![]() ![]() $$ \textĪn analyst is analyzing the spending habits of people belonging to different annual income categories. We use the following formula to determine the number of households from each region to be included in the sample: What is the number of homes that have been sampled in each region? Given the differences in the composition of each region, the firm decides to draw a sample of 50 households, taking into account the total number of families in each. ![]() There are 160 households in town A, 60 in town B, and 80 in C. Town B mainly harbors retirees while most people in town C practice agriculture. Town A is adjacent to a major factory where most residents work, with most having kids of school-going age. The district has three distinct towns – A, B, which are urbanized, and C, located in a rural area. The company decides to carry out a survey aimed at estimating the mean number of hours households spend watching TV per week. Example: Stratified Random SamplingĪn advertising firm wants to determine the extent to which it needs to invigorate television advertisements in a district. The number of members chosen from any one stratum depends on its size relative to the population as a whole. The method is most appropriate for large populations that are heterogeneousin nature.Ī simple random sample is then drawn from within each stratum and combined to form the overall, final sample that takes heterogeneity into account. Each stratum is composed of elements that have a common characteristic (attribute) that distinguishes them from all the others. In stratified random sampling, analysts subdivide the population into separate groups known as strata (singular – stratum). The underlying feature in random sampling is that all elements in the population must have equal chances of being chosen. We may then use a computer to randomly generate 50 numbers between 1 and 100,000, where a given number represents a particular candidate who can be identified by their name or admission number. Next, we would randomly draw 50 numbers from the basket, one after the other, without replacement.Ī more scientific approach may also involve the use of random numbers where all the 100,000 candidates are numbered in a sequence (from 1 to 100,000). One approach may involve numbering each of the 100,000candidates, placing them in a basket, and shaking the basket to jumble up the numbers. Imagine that we wish to come up with a sample of 50 level I candidates out of a total of 100,000 level I candidates. Note that simple random sampling is preferred when the population data is homogenous. That is where stratified random sampling comes in. Differences within a population prompt statisticians to divide the members of a population into different, distinctive categories. However, simple random sampling is not appropriate when there are glaring differences within a population. ![]() The method attempts to come up with a sample that represents a population in an unbiased manner. Simple random sampling involves the selection of a sample from an entire population such that each member or element of the population has an equal probability of being picked. Probability Sampling Methods Simple Random Sampling On the other hand, convenience sampling and judgemental sampling are types of non-probability sampling techniques. You will recall that simple random sampling, stratified random sampling, and cluster sampling are types of probability sampling techniques. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |