Research Methodology Vocabulary — Experiments, Case Studies & Survey Design
Learn research methodology vocabulary: controlled experiments, case studies, survey design, longitudinal studies, replication studies, sample size, statistical power, and confounding variables.
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What is the defining characteristic of a 'controlled experiment' in research methodology vocabulary?
Controlled experiment vocabulary: independent variable (what you manipulate), dependent variable (what you measure), control group (baseline, no treatment), treatment/experimental group (receives the manipulation), random assignment (participants randomly allocated to groups to avoid selection bias). 'Controlled' refers to controlling variables, not the environment. Controlled experiments support causal language: 'X caused Y.' Observational studies only support correlational language: 'X was associated with Y.'
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When is a 'case study' methodology appropriate in technical research, and what are its limitations?
Case study language: 'We conducted a case study of a 200-developer organisation migrating from monolith to microservices.' Strengths: rich contextual detail, examines real-world complexity, appropriate for exploratory research. Limitations: 'The findings of this case study may not generalise to organisations of different sizes or industries' (external validity threat). 'Our observations at Company X may reflect its specific culture rather than the technology itself' (confound). Multiple-case studies (comparing several organisations) increase external validity.
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What is a 'longitudinal study' and how does it differ from a 'cross-sectional study' in research design vocabulary?
Longitudinal study language: 'We tracked 50 development teams over 18 months, measuring deployment frequency at each quarter.' Advantage: reveals trends, development, and causal sequences. Disadvantage: attrition (participants drop out), expensive, time-consuming. Cross-sectional: 'We surveyed 500 developers at a single point in time.' Advantage: fast and cheap. Disadvantage: cannot distinguish age effects from cohort effects; cannot show causality. A longitudinal study of developer productivity over a career cannot be replaced by a cross-sectional snapshot.
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What is a 'confounding variable' and why must it be addressed in research methodology?
Confounding variable example: studying whether code review tools improve code quality. A confound: team experience level. Experienced teams may both use better tools AND produce higher quality code. If you do not control for experience, you may incorrectly attribute the quality improvement to the tool. Language: 'We controlled for team experience by stratifying our sample.' Or: 'Team size was a potential confound — we address this by...' Failing to address confounds: 'A limitation of this study is that we could not fully control for organisational culture, which may confound our results.'
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What is 'statistical power' and why is it reported in research methodology sections?
Statistical power language: 'We conducted an a priori power analysis (alpha = 0.05, power = 0.80, Cohen's d = 0.5) which indicated a minimum sample size of 64 per group.' A study with low power (e.g., 0.30) has a 70% chance of failing to detect a real effect — underpowered studies produce unreliable null results. Convention: 0.80 power (80% chance of detecting a true effect). Reporting power shows reviewers the study was not merely under-resourced. Post-hoc power analysis after a null result: 'The achieved power of 0.42 suggests the study may have been underpowered to detect small effects.'
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Reviewer: 'This experiment used a purely observational design. No control group was established to isolate the effect of the new feature.'
What does the reviewer *primarily* mean when discussing this experimental design?
The reviewer highlights the critical flaw: without a control group, it's impossible to determine if observed changes are due to the new feature or other factors. A control group provides a baseline for comparison, allowing researchers to isolate the impact of the intervention. This is fundamental to establishing causality – the core goal of an experiment.
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@alice: 'For our survey on user onboarding, we're using a case study approach. We're focusing intensely on one particularly engaged customer to understand their workflow.'
Alice is describing which research methodology?
Alice's description aligns with a case study methodology. It emphasizes deep understanding through detailed examination of a single instance – in this case, the engaged customer. This contrasts with broader surveys that rely on aggregate data and statistical analysis; case studies are inherently qualitative.
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PR Description: 'We've conducted a cross-sectional survey of 500 users to assess satisfaction with the new API v2. The data will be analyzed using descriptive statistics to identify key trends.'
Which research design is being described in this PR?
The description clearly outlines a 'cross-sectional' approach – the survey captures data from users at one specific moment. This contrasts with longitudinal studies that track changes over an extended period and experimental designs which actively manipulate variables to test hypotheses. The focus on descriptive statistics further reinforces this design.
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Ben: 'I'm currently working on a user research project. We're running a series of interviews to understand the pain points users experience when submitting support tickets.'
What methodological consideration is Ben *most likely* focused on?
Ben's mention of 'pain points' suggests an interest in understanding the *reasons* behind those issues. This immediately raises the possibility of confounding variables – factors other than the new feature (or whatever is being investigated) that might be contributing to user frustration and skewing results. Addressing these is crucial for accurate research.
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API Response: {
"status": "success",
"data": {
"study_type": "longitudinal",
"duration": "24 months",
"sample_size": 150,
"variables": [
"user_engagement",
"feature_usage",
"system_performance"
]
}
}
What does the API response indicate about the research methodology being employed?
The response clearly specifies a 'longitudinal' study, characterized by repeated measurements over an extended period. The inclusion of variables like 'user_engagement', 'feature_usage', and 'system_performance' indicates a focus on tracking changes *over time* - the core feature of this methodology.
What will I practice in "Research Methodology Vocabulary — Experiments, Case Studies & Survey Design"?
This is a Research English exercise set. It walks through 10 scenario-based multiple-choice questions built around real usage of research english terminology that IT professionals encounter on the job.
Is this exercise free to use?
Yes. Every exercise on CoderSlingo, including this one, is free to complete with no account, sign-up, or paywall.
How many questions are in this exercise?
This set contains 10 questions. Each one shows immediate feedback and a detailed explanation after you answer, so you learn the correct usage right away rather than waiting for a final score.
Do I need prior experience to complete this exercise?
No prior experience is required. Each question includes a full explanation covering the reasoning behind the correct answer, so the exercise itself teaches the research english vocabulary as you go.
Can I retry the exercise if I get questions wrong?
Yes — use the "Try again" button on the results screen to reset your answers and go through all the questions again. There is no limit on attempts.
Is my progress saved?
Your answers and score for the current session are tracked in the browser as you go. No account or login is needed, and there is nothing to install.
What if I don't understand a term used in a question?
Read the explanation shown after you answer each question — it breaks down the correct term in plain English with a real-world example. You can also check the site Glossary for quick definitions.
How is this different from reading a blog article on the topic?
Exercises like this one are interactive drills that test and reinforce specific vocabulary through multiple-choice questions, while blog articles explain concepts in prose. Practising here after reading builds active recall, not just passive recognition.
Where can I find more Research English exercises?
See the Research English exercises hub for the full set of related pages, or browse all exercise categories from the main Exercises index.
Can I use this exercise to prepare for a technical interview?
Yes — research english vocabulary comes up often in technical discussions and interviews. Pair this exercise with our dedicated Interview Preparation section for role-specific practice.