Lecture – 7 Sample Design and Sampling Procedure

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Business Research Methods

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Lecture – 7

Sample Design and Sampling Procedure

Determination of Sample Size: A review of Statistical theory

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Sample Design and Sampling Procedure


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Pragmatic reasons

Accurate and reliable results

Destruction of test units

Why Sampling

Budget and time constraints. Often,

Not be possible to contact the whole population

Samples are accurate only when researchers have taken care.

A sample may be more accurate than a census.

In a census there is greater likelihood of non-sampling errors.

A small, well-trained, closely supervised group may do a more accurate job

At times testing require the destruction

If all tested that way, there would be no product left after testing.

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EMBA-1Stages in Sample Selection

Define the target population

Select a sample frame

Determine if a probability or non probability sample will be chosen

Plan procedure for selecting sampling units

Determine sample size

Select actual sampling units

Conduct fieldwork


EMBA-1Types of Sampling

Non Probability Sampling

Probability Sampling


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Convenience sampling

Judgment sampling

Quota sampling

Snowball sampling



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Simple random sampling

Systematic sampling

Stratified sampling

Proportional versus disproportional strata

Cluster sampling



EMBA-1Internet Sampling

Internet surveys allow researchers to rapidly reach a large sample. This is both an advantage and a disadvantage.

Sample size requirements can be met overnight or in some cases almost instantaneously.

A major disadvantage of Internet surveys is the lack of computer ownership and Internet access among certain segments of the population.


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Determination of Sample Size: A review of Statistical theory


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Descriptive Statistics

Inferential statistics

Sample Statistics

Population parameters

Basic Terminology

Statistics used to describe or summarize information about population or sample

Statistics used to make inferences or judgments about a population on the basis of a sample

Variables in a population or measured characteristics of a population

Variables in a sample or measures computed from sample data


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Frequency Distribution

Percentage distribution

Central Tendency

Measure of Dispersion

Normal Distribution

Making the Data Useable


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Central Tendency


Normal Distribution


Mean

Median

Mode


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Central Tendency


Normal Distribution


Range

Deviation Scores

Variance

Standard Deviation


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Central Tendency


Normal Distribution


Z =Value of X – Mean

Standard Deviation

X

MeanZ

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error sampling small aX

Some Formula

Xcl SZ ERRORSAMPLING SMALL

XclSZ E E X2

Ezs

nn

SSx

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EMBA-1Factors of Sample Size

• Variance (standard deviation)

• Magnitude of error

• Confidence level

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Sample Size Formula - Example

Suppose a survey researcher, studying expenditures on lipstick, wishes to have a 95 percent confident level (Z) and a range of error (E) of less than $2.00. The estimate of the standard deviation is $29.00.

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2

E

zsn

2

00.2

00.2996.1

2

00.2

84.56

242.28 808


Suppose a survey researcher, studying expenditures on lipstick, wishes to have a 95 percent confident level (Z) and a range of error (E) of less than $2.00. The estimate of the standard deviation is $29.00.

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Suppose, in the same example as the one before, the range of error (E) is acceptable at $4.00, sample size is reduced.


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2

E

zsn

2

00.4

00.2996.1

2

00.4

84.56

221.14 202


Suppose, in the same example as the one before, the range of error (E) is acceptable at $4.00, sample size is reduced.

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99% Confidence

Calculating Sample Size

1389

265.372

253.74

2

2)29)(57.2(n

2

347 6325.18 2

453.74

2

4)29)(57.2(n

2

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npp

or

npq

ps

)1(

Standard Error of the Proportion

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pclSZp

Confidence Interval for a Proportion

2

2

EpqZ

n

Confidence Interval

XclSZ E

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2

2

Epqz

n

Where: n = Number of items in samples

Z2 = The square of the confidence interval in standard error units.

p = Estimated proportion of success

q = (1-p) or estimated the proportion of failures

E2 = The square of the maximum allowance for error between the true proportion and sample proportion or zsp squared.

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Calculating Sample Size at the 95% Confidence Level

753001225.

