POLS1009 Introduction to Data and Methods for Political Science and International Relations
The Complete Exam Bible · S2 2026POLS1009 Overview
- 6 units
- Semester 2 2026
- In person
- School of Politics and International Relations
- Two closed-book exams
Introduction to Data and Methods for Political Science and International Relations is a first-year course from ANU's School of Politics and International Relations, and it treats research and writing as a single skill. Every week pairs one step in the research process with one step in academic writing.
- Assessed by Two closed-book exams, three short writing tasks and active tutorial participation.
- Exam 2 shape Two hours of multiple-choice and short-answer questions on the second half of semester.
- Hardest step Applying a definition to a new case instead of reciting it, especially for measurement and hypotheses.
- How to prepare Confusable pairs from Weeks 7 to 12, then timed short answers that define, apply and qualify.
What you get in the full POLS1009 Exam Bible
- 14 chapters, 61 pages — every examinable topic, week by week
- 38 original practice questions, most with model answers in the Exam 1 format
- 14 worked examples, solved the way POLS1009 marks
- 18 computed figures, redrawn from the course’s own numbers
- Exam-morning plan for the Exam 1 (20%) — what to revise last, where marks are lost
Free on this page: assessment table, chapter map, one worked example, key terms, FAQ. The rest is in the full exam bible.
How POLS1009 is assessed
| Component | Weight | Format |
|---|---|---|
| Exam 1 | 20% | In person, closed book, 1 hour of multiple-choice questions on the first six weeks; held in the teaching break |
| Exam 2 | 40% | In person, closed book, 2 hours of multiple-choice and short-answer questions on the last six weeks; held in the formal examination period |
| Take home tutorial tasks (3 x 10%) | 30% | Written responses to three of the five designated tutorial writing tasks, 500 words each with a 10% margin; at least one submitted before Week 6 |
| Tutorial Participation | 10% | Attendance is required, and the mark rewards active participation in tutorial discussion rather than attendance alone |
These four weights come from the Semester 2, 2026 class summary on the ANU Programs and Courses website and total 100%; the Week 1 lecture slides list the same four tasks and weights. The general course page on the same website still shows an older indicative scheme of quizzes, a paper proposal and a final research paper, and the Week 6 slides describe Exam 1 as an online, invigilated Canvas exam rather than an in-person one, so check Canvas for the arrangements that apply to you. Both the class summary and Canvas warn that if you submit more than three tutorial tasks, the lowest marks are the ones counted.
What other students are asking about POLS1009
- Where can I find lecture notes or study guides for POLS1009 at ANU?
- The POLS1009 study guide in AskSia Library covers Introduction to Data and Methods for Political Science and International Relations in 14 chapters, with the assessment table, a chapter-by-chapter topic map, key terms and a worked example, free to read on this page.
- Is there a final exam in POLS1009, and how much is it worth?
- Yes. In the offering this AskSia Library guide is built from, Exam 1 carries 20% of the POLS1009 mark; the full split is Exam 1 20%, Exam 2 40%, Take home tutorial tasks (3 x 10%) 30%, Tutorial Participation 10%.
- What does POLS1009 cover on ontology, epistemology and critical reading?
- In AskSia Library's POLS1009 guide, the Ontology, Epistemology and Critical Reading chapter explains objectivism and constructivism · positivist and interpretivist standpoints · reading an article with a purpose · questioning content, structure and style.
- What does POLS1009 cover on research questions, puzzles and concepts?
- In AskSia Library's POLS1009 guide, the Research Questions, Puzzles and Concepts chapter explains rule zero and genuine puzzles · fake puzzles · Baglione's six checks · concepts and the ladder of abstraction.
- What does POLS1009 cover on writing introductions and conclusions?
- In AskSia Library's POLS1009 guide, the Writing Introductions and Conclusions chapter explains the introduction recipe · Swales's three moves · the conclusion as mirror image · the reader funnel.
