihood of the interviewer asking irrelevant questions or missing out questions.
They are also useful for prompt-questions as flash cards can be generated on the screen and shown to the respondents as required. This should mean respondents are more likely to see the flash-cards in the same way as there is no possibility for the researcher to arrange them in a different order for different respondents, as might be the case with physical flashcards.
Another advantage of computer assisted interviewing is automatic storage on the computer or cloud upload which means there is no need to scan paper interview sheets or enter the data manually at a later date.
Thus Computer Assisted Interviews should increase the level of standardisation and reduce the amount of variability error introduced by the interviewer.
The disadvantages of Computer Assisted Interviewing:
They may create a sense of distance and disconnect between the interviewer and respondents.
Miskeying may result in the interviewer entering incorrect data, and they are less likely to realise this than with paper interviews.
Interviewers need to be comfortable with the technology.
Conducting Structured Interviews
The procedures involved with conducting an effective structure interview include:
Knowing the interview schedule
Gaining access
Introducing the research
Establishing rapport
Asking questions and recording answers
Leaving the interview.
The processes above are specifically in relation to structured interviews, but will also apply to semi-structure interviews.
The interview schedule
An interview schedule is the list of questions in order, with relevant instructions about how the questions are to be asked. Before conducting an interview, the interviewer should know the interview schedule inside out.
Interviews can be stressful and pressure can cause interviewers to not follow standardised procedures. For example, interviewers may ask questions in the wrong order or miss questions out.
When several interviewers are involved in the research process it is especially important that all of them know the interview schedule to ensure questions are asked in a standardised way.
Gaining access
Interviews are the interface between the research and the respondents and are thus a crucial link in ensuring a good response rate. In order to gain access interviews need to:
Be prepared to keep calling back with telephone interviews. Keep in mind the most likely times to get a response.
Be self-assured and confident.
Reassure people that you are not a salesperson, but doing research for a deeper purpose.
Dress appropriately.
Be prepared to be flexible with time: finding a time that fits the respondent if first contact isn’t convenient.
Introducing the research
Respondents need to be provided with a rationale explaining the purposes of the research and why they are giving up their time to take part.
The introductory rationale may be written down or spoken. A written rationale may be sent out to prospective respondents in advance of the research taking place, as is the case with those selected to take part in the British Social Attitudes survey. A verbal rationale is employed with street-based market research, cold-calling telephone surveys and may also be reiterated during house to house surveys.
An effective introductory statement can be crucial in getting respondents to take part.
What should an introductory statement for social research include?
Make clear the identity of the interviewer.
Identify the agency which is conducting the research: for example a university or business.
Include details of how the research is being funded.
Indicate the broader purpose of the research in broad terms: what are the overall aims?
Give an indication of the kind of data that will be collected.
Make it clear that participation is voluntary.
Make it clear that data will be anonymised and that the respondent will not be identified in any way, by data being analysed at an aggregate level.
Provide reassurance about the confidentiality of information.
Provide a respondent with the opportunity to ask questions.
Establishing rapport with structured interviews
Rapport is what makes the respondent feel as if they want to cooperate with the researcher and take part in the research. Without rapport being established respondents may either not agree to take part or terminate the interview half way through!
Rapport can be established through visual cues of friendliness such as positive body language, listening and good eye contact.
However with structured interviews, establishing rapport is a delicate balancing act as it is crucial for the interviewers be as objective as possible and not get too close to the respondents.
Rapport can be achieved by being friendly with the interviewee, although interviewers shouldn’t take this too far. Too much friendliness can result in the interview taking too long and the interviewee getting bored.
Too much rapport can also result in the respondent providing socially desirable answers.
Asking Questions and Recording Answers
With structured interviews it is important that researchers strive to ask the same questions in the same way to all respondents. They should ask questions as written in order to minimise error.
Experiments in question-wording suggest that even minor variations in wording can influence replies.
Interviewers may be tempted to deviate from the schedule because they feel awkward asking some questions to particular people, but training can help with this and make it more likely that standardisation is kept in place.
Where recording answers is concerned, bias is far less likely with pre-coded answers.
