Research methodlogy unit-iv-measurement and data preperation_For BBA_B.com_MBA and for Other U.G and .P.G students

List ofTopics
Unit-IV
Measurement & Data
Preparation
Measurement
Types of Scales
 Scaling Technique
Data Processing
Data Analysis & Interpretation
Displaying of Data

Measurement is defined as the assignment of numbers to characteristics of objects or
events according to rules. the definition of measurement clearly states that the
researcher should know that the measurement scale measures the characteristics of the
objects or event and not the objects or events.
Researchers normally use four level of measurement scales. they are:
a)Nominal
scale
b) Ordinal
scale
c) Interval
scale
d) Ratio
Scale
T.MANOJ KUMAR RESEARCH METHODOLOGY
A S S T P R O F E S S O R
S K I M T

S K I M T

MEASUREMENT SCALES (TYPE OF SCALE)
Nominal scale
• A set of data is said to be nominal if the values / observations belonging to it can be assigned a
code in the form of a number where the numbers are simply labels.You can count but not order
or measure nominal data. Nominal Scale is the crudest among all measurement scales but it is
also the simplest scale. In this scale the different scores on a measurement simply indicate
different categories.The nominal scale does not express any values or relationships between
variables.
• For example, labelling men as ‘1’ and women as ‘2’ which is the most common way of labelling
gender for data recording purpose does not mean women are ‘twice something or other’ than
men. Nor it suggests that men are somehow ‘better’ than women.
• The nominal scale is often referred to as a categorical scale.The assigned numbers have no
arithmetic properties and act only as labels.The only statistical operation that can be performed
on nominal scales is a frequency count
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Ordinal Scales
Ordinal Scale involves the ranking of items along the continuum of the characteristic being scaled.
In this scale, the items are classified according to whether they have more or less of a
characteristic.
For example, you may wish to ask theTV viewers to rank theTV channels according to their
preference and the responses may look like this as given below:
TV Channel Viewers preferences
Doordarshan-1 1
Star plus 2
NDTV News 3
Aaaj Tak TV 4
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The main characteristic of the ordinal scale is that the categories have a logical or ordered
relationship.
This type of scale permits the measurement of degrees of difference, (that is,‘more’ or ‘less’) but
not the specific amount of differences (that is, how much ‘more’ or less’).
This scale is very common in marketing, satisfaction and attitudinal research.Another example is
that a fast food home delivery shop may wish to ask its customers:
Using ordinal scale data, we can perform statistical analysis like Median and Mode, but not the
Mean.
How would you rate the service of our staff?
(1) Excellent • (2) Very Good • (3) Good • (4) Poor • (5) Worst
S K I M T

MEASUREMENT SCALES
Interval Scale is a scale in which the numbers are used to rank attributes such that
numerically equal distances on the scale represent equal distance in the characteristic being
measured.An interval scale contains all the information of an ordinal scale, but it also one allows
to compare the difference/distance between attributes
The interval scales allow the calculation of averages like Mean, Median and
Mode and dispersion like Range and Standard Deviation.
An interval scale is a scale of measurement where the distance between any two adjacent units
of measurement (or 'intervals') is the same but the zero point is arbitrary. Scores on an interval
scale can be added and subtracted but cannot be meaningfully multiplied or divided.
Food supplied is:
Indicate your score on
the concerned blank
and circle the appropriate
number on each line.
Fresh 1 2 3 4 5
Tastes good 1 2 3 4 5
Value for money 1 2 3 4 5
Attractive packaging 1 2 3 4 5
Prompt time delivery 1 2 3 4 5
S K I M T

Ratio Scales
Ratio Scale is the highest level of measurement scales.This has the properties of an interval
scale together with a fixed (absolute) zero point.The absolute zero point allows us to construct
a meaningful ratio.
Examples of ratio scales include weights, lengths and times. In the marketing research, most
counts are ratio scales. For example, the number of customers of a bank’s ATM in the last three
months is a ratio scale.This is because you can compare this with previous three months.
Ratio scales permit the researcher to compare both differences in scores and relative magnitude
of scores
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SCALING TECHNIQUE
S K I M T

