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e. Misuse of data: The data collected has to be used only for the purpose it is collected
for not for making unethical usage. E.g. if the data of users is shared by a banking
institution with an advertising company it leads to invasion of privacy and rights of
the bank‘s clients.

CHECK YOUR PROGRESS-II

1. What are the various types of sample design?
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2. Write a note on ‗Methods of Collecting Primary Data‘.

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2.7 Summary
Sampling is a process used in statistical analysis in which a predetermined number of
observations are taken from a larger population. The methodology used to sample from a
larger population depends on the type of analysis being performed but may include
simple random sampling or systematic sampling.

2.8 Glossary
Budgetary limitation: Funds available guide us to decide the size, variation and quantum
of samples. This fact can even lead to the use of a non-probability sample.
Budgeted: Sample design must be practical and be within the limits of funds available
for the research study.

Cluster sampling. With cluster sampling, every member of the population is assigned to
one, and only one, group. Each group is called a cluster. A sample of clusters is chosen,
using a probability method (often simple random sampling). Only individuals within
sampled clusters are surveyed.

Confidentiality: Sharing information about a respondent with others for purposes other
than research is unethical. Identification of study population to put the findings into
context may be important but then it has to be assured that the information provided by
respondents remains anonymous.
Consideration of interest: In determining the sample design, one must consider the
question of the specific population stricture which is of interest. E.g. we may calculate the
number of walk in guest‘s from total arrivals at a hotel on daily basis to understand the
proportion and then to leave an optimum number of unreserved rooms everyday for such
guest.

Convenience Sampling: A convenience sample consists of people who are easily

approachable and can be reached out to in shorter time.

Data collection is the process of gathering and measuring information on targeted

variables in an established systematic fashion, which then enables one to answer relevant
questions and evaluate outcomes. Data collection is a component of research in all fields
of study including physical and social sciences, humanities, and business. It is a
component of research in all fields of study including physical and social
sciences, humanities, and business.
Direct Approach: The researcher asks direct questions about behaviors and thoughts. e.g.
Why don‘t you eat at MacDonald?
Error Free: Sample design should reduce the probability of errors. The minimum
numbers of errors in any sample ensure correct data obtained and analyzed.

Generalization of Results: Sample should be such that the results of the sample study
can be applied, in general, for the universe with a reasonable level of confidence.

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Inappropriate research methodology: Any instrument or process that may be
unsuitable or have negative effect on a study should be avoided. E.g. asking respondents
questions which lead to findings convenient to the researcher only.
Incentives: The data collected does not need to be exchanged for a price as this deters or
de-motivates the respondents to participate in a research study. Offering incentives, gifts,
etc for seeking information is unethical and equivalent to bribing.
Indirect Approach: The researcher might ask: ―What kind of people eat at
MacDonald‘s?‖
Intercept interviewing: It is an integral part of tourism research. It allows researcher to
reach known people in a shorter durations but at the same time it reaches out to
respondents whose details are not known. The interviewer has to make an effort to gain
attention and cooperation from respondents to assure apt responses. The interviews can be
conducted at different locations like residences, offices, public spaces, shopping
destinations etc. The interviewer uses own judgement to identify the respondents
depending on convenience and may also offer some compensations if the interaction is
prolonged.
Misrepresentation of facts: To report the findings in a way that changes or slants them
to serve your own or someone else‘s interest is unethical.
Misuse of data: The data collected has to be used only for the purpose it is collected for
not for making unethical usage. E.g. if the data of users is shared by a banking institution
with an advertising company it leads to invasion of privacy and rights of the bank‘s
clients.

Multistage Sampling: In this method of sampling, we select a sample by using

combinations of more than one sampling method.

No Bias: Sample design should be able to control systematic bias.

Non-probability sampling: Non-probability sampling is a sampling technique where the

samples are gathered in a process that does not give all the individuals in the population
equal chances of being selected.
Permission or consent: It is important for respondents to be free and under no pressure
to participate in a study being conducted. The information that is sought should be first
assessed to be ethical. The respondent should be able to give an informed consent. This
will lead to honesty on part of the respondent and the researcher. We should inform the
respondents about the type of data or information being sought, the purpose of such study,
and how the respondent can get involved in the study.
Personal Interviewing: It is very flexible and can also be used to collect large amounts
of information. Skilled interviewers are able to keep the respondent attentive and clarify
difficult questions in case of a doubt. They can guide interviews, explore issues, and
probe as the situation demands. Personal interview can be used in any type of
questionnaire and can be conducted fairly quickly.

