Introduction to Social Research Methodology

Hypotheses and research design

Ben Stanley

Department of Social Sciences, SWPS University

November 3, 2026

Course outline

Course outline: session 3 (today) Course outline: 15 stations. 1 — Nature and purpose of social research (done); 2 — Research goals and research questions (done); 3 — Hypotheses and research design (today); 4 — Research ethics (to come); 5 — Conducting a literature review (to come); 6 — Secondary data analysis (to come); 7 — Qualitative approaches (to come); 8 — Quantitative approaches and measurement (to come); 9 — Mixed methods research (to come); 10 — Sampling and participant recruitment (to come); 11 — Questionnaire design and survey fieldwork (to come); 12 — Interviews and focus groups (to come); 13 — Quantitative data analysis (to come); 14 — Qualitative data analysis (to come); 15 — Reporting research; course review (to come). 1 Nature and purpose of social research 2 Research goals and research questions 3 Hypotheses and research design TODAY 4 Research ethics 5 Conducting a literature review 6 Secondary data analysis 7 Qualitative approaches 8 Quantitative approaches and measurement 9 Mixed methods research 10 Sampling and participant recruitment 11 Questionnaire design and survey fieldwork 12 Interviews and focus groups 13 Quantitative data analysis 14 Qualitative data analysis 15 Reporting research; course review

Today’s lecture

  • From questions to hypotheses — assumptions, the definition and properties of a hypothesis, and the main types
  • Variables and units of analysis — independent, dependent, and control variables, and deciding what we are studying
  • Research designs — the main design types, and how to match a design to a managerial problem
  • Planning the research process — the stages of a project, and the structure of a short research proposal
  • By the end, you should be able to take a research question and turn it into a workable plan for answering it

From questions to hypotheses

Where we are

  • Last session, you learned to craft a research question — clear, researchable, specific, and worth answering
  • A question tells you what you want to know; it does not yet tell you what you expect to find or how you will find it
  • Today we take the next two steps:
    • turning the question into a testable hypothesis
    • choosing a research design capable of testing it
  • This is the stage where a study stops being an idea and becomes a plan

Running example: Meridian, a Polish retail-and-services firm — 450 employees, 32 stores, a growing e-commerce arm — where annual staff turnover has risen from 14% to 27% in two years. The board wants evidence, not hunches, and you are the research team.

Research assumptions

  • Every study rests on assumptions — statements we accept as true for the purposes of the research, without testing them directly
  • Common kinds include:
    • Theoretical — e.g., that job dissatisfaction increases the likelihood of quitting
    • Methodological — e.g., that employees will answer an anonymous survey honestly
    • Practical — e.g., that Meridian’s HR records of leavers are accurate and complete
  • Assumptions are unavoidable — no study can test everything at once
  • What matters is to state them explicitly, so that readers can judge whether they are reasonable — a hidden assumption is a weakness; a stated one is a boundary

What is a hypothesis?

Anatomy of a hypothesis At the top, a question: does the quality of store management affect staff turnover at Meridian? Below it, a proposed answer, the hypothesis: an arrow runs from the independent variable, quality of first-line management, to the dependent variable, staff turnover, labelled poorer to higher. In words: stores with poorer-quality first-line management have higher staff turnover. Three cards: at least two variables, named and linked by a stated relationship; a commitment, because it says what evidence would prove it wrong, namely no difference in turnover or the opposite one; and the engine of deduction, because theory suggests the hypothesis and the study is built to test it. Question: does the quality of store management affect staff turnover at Meridian? a proposed answer HYPOTHESIS INDEPENDENT VARIABLE Quality of first-line management DEPENDENT VARIABLE Staff turnover poorer → higher “Stores with poorer-quality first-line management have higher staff turnover.” At least two variables named, and linked by a stated relationship — not a description of one thing A commitment it says what evidence would prove it wrong: no difference in turnover, or the opposite one The engine of deduction theory suggests the hypothesis; the study is built to test it

