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Research Methodology Notes - Ch. 1

The Research Process

  • Psychologist's Research Steps: Aim/hypothesis, method selection, variable definition/control, ethics, participants, data analysis/conclusions, evaluation.
  • Why Research? To test hypotheses and find evidence.

Research Methods Overview

  • Three Main Types:
    1. Experimental: Lab, Field, Quasi/Natural. Can show cause-and-effect.
    2. Non-experimental: Self-reports, observations, case studies, correlations. Cannot show cause-and-effect. Can be used alone or with experimental for extra info.
    3. Longitudinal: Can be experimental or non-experimental; lasts longer, tracks changes over time.

EXPERIMENTAL RESEARCH METHODS

  • Goal: Determine if an Independent Variable (IV) causes changes in a Dependent Variable (DV).
  • Method: Create 2+ conditions (e.g., low vs. high background light) and measure effects on behavior.
  • Causality: Requires controlling extraneous variables.
  • Experimental Design: Compares experimental conditions (groups receiving IV) to a control condition (no IV).
    • Experimental groups: Receive IV.
    • Control group: Comparison, no IV.

1. TYPES OF EXPERIMENTAL RESEARCH METHODS

  • All aim to show cause-and-effect.

    A. Laboratory Experiments

    • IV impacts DV. Participants assigned to design (independent or repeated measures).
    • Setting: Artificial, highly controlled lab.
    • Pros: Fewer extraneous variables, high standardization, easy replication.
    • Cons: Lower ecological validity/mundane realism, researcher bias risk.

    B. Field Experiments

    • Setting: Participant’s natural environment.
    • Pros: Higher ecological validity, lower demand characteristics, natural behavior.
    • Cons: More uncontrolled variables, harder to replicate/check reliability, ethical issues (consent, deception).

    C. Quasi/Natural Experiments

    • Uses naturally occurring IVs (e.g., gender, age).
    • No random assignment; grouping is natural.
    • Causality: Harder to establish.
    • Strengths: Real-world relevance.
    • Weaknesses: No random assignment, participant variables, hard to infer causality.

TYPES OF NON-EXPERIMENTAL RESEARCH METHODS

  • Cannot show causality, but describe phenomena or reveal relationships.

    A. Self-Reports (Questionnaires and Interviews)

    • Questionnaires: Written. Open/closed questions (Likert, rating scales).
    • Pros: Quick, efficient, potentially truthful.
    • Cons: Social desirability, memory issues.
    • Interviews: Spoken. Structured, semi-structured, unstructured.
    • Pros: Reveal reasons, in-depth insight.
    • Cons: Interviewer bias, social desirability, memory issues.

    B. Observations

    • Directly observing behavior. Overt/covert, participant/non-participant, structured/unstructured, naturalistic/controlled.
    • Measurements: Time sampling (fixed intervals) or event sampling (every occurrence).
    • Strengths: Captures actual behavior, high ecological validity (natural).
    • Weaknesses: Observer bias, subjectivity, time-consuming.

    C. Case Studies

    • In-depth analysis of individuals/small groups using various methods (interviews, questionnaires, observations).
    • Usually longitudinal; no variable manipulation.
    • Strengths: Rich, detailed data; ecological validity.
    • Weaknesses: Hard to generalize, observer bias, low reliability, hard to replicate.

    D. Correlations

    • Explore relationships between variables, not causation.
    • Types: Positive (direct), negative (inverse), zero/no.
    • Strengths: Identify relationships for further study, fewer ethical concerns.
    • Weaknesses: Cannot infer causality, mainly quantitative data (unless combined). Correlation e causation.

LONGITUDINAL RESEARCH

  • Follows one group over time, assessing variables at intervals.
  • Can be experimental or non-experimental.
  • Advantages: Tracks individual changes over time (self-comparison), controls for situational variables.
  • Disadvantages: Generalizability issues due to attrition (dropouts), researchers/measurements may change (outdated), ongoing consent needed.

