SAMPLE - Dealing with Data by The Examcraft Group

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Dealing With Data Table of Contents Introduction – A note to teachers and students. ................................................................................................ 3 Section 1 – Key Concepts and Terminology ........................................................................................................ 5 Chapter 1 - The Research Process ................................................................................................................... 5 “Qualitative” vs “Quantitative” Data........................................................................................................... 6 Data Gathering .......................................................................................................................................... 10 Justifying their Claims (deductive, inductive, and abductive reasoning) .................................................. 12

Thinking about the History of Data – it hasn’t always been a constant! .................................................. 14 Chapter 2 – Common Data Sources .............................................................................................................. 17 Chapter 3 – Data Gathering – Sampling Methods......................................................................................... 23

Probability Sampling .................................................................................................................................. 24 Non-Probability Sampling .......................................................................................................................... 29 Weighted Samples ..................................................................................................................................... 36 Cross-Sectional Studies ‘vs’ Longitudinal Studies (Case Study: “Growing up in Ireland”) ........................ 38 Sample Size and “Margin of Error” Calculations ....................................................................................... 42 Chapter 4 – Representing the Data ............................................................................................................... 47

Infographics ............................................................................................................................................... 48 Chapter 5 - Indices ......................................................................................................................................... 56 Chapter 6 -‘Bad Data’ .................................................................................................................................... 60

Chapter 7 – Understanding the Structure of “Reports” ................................................................................ 67 Chapter 8 – Research Ethics .......................................................................................................................... 68

Section 2 – Exam Focus ..................................................................................................................................... 71 Chapter 9 – Dealing with the ‘Long Question’ (2022 & Beyond) .................................................................. 71 Chapter 10 – Sample Data-Based Questions – One Suggested Approach .................................................... 74 OL Sample 1 – Women in Politics (EU and Local Elections)....................................................................... 76 OL Sample 2 – Educational Inequality (PISA and Covid)............................................................................ 81 OL Sample 3 – Sustainable Development (Climate Justice and Fossil Fuel Divestment) .......................... 86 OL Sample 4 – Irish Political System – Right/Left Divide and Age & Gender Considerations. .................. 91 HL Sample 1 – UN Committee System & Shadow Reports........................................................................ 96 HL Sample 2 – Ethical Trade – Personal and Governmental Responsibility ............................................ 101 HL Sample 3 – Children’s Rights – Ombudsman and Growing Up in Ireland .......................................... 106 HL Sample 4 – Elections – Electoral Integrity and Electoral Commission ............................................... 111 HL Sample 5 – Migration – BBC Reporting and Central Statistics Office ................................................. 117

HL Sample 6 – Gender – Women in Irish Theatre and the General Workforce ...................................... 122 HL Sample 7 – Corruption – Transparency International and International Monetary Fund ................. 128 HL Sample 8 – Development Funding – The Guardian and Financial Justice Ireland .............................. 135 HL Sample 9 – Northern Ireland – Northern Ireland ‘Life and Times’ and the 2022 Assembly Elections 140 HL Sample 10 – Covid Rights & Duties and Irish Human Rights and Equality Commission..................... 146 HL Sample 11 – Sustainable Development Report 2021 and Social Justice Ireland................................ 151 HL Sample 12 – European Union – “Irexit” and European Movement Ireland ....................................... 157

Chapter 11 – Higher Level - Exemplar Answers .......................................................................................... 162

ISBN 9-781897-922286

Introduction – A note to teachers and students. Welcome to Dealing with Data – A Students’ Guide to the Data-Based Questions (DBQ) in Leaving Cert Politics and Society. Why has this book been written? It’s simple really. It’s the kind of book that I would want to have in my own classroom to help my own students’ exploration of this major component of the course. As a subject with a short pedigree (the first exam only took place in 2018), this part of the course has proved more problematic for students and teachers than virtually any other section – both in terms of the types of questions that could be asked, and the level of depth expected in the answers.

Currently worth 150 out of 400 marks in the written exam (and 150 out of 300 in ‘Covid Times’), this element of the exam is both the most time-consuming for teachers to prepare materials and the area where the materials prepared become “dated” the quickest. A DBQ on Media Ownership in Ireland in 2020, for example would have become largely obsolete when Denis O’Brien sold his controlling interest in large parts of the Irish media landscape in the spring of 2021. Therefore, this workbook will need to be updated on a “rolling basis” – with new sample questions replacing older material on what is hoped will be a two-year rotation (with older questions continuing to be available online for the more ambitious students).

