2.3 Primary, Secondary, Qualitative, and Quantitative Data

Four data distinctions

CHES candidates should be comfortable sorting data by source and type. Primary data are collected directly for the current assessment. Examples include a local survey, focus group, key informant interview, windshield survey, listening session, observation checklist, or environmental scan. Secondary data already exist, such as surveillance reports, census data, hospital discharge data, school attendance records, clinic quality measures, or published community health assessments.

Quantitative data use numbers to show how much, how many, how often, or how rates differ. They help estimate prevalence, compare groups, identify trends, and set measurable baselines. Qualitative data use words, narratives, and observations to explain meaning, barriers, beliefs, context, and acceptability. They help answer why and how questions.

Choosing the right source

The best answer depends on the question. If a team needs fast background on rates, secondary data may be efficient. If a team needs current local barriers not captured in existing datasets, primary data may be necessary. If a team needs both size and meaning, mixed methods may be best.

Strengths and limitations

Secondary data are often cheaper and faster, but they may be outdated, too broad, missing key subgroups, or collected for another purpose. Primary data can be tailored, but they require time, permissions, recruitment, and careful design. Quantitative data can show patterns but may not explain lived experience. Qualitative data can reveal context but usually cannot estimate prevalence by itself.

The exam may test whether the conclusion fits the data. A focus group with 12 parents can suggest barriers and themes, but it should not be used alone to claim a countywide rate. A county prevalence rate can show burden, but it cannot explain why a specific neighborhood avoids a clinic. Match the inference to the data source.

Data quality questions

Before using any source, ask about relevance, timeliness, credibility, representativeness, and cultural fit. Relevance asks whether the data answer the current question. Timeliness asks whether the data are recent enough for decisions. Credibility asks whether the source is trustworthy and methods are sound. Representativeness asks whether the data include the priority population. Cultural fit asks whether tools and procedures are understandable, respectful, and accessible.

For primary data, also consider literacy level, language, privacy, mode of administration, sampling method, and response burden. A long online-only survey may miss people without reliable internet. An English-only instrument may distort findings in a multilingual community. A focus group on a sensitive topic may require careful facilitation and confidentiality procedures.

Exam approach

When an item asks for the best data method, underline the assessment question mentally. If the team asks how common, choose a quantitative source. If it asks why, choose a qualitative method. If it asks whether local findings match state trends, choose comparable secondary data. If it asks about assets, look for scans, inventories, partner mapping, and stakeholder input.

The strongest assessment plans rarely rely on a single convenient source. They triangulate data, meaning they compare multiple sources to see whether findings support each other. Triangulation improves confidence and helps prevent one dataset from driving a narrow or biased conclusion.