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!