1. Prepare of list of all the players and number them 1 through 750 2. Randomly pick a starting place in the random number table 3. Select 10 three-digit numbers between 1 and 750
Using a Table of Random Numbers Example: 1. Choose a starting point randomly. Let’s say it’s 3:04pm, so we choose row 3, column 4 2. The number is 03759, so the first player is player number 37. 3. Move in any direction you want. Let’s say to the right. So the next player is player 447. 4. The next two column on the right has number > 750. So the next available column is 18910, so player 189 is selected.
If you do not have a list of the entire population to begin with, you can use the systematic random sample SYSTEMATIC RANDOM SAMPLE A random starting point is selected, and then every 𝑘 th member of the population is selected Systematic Sample
Example: Stood’s Grocery Store wants to study the length of time customers spend in their store. They decide to select 100 customers over 4 days (25/day), Mon-Thurs. Randomly select the days of the week, the times, and the starting point of the study, then systematically select the customers and measure the time each spends in the store Time Day Random Number Begin selection process Measure length of time for selected customer
▪ When the population can be divided into groups based on some characteristic, use stratified random sampling ▪ Guarantees that each group, called strata, is represented in the sample STRATIFIED RANDOM SAMPLE A population is divided into subgroups, called strata, and a sample is randomly selected from each stratum Stratified Sample
Example ▪ A study of 50 of the 352 largest US firms’ ad spending. The objective is to determine whether firms with high returns on equity spend more on advertising than firms with low returns on equity ▪ Begin by identifying the strata, then use random sampling within each group based on relative frequencies to collect the sample
Cluster sampling is a common type of sampling, used to reduce the cost of sampling over large geographic areas CLUSTER SAMPLING A population is divided into clusters using naturally occurring geographic or other boundaries. Then clusters are randomly selected and a sample is collected by randomly selecting from each cluster Clustered Sampling