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Prejudice: Any deliberate attempt to hide the findings of the study or highlight
something disproportionately to its true existence leads to a bias or prejudice. E.g. During
year end appraisal if only the shortcomings are highlighted the candidate may not be
evaluated honestly.
Primary Data: Primary data means original data that has been collected specially for the
purpose in mind. It means someone collected the data from the original source first hand.
Data collected this way is called primary data. The people who gather primary data may
be an authorized organization, investigator, enumerator or they may be just someone with
a clipboard. Those who gather primary data may have knowledge of the study and may be
motivated to make the study a success. These people are acting as a witness so primary
data is only considered as reliable as the people who gathered it.
Probability Sampling Methods: The main types of probability sampling methods are
simple random sampling, stratified sampling, cluster sampling, multistage sampling, and
systematic random sampling.

Proportional: Sample design must result in a truly representative sample. This means
that the sample selected should be exactly or almost similar to the population it represents
I terms of data and characteristics.
Provision or deprivation of a treatment: This may be understood as conducting an
experiment without having the confidence whether it would be fruitful or otherwise for a
study population. But at the same time on the other hand a constructive result may lead to
wonderful results and benefits. E.g. developing a new food product for health benefits.
Safety of respondents: During the course of collecting information the respondents
should not be subjected to unnecessary harassment, anxiety, or putting them through
experiments including hazards, discomfort, demeaning or dehumanizing procedures etc.
Sampling procedure: Finally, the researcher must decide the type of sample he will use
i.e., he must decide about the technique to be used in selecting the items for the sample.
In fact, this technique or procedure stands for the sample design itself. An ideal design is
the one that for a given sample size and for a given cost, has a smaller sampling error.

Sampling unit: The sampling unit can be anything that exists within the population of
interest. An assessment has to be taken with reference to a sampling unit before selecting
sample.
Secondary Data: Refers to data which is collected by someone who is someone other
than the user. Common sources of secondary data for social science include censuses,
information collected by government departments, organizational records and data that
was originally collected for other research purposes. Secondary data analysis can save
time that would otherwise be spent collecting data and, particularly in the case
of quantitative data, can provide larger and higher-quality databases that would be
unfeasible for any individual researcher to collect on their own. In addition, analysts of
social and economic change consider secondary data essential, since it is impossible to
conduct a new survey that can adequately capture past change and/or developments.
However, secondary data analysis can be less useful in marketing research, as data may
be outdated or inaccurate.

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Sensitive Information: Certain types of information can be regarded as sensitive or
confidential by some people thus asking for such information may upset or embarrass a
respondent. E.g. questions on drug use, pilferage, income, age, marital status etc are
invasive. Researcher has to be careful about the sensitivities of the participants. Any such
information may be requested provided the respondent is informed and explained the
purpose beforehand.
Size: The sample size should be justified, not be excessively large nor it should be too
small. Preferably the sample size should be optimal which fulfills the requirements of
efficiency, representativeness, reliability and flexibility and representative of the
population to obtain dependable outcomes. Population variance, population size,
parameters of interest, and budgetary constraints are some of the factors that impact the
sample size.

Source list: It is also known as ‗sampling frame‘ from which sample is to be drawn. It
contains the names of all items of a finite universe. If source list is not available,
researcher has to prepare it. Such a list should be comprehensive, correct, reliable and
appropriate. It is extremely important for the source list to be as representative of the
population as possible.

Stratified sampling. With stratified sampling, the population is divided into groups,
based on some characteristic. Then, within each group, a probability sample (often a
simple random sample) is selected. In stratified sampling, the groups are called strata.

Structured Surveys: Using formal lists of questions asked to all respondents in an

identical set.

Systematic Random Sampling: This begins with creation of a list of each member of the
population. From the list, we randomly select the first sample element from the
first k elements on the population list. Thereafter, we select every kth element on the list.

