MARKETING RESEARCH UNIT-1Applications of Market
ResearchMarketing research uses include: Advertising testing
research, branding research, customer satisfaction research,
pricing research, product positioning research, new product
assessments, marketing due diligence, and segmentation research. We
conduct these marketing research studies for firms of most sizes
from venture funded start ups to middle-market and large
enterprises.Applications of Market Research
Pricing Research
We provide pricing strategy consulting backed by strong pricing
research capabilities. Our perspective is broad when dealing with
pricing research and pricing strategy decisions, and focus on
finding for your business optimum price-product-feature
configurations in the context of market positioning opportunities.
We employ both qualitative and quantitative pricing research
tools.
Product Research
Product market research serves several goals: new product design
and market validation research, or assessing existing product
strength and line extension potential. We follow the product
development cycle integrating research with creative positioning
and technical product design efforts.
Concept Testing
Concept testing research evaluates advertising concepts, ad
theme concepts and appeals, new product concepts, pricing, brand
concepts, brand names, and positioning strategy concepts. We select
techniques -- qualitative and quantitative -- to both develop
concepts, refine, and screen to assess market potential.
Positioning Research
We offer experienced market positioning and creative branding
research capabilities to define and go-to-market with a high-impact
positioning strategy. First, it requires understanding the market
positioning concept, your current and potential markets, and the
process needed to generate brand name impact.
Marketing Due Diligence
We support venture investment firms with primary and secondary
marketing research in a stand alone or component marketing due
diligence study.
Customer Satisfaction Research
The buzz and interest around customer satisfaction research
sometimes deflates if the research design does not lead to
actionable results. Also, customer expectations generally rise
overtime as advances in technology in many categories boost the
consumer consciousness of what to expect. We build into our
customer satisfaction study design "action indicators" to point to
immediate use of customer satisfaction results.
Branding Research
Branding decisions drive branding marketing research strategy.
Corporate, product and advertising brand development is amix of
creativity and marketing informationto uncover brand positioning
opportunities in cluttered market spaces.
Brand Equity Research
Brand equity research measures the breadth and depth of brand
power in your target markets. We use both standard and custom
tailored brand equity survey measurements. A key to research design
is the goal of a brand equity measurement study.
Advertising Research
Advertising research design is determined by specific
advertising goals and the stage of ad development, or campaign. We
use a broad range of advertising research techniques including ad
recall surveys, message and theme salience and impact measures,
buying motivation and association with the ad message or
positioning theme. We employ both qualitative and quantitative
pricing research tools.
Market Segmentation
Market segmentation research maintains focus and delivers needed
marketing information in today's moving economy where new markets
and new product categories emerge and traditional market segments
fade away. Market segmentation research is a way to keep 'your eye
on the ball.' Often we start the market segmentation process
withqualitative researchto the range and breadth of customers. Then
we follow with quantitative research using appropriate multivariate
analysis (cluster, k-means factor, etc) to define meaningful
segments.
Sales Analysis
Data mining -- finding gems of insight from sophisticated or
basic analysis of your internal customer and sales and margin trend
data -- is a key first step in product and brand analysis. Simply
put, a marketing analysis data mining effort searches for meaning
and insight among the stacks of sales data and marketing data
already within a sales and marketing organization. Through these
tools we can better target your best customers, find which
advertising and promotion methods are most efficient and
effective.Limitations of Marketing ResearchFollowing are the
mainlimitations of Marketing Research: Marketing Research (MR) is
not an exact science though it uses the techniques of science.
Thus, the results and conclusions drawn upon by using MR are not
very accurate. The results of MR are very vague as MR is carried
out on consumers, suppliers, intermediaries, etc. who are humans.
Humans have a tendency to behave artificially when they know that
they are being observed. Thus, the consumers and respondents upon
whom the research is carried behave artificially when they are
aware that their attitudes, beliefs, views, etc are being observed.
