Mba 2 Sem Research Methodology (Bangalore University) (3)

8/8/2019 Mba 2 Sem Research Methodology (Bangalore University) (3)

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Sampling : Introduction

A sample is a definite plan for obtaining a

sample from a given population.

It refers to the technique or the procedure the

researcher would adopt in selecting items for

the sample.



Meaning:-

It is the aggregate of element about which we

wish to make inferences. A member of the

population is an element. It is the unit of

study.

A part of population is known as a sample.

The process of drawing a sample from a

large population is called sampling . The list of sampling unit from which a sample

is taken is called the sampling frame.



Example:-

A researcher wants to survey the brandpreference of households regardingpremium soaps in R.T. Nagar area of the

city of Bangalore. A house hold is the sampling unit

The total of all households in R.T. Nagar is thepopulation.

Suppose a list of households is not available,each block may be considered as sampling unit.

A List of such blocks will be used as thesampling frame.



Types of Sampling :

Probability Sampling Method

Simple Random Sampling

Systematic Sampling

Stratified SamplingCluster Sampling

Non-Probability Sampling Method

Convenience Sampling

Judgment Sampling

Quota Sampling



Sampling and

Sampling Distribution

Module-3

Types of Sampling



Probability Sampling Methods

It is based on the theory of probability. It is also

known as random sampling or chance sampling.

In probability sampling every population has a

chance of being selected. Such chance is knownas probability.

E.g. If a sampling frame is a list of 100 students of

a specific course of study, in a simple random

sample, each student has 1/100th chance of being

selected

Probability sampling yields a representative

sample.



Simple Random Sampling

This technique gives each element an equal &

independent chance of being selected.

Equal probability selection method is described as

Epsem sampling. An independent chance means that the draw of one

element will not affect the chances of other element

being selected.

Where some element are purposely excluded from thesample , the resulting sample is not a random one.

Hence all the element should be included in the sample

frame to draw a random sample.



Procedur e:

Enumeration of all element in the

population.

Drawing sample number by using :-Lottery method

A table of random numbers



Lottery Method:-

This is the simplest and most familiar procedure of

random sampling.

E.g. If a sample of 10 students is to be drawn out of 50 in a

section, Write the no¶s 1-50 in a slips and pick 10 slips ,

the units bearing the numbers of slips drawn constitute

the random sample.

They are two alternatives:-

After a number is selected by draw, it may be replaced

and consequently it has a chance of being selected again.

This is referred as unrestricted random sampling .

The Selected number is set aside, & in the subsequent

draws, it does not get a chance of being selected again.

This is referred as restricted random sampling.



The table of random numbers

To select a random sample out of a given

frame, One should start selecting the number

from a table of Random number at any

randomly selected point & with in the range of frame.

The table of random numbers is ideal for

obtaining a random sample from relatively

small populations. When population are quite

large say lakhs, drawing number from the table

becomes tedious.



Example:-

Let us suppose that the random sample of 50 is toselected from a college population of 500 commercestudents.

Sample of random numbers

100 732 225 337 762 522374 110 248 397 284 376

084 885 997 019 028 639

090 578 445 320 001 012

128 450 780 540 444 079

All the numbers within the range of 1-500 may be pickedup for the study.



Suitability:-

The simple random sampling is suitable only for a

small homogenous population/ group with reference to

the specified characteristics.

Where the population is relatively small.

Where a complete list of all elements is available or can

be prepared.

The simple random sampling is not suitable for drawing a

sample from a large heterogeneous population, as it

may not yield a representative sample of such

population.



Stratified Random Sampling

The members of the population are first assigned to strata or

groups and simple random sample is drawn from each stratum.

In simple words, when the population is very much heterogeneous

& it is divided into different strata on the basis of age, socio-ecostatus, occupation, educational background, Residence (urban,

sub-urban, Rural) in this case stratified random sampling will be

the best choice.

It is the process of selecting a sample in such a way that, identified

sub-group in the population are represented in the sample.



Ste ps for random sampling

Identify and define the population

Determine the desired sample size

Identify the variable & sub-group (strata) for whichyou want to guarantee appropriate representation.

Classify all members of the population as member of

one of the identified sub-groups.

Randomly select an appropriate number of individuals

from each of the sub-group.



Example:-

Population 400

10th grade student

70-High IQ Students 260 Avg IQ Students 70 Low IQ Students

30 High IQ boys 40 High IQ girls

Randomly Selected

15 High IQ boys & girls



Need for Stratification

Increasing a sample¶s statistical efficiency

Providing adequate data for analyzing the

various sub-population. Stratification is essential when the researcher

wants to study the characteristics of population

sub-group.

Suitability:- is appropriate for a large

heterogeneous population



Systematic Sampling

This method of sampling is an alternative to random

sampling. It consists of taking every k th item in the

population after a random start with an item from 1 to

k.

E. g. suppose it is desired to select a sample of 20

students, from a list of 300 students , divide the

population total of 300 by 20, the quotient is 15.

select a number at random between 1 & 15, using

lottery method. Suppose the selected number is 9, then

the students numbered 9 , 24(9+15), 39 (24+15),

54(39+15), 69,84«.. Are selected as sample.



Cluster Sampling

Divide the entire population into clusters and select entire groupsor clusters at random to collect information from these clusters orgroups.

E.g. Suppose a researcher wants to select a random sample of 1000households out of 40000 estimated households in a city for asurvey. A direct sample of individual household would be difficultto select , because a list of households does not exist and would beto costly to prepare.

Instead he can select a random sample of a few blocks/wards. The

no of blocks to be selected depends upon the average no of estimated households per block. Suppose the average no of households per block is 200, then 5 blocks comprises the sample, &from each sample blocks a certain number of households may beselected by systematic sampling.



Some illustration of cluster ar e:

Population Elements Cluster or sampling

unit

City Households Blocks

City Individuals Households

Affiliating UNIV Students Affiliated Colleges

Rural areas Households Villages



Non-Probability Sampling Methods

The probability of inclusion of any unit

(of population) in a sample is not known. It

involve human judgment rather than

pure chance. The degree of accuracy is

also unknown.



Contd:-

Convenience Sampling : The selection of the sample is left to theresearcher who is to select the sample unit in a just µhit and missµfashion. Most suitable in µexploratory research¶.

E.g. Interviewing the people whom we happen to meet.

Judgment Sampling :This involves selection of cases which we judge as the most appropriate ones for the given study. It is basedon the judgment of the researcher and some experts. It does notaim at securing a cross section of the population.

Quota Sampling : The population is stratified on the basis of age,sex, income, family size etc. More often compounded stratificationis used in groups with gender.



Characteristics of a Good Sample Design

Goal Orientation

Measurability

Usability

Cost Factor



Sampling and Non-Sampling Errors

Starting from data collection to inf erring r esults, two kinds of errorsmay come:

Sampling Errors : arise due to studying only a part of the total population. These may arise due to non-r e pr esentative ness of the sample and the inadequacy of sample size.

When several samples ar e drawn from a population, their r esults wouldnot be identical. The degr ee of variation of sample r esult is known asstandard error

Non-Sampling Errors : arise due to technically faulty observation or calculations during the processing of data.

Methods of data collection Incomplete coverage of the population

Inaccurate information provided by the participants

Error during tabulation, editing etc.



Conce pt of measur ement & Scaling

Measurement in research consists of assigning numbers

to empirical events in compliance with a set of rules.

The definition implies the measurement in three part

process:- Selecting observable empirical events.

Developing a set of mapping rules: a scheme for

assigning numbers to represent aspects of the event

being measured.

Applying the mapping rule to each observation of that

event.



Example:

(1) Do you feel respected by your colleagues/ coworkers?

Very desirable 5 4 3 2 1 Very undesirable

Sample element :- Colleagues/ Co-workers or Individuals

Empirical Observation :- Respected

Mapping rules :- Rating Scale

(2) Is there employee turnover in your organization?

(a) Yes b) No





Characteristics of sound measur ement

Validity :- refers to the extend to which a test

measures what we actually wish to measure.

Reliability :- has to do with the accuracy and

precision of a measurement procedure.

Practicality :- is concerned with a wide range of factors of economy, convenience, and

interpretability.



What is Scaling?

S caling :-

is a procedure for the assigning of numbers to

indicants of the properties of objects.Types of Response Scales:-

R ating Scales

R anking Scales



Contd:-

R ating Scales :- is used when respondents score an

object without making any comparison to other object.