Properties of a good hypothesis

  • Testable and falsifiable — there must be observable evidence that could, in principle, show it to be false
  • Clear and specific — the variables involved and the expected relationship are stated precisely
  • A statement of relationship — it links at least two variables rather than describing one thing
  • Grounded — it follows from theory, prior research, or informed observation, not from thin air
  • Value-neutral — it predicts what is, not what ought to be
  • “Bad management is unacceptable” fails every test; “stores whose managers score lower on supervisor support have higher turnover” passes them all

Directional and non-directional hypotheses

Directional and non-directional hypotheses Two panels. Non-directional: manager quality is related to staff turnover; the two variables are joined by a line without an arrowhead. Of three possible patterns of turnover against manager quality, it is supported both when turnover falls as manager quality rises and when turnover rises, and refuted only by no relationship: it states that a relationship exists, not which way it runs; use it when you genuinely do not know. Directional: the poorer the manager quality, the higher the staff turnover; an arrow is labelled poorer to higher. Only the falling pattern supports it; a rising pattern or no relationship refutes it. It is riskier and more informative, because it can fail in more ways, so surviving the test means more; use it when theory or prior evidence justifies an expectation. Non-directional “Manager quality is related to staff turnover.” Manager quality Staff turnover related Which patterns would support it? ✓ falling ✓ rising ✗ flat manager quality → turnover → States that a relationship exists, not which way it runs. Use when you genuinely do not know which way it runs. Directional “The poorer the manager quality, the higher the staff turnover.” Manager quality Staff turnover poorer → higher Which patterns would support it? ✓ falling ✗ rising ✗ flat manager quality → turnover → Riskier and more informative: it can fail in more ways, so surviving the test means more. Use when theory or prior evidence justifies an expectation.

Null and alternative hypotheses

Null and alternative hypotheses, and the logic of testing them Two hypotheses about manager quality and staff turnover. The null hypothesis, H0: manager quality has no effect on staff turnover; the link between the variables is crossed out. The alternative hypothesis, H1: stores with poorer manager quality have higher turnover; usually your research hypothesis. Below, the logic of testing: from our data we ask how likely these data would be if H0 were true. If very unlikely, we reject H0, which is evidence for H1; if plausible, we do not reject H0. We never prove H1; we accumulate evidence against H0, a cautious logic that protects us from seeing patterns that are not there. Null hypothesis (H₀) Manager quality Turnover ✗ “Manager quality has no effect on staff turnover.” Alternative hypothesis (H₁) Manager quality Turnover “Stores with poorer manager quality have higher turnover.” — usually your research hypothesis Our data How likely would these data be if H₀ were true? very unlikely reject H₀: evidence for H₁ plausible do not reject H₀ NEVER PROOF We never prove H₁; we accumulate evidence against H₀. The cautious logic protects us from seeing patterns that are not there.

From question to hypothesis: Meridian

Carving a broad question into testable hypotheses At the top, the board's question: why has staff turnover at Meridian doubled in two years? It is too broad to test directly. Dashed arrows carve it into three hypotheses. H1, directional: Employees who report lower supervisor support are more likely to quit within a year. Independent variable: supervisor support; dependent variable: quitting within a year. H2, comparative, directional: Turnover is higher in stores than in the e-commerce division. Independent variable: store or e-commerce; dependent variable: turnover. H3, non-directional: Perceived pay fairness is related to intention to quit. Independent variable: perceived pay fairness; dependent variable: intention to quit. Each slice has named variables, a stated relationship, and data that could prove it wrong. “Why has staff turnover at Meridian doubled in two years?” too broad to test directly: a topic wearing a question mark H1 Directional Employees who report lower supervisor support are more likely to quit within a year. IV: supervisor support DV: quitting within a year H2 Comparative, directional Turnover is higher in stores than in the e-commerce division. IV: store or e-commerce DV: turnover H3 Non-directional Perceived pay fairness is related to intention to quit. IV: perceived pay fairness DV: intention to quit Each slice: named variables, a stated relationship, and data that could prove it wrong