The key data concept of ‘Sample Size’ becomes a looming issue at this point, with only a handful of past Leaving Cert Questions available upon which to base the sample content and questions below. As any datacentric student should quickly realize, extrapolating patterns based on a limited sample is potentially disastrous. While some of the issues arising here will, hopefully, be addressed in subsequent iterations of the workbook, the general maxim by which the authors abide is: “Don’t let perfect be the enemy of good.” In other words, the content, sample questions, worked exemplars, and sample datasets provided here represent our best attempt to quantify a constantly evolving subject. How it is being taught by teachers, studied by students, and examined by the State Exams Commission will inform how this workbook itself evolves in the years to come.

Nothing within is designed to be prescriptive or definitive, but rather should be thought of as a useful guide to a new subject, which has already changed a lot since its inception in 2016 and which (it is hoped) will continue to evolve in the years ahead. The authors welcome any constructive feedback on the text with open ears, particularly where remediation is suggested! Rather than seeing this as a book that should be proceeded through mechanically from page 1 to 200, it is useful for teachers and students to think about dipping in and out of the text as and when it is needed. Where possible, it is suggested that teachers should integrate the data concepts and sample questions into the study of any given topic. When covering the Electoral System’ in Learning Outcome 2.5 “evidence about the effectiveness of representation” in Ireland, for example, a teacher might cover the opening ground themselves in class before assigning the students the Ordinary Level Sample Question 1 on “Women in Politics”. This will expose students to some of the key data they might need for a formative essay assignment that they might then prepare, while simultaneously introducing them to many key aspects of interrogating and evaluating data itself. Later in that scheme of work, students might be asked to engage with Higher Level Sample Question 4 on Electoral Integrity and the proposed Electoral Commission. This will provide insights into the kinds of reforms that might be deemed necessary by the student in their own evolving understanding of the strengths and weaknesses of our system, but now understood with a more datainformed position!

A key concept to grasp for both student and teacher is this:

Any Data-Based Question that had been completed becomes a Case Study for future reference. The data that students engage with here is specifically chosen to supplement and reinforce the key concepts outlined in the four, interweaving strand of the course. It should become clear that while some sample questions focus in on one specific “Learning Outcome”, many others touch simultaneously on numerous Learning Outcomes across different Topics and Strands. Ultimately, the goal of this workbook is to act as a steppingstone for students. Having walked through all of the components necessary in the identification, comprehension, and analysis of data in the controlled environment of the classroom and scaffolded by this workbook, they should gradually become more confident in interrogating unseen sources of qualitative and quantitative data of their own – both for the upcoming exam and more importantly in the real exam - LIFE.

By the time the students approach the end of 6th year, it is hoped that they will simply see this book as providing “SLOP” (Shed Loads of Practice!) and a useful point of reference for their exam revision. Like any good Pol-Soc teacher, this book should find itself being almost redundant by the time the students finalize their exam preparation, secure in the knowledge that they have a solid grasp on the key concepts and a high degree of flexibility in using the analytic skills they have developed.

The Indices section of this book (Chapter5) is designed to work alongside the “Pol-Soc Log Tables”, akin to the short booklets with which Maths, Applied Maths, Physics, and Chemistry students will be more familiar. I provide free, annually updated versions of these data sets for students to download on my website, www.polsocpodcast.com. This idea emerged not as a means of being prescriptive as to what student MUST know going into the exam, but rather as a shorthand guide to understanding the proliferation of useful (if often somewhat limited) indices and league tables against which Ireland’s performance is often ranked and handicapped. Herein, a one-page summary provides a quick overview, with Ireland’s relative position judged against both the top performers and the laggards in each area, designed to provide to give a ‘light-touch’ of context.

Regardless of how students and teachers end up using the book (and not doubting for a moment that many teachers will have innovative methods of their own to supplement and surpass its content), we hope that the workbook will ease the load of teachers, give some greater sense of certainty to students, and will make the prospective decision of potential Pol Soc students who are considering pursuing the subject seem a little more manageable in the future. The goal is not that students become cynical of data, or that they might have “had enough of experts” as the pro-Brexit faction of the UK political establishment so infamously (and depressingly) argued, but rather that they recognize both the inherent possibilities and limitations of these critical political and sociological tools. Dr Jerome Devitt July, 2022.

