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    Edward is conducting a survey of attitudes about the Head Start program. He wants to make sure he has members of all important subgroups of the American population in his sample. What sampling technique should he use?

    a. Simple random sampling

    b. Stratified random sampling

    c. Quota sampling

    d. Snowball sampling

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    SOLUTION This solution is FREE courtesy of BrainMass!

    Answer: b. Stratified random sampling

    Stratified Random Sampling: More often than not, you will want to not only examine the results from the overall population, but also understand the differences between key demographic subgroups within the population. For example, you might want to understand the differences between males and females or senior managers and regular employees. If you plan to look at distinct subgroups such as these, you should perform a stratified random sample. In a nutshell, this means you will need to select a separate random sample from each of the subgroups rather than just taking a single random sample from the entire group. The process is slightly more time consuming and will require you to survey a greater number of people overall, but this technique can be very valuable. If you want to conduct a stratified random sample, think carefully about the single most relevant demographic division that can be made between people within your population. It is probably not practical to conduct a stratified random sample on more than one demographic category as the process becomes much more complex and you will ultimately end up needing to survey almost the entire population if any of the subgroups are very small. In other words, if you want to look at age and position, you will need to look at each position/age combination and you might find very small numbers of people within some of these areas(http://www.custominsight.com/articles/random-sampling.asp).

    It is not quota sampling: why? Quota sampling has some similarity to stratified sampling however the selection of respondents within strata is non-random, but is left to the interviewer. This non-random element is its greatest weakness and quota versus probability has been a matter of controversy for many years. Statisticians criticise it for its theoretical weakness. Market and opinion researchers defend it for its cheapness and convenience. In quota sampling each interviewer is given an assignment of interviews based upon quotas that are representative of the whole population. The quota controls should form strata within the population having differing opinions on the subject understudy. Often they are age, sex, and social class. An example quota sample of size 20 with independent controls would be... (see http://www.cems.uwe.ac.uk/~pwhite/SURVEY1/node34.html for more information).

    I hope this helps and take care.

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