SCALING TECHNIQUE
• In comparative scaling, the respondent is asked to compare one object with another. For
example, the researcher can ask the respondents whether they prefer brand A or brand B of a
detergent. On the other hand, in non-comparative scaling respondents need only evaluate a
single object.Their evaluation is independent of the other object which the researcher is
studying.
• Respondents using a non-comparative scale employ whatever rating standard seems
appropriate to them. Non-comparative techniques consist of continuous and itemized rating
scales. Figure below shows the classification of these scaling techniques
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Comparative scaling techniques consist of:
a) Paired comparison scaling
b) Rank order scaling
c) Constant sum scaling and
d) Q-sort
a) Paired comparison scaling as its name indicates involves presentation of two objects and
asking the respondents to select one according to some criteria.The data are obtained using
ordinal scale..
As the number of items increases, the number of comparisons increases geometrically. If the
number of comparisons is too large, the respondents may become fatigued and no longer be able
to carefully discriminate among them.
S K I M T
SCALING TECHNIQUE

b) Rank order scaling
This is another type of comparative scaling technique in which respondents are presented with
several items simultaneously and asked to rank them in the order of priority.This is an ordinal
scale that describes the favoured and unfavoured objects, but does not reveal the distance
between the objects.
Like paired comparison, the rank order scale, is also comparative in nature.The resultant data in
rank order is ordinal data.
Example
S K I M T
SCALING TECHNIQUE

In this scale, the respondents are asked to allocate a constant sum of units such as points, rupees, or chips
among a set of stimulus objects with respect to some criterion.
For example, you may wish to determine how important the attributes of price, fragrance, packaging,
cleaning power, and lather of a detergent are to consumers. Respondents might be asked to divide a
constant sum to indicate the relative importance of the attributes using the following format.The advantage
of this technique is saving time.
The respondents may allocate more or fewer points than those specified
Example
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SCALING TECHNIQUE

Q-Sort Scale: This is a comparative scale that uses a rank order procedure to sort objects based
on similarity with respect to some criterion.The important characteristic of this methodology is that
it is more important to make comparisons among different responses of a respondent than the
responses between different respondents.Therefore, it is a comparative method of scaling rather
than an absolute rating scale. In this method the respondent is given statements in a large number
for describing the characteristics of a product or a large number of brands of a product.
S K I M T
SCALING TECHNIQUE

SCALING TECHNIQUE
Continuous Rating Scales
It is very simple and highly useful. In continuous rating scale, the respondent’s rate the
objects by placing a mark at the appropriate position
on a continuous line that runs from one extreme of the criterion variable to the other.
Examples of continuous rating scale are given below:
Question: How would you rate the TV advertisement as a guide for buying?
Scale Type A
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Itemised Rating Scales
Itemised rating scale is a scale having numbers or brief descriptions associated with each
category.The categories are ordered in terms of scale position and the respondents are required to
select one of the limited number of categories that best describes the product, brand, company, or
product attribute being rated. Itemised rating scales are widely used in marketing research
SCALING TECHNIQUE
S K I M T

a) Likert Scale
These scales are sometimes referred to as summated scales. It requires a respondent to indicate a degree of agreement or
disagreement with each of a series of statements related to the attitude object.
For Example:The service at a retail store is very important to me:
____ Strongly Agree ____ Agree ____ Neither Agree nor Disagree ____ Disagree ____ Strongly Disagree
To analyze a Likert Scale, each response category is assigned a numerical value.These examples could be assigned values such
as
Strongly Agree=1, through Strongly Disagree=5 or the scoring could be reversed., or a –2 through +2 system could be used.
They can be analyzed on an item-by-item basis, or they can be summed to form a single score for each individual.
Advantages
1. It is relatively easy to construct and administer.
2. Instructions that accompany the scale are easily understood; hence it can be used for mail surveys and interviews with
children.
Disadvantages
1. It takes a longer time to complete as compared to Semantic Differential Scales, etc.
2. Care needs to be taken when using Likert Scales in cross cultural research, as there may be cultural variations in
willingness to express disagreement.
SCALING TECHNIQUE
S K I M T

Example
Semantic Differential Scale
This is a seven point rating scale with end points associated with bipolar labels (such as
good and bad, complex and simple) that have semantic meaning. The Semantic Differential
scale is used for a variety of purposes. It can be used to find whether a respondent has a
positive or negative attitude towards an object. It has been widely used in comparing
brands, products and company images. It has also been used to develop advertising and
promotion strategies and in a new product development study
SCALING TECHNIQUE
S K I M T

