Measurement variables noir Flashcards
Lecture 6
objectives
1.Be able to define the different types of research variables and explain the
importance of defining them operationally.
- Understand the different measurement scales (NOIR).
- Be able to classify variables as discrete or continuous
the research process- VARIABLES
What data do we want to collect?
-What variables we want to include?
How will we collect this data?
-what collection tools do we want to use?
what are some variables we want to look for and include?
Height
* Weight
* Age
* Gender
* Scores on a test
* Income
* Country of birth
2 types of variables, what are they?
Discrete and concrete
define discrete variables
are numeric but are “distinct” and “separate” values
examples include
QUALI- words words
subjective and qualitative
surveys, questions
stress, aggressiveness, comfort
happiness, motivation, sleep, satisfaction, perception, perspective
explain what concrete or continuous variables
-It is numeric values that can be any value within a range
- variables that are easily defined
-quantitative approach
-QUANTI - numbers numbers
examples include
Age, speed
height, weight
time, distance
temperature
income
Research process - SCALES
ratio
interval
ordinal
nominal
what is ratio
Cannot have a zero such as weight and height cannot be zero
has order
name
interval
what is interval
equal spacing or decimal points
name and order
what is ordinal
rank and order
name
order
*****not interval because the spacing is not known
what is nominal
names, labels no true order
parametric data what are they?
interval and ratio
non parametric data- what are they?
ordinal and nominal
differences between ratio vs interval
ZERO does not exist
e.g race time cannot have a zero time
difference between ordinal and nominal?
what are parameters?
describe the population of interest
mean- average data set between population
standard deviation
measures the dispersion of the data set
calculated as a square root of the means describes the variance between data points
standard deviation
3 curves
normal/bell curve
distribution curve
sigmoid curve
parametric statistics
assumes data to be normative
follows normal curve
assumes variance between data points to be equal
assumes data is linear
assumes data is independent
**ratio and interval
