Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a stratified sample, divide the population into groups called strata and then take a proportionate number from each stratum. For example, you could stratify (group) your college population by department and then choose a proportionate simple random sample from each stratum (each department) to get a stratified random sample. To choose a simple random sample from each department, number each member of the first department, number each member of the second department, and do the same for the remaining departments. Then use simple random sampling to choose proportionate numbers from the first department and do the same for each of the remaining departments. Those numbers picked from the first department, picked from the second department, and so on represent the members who make up the stratified sample.
A survey of geographical regions can be done using stratified sampling where regions with similar habitat, elevation, and soil type can be divided into strata. Stratified random sampling can also be used to study elections' polling, people who work overtime hours, life expectancy, the income of varying populations, and income for different jobs across a nation.
This text is adapted from Openstax, Introductory Statistics, Section 1.2 Data, Sampling, and Variation in Data and Sampling
分层抽样方法通常用于研究异质总体(具有较大变化的总体)。
在这里,总体被划分为两个或多个具有共同特征的子组或层 – 在本例中,为一种共同颜色。每个层代表共享特征的一个同质组。
层是互斥的,这意味着主题必须仅存在于一个层中,例如红色必须仅存在于层 1 中。它们还必须详尽无遗,这意味着所有具有共同特征的主体,在本例中是所有相同颜色的球,必须存在于单个层中。
然后,从每个层中随机抽取一些受试者并组合形成样本。
例如,假设你想知道 7 到 12 年级学生的平均体重。由于该群体中有不同年龄段的学生,因此该群体内的权重差异很大。
因此,学生分为两个阶层。然后,从每个层中随机抽取学生形成样本,并计算平均权重。
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