SAMPLING STRATEGIES

HE MISIRIBLANTYRE, 14 JANUARY,2014

POPULATION

• This is any collection of objects – animate or inanimate- that we intend to study.

EXAMPLES

1. A woodlot comprising 1000 trees.2. A litter of piglets3. A fleet of cars produced in a day by mass

production4. Patients in a ward5. People in a village6. Bees in a hive

FINITE AND INFINITE POPULATIONS

• A finite population is a countable population .

• An infinite population is a population which due to some reasons is not countable. For example, a swarm of bees.

• This is the total number of subjects in a population.

EXAMPLES

1. If the maximum capacity of a hospital ward is 20 and all beds are occupied , then the population of patients in that ward is of size 20.

2. If the total number of students at a college of medicine is 100, then the population of students at that campus is of size 100.

STATISTICAL NOTATION

• The population size is usually denoted by upper case letter N.

• Thus in the above two examples, we have: N = 20 and N=100 respectively.

TARGET POPULATION

• This is the basic finite set of individuals that we are interested in.

• For example if we are interested in studying pregnant mothers attending a prenatal clinic, then our target population is all pregnant mothers attending that clinic regardless of their age, parity and trimester.

STUDY POPULATION

• This is the specific set of individuals we want to study. For example, if we want to study pregnant mothers who are in their first trimester of pregnancy, then this group is our study population.

• The study population is a subset of the target population if not the target population itself.

HETEROGENEITY AND HOMOGENEITY

• A population is said to be homogeneous with respect to some attribute if all the members of the population possess that attribute.

• A population is said to be heterogeneous with respect to a certain attribute if not all members of the population has that attribute or if they have varying degrees of that attribute.

EXAMPLES-HOMOGENEITY

Literacy • If all members of the population are literate,

then we say that that population is homogeneous with respect to literacy.

Employment: • If all members of a population have blue-collar

jobs then that population is homogeneous with respect to that.

EXAMPLES-HETEROGENEITY

Education• A heterogeneous population may have people

who are educated and those who are not. • Alternatively, people may be educated but

the levels of education may be diverse from one subject to another.

CENSUS AND SAMPLE

• A census is the complete enumeration of all members of the population.

• A sample is a collection of members from the population.

• This is just a set of individuals and not the whole population.

• The size of a sample is usually denoted by lowercase n.

EXAMPLE

• If my population comprises 18 first year medical students, then a group of students of size less that 18 is a sample from this population.

SAMPLING UNIT

• This is a potential member of a sample.

• Sometimes, a sampling unit may be a collection of several units. For instance, if you want to study children under 20 years of age in a village, then you will sample households.

SAMPLING FRAME

• This is a finite collection of all sampling units.• This is, in essence, a collection of all

population members. • This can be a list of all population members.• Alternatively, if you want to sample areas, the

whole area is divided into several parts which are numbered. Your sampling frame is a map divided into numbered areas.

REASONS FOR SAMPLING

• To reduce cost/expenses• To reduce time• Utility• Accessibility of Members of a Population• To increase the scope of the study• To increase the precision of estimates

Replacement of sampled subjects

Sampling With Replacement:• Is sampling where sampled subjects are

replaced to the population. There is a high possibility of sampling them again.

Sampling Without Replacement:• Is sampling where sampled members are not

replaced to the population

ACCESSIBILITY SAMPLING

• Only is subjects who are accessible to the researcher are selected to be members of a sample.

• There is a problem of selection bias.• People may be accessible because of being

friendly, agreeable, pleasant, and presentable, among other things.

• In general, accessible subjects do not necessarily represent the population.!

JUDGMENTAL SAMPLING

• The researcher uses his judgement to choose what he thinks is a representative group of subjects.

• If his judgement is expert judgement, the sample may be representative.

• If he is a novice, the resulting sample may be nonrepresentative.

• Sampling bias is inevitable.

QUOTA SAMPLING

• Market researchers and opinion poll experts often use these methods.

