# MKT 500 Lecture Notes - Lecture 9: Simple Random Sample, Nonprobability Sampling, Telephone Directory

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11 Aug 2016

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Mkt 500: Chapter 9

Chapter 9: Selecting the Sample

BASIC CONCEPTS IN SAMPLES AND SAMPLING

Population

- Population: the entire group under study as defined by research objectives

- Researchers must use the description of the population precisely, whereas

managers use it in a more general way

Census

- Census: requires information from everyone in the population

- Ex. If you want to know the average age of members in the population, you

would have to ask each and every population unit his or her age and then

compute the average

- Since researchers realized the impracticality and outright impossibility of

taking a cense of the population they went on to use subsets or samples to

represent the targeted population

Sample and Sample Unit

- Sample: a subset of the population, and the sample unit pertains to the basic

level of investigation

- Sample unit: is the basic level of investigation – Ex. For Weight Watchers it

would be one person – another example would be a survey of hospital

purchases of laser surgery equipment, in this case the sample unit would be

hospitals since they are the ones being researched

Sample Frame and Sample Frame Error

- Sample frame: a master source of sample units in the population

- The sample frame doesn’t always correspond perfectly to the population

- Sample frame error: the degree to which the sample frame fails to account

for all the population

Sampling Error

- Sampling error: is an error in a survey that occurs because a sample is used

- Sampling error is caused by 2 factors:

1. Sample frame error

2. the size of the sample

REASONS FOR TAKING A SAMPLE

- Taking a sample is less expensive than taking a census

- Typical research firms or the typical researcher cannot analyze the huge

amounts of data that is generated using a census

- Although yes computers can be used they still cant handle large sums of data

and slow down

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PROBABILITY VERSUS NONPROBABILITY SAMPLING METHODS

- Probability samples: are samples in which members of the population have a

known chance of being selected in the model

- Nonprobability samples: are samples where the chances of selecting

members from the population into the sample are unknown

- With probability sampling, the method determines the chances of a sample

unit being selected into the sample

- With non probability methods there is no way to determine the probability

even if the population size is known because the selection technique is

subjective

- Nonprobability sampling is sometimes called “haphazard sampling” because

it is prone to human error and even subconscious bias

Probability Sampling Methods

- There are 4 probability sampling methods: simple random sampling,

systematic sampling, cluster sampling, and stratified sampling

Simple Random Sampling

- with simple random sampling, the probability of selection into the sample is

“known” for all members of the population

- Formula: sample size / population size = probability of selection

oThe random device method

The random device involves using an apparatus of some sort to

ensure that every member of the population has the same

chance of being selected into the sample

Ex. Flipping a coin, lottery numbers being selected using ping

pong balls, roulette wheels, a hand dealt in a poker game

oThe random number method

A tractable and more sophisticated application of the simple

random sampling is to use computer-generated numbers based

on the concept of random numbers, which are numbers whose

chance nature is assured

A computer easily handles data sets of thousands of

individuals; it can quickly label each one with a unique number

or designation, generate random numbers, and match the

random numbers with the unique designations of the

individuals in the data set to select or “pull” the sample

oAdvantages and Disadvantages of Simple random sampling

Pros

Provides unbiased estimates of the population

Guarantees that every member of the population has an

equal chance of being selected in the sample meaning

that the resulting sample, no matter what size, will be a

valid representation of the sample

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find more resources at oneclass.com