Section 1 – Key Concepts and Terminology Chapter 1 - The Research Process At various points throughout this text we will return to the idea of the ‘Research Process’. In order for you to be critical of how data is presented, you will need to have a basic understanding of how that data was gathered, analysed, and presented, but also have some insight into the reasons why various research concepts were examined in the first place. In some ways, the key skill you need to approach your Politics and Society “Data-Based Question” is the ability to ‘reverse engineer’ the way thin which the data presented to you in the exam was gathered.

The diagram below is one that you should refer back to as you examine each new idea in order to help you understand where that idea fits into the ‘big picture’. The cycle described below is a generalization and follows a relatively straightforward timeline, but with so many different types of investigation and presentation of data possible, students should understand that some sections might overlap, if a problem arises with a later stage, sometimes researchers will need to return to an earlier point on the cycle, or even scrap what they have done and start from scratch if major 1. EXISTING KNOWLEDGE

2. Developing your 'research question' and goal

8. Presenting the data so as to contribute to the ....

7. Drawing conclusion on the data

3. Choice of Research 'Methodology' the "how" question

4. Selection of Cases and "variables" to observe

6. Data Analysis

5.Data Collection

problems arise.

Depending on the type of Citizenship Project that you are undertaking, this process may also prove useful, particularly if you are gathering data of your own. But remember, if this is the case, you’ll need to think about the ‘Ethics’ of data gathering (see Chapter 6). This schematic representation of the ‘Research Process’ is adapted from Dimiter Toshkov, “Research Design” in Lowndes, Marsh, and Stoker, “Theory and Methods in Political Science” 4 th Edition. Red Globe Press, 2018. Page 221.

“Qualitative” vs “Quantitative” Data The problems of gathering, analysing, and interpreting data is not a new problem. It has been an issue for policy makers, governments, scientists, and social scientists. One of the key decisions that any researcher needs to make before they undertake their research is the decision of what data they hope to gather and how that data will be gathered. In virtually all cases the answer to those

'There are three kinds of lies: lies, damned lies, and statistics.’ Variously attributed to British Prime Minister, Benjamin Disraeli & American Author, Mark Twain

questions will both influence, and be influenced by the structure of their research, the available resources (financial, personnel, computing power, and time) and ultimate goal of their research project.

Forms of Data

Identify the following categories of data – ‘Qualitative’, ‘Quantitative’, or ‘Both’ 2. A Pol-Soc teacher giving written feedback on an essay commenting on the fluency of the writing, the structure of the argument, and its register.

4. Da Vinci’s painting, Salvator Mundi is a haunting and enigmatic representation of Christ holding a sphere which represents the Earth.

5. Irish soccer fans were angry and disappointed that 25-year-old former Ireland Under 21 player, Jack Grealish, chose to play senior football for England

3. Leonardo da Vinci’s painting, Salvator Mundi, sold for over $450 million at Christie’s New York in 2016 during a post-war and contemporary art event.

1. Preliminary results from the CSO show that the population of Ireland was 5,123,536 on Census night. 2022. This is an increase of 7.6% since 2016

6. In 2020, 57,569 students were registered to study the Leaving Cert and Leaving Cert Applied. This represented a +2.9% increase on 2019.

What kinds of data (‘Qual’ or ‘Quant’) would you gather in order to investigate the following questions? In each case, you should justify your answer. Quantitative Data

Qualitative data is information about qualities, information that cannot be reliably measured. It is expressed in words, rather than numbers. It is a more subjective form of assessment because how different people might describe a similar phenomenon will depend on their use of language and their personal perspective. The researcher should be a rigorous and careful insider who understands people’s worldview and assess what their subjects say with this in mind.

Quantitative data is information about quantities, or numbers of things. It is information that can be measured and written down with numbers. In theory, at least, it tends to be more objective, and can be verified. The researcher should be an objective outsider who brings a hypothesis to the research. It is evident that anybody measuring the same phenomenon will arrive at the same result, if measurements are made under exactly the same conditions.

Some examples of qualitative data are the grace with which you run, how proud you feel of your achievements, your opinion of a movie you saw recently. (Give 3 other examples):

Some examples of quantitative data are your height, weight, the average number of students in a class, the average income of a group of people… (Give 3 other examples):

Key Term: Subjective “Based on or influenced by personal feelings, tastes, or 3. opinions.” If research is subjective, the position, experience, or background of the researcher may influence their findings.

