Sampling Methods

is systematic, controlled, empirical, and critical investigation of natural phenomena guided by theory and hypotheses about the presumed relations among such phenomena

is an organized and systematic way of finding answers to questions

is “a smaller (but hopefully representative) collection of units from a population used to determine truths about that population”

Why sample?

is the list from which the potential respondents are drawn

3 factors that influence sample representative- ness

When might you sample the entire population?

Who do you want to generalize to?

What population can you get access to?

How can you get access to them?

Who is in your study?

Sampling Breakdown

2 Types of Samples

Types of Probability (Random) Samples

Types of Non-Probability Samples

The sampling process comprises several stages:

can be defined as including all people or items with the characteristic one wishes to understand

scheme is one in which every unit in the population has a chance (greater than zero) of being selected in the sample, and this probability can be accurately determined.

When every element in the population does have the same probability of selection, this is known as an

When every element in the population does have the same probability of selection, this is known as an 'equal probability of selection' (EPS) design. Such designs are also referred to as

Any sampling method where some elements of population have no chance of selection (these are sometimes referred to as 'out of coverage'/'undercovered'), or where the probability of selection can't be accurately determined.

not allows the estimation of sampling errors.

It involves the selection of elements based on assumptions regarding the population of interest, which forms the criteria for selection

may turn any probability design into a nonprobability design if the characteristics of nonresponse are not well understood, since nonresponse effectively modifies each element's probability of being sampled.

• Applicable when population is small, homogeneous & readily available

• All subsets of the frame are given an equal probability. Each element of the frame thus has an equal probability of selection.

• It provides for greatest number of possible samples. This is done by assigning a number to each unit in the sampling frame.

• A table of random number or lottery system is used to determine which units are to be selected.

Estimates are easy to calculate

Disadvantages
• If sampling frame large, this method impracticable.

• Minority subgroups of interest in population may not be present in sample in sufficient numbers for study

Sampling schemes may be

- no element can be selected more than once in the same sample

an element may appear multiple times in the one sample

relies on arranging the target population according to some ordering scheme and then selecting elements at regular intervals through that ordered list.

involves a random start and then proceeds with the selection of every kth element from then onwards. In this case, k=(population size/sample size).

It is important that the starting point is not automatically the first in the list, but is instead randomly chosen from within the first to the kth element in the list

systematic sampling is an

ADVANTAGES:
• Sample easy to select
• Suitable sampling frame can be identified easily
• Sample evenly spread over entire reference population

DISADVANTAGES:
• Sample may be biased if hidden periodicity in population coincides with that of selection.
• Difficult to assess precision of estimate from one survey

Where population embraces a number of distinct categories, the frame can be organized into separate "strata." Each stratum is then sampled as an independent sub-population, out of which individual elements can be randomly selected

• Every unit in a stratum has same chance of being selected.

• Using same sampling fraction for all strata ensures proportionate representation in the sample.

• Adequate representation of minority subgroups of interest can be ensured by stratification & varying sampling fraction between strata as required.

Drawbacks to using _______.
• First, sampling frame of entire population has to be prepared separately for each stratum
• Second, when examining multiple criteria, stratifying variables may be related to some, but not to others, further complicating the design, and potentially reducing the utility of the strata.
• Finally, in some cases (such as designs with a large number of strata, or those with a specified minimum sample size per group), stratified sampling can potentially require a larger sample than would other methods

Draw a sample from each stratum

Choice-based sampling is one of the stratified sampling strategies. In this, data are stratified on the target and a sample is taken from each strata so that the rare target class will be more represented in the sample. The model is then built on this biased sample. The effects of the input variables on the target are often estimated with more precision with the choice-based sample even when a smaller overall sample size is taken, compared to a random sample

Stratification is sometimes introduced after the sampling phase in a process called

is an example of 'two-stage sampling'

• First stage a sample of areas is chosen;
• Second stage a sample of respondents WITHIN those areas is selected.
• Population divided into clusters of homogeneous units, usually based on geographical contiguity.
• Sampling units are groups rather than individuals.
• A sample of such clusters is then selected.
• All units from the selected clusters are studied.

Advantages :
• Cuts down on the cost of preparing a sampling frame.
• This can reduce travel and other administrative costs.

Disadvantages :
• sampling error is higher for a simple random sample of same size.
• Often used to evaluate vaccination coverage in EPI

Two types of cluster sampling methods

All of the elements within selected clusters are included in the sample.

A subset of elements within selected clusters are randomly selected for inclusion in the sample.

(1) _____ the best survey results occur when elements within strata are internally HOMOGENEOUS.

(2) However, with _________ , the best results occur when elements within clusters are internally HETEROGENEOUS.

• Complex form of cluster sampling in which two or more levels of units are embedded one in the other.

• First stage, random number of districts chosen in all states.

• Followed by random number of talukas, villages.

• Then third stage units will be houses.

• All ultimate units (houses, for instance) selected at last step are surveyed.

A method of assigning participants to groups in which pairs of participants are first matched on some characteristic and then individually assigned randomly to groups.

• The population is first segmented into mutually exclusive sub-groups, just as in stratified sampling.

• Then judgment used to select subjects or units from each segment based on a specified proportion.

• For example, an interviewer may be told to sample 200 females and 300 males between the age of 45 and 60.

• It is this second step which makes the technique one of non-probability sampling.

Sometimes known as grab or opportunity sampling or accidental or haphazard sampling.

• A type of nonprobability sampling which involves the sample being drawn from that part of the population which is close to hand. That is, readily available and convenient.

• The researcher using such a sample cannot scientifically make generalizations about the total population from this sample because it would not be representative enough.

• For example, if the interviewer was to conduct a survey at a shopping center early in the morning on a given day, the people that he/she could interview would be limited to those given there at that given time, which would not represent the views of other members of society in such an area, if the survey was to be conducted at different times of day and several times per week.

In social science research, snowball sampling is a similar technique, where existing study subjects are used to recruit more subjects into the sample.

Use results that are easy to get

The researcher chooses the sample based on who they think would be appropriate for the study. This is used primarily when there is a limited number of people that have expertise in the area being researched

• Method of first selecting a group of participants through a random sampling method and then asking that group for the same information again several times over a period of time.

• Therefore, each participant is given same survey or interview at two or more time points; each period of data collection called a "wave".

• This sampling methodology often chosen for large scale or nation- wide studies in order to gauge changes in the population with regard to any number of variables from chronic illness to job stress to weekly food expenditures.