MR is not a complete solution to any marketing issue as there are
many dominant variables between research conclusions and market
response. MR is not free from bias. The research conclusions cannot
be verified. The reproduction of the same project on the same class
of respondents give different research results. Inappropriate
training to researchers can lead to misapprehension of questions to
be asked for data collection. Many business executives and
researchers have ambiguity about the research problem and its
objectives. They have limited experience of the notion of the
decision-making process. This leads to carelessness in research and
researchers are not able to do anything real. There is less
interaction between the MR department and the main research
executives. The research department is in segregation. This all
makes research ineffective. MR faces time constraint. The firms are
required to maintain a balance between the requirement for having a
broader perspective of customer needs and the need for quick
decision making so as to have competitive advantage. Huge cost is
involved in MR as collection and processing of data can be costly.
Many firms do not have the proficiency to carry wide surveys for
collecting primary data, and might not also able to hire
specialized market experts and research agencies to collect primary
data. Thus, in that case, they go for obtaining secondary data that
is cheaper to obtain. MR is conducted in open marketplace where
numerous variables act on research settings.Four Factors to
Consider When Creating a Market Research Function. /Organising the
Marketing Research Function Todays marketers need to remember that
one of their primary jobs is providing the rest of the company with
a window into the customer. This takes research.Research enables
you to: Ask the questions you want to ask Begin with and test a
hypothesis Predict what might happen in the futureIf improving your
ability to retain and grow business with existing customers is
important to you, then conduct research. Research in its most basic
form is to inquire, to examine. There is a rigor to research.
Research begins with a question and the question helps you
formulate your approach or methodology.In addition to conducting
research, a number of companies are exploring adding a market
research function to their organization. Weve had a number of
questions regarding what factors should be considered when
assessing workload requirements. Here are four key factors to
consider:Project complexity. The number and type of people you will
need to staff your own research department is going to depend how
complex the research projects are going to be. Secondary research
projects require different skills sets than primary research
efforts. Short, closed ended online web surveys that require little
statistical analysis will require different skills and people than
conducting in-person product validation research around the
world.Frequency of research. A couple of research projects a year
will require different resources than a couple of research projects
a month. If you havent created a research calendar, this is a good
starting point. At a minimum the calendar should identify the
research topic, timing, methodology, participant profile, and
purpose.Project alignment.Market research is frequently aligned by
brand and the staff allocations for each brand are based on the
products lifecycle and category. The number of brands and markets
and the number of products and complexity of these products in each
brand will affect the staff and budget requirements. The research
calendar will help you determine staff and budget
requirements.Market potential.A products market potential can
influence the number of analysts assigned to a research project. It
is common for companies to have more analysts assigned to research
projects related to larger brands or products with the greatest
potential. It helps to have a way to evaluate each research effort.
One way to do this is to use a 2X2 grid, with one axis for
business/marketing value/ impact (high to low) and one axis for
effort or investment (high to low). Those research efforts in the
high value/impact quadrant should be prioritized first. We often
recommend tackling some of the easiest efforts first as a way to
rack up some fast wins.What are the various types of research
design?Various types of research design are as follow:1. Research
design for exploratory or formulative studies In this type of
design, a vague problem is selected and understood and is then
followed by an exploratory research to find a new hypothesis and
then carrying out conclusion research decisions to finally get new
ideas. Aims at finding a new hypothesis. Individual surveys,
referring to secondary sources of data etc. play an important role
in such research designs. Reviewing related literature, following
or surveying people having practical experience in the problem
related field act as very important and most commonly used methods
by an exploratory researcher.2. Research design for conclusive
studiesAlso referred to as the research design for the descriptive
studies and is further divided as follows a. Case Study method
Finds extensive use in commerce and industry. Very respectable
method of teaching and research in management. Helps greatly in
knowing the causes and the results of the incident of the
phenomenon.b. Statistical method Also trying to find its place in
commerce and industry. Act as method of correlation and
regressions, analysis, chi square etc. Has been made very rigorous
and sophisticated by coming up of the computers.3. Research design
for experimental studies Explains the structure of an experiment.