Variables and units of analysis

Independent and dependent variables

Independent and dependent variables, and how their roles depend on the hypothesis At the top: supervisor support, the independent variable or presumed cause, influences intention to quit, the dependent variable or presumed effect. Below: the roles come from the hypothesis, not from the variables. When we explain turnover, workload is the independent variable and turnover the dependent one; when we ask whether turnover damages customer satisfaction, turnover becomes the independent variable. INDEPENDENT VARIABLE Supervisor support DEPENDENT VARIABLE Intention to quit influences the presumed cause the presumed effect The roles come from the hypothesis, not from the variables: Workload DEPENDENT Turnover explaining turnover INDEPENDENT Turnover Customer satisfaction asking whether turnover damages satisfaction

Control variables

A control variable as a rival explanation We observe that stores with poor managers have high turnover: a dashed link with a question mark joins manager quality, the independent variable, to staff turnover, the dependent variable. A control variable, pay level, points at both: stores with poor managers also pay less, and low pay drives people to leave. Without controlling for pay, the conclusion is spurious: blaming managers for what wages are doing, and fixing the wrong thing. Typical controls in a Meridian study: pay level, local unemployment rate, store size, employee age and tenure, each a rival explanation taken seriously. INDEPENDENT Manager quality DEPENDENT Staff turnover ? observed: poor managers, high turnover CONTROL VARIABLE Pay level stores with poor managers also pay less low pay drives people to leave WITHOUT THE CONTROL a spurious conclusion: blaming managers for what wages are doing — and fixing the wrong thing TYPICAL CONTROLS pay level local unemployment rate store size employee age and tenure each a rival explanation taken seriously

Units of analysis

Units of analysis and the ecological fallacy Nested boxes: organisations such as stores, firms and branches contain groups such as teams, departments and shifts, which contain individuals such as employees, customers and voters. Beside them, social artefacts, human products studied as units: exit interviews, complaint records, job adverts. The same topic at two units: which employees quit is a question about individuals, because people quit; which stores have high turnover is a question about organisations, because turnover is a property of a store. The ecological fallacy: a store with 40% turnover does not tell you which of its employees are at risk, since the churn may all be among weekend staff; conclusions about one unit do not transfer automatically to another. Organisations stores, firms, branches Groups teams, departments, shifts Groups teams, departments, shifts Individuals: employees, customers, voters Social artefacts human products studied as units: exit interviews, complaint records, job adverts “Which employees quit?” unit: individuals — people quit “Which stores have high turnover?” unit: organisations — turnover is a store's property THE ECOLOGICAL FALLACY A store with 40% turnover does not tell you which of its employees are at risk — perhaps the churn is all among weekend staff. Conclusions about one unit do not transfer automatically to another.

Meridian: putting the pieces together

Component Choice
Hypothesis (H1) Employees who report lower supervisor support are more likely to quit within a year
Unit of analysis Individual employees
Independent variable Perceived supervisor support
Dependent variable Quitting within 12 months
Control variables Pay level, tenure, contract type, store location

Research designs

What is a research design?

  • A research design is the overall structure of a study — the plan that determines when, where, and on whom data are collected
  • It is the blueprint connecting the hypothesis to the evidence needed to test it (Creswell & Creswell call it the plan that runs from broad assumptions to detailed methods)
  • Design is not the same as method: a survey (method) can sit inside a cross-sectional, longitudinal, or comparative design
  • Five designs cover most organisational research: cross-sectional, longitudinal, case study, comparative, and experimental
  • The design question is always the same: what structure of evidence would allow this hypothesis to fail?

