1. Research Methods & Statistics
Lesson 2

2. Mysteries of Life We have questions Why do people behave that way?
Is global warming occurring?
Will my cancer come back?
To get answers, we need…
Information  Data
Explanation  analysis & interpretation ~

3. Research Methods & Statistics
Integral relationship
Must consider both during planning
Research Methods
How data *are collected
What kind of data
Statistics
Analysis depends on data & how it is collected ~

4. Scientific Inquiry Observing relationships between variables
Variable: any measurable characteristic
can take on many different values
Science is a way thinking & observing
1. systematic empiricism
2. production of public knowledge
3. examination of solvable problems
Guided by theory ~

5. Theories Descriptions Of observations Explanations
Relationships between observations
Generate hypotheses
testable predictions about relationships ~

6. The Research Process Systematic Empiricism Observe Explain Predict
Test
Science is cumulative
Theories tested
Supported or modified
No single experiment conclusive ~

7. Theories Scientific theories are falsifiable
Certain outcomes would disprove them
Non-scientific: Salsa dancing is the most exciting form of dance.
Scientific: Salsa dancing increases physical fitness ~

8. Scientific Validity Scientific conclusions
About relationships b/n variables
Validity
Soundness, legitimacy, truth
Internal validity
About cause & effect
External (ecological) validity
About broad applicability ~
Example: validity: don’t use thermometer to measure pulse
Reliability: checking blood pressure, pulse

9. How are data collected? 2 scientific approaches
Same or similar statistical analysis
But not same conclusions
Correlational methods
Observe co-occurrence of variables
Naturalistic observation, case studies, archival research, surveys, etc.
Experimental method
Manipulate a variable  observe effect on another variable ~

10. Experimental Variables
Independent (IV)
Predictor (or cause)
Manipulated
Dependent (DV)
Outcome (or effect)
Measured
Extraneous variables
Or confounding
Might also affect outcome (DV) ~

11. The Experimental Method
At least 2 variables:
Independent (IV) & Dependent (DV)
At least 2 groups (levels of IV)
control group - no treatment
experimental - receives treatment
random assignment to groups
Control confounding variables
Which might also affect DV
Weakens internal validity ~

12. Variation within an Experiment
Systematic
Variation due to manipulation of IV
Difference between groups
Unsystematic
Individual differences
Variation due to random or uncontrolled variables
Potentially confounding variables ~

13. Variation within an Experiment

14. Internal Validity Legitimacy of conclusions about cause & effect
High internal validity
Confident that only changes in IV cause change in DV
Low internal validity
Confounding variables influence outcome ~
Example: validity: don’t use thermometer to measure pulse
Reliability: checking blood pressure, pulse

15. Two Experimental Approaches
Treatment
Level of IV or group
Between-groups designs
AKA Between-subjects, independent groups
Each group gets different level of IV
Within-subjects designs
AKA repeated measures, dependent groups
Each individual experiences each level of IV ~

16. Randomization Important for validity Helps avoid bias
Random sampling (or selection)
Selection of participants for study
Representative sample from population
 external validity
Random assignment to condition (groups)
Minimize biasing of groups
 internal validity ~

17. Correlational vs. Experimental
Internal vs External validity
Inverse relationship based on control
Correlational?
 internal vs  external
Cannot determine causality
Experimental
 internal vs  external
Establishes cause & effect relationships
For useful conclusions need both ~

18. Planning Research Correlational or experimental research
Research design
Between-groups or within-subjects
Operational definition of variables
Data categorical or quantitative
Statistical analysis
Depends on all of the above ~