Involve plans for the testing of the causal hypothesis. Decides the
number of observations to be taken and also the order in which
experiments are to be carried out. Which randomization method to be
used. Which mathematical model to be used for explaining the
experiment.This research design can be further categorized into the
following 1. Informal experimental design After only design. After
only with control design. Before and after without control design.
Before and after with control design.2. Formal experimental design
Completely randomized design. Randomized block design. Latin square
design. Factorial design.UNIT-2Data Collection MethodsData
Collection is an important aspect of any type of research study.
Inaccurate data collection can impact the results of a study and
ultimately lead to invalid results.Data collection methods for
impact evaluation vary along a continuum. At the one end of this
continuum are quantatative methods and at the other end of the
continuum are Qualitative methods for data collection.Quantitative
and Qualitative Data collection methodsTheQuantitative data
collection methods, rely on random sampling and structured data
collection instruments that fit diverse experiences into
predetermined response categories. They produce results that are
easy to summarize, compare, and generalize.Quantitative research is
concerned with testing hypotheses derived from theory and/or being
able to estimate the size of a phenomenon of interest.Depending on
the research question, participants may be randomly assigned to
different treatments.If this is not feasible, the researcher may
collect data on participant and situational characteristics in
order to statistically control for their influence on the
dependent, or outcome, variable.If the intent is to generalize from
the research participants to a larger population, the researcher
will employ probability sampling to select participants.Typical
quantitative data gathering strategies include:
Experiments/clinical trials. Observing and recording well-defined
events (e.g., counting the number of patients waiting in emergency
at specified times of the day). Obtaining relevant data from
management information systems. Administering surveys with
closed-ended questions (e.g., face-to face and telephone
interviews, questionnaires etc).InterviewsIn Quantitative
research(survey research),interviews are more structured than in
Qualitative research. In a structured interview,the researcher asks
a standard set of questions and nothing more.(Leedy and Ormrod,
2001)Face -to -face interviewshave a distinct advantage of enabling
the researcher to establish rapport with potential partiocipants
and therefor gain their cooperation.These interviews yield highest
response rates in survey research.They also allow the researcher to
clarify ambiguous answers and when appropriate, seek follow-up
information. Disadvantages include impractical when large samples
are involved time consuming and expensive.(Leedy and Ormrod,
2001)Telephone interviewsare less time consuming and less expensive
and the researcher has ready access to anyone on the planet who
hasa telephone.Disadvantages are that the response rate is not as
high as the face-to- face interview but cosiderably higher than the
mailed questionnaire.The sample may be biased to the extent that
people without phones are part of the population about whom the
researcher wants to draw inferences.Computer Assisted Personal
Interviewing (CAPI):is a form of personal interviewing, but instead
of completing a questionnaire, the interviewer brings along a
laptop or hand-held computer to enter the information directly into
the database. This method saves time involved in processing the
data, as well as saving the interviewer from carrying around
hundreds of questionnaires. However, this type of data collection
method can be expensive to set up and requires that interviewers
have computer and typing
skills.QuestionnairesPaper-pencil-questionnairescan be sent to a
large number of people and saves the researcher time and
money.People are more truthful while responding to the
questionnaires regarding controversial issues in particular due to
the fact that their responses are anonymous. But they also have
drawbacks.Majority of the people who receive questionnaires don't
return them and those who do might not be representative of the
originally selected sample.(Leedy and Ormrod, 2001)Web based
questionnaires: A new and inevitably growing methodology is the use
of Internet based research. This would mean receiving an e-mail on
which you would click on an address that would take you to a secure
web-site to fill in a questionnaire. This type of research is often
quicker and less detailed.Some disadvantages of this method include
the exclusion of people who do not have a computer or are unable to
access a computer.Also the validity of such surveys are in question
as people might be in a hurry to complete it and so might not give
accurate responses.