Cross-sectional designs

Cross-sectional design Pictogram: ten cases drawn as horizontal timelines, all observed at a single moment, a vertical band across them. At Meridian: one employee survey across all 32 stores, fielded in a single week. Strengths: fast and relatively cheap; good for describing patterns and comparing groups at one moment. Weaknesses: weak on causal order, since we cannot tell whether dissatisfaction causes quitting or soon-to-quit staff become dissatisfied; it shows association, not which comes first. cases time → one moment AT MERIDIAN One employee survey across all 32 stores, fielded in a single week. STRENGTHS Fast and relatively cheap; good for describing patterns and comparing groups at one moment. WEAKNESSES Weak on causal order: does dissatisfaction cause quitting, or do soon-to-quit staff become dissatisfied? It shows association, not which comes first.

Longitudinal designs

Longitudinal designs: trend, cohort and panel Pictogram: three rows of data across three waves. Trend: a new sample each wave from the same population, drawn as differently coloured dots each time, as in annual engagement surveys. Cohort: one defined group, drawn as a box, followed across the waves, such as everyone hired in 2024. Panel: the same individuals, the same four coloured dots, measured at every wave, such as the same 200 staff every six months. Strengths: change can be observed directly, and causes can be placed before effects. Weaknesses: slow and expensive; panels suffer attrition, and in a turnover study the people we most want to follow are the ones who leave. wave 1 wave 2 wave 3 Trend new sample each wave annual engagement surveys Cohort one defined group everyone hired in 2024 Panel the same individuals the same 200 staff, every six months AT MERIDIAN Trend: annual engagement surveys. Cohort: everyone hired in 2024. Panel: the same 200 employees surveyed every six months. STRENGTHS Change can be observed directly, and causes can be placed before effects. WEAKNESSES Slow and expensive; panels suffer attrition — in a turnover study, the very people we most want to follow are the ones who leave.

Case study designs

Case study design Pictogram: a magnifying glass over one store in its context, with three sources of evidence around it: interviews, observation and documents. At Meridian: why does the Katowice store keep its staff when comparable stores cannot? Strengths: depth and context; it surfaces mechanisms and generates hypotheses for wider studies to test. Weaknesses: no statistical generalisation, since one exceptional store shows what is possible, not what is typical. one store in context interviews observation documents AT MERIDIAN Why does the Katowice store keep its staff when comparable stores cannot? STRENGTHS Depth and context; surfaces mechanisms and generates hypotheses for wider studies to test. WEAKNESSES No statistical generalisation: one exceptional store shows what is possible, not what is typical.

Comparative designs

Comparative design Pictogram: a high-turnover and a low-turnover store compared on the same dimensions; pay, store size and region are the same, management style differs, so the contrast points to what differs. At Meridian: stores against the e-commerce division, high- against low-turnover stores, Poland's regions against one another. Strengths: contrast does the explanatory work, approximating the logic of experiment when experiments are impossible. Weaknesses: compared cases differ in many ways at once, so every other difference is a rival explanation to rule out, one by one. High-turnover store Low-turnover store = pay same = store size same = region same ≠ management style differs the contrast points to what differs AT MERIDIAN Stores against the e-commerce division; high- against low-turnover stores; Poland's regions against one another. STRENGTHS Lets contrast do the explanatory work; approximates the logic of experiment when experiments are impossible. WEAKNESSES Compared cases differ in many ways at once, so every other difference is a rival explanation to rule out, one by one.

Experimental and quasi-experimental designs

Experimental and quasi-experimental designs Pictogram, top: in an experiment, 32 stores are randomly assigned, 16 to a new onboarding programme and 16 to continue as before, and turnover is compared; the groups are equivalent on average. Bottom: in a quasi-experiment, managers volunteer, so 16 volunteer stores get the programme and the other 16 do not; the groups may differ from the start. At Meridian: a new onboarding programme piloted in 16 randomly chosen stores; a quasi-experiment if it goes to the stores whose managers volunteered. Strengths: the strongest design for establishing cause, because random assignment makes the groups equivalent on average. Weaknesses: often impractical or unethical in organisations; self-selected groups may differ from the start, so causal claims need more caution. Experiment 32 stores random assignment 16: new onboarding 16: as before compare turnover groups equivalent on average Quasi-experiment 32 stores managers volunteer 16 volunteers: programme the other 16 ? groups may differ from the start AT MERIDIAN A new onboarding programme piloted in 16 randomly chosen stores while the other 16 continue as before; a quasi-experiment if it goes to the 16 stores whose managers volunteered. STRENGTHS The strongest design for establishing cause: random assignment makes the groups equivalent on average. WEAKNESSES Often impractical or unethical in organisations; in a quasi-experiment, self-selected groups may differ from the start, so causal claims need more caution.