922.

001225

)24)(.8416.3(

)035( .)4)(.6(.)96 1. (

n4.q

6.p2

2

As given:

Suppose a simple random sample shows 60% of the respondents (p) recognize the name. Researcher wishes to estimate with 95% confidence (I.e., Z=1.96) that the allowance for sampling error is not more that 3.5% (E).

Solution:

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? Any Question?

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Thanks for your

contribution

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EMBA-1Assignment

Gp Assignment

Case-23: Business Forum Industry

Submission date is 17th Jul

Submission time: 0630 p.m.

Selected person will present for 10 mins

Discussion to focus, how the data were analyzed

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See You Next Week


EMBA-1Stages in Sampling

What is the relevant population? In many cases this is not a difficult question, but in other cases, the decision may be a difficult one.

Answering questions about the crucial characteristics of the population is the usual technique for defining the target population. The question “Whom do we want to talk to?” must be answered.

Target population

Sample frame

Sampling Method Choice

Procedure for sampling units


Actual sampling units

Conduct fieldwork



A sampling frame is a list of elements from which the sample may be drawn.

The sampling frame is also called the working population, because it provides the list that can be operationally worked with.

Target population

Sample frame





Conduct fieldwork



Probability or Non probability sample

Target population

Sample frame





Conduct fieldwork

In probability sampling every element in the population has a known nonzero probability of selection; each member of the population has an equal probability of being selected.

In nonprobability sampling, the probability of any particular member of the population being chosen is unknown. Nevertheless, there are occasions when the nonprobability samples are best suited for the researcher’s purpose.



The sampling unit is a single element or group of elements subject to selection in the sample.

If the target population has been divided into stages, the term primary sampling unit (PSU), secondary sampling units, or tertiary sampling units is used .

When there is no list of population elements, the sampling unit is generally something other than the population element. For example, in a random digit dialing study the sampling unit will be telephone numbers.

Target population

Sample frame





Conduct fieldwork


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Judgment sampling

Quota sampling

Snowball sampling


Researchers generally use convenience samples to obtain a large number of completed questionnaires quickly and economically

Convenience samples are best utilized for exploratory research when additional research will subsequently be conducted with a probability sample


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Judgment sampling

Quota sampling

Snowball sampling


Judgment or purposive sampling is a nonprobability technique in which an experienced individual selects the sample upon his or her judgment about some appropriate characteristic required of the sample members


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Judgment sampling

Quota sampling

Snowball sampling


The purpose of quota sampling is to ensure that the various subgroups in a population are represented on pertinent sample characteristics to the exact extent that the investigators desire


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Judgment sampling

Quota sampling

Snowball sampling


Snowball sampling refers to a variety of procedures in which initial respondents are selected by probability methods, but additional respondents are then obtained from information provided by the initial respondents. This technique is used to locate members of rare populations by referrals.


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Systematic sampling

Stratified sampling


Cluster sampling


A simple random sample is a sampling procedure that assures that each element in the population will have an equal chance of being included in the sample


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Systematic sampling

Stratified sampling


Cluster sampling


Systematic sampling is extremely simple: An initial starting point is selected by a random process; then every nth number on the list is selected.


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Systematic sampling

Stratified sampling


Cluster sampling


In stratified sampling, a subsample is drawn utilizing a simple random sample within each stratum. The reason for taking a stratified sample is to have a more efficient sample than could be taken on the basis of simple random sampling


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Systematic sampling Stratified sampling Proportional versus

disproportional strata

Cluster sampling


If the number of sampling units from each stratum is in proportion to the relative population size of the stratum, the sample is a proportional stratified sample.


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Systematic sampling

Stratified sampling


Cluster sampling


The purpose of cluster sampling is to sample economically while retaining the characteristics of a probability sample. In a cluster sample, the primary sampling unit is no, longer the individual element in the population (for example, grocery stores) but a larger cluster of elements located in proximity to one another (for example, cities). The area sample is the most popular type of cluster sample.

Lecture – 7 Sample Design and Sampling Procedure

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