Research – Single-Subject and Group Designs

Why Research Design Matters (And Why You'll Actually Use This)

You know that feeling when you're scrolling through social media and see yet another article claiming "Scientists discover that coffee/chocolate/wine is good/bad for you"? Ever wonder why these findings seem to contradict each other every few months? The answer lies in research design.

Understanding research designs isn't just about passing the EPPP. When you're in practice, you'll need to evaluate whether that new therapy approach really works, determine if your client's progress is actually due to your intervention, and critically read research that could change how you treat people. Plus, if you ever want to conduct your own research or even track a single client's progress systematically, you'll need this knowledge in your toolkit.

Let's break down the main types of research designs in a way that actually makes sense.

The Big Picture: Qualitative vs. Quantitative

Before diving into specific designs, understand that research falls into two broad camps:

Qualitative research is about understanding the "what" and "why" of human experience. It produces rich, descriptive information that you interpret rather than count. {{M}}Think of it like having an in-depth conversation with a friend about their breakup{{/M}} – you're gathering detailed stories, emotions, and meanings, not tracking how many times they cried.

Quantitative research is about measuring and counting. It produces numbers you can analyze statistically. {{M}}It's like tracking your daily step count and sleep hours in your fitness app{{/M}} – concrete data you can graph and compare.

Qualitative Approaches You Should Know

Grounded Theory: Researchers develop a theory by deeply listening to participants' experiences. {{M}}Imagine you're trying to understand how people decide to switch careers. Instead of testing a pre-existing theory, you interview dozens of career-changers and let the patterns emerge naturally from their stories.{{/M}} The theory is "grounded" in what people actually say, not what textbooks predict.

Phenomenology: This approach focuses on the lived experience of a phenomenon. {{M}}If grounded theory is about finding patterns, phenomenology is about understanding what something feels like from the inside.{{/M}} For example, what's it really like to experience your first panic attack? How do people actually perceive and make sense of that moment?

Ethnography: Researchers immerse themselves in a culture or setting. {{M}}Picture a psychologist who wants to understand the culture of an online gaming community dealing with depression, so they join the community, participate in discussions, and observe interactions over months.{{/M}} They're not just asking questions from outside – they're living it.

Thematic Analysis: This is the method where researchers identify recurring patterns or themes in their data. It can stand alone or support other methods. {{M}}If you recorded ten therapy sessions and noticed clients repeatedly mentioning feelings of being "stuck" or "trapped," you'd be doing thematic analysis.{{/M}}

Making Research More Credible: Triangulation

Triangulation strengthens research by approaching a question from multiple angles. Four types exist:

{{M}}Think of it like checking your bank balance. You could just trust one source, but you feel more confident when the app, the ATM, and your monthly statement all show the same number.{{/M}}

Quantitative Research: Three Main Types

Descriptive Research: Taking a Snapshot

Descriptive research describes what exists without manipulating anything. You're simply documenting reality.

Surveys gather information from many people using questionnaires. {{M}}Like when Netflix asks what you watched and uses that data to understand viewing patterns.{{/M}}

Case studies provide in-depth information about one person, group, or situation. {{M}}Think of true crime documentaries that exhaustively examine one case from every angle{{/M}} – that's essentially a case study format.

Observational studies involve watching and recording behavior as it naturally occurs. Two key techniques:

Interval Recording (also called time sampling): You divide observation time into chunks and note whether the behavior occurred in each chunk. {{M}}Imagine watching a coworker during a meeting and checking every two minutes whether they're on their phone. You're not counting every single time they look at it, just marking yes/no for each two-minute window.{{/M}} Best for frequent behaviors without clear starts and stops (like "seems distracted" or "participates in discussion").

Event Recording (also called event sampling): You count every single occurrence and note when it starts and stops. {{M}}Like tracking every time a colleague leaves their desk during the workday – "Left at 10:15am, returned 10:23am; left at 11:47am, returned 12:02pm."{{/M}} Best for infrequent behaviors with clear beginnings and endings.

Correlational Research: Finding Relationships

Correlational research examines whether two or more variables are related. The key point: correlation doesn't equal causation. You're measuring things as they exist, not manipulating them.

{{M}}You might find that people who drink more coffee score higher on anxiety measures. But you can't say coffee causes anxiety – maybe anxious people seek out coffee, or maybe stressed jobs lead to both coffee consumption and anxiety.{{/M}}

The data from correlational studies often feeds into regression analysis, letting you predict one variable (criterion/Y variable) from another (predictor/X variable).

Experimental Research: Establishing Cause and Effect

This is where you can actually say "X causes Y" – but only if your design is solid.

True Experimental Design: You randomly assign participants to different groups (different levels of your independent variable). Random assignment is crucial because {{M}}it's like shuffling a deck of cards{{/M}} – it helps ensure groups are similar at the start, so any differences at the end are likely due to your treatment, not pre-existing differences.