(http://www.statcan.ca/english/edu/power/ch2/methods/methods.htm)Questionnaires
often make use of Checklist and rating scales.These devices help
simplify and quantify people's behaviors and attitudes.Achecklistis
a list of behaviors,characteristics,or other entities that te
researcher is looking for.Either the researcher or survey
participant simply checks whether each item on the list is
observed, present or true or vice versa.Arating scaleis more useful
when a behavior needs to be evaluated on a continuum.They are also
known as Likert scales. (Leedy and Ormrod, 2001)Qualitative data
collection methodsplay an important role in impact evaluation by
providing information useful to understand the processes behind
observed results and assess changes in peoples perceptions of their
well-being.Furthermore qualitative methods can beused to improve
the quality of survey-based quantitative evaluations by helping
generate evaluation hypothesis; strengthening the design of survey
questionnaires and expanding or clarifying quantitative evaluation
findings. These methods are characterized by the following
attributes: they tend to be open-ended and have less structured
protocols (i.e., researchers may change the data collection
strategy by adding, refining, or dropping techniques or informants)
they rely more heavily on iteractive interviews; respondents may be
interviewed several times to follow up on a particular issue,
clarify concepts or check the reliability of data they use
triangulation to increase the credibility of their findings (i.e.,
researchers rely on multiple data collection methods to check the
authenticity of their results) generally their findings are not
generalizable to any specific population, rather each case study
produces a single piece of evidence that can be used to seek
general patterns among different studies of the same
issueRegardless of the kinds of data involved,data collection in a
qualitative study takes a great deal of time.The researcher needs
to record any potentially useful data thououghly,accurately, and
systematically,using field notes,sketches,audiotapes,photographs
and other suitable means.The data collection methods must observe
the ethical principles of research.The qualitative methods most
commonly used in evaluation can be classified in three broad
categories: indepth interview observation methods document
reviewThe following link provides more information on the above
three methods.
Primary Data Collection:In primary data collection, you collect
the data yourself using qualitative and quantitative methods. The
key point here is that the data you collect is unique to you and
your research and, until you publish, no one else has access to
it.There are many methods of collecting primary data. The main
methods include: questionnaires interviews focus group interviews
observation case-studies scientific experimentsTen Steps to Design
a QuestionnaireDesigning a questionnaire involves 10 main steps:1.
Write a study protocolThis involves getting acquainted with the
subject, making a literature review, decide on objectives,
formulate a hypothesis, and define the main information needed to
test the hypothesis.2. Draw a plan of analysisThis steps
determineshowthe information defined in step 1should be analysed.
The plan of analysis should contain the measures of association
andthe statistical tests that you intend to use. In addition, you
should draw dummy tables with the information of interest. The plan
of analysis willhelp you to determine which type of results you
want to obtain. An example of a dummy table is shown
below.Exposurenr Cases (%)TotalAttack RateRR (CI95%)
Tomato salad
Chicken breast
3. Draw a list of the information neededFrom the plan of
analysis you can draw a list of the information you need to collect
from participants. In this step you should determine the type and
format of variables needed.4. Design different parts of the
questionnaireYou can start now designing different parts of the
questionnaire using this list of needed information.5. Write the
questionsKnowing the education and occupation level of the study
population, ethnic or migration background, language knowledge and
special sensitivities at this step is crucial at this stage. Please
keep in mind that the questionnaire needs to be adapted to your
study population. Please see "Format of Questions" section for more
details.6. Decide on the order of the questions askedYou should
start from easy, general and factual to difficult, particular or
abstract questions. Please consider carefully where to place the
most sensitive questions. They should be rather placed in the
middle or towards the end of the questionnaire. Make sure, however,
not to put the most important item last, since some people might
not complete the interview.7. Complete the questionnaireAdd
instructions for the interviewers and definitions of key words for
participants. Insure a smooth flow from one topic to the next one
(ex. "and now I will ask you some questions about your own
health..."). Insert jumps between questions if some questions are
