01. what is cluster sampling?

cluster sampling occurs when you have a group or cluster comprising of people with different characteristics.

for example, i may decide to form a cluster of OUM students of different grouping (_base_d on faculty). this group may comprises of students from the Faculty of Business, Faculty of Science, Faculty of Engineering & Faculty of IT.

in this way, we can then have a good representative of the students population. each of these clusters or group contains heterogenous collection of members with different interests, orientation, values, philosophy and vested interests drawn from different departments to offer a variety of perspectives.


02. Can you give examples of probability and non probability sampling techniques?

Random sampling is the purest form of probability sampling. Each member of the population has an equal and known chance of being selected. When there are very large populations, it is often difficult or impossible to identify every member of the population, so the pool of available subjects becomes biased.

Systematic sampling is often used instead of random sampling. It is also called an Nth name selection technique. After the required sample size has been calculated, every Nth record is selected from a list of population members. As long as the list does not contain any hidden order, this sampling method is as good as the random sampling method. Its only advantage over the random sampling technique is simplicity. Systematic sampling is frequently used to select a specified number of records from a computer file.

Judgment sampling is a common non-probability method. The researcher selects the sample _base_d on judgment. This is usually and extension of convenience sampling. For example, a researcher may decide to draw the entire sample from one "representative" city, even though the population includes all cities. When using this method, the researcher must be confident that the chosen sample is truly representative of the entire population.

Quota sampling is the non-probability equivalent of stratified sampling. Like stratified sampling, the researcher first identifies the stratums and their proportions as they are represented in the population. Then convenience or judgment sampling is used to select the required number of subjects from each stratum. This differs from stratified sampling, where the stratums are filled by random sampling.


03. what is a sampling design? let's just say you are targeting at OUM students in your research. how are you going to select them? at random? (probability sampling) [making sure that everyone has an equal chance of being selected (probability sampling)] or some of the respondents do not have a known and equal chance of being selected (non-probability sampling). What the various probability and non probability sampling design.

Sampling design is part of research plan where its explain how the sampling is going to be carried out and who will be the respondents or target group for the study.

There are two types of sampling techniques (1) probability or representative sampling - as suggested by the name, the chance of being selected is known and usually the same. It often used in surveying; and (2) non-probability sampling - where the chance of being selected is unknown and it is not suitable for research which requires statistical inferences, hence, it is often used in case study research.

Type of probability sampling:- (1) cluster sampling, (2) Double (sequential, multiphase) sampling, (3) simple random sampling, (4) systematic sampling, and (5) stratified sampling.

Type of non-probability sampling:- (1) convenience (haphazard) sampling, (2) purposive (judgmental) sampling, (3) quota sampling, and (4) snowball sampling.

04. What are nominal and ratio scales in a research?


Nominal Scale Is usually used for something for obtaining personal data such as gender, department in which one is working, and so on, where grouping of individuals or _object_ is useful as in the cases below:


1. Your gender 2. Your department

-male -production

-Female -sales

- accounting



Ratio scale measures provide information about relative position and about absolute amount. The intervals between numbers are equal. What differentiates ratio from interval measures is that ratio scales have a non-arbitrary, absolute zero.

Absolute Zero


Zero in a ratio scale measure is an absolute zero. It represents absence of whatever is being measured. There is no quantity less than absolute zero. If I have no (zero) money, I cannot have any less money. One implication of this is that there are no negative numbers in a ratio scale measure.