## Bivariate Analysis for Quantitative Social Research

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.

## 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.

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!)

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:

## 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.

Bryman, A (2016) Social Research Methods

## Univariate Analysis in Quantitative Social Research

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.

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!

#### Sources

Gov.UK (accessed July 2023) Schools, Pupils and their Characteristics 2022-23.

## Measures of Central Tendency

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

### 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.

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

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.

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.

#### Signposting and related posts

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.

## Secondary Data on Academic Progress

What are the strengths and limitations of using secondary data to research the academic progress of students in schools?

This challenging question came up in the methods in Context section of the November 2021 AQA A-Level Sociology exam, and students found it difficult according the Examiners Report, with significant numbers focussing only on quantitative secondary data, rather than both quantitative and qualitative, and many answers making generalisation and failing to pick up on the specifics of different types of data, let alone APPLY these to the topic at hand which was student progress.

So this applied research methods topic is probably worth going over in some depth! (Remember, even though this came up relatively recently it can still come up this year, especially since the examiners know it’s a challenging topic for many students!).

## The Question and Item

Applying material from Item C and your knowledge of research methods, evaluate the strengths and limitations of using secondary data to investigate the
academic progress of pupils in schools.

The item suggest that you should focus on both quantitive and qualitative forms of secondary data.

And with methods in context questions you need to at least try and apply the strengths and limitations of the data to the actual topic in the question: academic progress!

### Secondary quantitative data to research academic progress

This topic is partly dealt with in this post: Official Statistics on Education: Strengths and Limitations

Official Statistics include exam results and SATs. They have excellent representativeness and usually these are easy to compare, but with education statistics, there are several different versions to measure progress and this can get confusing, also GSCE results changed from A-C to numerical form which makes comparing more difficult over time.

However, official stats do not tell us WHY students achieve at different rates, also for Gypsy and Roma children, many don’t sit formal exams so there is missing data here.

Schools may also record their own quantitative data in the form of internal tests (not official statistics) which provide more insight than official statistics but there are access issues.

### Secondary qualitative data to research academic progress

Secondary qualitative data will give you more insight into WHY students achieve at different rates, and such data includes OFSTED reports, school progress reports, the written work of students and even personal documents such as diaries.

Written work in particular can give you an insight into the quality of feedback students get and also how much effort they are making, while personal documents can tell you what is going on in students’ lives outside of education.

The main problem with both of these sources is access.

This topic is covered in depth in this post: Assessing the usefulness of secondary data for researching education. NB this post is broader than this topic, and some of the sources mentioned in it may not be useful for measuring academic progress.

##### Sources

The AQA’s mark scheme for the November 2021 Sociology A-level Education with Theory and Methods exam paper.

For more information on exams see my exams and essay writing page.

## Bias in Presenting Quantitative Data

Newspapers can ‘bias’ the presentation of quantitative data by stretching out the scale of the data they present, making differences between bars seem larger than they actually are (or vice versa!).

Quantitative research methods are usually regarded as being more objective than qualitative research methods as there is less room for the subjective biases and interpretations of researchers to influence the data collection process in quantitative research.

However, bias can still creep into quantitative research and one way this can happen is over the decision in how to present the data in even a basic visualisation.

Specifically, one can take the same data and stretch out the scale of a graph displaying that data and give the impression that the differences between the subjects under investigation are wider than in the original presentation.

## Bias in scaling graphs

A recent example of what I’m going to call ‘bias in scaling graphs‘ can be found in how an article by The Guardian displays recent data on how much GDP (Gross Domestic Product) has grown in different European Countries between 2019 to 2022.

the same data from the Office for National Statistics in a more ‘stretched out’ scale which

The Guardian article (September 2022) in question is this one: UK is only G7 country with smaller economy than before Covid-19 which displays the following graphical data to show how the UK’s GDP is falling compared to other G8 Nations.

Now you might think ‘this is quantitative data so it’s objective’ and on that basis no one can argue with what it’s telling us – the U.S. economy is doing VERY WELL compared to most Euro nations, growing more than TWICE as fast is the impression we get.

And after all, this is fair enough – a 2.6% growth rate is more than twice as fast as a 1% or less growth rate!