Choosing among designs

Design Best for Main limit
Cross-sectional Describing patterns now; comparing groups Weak on causal order
Longitudinal Tracking change; ordering cause and effect Slow, costly, attrition
Case study Understanding how and why in depth No statistical generalisation
Comparative Learning from contrasts between units Many differences at once
Experimental Establishing cause Often impractical or unethical

Discuss: which design?

  • For each Meridian problem, which design would you choose — and why?
    • The board wants to know current engagement levels across all 32 stores by the end of the month
    • HR wants to know whether the new onboarding programme actually reduces first-year turnover
    • Directors cannot understand why the flagship Warsaw store has the worst turnover despite the best pay
    • Marketing wants to know whether customer satisfaction changes as stores shift services online over the next two years
  • Notice how the wording of each problem — now, whether it works, why this case, changes over time — points towards a design
  • Managers rarely ask for a “research design”; translating their problem into one is precisely the researcher’s job

Planning the research process

The stages of a research project

  • Babbie pictures a research project as a connected structure, flowing from idea to application:
    1. Interest, idea, theory — the problem and the hunches about it
    2. Conceptualisation — specifying what the key concepts mean (session 8)
    3. Choice of research method — survey, field research, existing data…
    4. Operationalisation — deciding how variables will be measured (session 8)
    5. Population and sampling — whom the conclusions are about (session 10)
    6. Observations — collecting the data (sessions 11–12)
    7. Data processing and analysis — turning data into answers
    8. Application — reporting results and assessing their implications
  • The stages loop as well as flow: analysis feeds back into theory, and one study’s findings begin the next cycle of inquiry

Planning: schedule and resources

  • A plan is not just a list of stages — it attaches time, money, and people to each one
  • Build a schedule: even a rough timeline for each stage exposes whether the project fits the deadline — a board that wants answers in six weeks has just ruled out an eighteen-month panel study
  • Budget the real costs: staff time, incentives for participants, software, travel, data access
  • Check practicalities early: can you access the stores? will managers grant time for interviews? who are the gatekeepers?
  • Most research failures are planning failures — the design was fine, but the time and access it required were never there

The research proposal

  • Before a study runs, its plan is usually written down for someone else’s review — a supervisor, a client, a board, a funder
  • The research proposal lays out what you want to study, why it matters, and exactly how you will proceed
  • Writing it is not bureaucracy — it is the discipline of committing to decisions before data collection makes them irreversible
  • A proposal that cannot be written clearly is a study that has not been thought through clearly
  • For Meridian’s board, the proposal is the product: it is what turns “we should look into turnover” into a project someone can approve, fund, and hold you to

Structure of a short proposal

Section Question it answers
Problem / objective What exactly will you study, and why is it worth studying?
Literature review What is already known, and what remains unresolved?
Research question & hypothesis What do you ask, and what do you expect to find?
Subjects for study Whom or what will you study, and how will they be selected?
Measurement What are the key variables, and how will you measure them?
Data-collection method How will the data actually be gathered?
Analysis How will the data answer the question?
Schedule & budget How long will it take, and what will it cost?