PROVIDING Clear instructions
Interviews need to follow clear instructions through the progress of the interview. This is important if an interview schedule includes filter questions.
Filter questions require the interviewer to ask questions of some respondents but not to others. Filler questions are usually indented on an interview schedule.
For example:
Did you vote in the last general election…? YES / NO
1a (to be asked if respondent answered yes to Q1)
Which of the following political parties did you vote for? Conservatives/ Labour/ Lib Dems/ The Green Party/ Other.
The risk of not following instructions is that the respondent may be asked questions that are irrelevant to them, which may be irritating.
Question order
Researchers should stick to the question order on the survey.
Leapfrogging questions may result in questions skipped not being asked because the researcher could forget to go back to them.
Changing the question order may also lead to variability in replies because questions previously asked may affect how respondents answer questions later on in the survey.
Three specific examples demonstrate why question order matters:
People are less likely to respond that taxes should be lowered if they are asked questions about government spending beforehand.
In victim surveys if people are asked about their attitudes to crime first they are more likely to report that they have been a victim of crime in later questions.
One question in the 1988 British Crime Survey asked the following question:
‘Taking everything into account, would you say the police in this area do a good job or a poor job?
For all respondents this question appeared early on, but due to an admin error the question appeared twice in some surveys, and for those who answered the question twice:
66% gave the same response
22% gave a more positive response
12% gave a less positive response.
The fact that only two thirds of respondents gave the same response twice clearly indicates that the effect of question order can be huge.
One theory for the change is that the survey was about crime and as respondents thought more in-depth about crime as the interview progressed, 22% felt more favourable to the police and 13% less favourable, this would have varied with their own experiences.
Rules for ordering questions in social surveys
Early questions should be clearly related to the topic of the research about which the respondent has already been informed. This is so the respondent immediately feels like the questions are relevant.
Questions about age/ ethnicity/ gender etc. should not be asked at the beginning of the interview
Sensitive questions should be left for later.
With a longer questionnaire, questions should be grouped into sections to break up the interview.
Within each subgroup general questions should precede specific ones.
Opinions and attitudes questions should precede questions about behaviour and knowledge. Questions about the later are less likely to be influenced by question order.
If a respondent has already answered a later question in the course of answering a previous one, that later question should still be asked.
Probing questions in structured interviews
Probing may be required in structured interviews when
respondents do not understand the question and either ask for or it is clear that they need more information to provide an answer.
The respondent does not provide a sufficient answer and needs to be probed for more information.
The problem with the interviewer asking additional probing questions is that they introduce researcher-led variability into the interview context.
Tactics for effective probing in structured interviews:
Employ standardised probes. These work well when open ended answers are required. Examples of standardised probes include: ‘Could you say a little more about that?’ or ‘are there any other reasons why you think that?’.
If a response does not allow for a pre-existing box to be ticked In a closed ended survey the interviewer could repeat the available options.
If the response requires a number rather than something like ‘often’ the researcher should just persist with asking the question. They shouldn’t try and second guess a number!
Prompting
Prompting occurs when the interviewer suggests a possible answer to a question to the respondent. This is effectively what happens with a closed question survey or interview: the options are the prompts. The important thing is that the prompts are the same for all the respondents and asked in the same way.
During face to face interviews there may be times when it is better for researchers to use show cards (or flash cards) to display the answers rather than say them.
Three contexts in which flashcards are better:
When there is a long list of possible answers. For example if asking respondents about which newspapers they read, it would be easier to show them a list rather than reading them out!
With Likert Scales, ranked for 1-5 for example, it would be easier to have a showcard with 1-5 and the respondent can point to it, rather than reading out ‘1,2,3,4,5’.
With some sensitive details such as income, respondents might feel more comfortable if they are shown income bands with letters attached, then they can say the letter. This allows the respondent to not state what their income is out loud.
Leaving the Interview
On leaving the interview thank the respondent for taking part.
Researchers should not engage in further communication about the purpose of the research at this point beyond the standard introductory statement. To do so means this respondent may divulge further information to other respondents yet to take part, possibly biassing their responses.
