![]() ![]() Say we want to study a population by its age. This would give a sample that more accurately represents the characteristics of the overall population. You could divide the population into subgroups by a similar attribute such as age, race, gender, income level, etc. If you want a more precise representation of the overall population, you could use stratified random sampling instead. A random sample is easier to create than a stratified random sample, but it may not tell you much about the characteristics of the population. If you have 100 people in a population size and you want a random sample size of 10% of the population, you could put 100 names in a hat, pull out 10 names, and create a simple random sample of 10% of the population. The population is not broken into subpopulations before the random sample is selected. Simple random sampling is a sampling method where every member of the population has an equal chance of being selected. Then we would randomly select 20 lemon ones, and so on, to create our stratified random sample of 100 gummies. In this example, we would randomly select 10 orange gummy bears from the original 100 possible choices to create that stratum of our sample. We select gummy bears of each flavor for our smaller bag of 100 in the same ratio as the flavor in the giant bag of 1000, as shown in column 3.We figure the ratio of each flavor to the total population, as shown in column 2. ![]() We count the number of orange, lemon, lime, and raspberry gummy bears, as shown in column 1.Say we have a giant bag of 1,000 gummy bears of assorted flavors, and we want to divide the giant bag into 10 smaller bags of 100 gummy bears each, with each smaller bag containing the exact ratio of flavors that the giant bag has. Then you randomly select individuals from each stratum relative to the percentage that each group exists in the total population. Stratified random sampling is a method of sampling that ensures the ratio of each subgroup (stratum) to the entire population size is the same as the ratio of its sample counterpart stratum to the sample population size.įirst, you divide the population into strata based upon a particular characteristic. Each of the assets selected for the portfolio would be in proportion in market capitalization to those in the index. A portfolio manager might use the market capitalization of the assets in an index to create a stratified sample. Indexes can be divided into subgroups using one or more characteristics, such as market capitalization or industry. A portfolio manager can select assets for an index-tracking portfolio so that it copies the structure of the index with fewer assets. ![]() Stratified random sampling is a sampling technique portfolio managers commonly use to create an investment portfolio that replicates a stock or bond index without having to buy all of the stocks or bonds in the index. Then you randomly select individual subjects from within each subgroup (stratum) to create an accurate mini-sample that is proportional to the overall population. Stratified random sampling (aka proportionate stratified random sampling) is a type of probability sampling where you divide an entire population into different subgroups (strata). There are many different sampling designs. Viewing a smaller sample of a population is more accessible than considering an entire population. Sampling is a statistical technique that takes a representative sample size from a target population to study the characteristics of the overall population. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |