Primary and secondary data

• Primary data are collected by the researcher, for the researcher’s own question, using an instrument the researcher designed
  • A survey you commission, interviews you conduct, an experiment you run
• Secondary data are data that already exist — collected by someone else, usually for some other purpose
  • Government statistics, a rival consultancy’s survey, your own company’s HR records
• The distinction is about origin and purpose, not about quality: excellent studies are built on each

What is secondary analysis?

• Secondary analysis is a form of research in which data collected and processed by one researcher are reanalysed — often for a different purpose — by another (Babbie)
• The analysis is new; the data are not
• It is a genuine data collection method: “collection” here means locating, obtaining, and preparing existing data rather than generating fresh observations
• It has grown enormously as governments, organisations, and large academic projects have opened their datasets to reuse
• For a manager, it is usually the first method to consider: the answer may already be sitting in a filing cabinet or a public database

Not the same as a literature review

• In the previous session you learned to review the literature; today you learn to reuse the data — and the two are different operations

Why managers should care

• Organisational questions rarely arrive with a budget for a bespoke study attached
• Before commissioning anything, a competent junior research team asks: what do we already have, and what already exists out there?
• Data that already exist can establish context: is our problem unusual, or is the whole sector experiencing it?

Internal sources: the organisation’s own records

• Most organisations are sitting on more data than they ever analyse
• HR records — headcount, contracts, tenure, absence, pay grades, exits by store and month
• Sales and CRM data — transactions, customer complaints, satisfaction scores, loyalty-card behaviour
• Exit interviews — routinely conducted, rarely systematically analysed
• Past employee surveys — engagement or satisfaction studies from previous years, often run as CAWI questionnaires
• These were collected for administration, not research — which is both their weakness and their innocence: nobody shaped them to please a researcher

Internal sources at Meridian

• What could Meridian’s own records tell the board before any new data are collected?
  • HR records: who leaves — which stores, which roles, which tenure bands, which managers?
  • Exit interviews: what reasons do leavers give — and were the interviews conducted consistently enough to compare?
  • Past engagement surveys: did warning signs appear before the turnover rise — and were the same questions asked each year?
  • Sales and rota data: do high-turnover stores also show falling sales or heavier weekend scheduling?

External sources: official statistics

• Statistics Poland (GUS) — the national statistical office: employment, wages, and labour demand by sector and region, much of it in the Local Data Bank
• Eurostat — the EU’s statistical office: harmonised labour-market data (including the EU Labour Force Survey), comparable across member states
• OECD — cross-national indicators on employment, job tenure, and earnings across rich democracies
• Collected under statutory mandates, with published methodologies and large, carefully designed samples
• Their strength is credibility and comparability; their limit is that they describe sectors and countries, not your firm

External sources: reports and academic surveys

• Industry-association and consultancy reports — retail federations, HR consultancies, and job portals publish studies of turnover, pay, and engagement
  • Often timely and sector-specific — but ask who paid for them and what they are selling
• Large academic surveys — designed for reuse from the outset:
  • European Social Survey (ESS) — attitudes and behaviour across Europe, every two years, with full documentation
  • European Values Study (EVS) — long-running study of values, including attitudes to work
• Academic surveys publish their questionnaires, sampling designs, and data files openly — the gold standard of documentation

Digital trace and big data

• A newer category: data generated as a by-product of behaviour rather than by asking anyone anything
  • Website clickstreams, till and loyalty-card transactions, staff rota and badge-swipe logs, social media posts, online job-board activity
• Enormous in volume, fine-grained, and collected continuously — behaviour recorded as it happens, not recalled afterwards
• But it is found data, not designed data: no sampling frame, no consent for research, and it covers only those who leave traces
• Treat it as powerful raw material that needs more methodological caution, not less

Benefits: cost, speed, scale

• Cost — the most expensive phases of research (sampling, fieldwork, data entry) have already been paid for by someone else
• Speed — analysis can begin in days; a bespoke survey takes months to design, field, and clean
• Scale — national statistical offices and academic surveys achieve sample sizes and coverage no single organisation could fund
• For a student, a start-up, or a junior research team, secondary analysis makes otherwise impossible studies feasible

Benefits: reach and burden

• Longitudinal reach — existing series stretch back years or decades, so you can study change: trends, turning points, before-and-after comparisons
  • No new study, however lavish, can go back in time and measure last decade
• No participant burden — nobody is interrupted, surveyed, or observed again
  • Ethically lighter: no new consent to seek, no fatigue imposed on over-surveyed employees
  • Practically valuable at Meridian, where staff already distrust head office questionnaires

Risks: fit and quality

• Fit to your question — the data were collected for someone else’s purpose; the variable you need may be missing, or measured in a way that misses your concept
  • A national “job satisfaction” item is not a measure of Meridian employees’ satisfaction
• Unknown quality — you did not oversee the collection: response rates, interviewer conduct, and data cleaning are outside your control and sometimes outside your knowledge
• Validity and reliability must be assessed at second hand, through documentation — if the documentation exists

Risks: mismatch and missing documentation

• Unit-of-analysis mismatch — the data describe one level (the sector, the country, the region); your question concerns another (one firm, one store, one team)
  • Retail turnover for Poland as a whole cannot tell you whether store 17’s manager is the problem
  • Inferring individual-level facts from aggregate data is the road to the ecological fallacy
• Missing documentation — without the questionnaire, the sampling design, and the definitions, you cannot judge what the numbers mean
  • An undocumented dataset is a rumour with decimal places

Risks: definitions change over time

• Long series are the great gift of secondary data — and definitions quietly shifting underneath them are the great trap
• Statistical offices revise classifications and methods; what counts as “employed”, “vacancy”, or “retail” can change between years
• Organisations do it too: if Meridian redefined “leaver” two years ago — say, from voluntary quits only to all departures including seasonal contract expiries — part of the rise from 14% to 27% may be an artefact of the definition
• Rule: before interpreting any trend, ask whether the measure changed before believing the world did

Benefits and risks: a summary

The fitness-for-purpose checklist

• Before using any secondary dataset, interrogate it like a witness:
  1. Who collected it — and are they competent and independent?
  2. Why — for what purpose, and with what interest in the result?
  3. When — is it current enough, and consistent over time?
  4. From whom — what population, what sample, what response rate?
  5. How — by what mode and procedure (PAPI, CAWI, CATI, administrative records, digital trace)?
  6. With what instrument — what exact questions or measures?
  7. Under what definitions — how are the key concepts defined, and have the definitions changed?
• If the documentation cannot answer these questions, that silence is itself a finding

Who collected it, and why

• Who: a national statistical office, a university consortium, an industry body, a consultancy, a vendor with a product to sell — each has different competence and different incentives
• Why matters because purpose shapes design:
  • Data collected to administer (payroll, tills) record what the process needed, no more
  • Data collected to persuade (some consultancy reports) may sample and word questions conveniently
  • Data collected to research (GUS, Eurostat, ESS) are designed for inference — but for someone else’s inference

When, and from whom

• When: a 2019 study of retail employment describes a pre-pandemic labour market; treat vintage as a substantive issue, not a footnote
• For trends, you need consistent measurement across waves — the same question, the same definition, the same population each time
• From whom: identify the population, the sampling method, and the achieved response rate
  • A “national survey of employees” recruited from one job portal’s users is a survey of that portal’s users
  • Aggregates hide composition: sector-level wages mix hypermarkets, boutiques, and e-commerce warehouses

How, with what instrument, under what definitions

• How: mode shapes answers — CAWI panels, CATI interviews, and administrative registers each have characteristic biases (face-to-face and telephone modes, for instance, invite socially desirable answers — more in session 8)
• With what instrument: get the exact wording; “are you satisfied with your job?” and “would you recommend working here?” are different variables wearing similar labels
• Under what definitions: the decisive question for comparability
  • Does “turnover” mean all separations, voluntary quits, or replacements hired?
  • Does “retail” include e-commerce fulfilment? Does “employee” include agency staff?
• Only when definitions match can you compare Meridian’s 27% with anyone else’s number

Applying the checklist at Meridian

• Suppose we find an industry report: “retail staff turnover in Poland averages 31%” — reassuring for the board?
• Run the checklist before relaxing:
  • Who/why: a recruitment agency that profits from portraying turnover as universal
  • From whom: an online panel of 400 HR managers, self-selected, response rate unreported
  • Definitions: turnover includes planned seasonal contract expiries — Meridian’s figure excludes them
• The comparison collapses: the 31% and the 27% are not measuring the same thing

Secondary first, primary second

• In organisational diagnosis, the efficient sequence is almost always: exhaust the existing data before collecting new data
• Secondary analysis does three preparatory jobs:
  • Establishes context — is the problem unusual, or sector-wide?
  • Sharpens the question — internal records show where and among whom the problem sits, so primary research can ask why
  • Provides benchmarks — external figures give the yardstick against which your own results will be judged
• It also disciplines spending: never pay to collect what already exists

What secondary data cannot do

• It cannot answer questions nobody’s data address — no public dataset contains Meridian employees’ reasons for leaving
• It cannot rescue a mismatched unit of analysis — sector aggregates will never identify which stores, teams, or managers drive the problem
• It cannot tell you about meanings and motives — administrative records show that people left, not what leaving meant to them
• And it inherits every flaw of the original collection, permanently: no reanalysis can repair a biased sample or a leading question

A combined design for Meridian

• A defensible research plan for the board, in sequence:
  1. Internal secondary: analyse HR records — turnover by store, role, tenure, and manager; check whether the definition of “leaver” changed
  2. External secondary: benchmark against GUS/Eurostat retail employment and wage data — is Meridian an outlier, or riding a sector-wide wave?
  3. Primary qualitative: interviews with recent leavers from the worst-affected stores, targeting the why the records cannot reach
  4. Primary quantitative: a CAWI survey of current staff, designed around what stages 1–3 revealed
• Each stage is cheaper than the next, and each makes the next one smarter

Conclusion

• Secondary analysis reuses data, not findings — that is what separates it from the literature review, and what makes it a data collection method in its own right
• The sources span your own organisation’s records and a rich external ecosystem: GUS, Eurostat, OECD, industry reports, and academic surveys such as the ESS and EVS
• Its benefits — cost, speed, scale, longitudinal reach, no participant burden — are bought by surrendering control over design, and the risks all flow from that surrender
• The fitness-for-purpose checklist — who, why, when, from whom, how, with what instrument, under what definitions — is how you decide whether someone else’s data can carry your conclusion
• Used well, secondary analysis is the opening move of a diagnosis, not the whole game: it tells you where to point the primary research that follows
• Questions and discussion are welcome

Exercise

Study guide