EXPERIMENTAL DESIGN (RESEARCH DESIGN)

  • Only for experiments; how subjects are allocated to IV levels/groups. A. Independent measures (between-subjects)
    • Different subjects for each IV level; compare data between groups.
    • Random assignment or matching to control participant variables.
    • Strengths: Lower demand characteristics, fewer carry-over effects.
    • Weaknesses: Participant variables, more participants needed.
    • Matching: (Matched Pairs) reduces participant variables.
      B. Repeated measures (within-subjects)
    • Same participants in all IV levels; compare data within individuals.
    • Strengths: Each person is own baseline, reduces participant variables, fewer participants needed.
    • Weaknesses: Order effects (practice, fatigue), demand characteristics.
    • Control: Counterbalancing and randomization for order effects.

RESEARCH METHODS COMPARISON

  • Experimental: Show cause-and-effect (Lab, Field, Quasi/Natural). Need an experimental design.
  • Non-experimental: No causality (Self-reports, Observations, Case Studies, Correlations). No fixed research design.

VARIABLES AND OPERATIONALIZATION

  • DV (Dependent Variable): What is measured.
  • IV (Independent Variable): What the researcher manipulates to create groups/conditions.
  • Ask: DV = what is measured? IV = how are groups different?
  • Operationalizing: Turning abstract concepts (e.g., helpfulness) into measurable indicators.
  • DV on Y-axis for graphs.

STANDARDIZATION, CONTROLS, AND EXTRANEOUS VARIABLES

  • Why control? Ensure only IV affects DV; strengthens causal inference.
  • Standardizing procedures: All participants follow same sequence, instructions, materials, location.
    • Benefits: Easier replication, reliability, increases validity.
  • Controls vs. control conditions:
    • Controls: Procedures minimizing extraneous variables.
    • Control conditions: Baseline for comparison.
  • Extraneous vs. confounding variables:
    • Extraneous: Other variables influencing DV (threats to validity).
    • Confounding: Extraneous variables with a systematic effect, distorting IV-DV relationship.
  • Pilot studies: Small pre-studies to identify variables needing control.
  • Types of extraneous variables:
    1) Situational: Environment (temperature, noise).
    2) Participant: Traits (intelligence, mood, sleep).
    3) Order effects: (In repeated measures) practice, boredom, fatigue.
    4) Experimenter effect: Bias, expectations of researcher.
    5) Demand characteristics: Participants guess study aim, alter behavior.
  • Controlling extraneous variables: Counterbalancing, random allocation, matching, standardized procedures, double-blind designs.
  • Double-blind tests: Neither participant nor researcher knows condition.

AIMS AND HYPOTHESES

  • Aim: Study purpose, stated before data collection.
  • Hypotheses: Testable, falsifiable, concise statements with IV and DV. "If (IV manipulation) then (DV outcome)."
  • A. Alternative (H1) hypotheses:
    • Directional (one-tailed): Predicts specific direction (e.g., increase/decrease).
    • Non-directional (two-tailed): Predicts a difference, but not direction.
  • B. Null hypothesis (H0): Predicts no effect/difference; any difference is due to chance. Aim is to reject H0. Example: "There will be no difference between groups."
  • Theory vs. Hypothesis:
    • Hypothesis: Testable prediction for specific study.
    • Theory: Well-substantiated explanation for verified facts, supported by evidence.

SAMPLING METHODS

  • Sample: Smaller group representing wider population.

  • Population/Target Population: Larger group of interest.

  • Purpose: Recruit representative sample for generalizability.

    A. Opportunity sampling: Use readily available people.

    • Strengths: Quick, large numbers.
    • Weaknesses: May not be representative.
      B. Volunteer (self-selecting) sampling: Participants volunteer via ads.
    • Strengths: Lower drop-out rates.
    • Weaknesses: Not representative (self-selection bias).
      C. Random sampling: Every population member has equal chance of selection (gold standard).
    • Strengths: Higher representativeness.
    • Weaknesses: Difficult to obtain participation/reach individuals.

TYPES OF DATA

  • Data: Results collected.
    1. Quantitative data (QT): Numerical measurements.
      • Strengths: Comparison, statistics, objective.
      • Weaknesses: Oversimplifies behavior, limited to numbers.
    2. Qualitative data (QL): Descriptive, word-based.
      • Strengths: Captures explanations, meanings.
      • Weaknesses: Subjective interpretation, researcher bias.

