Business Static's

Business Static's

Business Static's

Question 1:

Answer 1: (a)

Target population

Target population is the age and group of people the experimenter plans to generalise the findings on. In this experiment the target population was sixth formers at St. Paul's school. It was hard to generalise due to the method of opportunity sampling. This method was biased because only students who were available participated in the experiment. It could be improved if a wider range of students and not only people who were 'free' to participate therefore this was not a representative sample. The number of participants who took part was only 24. This was too little to generalise to a school of 1080 students. It was hard to generalise beyond the target population, as there are individual differences, psychological differences and cultural differences between much of the population. In addition my sample was too small to generalise beyond target population.

Sampling frame

Sampling therefore is a very important part of the Market Research process. If you have surveyed using an appropriate sampling technique, you can be confident that your results will be generalised to the population in question. If the sample were biased in any way, for example, if the selection technique gave older people more of a chance of selection than younger people, it would be inadvisable to make generalisations from the findings.

Sampling methods are classified as either probability or nonprobability. In probability samples, each member of the population has a known non-zero probability of being selected. Probability methods include random sampling, systematic sampling, and stratified sampling. In non-probability sampling, members are selected from the population in some nonrandom manner. These include convenience sampling, judgment sampling, quota sampling, and snowball sampling. The advantage of probability sampling is that sampling error can be calculated. Sampling error is the degree to which a sample might differ from the population. When inferring to the population, results are reported plus or minus the sampling error. In non-probability sampling, the degree to which the sample differs from the population remains unknown.

Stratified sampling

Stratified sampling is commonly used probability method that is superior to random sampling because it reduces sampling error. A stratum is a subset of the population that share at least one common characteristic. Examples of stratums might be males and females, or managers and non-managers. The researcher first identifies the relevant stratums and their actual representation in the population. Random sampling is then used to select a sufficient number of subjects from each stratum. "Sufficient" refers to a sample size large enough for us to be reasonably confident that the stratum represents the population. Stratified sampling is often used when one or more of the stratums in the population have a low incidence relative to the other stratums.

A stratified sample is obtained by taking samples from each stratum or sub-group of a population. When we sample a population with several strata, we generally require that the proportion of each stratum in the sample should be the same as in the ...