Key Term: Objective “NOT influenced by personal feelings or opinions in 3. considering and representing facts. In research, this implies a high degree of impartiality and an ability to ‘step outside’ the topic under investigation.

Qualitative Data

1. The average income of Irish adults: ________________________________ Justification:

2. How students feel about bullying in schools: ________________________ Justification:

3. The average age of students in your year in school: _____________________ Justification:

4. The number of Leaving Cert points needed to qualify for a place in Medical School in the last 10 years: _________________________ Justification:

5. The experience of recent immigrants in ‘Direct Provision’: _____________________ Justification:

6. The number of votes needed to qualify for a ‘quota’ in your constituency: _______________ Justification:

Throughout this workbook, you will see the idea of evaluating or critically evaluating different aspects of data. This means we must identify the Strengths and Weaknesses of a certain way of doing things and then come to a conclusion. Here’s a shorthand version of what to look for. Type of Data Quantitative data

Strengths

Weaknesses

Precision: Statistical and numerical, clearly outlines measurable data for reader/user.

Scale: This Useful for ‘Big Picture’ investigations. Many thousands of people can fill out the same form, where interviewing that number of participants would be impractical.

Survey Fatigue: If the survey itself is too long participants won’t do it, might fail to complete it, or might rush through the final questions without really putting too much thought into the answers. Conversely, if the survey is too short, or if similar surveys are being completed quite frequently, it may not provide enough context to draw meaningful conclusion.

Engagement: Usually surveys (such as exit polls) are relatively quick. The participants don’t feel it is too onerous on their time to participate. Similarly, anonymous responses can aid authentic replies to questions.

Lacks Nuance: It can give a numerical account of a situation without accounting for the causes of the issues being examine. This approach can often neglect more meaningful questions.

Reliability? There can be unreliable, selective or partisan gathering & collation of quantitative data. Eg Twitter polls

Replicable: In theory, any researcher undertaking the same investigation of the same group should generate the same findings and same results.

Shallow, but Broad: This approach gathers ‘a little’ detail from lots of participants. Obviously, this could be a ‘strength’ or a ‘weakness’ depending on what you are investigating! Qualitative data

Detail: This approach gathers far more in-depth detail and information and offers more themes and meaning from participants. Nuance: Because participants can explain exactly what they mean, the researcher can gain far greater insight. Provides far more nuanced detail

Variation: Because there is more detail being provided, it can be harder to categorize or generalize the responses. Reliability: This approach can sometimes be susceptible to false data if subjects are insincere. In order to impress or avoid the scorn of the investigator, such as being an outlier in a focus group, some respondents might tell the investigator “What they want to hear!” – invalidating the results. This requires shrewd judgment on the part of the researcher.

Narrow, but deep: This approach garners a lot of detail from a small number of participants (how representative is that detail?). Lack of anonymity can lead to subject offering inauthentic responses. As with the Quantitative approach, this could also be a ‘strength’ or a ‘weakness’ depending on what you are investigating!

With all things, we have to recognize that it’s not always as clear cut at these straightforward categorizations suggest. Here, as elsewhere in this book, it can help to see what the experts say: What the Experts Say

“Qualitative research is often criticized as biased, small scale, anecdotal, and/or lacking rigor; however, when it is carried out properly it is unbiased, in depth, valid, reliable, credible and rigorous. In qualitative research, there needs to be a way of assessing the “extent to which claims are supported by convincing evidence.” Although the terms reliability and validity traditionally have been associated with quantitative research, increasingly they are being seen as important concepts in qualitative research as well. Examining the data for reliability and validity assesses both the objectivity and credibility of the research. Validity relates to the honesty and genuineness of the research data, while reliability relates to the reproducibility and stability of the data.” Remember to “Cite your sources”!!! – This extract is drawn from the work of Dr Claire Anderson in the American Journal of Pharmaceutical Education, vol.74 (8); October 2010.

We might think about this from the opposite side too. Are we really capable of being fully objective, even despite our best efforts? Students who find this topic interesting might enjoy digging further into the psychology behind the study of ‘Biases’. (Find out more about Anchoring Bias, Recency Bias, and particularly Confirmation Bias, which is a major issue for students who try to do a quick Google Search instead of doing a rounded investigation!) What the Experts Say

“We tend to think that how we see and view things reflects an objective reality, but this is often not the case. To take a quick example, if you ask people if they are better than average at something, pretty much no matter what it is, more than 50% of people will say that they are. One study asked this question about car driving and found that over 90% of people report thinking they are better than the average driver, and the numbers weren't that far off for people who had been in multiple car accidents!” Cite your sources! – This view comes from Dr Nathan Heflick in ‘Psychology Today’, May 2011.