Problems with structured interviews
Four problems with structured interviews include:
the characteristics of the interviewer interfering with the results.
Response sets resulting in reduced validity (acquiescence and social desirability).
The problem of lack of shared meaning.
The feminist critique of the unequal power relationship between interviewer and respondent.
Interviewer characteristics
The characteristics of the interviewer such as their gender or ethnicity may affect the responses a respondent gives. For example, a respondent may be less likely to open up on sensitive issues with someone who is a different gender to them.
Response Sets
This is where respondents reply to a series of questions in a consistent way but one that is irrelevant to the concept being measured.
This is a particular problem when respondents are answering several Likert Scale questions in a row.
Two of the most prominent types of response set are ‘acquiescence’ and ‘social desirability bias’
Acquiescence
Acquiescence refers to a tendency of some respondents to consistently agree or disagree with a set of questions. They may do this because it is quicker for them to get through the interview. This is known as satisficing.
Satisficing is where respondents reduce the amount of effort required to answer a question. They settle for an answer that is satisfactory rather than making the effort to generate the most accurate answer.
Examples of satisficing include:
Agreeing with yes statements or ‘yeasaying’.
Opting for middle point answers on scales.
Not considering the full-range of answers in a range of closed questions, for example picking the first or last answers.
The opposite of satisficing is optimising. Optimising is where respondents expend effort to arrive at the best and most appropriate answer to a question.
It is possible to weed out respondents who do this by ensuring there is a mix of positive and negative sentiment in a batch of Likert questions.
For example you may have a batch of three questions designed to measure attitudes towards Rishi Sunak’s performance as Primeminister.
If you have two scales where ‘5’ is positive and one where 5 is Negative, for example:
Rishi Sunak is an effective leader 1.2.3.4.5
Rishi Sunak has managed the economy well 1.2.3.4.5
Rishi Sunak is NOT to be trusted 1.2.3.4.5
If someone is acquiescing without thinking about their answers, they are likely to circle all 5s, which wouldn’t make sense. Hence we could disregard this response and maybe even the entire survey from this individual.
Social desirability bias
Socially desirable behaviours and attitudes tend to be over-reported. This can especially be the case for sensitive questions.
Strategies for reducing social interviews bias
Use self-completion forms rather than interviewers.
Soften the question for example ‘even the calmest of car drivers sometimes lose their temper when driving, has this ever happened to you?
The problem of meaning
Structured surveys and interviews assume that respondents share the same meanings for terms as the interviewers.
However, from an interpretivist perspective interviewer and respondent may not share the same meanings. Respondents may be ticking boxes but mean different things to what the interviewer thinks they mean.
The issue of meaning is side-stepped in structured interviews.
The feminist critique of structured interviews
The structure of the interview epitomises the asymmetrical relationship between researcher and respondent. This is a critique made of all quantitative research.
The researcher extracts information from the respondent and gives little or nothing in return.
Interviewers are even advised not to get too familiar with respondents as giving away too much information may bias the results.
Interviewers should refrain from expressing their opinions, presenting any personal information and engaging in off-topic chatter. All of this is very impersonal.
This means that structured interviews are probably not appropriate for very sensitive topics that involve a more personal touch. For example with domestic violence, unstructured interviews which aim to explore the nature of violence have revealed higher levels of violence than structured interviews such as the Crime Survey of England and Wales.
Sources and signposting
Structured interviews are relevant to the social research methods module within A-level sociology.
This post was adapted from Bryman, A (2016) Social Research Methods.
500 000 more Americans die every year compared to equivalent economies. The death rate in America is higher for several causes of death than in Europe.
US life expectancy fell from 78.8 to 76.1 between 2019 to 2021. In all other developed countries it continued to increase.
In 2020 the EU reported 5800 overdose deaths in a population of 330 million. America reported 68 000 overdose deaths in a similar population of 440 million.
In 2021 there were 26 000 murders in America compared to just a few hundred in most European countries with larger populations.
The US car crash death rate is four times that of Germany.
Americans are nearly twice as likely to die in fires compared to in Western Europe.
Relevance to A-level sociology
This material is relevant to global development, an option within A-level sociology.
