Research Design Builder

Purpose

To establish cause and effect.

When it is commonly used

When the question is whether something causes a change, and the cause can be manipulated ethically and practically.

Strengths

• The strongest general basis for claims about cause and effect.
• Clear, repeatable procedures.

Limitations

• Controlled conditions may not reflect real life.
• Many causes can't ethically or practically be manipulated.

Typical data

• Measurements of the outcome before and after the intervention
• Records of which condition each participant received

Common analysis methods

• Comparisons of group means
• Analysis of variance and regression

Examples

Comparing test scores of students taught with two revision methods.

References

References awaiting academic review.

Purpose

To establish cause and effect with the strongest protection against other explanations.

When it is commonly used

When random assignment is possible and ethical.

Strengths

• Random assignment balances known and unknown influences between groups.
• Supports the strongest causal conclusions of any design.

Limitations

• Random assignment is often impossible or unethical in real settings.
• Participants may behave differently because they know they are in a study.

Typical data

• Pre-test and post-test measurements
• Outcome measurements for treatment and control groups

Common analysis methods

• Comparisons of treatment and control groups
• Analysis of covariance

Examples

A randomised trial of a new reading programme against usual teaching.

References

References awaiting academic review.

Purpose

To estimate the effect of an intervention where random assignment isn't possible.

When it is commonly used

When groups are already formed, or random assignment would be impractical or unethical.

Strengths

• Works in real settings with existing groups.
• Often more practical and ethical than a true experiment.

Limitations

• Groups may differ in ways other than the intervention.
• Causal conclusions are weaker than with random assignment.

Typical data

• Outcome measurements for intervention and comparison groups
• Measurements before the intervention, to compare the groups' starting points

Common analysis methods

• Comparisons adjusted for starting differences
• Regression with control variables

Examples

Comparing two schools, one of which adopts a new timetable.

References

References awaiting academic review.

Purpose

To take a first look at whether an intervention may have an effect.

When it is commonly used

In pilot studies, or when no comparison group is available.

Strengths

• Simple and quick to carry out.
• Useful for piloting an intervention or instrument.

Limitations

• Without a comparison group, other explanations for a change can't be ruled out.
• Rarely supports causal conclusions on its own.

Typical data

Measurements before and after the intervention for one group

Common analysis methods

• Before-and-after comparisons
• Descriptive statistics

Examples

Measuring staff confidence before and after a single training day.

References

References awaiting academic review.

Purpose

To describe characteristics, attitudes or behaviours, and how they are related, across a population.

When it is commonly used

When information is needed from many people, and the questions can be standardised.

Strengths

• Reaches many people efficiently.
• Standardised answers can be compared and analysed statistically.

Limitations

• Answers are limited to the questions asked.
• Relies on what people report, which may differ from what they do.

Typical data

• Questionnaire responses
• Structured interview answers

Common analysis methods

• Descriptive statistics
• Cross-tabulation, correlation and regression

Examples

A questionnaire on study habits sent to all first-year students.

References

References awaiting academic review.

Purpose

To identify and describe relationships between variables.

When it is commonly used

When variables can't or shouldn't be manipulated, but their relationship matters.

Strengths

• Studies variables that can't be manipulated.
• Shows the strength and direction of relationships.

Limitations

• A relationship doesn't show cause and effect.
• A third variable may explain the relationship.

Typical data

Measurements of each variable for the same participants

Common analysis methods

• Correlation
• Regression

Examples

Examining whether working hours are related to burnout among nurses.

References

References awaiting academic review.

Purpose

To understand a complex situation in detail, where context matters.

When it is commonly used

When “how” and “why” questions are asked about a contemporary situation the researcher can't control.

Strengths

• Rich understanding of how things work in practice.
• Can combine several kinds of evidence.

Limitations

• Findings from a few cases can't be generalised statistically.
• Defining the boundaries of the case can be difficult.