Current POLS1009 dates
| Date | Item | Control |
|---|---|---|
| 27 July to 30 October 2026 | Class dates | Class start and end dates for the Semester 2, 2026 offering. |
| 7 to 18 September 2026 | Exam 1 window | The one-hour multiple-choice exam was sat during the teaching break. |
| Formal examination period | Exam 2 | The two-hour end-of-semester exam is scheduled in the examination period; the exact date is set by the University timetable. |
Dates are as published in the Semester 2, 2026 class summary and the Week 6 lecture slides. Confirm exact deadlines and submission settings in the live LMS.
What POLS1009 covers
Fourteen chapters follow the teaching weeks, pairing each research step with the writing skill that carries it to a reader.
Science, Falsifiability and the Scientific Method
Science as a method · falsifiability and tautologies · five stages from puzzle to evaluation · rules of the roadThe course begins by asking what it means to study politics scientifically, and its answer is about method rather than subject matter.
- Science as a critical, systematic method whose explanations stay provisional
- Falsifiability and why a refuted claim can still be scientific
- Tautologies and claims about the unobservable
- Five stages: question, theory, hypotheses, empirical test, evaluation
- Why passing a test supports a theory but cannot prove it
- Polity, politics, policy and relations between polities
Ontology, Epistemology and Critical Reading
Objectivism and constructivism · positivist and interpretivist standpoints · reading an article with a purpose · questioning content, structure and styleBefore choosing a method, a political researcher holds views about what the social world is and how anyone can know it, often without saying so. This chapter makes those views explicit.
- Ontology and the objectivist and constructivist streams
- Epistemology and the questions it asks about knowledge
- Five standpoints from positivism to interpretivism
- Three main approaches and the empirical-analytical focus
- Methods as tools, data as any information, qualitative and quantitative work
- Reading an article section by section for a stated purpose
Research Questions, Puzzles and Concepts
Rule zero and genuine puzzles · fake puzzles · Baglione's six checks · concepts and the ladder of abstractionEvery paper in the course is steered by its research question, so this chapter covers how to find one, test it and build it out of usable concepts.
- Rule zero and the denominator problem
- Well-informed surprise and what makes a genuine puzzle
- Three fake puzzles and the Week 6 review shorthand
- Variation over time and across units as starting points
- Sources of questions and what to do when yours is already studied
- Topics versus questions, scope and Baglione's six checks
Writing Introductions and Conclusions
The introduction recipe · Swales's three moves · the conclusion as mirror image · the reader funnelThe opening and closing sections of a paper do the most work with the fewest words, and the course treats them as a matched pair.
- Six sections of an empirical paper and the question each answers
- Why the introduction and conclusion are written last
- The reader funnel from title to whole paper
- The seven-element introduction recipe
- Swales's CARS model and its three moves
- The five-element conclusion and the bookend idea
Causality, Counterfactuals and Experiments
Research design · counterfactuals and the fundamental problem · random assignment · internal and external validity · limits of experimentsWhy questions in politics are causal questions, and this chapter shows what a causal claim actually asserts.
- Research design as a plan made before seeing the evidence
- Two shortcuts to avoid when choosing a design
- Two worlds, factual and counterfactual, behind every causal claim
- Treatment, causal effect and the fundamental problem of causal inference
- Experiments, control groups and random assignment
- How randomisation breaks the link between a confounder and the treatment
Observational Designs, Datasets and Drafting
Observational research · qualitative designs and comparable cases · cross-sectional, time-series and panel data · plan, draft, reviseMost political questions cannot be answered by experiment, so this chapter turns to observational designs, in which the researcher records outcomes as they occur and must work to make comparisons fair because confounding is always possible.
- Observational design and why confounding is always possible
- Qualitative and quantitative families as complements
- Process tracing, comparative case studies and content analysis
- Mill's comparable-cases logic
- Rows, columns and the unit of observation in a dataset
- Cross-sectional, time-series and panel data
Theories and the Four Causal Hurdles
Four parts of a theory · rival mechanisms · mechanism, direction, covariation and confounders · correlation pitfallsA theory is the proposed answer to a research question, and this chapter explains what a usable theory contains and how to judge the causal claim inside it.
