1. Classification, Prediction, and Description
The Scientific Method in a Nutshell

2. Intuition vs Empiricism
Correlation vs Cause
Confirmation bias
Thus, we seek to falsify, rather than to confirm
Empiricism and Positivism

3. Causal Inferences require
Covariation (correlation)
A temporal relationship
Elimination of other causes (confounds)
Thus, experiments
Internal Validity

4. Correlations are Not Useless!
Just not causative
But it can be predictive
Much of our neurological research is correlative
When cannot experimentally, directly explore because:
Ethics
Resources

5. Choose your Variables! Independent Variable Dependent Variable
Intervening Variables
Invisible
Indirectly measured
Covariate
Confound
Continuous vs Discrete

6. Choose your Statistics!
Discrete IV’s with Continuous DVs
ANOVA
t-test
Discrete IV’s with Discrete DVs
Chi Squared
Continuous IV’s and Continuous DVs
Predictors and Predicted variables
Correlation
Regression

7. Reporting Detailed without getting bogged down in trivia
Consider it a blueprint
Constructs and Operational Definitions
Intelligence
Acceptance
Burnout
Depression
Anxiety

8. Choosing Your Measures
Validity
Does it measure what it says it measures?
Reliability
Consistency across situations and administrations
Precision
Gall and Phrenology

9. Types of Validity Construct Content Predictive Concurrent
Shades of construct and predictive

10. Reliability Interobserver agreement/Interrater reliability
Internal consistency
Test-retest reliability and the problem of practice
Split half
Alternate forms

11. The Hypothetico-Deductive Method
Form the theory
The theory makes a prediction
The prediction forms a hypothesis
Test the hypothesis
Retain or revise the theory
If revise, this creates a new prediction, and so on…

12. Nomothesis vs Ideothesis
The Nomothetic approach
Averages
Ever meet the average person?
Seeks generalizations to apply to the broader population
Thus, need a sample that represents that population
External validity
The Ideothetic approach
Individuals
Not terribly generalizable
Skinner

13. Quality vs Quantity Quantitative approach Qualitative approach
Statistical
Based largely on specific measures
Measures of central tendency and dispersion
Qualitative approach
More summary and thematic interpretation
Minimal statistical evaluation
Frequency counts of themes
Interobserver agreement