• It is not probablistic• It can be outlined as below:

SAMPLING PROCEDURE

1. The categories of people to be sampled are known in advance. E.g. Political Party Affiliation.

2. The number of people to be interviewed for each category is specified in advance. This is known as a quota.

3. The interviewer then has the mandate to choose whom he wants to interview for each category.

4. The process of interviewing people is carried out until all the categories are filled.

SAMPLING PROCEDURE

• i. The categories of people to be sampled are known in advance. E.g. Political Party Affiliation.

• ii. The number of people to be interviewed for each category is specified in advance. This is known as a quota.

• iii. The interviewer then has the mandate to choose whom he wants to interview for each category.

• iv. The process of interviewing people is carried out until all the categories are filled.

MAJOR DRAWBACKS

1. There is bias in the selection of subjects –selection bias.

2. There is no way of measuring the precision of estimates obtained from surveys using such methods of sampling.

PROBABILITY-BASED METHODS

• SIMPLE RANDOM SAMPLING• SYSTEMATIC SAMPLING• STRATIFIED SAMPLING• CLUSTER SAMPLING

SIMPLE RANDOM SAMPLING

• This is a method of sampling where each member of the population has an equal chance of selection.

• This can be done with or without replacement.

• For sampling without replacement, the procedure is as follows:

i. Compile a sampling frame. Numbers from 1 to N will index the population members.

ii. Specify the size of your sample which is usually denoted by n.

iii. Using random number tables (A computer program like EPI INFO can produce them for you) draw a sample of n numbers from 1 to N inclusive.

iv. Use these sampled numbers to identify your sample members.

• This method is handy. • It produces representative samples. • However, if the population is very big, this

method is laborious and expensive as the sample produced may be staggered in the whole population.

SYSTEMATIC SAMPLING

• This technique relies on the existence of a sampling frame.

• A subject is chosen from the population at random.

• Every kth member after the first is chosen as a subsequent member of your sample. k is known as the sampling interval.

EXAMPLE

• Suppose the sample size is 23 and the sampling interval is 5.

• Draw a systematic sample of size 5:

POSSIBLE SYSTEMATIC SAMPLES1ST sample 2nd sample 3rd sample 4th sample 5th sample

1 2 3 4 5

6 7 8 9 10

11 12 13 14 15

16 17 18 19 20

21 22 23

ADVANTAGES

• It is easy• A sample can be drawn on site• Intuitively speaking, it produces a sample which

is spread more evenly over the population.• If the list is random, this method is equivalent to

simple random sampling• If the sampling frame is periodic, it produces a

better (representative) sample than when simple random sampling is used.

DISADVANTAGES

• In the case of a periodic frame, if the sampling interval is equal to the period of the frame or a multiple of it, it will not yield good results.

• It yields samples whose size may not be equal to the desired sample size.

CLUSTER SAMPPLING

• A cluster is a non-overlapping group of population members.

• Clusters may occur naturally. A good example is a village. Aletrnatively, clusters may be an artefact of the researcher.

• Clusters may be either of equal sizes or of different sizes.

EXAMPLES

• A village is a collection of households• A household may comprise several people• A traditional authority may comprise several

villages

SAMPLING PROCEDURE

• Identify clusters.• Compile a list of clusters• Obtain a random sample of clusters.• Study members of clusters.

MULTI-STAGE SAMPLING

• If a sample of clusters is obtained and every member of each sampled cluster is included in the study then this is known as a first-stage cluster-sampling scheme.

• Sometimes this is done for several stages.

• This is known as multistage sampling.

EXAMPLE

• Sample several districts. • From each sampled district, sample several

schools. • From each sampled school, sample 100 pupils.

STRATIFICATION

• In some cases, the population is divided into several divisions based on some attribute e.g. sex, race, religion, district, tribe etc.

• Within each subdivision, members are homogeneous and between subdivisions, members are heterogeneous.

• The process of dividing populations into such categories is called stratification

REASONS FOR STRATIFICATION

• To achieve gain in precision.• Administrative convenience• To collect data of known precision for certain

divisions of the population.• To reduce sampling costs: Sampling problems

may differ in different parts of the population.

STRATIFIED RANDOM SAMPLING

• Random samples of equal or different sizes are each drawn from each stratum.

• THE END