2.2 Psych: Approaches to Research

Overview of Research Methods in Psychology

• Purpose: understand, describe, and explain behavior and underlying cognitive/biological processes.
• Approaches vary: observational techniques vs researcher-participant interactions (surveys, interviews), up to well-controlled experiments.
• Each method has strengths, weaknesses, and is suitable for certain questions.
• Key theme: correlational methods show relationships but do not establish causation; experiments provide control to infer causality but may sacrifice ecological validity and raise ethical concerns.
• Examples illustrate real-world trade-offs between depth vs breadth, generalizability vs detail, and practicality vs control.

Clinical or Case Studies

• Definition: in-depth study of one person or a small number of individuals.
• Example (historical): Krista and Tatiana Hogan, Canadian conjoined twins connected at the head via the thalamus (a major sensory relay center).
  • Thalamic connection could hypothetically allow shared sensory experiences; e.g., Krista’s stimulus could influence Tatiana’s responses.
  • Research goal: gain deep, long-term insight into brain function and sensory processing in rare cases.
  • Ongoing observation: scientists may follow such rare cases for many years with family consent (Dominus, 2011; Egnor, 2017).
• Benefits: rich, detailed information; deep understanding of the particular phenomenon.
• Major weakness: limited generalizability to the broader population because subjects are atypical.
• Generalization: difficulty applying findings to “the average person.”
• Why used despite weaknesses: when subjects have rare characteristics, case studies can illuminate aspects of behavior or brain function that other methods cannot.

Naturalistic Observation

• Definition: observe behavior in the natural setting, with minimal interference.
• Key challenge: people may alter behavior when they know they are being watched (demand characteristics, reactivity).
• Unobtrusiveness: researchers should blend into the environment to maximize natural behavior.
  • Example: Suzanne Fanger and colleagues observed preschoolers on a playground using wireless microphones tucked on some children; the setting was a laboratory preschool where observers were familiar but unobtrusive (Fanger, Frankel, & Hazen, 2012).
• Purposeful unobtrusiveness improves ecological validity (real-world applicability).
• Animal naturalistic observation: field studies of animals (e.g., ground squirrels, gorillas, chimpanzees).
  • Jane Goodall’s decades-long chimpanzee observations illustrate non-human naturalistic work.
  • Controversy: naming individual animals vs. numbering for objectivity.
• Strength: high ecological validity; data reflect genuine behavior in real contexts.
• Major weaknesses: limited control, time-intensive, expensive, chance of missing relevant behavior, luck can influence findings.
• Real-world concern: “reality” programs or camera crews disrupt natural behavior, reducing realism.
• Concepts to know:
  • Ecological validity: realism and applicability of findings to real-world settings.
  • Inconspicuous observation: critical for minimizing behavioral change due to observation.
  • Time/money/luck factors: significant investments needed for naturalistic studies.
  • Structured vs. unstructured observation: some studies use set tasks or phases to guide observation (e.g., Strange Situation).

Observational Methods: Bias and Reliability

• Observer bias: researchers’ expectations may unconsciously influence what they record.
• Mitigation strategies:
  • Clear criteria for behaviors recorded.
  • Classification rules for behaviors.
  • Inter-rater reliability: multiple observers record the same event to assess consistency.
• Importance: helps ensure observations are objective and replicable.

Structured Observation and the Strange Situation

• Structured observations involve watching behavior during specific tasks or phases.
• Example: Mary Ainsworth’s Strange Situation to assess infant-caregiver attachment.
  • Phases include introduction of a stranger, caregiver leaving, and caregiver returning.
  • The infant’s behavior on reunion with the caregiver is most informative for attachment style.
• Purpose: standardize observations to enable comparison across cases.

Surveys

• Definition: lists of questions to gather data from participants.

• Modes: paper-and-pencil, electronic, or verbal administration.

• Strengths: quick, scalable to large samples; facilitates generalization to a population if the sample is representative.

• Weaknesses: self-report biases (social desirability, recall errors); participants may lie, forget, or misrepresent themselves.

• Key concepts:

  • Sample vs population: sample is a subset from the population; researchers generalize findings from the sample to the population.
  • Measures of central tendency: used to summarize survey data.

• Measures of central tendency (three):

  • Mode: most frequently occurring value.
  • Median: middle value when data are ordered.
  • Mean: arithmetic average, defined as

  $\bar{x} = \frac{1}{n} \, \sum<em>{i=1}^{n} x</em>i.$

• Strength comparison with case studies: larger samples improve generalizability; deeper information per person is typically less in surveys.

• Example study: Jenkins, Ruppel, Kizer, Yehl, and Griffin (2012) examined Arab-American attitudes post-9/11.

  • Design: 140 participants, 10 questions including direct prejudice items and indirect social-interaction questions.
  • Findings: participants concealed prejudicial attitudes in direct questions but showed reduced willingness to interact with Arab-Americans in hypothetical scenarios, indicating social desirability effects.

Archival Research

• Definition: use existing records or data sets to answer research questions without direct participant interaction.
• Characteristics:
  • Low time and monetary costs because data already exist.
  • No control over how data were originally collected.
  • Potential inconsistencies across sources, making comparisons challenging.
• Practical use: study historical trends, outcomes, or risk factors using past records.
• Example: data from academic records over the past decade to identify factors related to degree completion and risk of struggling students; potential bias in data collection and social factors noted (Jenkins et al., 2012).

Longitudinal vs Cross-Sectional Research

• Longitudinal research:
  • Definition: data gathered repeatedly from the same participants over an extended period.
  • Example: CPS-3 (Cancer Prevention Study-3) following hundreds of thousands of participants over 20 years with periodic surveys to track cancer development and related factors.
  • Benefits: reduces cohort effects, allows study of changes within individuals over time; findings can be generalized to the population if the sample remains representative.
  • Limitations: enormous time and financial commitments; high attrition risk as participants move, change names, or drop out; ongoing monitoring needed to maintain representative samples.
  • Real-world impact: longitudinal studies helped establish links between smoking and increased cancer risk (historical contributions from earlier CPS studies; American Cancer Society).
• Cross-sectional research:
  • Definition: compare different age or demographic groups at the same point in time.
  • Example: compare dietary habits across 20-, 30-, and 40-year-olds to infer age-related differences.
  • Benefits: quicker and less costly than longitudinal designs.
  • Limitations: cohort effects—differences between generations that are not due to age per se but to social/cultural experiences—can confound interpretations about aging.
• Important distinction: longitudinal designs track changes within the same individuals; cross-sectional designs compare different groups at one time.

Correlation vs Causation

• Core idea: many research methods yield correlational data showing relationships between variables, not cause-and-effect.
• Correlational finding: can identify whether two variables move together (positive/negative relationships) but cannot determine which variable causes the other.
• Causation requires experimental control:
  • Random assignment, manipulation of the independent variable, control of confounding variables.
  • Note: experiments may be conducted in artificial settings, which can threaten external validity (generalizability to real-world settings).
  • Ethical constraints: some questions cannot be pursued experimentally due to harm, privacy, or consent concerns.
• Summary takeaway: correlation ≠ causation; experiments are needed to infer causality, but must consider ecological and ethical validity.

Real-World Connections and Implications

• Practical implications:
  • Choice of method depends on research question, ethical considerations, and feasibility.
  • Balancing depth (case studies, naturalistic observation) with generalizability (surveys, archival, longitudinal) is crucial.
• Ethical considerations:
  • Some questions cannot be pursued via experimentation due to potential harm.
  • Naturalistic observation requires minimizing intrusion to avoid altering behavior; observer presence must be managed to protect participant welfare and data integrity.
• Philosophical/interpretive notes:
  • Distinguishing between description (what is) and explanation (why it happens) guides method selection.
  • Some debates concern objectivity vs. empathy in studying animals and humans (e.g., naming animals vs. numbering for scientific detachment).
• Summary of major trade-offs:
  • Depth vs breadth: Case studies offer depth; surveys offer breadth.
  • Control vs realism: Experiments offer control; naturalistic and observational methods offer realism but less control.
  • Speed vs time investment: Cross-sectional is quick; longitudinal requires long-term commitment.
  • Generalizability vs specificity: Archival and survey data support generalizability; case studies offer unique insights with limited generalization.

Key Terms and Concepts to Remember

• Ecological validity: the extent to which findings generalize to real-world settings.
• Generalizability: the extent to which results from a sample apply to the larger population.
• Observer bias: when observers’ expectations influence their records; mitigated by clear criteria and multiple observers.
• Inter-rater reliability: consistency of observations across different observers.
• Cohort effects: differences between groups due to the era or generation they grew up in, not age per se.
• Attrition: loss of participants over time in a longitudinal study; leads to potential bias if dropouts are systematic.
• Central tendency measures (for survey data):
  • Mode: most frequent value.
  • Median: middle value in ordered data.
  • Mean: arithmetic average, ar{x} = rac{1}{n}
    \sum{i=1}^{n} xi.

Figures and Examples Referenced (Contextual Mentions)

• Hand-washing scenario: classic demonstration of reactivity and social desirability biases.

• Police car example: illustrates how awareness of being watched can change behavior in everyday tasks (driving behavior).

• Strange Situation: foundational observational protocol for assessing infant attachment styles.

• Jane Goodall’s chimpanzee research: example of long-term naturalistic observation in animals.

• CPS-3 study: large-scale longitudinal effort to identify cancer risk factors, illustrating the scale and potential long-term impact of longitudinal research.

• Jenkins et al. (2012): example of archival survey data revealing differences between direct prejudice responses and social-interaction willingness, highlighting self-report biases.

• Key takeaway: Different methods illuminate different aspects of behavior and cognition. A robust research program often triangulates findings across methods to build a coherent understanding while acknowledging limitations, biases, and ethical considerations.