Market Research: Chapter 12

Front 1

Population

Back 1

-entire group under study as defined by research objectives

Front 2

Sample

Back 2

-subset of the population that should represent the entire group

Front 3

Sample Unit

Back 3

-basic level of investigation

Front 4

Census

Back 4

-accounting of the complete population

Front 5

Sampling Error

Back 5

-any error in a survey that occurs because a sample is used

Front 6

Sample Frame

Back 6

-master list of the entire population

Front 7

Sample Frame Error

Back 7

-degree to which the same frame fails to account for all of the population
-ex. telephone book does not contain ALL numbers

Front 8

Reasons for Taking Sample

Back 8

-practical considerations such as cost and population size
-inability of researcher to analyze huge amounts of data
-sample can produce precise results

Front 9

Probability Samples

Back 9

-members of the population have a known chance of being selected in the sample

Front 10

Non-probability Samples

Back 10

-the chances of selecting members for the population into the sample are unknown

Front 11

Simple Random Sampling

Back 11

-probability of being selected into the sample is known and equal for whole population

Front 12

Advantage of Random Sampling

Back 12

-known and equal chance of selection makes it a probability sample

Front 13

Disadvantage of Random Sampling

Back 13

-complete accounting of population needed
-cumbersome to provide unique designations to every member

Front 14

Systematic Sampling

Back 14

-way to select a random sample from a directory or list that is more efficient than random sampling-easier to do when you do not have data in electronic format
-skip interval

Front 15

Advantages of Systematic Sampling

Back 15

-approximate known and equal chance of selection
-efficiency (do not need whole pop.)
-less $

Front 16

Disadvantages of Systematic Sampling

Back 16

-small loss in sampling precision

Front 17

Cluster Sampling

Back 17

-method in which the population is divided into group, any of which can be considered a representative sample

Front 18

Advantages of Cluster Sampling

Back 18

-economic efficiency

Front 19

Disadvantages of Cluster Sampling

Back 19

-cluster specification error
-the more homogeneous the clusters, the more precise the sample results

Front 20

Stratified Sampling

Back 20

-method in which the population is separated into different strata and sample is take from each (freshman, sophomore..)

Front 21

Advantages of Stratified Sampling

Back 21

-more accurate overall sample of skewed populations

Front 22

Disadvantage of Stratified Sampling

Back 22

-more complex sampling plan requiring different sample size for each stratum

Front 23

Non-probability Sampling

Back 23

-selection is not based to fairness, equity or equal chance
1. convenience sampling
2. judgment sampling (guess of who should represent population)
3. referral sampling (snowball effect)
4. quota sampling (specific quota for certain types of people)