These statistics suggest that social development does not automatically follow on from economic development. America is one of the richest countries on earth, but its social development is relatively retarded. The money made in America isn’t being used to benefit ordinary people by making the country a safer place to live!
Millions of young people are exploited at work through unpaid trial shifts, lower minimum wages for example.
Millions of young people in the UK are being treated unfairly at work. Around half of young workers are exploited in some way, for example through being underpaid. Young people lose up to £1.65 billion each year through wage theft, and over 100,000 are never paid for overtime at all.
This is according to some recent research from the Equality Trust published in 2023: Your Time, Your Pay. The primary purpose of this research was to assess young people’s knowledge, awareness and application of their employment rights. The research was based on a sample of 1018 16-24 years olds.
Examples of how young people are exploited at work include…
Unpaid trial shifts
Working without a contract
Zero hours contracts
Lower minimum wages
Not being auto-enrolled onto Pensions
Lack of education about employment rights.
The rest of this post outlines the economic challenges young people face, statistics and cased studies about young people being exploited at work and recommendations about how to improve things.
Economic challenges young people face
Young people are exploited in work despite facing huge economic challenges:
Young people suffered more from the Pandemic with school closures and higher rates of job losses. Under 25s accounted for 60% of job losses during lockdowns between February 2020 – March 2021.
Young people face wage discrimination as employers are legally allowed to pay them less. The minimum wage for under 18s is a dismal £5.28 an hour.
Relative scarcity of housing means rents have increased, and buying is simply out of reach for most under 25 year olds. For those who want to buy they have to save tens of thousands of pounds for a deposit.
For those who choose to go to university, they are saddled with tens of thousands of pounds of debt.
Recent high rates of inflation mean the cost of living is relatively higher for young people today compared to their parents when they were younger.
As a result, it is the norm for young people to face ‘financial precarity’. A 2022 report found that 47% of young people (aged 16-25) are experiencing financial precarity. This number grew as young people got older, with 57% of 22-24-year-olds in a precarious financial situation.
And yet despite these challenging times, many young people who have to work out of necessity or choose to work to get ahead suffer massive exploitation at the hands of their employers…
Young people being exploited at work: statistics
The Equality Trust report found that…
42% of young workers have been asked to work for no pay.
51% of young people work overtime, over half have not always been paid for it.
38% of young people either do not have or do not know whether they have a written employment contract.
16-17 year olds were the least likely age cohort to have a written employment contract with only 34% having a written contract. This compares to 59% of 18-21 year olds and 67% 22-24 year olds.
40% of young people have been employed on a zero hour contract.
Almost two thirds of young people did not receive, or don’t know if they received, information about employment rights at school.
73% of young people are not members of a trade union.
Only 37% of young people think their standard of living is better than their parent(s) or guardian(s).
Young people being exploited at work: case studies
The focus group revealed the following examples:
Unpaid trial shifts: One worker who did a 4 hour trial shift in an expensive homeware store who made a £50 sale despite receiving no training during that 4 hours and being reprimanded for using the till incorrectly.
Working without a contract: On person worked as an age verification checker where she went into off-licences and betting shops to see if they asked her proof of her age. She had to write detailed reports but had no formal contract. She saw the job advertised specifically to students on TikTok.
Working as bar staff at a music festival – One respondent reported that they had to spend £40 on their train ticket to event despite being told travel costs would be paid at their interview. The agency oversubscribed workers assuming some wouldn’t turn up so when she arrived for a shift at 7.00 a.m. she was told she wouldn’t be starting until 18.00.
One respondent reported a positive experience on working a zero hours contract for an administrative body where the flexibility was mutually beneficial.
Research Methods used in this report
The polling was conducted by Survation in November 2022 and they surveyed 1,018 young people from across the UK.
They also ran two focus groups with a total of nine young people; one to co-produce the questions for the survey and the second to analyse the results.
They ensured they sampled a diverse group of young people from a range of socio-economic backgrounds.
Recommendations
Based on the above findings the Equality Trust recommends that…
The government should abolish the National Minimum Wage rates based on a person’s age.
