ba260 module 4 mba degree

8/12/2019 BA260 Module 4 MBA Degree

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BA 26

RESEARCH METHODS FOR MANAEMENT SCIENCE

SAMPLING AND SAMPLING TECHNIQUES

POPULATION

CHOOSING A SAMPLE

THE DIFFERENT SAMPLING TECHNIQUES

THE DIFFERENT WAYS OF DERIVING SAMPLE

THE DIFFERENT PROBABILIT SAMPLING TECHNIQUE

THE DIFFERENT NON PROBABILITY SAMPLING

TECHNIQUE

POPULATION

In data gathering phase, the information is taken from a unit, which is part of a

collection of all such units called a population.

To illustrate:

If the data to be collected are the test results in BA260, the smallest unit under

investigation is a BA261 student and the population is the set of all students whoare taking BA260.

If you are interested in the number of television sets in Ozamiz City, the unit from

which data is collected can be a household, and the population is all the

households in Ozamiz City.

Population

A population is a collection of all units from which data is to be collected.

A unit in a population is also called an element of the pop ulat ion.

Because there is very rarely enough time or money to gather information from

everyone or everything in a population, the goal becomes finding a

representative sample (or subset) of that population.Composed of Two Groups

Target Population

Accessible population

1. Target population (universe)

The entire group of people or objects to which the researcher wishes to

generalize the study findings


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Meet set of criteria of interest to researcher

Examples:

All people with AIDS

All low birth weight infants

All pregnant teens

2. Accessible population

the portion of the population to which the researcher has reasonable access;

may be a subset of the target population

May be limited to region, state, city, county, or institution

Examples:

All people with AIDS in the metropolitan Manila area

All low birth weight infants admitted to the Misamis University Medical Center

(MUMC)

All pregnant teens in Ozamiz City


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Define the Target Population

The target population is the collection of elements or objects that possess the

information sought by the researcher and about which inferences are to be made. The

target population should be defined in terms of elements, sampling units, extent, and

time.

An elementis the object about which or from which the information is

desired, e.g., the respondent.

A sampling unitis an element, or a unit containing the element, that is

available for selection at some stage of the sampling process.

Extentrefers to the geographical boundaries.

Timeis the time period under consideration.

Sample

It is a subset or a representative part of the population.

The sample must be:

1. representativeof the population;

2. appropriately sized(the larger the better);

3. unbiased;

4. random(selections occur by chance); The above criteria are interrelated.

Frame

It is a listing of all the elements of the population.

A set of items or events possible to measure.

Is the list from which the potential respondents are drawn

Is needed so that everyone in the population is identified so they will have an

equal opportunity for selection as a subject (element).

Examples:

A list of all low birth weight infants admitted to the Misamis University MedicalCenter (MUMC).

A list of all pregnant teens in the city of Ozamiz.

Census

It is a process when the information is gathered for all units in the population.

Primary data is collected from every member of the target population.


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Census Sample

A census study occurs if the entire population is very small or it is reasonable to

include the entire population (for other reasons).

It is called a census sample because data is gathered on every member of the

population.

Sampling or Sample Survey

It is a process when only a part of a population is used to obtain data.

The process of selecting a group of people, events, behaviours, or other

elements with which to conduct a study.

Why sample?

The population of interest is usually too large to attempt to survey all of its

members.

A carefully chosen sample can be used to represent the population.

The sample reflects the characteristics of the population from which it is

drawn.When the size of the population is large, a census becomes long and tedious

process aside from having a prohibitive cost. To save on cost and time, a sample

survey is a convenient alternative. The information derived from the data in the sample

is then used to make some generalizations about the population. However, in making

this option, errors are unavoidable.

Different Sampling Techniques

1. Probability Sampling

- is one in which every unit in the Population has a chance (greater than zero) ofbeing selected in the Sample.

Probability samples are based on the mathematical theory of probability.

The surest way of providing equal probability of selection is to use the principle of

random selection.

This involves listing all members of the population (this list is called a sampling frame)

and then, in effect, 'pulling names out of a hat'; although you can use a random number

table to do this.

There are still likely to be differences between the sample and the total population, but

using a probability sample means that this should be by chance alone.


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Advantages of Probability Sampling

Probability sampling does not depend upon the existence of detailed information

about the universe for its effectiveness.

Probability sampling provides estimates which are essentially unbiased and have

measurable precision.

It is possible to evaluate the relative efficiency various sample designs only when

probability sampling is used.