## Same data different scale…

However you might think differently about the above when you see the same data (almost) displayed by the UK Government in this publication: GDP International Comparisons: Key Economic Indicators which features the graph below:

Note that the data is ALMOST the same – except for Britain’s data being different at 0.6% positive rather than negative – the Guardian article was written after the UK Gov report on the basis of the UK Economic growth forecast being downgraded, but everything else is the same.

My point here is that the data above is (almost) the same and yet the graph has been ‘squashed’ compared to the graph showing the same data in The Guardian article – note the scaling is the same – if you look above you can see that the US Bar is twice as high as the EU bars, but the difference APPEARS smaller because it’s not as stretched.

The Guardian achieves its stretched out scale by displaying the bars horizontally rather than vertically – that way there is more room to stretch them out and make the differences appear larger in a visual sense.

And with the UK now in an economic downturn it makes Britain seem further behind compared to other countries than what would have been the case with the more squished presentation in the Government’s version.

## But aren’t they both biased…?

In a word yes – someone has to decided the format in which to present the data which is going to skew what people see.

But the reason I’m calling out The Guardian on this is for two reasons:

1. it’s unusual to display bars horizontally, the standard is vertically, but there’s not way you can stretch out the visualisation vertically without it looking very odd.
2. The differences are quite small – we are talking 1-2% points of change so having a more squished scale to represent the small differences seems appropriate, The Guardian has chosen to exaggerate these from the original display possible to make them seem larger than they actually are.

## Signposting and Related Posts

This material should be of interest to anyone studying Research Methods.

It’s also a useful example of Left Wing bias in the media, most sociologists focus on right wing bias!

## The Mass Shooter Database…

Mass shootings per year in America are increasing, and some recent research from the Violence Project aims to help us understand why this is.

For students of A-level Sociology this is a useful case study relevant to both research methods and crime and deviance.

The project has interviewed hundreds of people convicted of mass shootings and their family members to better understand their life histories (nice link to secondary qualitative data here!) and then fed this information into a database in oder to quantify it and to see what the main characteristics of mass shooters are.

Interestingly the data shows that there is a broad difference between people who do mass shootings in restaurants, bars and retail establishments compared to people who shoot up workplaces, religious institutions or schools and colleges. In the former, the victims tended to be strangers to the shooters, in the later type the shooters were much more likely to have known their victims.

## The main characteristics of mass shooters in America….

• Out of 172 cases only four were women, two of these acted with a man.
• 50% are white, 50% from other ethnic backgrounds
• 65% of shooters had a criminal record, 63% had a history of violence
• The most common ‘motivation’ was a history of psychosis (30% of shooters) where the shooter was loosing their grip on reality.
• Half the shooters acquired their guns legally.

You can explore the database for yourself at the link below.

## These seem to be a very ‘postmodern’ set of findings…

The researchers note that the data reveals that there is ‘no one type of shooter’ – mass shooters in America come from a diverse array of backgrounds and have diverse motives for what they are doing.

Although personally i can see one clear trend from the data which is the huge bias towards to males – as is the case with many other crimes!

And another is the recent shift to grocery store shootings – the first of these wasn’t until 2018, and since then there have been ‘copycat’ cases following it – Shooters tend to take lessons from other shooters who have done the same before!

## Controlling Gun Crime…

The project suggests two main solutions to bring down the number of mass shootings…..

1. Monitoring people with high risk characteristics and restricting gun sales to these people (nice link to Actuarialism here within crime and deviance).
2. Stopping giving attention to mass shooters – which should help stop the copycat spreading of such hideous acts!

## Teaching Resources for A-level Sociology: Research Methods

teaching resources for A-level sociology AQA focus 2020

I’ve just released the latest research methods teaching resources for sale as part of my sociology teaching resources subscription package, available for only £9.99 a month!

## Research Methods teaching resources

1. Unstructured interviews
2. Participant Observation lesson
3. Participant observation lesson 2
4. Non-Participant observation
5. Official Statistics
6. Cross National Comparisons
7. Secondary Qualitative data
8. Content analysis
9. Bringing it all together: stages of the research process

NB the October release contained all of the preceding methods (intro, surveys and experiments) and the next release in December will include Methods in Context material – the monthly subscription will give you access to all the methods material, and all material to date (education and families and households), so that’s almost the entire first year of A-level sociology teaching!)