Typical data

• Interviews
• Documents, observation and records

Common analysis methods

• Thematic analysis
• Pattern matching and explanation building

Examples

How one hospital introduced electronic patient records.

References

References awaiting academic review.

Purpose

To understand the essence or meaning of an experience.

When it is commonly used

When the question is about what an experience is like for the people who have it.

Strengths

• Detailed insight into lived experience.
• Centres participants' own perspectives.

Limitations

• Relies on small, carefully chosen samples.
• Requires careful handling of the researcher's own assumptions.

Typical data

• In-depth interviews
• Written accounts of experience

Common analysis methods

• Interpretative phenomenological analysis
• Thematic analysis of meaning

Examples

What returning to study after a long break is like for mature students.

References

References awaiting academic review.

Purpose

To build a new explanation of a process where existing theory is limited.

When it is commonly used

When little theory exists about a process, or existing theory doesn't fit the setting.

Strengths

• Theory closely grounded in evidence.
• Systematic procedures for analysis.

Limitations

• Time-consuming, because data collection and analysis alternate.
• Different versions follow different procedures, so the version must be named.

Typical data

• Interviews
• Observation and documents

Common analysis methods

• Coding and constant comparison
• Theoretical sampling to saturation

Examples

Developing a theory of how new managers learn their role.

References

References awaiting academic review.

Purpose

To understand a group's shared practices and meanings from the inside.

When it is commonly used

When the question is about a group's culture, and long-term access to the group is possible.

Strengths

• Deep, first-hand understanding of everyday life.
• Shows what people do, not only what they say.

Limitations

• Very time-consuming.
• The researcher's presence may change how people behave.

Typical data

• Participant observation and field notes
• Informal interviews and documents

Common analysis methods

• Thematic and interpretive analysis of field notes
• Thick description

Examples

A year with a community sports club, studying belonging.

References

References awaiting academic review.

Purpose

To understand how people make sense of events over the course of their lives.

When it is commonly used

When experience unfolds over time and is best understood through personal stories.

Strengths

• Keeps the richness and order of personal experience.
• Gives participants a voice.

Limitations

• Usually involves few participants.
• Stories are shaped by memory and by the way they are told.

Typical data

• Life-story interviews
• Diaries, letters and other personal documents

Common analysis methods

• Narrative analysis
• Thematic analysis across stories

Examples

The career stories of women in engineering.

References

References awaiting academic review.

Purpose

To solve a real problem while generating knowledge about it.

When it is commonly used

When the researcher is part of, or works closely with, the setting they want to improve.

Strengths

• Produces practical change as well as knowledge.
• Involves the people affected.

Limitations

• Findings are closely tied to one setting.
• The researcher's involvement makes objectivity harder to claim.

Typical data

• Reflective journals and meeting notes
• Interviews, focus groups and before-and-after measures

Common analysis methods

• Reflective and thematic analysis
• Simple comparisons across cycles

Examples

Teachers improving feedback practices over a school year.

References

References awaiting academic review.

Purpose

To understand what happened in the past, how and why.

When it is commonly used

When the question is about the past, or about how a present situation developed.

Strengths

• Gives access to periods that can't be observed directly.
• The data aren't affected by the researcher's presence.

Limitations

• Records may be incomplete, biased or lost.
• Interpretation depends on understanding why the records were made.

Typical data

• Archival documents and official records
• Letters, diaries, newspapers and oral histories

Common analysis methods

• Source criticism
• Documentary and thematic analysis

Examples

Tracing how a national curriculum changed between 1988 and 2014.

References

References awaiting academic review.

Purpose

To use one kind of data to explain, or build on, the other.

When it is commonly used

When one phase's results are needed to design or interpret the next.

Strengths

• Each phase can be designed with the other's results in mind.
• Clear structure that is easy to report.

Limitations

• Takes longer, because the phases can't overlap.
• Participants may need to take part twice.

Typical data

Questionnaire data and interviews, collected in turn

Common analysis methods

• Separate analysis of each phase
• Integration of findings in the interpretation

Examples

A survey of staff wellbeing, followed by interviews with staff whose scores were very high or very low.