PROCESSING OF DATA
DATA PROCESSIING
Data continues to be in raw form, unless and until they are processed and analyzed.
Processing is a statistical method by which the collected data is so organized the further analysis
and interpretation of data become easy. It is an intermediary stage between the collection of data
and their analysis and interpretation.
Processing stages
There are four important stages in the processing of data.They are;
1. Editing
2. Coding
3. Classification
4. tabulation
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Editing
As soon as the researcher receives the data, he should screen it for accuracy. Editing is the
process of examining the data collected through various methods to detect errors and omissions
and correct them for further analysis.
Though editing, it is ensured that the collected data are accurate,consistent with other facts
gathered, uniformly entered and well-arranged so that further analysis is
made easier
EDITING
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EDITING
Practical guidelines for editing
While editing care has to be taken to see that the data are as accurate and complete as possible.
The following points are to be noted;
1.The editor should familiarize with the copy of instructions given to the interviewers.
2.The original entry, if found incorrect, should not be destroyed or erased. On the other hand,
it should be crossed out in such a manner that it is still eligible.
3.Any, modification to the original entry by the editor must be specifically indicated.
4.All completed schedules must bear signature of the editor and the date.
5. Incorrect answer to the questions can be corrected only if the editor is absolutely sure of the
answer, otherwise leave it as such.
6. Inconsistent, incomplete or missing answers should not be used.
7. Sere that all numerical answers are converted to same units.
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Coding
Coding is the process by which r response categories are summarized by numerals or other
symbols to carry out subsequent operations of data analysis.This process of assigning numerals or
symbols to the responses is called coding.
It facilitates efficient analysis of the collected data and helps in reducing several replies to a small
number of classes which contain the critical information
required for analysis.
In general it reduces the huge amount of information collected in to a form that is amenable to
analysis.
CODING
S K I M T

CODING
Steps in coding
1. Study the answers carefully.
2. Develop a coding frame by listing the answers and by aligning codes to each of them.
3. Prepare a coding manual with the detail of variable names, codes and instructions.
4. If the coding manual has already been prepared before the collection of the data, make the
required additions for the open ended and partially coded questions.
Coding rules
1. Give each respondent a code number for identification.
2. Provide code number for each question.
3.All responses including ‘don’t know’,‘no opinion’. Etc is to be coded.
4.Assign additional codes to partially coded questions.
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CLASSIFICATION
Classification
Classification is the process of reducing large mass of data in to homogeneous groups for
meaningful analysis.
It converts data from complex to understandable and unintelligible to
intelligible forms.
It divides data in to different groups or classes according to their similarities and
dissimilarities.When the data are classified, they give summary of whole information.

CLASSIFICATION
Objectives of classification
1.To organize data in to concise, logical and intelligible form.
2.To take the similarities and dissimilarities s between various classes clear.
3.To facilitate comparison between various classes of data.
4.To help the researcher in understanding the significance of various classes of data.
5.To facilitate analysis and formulate generalizations

CLASSIFICATION
Types of classification
A. Classification according to external characteristics
In this classification, data may be classified either on geographical basis or periodical basis.
Classification on geographical basis
In this type of classification, the data that are collected from different places are placed in different
classes.
Classification on periodical basis (chronological classification)
In this type of classification, the data belonging to a particular time or period are put under one
class.This type of classification is based on period.
B. Classification according to internal characteristics
Data may be classified either according to attributes or according to the magnitude of variables
Classification according to Attributes
In this type data are classified on the basis of some attributes an characteristics.

CLASSIFICATION
Simple Classification
If the classification is based on one particular attribute only it is called simple classification.
Eg; classification on the basis of sex.
Manifold Classification
If the classification is based on more than one or several attributes it is called manifold or multiple
classifications. in this data are classified in several groups.
C. Classification according variables
Here the data are classified to some characteristics that can be measured. Data are classified on
the basis of quantitative characteristics such as age, height; weight etc. quantitative variables are
grouped in to two
a) Discrete variable
If the variables can take only exact value, it is called discrete variable.
b) Continuous variable
The variables that can take any numerical value within a specified range are called
continuous variable