- Theory as the because sentence and hypothesis as its prediction
- Four parts of a theory: prediction, mechanism, assumptions, scope conditions
- Empirical and normative, grand and middle-range, deterministic and probabilistic
- Inductive and deductive routes to theory
- Rival mechanisms for the same variables
- Four causal hurdles and why covariation is the easiest
Writing and Testing Hypotheses
Independent, dependent and third variables · hypothesis types · six common errors · the null hypothesis and threats to inferenceThis chapter covers the Week 6 research strand, where a theory becomes something you can test.
- What a hypothesis is and how the course notation writes it
- Indicators, level of analysis and probabilistic claims
- Deductive and inductive routes to a hypothesis
- Six variable roles, including mediators, moderators and confounders
- Four criteria and the one-sign rule
- Six common errors and how to repair each
Academic Language and Paragraph Structure
Know your reader · structure and register · MEAL paragraphs · topic sentences and reverse outlinesThis chapter brings together the writing strand of Weeks 6 and 7, which is about delivering an argument to someone who only has the page in front of them.
- Writing for a time-poor reader with a visible structure
- Language conventions: terminology, register, contractions and openers
- Active and passive voice
- The paragraph as a unit of thought, and its length
- The MEAL scaffold and its four moves
- Topic sentences and the point-first shape
Operationalisation, Validity and Reliability
Concept, indicator, variable · indices · kinds of validity · reliability checks · random and systematic error · trustworthinessWeek 7 asks how political scientists turn ideas they cannot see into things they can record, and how to judge whether the result is any good.
- Concept, attribute, indicator and variable as one chain
- Building an index and declaring its weights
- Validity as accuracy and reliability as consistency
- Construct, face, content, convergent and discriminant validity
- Test and retest, inter-coder agreement and internal consistency
- Random error against systematic error
Qualitative, Quantitative and Mixed Methods
Methods as tools · qualitative and quantitative strategies · data collection and analysis · five mixed-method objectivesThis chapter covers the Week 8 research lecture, which steps back from individual designs to ask what research methods are and how to choose them.
- Methods as tools for collecting and for analysing data
- What counts as qualitative and as quantitative research
- Why the research question decides the method
- Setting boundaries before collecting data
- Surveys, interviews, focus groups, online data and ethnography
- Text analysis and quantitative analysis
Sources, Citation and Academic Integrity
Why we cite · primary, secondary and general sources · quoting, paraphrasing, summarising · plagiarism, collusion, fabrication, recyclingThis chapter covers the part of the course that deals with other people's work: how to find it, how to judge it, how to bring it into your own writing and how to stay inside the University's integrity rules.
- Two reasons to cite and what counts as common knowledge
- Back-to-back citations, stacking, poor weighting and lazy sourcing
- General, secondary and primary academic sources on a scale of rigour
- Finding sources and checking that they were peer reviewed
- Quoting, paraphrasing and summarising, from narrow to broad
- Plagiarism, collusion, fabrication and recycling
Text Analysis in Political Research
Inductive and deductive coding · content, framing and discourse analysis · coding party manifestos · automated text analysisThis chapter follows the course's treatment of text analysis, which much qualitative research uses to extract meaning from documents, speeches, transcripts and field notes.
- What counts as a text and the six questions about its producer
- Statements as information versus words as data
- Inductive, bottom-up coding and the grounded theory approach
- Deductive, top-down coding with a category scheme
- Units of observation, codes and rules for ambiguity
- The Manifesto Project as a large coding example
Self-Editing, Proofreading and Concise Writing
Plan, write, polish · proofreading tactics · tightening bloated sentences · wordiness, redundancy and punctuationThis chapter covers the second, writing-focused half of the Week 9 material: what to do once a full draft exists.
- Writing as understanding, planning, writing and polishing
- Why revising comes before proofreading
- Checking the question, the plan and the matching bookends
- Three proofreading methods: aloud, another reader, software
- The need-to-know test for paragraphs, examples, tables and figures
- Sentence bloat and the two-line test
On the research side you learn what makes a claim scientific, how a genuine puzzle becomes a research question, why a research design exists to set up a fair comparison, how a theory yields hypotheses you can test, how abstract concepts become measures, and how qualitative, quantitative and text-based methods turn information into evidence.
