Chapter 4 Flashcards
What’s the difference between numerical and categorical variables?
Numerical: number ex. grade
Categorical: category ex. gender level of ed
What is the difference between non-experimental and experimental variables?
Non-experimental:
- No manipulation of variables (no change, just observe and describe what you see)
- Goal to describe behaviour
- Observations of physiological responses
Experimental variables:
- Manipulation of variable
- goal to explain a behaviour
- allow for cause and effect inferences
- determine cause and effect
Independent vs dependent variable
Independent variable:
- Manipulated (changed) to cause an effect
- ex. amount or strength of alcohol
Dependant variable
- Measured to determine outcome (effect)
- changes as a result of Iv
ex sleep
I need to change to be independent
dependent depends on independant
What are the 3 categories of variables?
Situational
- A situational variable is something in the environment or surroundings that can influence how people behave. For example, if you’re studying how people act in a stressful situation, the amount of noise in the room or whether they’re in a comfortable setting would be situational variables.
Response
- the outcome or result being measured in an experiment, like how much someone learns after a lesson or how fast they complete a task.
Participant
- Characteristic individual brings to the study
What are operational definitions?
- they define the technique the researcher will use to measure or manipulate a variable
- Necessary for empiracl study
What are confounding variables
- can invalidate or weaken research
- A confounding variable is an outside factor that can influence both the independent and dependent variables in an experiment, making it hard to tell if the results are due to the factor being studied or the confounding variable.
- Must measure them so you can properly take them into account
what are positive Linear Relationships
-Increases in one variable
relate to increase in
another variable
Negative Linear Relationships
Increases in one variable
relate to decrease in another
variable
Curvilinear Relationship
Increases in the values of
one variable are
accompanied by both
increases and decreases in
the values of the other
variable
No relationship
increases in one variable
lead to no systematic
changes in the other
variable
Correlation Coefficients
- Symbolized by the letter
“r”
A statistic that reflects the association
of two numeric (quantitative) variables - “consumption is strongly
related to a sense of well-
being, r = .88, p < .05.” - between - 1 and 1
Strength of Association
- Strong: -1.0 to -0.5 or 0.5 to 1.0
- Moderate: -0.5 to -0.3 or 0.3 to 0.5
- Weak: -0.3 to -0.1 or 0.1 to 3
- None: -0.1 to 0.1
Values range from -1.00 to 1.00
Interpreting Correlations
- A positive linear relation means
higher scores on one make it more
likely one will have higher scores
on the other
Correlations are probabilistic - It does not make it inevitable
Issues with Interpreting Non-Experimental
Results
- cant make causal statements
Cannot establish
temporal
precedence - The “Third-
variable” problem
What is the third variable problem
When an unmeasured factor affects both variables being studied, potentially creating a false or misleading correlation between them.
