Three Examples of Quantitative Research Methods for Academic Writing

Three Examples of Quantitative Research Methods for Academic Writing

Introduction: What Quantitative Research Really Involves

Quantitative research is often defined as “research that involves numbers,” but this simple description hides a great deal of diversity. At its core, quantitative research seeks to answer questions by collecting numeric data and analysing it using statistical methods. That data might come from survey responses, experiments, physiological measurements, tests, or counts of behaviour – and the design you choose makes a huge difference to the strength and scope of your conclusions.

In academic and scientific writing, reviewers and examiners look closely at your methods. They want to know whether you used an appropriate design, how you sampled participants, how you measured key variables, and how you analysed your data. Clear, precise descriptions of your quantitative methods help readers trust your findings and understand their limitations.

To illustrate how different quantitative designs work in practice, this article presents three fictional but realistic examples:

1. A survey study of shopping habits.
2. An experimental study of animal-assisted therapy for elderly patients with dementia.
3. A naturalistic observational study of customer behaviour in a fast-food restaurant.

Each example is presented in an outline format similar to what a researcher might develop in the planning stages of a project. These outlines are simplified for clarity – a real ethics application or methods section would require more detail – but they highlight the distinctive features, strengths, and challenges of each approach.

Example 1: Survey Research on Grocery Shopping Habits

1.1 Research focus and objective

Our first example uses a survey to explore the grocery-shopping habits of low-income families in a specific urban area. The researcher is interested in how price and availability influence decisions about buying high-fat versus low-fat foods.

Research question: To what extent do the price and availability of unhealthy high-fat foods compared to healthy low-fat foods shape the grocery-shopping choices of low-income families?

Hypothesis: Lower prices and greater availability of high-fat foods encourage low-income shoppers to choose these options more frequently than healthier low-fat alternatives.

1.2 Sampling and recruitment

The study is conducted in collaboration with East Side Social Services, which serves families living on the east side of a city called Civitas. To obtain a reasonable sample size and avoid selection bias, the researcher plans to invite 1,000 families to participate:

• Potential participants are identified from Social Services client lists, Food Bank records, and publicly available community mailing lists.
• Families are selected using random sampling to increase representativeness.
• Participation is voluntary, and only respondents aged 17 or over are included in the analysis.

1.3 Survey design

The instrument is a structured email questionnaire containing 30 closed-ended questions. These questions include:

• Yes/no items: e.g. “Do you read nutritional labels while shopping for food?”
• Multiple-choice questions: e.g. “Which of the following best describes your behaviour when shopping for foods for your children’s lunches?” with options such as “I prioritise price,” “I prioritise nutritional value,” etc.
• Likert-scale items: e.g. “How strongly do you agree with the statement: ‘I always check labels for fat content before buying processed food’?” with options ranging from “Strongly disagree” to “Strongly agree.”

The survey also gathers demographic information (age, gender, marital status, household income, number and ages of children) to allow subgroup analyses and ensure the sample reflects the target population.

1.4 Incentives and ethical considerations

To encourage participation, each family that completes the survey within two weeks is offered a $10 grocery gift card, donated by the East Side Grocers Association. Ethical considerations include:

• an information sheet explaining the study’s purpose and procedures;
• an informed consent form confirming voluntary participation and the right to withdraw;
• procedures for protecting confidentiality (e.g. storing data securely, reporting only aggregated results).

1.5 Data analysis and reporting

Survey responses are entered into statistical software for analysis. The researcher calculates:

• descriptive statistics – frequencies and percentages for each response option;
• cross-tabulations – e.g. comparing responses across income brackets or family sizes;
• where appropriate, inferential tests (chi-square tests, t-tests, or logistic regression) to examine relationships between variables.

Results are presented in tables and graphs, with key findings described in the text. An optional open-ended question at the end of the survey (“Is there anything else you would like to tell us about your shopping habits?”) provides qualitative comments that help interpret the numbers and suggest avenues for further research.

Example 2: Experimental Research on Animal-Assisted Therapy

2.1 Research focus and design

The second example adopts an experimental design to evaluate whether a specific therapy causes measurable changes in patient outcomes. Here, the investigator examines the impact of animal-assisted therapy on social, emotional, and cognitive functioning in elderly patients with dementia or Alzheimer’s disease living in long-term care.

Research questions:

• Does structured interaction with therapy cockatoos improve social, emotional, or cognitive functions among elderly dementia and Alzheimer’s patients?
• How do these effects compare to those of standard recreational activities?
• Are any observed improvements sustained over several days?

2.2 Participants and groups

The study is conducted at two care facilities – Shady Grove Retirement Villa and Sunny Shores Care Home. The sample includes:

• Experimental group: 13 residents at Shady Grove and approximately 12 at Sunny Shores who participate in weekly group activities. These patients have been diagnosed with dementia or Alzheimer’s disease and can attend a one-hour session in a communal room.
• Control group: 11 residents at Sunny Shores who attend a similar weekly recreation session but do not receive animal-assisted therapy.