On the writing side you learn to read an article with a purpose, build introductions and conclusions, write paragraphs that carry one idea, cite and integrate sources honestly, and edit a draft until every sentence earns its place.
The grade rests on two closed-book exams worth 60% between them, three short take-home writing tasks and tutorial participation.
Exam 1 was a one-hour multiple-choice paper on the first six weeks. Exam 2 is worth 40%, runs for two hours in the formal examination period, mixes multiple-choice and short-answer questions, and covers the last six weeks: measurement, research methods and sources, qualitative analysis and self-editing, collecting and describing information, quantitative analysis and writing strategy.
The class summary says it tests your understanding of the research process and your ability to apply the writing skills from the whole semester.
The guide follows the teaching weeks in fourteen chapters.
Each one restates the course's definitions and checklists in plain words, works an original political example through to a finished answer, and ends with drill questions in the two formats the exams use, so you can rehearse the reasoning before you sit down in the exam room.
Worked example · free
Telling random error from systematic error in two coders' scores
- 1Compute Coder P's gaps. Coder minus panel gives +4, −4, +3, −3 and 0; the signs are mixed and the gaps sum to zero.
- 1Compute Coder Q's gaps. They are −8, −7, −9, −8 and −8, every one below the benchmark, an average of −40 ÷ 5 = −8.
- 1Name each error. Coder P's misses cancel out, which is random error; Coder Q's push every score the same way, which is systematic error, or bias.
- 1State the cost. Coder P's noise costs precision, so two manifestos only a few points apart could swap places; Coder Q keeps the order of all five but understates every level by about 8 points.
Key terms
- Falsifiability
- The property that makes a claim scientific: it must be possible to describe an observation that would show the claim to be false, whether or not anyone has made it yet.
- Ontology
- A position on what social and political reality consists of, for example whether institutions exist apart from the people who act in them or are continually remade by those people.
- Epistemology
- A theory of how knowledge about the world can be gained and checked, which decides whether a researcher expects to measure truth, approximate it or interpret perspectives on it.
- Research puzzle
- An observed event or pattern that looks counterintuitive against existing theory or informed expectations, and so gives a research question something worth explaining.
- Ladder of abstraction
- An ordering of concepts from general to specific, where each added defining attribute narrows the concept and shrinks the set of cases it covers.
- Research design
- The plan, fixed before the evidence is examined, that states which cases are studied, how concepts are measured, what data are collected and how they are analysed.
- Counterfactual
- The unobserved alternative in which the same unit did not receive the treatment; a causal effect is the difference between what happened and this missing world.
- Confounding variable
- A third factor linked to both the explanation and the outcome, which can produce a relationship between them that disappears once it is taken into account.
- Null hypothesis
- The statement that no relationship exists between the variables; researchers test whether the evidence lets them reject it, and it is never their own research claim.
- Operationalisation
- Turning the attributes of an abstract concept into observable indicators, and those indicators into variables whose values can be recorded for each case.
- Measurement validity
- Accuracy of a measure: how far an indicator captures the concept it is meant to stand for rather than some other quality.
- Measurement reliability
- Consistency of a measure: whether repeating it across occasions, coders or groups returns the same result, which is needed but does not prove accuracy.
- Content analysis
- A text-analysis method that cuts documents into defined units, assigns codes from a category scheme, and then counts or reads the coded units to answer a question.
POLS1009 FAQ
How is this political science methods course assessed?
Four tasks make up the grade: Exam 1 was worth 20%, Exam 2 is worth 40%, three take-home tutorial writing tasks are worth 10% each, and tutorial participation is worth 10%. The Semester 2, 2026 class summary describes both exams as closed book and places Exam 2 in person in the formal examination period; the Week 6 slides say Exam 1 was sat online in Canvas. Participation means active discussion, not attendance alone.
What is on the end-of-semester exam?
