1.2) Random Sampling

Question 1

Simple Random Sampling (What & How?)

Answer 1

What?

Sampling where every sampling unit has an = chance of being selected

How?

-In Sampling Frame each item has identifying number
-Random number generator/‘lottery sampling’ (names in a hat)

Question 2

Simple Random Sampling (Pros)

Answer 2

-Bias free
-Easy and cheap to implement
-Each Sampling Unit has a known, = probability of being selected

Question 3

Simple Random Sampling (Cons)

Answer 3

-Not suitable when population size is large (strat sampling better here)
-Sampling Frame needed to work

Question 4

Systematic Sampling (What & How?)

Answer 4

What?

Where required elements/Sampling Units are chosen at regular intervals in an ordered list

How?

i.e Take every kth element where:

k= pop size (N) divided by sample size (n)

Starting at a randomised unit between 1 and k

Question 5

Systematic Sampling (Pros)

Answer 5

-Simple and quick to use
-Suitable for large samples/populations

Question 6

Systematic Sampling (Cons)

Answer 6

-Sampling Frame needed
-Can introduce bias if sampling frame not random

Question 7

Stratified Sampling (What & How?)

Answer 7

What?

Where a population is divided into groups (strata) and a simple random sample carried out in each group/strata

Used when sample is large and population naturally divides into groups

How?

Same proportion {pop size (N) divided by sample size} sampled within each strata

Question 8

Stratified Sampling (Pros)

Answer 8

-Reflects Population structure
-Guarantees proportional representation of groups within population

Question 9

Stratified Sampling (Cons)

Answer 9

-Population must be clearly classified into distinct strata
-Selection within each strata has same Cons as simple random sampling