A Meridian mini-proposal

A Meridian mini-proposal in five entries Five entries of a short research proposal. Problem: Turnover has doubled to 27% in two years; the board needs to know why before budgeting counter-measures. Question: Does the quality of first-line management explain differences in turnover across Meridian's stores? Hypothesis: Stores whose employees report lower supervisor support have higher annual turnover, controlling for pay and local labour-market conditions. Design: A cross-sectional survey of all store employees (CAWI), linked to store-level HR turnover records. Data needed: Survey measures of supervisor support and quit intention; HR data on leavers, pay and tenure; regional unemployment figures. Problem Turnover has doubled to 27% in two years; the board needs to know why before budgeting counter-measures. Question Does the quality of first-line management explain differences in turnover across Meridian's stores? Hypothesis Stores whose employees report lower supervisor support have higher annual turnover, controlling for pay and local labour-market conditions. Design A cross-sectional survey of all store employees (CAWI), linked to store-level HR turnover records. Data needed Survey measures of supervisor support and quit intention; HR data on leavers, pay and tenure; regional unemployment figures.

Conclusion

Conclusion

  • A hypothesis converts a research question into a testable expectation — specific, falsifiable, and built from named variables
  • Variables have roles: the independent variable does the explaining, the dependent variable is explained, and control variables guard against spurious conclusions — all relative to a stated unit of analysis
  • Research designs — cross-sectional, longitudinal, case study, comparative, experimental — are structures of evidence, and the craft lies in matching the design to the problem
  • Planning turns method into management: stages, schedule, resources, and a proposal that commits the plan to paper before the data commit you
  • We took Meridian’s turnover crisis from a board-room worry to a fundable one-page plan — the same journey your exercise, and later your own projects, will make
  • Questions and discussion are welcome

Exercise

Today’s exercise: From question to design

QR code linking to the exercise worksheet

bdstanley.netlify.app/social-research-methodology-3-exercise

Model answers: from questions to hypotheses

Model answers to Task 1: from questions to hypotheses A table of model answers with one column per research question and one row per component: hypothesis, unit of analysis, independent variable, dependent variable, and a control variable with the rival explanation it rules out. Q1, managers and turnover: hypothesis, Stores whose employees report lower supervisor support have higher annual turnover. Unit of analysis: Stores (32). Independent variable: Quality of first-line management (mean reported supervisor support). Dependent variable: Annual staff turnover rate. Control: Pay level: weaker managers may sit in lower-paying stores, so pay could explain the turnover instead. Q2, turnover and satisfaction: hypothesis, Stores with higher staff turnover have lower average customer satisfaction scores. Unit of analysis: Stores (32). Independent variable: Staff turnover rate. Dependent variable: Average customer satisfaction score. Control: Staffing level: understaffed stores may both lose staff and serve customers badly. Q3, onboarding and staying: hypothesis, Employees who complete the onboarding programme stay with Meridian longer than those who do not. Unit of analysis: Individual employees. Independent variable: Onboarding completed (yes / no). Dependent variable: Length of stay (months until leaving). Control: Contract type: temporary staff may both skip onboarding and leave sooner by design. These are one good answer among several. One good answer among several: any falsifiable hypothesis with named variables, a stated relationship and a plausible rationale Hypothesis Unit of analysis Independent variable Dependent variable Control + why Q1 Managers and turnover Stores whose employees report lower supervisor support have higher annual turnover. Stores (32) Quality of first-line management (mean reported supervisor support) Annual staff turnover rate Pay level: weaker managers may sit in lower-paying stores, so pay could explain the turnover instead. Q2 Turnover and satisfaction Stores with higher staff turnover have lower average customer satisfaction scores. Stores (32) Staff turnover rate Average customer satisfaction score Staffing level: understaffed stores may both lose staff and serve customers badly. Q3 Onboarding and staying Employees who complete the onboarding programme stay with Meridian longer than those who do not. Individual employees Onboarding completed (yes / no) Length of stay (months until leaving) Contract type: temporary staff may both skip onboarding and leave sooner by design.