Exam 2 runs for two hours during the formal examination period and mixes multiple-choice with short-answer questions. It covers the last six weeks of teaching: measurement and paragraphs, research methods and sources, qualitative analysis and self-editing, collecting and describing information, quantitative analysis and writing strategy.
The class summary adds that the paper assesses the research process and your ability to apply academic writing skills from across the semester.
How hard is the course?
Both exams are closed book, so the definitions and checklists have to be known well enough to apply to an example you have not seen. The Week 6 slides name two traps: improperly specified comparisons are the most common error in stating hypotheses, and covariation is the easiest causal hurdle to clear yet it is the hurdle people most often confuse with the whole test.
Knowing the terms well enough to apply them to a new case is the safest preparation.
How do the tutorial writing tasks work?
You choose three of the five designated writing tasks, and each one is worth 10%. Each response has a 500-word limit with a 10% margin either way, and at least one must go in before Week 6. Canvas sets the due date as the Sunday after the tutorial fortnight that covered the task.
Tasks are marked on structure, synthesis, critical thinking and clarity, and suspected AI-generated content caps the critical thinking criterion at Pass.
What did the mid-semester exam cover?
Exam 1 was a one-hour multiple-choice paper worth 20% on everything taught in Weeks 1 to 6: the lectures, the slides and the compulsory readings. The Week 6 slides set the sitting window from 7 to 18 September, during the teaching break. Those six weeks still matter for Exam 2, because the later topics on measurement, methods and writing rely on the earlier ideas about questions, theory, hypotheses and design.
Is there practice material for the exams?
The Week 6 slides advise working back through the lectures and compulsory readings, redoing the in-lecture pop quizzes from Weeks 1, 3 and 4, and applying the vocabulary to genuine arguments by identifying the independent variable, the dependent variable, the mechanism and any hurdle left uncleared. A practice exam on Canvas was there to test the online exam software, and its questions had nothing to do with course content.
How should I revise for the end-of-semester exam?
Exam 2 covers Weeks 7 to 12, so work through all six weeks: kinds of validity, the reliability checks, random against systematic error, the mixed-method objectives, the source types and integrity breaches, the text-analysis approaches, and the Week 10 and 11 material on collecting, describing and analysing data from the lecture slides on Canvas.
Then rehearse short answers that define a term, apply it to a fresh political example, and name its limitation, all in three or four precise sentences.
Questions students ask in each POLS1009 chapter
- Can a claim be scientific if it turns out to be false?
- Yes. Under Popper's criterion a claim is scientific when an observation could in principle refute it, so a belief that was tested and refuted, such as the earth-centred model of the heavens, was wrong but scientific. What disqualifies a claim is being untestable, not being mistaken. Chapter 01 · Science, Falsifiability and the Scientific Method
- What is the difference between ontology and epistemology?
- Ontology asks what exists in the social and political world, for instance whether a category such as social class is out there or made through interaction. Epistemology asks how claims about whatever exists can be known and justified, for instance whether measurement can reveal it. Your answer to the first usually shapes your answer to the second. Chapter 02 · Ontology, Epistemology and Critical Reading
- What makes a good research question in political science?
- The course uses six checks: interesting to you, important to others, counterintuitive to informed readers, short, direct rather than multi-part, and doable with your time, data and skills. Scope matters too: a question about all governments is a career, while one about a single decision on a single day is too thin for a paper. Chapter 03 · Research Questions, Puzzles and Concepts
- Should I write the introduction of a research essay first or last?
- Last, or at least finish it last. You can draft a frame early, with a placeholder where the findings will go, but you cannot promise what the paper delivers until the body is written. The course suggests starting with any of the first three sections, since you do not have to begin with the introduction, and finalising the introduction and conclusion last. Chapter 04 · Writing Introductions and Conclusions
- What is the fundamental problem of causal inference?
- It is the observation, associated with Holland in 1986, that the same unit cannot be seen both with and without the treatment at the same time. A city either runs a programme in a given year or it does not, and only one of those outcomes is ever recorded. Every causal claim therefore depends on inferring an unseen outcome, and research design is the tool for making that inference believable. Chapter 05 · Causality, Counterfactuals and Experiments
- Is observational research less scientific than an experiment?