Type of universe: The accuracy of the results in any study depends on how clearly the
universe or population of interest is defined. The universe can be finite or infinite,
depending on the number of items it contains.
Unstructured Surveys: The interviewer probes the respondents and guides the interview
according to their answers. E.g. Debates on political issues on Television Channels.
Voluntary Sampling: This constitutes of people who have keen interest in the topic of
survey being conducted and are themselves getting involved to contribute as respondents.

2.9 References/Bibliography
 Kumar Ranjit: Research Methodology: A Step by Step Guide for Beginners, Sage
Publication, 2014.
 Kothari C.R. : Research Methodology, New Age International, 2011.
 Shajahan S. : Research Methods for Management, 2004.
 Thanulingom N : Research Methodology, Himalaya Publishing

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 C. Rajendar Kumar : Research Methodology , APH Publishing
 J. R. Brent Ritchie, Charles R. Goeldner : Travel, Tourism, and Hospitality
Research: A Handbook for Managers and Researchers, Wiley Publishers
Publishers Ltd, UK

2.10 Suggested Readings

 Kumar Ranjit: Research Methodology: A Step by Step Guide for Beginners, Sage
Publication, 2014.
 Kothari C.R. : Research Methodology, New Age International, 2011.
 Shajahan S. : Research Methods for Management, 2004.
 Thanulingom N : Research Methodology, Himalaya Publishing
 C. Rajendar Kumar : Research Methodology , APH Publishing
 J. R. Brent Ritchie, Charles R. Goeldner : Travel, Tourism, and Hospitality
Research: A Handbook for Managers and Researchers, Wiley Publishers
Publishers Ltd, UK

2.11 Terminal Questions

1. Define sampling?
2. What are the different methods of sampling?
3. Define sample design?
4. List the various types of sample design.

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UNIT 3
PROCESSING
AND
ANALYSING DATA
Structure
3.1 Introduction
3.2 Objectives
3.3 Defining data processing and analysis
3.4 Editing
3.5 Coding
3.6 Classification and tabulation
3.7 Presentation of Data
3.8 Interpretation of Data meaning
3.9 Methods of data analysis
3.10 Summary
3.11 Glossary
3.12 References/Bibliography
3.13 Suggested Readings
3.14 Terminal Questions

3.1 Introduction
The data collected from the survey tool, observation, and interview is raw and is of no
value unless and until it is presented in usable manner. The data collected from the
samples is arranged in meaningful way by editing, coding, and presented in tabular form
for drawing useful inferences. In this unit we will be learning the various ways by which
raw data is converted into important information,

3.2 Objectives
After reading this unit the learner will be:

 able to understand the editing of data

 able to convert raw data into useful information
 able to classify data
 able to prepare tables and graphs using raw data

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3.3 Defining data processing and analysis

Processing and analyzing data involves a number of closely related operations which are
performed with the purpose of summarizing the collected data and organizing these in a
manner that they answer the research questions (objectives).

3.4 Editing
It is a process of examining the collected raw data to detect errors and omissions and to
correct these when possible. It is also defined as the process relating to the review and
adjustment of collected survey data with an aim to control the quality of the
collected data. Data editing can be performed manually, with the assistance of a computer
or using a combination of both the methods.

Data editing is crucial as it helps in take full advantage of the available data to be
converted into useful data, ensuring that the errors arising during collection, entry,
assimilation are omitted or minimized. It also assures that the consistency is coherent and
consistent, since such characteristics have a constructive impact on the final analysis and
outcomes.

3.5 Coding
The purpose of data coding is to bring out the essence and meaning of the data that has
been collected from the respondents. In order to make sense of the data, it must be
analyzed.

Analysis begins with the labeling of data as to its source, how it was collected, the
information it contains, etc. When we have received hundreds of questionnaires, forma
and formats containing the data it seems impossible to figure out any outcomes just by
looking at the quantum. E.g. if the Hotel guest‘s feedback is received in letter forms with
no specific format it would be nearly impossible to assess the satisfaction levels, major
complaint areas or just finding out who has been recommended by most of the guests as
the best employee at the hotel.

Coding facilitates the researcher to reduce the bulk od information and data to a form that
is easily understandable and can be interpreted soon either manually or through software
programming. For example, the injury rate at different levels of intensive physical labor
demanding operations in various hotels in the city may not be sorted under name but each
of the hotels can be assigned a numeric or alphabetical code. The content analysis
computer programs help researchers to code textual data for qualitative or quantitative
analysis.

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3.6 Classification and Tabulation

It is the process of arranging data in groups or classes on the basis of common
characteristics such as descriptive or numerical.
Simple Classification: This means that one attribute is considered and the universe is
divided into two classes. With one class consisting of items possessing the given attribute
and the other class consisting of items which do not possess the given attribute.

Class interval Classification: This is more relevant when we use quantitative data like
number of guests, number of spa users, age groups of tourists, income levels of travelers,
daily occupancy and other statistical data.

E.g. Pocket Money Received by IHM Students

Income Range Frequency %
Rs.1001-2000 10 50
Rs.2001-3000 8 40
Rs.3001-4000 2 10
Total 20 100

3.6.1 Tabulation

It is the process of summarizing raw data and displaying the same in compact form for
further analysis. It is an orderly arrangement of data in columns and rows.
Tabulation is essential because:
a. It conserves space and reduces explanatory and descriptive statement to a
minimum.
b.It facilitates the process of comparison.
c. It facilitates the summation of items and the detection of errors and
omissions.
d.It provides the basis for various statistical computations.
Tabulation may also be classified as simple and complex tabulation. Simple tabulation
generally results in one-way tables which supply answers to questions about one
characteristic of data only. Complex tabulation usually results on two-way tables that give
information about two interrelated characteristics of data, three –way tables or still higher
order tables known as manifold tables.
Components of Data Tables
The components of data tables are as under:

 Table Number
 Title
 Head notes
 Stubs
 Caption
 Body or field

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 Footnotes
 Source
 Table Number: Each table should have a specific table number for ease of access and
locating. This number can be readily mentioned anywhere which serves as a reference
and leads us directly to the data mentioned in that particular table.

 Title: A table must contain a title that clearly tells the readers about the data it
contains, time period of study, place of study and the nature of classification of data.

 Head notes: A headnote further aids in the purpose of a title and displays more
information about the table. Generally, headnotes present the units of data in brackets
at the end of a table title.

 Stubs: These are titles of the rows in a table. Thus a stub display information about the
data contained in a particular row.

 Caption: A caption is the title of a column in the data table. In fact, it is a counterpart
if a stub and indicates the information contained in a column.

 Body or field: The body of a table is the content of a table in its entirety. Each item in
a body is known as a ‗cell‘.

 Footnotes: Footnotes are rarely used. In effect, they supplement the title of a table if
required.

 Source: When using data obtained from a secondary source, this source has to be
mentioned below the footnote.

CHECK YOUR PROGRESS-I

1. What coding of data?
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2. What do you mean by editing data?
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3.7 PRESENTATION OF DATA

The various types of data that can be presented are:
 Textual presentation
 Data tables
 Diagrammatic presentation
 Time Series Data
 Bar Charts
 Combo Charts
 Pie Charts
 Tables
 Geo Map
 Scorecard
 Scatter Charts
 Bullet Charts
 Area Chart
 Text & Images

Presenting and Analyzing data:

1. Frame the objectives of the study and make a list of data to be collected and its
format.
2. Collect/obtain data from primary or secondary sources.
3. Change the format of data, i.e., table, maps, graphs, etc. in the desired format

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4. Sort data through grouping, discarding the extra data and deciding the required
form to make data comprehensible
5. Make charts and graphs to help to add visual part and analyze trends.
6. Analyze trends and relate the information to fulfill the objectives.

Presenting the results:

 The results should be presented such that a progression of arguments is in

support of the study beginning with a statement defining the purpose of study
and subsequently a logical presentation making objectives clear and related to
the aim of study.
 Bigger objectives should be broken down into smaller ones i.e. define each
objective as per need and outcome. Prepare a list of data to be collected, the
sources of data, form in which data exists and needs to be obtained and
conducting a primary survey for information which does not exist.
 Form and explain the methodology adapted to carry out a study.
 Sampling methods should be clear and confirmed for ease of collecting data that
results in efficient and lesser errors in the process.
 Present only the required information and skip the background research to make
your point more clear.
 Credits and references should either be provided in the end and wherever
obligatory.
 The presentation methods depend upon the availability of resources and type of
results expected out of the final presentation. PowerPoint, Models, Paper Charts,
Smart Boards, Analytical software e.g. Google analytics etc can be used to make
the presentation effective and crisp.

