Download Introduction to Quantitative Research

Introduction to
Quantitative Research
SCALES OF DATA
NOMINAL
ORDINAL
INTERVAL
RATIO
Nominal Scale
NOMINAL
Assigning numbers to categories!
They have no numerical meaning:
a number on a football shirt: does not mean the player with the
number 52 is twice as anything as the player with the number
26.
Nominal data is generally used for categorical data:
gender, age group (30-35, 35-40), subject taught, type of
school, socio-economic status, etc.
Ordinal Scale
ORDINAL
Introducing an order into the data:
Rating scales (strongly agree, agree, …), weakest to
strongest, smallest to biggest, first-second-third,
Thus, the distance between each point of the scale may not
be equal.
Distance between ‘very little’ and ‘a little’ may not be the
same as the distance between ‘a lot’ and ‘a very great
deal’
E.g. Likert scale is a kind of ordinal data!
Interval Scale
INTERVAL
Equal interval between each data point:
We know how far apart are the individuals, events, etc from the
focus of inquiry.
However, there is no true zero.
The zero point on an interval scale does NOT mean a total absence of
what is being measured. E.g ‘00 C’ does not mean there is no
temperature. It is possible to say ‘-100 C’
In Fahrenheit degrees, 32 is the freezing point, not zero. So, we
cannot say 100 degrees is twice as hot as 50 degrees.
Not used very often in social sciences!
Ratio Scale
RATIO
Includes all the previous three scales (classification,
order and an equal interval), plus a true zero
Possible to determine proportions (twice as many as,
half as much as,…)
Because there is an absolute zero, possible to use all
arithmetical processes: (addition, subtraction,
multiplication, division)
KINDS OF STATISTICS
• Parametric statistics: assumes a particular underlying
theoretical population distribution, e.g., the normal distribution
• i.e. characteristics of, or factors in the population are known
• interval and ratio data (experiments and tests --exam scores)
• Non-parametric statistics: does not assume a particular
underlying theoretical population distribution
• i.e. characteristics of, or factors in, the population are unknown.
• nominal and ordinal data (questionnaires, surveys --though they
might also be parametric data)
KINDS OF STATISTICS
• Descriptive statistics: to summarize/describe features
of the sample or simple responses of the sample (e.g.
frequencies or correlations).
• No attempt is made to infer or predict population
parameters.
• Inferential statistics: to infer or predict population
parameters or outcomes from simple measures, e.g.
from sampling and from statistical techniques.
• Based on probability.
Dependent and Independent
Variables
• An independent variable (the input variable) causes partial or
total outcome.
• A dependent variable (the outcome variable) is the effect,
consequence of, or response to an independent variable
• If you need to use tests that require independent and
dependent variables, you have to be careful while assuming
which is or is not the dependent or independent variable!