Ch 1-3 (Research Methods) Power Point Lecture
Scientific Reasoning
1. Why Do We Believe the Things We Do?
Sources of Belief: Understanding the foundations of our beliefs can be categorized into four main sources:
Personal Experience: Individual experiences shape our beliefs and perceptions.
Intuition: Gut feelings or instincts can influence our beliefs without empirical evidence.
Authority: Trust in authoritative sources or figures can lead to the acceptance of certain beliefs.
Science: Scientific inquiry and evidence are critical in forming rational beliefs.
3. The Scientific Method
Characteristics of the Scientific Method:
Empirical Approach: Science relies on observation and experience to gather data and validate theories.
Skeptical Attitude: A critical approach that questions assumptions and seeks evidence before accepting claims.
4. Perceptive Clever Hans and Critiques of Pseudoscience
Clever, a highly intelligent horse, is capable of answering questions from his owner and others, showcasing remarkable perceptiveness.
Clever can solve basic math problems and distinguish the time of day, generating public interest.
Researchers, skeptical of these claims, conducted experiments to explore Clever's abilities.
Findings indicated Clever could not answer correctly without cues from his interviewer or when the questioner did not know the answer.
The key takeaway: Clever's perceived intelligence stemmed not from innate intelligence but from being perceptively attuned to human reactions.
5. Skepticism Toward Pseudoscience
Importance of critical thinking in assessing claims and engaging with pseudoscientific ideas.
Recent examples of pseudoscience include:
Gwyneth Paltrow's Goop selling "healing body stickers" made from non-existent technology.
False advertising resulted in significant penalties (settlement of $5,057,000,000).
Conclusion: Caution against unsubstantiated claims is vital, especially given the influence of social media.
6-8. Origin of Research Ideas
Research ideas can originate from a variety of sources, which might include:
Personal Observations of phenomena that prompt questions.
Social Dynamics, and underrepresentation questions, such as gender disparities in STEM fields.
Example: Why are women underrepresented in math and science?
Influence of Popular Culture, e.g., songs inspiring research studies, such as investigating the concept of “beer goggles” from country music.
Study demonstrated that as individuals drink more alcohol, they rated the attractiveness of people more favorably.
Reference: Pennebaker, 1979.
9-10. Theory-Data Cycle and Research Design
The theory-data cycle begins with a Theory: defined as a set of statements that describe general principles on how variables relate.
Research Questions: which lead to an appropriate
Development of research questions emerges from theoretical frameworks.
Research Design: to test a specific hypothesis
The Hypothesis is ideally Preregistered before they collect data
Collect Data: which feed back into the cycle
If Data Supports, it strengthens the theory. Nonsporting Data lead to revised theories or improved research designs.
14. Theory-Data Cycle: Hypothesis
Hypothesis: A testable statement about the expected relationship between study variables, ideally preregistered to enhance transparency.
15. Theory-Data Cycle: Data Collection and Results
Steps:
Collect Data.
Revise Theory as necessary based on findings.
Report and Share Results through the peer review process.
17-18. Different types of research designs include:
Descriptive Research: Focuses on observing and describing behavior.
Correlational Research: Assesses relationships between variables that are measured but not manipulated
Experimental Research: Involves manipulation of an independent variable to determine its effect on a dependent variable.
11-12. Two Factor Theory of Emotion
Variable: Factors that can vary or change (what is being studied), can be measured
or manipulated
The Two Factor Theory suggests that physiological arousal is interpreted based on situational cues.
Example of an experiment using the Capilano Suspension Bridge:
Hypothesis was that if physiological arousal occurs due to fear, it may be misattributed to attraction towards a nearby attractive experimenter.
Variables were identified:
Independent Variable : Type of bridge (suspension vs. sturdy bridge).
Dependent Variable : Rate of calls made to the female experimenter.
Results confirmed that men misattributed their physiological arousal caused by fear to feelings of attraction.
12. Operational Definitions
Operational Definitions: Detailed descriptions of how variables will be measured or manipulated.
Example: Class motivation might be operationalized through attendance, assignment completion, or participation in class.
Challenges: Need for clarity in operational definitions to ensure the validity of conclusions and measurements.
16. Types of Claims in Research
Three Main Claim Types:
Frequency Claims: Represents a particular rate or degree of a single variable.
Example: Nearly 60% of teens text while driving.
Association Claims: Describes relationships between two measured variables.
Example: Higher self-esteem correlates with better grades.
Causal Claims: Indicates that one variable causes a change in another.
Example: Venting anger physically leads to increased anger levels.
17-18. Types of Research Designs
Descriptive Methods: Used for observing and documenting behaviors, allowing for frequency or association claims.
Observational Studies: Systematic observation to record behaviors in natural settings.
Example: Studying behavior of intoxicated individuals.
Content Analysis: Analyzing textual data to derive findings.
Example: Analyzing presidential speeches for leadership insights.
Existing Data Research: Utilizing pre-existing data for investigation.
Example: Analyzing crime rate trends over a five-year span.
Surveys: Tools for collecting information through questionnaires or interviews regarding experiences, beliefs, or attitudes of a population.
19. Correlational Designs
Goal: To make association claims, examining how strongly two variables are related.
Correlation Coefficient: Ranges from -1.00 to +1.00, reflecting the strength and direction of a relationship.
Interpretations:
Perfect Positive Correlation (+1.00)
No Relationship (0.00)
Perfect Negative Correlation (-1.00)
20-24. Correlation Types
Positive Correlation: Both variables increase together, or decrease together
Example: Higher self-esteem correlates with a higher GPA.
Negative Correlation: As one variable increases, the other decreases or vice versa
Example: Increased wine consumption correlates with poorer decision-making.
25. Key Concept: Correlation Does Not Imply Causation
Understanding Correlation: Just because two variables are correlated does not mean one causes the other, exemplified by the third variable problem.
Example: Ice cream consumption does not cause drowning incidents
Potentials for Prediction: Correlations can help predict outcomes but must be interpreted cautiously.
29-20. Experimental Methods: Establishing Causal Relationships
Through manipulation of independent variables and control over experimental conditions.
Essential for determining temporal precedence and internal validity.
Covariation: Changes in one variable correspond with changes in another.
Temporal Precedence: The cause must occur before the effect.
Internal Validity: Confidence that results stem from manipulation of the independent variable, ruling out confounding variables.
Example: Study on gratitude and its causal effects on happiness.
31. Understanding Confounding Variables
Definition: Variables that can affect the outcome and obscure the true relationship between independent and dependent variables.
Example Situation: A psychologist testing a therapy's effectiveness may find no difference in outcomes due to external factors influencing both groups.
32-33. Validity Types to Assess Claims
Four Major Validities:
Construct Validity: Assesses how well the variable was defined and measured.
Relevant for all claims.
External Validity: Examines whether findings can generalize to other settings or groups.
Relevant for all claims.
Statistical Validity: Evaluates the accuracy of findings and the strength of the observed relationships.
Important for determining margin of error in frequency claims and effect size in association or causal claims.
Internal Validity: Measures the degree of confidence that results are genuinely the outcome of experimental manipulation.
Primarily relevant for causal claims (experiments).