Quasi-Experimental Design: You can't randomly assign participants (maybe you're comparing existing groups like "people with PTSD vs. people without PTSD," or you only have one group to work with). Without random assignment, you can't be as confident that your independent variable caused changes in your dependent variable.

Single-Subject Designs: When One Person Is Enough

Single-subject designs are powerful tools for clinical practice. They share key features:

• At least two phases: baseline (A) and treatment (B)
• Multiple measurements during each phase
• You don't start treatment until baseline is stable

{{M}}Think of these like A/B testing that tech companies do, but with one person.{{/M}}

AB Design: The Basic Version

You measure behavior without treatment (baseline), then apply treatment and keep measuring.

The design controls for maturation (gradual changes over time like fatigue or natural development) because those would show up as gradual trends. However, it doesn't control for history – any one-time event that happens to coincide with treatment.

{{M}}Imagine you start meditating and notice your anxiety drops. But did the meditation help, or did you also just finally resolve that conflict with your roommate the same week?{{/M}} That's the history problem.

ABAB Design: More Convincing

Add another baseline phase and another treatment phase (baseline-treatment-baseline-treatment).

{{M}}It's like checking if your wireless earbuds are really causing that connection issue by disconnecting them (first baseline), connecting them (first treatment), disconnecting again (second baseline), and connecting again (second treatment).{{/M}} If the problem consistently appears and disappears with the earbuds, you know they're the cause.

When behavior returns to baseline after treatment withdrawal and improves again when treatment is reintroduced, you have strong evidence the treatment works.

Multiple Baseline Design: No Need to Withdraw Treatment

Instead of withdrawing treatment, you apply it sequentially to different baselines (different behaviors, settings, or people).

Here's how it works: {{M}}Suppose you're helping someone reduce three problematic behaviors: interrupting others, arriving late, and forgetting commitments. You track all three behaviors. After establishing baseline for all three, you apply your intervention to just interrupting while continuing baseline for the other two. Once that improves, you add the intervention for lateness while maintaining it for interrupting and continuing baseline for forgetting. Finally, you apply it to all three.{{/M}}

If each behavior only improves when the intervention is applied to it specifically, you've demonstrated effectiveness without ever having to withdraw treatment (which is both more ethical and more practical).

Group Designs: Between, Within, and Mixed

Between-Subjects Design

Different groups get different treatments. Each person experiences only one condition.

{{M}}Like testing three different study methods by assigning different students to each method{{/M}} – flashcards group, practice-testing group, and re-reading group.

Randomized Controlled Trials (RCTs) are the gold standard here. They randomly assign people to treatment or control groups in controlled conditions. The random assignment increases internal validity (confidence that the treatment caused the effect) but the strict conditions can limit external validity (generalizability to real-world settings).

Within-Subjects Design

Each person experiences all (or multiple) conditions at different times.

{{M}}Instead of having different students try different study methods, you have the same students try all three methods for different exam units.{{/M}} Everyone is their own control group.

Time-series design is essentially a group version of the AB design – you measure everyone repeatedly before and after an intervention.

Mixed Design

You have at least two independent variables: one between-subjects and one within-subjects.

{{M}}You're comparing three workout programs (between-subjects: each person does only one program), but you're measuring fitness weekly for eight weeks (within-subjects: everyone gets measured repeatedly over time).{{/M}}

Program is between-subjects (you do either yoga, running, or weightlifting). Time is within-subjects (everyone gets measured each week).

Factorial Designs: Testing Multiple Variables at Once

When you have two or more independent variables, you have a factorial design. The major advantage: you can examine main effects and interaction effects.

Main effect: The effect of one independent variable by itself. Interaction effect: When the effect of one variable depends on the level of another variable.

{{M}}Here's a real-world scenario: You're testing whether caffeine and sleep affect test performance. You might find a main effect of sleep (more sleep = better scores), a main effect of caffeine (caffeine = better scores), but also an interaction: caffeine barely helps well-rested people but dramatically helps sleep-deprived people.{{/M}} That interaction is crucial information you'd miss with simpler designs.

When there's a significant interaction, be cautious interpreting main effects – the interaction might tell the more important story.

Special Research Approaches

Analogue Research

This involves studying situations that approximate but don't replicate real-life conditions.

{{M}}Like how flight simulators train pilots{{/M}} – not exactly like flying a real plane, but close enough to be useful and much safer for learning.

Common examples: using college students instead of clinical populations, or conducting therapy studies in laboratories instead of actual therapy offices.

Advantage: Better internal validity (more control over variables) Disadvantage: Worse external validity (findings may not generalize)

Developmental Research

These designs study change over time.

Longitudinal: Follow the same people over time. {{M}}Like having yearly check-ins with the same friend group and watching how everyone's careers evolve over a decade.{{/M}}

Cross-sectional: Compare different age groups at one time point. {{M}}Instead of following one group for ten years, you interview 25-year-olds, 35-year-olds, and 45-year-olds all this year.{{/M}}

Cross-sequential: Combine both approaches. {{M}}You interview 30-year-olds, 40-year-olds, and 50-year-olds today, then follow all three groups and interview them again in ten and twenty years.{{/M}}

More expensive than cross-sectional, less than longitudinal. Helps separate age effects from cohort effects.

Sampling: Who's in Your Study?

Probability Sampling (Random Selection)

Everyone in the population has an equal (or known) chance of selection. This helps ensure your sample represents the population.

Simple random sampling: {{M}}Like drawing names from a hat{{/M}} – pure chance determines who's selected.

Systematic random sampling: Select every nth person from a list (every 10th person, every 25th person).

Stratified random sampling: {{M}}Imagine you're selecting participants and want to ensure various age groups are represented. You divide your population into age categories (strata), then randomly select from each category.{{/M}}

Cluster random sampling: {{M}}Instead of randomly selecting individual users from all of Twitter (impossible), you randomly select specific hashtag communities, then sample from within those communities.{{/M}}

Even with random sampling, sampling error can occur – your sample isn't perfectly representative just due to chance, especially with small samples.

Non-Probability Sampling (Non-Random Selection)

Not everyone has an equal chance of selection. This introduces sampling bias (also called selection bias or systematic error).

Convenience sampling: You use whoever's easily available. {{M}}Like surveying people in your apartment building{{/M}} – easy, but hardly representative.

Voluntary response sampling: People volunteer to participate. {{M}}Think of online polls where people choose to respond{{/M}} – the most motivated (or most extreme) opinions are overrepresented.

Purposive/judgmental sampling: You deliberately select people who fit your needs. {{M}}If you're studying therapist burnout, you intentionally recruit therapists, not random healthcare workers.{{/M}}

Snowball sampling: You ask participants to recommend others. {{M}}Like when you ask a freelancer you hired for recommendations of other freelancers in their network.{{/M}} Especially useful for hard-to-reach populations.

Community-Based Participatory Research (CBPR)

CBPR is action research that aims to improve social problems by involving community members as equal partners throughout the research process.

Instead of researchers studying a community from outside, community members help design the study, collect data, interpret findings, and implement changes.

Key principles include:

• Recognize community identity and strengths
• Share power equally among all partners
• Focus on problems the community actually cares about
• Commit to long-term sustainability
• Ensure all partners learn from each other

{{M}}Rather than a university researcher studying homelessness by interviewing homeless individuals, CBPR would involve homeless individuals as research partners who help shape research questions, interpret findings, and develop solutions.{{/M}}

Common Misconceptions to Avoid

1. "Quasi-experimental means low quality": Not true. Sometimes random assignment is impossible or unethical. Quasi-experimental designs can be rigorous and valuable.

2. "Single-subject designs aren't real research": Wrong. They provide strong evidence of treatment effectiveness and are incredibly practical for clinical work.

3. "You need huge samples for good research": Sample size depends on your design and goals. Single-subject designs work with one person. Small qualitative studies can provide deep insights.

4. "Qualitative research is just opinions": No. Rigorous qualitative research follows systematic methods and uses strategies like triangulation to ensure credibility.

5. "Correlational research is useless because it doesn't show causation": Correlational research is valuable for prediction, identifying relationships worth studying experimentally, and studying variables you can't ethically manipulate.

Memory Aids for the EPPP

For single-subject designs: A = Away from treatment, B = Bringing in treatment. More phases = more confidence.

For developmental designs:

• Longitudinal = Long-term with same people
• Cross-sectional = Cross-section of different ages now
• Cross-sequential = Crosses both approaches

For sampling: If it has "random" in the name, it's probability sampling. If not, it's non-probability.

For factorial designs: "Main" effects are simple (one variable). "Interaction" effects are complex (variables depend on each other).

For observational recording:

• Interval = In chunks of time
• Event = Every occurrence

Key Takeaways

• Qualitative research provides rich description and understanding; quantitative research provides measurable data
• Triangulation strengthens research credibility by approaching questions from multiple angles
• True experimental designs allow causal claims through random assignment; quasi-experimental designs cannot
• Single-subject designs (AB, ABAB, multiple baseline) are powerful for evaluating individual treatment effects
• Between-subjects = different people in different conditions; within-subjects = same people in all conditions
• Factorial designs reveal both main effects and interactions between variables
• RCTs maximize internal validity but may sacrifice external validity
• Longitudinal research tracks change in the same people; cross-sectional compares different age groups at once
• Probability sampling allows generalization; non-probability sampling is vulnerable to bias but useful for exploration
• CBPR involves community members as equal partners throughout research

Remember: Research design isn't just academic busy-work. Every time you read a study claiming a treatment works, evaluate if someone's progress is real, or track your own clinical outcomes, you're using these concepts. Master them, and you'll be a more critical consumer and producer of psychological knowledge.