Participants are not randomly assigned in this simplified example, but in a full-scale study, randomisation would strengthen causal inferences.

2.3 Intervention and procedures

The intervention consists of one-hour sessions in which patients interact with a pair of trained galahs (roseate cockatoos) named Harry and Hermione. The sessions are designed in collaboration with a qualified animal trainer and the care-home staff.

The study proceeds as follows:

1. Baseline assessment: On the day before the therapy session, each participant is asked simple orientation and wellbeing questions (e.g. “What is your name?”, “How old are you?”, “What year is it?”, “How are you feeling today?”). Responses are recorded using a standardised form.
2. Therapy session: During the one-hour session, the cockatoos are introduced into the recreation room. The trainer and a research assistant manage the birds, while the researcher and another assistant observe and record patient behaviours according to pre-defined criteria (smiling, laughing, initiating conversation, recalling past events, physical affection, participation in activities, etc.).
3. Follow-up assessments: The same orientation and wellbeing questions are repeated one day after and three days after the session to assess any short-term and slightly longer-term changes.

The control group at Sunny Shores follows the same assessment schedule (before, one day after, and three days after a standard recreation session without animals), allowing comparison between those who received the therapy and those who did not.

2.4 Measurement and data analysis

To turn observed behaviour into quantitative data, the researcher uses behavioural coding schemes. For example:

• Each smile, laugh, or verbal interaction is counted as an instance of positive emotional or social response.
• Correct answers to orientation questions are scored to produce a simple cognitive-function score at each time point.

Statistical analyses might include:

• within-group comparisons (e.g. using repeated-measures ANOVA or non-parametric equivalents to see whether scores change from baseline to follow-up in the experimental group);
• between-group comparisons (e.g. comparing changes in the experimental group with those in the control group).

Video recordings, if permitted, can be reviewed to ensure accurate coding and to calculate inter-rater reliability – an important indicator that behavioural measures are consistent between observers.

2.5 Ethical considerations

Because this study involves vulnerable participants and animals, ethics are central:

• Informed consent: Obtained from participants who can give consent, or from legal guardians/next of kin for those who cannot. Participants can withdraw at any time.
• Welfare of animals: Ensuring that the cockatoos are handled humanely and not over-stressed, in line with veterinary and ethical standards.
• Approval: Secured from a regional medical board and the university’s research ethics committee.

Clear documentation of these procedures is essential in any published methods section.

Example 3: Naturalistic Observation in a Fast-Food Restaurant

3.1 Research focus and context

The third example uses naturalistic observation to study how people behave in a real-world setting without interference from the researcher. The setting is Pudgy’s Burgers, a local fast-food restaurant in the small town of Quaintville. The franchise owner plans to close the restaurant, but some residents object, claiming that it is the only place where working families can afford a decent meal and let their children play indoors.

Research questions:

• Do families with children make up the majority of the restaurant’s customers?
• Do customers who are families tend to purchase healthier options or mainly high-fat items?
• Do families spend significant time in the restaurant, using it as a comfortable social space?
• How often is the children’s play area actually used?

The researcher has a working hypothesis – based on both local claims and existing literature on fast food and family health – that Pudgy’s might be an exception to broader trends by truly serving as a family-oriented space.

3.2 Observational plan

To answer these questions, the researcher plans a series of four-hour observation sessions across different days and times:

• Observation periods are scheduled so that every opening time slot (morning, lunchtime, afternoon, evening) and every day of the week is covered at least twice over two winter months (January and February).
• During each session, the researcher sits at a staff table in a quiet corner with the permission of the manager, unobtrusively observing customer behaviour.
• The researcher does not interact with customers, to avoid influencing their behaviour (a key principle in naturalistic observation).

3.3 Data-collection instrument: Customer Fact Sheet

To ensure consistent data collection, the researcher uses a pre-designed Customer Fact Sheet for each customer or group. Variables recorded include:

• date and time of visit;
• weather conditions (which may affect restaurant use);
• group composition (number of adults, number and estimated ages of children);
• whether the order is eat-in or take-out;
• types of food and drinks purchased (with a coding scheme for “healthy” vs “unhealthy” items);
• use of the children’s play area;
• approximate length of stay;
• observable social interactions (e.g. conversations among family members, interactions between groups, evidence of lingering).

To supplement these observations, the manager provides anonymous receipt data (e.g. items purchased and times), which the researcher can use to validate observational counts and explore purchase patterns more precisely.

3.4 Sampling and analysis

The goal is to gather data on at least 500 customer units (individuals or groups). Once data collection is complete, the researcher analyses:

• the proportion of customers who are families vs individuals or other groups;
• the frequency with which healthy menu items are chosen;
• average time spent in the restaurant by family vs non-family groups;
• patterns in play-area usage (e.g. by time of day, age of children).