References

References awaiting academic review.

Purpose

To compare two kinds of evidence on the same question and see whether they agree.

When it is commonly used

When both kinds of data are needed and can be collected together.

Strengths

• Takes less time than collecting data in two phases.
• Two kinds of evidence can confirm or challenge each other.

Limitations

• Needs a plan for when the two kinds of results disagree.
• Merging different kinds of data takes skill.

Typical data

Questionnaire data and interviews, collected at the same time

Common analysis methods

• Separate analysis of each strand
• Side-by-side comparison or joint displays

Examples

A questionnaire and interviews with patients on the same ward in the same month.

References

References awaiting academic review.

Purpose

To answer a secondary question that the main design can't answer on its own.

When it is commonly used

When a main design, often an experiment, needs a smaller strand of another kind of data.

Strengths

• Adds understanding without a second full study.
• Fits within an existing design.

Limitations

• The supporting strand can be too small to stand on its own.
• The two strands' purposes must be kept clear.

Typical data

The main study's data, plus a smaller strand of the other kind

Common analysis methods

The main design's analysis, with the supporting strand analysed to inform it

Examples

Interviewing some participants in a trial about how they experienced the intervention.

References

References awaiting academic review.

Purpose

To describe a situation, or compare groups, at one moment.

When it is commonly used

When time is limited, or change over time isn't the question.

Strengths

• Quicker and cheaper than following people over time.
• Allows comparisons between groups at one moment.

Limitations

• Can't show change over time.
• Makes cause and effect hard to establish, because everything is measured at once.

Typical data

A single round of questionnaires, interviews or measurements

Common analysis methods

• Descriptive statistics and group comparisons
• Correlation and regression

Examples

A survey of student wellbeing in one semester.

References

References awaiting academic review.

Purpose

To study change and development over time.

When it is commonly used

When the question is about change, development or the order in which things happen.

Strengths

• Shows how things change over time.
• Gives stronger evidence about the order in which things happen.

Limitations

• Time-consuming and costly.
• Participants may drop out between rounds.

Typical data

• Repeated questionnaires, measurements or interviews
• Existing records over time

Common analysis methods

• Analysis of change over time
• Repeated-measures and growth models

Examples

Following graduates' careers over five years.

References

References awaiting academic review.

Purpose

To give an accurate picture of a situation, population or phenomenon.

When it is commonly used

When little is documented about a situation, or a baseline is needed before further research.

Strengths

• Provides a clear, factual picture.
• A useful basis for later explanatory research.

Limitations

• Says what is happening, not why.
• Its value depends on accurate measurement and a representative sample.

Typical data

Questionnaires, observation or existing records

Common analysis methods

• Frequencies, percentages and averages
• Descriptive summaries of qualitative data

Examples

Describing how often patients miss follow-up appointments.

References

References awaiting academic review.

Purpose

To identify causes, mechanisms or reasons.

When it is commonly used

When a pattern is known and the question is why it occurs.

Strengths

• Goes beyond description to explanation.
• Can test or develop theory.

Limitations

• Explanations need a design that can support them.
• Other explanations must be ruled out.

Typical data

Measured variables, or detailed qualitative accounts of processes

Common analysis methods

• Regression and modelling
• Explanation building from qualitative data

Examples

Explaining why some students leave university in their first year.

References

References awaiting academic review.

Purpose

To gain initial understanding and generate ideas or hypotheses.

When it is commonly used

When a topic is new, poorly understood, or hasn't been studied in this setting.

Strengths

• Flexible and open to the unexpected.
• Identifies what later research should examine.

Limitations

• Findings are tentative.
• Rarely supports firm conclusions or generalisation.

Typical data

• Interviews, focus groups and observation
• Documents and early pilot data

Common analysis methods

• Thematic analysis
• Descriptive summaries

Examples

Exploring how small businesses used social media during a crisis.

References

References awaiting academic review.