Student Reflection: Bearing in mind all that you have learned in this section, jot down your key ‘take-aways’ – the ideas that stuck you as being the most interesting, which offered you the most insight, or the ones that challenged the way you had previously thought about a topic.

Data Gathering Once you know what kind of data you’re dealing with, or the kind of data you’re hoping to deal with, you need to consider the means by which these data will actually be gathered. This also dictates the ways in which you might respond to and question certain data sets and how you would critique source documents that are presented to you in the exam. After all, if you are asked to evaluate the data, how can you do that without understanding the way in which that data is gathered? This is particularly relevant in terms of recently gathered data. Covid-19 restrictions meant that many longitudinal studies had to adapt how they gathered qualitative and quantitative evidence. (For a useful sample exercise, refer to the ‘Methodology’ section of the “Northern Ireland Life and Times Survey” data in HL Sample DBQ 9!) Data Collection Approaches: Quantitative Data Collection Methods

Qualitative Data Collection Methods

“Qual”

●

● ● ●

“Open-response” Interview Focus groups Observations Field Work Case Study Life History Content Analyses Filming of Interactions

“Fixed-response” questions in Interview ● Surveys with fixed response options (multiple choice/Likert scale, etc.) ● Quantified observations of behaviours measured on a scale or index ● Meta-Analyses (taking in previously gathered quantitative data from multiple studies)

“Quant”

● ● ● ●

●

Given that data have different characteristics and are used in different ways, we will also notice that HOW you gather a given set of data will have a big influence on the utility of data you will ultimately end up with.

Quantitative Data Collection Methods: In this section, you should try and identify what the most useful and appropriate use of each data gathering method might be. Some examples have been completed for you. Fixed response questions in interviews: Opinion Polls, Exit Polls at an election…

Surveys with fixed response options: Conducting a survey in school where students have to tick a box to indicate their preferred option for a school trip.

Observations of behaviours on indices or scales: A psychologist using a behavioural rating scale as an “assessment instrument”, where they measure the behaviour of a student that includes items that assess one or more targeted behaviours, such as in assessing dyslexia or another specific learning difficulty.

Meta-Analyses: Drawing together and analysing homelessness data from a variety of sources, such as Government, NGOs, Charities, and Human Rights organizations

Examples of Qualitative Data Collection Methods: Add your own examples of the following types of “qual” data gathering methods. Some are partially completed for you, but you can still add your own examples. Interviews: After completing a quantitative survey, individuals are brought in for an interview to give greater depth to explain their survey responses

Focus Group: A political party who is trying to design political ads in the run up to an election gather a small group of people. They show multiple versions of similar ads to establish which is the most effective.

Observations:

Case Studies:

Fieldwork: An anthropologist who is conducting research into the impact of industrial mining operations in Queensland, Australia. (This is a fieldwork project that Key Thinker Thomas Hylland Eriksen worked on!)

Life Histories: Holocaust survivor Tomi Reichental’s testimony on his experiences in Nazi Concentration camps as a young boy.

Content Analysis: Looking at a large number of cartoons on Irish television to examine how gender, or social class are represented on screen to see how that might help shape how the young viewers understand the idea of gender.

Filming of Interactions: Sometimes, the presence of the investigator might distort the response of the subject of the investigation. Imagine how differently to their normal behaviour a toddler might act in the presence of a stranger (such as a child psychologist) who is trying to observe the issues around early childhood development!

Now imagine that you’re undertaking a ‘data-gathering’ exercise as part of your Citizenship Project. Which of the following approaches to gathering those data would be most relevant to the specific goals of the project you’re undertaking? Justify your response

Justifying their Claims (deductive, inductive, and abductive reasoning) When we are trying to decide whether or not researchers have ‘justified their claims’ in a piece of research, we have to apply some kind of coherent criteria or logic on which to base our conclusions. Read the article below to see if (or how!) having a solid understanding of REASONING can help you to respond to these questions. This might be just one tool that you use in assessing this problem. It might also be the case that because you only get a page of information on each document, you can only make a provisional judgment.