Descriptive statistics (percentages, means, standard deviations) summarise behaviour, while cross-tabulations and chi-square tests can examine associations (e.g. between group type and likelihood of using the play area).

3.5 Ethical considerations in observational research

Although observation in public places often does not require individual informed consent, ethical research still demands:

• clarity about what is being recorded (no names or identifying details, only general behaviours);
• transparency with gatekeepers, such as the restaurant manager, about the aims and scope of the study;
• careful handling of any data that might indirectly identify individuals.

These considerations are especially important when writing up the methods and ethics sections for publication or review by a thesis committee.

From Examples to Practice: Lessons for Your Own Quantitative Research

These three examples – survey, experiment, and observational study – illustrate some of the main categories of quantitative research. Each method has distinctive strengths and limitations, and the method you choose should always follow from your research question and practical constraints.

4.1 Match your method to your question

• If you want to describe patterns or relationships across a population (e.g. what proportion of people hold a particular view), a survey may be appropriate.
• If you want to test causal effects (e.g. whether an intervention changes outcomes), an experiment with control or comparison groups is usually required.
• If you want to observe behaviour in a natural context without interference, naturalistic observation may be the best option.

4.2 Consider validity, reliability, and bias

Regardless of method, you must think about:

• Internal validity: Are you measuring what you think you are measuring? Could other factors explain your results?
• External validity: Can your findings be generalised beyond your specific sample or context?
• Reliability: Would another researcher, using the same method, obtain similar results?
• Bias: Are there systematic factors that might distort your findings (e.g. self-selection in surveys, observer bias in observation, placebo effects in experiments)?

Good research design includes strategies to reduce these threats – for example, random sampling in surveys, blinding in experiments, clearly defined coding schemes in observation, and triangulation across methods where possible.

4.3 Analyse and interpret your data thoughtfully

Collecting numbers is only the beginning. To make your research publishable, you must:

• choose appropriate statistical tests for your design and data type;
• check assumptions (normality, independence, equal variances, etc.);
• report not only p-values but also effect sizes and confidence intervals where relevant;
• interpret your findings in light of the existing literature, not in isolation.

For instance, in the survey example, finding that 70% of respondents prioritise price over nutrition is interesting, but becomes much more meaningful when compared to national data or previous studies in similar communities. Similarly, a small but statistically significant improvement in cognitive scores in the animal-assisted therapy study must be discussed in relation to its practical significance for patient quality of life.

4.4 Acknowledge limitations honestly

No study is perfect. The fictional projects described above all have limitations:

• The survey may suffer from non-response bias if certain types of families are less likely to complete it.
• The experimental study may have limited generalisability due to small sample size and lack of random assignment.
• The observational study may be influenced by seasonal effects (winter behaviour may differ from summer behaviour) or by the presence of the observer, even if partially concealed.

In academic writing, acknowledging these limitations does not weaken your work; it strengthens your credibility. It shows that you are aware of what your methods can and cannot demonstrate, and it opens the way for future research to build on your findings.

Writing Up Your Quantitative Methods for Publication

When you come to write your methods and results sections, clarity and transparency are crucial. Editors and reviewers will expect you to provide enough detail for others to evaluate – and potentially replicate – your study.

• Describe your design: Specify whether your study is cross-sectional, longitudinal, experimental, quasi-experimental, or observational.
• Explain your sampling: State how participants or cases were recruited, any inclusion/exclusion criteria, and the final sample size.
• Detail your instruments and measures: For surveys, describe how you developed or chose your questions and scales; for experiments and observations, provide operational definitions of key variables.
• Outline your procedures: Describe exactly what happened, in what order, and under what conditions.
• Specify your analysis plan: Explain which statistical tests you used and why.

Finally, ensure that your writing is clear and concise, free from grammatical and typographical errors. Journals and examiners expect a high standard of written English, especially in the methods and results sections where precision matters most. If you are unsure about your language or formatting, an experienced academic proofreader can help you refine your manuscript before submission.

Conclusion: Choosing and Using Quantitative Methods Wisely

Quantitative research is powerful because it allows you to summarise patterns, test hypotheses, and draw conclusions that are grounded in systematically collected data. But that power comes with responsibility: you must choose methods that fit your questions, apply them rigorously, and report them honestly and clearly.

The three fictional examples in this article – a survey of shopping habits, an experiment with animal-assisted therapy, and an observational study in a restaurant – are only a small sample of the many possible quantitative designs. They illustrate, however, the kinds of decisions all researchers must make: what to measure, how to measure it, whom to study, how to analyse the data, and how to interpret the results.

As you plan your own projects, use these examples as starting points, not templates. Adapt them to your discipline, your research question, and your ethical and practical constraints. Combine them where appropriate (for instance, using surveys alongside observation), and always be guided by the standards and expectations of your field. With thoughtful design, careful analysis, and clear academic writing – supported by expert human proofreading where needed – your quantitative research can make a persuasive, well-supported contribution to the scholarly conversation.