R anking Scales :- constraints the study

participants to make comparison among two or more

indicants or objects .



Types of Rating Scales

Simple category

Multiple choice :single response

Multiple choice :multiple response

Likert scale

Semantic differential

Numerical

Multiple rating

Graphic rating



Types of Ranking Scales

Paired-comparison

Forced Ranking

Comparative



Types of Rating Scales:

Simple category : also called dichotomous scale.

It offers two mutually exclusive response choice. Theyare yes & no, agree & disagree etc

E.g. Does compensation leads to motivation directly?

(a) Yes b) No



Contd:-

Multiple choice single response / multiple

response: Allows the rater to select one or several

alternatives.

E.g. What sort of non-monetary benefits would you prefer

in your Company?

a) Training facilities d) Flexible work hours

b) Rewards for referrals e) Overseas assignment

c) Gifting of leave f) Recognitions



Contd:-

Likert scale :- The Likert Scales consists of a number of

statements which either a favorable or unfavorable attitude

towards the given object to which the respondent is asked to

react.

E.g. An advertisement helps my decision in choosing the brand?

Strongly Agree [ ]

Moderately Agree [ ]

Neither Agree nor Disagree [ ]

Moderately Disagree [ ]

Strongly Disagree [ ]



Contd:-

Semantic differential :- This method consists of a

set of bipolar rating scale, usually with seven points, by

which one or more respondents rate one or more

concept on scale item.

E.g. Other than brand attributes like quality, value for

money, how much do you think celebrity endorsement

is important?

Most important :_: _ :_ :_ :_ :_ :_ : Not at all important

Severe :_: _ :_ :_ :_ :_ :_ : Lenient

3 2 1 0 -1 -2 -3



Contd:-

Numerical Scales :- are 5-points scale. The

respondent writes a number from the scale next to

each item.

E.g. Do you receive timely information relating to your work?

5 4 3 2 1



Contd:-

Multiple rating Scale:- is similar to the numerical

scale but differs in two ways:

1) It accepts a circled response from the rater

2) The layout allows visualization of the results.

E.g. ³Please indicate how important or unimportant each

service characteristic is.´

Important unimportant

Fast reliable repair 7 6 5 4 3 2 1

Services at my location 7 6 5 4 3 2 1

Knowledgeable technicians 7 6 5 4 3 2 1



Contd:-

Graphic rating Scale:- This scale uses pictures, icons,

other visuals to communicate with the rater and

represent a variety of data types.

E.g. ³How likely are you to recommend complete care toothers ?´ ( place an x at the position along the line that

best reflects your judgment )

Very likely _____________________ Very Unlikely



Types of Ranking Scales

Paired-comparison Scale :- the respondents can

express attitude unambiguously by choosing between

two objects.

E.g. For each pair of sports car listed, place a check

beside the one you would prefer if you had to choose

between the two.

a)________ BMW Z3 ________ Chevrolet

b)________ Porsche Boxster ________ PorschBoxster



Forced ranking scale

This method is faster than paired comparisons and is usually

easier and more motivating to the respondents. In this scale

attributes are given/listed, respondents are asked to rank relative

to each other.

E.g. Acc to your pr ef er ence rank the folg non-monetary

benefits provided in your Company?

a) Training facilities d) Flexible work hours

b) R ewards for r ef errals e) Overseas assignment

c) Gifting of leave f) R ecognitions



Comparative Scales:-

Is ideal for comparison of attributes using ranking scale,

if the respondents are familiar with the standard.

E.g. ³Compared to your previous mutual fund¶s

performance, the new one is.´

Superior Same Inferior

_____ _____ ____ ____ _____

1 2 3 4 5



Scale Design Techniques

Arbitrary scaling :- A scale is custom-designed to

measure a property or indicant.

Consensus scaling :- Requires items to be selected

by

a panel of judges and then evaluated.

Item Analysis scaling :- is a procedure for

evaluating an item based on how well it discriminates

between those person. The most popular scale using

this approach is Likert scale.

Cumulative scaling :- Scales are chosen for their

conformity to a ranking of items with ascending and

descending discriminating power.

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Mba 2 Sem Research Methodology (Bangalore University) (3)