Deductive versus inductive reasoning: what’s the difference? From detective work to science, both types of reasoning can prove invaluable. Source: ZME Science, by Tibi Puiu (Adapted) https://www.zmescience.com/science/difference-deductive-inductivereasoning/

Sir Arthur Conan Doyle’s fictional Sherlock Holmes is supposedly the best detective in the world. What’s the secret behind his astonishing ability to gather clues from the crime scene that the police always seem to be missing? The answer is quite elementary, my dear reader. While typical police detectives might use deductive reasoning to solve crimes, Sherlock on the other hand is a master of inductive reasoning. But what’s the difference?

What is deductive reasoning? Deductive reasoning involves drawing a conclusion based on premises that are generally assumed to be true. If all the premises are true, then it holds that the conclusion has to be true. Deduction always starts with a general statement and ends with a narrower, specific conclusion, which is why it’s also called “top-down” logic. The initial assumption presumes that if something is true, then it must be true in all cases. A second premise is made in relation to the first statement, and since the initial premise is supposed to be true, so must be the second statement as well. The association between two statements — a major and a minor statement — to form a logical conclusion is called a syllogism.

In mathematical terms, you can think of it this way: A=B, B=C, therefore A=C.

We use deduction in our day-to-day lives, but this reasoning method is most widely used in research, where it forms the bedrock of the scientific method that tests the validity of a hypothesis. Here are some examples: Premise A: All people are mortal. → Premise B: Socrates is a person. → Conclusion: Socrates is mortal. Premise A: All mammals have a backbone. → Premise B: Dogs are mammals. → Conclusion: Dogs have backbones. Premise A: Multiplication is done before addition. → Premise B: Addition is done before subtraction. → Conclusion: Multiplication is done before subtraction. Premise A: Oppositely charged particles attract one another. → Premise B: These two molecules repel each other. → Conclusion: The two molecules are either both positively or negatively charged. What is inductive reasoning? Inductive reasoning is the opposite of deductive reasoning, in the sense that we start with specific arguments to form a general conclusion, rather than making specific conclusions starting from general arguments. For this reason, inductive reasoning is often used to formulate a hypothesis from limited data rather than supporting an existing hypothesis. Also, the accuracy of a conclusion inferred through induction is typically lower than through deduction, even if the starting statements themselves are true.

For instance, take these examples of inductive logic: ● ● ●

The first marble from the bag is black, so is the second, and so is the third. Therefore, all the marbles in the bag must be black. Every cat I meet has fur. All cats then must have fur. Whenever I get a cold, people around me get sick. Therefore, colds are infectious.

Deductive versus inductive reasoning: which one is better? Deductive inference goes from the general to the specific, while Inductive inference goes from the specific to the general. Deductive reasoning cannot be false if its premises are true, whereas inductive reasoning can still be false due to the fact that you cannot account for those instances where you are not correct. In deduction, the conclusion either follows or it doesn’t. There is no in-between like there are degrees of strength or weakness in induction.

Neither deduction nor induction is necessarily superior to one another. Instead, there’s a constant interplay between the two, depending on whether we’re making predictions based on observations or on theory. Sometimes, it makes sense to start with a theory to form a new hypothesis, then use observation to confirm it. Or we can form a hypothesis from observations that seem to form a pattern, which can turn into a theory.

Both methods allow us to get closer and closer to the truth, depending on how much or how little information we have to hand. However, we can never prove something with absolute certainty, which is why science is a tool of approximation — the best there is, but still an approximation. That being said, each method is far from perfect and has its drawbacks. A deductive argument might be based on non-factual information (the premise is wrong), while an inductive statement might lack sufficient data to form a reliable conclusion, for instance.

As an example of when deduction can go hilariously wrong, look no further than Diogenes and his naked chicken. Diogenes was an ancient Greek philosopher who was a contemporary of Plato — and the two couldn’t be more different. Diogenes slept in a large jar in the marketplace and begged for a living. He was famous for his philosophical stunts, such as carrying a lit lamp in the daytime, claiming to be looking for an honest man. Plato would often quote and interpret the teachings of his old mentor, Socrates. On one occasion, Plato held a talk about Socrates’ definition of a man as a “featherless biped”. Diogenes cleverly plucked a chicken and with a wide grin on his face proclaimed “Behold! I’ve brought you a man.”

