JM

Final Exam Review - Psych 2910

Lecture 2: Knowledge Metaphysical Systems

  • Supernatural Explanations: Attributes behavior to non-physical forces like spirits and deities.

    • Animism: Belief that natural phenomena are alive and influence behavior.

      • Example: Possessing an eagle’s feather grants certain properties to the owner.

    • Mythology and Religion: Non-physical forces affect human behavior, differing from scientific assumptions.

    • Astrology: Suggests that celestial bodies influence human behavior and predict actions.

Philosophical Systems

  • Shift towards logic and empirical observation.

    • Empiricism (David Hume): Knowledge should be based on observations.

    • Positivism (Auguste Comte): Focus on knowledge derived solely from sense perceptions.

Methods of Acquiring Knowledge

  • Intuition: Knowledge based on instinctive feeling rather than conscious reasoning.

    • Problem: Conclusions often drawn without sufficient evidence.

  • Authority: Trusting figures of authority to provide information.

    • Problem: Questioning the authenticity of the authority.

  • Scientific Skepticism: Withholding judgment and systematically evaluating claims helps consider all possibilities.

  • Science: Requires adapting views based on new evidence.

Key Concepts

  • Determinism: The universe operates in a systematic, orderly manner; events have meaningful causes.

    • Causation:

      • Covariation of Cause and Effect: Presence of cause corresponds with presence of effect.

      • Temporal Precedence: Cause must occur before the effect.

      • Elimination of Alternative Explanations: Ensuring no other variable influences the effect.

Goals of Scientific Psychology

  • Objectives:

    • Describe behavior.

    • Predict behavior.

    • Determine causes of behavior.

    • Explain behavior.

Types of Research

  • Basic Research: Addresses fundamental questions.

  • Applied Research: Focuses on practical problems.

Four Keys of Research

  1. Replication: Detailed description for duplicating studies and results.

  2. Testability/Falsifiability: Rejecting untestable ideas.

  3. Peer Review: Validating research to ensure study quality.

  4. Adversarial Process: Evaluating opposing theories for experimental comparison.

Pseudoscience**: Characteristics include:

  • Unfalsifiable hypotheses.

  • Non-scientific methodology.

  • Anecdotal evidence or reliance on authority.

  • Lack of peer-reviewed citations and revisions based on new data.

  • Ignoring conflicting evidence.

Specific Concepts in Pseudoscience

  • Biorhythms: Claiming human behavior follows physical, emotional, and intellectual cycles of specific durations.

  • Homeopathy: Substance that induces symptoms in healthy individuals cures similar symptoms in patients.

  • Phrenology: Inferring personality traits based on skull bumps.

Critical Evaluation

  • Assess data source for credibility.

  • Evaluate methods of study conduct.

  • Analyze statistical methods used.

  • Review conclusions drawn from analysis.

Lecture 3: Intro to Statistics

  • Why Statistics?: Trust issues due to biases; control of extraneous variables; direct study limitations necessitating statistical methods.

  • Statistics Defined: Method of understanding data, aiding in decision-making.

Types of Statistics**:

  • Descriptive Statistics: Numbers summarizing data (mean, median, standard deviation).

  • Inferential Statistics: Making predictions about a population from sample data.

Theoretical Frameworks

  • Theory: General statements on relationships among variables, providing organizational frameworks and generating new knowledge.

Key Questions in Research

  • Sample characteristics, reasons for participation, controls, sample size, wording, causation, funding sources, and peer-reviewed publication.

Key Terms in Research Methodology

  • Population: Entire group of interest.

  • Sample: Subset of the population tested.

  • Independent Variable (IV): Manipulated variable.

  • Dependent Variable (DV): Measured outcome.

  • Construct: Internal attribute not directly observable.

  • Operational Definition: Specifies procedures to represent a construct, e.g., measuring hunger.

Lecture 4: Measurement Scales

  • Variable Types:

    • Discrete Variable: Indivisible categories (e.g., number of children).

    • Continuous Variable: Measurable characteristics (e.g., height, weight).

    • Dichotomous Variable: Only two possible outcomes.

Measurement Scales

  1. Nominal Scale: Categorizes data without implying order. Frequency calculations only; no intermediate values.

  2. Ordinal Scale: Includes order but does not quantify the gap between points.

  3. Interval Scale: Provides numeric intervals but lacks a true zero point; zero is arbitrary (e.g., temperature).

  4. Ratio Scale: Has all properties of interval scales but includes an absolute zero.

Lecture 5: Central Tendency

  • Mean: Average score; influenced by extremes but computationally simple.

  • Mode: Most common score in a dataset; relevant for nominal scales.

  • Median: Middle value dividing data into halves; especially useful in skewed distributions.

Lecture 6: Variability

  • Variability Defined: Quantifies how spread out the scores are within a distribution.

  • Range: Difference between highest and lowest scores; influenced by extremes.

  • Interquartile Range: Range of middle 50% of data, calculated as Q3-Q1.

  • Standard Deviation: Average distance from the mean, widely used measure of variability.

Lecture 7: Ethics in Research**

  • Key Ethical Organizations: CIHR, NSERC, SSHRC.

  • Concerns: Protecting subjects from risks, informed consent, and ethical conduct in experiments.

  • Experiments During WWII: Resulted in establishing ethical guidelines (Nuremberg Code, Helsinki Declaration, Belmont Report).

  • Milgram Study Insights: Experiment demonstrating obedience—65% of subjects administered maximum shocks when instructed.

Core Ethical Principles**:

  1. Respect for Persons: Ensuring voluntary participation.

  2. Concern for Welfare: Balancing risks against potential benefits.

  3. Justice: Fair treatment of participants.

Risk vs. Benefit Analysis**

  • Consider psychological, physical, and privacy-related risks while weighing the benefits to society.

Informed Consent**

  • Required prior to participation; ensuring participants are fully briefed.

Vulnerable Populations**: Special considerations should include children, prisoners, and individuals lacking capacity to consent.

Lecture 8: Percentiles**

  • Definition: Percentiles indicate the position of a score within a distribution.

Lecture 9: Standard Scores (z-scores)

  • Purpose of z-scores: Standardize scores by indicating how many standard deviations they are from the mean.

Lecture 10: Sampling

  • Good Sample Characteristics: Representative and sufficiently large to minimize errors.

  • Central Limit Theorem (CLT): As sample size increases, the distribution of sample means approaches normality.

Expected Value**: The mean of a distribution of sample means equals the population mean.

Lecture 11: Inferential Statistics**

  • Distinction between descriptive (summarizes data) and inferential statistics (draws conclusions about populations).

Testing Hypotheses**: Impact of hypotheses on interpreting results; understanding p-values as indicators of statistical significance.

Lecture 12: t-tests**

  • Differences between z-tests and t-tests based on population standard deviation availability.

Lecture 13: Independent vs. Dependent Groups**

  • Clarification of designs: Independent samples versus dependent samples in testing means.

Lecture 14: Correlation**

  • Importance of understanding correlation and causation; types of correlations analyzed.

Lecture 15: Probability**

  • Primary concepts include analytical probability, mutual exclusivity, and addition/multiplication rules.

Lecture 16: Research Design**

  • Types of Studies: Differentiating experimental from non-experimental designs.

Lecture 17: Measurement**

  • Discussing reliability, validity, and various types of reliability and validity tests.

Lecture 18: Observation**

  • Overview of observational methods including naturalistic and systematic observation, as well as concerns inherent in qualitative research.