DATA ANALYSIS – STATISTICS

  • Normal distribution: Symmetrical, bell-shaped; mean, median, mode are equal.
  • Skewed distribution: Asymmetrical data.
  • Measures of central tendency:
    • Mean: ar{x} =\frac{1}{n} extstyle \int\sum{i=1}^{n} xi
    • Median: Middle score.
    • Mode: Most frequent score.
  • Measures of spread:
    • Range: \text{Range} = x{\text{max}} - x{\text{min}}
    • Standard deviation (SD):
    • Smaller SD = scores closer to mean.
    • Larger SD = more variability.
  • Graphs:
    • Bar chart: Categorical data, separated bars.
    • Histogram: Continuous data, bars touch.
    • Scatter plot: Plots IVs vs. DVs; shows relationships, trend line, outliers.
    • Strong positive correlation: Points close to line of best fit.
    • Moderate/weak: More scatter.
  • Validity/Reliability (overview):
    • Validity: Measures what it intends to, accurate conclusions.
    • Reliability: Consistency of results.
    • Transferability/Generalizability: Apply findings to other contexts.
  • p-value: (AICE note) "p value lower than 0.5" for rejecting null hypothesis (conventional is 0.05). Not typically asked on AICE.

RELIABILITY AND VALIDITY

  • Reliability: Consistency of study/measurement. Replicable? Same results?
    • Improve: Standardize procedures, control extraneous variables, consistent data collection.
    • Assess: Test-retest (same results twice), Inter-rater (observers agree).
    • Qualitative methods have limited replication reliability.
  • Validity: Measures what it's supposed to, generalizable results.
    1. Control: How well extraneous variables are controlled.
    2. Realism (ecological validity): Natural behavior? Minimized demand characteristics? Realistic task?
    3. Generalizability: Applicable beyond sample/setting?

ETHICS OVERVIEW

  • Why? Respect participants' rights and dignity (BPS, 2014).
  • Core categories: Humans, Animals.
  • Key principles: Respect, informed consent, right to withdraw, protection from harm, confidentiality, privacy, debriefing.

ETHICS GUIDELINES RELATED TO HUMANS

  1. Informed consent: Sufficient, clear info for decision.
  2. Right to withdraw: Participants can leave anytime, data removed; no coercive incentives.
  3. Protection: No physical/psychological harm; end in similar state as started.
  4. Deception: Only if necessary/justifiable, no distress likely.
  5. Confidentiality: Personal data private; use anonymity.
  6. Privacy: Right to ignore questions; observe only expected behavior.
  7. Debriefing: Post-study explanation; mitigate negative mood.

ETHICS GUIDELINES RELATED TO ANIMALS

  1. Replacement: Use simulations if possible.
  2. Species/strain: Choose least suffering risk.
  3. Number: Smallest necessary.
  4. Pain/distress: Avoid physical/psychological pain.
  5. Housing: Respect species needs.
  6. Reward/deprivation/aversive stimuli: Prefer rewards; avoid deprivation/aversive.
  7. Anesthesia, analgesia, euthanasia: Use anesthesia/analgesia; humane euthanasia if needed.

EVALUATING RESEARCH – RELIABILITY AND VALIDITY (RECAP)

  • Reliability: Replicable with same methods/data? Standardization helps.
  • Validity: Measures what it intends? Conclusions warranted? Focus on internal, external, construct validity.

RESEARCH METHODS GRAPHIC ORGANIZER

  • Visual tool mapping Experimental vs. Non-Experimental methods and longitudinal applicability.

ADDITIONAL NOTES

  • Methodology question (exams): Evaluate researcher decisions (design, sampling, validity/reliability, generalizability, ethics) and impact on results.
  • General Study Path: Understand question, select method, specify IV/DV, controls/standardization, ethics, sampling, data types, analysis, anticipate validity/reliability.
  • Notation Recap:
    • IV: Manipulated variable.
    • DV: Measured outcome.
    • Extraneous: Unwanted variables affecting DV.
    • Confounding: Systematic extraneous bias.

KEY FORMULAS AND IDENTIFIERS

  • Mean: \bar{x} =\frac{1}{n}
  • Range: \text{Range} = x{\text{max}} - x{\text{min}}
  • Standard deviation (sample): s (Use standard formula with sum of squared deviations from mean).
  • Hypotheses structure: "If (IV manipulation) then (DV outcome)."
  • Null hypothesis: "No difference between groups; any difference due to chance."
  • p-value: