Module 1 Flashcards
why care about learning statistics?
because stats are the language underlining science
Stats demonstrate the observable evidence procurred by the scientific process
variable types
is science social?
YES.
It is done in teams, we share our work with the public, present at conferences.
what is science fundamentally about?
asking questions, NOT searching for facts
For example:
Why do people make Dispositional attributions about others?
Why do people feel less compassion for outgroup members?
what three ways does psychology use statistics?
- To develop measures – for example, can be used to improve work/life
- To Inform Best Practices – For example, working in an ABA Clinic.
- To modify theory – helps us to better understand ourselves
observation
a single person’s score on a scale measuring a given variable
qualitative/categorical variables
represent data that can be sorted into groups but CANNOT be ordered or measured
Each category can be identified by a distinct label and data points are allocated to these labels based on qualitative properties
types of categorical/qualitative variables
- nomial
- ordinal
categorical variable
nominal variables
- the least sophisticated of the cat. variables
- least numerical measurement possible
- categories that cannot be ordered based on size or magnitude
Examples:
cat people vs dog people
hair color
Demographic data, genotype, country of birth, blood type, sexual orientation, gender, hair color, race, religion, eye color, and political party
categorical variable
ordinal variables
- the same as nominal data but the categories do have some meaningful rank or order
Examples:
oldest vs youngest child
placement in a race (1st, 2nd, 3rd…)
low income, middle income, high income
<$50k, $50k-$100k, >$100k
High school diploma, bachelors, masters, or PhD
quantitative/numeric variables
represent measurable quantities and can be expressed as numbers.
Provide information about **the magnitude, quantity, or degree of the variable being measured **
types of quantitative variables
- interval
- ratio
quantitative variables
interval data
not the most specific type of quant. data (second to ratio data)
measured on a scale, can dip below zero (zero is not an absolute reference that literally references the lack of something)
*The distance between numbers on a scale are equal
Examples:
likert scales (there are exceptions)
Temperature
year
time of day
IQ test scores
ACT/SAT
quantitative variables
ratio data
the most specific, sophisticated, and accurate type of quant. data
measured on a scale, cannot fall below zero (zero is used as an absolute reference point)
Examples:
Income
height
weight
product defect rate
salary
speed
experiment vs. non-experiment design
difference is the groupings
If groupings are assigned by the experimenter, it’s an experimental design
If groupings are assigned by randomly/are unassigned (meta variables), it’s a non-experimental design
populations
we don’t actually study these in psychology directly, we generalize to them