3.8 METHODS OF DATA PRESENTATION

Bar Charts/Bar
Graphs: These are one of the
most widely used charts for
showing the grown of a
company over a period. There
are multiple options available
like stacked bar graphs and
the option of displaying a
change in numerous
entities. A bar graph is a way
of summarizing a set of
categorical data. It displays
the data using a number of
rectangles, of the same width,
each of which represents a
particular category. Bar graphs can be displayed horizontally or vertically and they are
usually drawn with a gap between the bars (rectangles).

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Line Chart: These
are best for showing
the change in
population, i.e., for
showing the trends.
These also work well
for explaining the
growth of multiple
areas at the same
time.
Pie Charts: These
work best for
representing the
share of different
components from a
total 100%. For, eg.
Contribution of
different sectors to
GDP, the population
of different states in
a country, etc. A pie
chart is used to
display a set of
categorical data. It
is a circle, which is
divided into
segments. Each
segment represents
a particular category. The area of each segment is proportional to the number of cases in
that category.
Combo
Chart: As the
name suggests it
is a combination
of more than one
chart type. The
one shown in the
figure below is a
combination of
line and bar
graph. These
save space and
are at times more
effective than using two different charts. There can even be 3 or more charts depending
on the requirement.

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Histogram - A histogram is a way of summarizing data that are measured on an interval
scale (either
discrete or
continuous). It is
often used in
exploratory data
analysis to
illustrate the
features of the
distribution of the
data in a
convenient form.
Line graph - A
line graph is
particularly useful
when we want to
show the trend of
a variable over
time. Time is
displayed on the
horizontal axis (x-
axis) and the
variable is displayed on the vertical axis (y- axis).

A) DESCRIPTIVE MEASURES:
Measures of central tendency and dispersion are common descriptive measures for
summarizing numerical data.

1. Measures of central
tendency: Measures of
central tendency are
measures of the location
of the middle or the center
of a distribution. The most
frequently used measures
of central tendency are

the mean, median and mode.

The mean is obtained by summing the values of all the observations and dividing by the
number of observations. Add up values and divide by number of values.

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𝑥
x=
𝑛

The median (also referred to as the 50th percentile) is the middle value in a sample of
ordered values. Half the values are above the median and half are below the median. It is
the middle value of data when ranked.
The mode is a value occurring most frequently. It is rarely of any practical use for
numerical data. Figure that appears most often in the data
A comparison of the mean, median and mode can reveal information about skewness, as
illustrated in figure below. The mean, median and mode are similar when the distribution
is symmetrical. When the distribution is skewed the median is more appropriate as a
measure of central
tendency.
2. Measures of
Dispersion:
A measure of dispersion
is a numerical value
describing the amount of
variability present in a
data set.
The standard deviation
(SD) is the most
commonly used measure
of dispersion. With the
SD you can measure
dispersion relative to the
scatter of the values about their mean.
The range can also be used to describe the variability in a set of
data and is defined as the difference between
the maximum and minimum values. The range is an appropriate
measure of dispersion when the distribution is skewed.

CHECK YOUR PROGRESS-II

1. What pie chart?
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2. Write a note on ‗Histogram‘.
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3.9 Data Analysis Methods:

Data analysis methods are as under:
 Qualitative Data Analysis
 Quantitative Data Analysis
 Manual Data Analysis
 Computerized Data Analysis

3.9.1 Qualitative Data Analysis

Qualitative Data Analysis can be defined as researcher‘s movement from quantitative

data to qualitative i.e. when an interpretation, explanation or understanding is generated
based on the quantitative data. This gives meaning and better elucidation of facts and
figures presented in form of charts or graphs.

In general the following terms are indicative of qualitative data analysis:

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 Theory: A set of interrelated concepts, definitions and propositions that presents a
systematic view of events or situations by specifying relations among variables.

 Themes: Clear-cut ideas that emerge from grouping of lower-level data points.