The implication is that a deductive conclusion is only as good as its premise. Inductive reasoning leads to a logical conclusion only when the available data is robust.

For instance, penguins are birds. Penguins can’t fly. Therefore, all birds can’t fly, which is obviously wrong if you know more birds than just penguins or weird plucked chickens. These reasoning techniques are important tools in any critical thinking arsenal, with each having its own time and place. Whether starting from the general or the specific, you have everything you need to win your next argument in style. How does this help a Pol-Soc student? In an exam, it’s entirely possible that you might be asked to evaluate whether the authors of a study or report have fully justified their claims. In this case, you can evaluate the work based on TWO factors: Did they present sufficient evidence within the document? Did the logic of the argument presented follow a clear deductive or inductive reasoning process. In an ideal world, if a conclusion was to be found to be fully convincing, you would want BOTH of these criteria to be met. You could structure your response to bear both factors in mind in your answer.

Thinking about the History of Data – it hasn’t always been a constant! What students might not be aware of is that the English rule of Ireland actually pushed forward many of those data gathering and data interpretation ideas that we now take for granted! This short article by UCC historian Dr Clíona Ó Gallchoir will help ambitions students, thinking about aiming for a “H1”, think in a more nuanced way about some of the issues we cover in Politics and Society.

How Oliver Cromwell's doctor pioneered the use of statistics By Clíona Ó Gallchoir UCC. Friday, 25 Sep 2020 (Adapted) Source: https://www.rte.ie/brainstorm/2020/0925/1167432-william-petty-oliver-cromwell-ireland-statistics/

About the Source: RTÉ’s ‘Brainstorming’ project describes itself in the following terms: “…an unique partnership between RTÉ and Irish third level institutions, namely our Founding Partners UCC, NUIG, UL, DCU, Technological University Dublin and Maynooth University. We're pleased to welcome the Irish Research Council and Teagasc as our Strategic Partners. RTÉ Brainstorm is where the academic and research community will contribute to public debate, reflect on what’s happening in the world around us and communicate fresh thinking on a broad range of issues.” In other words, University academics, who write short summaries of big ideas using their expertise. Their goal is to make academic ideas easily accessible to novice readers. A laudable project and a must for Pol-Soc Students!!!

Analysis: William Petty's use of numerical information about land and people shows that there are no such things as simple or neutral facts

Numbers and statistics have acquired a grim importance and urgency of late thanks to the pandemic. We have very quickly become familiar with forms of analysis used by epidemiologists, scientists who study health at the level of populations, rather than individuals. We can discuss rates of infection, mortality rates, positivity rates, and we anxiously track deaths per million or per 100,000. A sobering league table has emerged, that no country wants to lead. These numbers and statistics are vital to our understanding of this new disease, and to our ability to tackle it.

But the framing of death and disease in terms of numbers, rather than individuals, is also problematic. Comparing mortality rates as numbers and data erases the humanity of the individuals whose deaths make up those numbers. This was poignantly expressed by Dorothy Duffy in “My Sister is not a Statistic”, her poem about the death of her sister from Covid-19. The morally troubling aspect of statistics has been apparent since its emergence as a new form of knowledge. Statistics and the study of populations is usually seen as part of the scientific revolution of the 17th century, when religious interpretations of the world gave way to investigations of empirical reality and the insistence on observable facts as the basis for theories. One of the reasons that the Royal Society in England, founded in 1663, was committed to the promotion of empirically-based research was that it was regarded as a way of avoiding and moving beyond the religious and philosophical disputes that had raged since the Reformation. However, it is very tricky to separate supposedly neutral ‘facts’ from ideological controversy, as is seen in the career of William Petty. A polymath and founder member of the Royal Society, Petty was educated in Oxford, France and the Netherlands and came to Ireland in 1652 as physician to Oliver Cromwell's army. Petty’s wide-ranging abilities soon saw him appointed to other duties, including the redistribution of Catholic-owned lands seized during Cromwell’s campaign. (Remember “To Hell or to Connacht” from your 2nd year History course? – JD) This resulted in the first attempt at a comprehensive survey of land and population in Ireland, known as the ‘Down Survey’.

