Sampling.pptx

SAMPLING Dr Ayaz Muhammad Khan

SAMPLING

A sample is a finite part of a statistical population whose properties are studied to gain information about the whole(Webster, 1985). When dealing with people, it can be defined as a set of respondents (people) selected from a larger population for the purpose of a survey.

A population is a group of individuals persons, objects, or items from which samples are taken for measurement for example a population of presidents or professors, books or students.

SAMPLING

A process used in statistical analysis in which a predetermined number of observations will be taken from a larger population.

The methodology used to sample from a larger population will depend on the type of analysis being performed, but will include simple random sampling, systematic sampling and observational sampling.

The sample should be a representation of the general population.

Probability sampling:

1.Simple random sampling

2.Systematic sampling

3.Stratified sampling

4.Cluster sampling

5.Multistage Sampling

RANDOM PROBABILITY SAMPLING

Each individual in the population of interest has an “EQUAL CHANCE” of selection BEING Random still it is very strict in its meanings

– it does not mean that we are free to take the sample from anywhere Any variation between the sample characteristics and the population characteristics is only a matter of chance.

STAGES IN RANDOM SAMPLING:

Define population

Develop sampling

frame

Assign each unit a

number

Randomly select the required

amount of random numbers

Systematically select random numbers

until it meets the sample

size requirements

2.Systematic sampling:

Select every nth name from the population list, so

estimate the needed sample size and dividing the

number of the names on the list by the sample size.

ex:1000/100=10

Pro: not need to have an exact list of all the

sampling units.

Con: If the files are arranged in a specific pattern,

that could result in choosing a biased sample.

Define populatio

n

Develop sampling

frame

Decide the

sample size

Work out what

fraction of the frame

the sample size

represents

Select according to fraction (100 sample from 1,000 frame then

10% so every 10th unit)

First unit select by random numbers

then every nth

unit selected (e.g. every

10th)

CONT.

• As a random sample:

Stratified Random Sample

Define population

Develop sampling

frame according to

characteristics required

Determine the proportion of

each population variable of

interest

Systematic sampling

methods can then be followed to select sample

unit

CLUSTER SAMPLING

– For many pop where it is impossible to obtain a list of the elements as in large scale Quantitative studies. Here there is a successive random sampling of units.

Ex: city blocks or classroom in a school, and

study all the samples there.

Pro: save time and money by collecting data

at a limited number of sites.

Con: small sample size, less precision in

estimating the effect.

5.MULTISTAGE SAMPLING Multi-stage random – used when the study

population is large. The population is then broken into strata for convenience and randomness.

Ex: cluster sampling +random sampling Pro: more reliable Con: complex calculations

NON-PROBABILITY SAMPLING

DEFINITION

The process of selecting a sample from a population without using (statistical) probability theory.

NOTE THAT IN NON-PROBABILITY SAMPLING each element/member of the population DOES

NOT have an equal chance of being included in the sample, and

the researcher CANNOT estimate the error caused by not collecting data from all elements/members of the population.

TYPES OF NON-PROBABILITY SAMPLING1. Convenient (or Convenience) Sampling

2. Quota Sampling

3. Theoretical sampling 4. Snowball Sampling

CONVENIENT SAMPLING

DEFINITION Selecting easily accessible participants with no randomization.

For example, asking people who live in your dorm to take a survey for your project.

– This entails the use of most convenient available people as study participants. Stopping people at the street corner to ask them question is sampling by convenience.

cannot generalise findings (do not know what population group the sample is representative of) so cannot move beyond describing the sample.

• Aim is to sample reflecting proportions of population in different categories or quotas (e.g. gender, age, ethnicity).

• Used in often in market and opinion poll research.

• + easy to manage, quick

• – only reflects population in terms of the quota, possibility of bias in selection, no standard error

•For example you include exactly 50 males and 50 females in a sample of 100.

Quota Sampling

THEORETICAL SAMPLING

DEFINITIONSelecting participants because they have certain predetermined characteristics, no randomization.

For example, you want to be sure include African Americans, EuroAmericans, Latinos and Asian Americans in relatively equal numbers.

SNOWBALL SAMPLING

• Useful when a population is hidden or difficult to gain access to.

• The contact with an initial group is used to make contact with others.

• + access to difficult to reach populations (other methods may not yield any results).

• - not representative of the population and will result in a biased sample as it is self-selecting.