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To collect and analyze data well, start with the question you need to answer—not with a survey or spreadsheet. Define the decision the findings will support, decide what evidence would answer it, choose a suitable source and collection method, and plan the analysis before gathering anything. Numerical, qualitative, and mixed-methods data call for different approaches; the right choice depends on the question, people or units involved, time and expertise, ethics, and the quality of evidence required.
1. Define the question and the decision
Write down what you need to know and who will use the answer. The intended use helps determine how much detail, certainty, and breadth the work needs.
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- Questions such as “How many?”, “How often?”, or “Did the outcome change?” generally call for measurable, comparable evidence.
- Questions such as “How did this happen?”, “Why did people respond this way?”, or “What was the experience like?” call for context and detailed accounts.
- If you need both a measure of what happened and an explanation of how or why, a study can combine the two.
Translate the question into evidence you can actually collect. Specify the people, events, records, or other units you will observe, what indicator or measure counts as evidence, and when you need it. The CDC’s guidance on gathering credible evidence emphasizes matching sources and measures to the evaluation question.
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2. Identify what kind of data will answer it
Quantitative data: amounts, counts, and comparisons
Quantitative data are numerical measurements or values. They are useful for describing how many or how often, tracking change, comparing groups, or examining relationships. Analysis might include frequency distributions, charts, descriptive statistics, and appropriately designed comparisons or relationship analyses. The numbers do not determine the analysis on their own: the study design and question matter.
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Qualitative data: meaning, experience, and context
Qualitative data include spoken, written, and behavioral material—such as interview accounts, observations, documents, audio, and video. They help explain meanings, experiences, context, and processes. Depending on the question and material, analysis may involve coding and developing themes, or use document, discourse, or multimodal analysis.
Mixed methods: use both strands deliberately
Mixed methods combines the collection and analysis of quantitative and qualitative data within one study. It can pair a measure of an outcome with accounts that help explain how or why it occurred. The design needs to specify how the two strands inform one another; collecting both kinds of data without a plan for integration does not, by itself, make the results useful together. Mixed methods also adds design, staffing, time, and cost demands. See the Office for Health Improvement and Disparities’ mixed-methods guidance.
Primary and secondary describe where data came from
Primary data are collected for the current study. Secondary data were collected earlier, often for another purpose. This distinction is separate from whether data are numerical or qualitative: an existing dataset may contain counts, text, or both. Administrative records, census or population data, surveillance information, program records, and prior studies may provide useful evidence or spare people unnecessary new data collection. Check why the material was originally gathered, what it measures, and whether its coverage and quality fit your current question. The Australian Institute of Family Studies survey guide and the CDC evidence guide discuss selecting and assessing sources.
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3. Choose a collection method that fits the evidence
Different methods produce different kinds of evidence. Compare alternatives against question fit, depth or breadth, comparability, time, cost, staff expertise, ethics, validity, reliability, and whether you need findings to generalize beyond the cases observed. Use the simplest design that can answer the question reliably; no single method is best for every purpose.
| Need | Likely method | Strength | Constraint |
|---|---|---|---|
| Comparable answers from many people or change over time | Structured survey or questionnaire | Standardized responses make breadth and comparison possible. | Fixed response options and wording may limit context or introduce bias. |
| Detailed accounts of experience, motivation, or emotion | Individual or group interviews | Follow-up questions can uncover detail. | Collection and analysis take time; reduced anonymity may affect responses. |
| Behavior in its natural setting | Observation | Captures behavior and context rather than relying only on self-report. | Requires attention to ethics, sampling, and observer objectivity. |
| Existing information or records | Record review or secondary dataset | May reduce new collection and supply context. | The original purpose and data quality may not fit the current question. |
| Both numerical outcomes and experiential explanations | Mixed methods | Can show what happened alongside how or why. | Requires more resources and a plan to integrate findings. |
Other possible methods include tests that measure performance against a standard, physiological assessments, and biological samples for defined physical measurements. These are examples, not interchangeable choices; each needs a clear measure and suitable collection procedure. The U.S. Office of Research Integrity describes these and other methods of information collection.
4. Plan the analysis before collecting
Decide in advance how each answer, observation, or measurement will be represented and used. The Open University’s overview of research methodology underscores the importance of aligning method and analysis with the question. A simple analysis plan can state:
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- Which variables, responses, records, or observations address each part of the question.
- How responses will be recorded, categorized, or measured.
- Which summaries, comparisons, coding approach, or interpretive method will be applied.
- How missing information, unusual responses, or disagreement between observers will be handled.
- If using mixed methods, how findings from each strand will be brought together and what happens if they differ.
Analyze numerical data
Begin by checking and summarizing distributions, then visualize the values. Frequency distributions, descriptive statistics, and charts can reveal patterns and data-quality problems before you compare groups or examine relationships. Choose comparisons that fit the design; a numerical difference alone does not establish that one factor caused another.
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Organize the material in a way that preserves enough context to interpret it. For an approach based on coding, apply a consistent process to identify relevant content, then examine patterns and differences in relation to the question. Thematic coding is one option, not a universal requirement: document, discourse, and multimodal approaches may be more suitable for other materials or questions.
Integrate mixed-methods findings
State what each strand contributes and how they will be compared or connected. For example, one strand may describe a measured outcome while another explores participants’ experiences of the process. Treat agreement, complementarity, and disagreement as findings to interpret, rather than assuming one strand automatically confirms the other.
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5. Protect data quality and participants
The Office of Research Integrity advises carrying out collection with precision, accuracy, and minimal error. In practical terms, consider:
- Validity: Does the method measure what you intend it to measure?
- Reliability: Could the procedure produce consistent or reproducible findings under the stated conditions?
- Consistency: If several people collect or code data, have they aligned how they record, count, or interpret it?
- Ethics and sensitivity: Is the collection appropriate for the people and information involved?
- Feasibility and burden: Can the work be done with available time, skills, and resources without asking respondents for unnecessary effort?
These checks are relevant across methods. For instance, survey question wording can affect responses, while observation needs a considered approach to sampling and observer judgment. Consult the Office of Research Integrity’s collection-method guidance, the CDC’s credible-evidence guidance, and the Australian Institute of Family Studies’ survey guide for further detail.
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Report what the evidence supports, who or what was observed, the context, and relevant missing or weak evidence. A numerical summary does not automatically show that a result represents a wider population or that one factor caused another. Qualitative depth can clarify experience and context, but does not on its own establish how common an experience is across a population. Keep conclusions within the reach of the design and data.
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