## Resources in the bundle include:

• 5 workbooks covering  the methods above
• 5 Power Points covering most of the above lessons
• 9 lesson plans covering all of the above lessons.
• Various supplementary hand-outs for some of the above lessons as necessary.

## Fully modifiable resources

Every teacher likes to make resources their own by adding some things in and cutting other things out – and you can do this with both the work pack and the PowerPoints because I’m selling them in Word and PPT, rather than as PDFs, so you can modify them!

NB – I have had to remove most the pictures I use personally, for copyright reasons, but I’m sure you can find your own to fit in. It’s obvious where I’ve taken them out!

## More resources to come…

I’m making resources available every month as part of this teacher resource subscription package. The schedule of release of resources is as below:

• March – June 2020 – Education Resources
• July – September 2020 – Research Methods, including methods applied to education
• October – December 2020 – Families and Households
• January – April 2021 – Global Development
• May – August 2021 – Crime and Deviance
• September – October 2021 – Theory and Methods
• November 2021 – January 2022 – Revision Material
• February 2022 – Intro material.

## Issues surrounding researching in schools

There are tens of thousands of schools in the United Kingdom, which means that observational research which focuses on just one, or a handful of schools will be unrepresentative. This is also a  problem with any of the popular documentary programmes which focus on just one school – they are very interesting as they focus on the stories of the school, and some (but only some) of the pupils and teachers, but they are never going to be representative of all schools!

There are a lot of official statistics available on schools, much of it freely available on the DFES website – information on results, attendance, exclusions are all available, as are the latest OFSTED reports, so using a mixture of secondary qualitative and quantitative data may be a good choice for researchers given that schools are ‘data rich’ institutions.

A researcher could also use official statistics to easily select a sample of schools which represent all the regions in the UK, different OFSTED grades, and/ or different school types.

However, official statistics on education can be misleading – exam results may not reflect the underlying ethos of a school, or show us the difficulties a particular school faces, and schools can manipulate their data to an extent – for example, they can reduce their exclusion statistics by ‘off-rolling pupils’ – getting parents to agree to withdraw them before they exclude them.

Schools are potentially very convenient places to conduct research – because the law requires pupils to attend and teachers/ managers need to attend to keep their jobs, you can be reasonably certain that most people you want to research are going to be in attendance! You have a captive audience!

However, school gatekeepers (i.e. head teachers) may be reluctant to allow researchers into schools: they may see research as disruptive, fearing it may interfere with their duty to educate students.

Schools are also highly organised, ‘busy’ institutions – researchers may find it difficult to find the time to ask questions of pupils and teachers during the day, meaning interviews could be a problem, limiting the researcher to less representative observational research.

The researcher will also need to ensure they blend-in, otherwise they may be seen as an outsider by teachers and students alike, which would not be conducive to getting respondents to open up and provide valid information.

## Researching Parents

Home factors have more of an influence on pupil performance than school factors, and parents are certainly the biggest influencers of pupils at home, especially in their early years.

Parents can influence a child’s attitude towards education in various ways:

• The amount of time they spend reading with their children in early years
• How they play with their children more generally, and how educational that play is.
• How strict they enforce rules.
• The importance they attribute to education themselves
• The amount of interest they show in their child’s education

As a result of early socialisation, children end up being either culturally deprived or having cultural capital (or somewhere in between), which means they are either ill-prepared for school or very well prepared, which will make an enormous difference in how well they adapt to school life when they first start.

If you wish to research pupils, you may well need the consent of parents, so some minimal contact may well be necessary even if it’s not them you are actually researching.

# Problems of researching parents

## Validity issues

Middle class, pro-school parents are more likely to want to engage with research about education, as they will be more interested and will probably be able to use it as an opportunity for self-validation – they can show off how much they care about their children’s education. They will also be more familiar with filling in questionnaires, and engaging in social research, which are quite ‘middle class’ pursuits.

Working class parents, who themselves maybe didn’t have such a positive experience of schooling, might be more reluctant to take part in research, feeling less comfortable engaging with the middle class researchers.

Parents may also try to ‘impression manage’ with researchers, exaggerating their involvement in their children’s education for example, because this paints them in a more positive light.

## Practical Problems

Gaining access to parents could be difficult – if you don’t want to hang around the school gates or at parents evenings then you would have to approach them either at home or via phone/ email.