- No. Observational designs answer most of the questions political scientists care about, because many causes cannot be assigned at random. They do demand extra care: the researcher has to select comparable cases, hold rival explanations constant and use statistical controls so that a comparison approaches the fairness an experiment would give automatically. Chapter 06 · Observational Designs, Datasets and Drafting
- What is the difference between a theory and a hypothesis?
- A theory explains why something happens by naming a mechanism that connects cause and outcome. A hypothesis states what you should observe if that theory is right, using indicators that can actually be measured. One theory can and should produce several hypotheses, so that it faces more than one independent test. Chapter 07 · Theories and the Four Causal Hurdles
- Which criteria must a well-formed hypothesis meet?
- The Week 6 slides list four: it contains no proper names, it is completely specified, it is testable and it is falsifiable. It should also link an outcome to a proposed explanation, say in which way the two are connected, and make clear what a test would look for. A hypothesis that puts both signs in front of the dependent variable fails, because any result would count as support. Chapter 08 · Writing and Testing Hypotheses
- How long should a paragraph be in a politics essay?
- The course gives two figures. The Week 6 slides suggest roughly 150 to 250 words, enough for a point with its evidence, while Week 7 offers 250 words as a conservative upper limit. Both stress that a paragraph is defined by holding one idea rather than by a word count, so a short paragraph that completes its point is fine. Chapter 09 · Academic Language and Paragraph Structure
- What is the difference between validity and reliability?
- Validity is about accuracy: whether a measure captures the concept you intend rather than something else. Reliability is about consistency: whether repeating the measure across time, coders or groups gives the same result. The two are independent, so a measure can be consistent and still wrong, and a measure that is right on average can still be too noisy for one case. Chapter 10 · Operationalisation, Validity and Reliability
- Is quantitative research more scientific than qualitative research?
- No. The course states that neither approach is better: each has advantages and disadvantages. Qualitative work is strong on depth, context and meaning, while quantitative work is strong on comparison and generalisation across many cases. Which one suits a project depends on what the research question asks, not on any ranking of the methods. Chapter 11 · Qualitative, Quantitative and Mixed Methods
- Do I need to cite something everyone already knows?
- Not if it is common knowledge for the readers you are writing for. A political science audience may accept a widely known fact without a reference that a general audience would need sourced. The test depends on audience, so when you are unsure, the lecture's advice is simply to ask rather than guess. Chapter 12 · Sources, Citation and Academic Integrity
- What is the difference between inductive and deductive text analysis?
- They run in opposite directions. Inductive analysis starts from the raw material and lets codes and categories emerge, so it is suited to building theory. Deductive analysis brings a category scheme derived from theory to the material, codes it and then counts or extracts the coded passages, so it is suited to testing a theory. Chapter 13 · Text Analysis in Political Research
- What is the difference between revising and proofreading?
- Revising changes the substance: structure, organisation and how clearly ideas come across, and it may mean rewriting or deleting whole paragraphs. Proofreading comes afterwards and fixes surface errors such as typos and grammar. Doing them in that order saves effort, because there is no point perfecting a paragraph that revision later removes. Chapter 14 · Self-Editing, Proofreading and Concise Writing
How to study for the exam
Work in two passes. In the first pass, rebuild the vocabulary of the last six weeks, starting from Chapter 9 and adding the Week 10 and 11 lecture material from Canvas: write each term, its definition in your own words, one political example and the term it is most often confused with, such as validity against reliability, framing against discourse analysis, or paraphrasing against summarising.
Keep the earlier chapters in view, because the later weeks keep reusing their ideas: a measure is only judged against a concept, and a method is only chosen against a research question. In the second pass, practise the two exam formats under time. For multiple-choice questions, eliminate options by naming exactly why each wrong answer fails.
For short answers, use a three-move shape: define the term, apply it to the case in the question, and state one limitation or consequence. Finish each session by editing one of your own answers with the self-editing checklist, cutting redundant words and checking that the first sentence states the point.
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