Unpaid trial shifts should be made illegal.
Expand automatic pension enrolment to qualifying over-16s.
Schools and colleges need to do more employment rights based education.
Trades Unions could do more to attract younger people.
Signposting and relevance to A-level sociology
This is a fantastic example of how to use focus group interviews in social research. Focus groups really work here because they give respondents a chance to share their experiences of being exploited with their peers. By being able to listen and respond in a supportive environment this should help encourage respondents to open up. The topic isn’t so sensitive as to require one on one interviews.
The wealth of billionaires in Britain has increased by 1100% in the 32 years between 1990 and 2022.
In 1990 there were 15 billionaires who controlled £53.9 billion in wealth.
By 2022 there were 177 billionaires who controlled £653.1 in wealth.
These increases reflect a wider increase in inequality in the UK more generally. However the increase in wealth at the very top, such as billionaires, has been the most extreme.
Most of the increase has been driven by an increase in the number of billionaires, but there has also been a concentration at the very top. The top two billionaire households in 2022 controlled as much wealth as the bottom half of billionaire households in 1990.
Billionaire wealth has increased due to the structure of the UK economy. It has continued to increase post-Covid despite the wider population facing economic crises.
Billionaires are not uniquely hard working, or intelligent, or creative. Instead, billionaires are better seen as the primary beneficiaries of an economic system which produces huge levels of poverty and inequality, and has left the UK particularly vulnerable to the multiple, overlapping crises we have faced over the past few years.
This blog post is a summary of ‘Billionaire Britain’, a report from the Equality Trust.
Measuring Billionaire Wealth
There is no quality data source on wealth in the UK at the national level. This is because there is no systematic recording of wealth when it is taxed. The Wealth and Assets survey suffers under-reporting from the very wealthiest households. The Times Richlist is the most comprehensive source, but this could miss out on various assets and under-report wealth.
Billionaire Britain uses data from the Times Rich List.
From a research methods perspective this is an interesting example of how power shapes data collection. The very richest are the most powerful and the UK government doesn’t systematically track data on their wealth. In fact, tracking is poor that Billionaire Britain estimates at least £4.4 billion of property investment in the UK has been bought by corrupt individuals.
Why are there more billionaires in Britain?
Two underlying structural changes have enabled massive accumulation by those at the top:
Firstly, the financialisation of the UK means that those with wealth now have greater returns on their investments. This is due to corporations focusing on profits over wages and the inflation of asset prices.
Secondly, deregulation has resulted in less restrictions and fewer taxes on wealth. This has attracted more wealth to the UK.
Of the 177 on the 2022 billionaire rich list 42 gained their wealth through investing and 39 through real estate.
Financialisation is where the financial industry becomes more important to the economy as a whole.
The finance sector consists of a range of different industries from investment companies (including real estate investments), stocks and shares funds, hedge funds, and insurance and pensions.
In a primarily finance based economy, the production of tangible products is less important, and many of the financial services seek to make returns trading financial instruments without creating anything of any value.
One consequence of a financialized economy is asset price inflation. Financial companies invest in assets such as houses and land for a return (rather than seeking to develop land or improve houses themselves) which pushes the prices up.
A second and related consequence is more households taking on debt. This is increasingly required to buy more expensive assets, such as housing.
A third consequence is more companies seeking profit over wages and quality services. They become more concerned with providing dividends to shareholders over paying decent wages.
In terms of service provision, energy and water companies have extracted billions in profits over the last years. Shareholders have got richer as a result. However the infrastructure is now crumbling in many cases, as evidenced with things such as leaky water pipes.
All of the above has resulted in a more unequal society as a few benefit from financialisation. Meanwhile at the bottom end people have relatively less money AND worse services.
What are the solutions to increased wealth and the inequality this causes?
The Equality Trust suggests five courses of action…
Introduce a progressive wealth tax. That means the wealthier you are the more tax you pay!
Make corporate ownership more democratic, so more people have a say in what happens to profits.
Regulate the financial sector more.
Return essential services to public ownership.
Improve tax transparency and end tax havens.
Create more community wealth funds to invest in areas that need it most.