Model answers: matching designs to problems

Model answers to Task 2: matching designs to managerial problems Four cards, one per scenario, each with the best-fitting design, why it fits, and its main limitation. Scenario A, two kraków stores, opposite turnover: comparative (or: a two-case case study). Alike in age, size and pay but opposite in turnover: the contrast isolates whatever else differs between them. Limitation: They still differ in many ways at once — manager, staff mix, location — and each is a rival explanation to rule out. Scenario B, unpaid overtime, before the pay talks: cross-sectional. How common, store by store and role by role, within three weeks: one fast snapshot of all 32 stores. Limitation: A snapshot shows no trend, so it cannot test “now routine”; self-reported overtime may be over- or under-stated. Scenario C, a shift-swap app in five stores: quasi-experimental. Five stores with the app, 27 without: a treatment and a comparison group — but the five chose themselves. Limitation: Volunteer managers may be the more engaged ones, whose absences would have fallen anyway. Next phase: randomly choose which stores get the app next. Scenario D, sixty september hires: longitudinal (a cohort, followed as a panel). Follow the same 60 people over the coming months to see when their commitment starts to slip. Limitation: Attrition: the leavers, who matter most, drop out of the study — keep their HR exit records. A Two Kraków stores, opposite turnover COMPARATIVE or: a two-case case study Alike in age, size and pay but opposite in turnover: the contrast isolates whatever else differs between them. − They still differ in many ways at once — manager, staff mix, location — and each is a rival explanation to rule out. B Unpaid overtime, before the pay talks CROSS-SECTIONAL How common, store by store and role by role, within three weeks: one fast snapshot of all 32 stores. − A snapshot shows no trend, so it cannot test “now routine”; self-reported overtime may be over- or under-stated. C A shift-swap app in five stores QUASI-EXPERIMENTAL Five stores with the app, 27 without: a treatment and a comparison group — but the five chose themselves. − Volunteer managers may be the more engaged ones, whose absences would have fallen anyway. → Next phase: randomly choose which stores get the app next. D Sixty September hires LONGITUDINAL a cohort, followed as a panel Follow the same 60 people over the coming months to see when their commitment starts to slip. − Attrition: the leavers, who matter most, drop out of the study — keep their HR exit records.

Model answers: a proposal skeleton

Model answer to Task 3: a one-page proposal skeleton A model proposal skeleton in five rows, worked for research question 3, one good answer among several. Problem: Most leavers go within their first year, and turnover has doubled to 27% in two years. Meridian runs an onboarding programme but does not know whether it keeps people. Research question: Do employees who complete Meridian's onboarding programme stay with the company longer than those who do not? Hypothesis: Employees who complete onboarding are less likely to leave within twelve months than those who do not, controlling for contract type, role and store. Design: Longitudinal (cohort): follow everyone hired over the next year from their first day. Completers are not randomly assigned, so the comparison is quasi-experimental and the controls matter. Data needed: HR records already held: start and exit dates, onboarding completion, contract type, role, store. New data: a short commitment survey at months 1, 3 and 6 — PAPI in the stores, where many staff have no company e-mail, and CAWI for e-commerce and head office. One good skeleton among several, worked for Q3 (the lecture's mini-proposal worked Q1) Problem Most leavers go within their first year, and turnover has doubled to 27% in two years. Meridian runs an onboarding programme but does not know whether it keeps people. Research question Do employees who complete Meridian's onboarding programme stay with the company longer than those who do not? Hypothesis Employees who complete onboarding are less likely to leave within twelve months than those who do not, controlling for contract type, role and store. Design Longitudinal (cohort): follow everyone hired over the next year from their first day. Completers are not randomly assigned, so the comparison is quasi-experimental and the controls matter. Data needed HR records already held: start and exit dates, onboarding completion, contract type, role, store. New data: a short commitment survey at months 1, 3 and 6 — PAPI in the stores, where many staff have no company e-mail, and CAWI for e-commerce and head office.

Study guide

Full summary of this session, for revision: Hypotheses and research design

QR code linking to the session handout

bdstanley.netlify.app/social-research-methodology-3-handout