BYBY

MICHAEL POKU-BOANSIMICHAEL POKU-BOANSI

In November 1988, George Bush was elected president of the US with 54% of the popular vote as against 46% for Michael Dukahis.

Prior to the election, a number of political polls had predicted the Bush victory.

Bush Dukahis

CBS/New York Times 57 43

ABC/Washintong Post 56 44

Gallop Poll 56 44

Gordon Black/USA Today/CNN 55 45

KRC/APN 55 45

Harris Poll 53 47

NBC/Wall Street Journal 53 47

Although the poll estimates were varied, you can see how they were clustered around the actual election-day results (54%, 46%).

Ques. Now how many interviews do you suppose it took each of these pollsters to come with a few % points in estimating the behaviour of about a 100 million voters?

Ans. Fewer than 2000

The level of accuracy in due to the fact that, the sample population provided useful descriptions of the total population and did contain essentially, the same variations that exist in the population.......This is what sampling is all about.

Sampling is the process of selecting observations. In order words, it is selecting a part to represent a whole.

• Sampling allows researchers to draw precise inferences on all the units (a set) base on relatively small number of units (a subset) when the subsets accurately represent the relevant attributes of the whole set.

• Is associated with coverage and scale of a survey.

Rationale i. Saves money – although unit cost of total

coverage will be lowerii. Saves labouriii. Saves time.

Element – an element is that unit about which information is collected and that provides the basis of analysis. It can be people or certain types of people, families, social clubs or company.

NB. Elements and units of analysis are often the same in a given study, though the former refers to sample selection and the latter refers to data analysis.

Population – It is aggregation of study elements. In other words it is the entire set of relevant units or cases or individuals that fit a certain specification e.g. hhs,, hs, teachers, students etc.

Study population – Is that aggregation of elements from which the sample is actually selected.

Sampling Unit – Is that element or set of elements considered for selection in some stage of sampling. In a simple-stage sample, the sampling unit are the same as the elements. In a more complex samples, different levels of sampling units may be employed e.g Census blocks in a city – selection of sample households in the selected blocks – selection of sample of adults from the selected households.

Sample Frame – Is the actual list of sampling units from which the sample is selected. In a single-stage sampling design, the sample frame is simply a list of the study population e.g. Student roster could be the sample frame when students are selected from it. If available, its reliability has to be assessed – updating.

Limitations of Sample Frame:• Incomplete frames - when some units are

missing • Cluster elements - when samples are listed in

clusters rather than individuals. Individual clusters must be listed.

• Blank foreign elements – when some of the units are not included in the research population e.g. Non-students in a youth list – new settlers, Error in sampling Frame – 1936 Us Election.

Observation Unit – An observation unit or unit of data collection is an element or aggregation of elements from which information is collected.

The unit of analysis and unit of observation are often the same, but they need not be the same always. For example, a researcher may interview heads of households (observation units) to collect information about all members of the households (the unit of analysis).

Sample size - the selected number of the population to be surveyed. How is this determined?

 Sample fraction - Sample size

expressed as a ratio of the frame.

Sample randomness - distribution – spread of sample size

There are two types of sampling methods – Probability and Non-probability sampling methods.

Probability Sampling The ultimate purpose of sampling is to select a

set of elements from a population in such a way that descriptions of these elements accurately portray the parameters of the total population from which the elements are selected.

Probability sampling enhances the likelihood of accomplishing this aim and also provides methods for estimating the degree of probable success.

• Random selection is the key to this process.

• In a random selection, each element has an equal chance of selection independent of any other event in the selection process.

• Mean of the sample population will be close to the mean of the total population.

• The bigger the sample, the closer the sample mean to the total population mean.

Types of Probability Sampling1. Simple random – it is the basic probability design and is

incorporated in almost all elaborate sample design. It is a process by which each member of the sample population has an equal chance or known non-zero chance of being selected.

Procedure

Identification number must be given to every unit on member of the population.

a) Lottery Approach b) Use of random tables  Is applicable when a sample frame can be secured or

compiled.

2. Systematic sampling – every Kth element in the total list is chosen systematically for inclusion in the sample. The first element is selected by simple random process.

3. Stratified sampling – This method is not an alternative to the simple random and systematic sampling methods but it represents a possible modification in their use. Stratified sampling ensures a greater degree of representativeness – thereby reducing the probable sampling error.

- disaggregating of the population into coherent sub-groups so that the sample becomes more representative.

- ensures that different groups of population are adequately represented in the sample.

- it combines homogeneity and heterogeneity at different levels

4. Cluster sampling - It is often used in large surveys because it is not very expensive. It involves selecting larger grouping called clusters from which the sampling units are chosen.

Different categories or levels  • Major – states, regions, districts, cities• Medium – village, town, NB. Etc• Compound housed, classes

- Used in situations where no reliable sample frame exist- Can be used to estimate population when census data are not available

5. Multi-stage Sampling - Sub – Sampling in stages – combination of sampling techniques E.g. choosing about 1000 farmers for a study Region, Districts then to individuals.

Even though no probability sample will be perfectly representative in all respect, controlled selection methods permit the researcher to estimate the degree of expected error in that regard.

In spite of the above comment, it is sometimes not possible to use standard probability sample methods or sometimes, it may be even appropriate to use non-probability sampling methods.

1. Purposive or Judgemental Sampling

• The use of purposive sampling is heavily dependent on the subjective decision of the researcher.

• Samples are selected because they satisfy certain criteria of interest. choice of informants/people with necessary information or knowledge or experience e.g.

- Mining & AIDS- Ethnic conflict- Disaster

• This method is very cheap.

• In spite of the subjectiveness of this approach in the selection of units, social scientist have used it with very good success.

2. Accidental Sampling

• This method is also known as “Convenience Sample”.

• It includes selecting anyone who is handy – i.e. anyone the interviewer meets on the street.

- Interviewers are influenced by a lot of factors in choosing the sample.

- Researcher must be aware of biases

3. Quota sampling

• This method is an improvement over Accidental sampling. The main objective is to select a sample that is similar as much as possible to the sampling population.

• Certain parameters and characteristics are defined for interviewers - Sex, age, ethnicity, education, etc.

- Interviewers are however, not told precisely who to interview 

- Although certain categories are defined, they are not necessarily proportional.

Problems associated with Quota Sampling

• Lack of information on which to base the quota

• Small number of variables can be used in quota - not all characteristics are visible e.g. social classes in Africa.

• Unavailability of certain segments of the population - e.g. sick and confined.

• Difficult to supervise - More useful in a situation where a small sample is

taken from a large population.- Be careful to generalise - check the

representativeness.

Determination of sample size