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CHAPTER 7SAMPLING DESIGN

7.1 REASONS FOR SAMPLING

7.2 SAMPLE SIZE DECISION

7.3 SAMPLING METHOD

7.4 ERRORS IN SAMPLING

SAMPLING

SAMPLING

The process of selecting a sufficient number of elements from the

population, so that results from analyzing the sample are

generalizable to the population.

REASONS FOR

SAMPLING

Less cost

Less errors due to less

fatigue

Less time

Destruction of elements

avoided

SAMPLE SIZE DECISION

There are variety sample size decision that available . The choice can be defend on the following: Population The element Population frame Sample Sampling unit The subject

SAMPLE SIZE DECISION

a) Population -Refer to the entire group of people, events or

things of interest that the population that the researches wishes to investigate.

b) Element - Single member of the population. The census

is a count of all elements in the human population.

c) Population frame - the listing of all the element in the

population from which the sample is drawn. It is also known as sampling frame.

SAMPLE SIZE DECISION

d) Sample -Subset of the population. It is a

subgroup of the population selected using sampling method or design.

e) Sampling unit -the element or set of the elements that

is available for selection in some stage of the sampling process.

f) Subject -a subject is a single member of the

sample.

The sampling process

Define the population

Determine the sample

frame

Determine the sampling design

Determine the appropriate sample size

Execute the sampling process

Sample Size

Most research

• > 30 < 500 are appropriate

Sub-samples

• Min 30 for each category

Multivariate research

• At least 10 times more than the number of variables

Experimental research

• Can be low as 10 to 20

Sample size

Precision• How close the estimate to

the true population characteristics with low margin of error

Confidence

•How certain the estimate will really hold true for the population.•Commonly accepted confidence level ≤0.05 (95% confidence)

Population

Defined in terms Elements

Geographical

Boundaries & Time

Sample Frame

Physical representation of all the elements in the population from which the sample is drawn

Make sure that sample frame the latest and most up-to-date to avoid coverage error

Sampling DesignTarget population

of focus to the study

The exact parameters need to be investigated

Availability of sampling frame

Sample size needed

Costs associated to the sampling

design

Time frame available for

data collection

Sampling Design

Probability

sampling

Non-probabili

ty sampling

SAMPLING TECHNIQUES

Probability SamplingSimple Random Sampling

Systematic Sampling

Stratified Random Sampling

Cluster Sampling

Simple Random Sampling

PROCEDURE– Each element has a known

and equal chance of being selected

CHARACTERISTICS– Highly generalizable

– Easily understood

– Reliable population frame necessary

Systematic Sampling

PROCEDURE– Each nth element, starting with

random choice of an element between 1 and n

CHARACTERISTICS– Easier than simple random

sampling– Systematic biases when

elements are not randomly listed

Cluster Sampling

PROCEDURE

– Divide of population in clusters– Random selection of clusters– Include all elements from selected clusters

CHARACTERISTICS

– Intercluster homogeneity– Intracluster heterogeneity– Easy and cost efficient– Low correspondence with reality

Stratified SamplingPROCEDURE– The process of dividing

members of the population into homogeneous subgroups before sampling

– There are two types of stratified random sampling:• Proportionate

Stratum A B C

Population size 100 200 300

Sampling fraction 1/2 1/2 1/2

Final sample size 50 100 150

CHARACTERISTICS– Interstrata heterogeneity– Intrastratum homogeneity– Includes all relevant

subpopulations

•DisproportionateStratum A B C

Population size 100 200 300

Sampling fraction 1/2 ¾ 1/3

Final sample size 50 150 100

Nonprobability Sampling

Convenience Sampling

Judgment Sampling

Quota Sampling

Members of the population are chosen based on their relative ease of access.

The researcher chooses the sample based on who they think would be appropriate for the study.

A quota is established (say 65% women) and researchers are free to choose any respondent they wish as long as the quota is met.

5 Common Sampling Errors

oPOPULATION SPECIFICATION ERROR

oSAMPLE FRAME ERROR

oSELECTION ERROR

oNON-RESPONSE

oSAMPLING ERRORS

Measurement of Variables

Operational definition Scales

One lends itself to objective and precise

measurement;The other is more

nebulous and does not lend itself to accurate

measurement because of its abstract and

subjective nature.

Type of variables

Object – house, countries, restaurants.

Examples of characteristics of

objects are arousal seeking tendency,

achievement motivation,

organizational effectiveness

(Characteristics of) Objects

the assignment of numbers or other

symbols to characteristics (or

attributes) of objects according to a pre-specified

set of rules.

Measurement

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Operationalizing Concepts

Operationalizing concepts: reduction of

abstract concepts to render them

measurable in a tangible way.

Operationalizing is done by

looking at the behavioural dimensions,

facets, or properties denoted by

the concept.

Example

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ScaleTool or mechanism by which individuals are

distinguished as to how they differ from one

another on the variables of interest to our study.

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4 TYPES OF

SCALES

Nominal Scale• A nominal scale is one that allows the

researcher to assign subjects to certain categories or groups.

• What is your department?O Marketing O Maintenance

O Finance O Production O Servicing

O Personnel O Sales O Public Relations O Accounting

• What is your gender?O MaleO Female 30

Ordinal Scale

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Ordinal scale: not only categorizes variables in such a way as to denote differences among various categories, it also rank-orders categories in some meaningful way.

What is the highest level of education you have completed?

O Less than High School O High School O College Degree O Masters Degree O Doctoral Degree

Interval Scale• Interval scale: whereas the

nominal scale allows us only to qualitatively distinguish groups by categorizing them into mutually exclusive and collectively exhaustive sets, and the ordinal scale to rank-order the preferences, the interval scale lets us measure the distance between any two points on the scale.

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• Circle the number that represents your feelings at this particular moment best. There are no right or wrong answers. Please answer every question.

1. I invest more in my work than I get out of it

I disagree completely 1 2 3 4 5 I agree completely

2. I exert myself too much considering what I get back in return

I disagree completely 1 2 3 4 5 I agree completely

3. For the efforts I put into the organization, I get much in return

I disagree completely 1 2 3 4 5 I agree completely33

Ratio scale

• Indicates not only the magnitude of the differences but also their proportion.

THE END!!!

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