purposive sampling

(noun)

occurs when the researchers choose the sample based on who they think would be appropriate for the study; used primarily when there is a limited number of people that have expertise in the area being researched

Related Terms

  • nonresponse

Examples of purposive sampling in the following topics:

  • Variation in Samples

    • Doreen uses systematic sampling and Jung uses cluster sampling.
    • Doreen's sample will be different from Jung's sample.
    • Even if Doreen and Jung used the same sampling method, in all likelihood their samples would be different.
    • Samples of only a few hundred observations, or even smaller, are sufficient for many purposes.
    • Be aware that many large samples are biased.
  • Using Chance in Survey Work

    • In order to conduct a survey, a sample from the population must be chosen.
    • Probability sampling includes: Simple Random Sampling, Systematic Sampling, Stratified Sampling, Probability Proportional to Size Sampling, and Cluster or Multistage Sampling.
    • Hence, because the selection of elements is nonrandom, non-probability sampling does not allow the estimation of sampling errors.
    • Information about the relationship between sample and population is limited, making it difficult to extrapolate from the sample to the population.
    • Non-probability sampling methods include accidental sampling, quota sampling, and purposive sampling.
  • Sampling

    • Sampling involves providing a sample of a consumer product to consumers so that they may try said product before committing to a purchase.
    • The purpose of a free sample is to acquaint the consumer with a new product, and is similar to the concept of a test drive, in that a customer is able to try out a product before purchasing it.
    • According to the Product Sampling Study by Arbitron, sampling successfully reaches 70 million consumers every quarter, and one-third of customers who try a sample will buy the sampled product in the same shopping trip, and 58 percent of those surveyed reported that they would buy the product again.
    • Marketers who are considering sampling their next product introduction should define the objectives of the sampling program.
    • There are a number of popular sampling techniques:
  • F Distribution and One-Way ANOVA: Purpose and Basic Assumptions of One-Way ANOVA

    • The purpose of a One-Way ANOVA test is to determine the existence of a statistically significant difference among several group means.
    • Each population from which a sample is taken is assumed to be normal.
  • Sampling

    • Paint chips are samples of paint colors that are sometimes offered as free samples.
    • The purpose of a free sample is to acquaint the consumer with a new product.
    • Paint chips are samples of paint colors that are sometimes offered as free samples.
    • Sampling has been around for ages.
    • Taking a sample doesn't turn people into customers unless you have asked permission to give them the sample.
  • Continuous Sampling Distributions

    • In the previous section, we created a sampling distribution out of a population consisting of three pool balls.
    • Now we will consider sampling distributions when the population distribution is continuous.
    • Note that although this distribution is not really continuous, it is close enough to be considered continuous for practical purposes.
    • As before, we are interested in the distribution of the means we would get if we sampled two balls and computed the mean of these two.
    • Therefore, it is more convenient to use our second conceptualization of sampling distributions, which conceives of sampling distributions in terms of relative frequency distributions-- specifically, the relative frequency distribution that would occur if samples of two balls were repeatedly taken and the mean of each sample computed.
  • Introduction to Sampling Distributions

    • Similarly, if you took a second sample of 10 people from the same population, you would not expect the mean of this second sample to equal the mean of the first sample.
    • Specifically, it is the sampling distribution of the mean for a sample size of 2 (N = 2).
    • Then this process is repeated for a second sample, a third sample, and eventually thousands of samples.
    • It is also important to keep in mind that there is a sampling distribution for various sample sizes.
    • (Although this distribution is not really continuous, it is close enough to be considered continuous for practical purposes. ) As before, we are interested in the distribution of means we would get if we sampled two balls and computed the mean of these two balls.
  • Applications of Statistics

    • In calculating the arithmetic mean of a sample, for example, the algorithm works by summing all the data values observed in the sample and then dividing this sum by the number of data items.
    • This single measure, the mean of the sample, is called a statistic; its value is frequently used as an estimate of the mean value of all items comprising the population from which the sample is drawn.
    • Once a sample that is representative of the population is determined, data is collected for the sample members in an observational or experimental setting.
    • This data can then be subjected to statistical analysis, serving two related purposes: description and inference.
    • Representative sampling assures that inferences and conclusions can safely extend from the sample to the population as a whole.
  • Sampling

    • (1) Qualitative marketing research: This approach is generally used for exploratory purposes with asmall number of respondents and is not generalizable to the whole population.
    • Samples are collected and statistics are calculated from the samples so that one can make inferences or extrapolations from the sample to the population as a whole.
    • This process of collecting information from a sample is referred to as sampling.
    • The best way to avoid a biased or unrepresentative sample is to select a random sample, also known as a probability sample.
    • A random sample is defined as a sample where each individual member of the population has a known, non-zero chance of being selected as part of the sample.
  • The Problems with Polls

    • In practice, pollsters need to balance the cost of a large sample with the reduction in sampling error.
    • Since some people do not answer calls from strangers or refuse to answer the poll, poll samples may not be representative samples from a population due to a non-response bias.
    • Error due to bias does not become smaller with larger sample sizes--taking a larger sample size simply repeats the same mistake on a larger scale.
    • In statistics, self-selection bias arises in any situation in which individuals select themselves into a group, causing a biased sample with non-probability sampling.
    • There may be a purposeful intent on the part of respondents leading to self-selection bias whereas other types of selection bias may arise more inadvertently, possibly as the result of mistakes by those designing any given study.
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