Psychology Research Methods

The Scientific Method in Psychology

Steps of Scientific Investigation

1. Observation: Identify phenomenon of interest
2. Question: Formulate research question
3. Literature Review: Examine existing research
4. Hypothesis: Develop testable prediction
5. Method Design: Plan data collection
6. Data Collection: Gather information systematically
7. Analysis: Examine and interpret data
8. Conclusion: Accept or reject hypothesis
9. Replication: Repeat for verification
10. Theory Building: Integrate findings

Key Scientific Principles

• Empiricism: Knowledge through observation
• Objectivity: Minimize bias and subjectivity
• Replicability: Results must be reproducible
• Falsifiability: Hypotheses must be testable
• Parsimony: Prefer simpler explanations
• Systematic: Organized and methodical approach

Types of Research Questions

• Descriptive: What is happening?
• Correlational: How are variables related?
• Causal: Does X cause Y?
• Comparative: How do groups differ?
• Developmental: How do things change over time?

Research Designs

Between-Subjects Design

Different participants in each condition:

• Advantages: No order effects, shorter sessions
• Disadvantages: Requires more participants, individual differences
• Control: Random assignment essential
• Analysis: Independent samples t-test, ANOVA

Within-Subjects Design

Same participants in all conditions:

• Advantages: Controls individual differences, fewer participants
• Disadvantages: Order effects, carryover effects
• Control: Counterbalancing required
• Analysis: Paired samples t-test, repeated measures ANOVA

Mixed Design

Combination of between and within factors:

• Example: Treatment type (between) × time (within)
• Advantages: Flexible, comprehensive
• Complexity: More complex analysis required
• Analysis: Mixed ANOVA

Factorial Design

Multiple independent variables:

• Main Effects: Effect of each variable independently
• Interactions: Combined effects of variables
• Notation: 2×2, 2×3, etc.
• Power: Efficient testing of multiple hypotheses

Experimental Methods

True Experiments

Gold standard for establishing causation:

• Manipulation: Independent variable controlled
• Random Assignment: Participants randomly allocated
• Control: Confounding variables minimized
• Measurement: Dependent variable observed

Variables in Experiments

• Independent Variable (IV): Manipulated factor
• Dependent Variable (DV): Measured outcome
• Confounding Variables: Unwanted influences
• Moderator Variables: Affect relationship strength
• Mediator Variables: Explain relationship mechanism

Experimental Control

• Random Assignment: Equalizes groups
• Control Groups: Baseline comparison
• Placebos: Control for expectations
• Blinding: Single or double-blind procedures
• Standardization: Consistent procedures
• Counterbalancing: Controls order effects

Quasi-Experiments

When random assignment isn't possible:

• Natural Groups: Pre-existing differences
• Examples: Age, gender, clinical diagnosis
• Limitation: Cannot infer causation definitively
• Designs: Nonequivalent groups, interrupted time series

Field Experiments

Experiments in natural settings:

• Ecological Validity: Real-world relevance
• Less Control: More confounding variables
• Examples: Bystander intervention studies
• Ethics: Informed consent challenges

Descriptive Research Methods

Observational Studies

Naturalistic Observation

• Setting: Natural environment
• Intervention: No manipulation
• Advantages: High ecological validity
• Challenges: Observer effects, lack of control
• Example: Playground aggression studies

Structured Observation

• Setting: Controlled environment
• Standardization: Specific situations created
• Advantages: More control, replicable
• Example: Strange Situation attachment study

Participant Observation

• Role: Researcher as participant
• Insight: Insider perspective
• Challenges: Objectivity, dual roles
• Example: Ethnographic studies

Survey Research

• Purpose: Gather self-report data
• Formats: Questionnaires, interviews, online
• Scales: Likert, semantic differential, ranking
• Advantages: Efficient, large samples
• Limitations: Response bias, social desirability

Case Studies

• Focus: In-depth individual or group analysis
• Data: Multiple sources and methods
• Advantages: Rich detail, rare phenomena
• Limitations: Generalizability issues
• Famous Examples: Phineas Gage, H.M.

Correlational Studies

• Purpose: Examine relationships between variables
• Correlation Coefficient: -1 to +1
• Direction: Positive or negative
• Strength: Weak, moderate, strong
• Limitation: Correlation ≠ causation

Longitudinal Studies

• Design: Same participants over time
• Advantages: Developmental changes, within-person
• Challenges: Attrition, time, cost
• Example: Grant Study of Adult Development

Cross-Sectional Studies

• Design: Different groups at one time
• Advantages: Quick, economical
• Limitation: Cohort effects
• Use: Age differences research

Data Collection Methods

Self-Report Measures

Questionnaires

• Closed-ended: Multiple choice, rating scales
• Open-ended: Written responses
• Design: Clear, unbiased questions
• Administration: Paper, online, mobile

Interviews

• Structured: Standardized questions
• Semi-structured: Flexible follow-up
• Unstructured: Open conversation
• Skills: Rapport, probing, recording

Behavioral Measures

• Direct Observation: Recording specific behaviors
• Coding Systems: Systematic categorization
• Frequency: Count occurrences
• Duration: Time measurements
• Intensity: Severity ratings

Physiological Measures

• Brain Imaging: fMRI, EEG, PET
• Autonomic: Heart rate, skin conductance
• Hormonal: Cortisol, testosterone
• Eye Tracking: Attention and processing
• Reaction Time: Processing speed

Psychological Tests

• Intelligence: IQ tests (WAIS, Stanford-Binet)
• Personality: Big Five, MMPI
• Clinical: Depression scales, anxiety inventories
• Neuropsychological: Memory, attention tests
• Projective: Rorschach, TAT

Archival Research

• Sources: Records, databases, documents
• Advantages: No participant burden, historical data
• Challenges: Data quality, missing information
• Examples: Census data, medical records

Sampling Techniques

Probability Sampling

Simple Random Sampling

• Every member has equal chance
• Random number generation
• Unbiased but may not represent subgroups

Stratified Random Sampling

• Population divided into strata
• Random sampling within strata
• Ensures subgroup representation

Cluster Sampling

• Groups selected randomly
• All members within clusters studied
• Efficient for geographic distribution

Systematic Sampling

• Every nth member selected
• Starting point random
• Simple but may have periodicity issues

Non-Probability Sampling

Convenience Sampling

• Easily accessible participants
• Quick and economical
• Limited generalizability
• Common in pilot studies

Purposive Sampling

• Specific characteristics sought
• Expert judgment used
• Good for specific populations

Snowball Sampling

• Participants recruit others
• Useful for hidden populations
• Network bias possible

Quota Sampling

• Predetermined quotas filled
• Non-random within quotas
• Ensures demographic representation

Sample Size Determination

• Power Analysis: Statistical power calculation
• Effect Size: Expected magnitude of effect
• Alpha Level: Significance threshold (usually .05)
• Power: Probability of detecting effect (usually .80)
• Resources: G*Power software

Statistical Analysis

Descriptive Statistics

Central Tendency

• Mean: Average score
• Median: Middle value
• Mode: Most frequent value

Variability

• Range: Maximum - minimum
• Variance: Average squared deviation
• Standard Deviation: Square root of variance
• Interquartile Range: 75th - 25th percentile

Distribution Shape

• Skewness: Asymmetry of distribution
• Kurtosis: Peakedness of distribution
• Normal Distribution: Bell curve

Inferential Statistics

Hypothesis Testing

• Null Hypothesis (H₀): No effect/difference
• Alternative Hypothesis (H₁): Effect exists
• Type I Error: False positive (α)
• Type II Error: False negative (β)
• p-value: Probability of results if H₀ true

Common Tests

• t-tests: Compare two means
• ANOVA: Compare multiple means
• Chi-square: Categorical associations
• Correlation: Relationship strength
• Regression: Prediction models

Effect Size

• Cohen's d: Standardized mean difference
• r: Correlation coefficient
• η²: Eta squared for ANOVA
• Interpretation: Small, medium, large

Confidence Intervals

• Definition: Range of plausible values
• Common: 95% CI
• Interpretation: Precision of estimate
• Relationship: To hypothesis testing

Qualitative Research Methods

Approaches

Phenomenology

• Lived experience exploration
• Essence of phenomena
• In-depth interviews
• Bracketing researcher assumptions

Grounded Theory

• Theory development from data
• Constant comparison method
• Theoretical sampling
• Saturation point

Ethnography

• Cultural group study
• Participant observation
• Extended fieldwork
• Thick description

Case Study

• Bounded system investigation
• Multiple data sources
• Within-case and cross-case analysis
• Instrumental or intrinsic

Data Analysis

Thematic Analysis

• Identifying patterns/themes
• Coding process
• Theme development
• Interpretation

Content Analysis

• Systematic categorization
• Quantifying qualitative data
• Manifest and latent content
• Inter-rater reliability

Narrative Analysis

• Story structure examination
• Temporal sequencing
• Personal meaning-making
• Identity construction

Quality Criteria

• Credibility: Truthfulness of findings
• Transferability: Applicability to other contexts
• Dependability: Consistency of findings
• Confirmability: Neutrality of findings
• Reflexivity: Researcher self-awareness

Research Ethics

Ethical Principles

• Beneficence: Maximize benefits
• Nonmaleficence: Do no harm
• Autonomy: Respect for persons
• Justice: Fair distribution
• Fidelity: Trust and integrity

Informed Consent

• Information: Study purpose, procedures, risks
• Comprehension: Understanding verified
• Voluntariness: Free choice to participate
• Documentation: Written consent forms
• Ongoing: Right to withdraw

Special Populations

• Children: Assent and parental consent
• Vulnerable Groups: Extra protections
• Deception: Justification and debriefing
• Anonymous: No identifying information
• Confidential: Protected identity

Institutional Review Board (IRB)

• Purpose: Protect human subjects
• Review Types: Exempt, expedited, full
• Submission: Protocol, consent forms
• Monitoring: Ongoing oversight
• Reporting: Adverse events

Data Management

• Storage: Secure and encrypted
• Access: Limited to research team
• Retention: Time limits specified
• Destruction: Secure disposal
• Sharing: De-identified datasets

Reporting Research

APA Format Structure

• Title Page: Title, authors, affiliations
• Abstract: 150-250 word summary
• Introduction: Background, rationale, hypotheses
• Method: Participants, materials, procedure
• Results: Statistical analyses, findings
• Discussion: Interpretation, implications
• References: APA style citations

Writing Guidelines

• Clarity: Clear, concise language
• Objectivity: Avoid bias
• Precision: Specific details
• Organization: Logical flow
• Voice: Active preferred
• Tense: Past for method/results

Tables and Figures

• Purpose: Supplement text
• Clarity: Stand-alone understanding
• Format: APA guidelines
• Numbering: Sequential
• Captions: Descriptive titles

Statistical Reporting

• Test Statistics: Include all values
• Effect Sizes: Always report
• Confidence Intervals: When appropriate
• Format: F(2, 147) = 8.92, p < .001, η² = .11
• Rounding: Two decimal places