 Characteristic: It is the smallest unit of analysis i.e. a single item or event in a text,
similar to an individual response to a variable or indicator in a quantitative research.

 Coding: The process of attaching labels to lines of text so that the researcher can
group and compare similar or related pieces of information.

 Coding sorts: Compilation of similarly coded blocks of text from different sources in
to a single file or report.

 Indexing: The process that generates a word list comprising all the substantive words
and their location within the texts entered in to program.

QDA can be performed in two ways:

Deductive Approach: The research questions are used to group the data and then finding
out the similarities and differences. It is used when time and resources are limited and
when qualitative research is a smaller component of a larger quantitative study.

Inductive Approach: This is used when qualitative research is a major design of the
inquiry. This uses emergent framework to group the data and then looks for relationships.

3.6.2 Quantitative Data Analysis

This is another systematic approach where the researcher converts or transforms the
observations and collected information into numerical data. It is suited to surveys that are
performed on a larger scale, are well administered and use carefully constructed
questionnaire.

Different methods used are as follows:

 Trend analysis: As the name suggests it is interpretation of data that has been
collected over a longer period of time thus making it easier to understand the changes
that have come through. In this analysis usually one of the variables being studied
remains constant.

 Cross-tabulation: This method used a basic table to draw inferences between different
data sets available for a study. The qualities of data used are that they are either related
to each other or are mutually exclusive.

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 SWOT analysis: Strength, Weaknesses, Opportunities and Threats for a subject,
individual, organization may be conducted to present a more holistic picture of
competition. This is generally used when effective business strategies are to be formed.

 MaxDiff analysis: This is a method that is used in studying purchase preferences of

customers and to understand why a particular factor is given more importance than
other. E.g a sun and sand tourist would prefer cleaner and less crowded beaches
compared to the beaches that have better availability of snacks and drinks options.

 Conjoint analysis: Like in the above method, conjoint analysis is a similar quantitative
data analysis method that analyzes parameters behind a purchasing decision. This
method possesses the ability to collect and analyze advanced metrics which provide an
in-depth insight into purchasing decisions as well as the parameters that rank the most
important.

 TURF analysis: Total Unduplicated Reach and Frequency Analysis is used when
researcher has to find out the market reach of a product or service or a mix of both. It is
helpful to develop a marketing plan when a product or service is exclusive yet has
limited buyers.

 Text analysis: It is an advanced statistical method where unstructured raw data is

collected and then it has to be converted into structured form for clearer understanding.
Open ended questionnaires provide data that needs conversion to statistical units for
correct analysis thus Text Analysis method is appropriate as it uses intelligent tools.

 Gap analysis: When it is important to understand the differential between actual and
perceived values of a product or service gap analysis method is applicable. E.g. a guest
may order a flashy looking cocktail perceiving light taste but may end up getting a
drink that has stronger taste.

Manual Data Analysis: This analysis is suitable when there are limited variables and the
number of respondents is also very small. This is applicable when simple cross
tabulations are done and also it is needed to calculate frequency distribution. The easiest
way to do this is to code it directly onto large graph paper in columns. Each column can
be given a number or a distinctive heading to identify and code information
corresponding to the question. This analysis begins with manually counting various codes
in a column and then decode them. For statistical testing manual calculation is done
depending on the researcher‘s expertise and how the results need to be communicated.

Computerized Data Analysis: Computerized data analysis needs the user to be familiar
with appropriate programs to be used along with an understanding of systems, statistical
data and software available. The most common software is SPSS for windows. However,
data input can be long and laborious process, and if data is entered incorrectly, it will
influence the final results.

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Researching for Hospitality and Tourism Management BHM-503T
CHECK YOUR PROGRESS-III
1. Write a note on ‗Qualitative Data Analysis‘?
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2. Write a note on ‗Quantitative Data Analysis‘.

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3.10 Summary

Processing and analyzing data involves a number of closely related operations which are
performed with the purpose of summarizing the collected data and organizing these in a
manner that they answer the research questions (objectives). Manual Data Analysis is
suitable when there are limited variables and the number of respondents is also very
small. This is applicable when simple cross tabulations are done and also it is needed to
calculate frequency distribution. The easiest way to do this is to code it directly onto

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