Probability Sampling Includes:

Simple random sampling

Systematic sampling Cluster sampling

Stratified sampling

Multi-stage sampling

2. Non - Probability Sampling

- Any Sampling methods where some elements of population have no chance

of selection, or where the probability of selection cant be accurately determined.

Principles of non-probability sampling

There are theoretical and practical reasons for using non-probability sampling.

Theoretical reasons

Non-probability sampling techniques can often be viewed as an inferior

alternative to probability sampling techniques.

Non-probability sampling techniques can often be viewed in such a way because

units are not selected for inclusion in a sample based on random selection, unlike

probability sampling techniques.

As a result, researchers following a quantitative research design often feel that they are

forced to use non-probability sampling techniques because of some inability to use

probability sampling (e.g., the lack of access to a list of the population being studied).


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Practical reasons

Non-probability sampling is often used because the procedures used to select units for

inclusion in a sample are much easier, quicker and cheaper when compared with

probability sampling.

Types of non-probability sampling

There are five types of non-probability sampling techniques:

Quota sampling

Convenience sampling

Purposive sampling

Self-selection sampling

Snowball sampling

Probability sampling

Involves random selection in choosing the subjects or elements. Randomization

or random choice, is the hallmark of probability sampling. This is the process of

giving every member or each element of the population an equal chance to be

included in the sample representing the total population. This is the method of

choice for obtaining a representative sample.

TYPES OF PROBABILITY SAMPLING

1. Simple random sampling

2. Stratified random sampling

3. Cluster sampling

4. systematic sampling


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1. SIMPLE RANDOM SAMPLING

All elements are enumerated and listed in a sampling frame ( the technical name

for the list of elements from which the sample will be chosen) and selection is

done at random.

Each element has an equal chance or probability of being chosen as subjects of

the study. Simple random sampling is the basic technique of probability

sampling.

Random sample from whole population.

it is considered fair and therefore allows findings to be generalized to the whole

population from which the sample was taken.

It is sometimes called the lottery method

EXAMPLE: Select ing a simple random samp le of students

A simple random sample of 25 students is to be selected from a school of 500 students

Using a list of all 500 students, each student is given a number (1 to 500), and these

numbers are written on small pieces of paper. All the 500 papers are put in a box, after

which the box is shaken vigorously to ensure randomisation. Then, 25 papers are taken

out of the box, and the numbers are recorded. The students belonging to these

numbers will constitute the simple random sample.

ADVANTAGE:

Highly representative if all subjects participate; the ideal.

DISADVANTAGES:

Not possible without complete list of population members;

potentially uneconomical to achieve;

can be disruptive to isolate members from a group;

time-scale may be too long, data/sample could change

2. STRATIFIED RANDOM SAMPLING The researcher divides the population into two or more homogenous strata or

subsets from which an appropriate number of elements (equal or unequal

number) are selected at random. Although stratified random sampling requires

more labour and effort than simple random sampling, it enables researchers to

sharpen the precision and to improve representativeness of the final sample.


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Stratification may be based on common demographic attributes such as age,

gender, educational level, educational attainment, income level.

Random sample from identifiable groups (strata), subgroups, etc.

EXAMPLE: Strat i f ied samp l ing of ho useholds

A survey is conducted on household water supply in a district comprising 2,000

households, of which 400 (or 20%) are urban and 1,600 (or 80%) are rural. It is

suspected that in urban areas the access to safe water sources is much more

satisfactory than in rural areas (Figure 15.5). A decision is made to sample 200

households altogether, but to include 100 urban households and 100 rural households.

ADVANTAGE:

Can ensure that specific groups are represented, even proportionally, in the

sample(s) (e.g., by gender), by selecting individuals from strata list.

DISADVANTAGES:

More complex, requires greater effort than simple random; strata must be

carefully defined

3. CLUSTER OR MULTISTAGE SAMPLING

The researcher selects random samples from larger(general population) to

successively smaller units using either the simple random or stratified random

method.

Although cluster sampling tends to be less accurate than simple or stratified

random sampling, it is more economical and practical, particularly with a large

and widely dispersed population.

Random samples of successive clusters of subjects (e.g., by institution) until

small groups are chosen as units

ADVANTAGES:

Possible to select randomly when no single list of population members exists, but

local lists do;

data collected on groups may avoid introduction of confounding by isolating

members


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DISADVANTAGE:

Clusters in a level must be equivalent and some natural ones are not for

essential characteristics (e.g., geographic: numbers equal, but unemployment

rates differ)

4. SYSTEMATIC SAMPLING

The researcher first randomly picks the first item or subject from the population.

Then, the researcher will select each n'th subject from the list.

EXAMPLE:

if you wanted to select a random group of 1,000 people from a population of

50,000 using systematic sampling, you would simply select every 50th person,

since 50,000/1,000 = 50.

ADVANTAGE:

The main advantage of using systematic sampling is its simplicity. It allows the

researcher to add a systematic element into the random selection of subjects, yet

it is very easy to do.

DISADVANTAGE:

the process of selecting the sample can interact with a hidden periodic trait within

the population.

The non-probability sampling technique

Learning objectives:

Develop an understanding about different NON PROBABILITY sampling

methods

Distinguish between probability & non probability sampling

Advantage and disadvantage of the different techniques


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Some terms to remember

Sampling the process of selecting a portion of the population to represent the

entire population

Population the entire aggregation of cases in which a researcher is interested

Sample a subset of population elements

Element the basic unit about which information is collected

Sampling design Used in the selection of a sample within a population intended to

yield knowledge, especially for the purposes of making

predictions based on statistics.

Probability Sampling Involves random selection of elements in which each

element has a chance of being selected

Non Probability Sampling Involves non-random methods in the selection of elements in

which not all have equal chances of being selected

Sampling Bias The over-representation or under-representation of some

segment of the population in

terms of a characteristics relevant to the research question

Non Probability Sampling Techniques

1. Convenience Sampling

2. Snowball Sampling3. Purposive Sampling

4. Quota Sampling

The Non- Probability Sampling Technique

A big part of the population is ignored in the selection of respondents or they

have a zero chance of being selected.

Any sampling method where some elements of population have no chance of

selection (these are sometimes referred to as 'out of coverage'/'undercovered'),

or where the probability of selection can't be accurately determined. It involves

the selection of elements based on assumptions regarding the population of

interest, which forms the criteria for selection. Hence, because the selection of

elements is nonrandom, nonprobability sampling not allows the estimation of

sampling errors.

Sometimes known as grab or opportunity sampling or accidental or

haphazard sampling.


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A type of nonprobability sampling which involves the sample being drawn from

that part of the population which is close to hand. That is, readily available and

convenient.

Example: We visit every household in a given street, and interview the first

person to answer the door. In any household with more than one occupant, thisis a nonprobability sample, because some people are more likely to answer the

door (e.g. an unemployed person who spends most of their time at home is more

likely to answer than an employed housemate who might be at work when the

interviewer calls) and it's not practical to calculate these probabilities.

Convenience Sampling

A researcher decides on the respondents of the study on the basis of

convenience.

Purposive Sampling Sampling respondents are chosen based on the judgment or opinion of the

researcher or upon the advice of certain experts

This is used primarily when there is a limited number of people that have

expertise in the area being researched

Quota Sampling

Is like a stratified random sampling but without randomization.

The population is normally subdivided into subgroups, like gender or year level of

students

The population is first segmented into mutually exclusivesub-groups, just as in

stratified sampling. Then judgment used to select subjects or units from each segment based on a

specified proportion.

For example, an interviewer may be told to sample 200 females and 300 males

between the age of 45 and 60.

It is this second step which makes the technique one of non-probability sampling.

In quota sampling the selection of the sample is non-random.

For example interviewers might be tempted to interview those who look most

helpful. The problem is that these samples may bebiasedbecause not everyone

gets a chance of selection. This random element is its greatest weakness and

quota versus probability has been a matter of controversy for many years

Snowball Sampling

Is done with the help of study subjects to choose other potential subjects.

A useful tool for building networks and increasing the number of participants.

Depends greatly on the initial contacts and the connections made.
http://en.wikipedia.org/wiki/Mutually_exclusivehttp://en.wikipedia.org/wiki/Mutually_exclusivehttp://en.wikipedia.org/wiki/Stratified_samplinghttp://en.wikipedia.org/wiki/Stratified_samplinghttp://en.wikipedia.org/wiki/Randomhttp://en.wikipedia.org/wiki/Randomhttp://en.wikipedia.org/wiki/Randomhttp://en.wikipedia.org/wiki/Biased_sampleshttp://en.wikipedia.org/wiki/Biased_sampleshttp://en.wikipedia.org/wiki/Biased_sampleshttp://en.wikipedia.org/wiki/Biased_sampleshttp://en.wikipedia.org/wiki/Randomhttp://en.wikipedia.org/wiki/Stratified_samplinghttp://en.wikipedia.org/wiki/Mutually_exclusive

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