Chapter 9

Question 1

Procedure for Drawing a Sample

Answer 1

1. Define the target population
2. identify the sampling frame
3. Select a Sampling Procedure
4. Select a Sampling Procedure
5. Select the Sample Elements
6. Collect the Data from the Designated Elements

Question 2

How to define a target population

Answer 2

A population (N) is all cases that meet designated specifications for membership in the group

Example – Target population: Households in the city limits of Sacramento, CA, with one or more children under the age of 18 living at home

Question 3

Parameter

Answer 3

A characteristic or measure of a population

Question 4

Statistic

Answer 4

A characteristic or measure of a sample

Question 5

identify the sampling frame

Answer 5

The list of population elements from which a sample (n) will be drawn

Question 6

Nonprobability Sample

Answer 6

A sample that relies on personal judgment in the element selection process

Question 7

Probability Sample

Answer 7

A sample in which each target population element has a known, nonzero chance of being included in the sample

Question 8

Convenience Sample (Nonprobability Technique)

Answer 8

Population elements are sampled simply because they are in the right place at the right time

Question 9

Judgment Sample (Nonprobability Technique)

Answer 9

Population elements are handpicked because they are expected to serve the research purpose

Example – Hire panelists who are knowledgeable about the issue being researched rather than selecting them at random

Question 10

Snowball Sample (Nonprobability Technique)

Answer 10

Initial sample chosen by a probability technique (e.g., systematic sampling) then the population elements are asked for referrals of others they know who might be interested in participation

Example – A demand study for a new product where initial respondents know people with a high interest level within the product category

Question 11

Quota Sample (Nonprobability Technique)

Answer 11

Sample chosen so that the proportion of sample elements with certain characteristics is about the same as the proportion of the elements with the characteristics in the target population

Stated more simply, certain important characteristics of the population are represented proportionately in the sample

Question 12

Simple Random Sample (Probability Technique)

Answer 12

Walking down the street and passing out surveys to unknown people “at random” is “random” in the everyday sense, but not random in a scientific sample sense

Question 13

Systematic Sample (Probability Technique)

Answer 13

Sample in which every kth element (k = sampling interval) in the population is selected for the sample pool after a random start

Example – Research Problem: Investigate 250 undergraduate student attitudes toward controversial new technology fee

Question 14

Stratified Sample (Probability Technique)

Answer 14

Sample in which (1) the population is divided into mutually exclusive and exhaustive subsets and (2) a simple random sample of elements is chosen independently from each group/subset

Question 15

Cluster Sample (Probability Technique)

Answer 15

Like stratified sampling, (1) the population is divided into mutually exclusive and exhaustive subsets

Unlike stratified sampling, (2) a simple random sample of subsets (i.e., clusters) is chosen

Most appropriate when subsets (or strata) are heterogeneous within but homogeneous between with respect to key variables

Question 16

Area Sampling (Probability Technique)

Answer 16

A form of cluster sampling that uses census tracks or city blocks as sampling units

Question 17

Nonprobability Sample

Answer 17

Neither sampling error nor the margin of sampling error can be estimated or calculated

Thus, results are not generalizable from the sample to the population

Question 18

Probability Sample

Answer 18

One can statistically assess level of sampling error

Thus, results are generalizable from the sample to the population

Question 19

Determine the Sample Size

Answer 19

Three basic factors affect the size of sample needed when working with a probability sample
Amount of Diversity or Variation
As diversity/variation increases, larger samples are required
Degree of Precision
As need for precision increases, larger samples are required
Degree of Confidence
Confidence increases as sample size increases
At any given sample size, there is a trade-off between confidence and precision.
Higher precision means lower confidence unless we can increase the sample size