Types of Research Methods

Why This Matters

Research methods are the backbone of everything you'll study in AP Psychology—every claim about memory, development, emotion, or social behavior rests on how psychologists gathered their evidence. When you encounter a study about infant depth perception (like the visual cliff) or implicit attitudes (like the IAT), you're being tested not just on what researchers found, but on why they chose that particular method and what conclusions it actually supports. Understanding the difference between correlation and causation, or why some studies use random assignment while others can't, will show up repeatedly across units.

The AP exam loves to test your ability to evaluate research quality and identify methodological limitations. Can this study establish cause and effect? What's a potential confound? Why might results not generalize? These aren't just abstract questions—they're the lens through which psychologists interpret findings on everything from operant conditioning to stereotype formation. Don't just memorize method names; know what each method can and cannot tell us about human behavior.

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Establishing Cause and Effect

These methods involve manipulation and control, allowing researchers to determine whether one variable actually causes changes in another. The key mechanism is isolating variables so that alternative explanations can be ruled out.

Experimental Method

• Random assignment and manipulation of variables—the only method that can establish true cause-and-effect relationships between an independent variable (IV) and dependent variable (DV)
• Control groups provide a baseline for comparison, helping researchers isolate the effect of the IV while minimizing confounding variables
• Replication strengthens findings; if results can be reproduced across different samples and settings, confidence in the causal relationship increases

Quasi-Experimental Design

• Lacks random assignment—researchers compare pre-existing groups (e.g., smokers vs. non-smokers, different age cohorts) rather than randomly assigning participants
• Useful when true experiments aren't feasible or ethical, such as studying the effects of trauma or comparing developmental stages
• More vulnerable to confounds because group differences may exist before the study begins; results suggest but don't prove causation

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Measuring Relationships Without Manipulation

These methods examine associations between variables but cannot determine causation. The core principle is that observing a relationship doesn't tell us why it exists or which variable (if either) influences the other.

Correlational Method

• Correlation coefficient (r) ranges from $-1$ to $+1$, indicating the strength and direction of a relationship—positive means variables move together, negative means they move in opposite directions
• Does not establish causation; a correlation between sleep and test scores doesn't tell us if poor sleep causes lower scores, if stress causes both, or if the relationship is coincidental
• Third variables (confounds) may explain observed relationships, which is why correlational findings require cautious interpretation

Cross-Sectional Studies

• Compares different groups at a single point in time—for example, testing memory in 20-year-olds, 40-year-olds, and 60-year-olds simultaneously
• Efficient and cost-effective compared to tracking the same people over decades, making it practical for studying age differences
• Cohort effects are a major limitation; differences between age groups may reflect generational experiences rather than actual developmental changes

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Tracking Change Over Time

These methods follow the same participants across extended periods, allowing researchers to observe developmental trajectories and long-term effects. The key advantage is capturing within-person change rather than just between-group differences.

Longitudinal Studies

• Repeated observations of the same participants over months, years, or even decades—essential for studying development, as seen in research on critical periods for language acquisition
• Can identify causal patterns by showing that Variable A precedes and predicts Variable B over time
• Participant attrition (dropout) is a major threat; people who stay in the study may differ systematically from those who leave, potentially biasing results

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Observing Behavior in Context

These methods prioritize ecological validity—studying behavior as it naturally occurs rather than in artificial laboratory settings. The tradeoff is that reduced control makes it harder to isolate specific causes.

Naturalistic Observation

• Observing subjects in their natural environment without interference—researchers watch and record behavior as it spontaneously occurs (e.g., studying children's play on a playground)
• High ecological validity means findings reflect real-world behavior, but lack of control makes cause-and-effect conclusions impossible
• Observer bias can distort findings if researchers interpret ambiguous behaviors in ways that confirm their expectations; ethical concerns about privacy must also be addressed

Case Studies

• In-depth analysis of a single individual, group, or event—critical for studying rare phenomena like the case of Genie (language critical periods) or patients with unique brain injuries
• Rich qualitative data can generate hypotheses and reveal complexities that large-scale studies miss
• Limited generalizability—findings from one unusual case may not apply to broader populations, and researcher subjectivity can influence interpretation

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Gathering Self-Report Data

This method relies on participants reporting their own thoughts, feelings, and behaviors. The key challenge is that what people say may not match what they actually think or do.

Surveys and Questionnaires

• Collect data from large samples efficiently—can reach thousands of respondents through online, paper, or interview formats
• Useful for measuring attitudes, beliefs, and self-reported behaviors that can't be directly observed, such as implicit attitudes measured by tools like the IAT
• Response biases threaten validity—social desirability bias (answering to look good) and acquiescence bias (tendency to agree) can distort results; careful question design is essential

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Isolating Nature vs. Nurture

This method specifically addresses the heritability question—how much of a trait is attributable to genetics versus environment. By comparing individuals with different degrees of genetic similarity raised in similar or different environments, researchers can estimate genetic influence.

Twin Studies

• Compares identical (monozygotic) twins sharing 100% of genes with fraternal (dizygotic) twins sharing ~50%—if identical twins are more similar on a trait, genetics likely plays a role
• Helps disentangle heredity from environment, especially when studying twins raised apart; foundational for understanding heritability of traits like intelligence and psychological disorders
• Assumptions can be problematic—identical twins may be treated more similarly than fraternal twins, inflating apparent genetic effects; ethical issues around consent and privacy also apply

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Synthesizing Existing Research

This method doesn't collect new data but instead combines and analyzes findings from multiple studies. The power comes from aggregating evidence to identify patterns that single studies might miss.

Meta-Analysis

• Statistically combines results from many studies on the same question, increasing power to detect effects and providing a more reliable estimate of true effect sizes
• Resolves conflicting findings by weighing studies based on sample size and methodology—essential for evidence-based practice in clinical psychology
• Quality depends on included studies—"garbage in, garbage out" applies; careful selection criteria and assessment of publication bias are critical

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Quick Reference Table

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Self-Check Questions

1. A researcher wants to determine whether sleep deprivation causes decreased memory performance. Which method should they use, and what feature of that method makes causal conclusions possible?

2. Compare and contrast longitudinal and cross-sectional studies: What question can each answer, and what is the primary limitation of each approach?

3. A study finds a strong positive correlation ($r = 0.72$) between hours of social media use and anxiety levels in teenagers. Why can't researchers conclude that social media causes anxiety? Name two alternative explanations.

4. Which two methods would be most appropriate for studying a rare condition like prosopagnosia (face blindness), and what are the tradeoffs of each?

5. An FRQ asks you to evaluate a study comparing stress levels in nurses versus accountants. Is this an experimental or quasi-experimental design? What confounding variables might explain any differences found?