provided so users do not need to run the analysis several times. It shows how sensitive
sample size is to variations in p of ±5% and ±10% and variations in the allowable error of
±.5% and ±1%. An extension of this feature is to add a cost factor (such as x dollars per
respondent) and to show how the sensitivity estimates affect the cost of the sample.
7. The equiprobable aspects of a random sample can be demonstrated in a number of different
ways. Here are two examples:
Some Internet sites are devoted to lotteries and gambling. Although we do not have a
specific one in mind, you might consider assigning a student or a team of students to
search out one or more that lists the lottery numbers selected over time, perhaps for your
state if you have a state lottery. Have students calculate the percent of times each number
was drawn and comment on whether or not they have found the random selection process
used to embody equal probability in all cases.
Put 3 x 5 index cards with students’ names in a hat or a box and have a series of actual
blind draw samples. Replace the drawn names to the population pool after each sample.
Maintain a record of how often each student’s name is selected.
8. The greater efficiency of systematic sampling over simple random sampling can be
demonstrated with a class exercise. Identify two groups of students and give each a copy of
the same page from the telephone book. Tell the first group to select 10 household names
using a table of random numbers or random numbers generated via a spreadsheet program
such as Microsoft Excel (simple random sampling), and tell the second group to select 10
names using systematic sampling. The second group should finish before the first group.
9. A class exercise that illustrates basic differences between various sample methods is to use a
data set with 10 or fewer variables listed in labeled columns and containing 200 or more
respondents (an Excel or other spreadsheet printout will work). Break the class into several
teams and give specific instructions for each team to draw 30 respondents and calculate the
percentage distribution or mean of the variables. Each team should be assigned a different
sampling method (convenience, simple random sampling, etc.). Have students time their
teams or raise their hands when they have completed the work. Student teams using
nonprobability sampling (except quota) will finish first, while teams using the more tedious
probability methods such as stratified simple random sampling will finish last. However,
nonprobability sampling method teams will have findings different from the true percentage
distributions and means, although probability sampling method teams will have findings
close to the actuals. (Note: Instructors will have to provide a table or list of random numbers
to teams needing them. Such tables can be generated by the random numbers feature of the
XL Data Analyst.)
10. When learning about the skip interval used in systematic sampling, students sometimes ask
how to determine the population size when a directory or phone book is used. In the absence
of a precise number, the size is usually estimated by multiplying the approximate number of
names on each page by the number of pages. Some adjustments may need to be made for
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