TABULATION
Tabulation
Tabulation is the next step to classification. It is an orderly arrangement of data in rows and
columns. It is defined as the “measurement of data in columns and rows”. Data presented in tabular
form is much easier to read and understand than the data presented in the text the main purpose of
tabulation is to prepare the data for final analysis. It is a stage between classification of data and
final analysis.
Objectives ofTabulation
1.To clarify the purpose of enquiry
2.To make the significance of data clear.
3.To express the data in least possible space.
4.To enable comparative study.
5.To eliminate unnecessary data
6.To help in further analysis of the data.
S K I M T

TABULATION
Parts of a statistical table
Following are the important parts of a statistical table.
1.Title of the table
The title of the table is placed above the table. If there are more than one table in a research, each
should bear a number for easy reference.
2. Caption or title of the column
It is also termed as “box head”.There may be sub- captions under the main caption.
3. Stub (row heading)
Stub refers to the title given to rows
4. Body (main data)
This is the main body of information needed for the research work.
5. End note (foot note)
This is placed below the table to convey the expansions of abbreviations to caption, stub or main
body.
6. Source note
If the table is based on outside information, it should be mentioned in the source note below.
S K I M T

TABULATION
Types ofTables
SimpleTable
Complex table
(a) One- way table
(b) Two- way table
(c)Three-way table
(d) Manifold tables
S K I M T

ANALYSIS OF DATA
ANALYSIS OF DATA
Analysis of data is considered to be highly skilled and technical job which should be
carried out .Only by the researcher himself or under his close supervision.Analysis of data means
critical examination of the data for studying the characteristics of the object under study and for
determining the patterns of relationship among the variables relating to it’s using both quantitative
and qualitative methods.
S K I M T

ANALYSIS OF DATA
Steps in Analysis
Different steps in research analysis consist of the following.
1.The first step involves construction of statistical distributions and calculation of simple
measures like averages, percentages, etc.
2.The second step is to compare two or more distributions or two or more subgroups within a
distribution.
3.Third step is to study the nature of relationships among variables.
4. Next step is to find out the factors which affect the relationship between a set of variables
5.Testing the validity of inferences drawn from sample survey by using parametric tests of
significance.
S K I M T

ANALYSIS OF DATA
Types of Analysis
Statistical analysis may broadly classified as descriptive analysis and inferential analysis
Descriptive Analysis
Descriptive statistics are used to describe the basic features of the data in a study.They provide
simple summaries about the sample and the measures. Descriptive statistics is the discipline of
quantitatively describing the main features of a collection of data or the quantitative description
itself. In such analysis there are univariate analysis bivariate analysis and multivariate analysis.
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Univariate analysis
Univariate analysis involves describing the distribution of a single variable, including its central
tendency (including the mean, median, and mode) and dispersion (including the range and
quartiles of the data-set, and measures of spread such as the variance and standard deviation).The
shape of the distribution may also be described via indices such as skewness and kurtosis.
Characteristics of a variable's distribution may also be depicted in graphical or tabular format,
including histograms and stem-and-leaf display.
Bivariate analysis
Bivariate analysis is one of the simplest forms of the quantitative (statistical) analysis. It involves
the analysis of two variables (often denoted as X, Y), for the purpose of determining the empirical
relationship between them. Common forms of bivariate analysis involve creating a percentage
Table or a scatter plot graph and computing a simple correlation coefficient
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Multivariate analysis.
• In multivariate analysis multiple relations between multiple variables are examined
simultaneously.
Multivariate analysis (MVA) is based on the statistical principle of multivariate statistics, which
involves observation and analysis of more than one statistical outcome variable at a time. In design
and analysis, the technique is used to perform trade studies across multiple dimensions while
taking into account the effects of all variables on the responses of interest
Inferential Analysis
Inferential statistics is concerned with making predictions or inferences about a population from
observations and analyses of a sample.That is, we can take the results of an analysis using a sample
and can generalize it to the larger population that the sample represents.Ther are two areas of
statistical inferences (a) statistiacal estimation and (b) the testing of hypothesis
S K I M T