Gaining access to parent’s private addresses is going to be difficult because schools will not share that data with you because of GPDR, thus if you wanted a representative sample by postcode then you wouldn’t be able to get it.

Schools might agree to send out questionnaires or letters asking for interviewees to parents on your behalf, but then you’ve got the problem of getting a self-selecting biased sample back. The chances are only those parents who are pro-education would want to take part in your research.

It could be very difficult to gain access to the parents of traditionally underachieving groups – white working class parents or traveler parents for example.

When it comes to researching, it would be more difficult to get parents into a group to research them (also, this might be pointless anyway), so you’d probably have to do one on one research which could be more time consuming.

## Researching Teachers in Education

Teachers are the ‘front line’ of education, with the primary day to day responsibility students’ education and well-being.

If you want to understand the impacts that education policies are having on different types of student, then teachers are probably best placed to be able to tell you.

However, there are a number of potential problems when researching teachers:

## Teachers have hectic working lives

Teachers work very long hours and often suffer with high stress levels, and they may not be willing or able to spend more time to engage with researchers.

For this reason questionnaires may be a better choice of method than interviews and observations may also be a good choice as these don’t really take up any time, but they could add to teacher stress, so it might be difficult to get teachers to agree to being observed.

## Teacher professionalism

The validity of information you get from teachers may be compromised because of their professional status.

Teachers are bound by the GDPR and have a duty of care towards their students and so probably will not share data about their students with researchers from outside of the school.

Teachers could also be concerned about ‘impression management’ – they may want to present themselves in the best light possible and some may feel duty bound to present their school in a good light, because to do so is good for marketing and student recruitment, which could limit the critical views you get from teachers as a researcher.

On the other hand, there are also ‘jaded’ teachers that are fed up with their jobs, and are just time-serving their way to retirement – if you got a group of these together in a group interview, you might just get unrepresentative biased moaning about how bad life is as a teacher.

## Line managers

If you want to gain access to teachers in a school you will have to approach the senior management team, and these may limit your access to the teachers you can research, possibly directing you towards the better and more compliant teachers to pain their school and the management in a positive light.

Even if you had unlimited access to teachers, they may not wish to be critical of the school for fear of this getting back to their superiors. In some schools there may well be very few critical teachers, and if research findings showed negative views of the school, in such cases it would probably be obvious which teachers were responsible for such negative comments, even if data was anonymous.

## Home may be the best place to research teachers?

You don’t have to research teachers in their school setting don’t forget, you may get more valid information if you interview them in their homes, away from the school setting, away from the ‘front stage’ where they are performing their teacher role.

## Starters for An A-level Sociology Non-Participant Observation Lesson

Non-Participant Observation involves the researcher observing respondents, but keeping their distance, and not engaging with those respondents.

As with many of the ‘minor’ research methods in A-level sociology, this one can be a bit of a struggle to make interesting, but here are three starter activities to get your students in the mood for making observations…

## Starter 1: How many passes does the team in white make?

I won’t give too much away, but it does show one of the limitations of doing narrowly focused structured observations where you are only looking for one thing!

## Starter 2: Whodunnit?

Again, I don’t want to give too much away, but this demonstrates how difficult observation can be, in terms of the amount of things you might miss if you’re not paying close attention!

## Starter 3: Street Life:

This is a bit of an old video, but it introduces students to some of the strengths and limitations problems of qualitative, unstructured observations.

You might like to think about showing this in contrast to a video of street life in a very underdeveloped country, and comparing the differences.

## How to use these starters

I use these three starters one after the other before I get students to go out and perform their own structured and unstructured observations of street-life in the local high street.

The first two are really just a bit of fun, but they do drive home the fact that you might miss a lot if you are just focusing on a few factors when doing structured observations.

If you like this sort of thing and want to see how these starters blend into the rest of my A-level sociology lesson on non-participant observation you might like to subscribe to my A-level sociology teacher resources.

Non Participant Observation material is scheduled for release in October 2020.

## A-Level Sociology Teaching Resources

NB – you get All of these starters and more as part of my A-level sociology teaching resources, available as a monthly subscription, for only £9.99 a month! The subscription includes lesson plans and modifiable student hand-outs and PPTs. Activities such as these starters are embedded into the student learning materials.

I hope you find these resources useful, and happy teaching,

Karl, September 2020.