The fact that the surveying and mapping of land was frequently prompted by the concerns of the military, or in the context of the seizure and settlement of land, is no great secret in Ireland. Many people are familiar with Brian Friel’s play Translations, which represents the 19th-century ordnance survey project as both an outcome of colonization and a further act of erasure of Irish culture through the replacement of place names with anglicized equivalents. It is less well known that population statistics had a very similar point of origin. English administrations in Ireland, such as the one of which Petty was a part, were obviously keen to maximize the productivity of the land. Doing so both contributed to individual and state coffers and served the ideological function of proving that the country and its people were better off under the guidance of a civilized and ‘improving’ regime.

Petty was among the very first writers to recognise the importance of gathering numerical information about land and people in order to bolster this project through the application of the new scientific method, founded on ‘facts’. He even gave his new science a new name: ‘Political Arithmetic’. One of the key principles of political arithmetic was that population size was a key indication of the growing wealth of a nation, and thus his Political Anatomy of Ireland (written in 1691) gives a great deal of space to calculating population from the very patchy information available, frequently relying on estimates of births and deaths.

But it is clear from Petty’s work that not all lives ‘count’ the same. Because his study of population and population increase is motivated by concerns about productivity and wealth, he distinguishes between those capable of work, or women capable of childbearing, and those who are not ‘productive’, effectively ‘subtracting’ from his totals the elderly and infirm, and children ‘not fit for labour’. Sectarianism was also embedded in the thought of the period, and Petty’s figures therefore also include estimates of the numbers of Catholics, Anglicans and dissenting Protestants in Ireland, not only in the interests of accuracy, but also because Catholics were generally regarded as lazier and less productive.

The confluence of statistical science with sectarianism and colonial ideas of productivity and value that we see in Petty continued into the 18th century. In his Essay on the Trade and Improvement of Ireland (1729-31), for example, Arthur Dobbs attributes population growth in Ireland to the impact of King William’s victory over James II at the Battle of the Boyne in 1690 and the consequent safeguarding of Protestant rule. However, the facts were not in Dobbs’s favour: a combination of financial instability and poor harvests in the 1720s led to great hardship and localized famines in Ireland. It was the gap between the rhetoric of writers like Dobbs, and the actual facts of poverty and hardship that prompted Jonathan Swift to write his savagely satirical A Modest Proposal in 1729. In what appears at first to be a pamphlet on ways to 'improve' Ireland economically, Swift declared that Ireland was an anomaly, because it was a country in which an increase in population did not signal wealth, but in fact lead to even greater poverty. The solution, or ‘modest proposal’, that he outlined was simple: to reduce the population and address poverty, the poor should be encouraged to sell their infant children to the rich as food. Swift’s pamphlet is astonishingly prescient in the way it identifies the potential for dehumanization that lurked within the new ‘scientific’ methods for counting and categorizing people. Since the start of the pandemic, we have been confronted with the uncomfortable implications of similar forms of arithmetic. Deaths are categorized by age, and by ‘underlying condition’. These categorizations are essential to understanding how the disease works, and who is most at risk, but the focus on the relatively low risks to the younger and healthier seems to suggest that some lives are less valuable, and some deaths less significant.

William Petty pioneered the use of statistics as a vital form of knowledge, but his work also reveals that there are no such things as simple or neutral facts. The reasons for gathering statistics also shape the knowledge and understanding they offer, and our values play a vital role in the use we make of statistical knowledge.

Key Vocabulary/Terminology for you to look up in the Dictionary:

Anglicized

Empirical Reality State Coffer

Sectarianism

Prescient

Ordnance Survey

Confluence

Rhetoric

Erasure Satirical

Implications

Anomaly

Polymath

Epidemiologists

Questions

How does the author’s presentation of the idea that “framing of death and disease in terms of numbers, rather than individuals, is also problematic” help us to understand the possibilities and limitations of Quantitative ‘vs’ Qualitative Analysis? Do you agree with the author’s assertion that: “it is very tricky to separate supposedly neutral ‘facts’ from ideological controversy”? Justify your answer with reference to the author’s argument and your own perspective/learning?

How does the author define Petty’s idea of “Political Arithmetic”? (Remember to try and weave direct quotes from the text into your answer) How does the author criticize Petty’s Methodology, specifically her claim that “it is clear from Petty’s work that not all lives ‘count’ the same”? Cite evidence from the article in your answer. How do we know that the suggestion of Jonathan Swift in his work A Modest Proposal (1721) is really a satirical proposal?

Longer Answer (50 Marks)

Is the author successful in using this historical perspective to improve our understanding of how data and statistics are used during the Covid-19 Pandemic? Justify your answer based on the article and your own learning.

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