ANALYSIS
Steps in Analysis
Different steps in research analysis consist of the following.
1.The first step involves construction of statistical distributions and calculation of simple
measures like averages, percentages, etc.
2.The second step is to compare two or more distributions or two or more subgroups within a
distribution.
3.Third step is to study the nature of relationships among variables.
4. Next step is to find out the factors which affect the relationship between a set of variables
5.Testing the validity of inferences drawn from sample survey by using parametric tests of
significance.
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DISPLAYING OF DATA
Graphs and Diagrams
In research, the data collected may be of complex nature. Diagrams and graphs is one of the
methods which simplifies the complexity of quantitative data and make them easily intelligible.
They present dry and uninteresting statistical facts in the shape of attracting and appealing pictures.
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DISPLAYING OF DATA
1. Line Graphs
A line graph displays information in a series of data points that each represents an
individual measurement or piece of data.The series of points are then connected by a line to show a
visual trend in data over a period of time.The line is connected through each piece chronologically.
For eg; following data show birth rate per thousands of six countries over a period.
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DISPLAYING OF DATA
2. BAR CHARTS
The bar graph is a common type of graph which consists of parallel bars or rectangles with lengths that
are equal to the quantities that occur in a given data set.The bars can be presented vertically or
horizontally to show the contrast and record information. Bar graphs are used for plotting discontinuous
(discrete) data.
Discrete data contains discrete values and are not continuous
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DISPLAYING OF DATA
3. Circle Charts or Pie Diagram
A pie graph is a circle divided into sections which each display the size of a relative piece of
information.
Each section of the graph comes together to form a whole. In a pie graph, the length of each
sector is proportional to the percentage it represents.
Pie graphs work particularly well when each slice of the pie represents 25 to 50 percent of the
given data.
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DISPLAYING OF DATA
Example of Circle Charts or Pie Diagram
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DISPLAYING OF DATA
A histogram is a graphical representation that organizes a group of data points into user-specified
ranges. It is similar in appearance to a bar graph.The histogram condenses a data series into an
easily interpreted visual by taking many data points and grouping them into logical ranges or bins.
• histogram is a bar graph-like representation of data that buckets a range of outcomes into
columns along the x-axis.
• The y-axis represents the number count or percentage of occurrences in the data for each
column and can be used to visualize data distributions.

DISPLAYING OF DATA
4.Pictograms
A pictogram, also called a pictogram or pictograph, is an ideogram that conveys its meaning
through its pictorial resemblance to a physical object. Pictographs are often used in writing and
graphic systems in which the characters are to a considerable extent pictorial in appearance.
Pictography is a form of writing which uses representational, pictorial drawings. It is a basis of
cuneiform and, to some extent, hieroglyphic writing, which also uses drawings as phonetic letters
or determinative rhymes.
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INTERPRETATION
MEANING OF INTERPRETATION
Interpretation refers to the task of drawing inferences from the collected facts after an analytical
and/or experimental study. In fact, it is a search for broader meaning of research findings.
The task of interpretation has two major aspects viz., (i) the effort to establish continuity in
research through linking the results of a given study with those of another, and (ii) the establishment
of some explanatory concepts.
“In one sense, interpretation is concerned with relationships within the collected data,partially
overlapping analysis.
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TECHNIQUE OF INTERPRETATION
The task of interpretation is not an easy job, rather it requires a great skill and dexterity on the part of
researcher. Interpretation is an art that one learns through practice and experience.The researcher may, at times, seek the
guidance from experts for accomplishing the task of interpretation.
The technique of interpretation often involves the following steps:
(i) Researcher must give reasonable explanations of the relations which he has found and he must interpret the lines of
relationship in terms of the underlying processes and must try to find out the thread of uniformity that lies under the
surface layer of his diversified research findings. In fact, this is the technique of how generalization should be done and
concepts be formulated.
(ii) Extraneous information, if collected during the study, must be considered while interpreting the final results of research
study, for it may prove to be a key factor in understanding the problem under consideration.
INTERPRETATION
S K I M T

(iii) It is advisable, before embarking upon final interpretation, to consult someone having insight into
the study and who is frank and honest and will not hesitate to point out omissions and errors in
logical argumentation. Such a consultation will result in correct interpretation and,
thus, will enhance the utility of research results.
(iv) Researcher must accomplish the task of interpretation only after considering all relevant factors
affecting the problem to avoid false generalization. He must be in no hurry while interpreting results,
for quite often the conclusions, which appear to be all right at the
beginning, may not at all be accurate.
INTERPRETATION
S K I M T

THANKS and REGARDS
mail id : manothamu@gmail.com WhatsApp :+919150860613

Research methodlogy unit-iv-measurement and data preperation_For BBA_B.com_MBA and for Other U.G and .P.G students

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