only targeted at a subgroup of the respondents.8. Verify the
content and style of the questionsVerify that each question answers
to one of the objectives and all your objectives are covered by the
questions asked. Delete questions that are not directly related to
your objectives. Make sure that each question is clear,
unambiguous, simple and short. Check the logical order and flow of
the questions. Make sure the questionnaire is easy to read and has
an clear layout. Please see theHints to Design a good
Questionnairesection for more details.9.Conduct a pilot studyYou
should always conduct a pilot study among the intended population
before starting the study.Please see thePiloting
Questionnairessection for more details.10. Refine your
questionnaireDepending on the results of the pilot study, you will
need toamend the questionnairebefore the main survey
starts.Guidelines on how to design a good questionnaire Good
questionnaire should not be too lengthy. Simple English should be
used and the question shouldnt be difficult to answer. A good
questionnaire requires sensible language, editing, assessment, and
redrafting.Questionnaire Design Process1. State the information
required-This will depend upon the nature of the problem, the
purpose of the study and hypothesis framed. The target audience
must be concentrated on.2. State the kind of interviewing
technique-interviewing method can be telephone, mails, personal
interview or electronic interview. Telephonic interview can be
computer assisted. Personal interview can be conducted at
respondents place or at mall or shopping place. Mail interview can
take the form of mail panel. Electronic interview takes place
either through electronic mails or through the internet.3. Decide
the matter/content of individual questions-There are two deciding
factors for this-a. Is the question significant? - Observe
contribution of each question. Does the question contribute for the
objective of the study?b. Is there a need for several questions or
a single question? - Several questions are asked in the following
cases: When there is a need for cross-checking When the answers are
ambiguous When people are hesitant to give correct information.4.
Overcome the respondents inability and unwillingness to answer-The
respondents may be unable to answer the questions because of
following reasons-. The respondent may not be fully informed. The
respondent may not remember. He may be unable to express or
articulateThe respondent may be unwilling to answer due to-. There
may be sensitive information which may cause embarrassment or harm
the respondents image.. The respondent may not be familiar with the
genuine purpose. The question may appear to be irrelevant to the
respondent. The respondent will not be willing to reveal traits
like aggressiveness (For instance - if he is asked Do you hit your
wife, sister, etc.)To overcome the respondents unwillingness to
answer:iv. Place the sensitive topics at the end of the
questionnaireiv. Preface the question with a statementiv. Use the
third person technique (For example - Mark needed a job badly and
he used wrong means to get it - Is it right?? Different people will
have different opinions depending upon the situation)iv. Categorize
the responses rather than asking a specific response figure (For
example - Group for income levels 0-25000, 25000-50000, 50000 and
above)1. Decide on the structure of the question-Questions can be
of two types:e. Structured questions-These specify the set of
response alternatives and the response format. These can be
classified into multiple choice questions (having various response
categories), dichotomous questions (having only 2 response
categories such as Yes or No) and scales (discussed already).e.
Unstructured questions-These are also known as open-ended question.
No alternatives are suggested and the respondents are free to
answer these questions in any way they like.1. Determine the
question language/phrasing-If the questions are poorly worded, then
either the respondents will refuse to answer the question or they
may give incorrect answers. Thus, the words of the question should
be carefully chosen. Ordinary and unambiguous words should be used.
Avoid implicit assumptions, generalizations and implicit
alternatives. Avoid biased questions. Define the issue in terms of
who the questionnaire is being addressed to, what information is
required, when is the information required, why the question is
being asked, etc.1. Properly arrange the questions-To determine the
order of the question, take decisions on aspects like opening
questions (simple, interesting questions should be used as opening
questions to gain co-operation and confidence of respondents), type
of information (Basic information relates to the research issue,
classification information relates to social and demographic
characteristics, and identification information relates to personal
information such as name, address, contact number of respondents),
difficult questions (complex, embarrassing, dull and sensitive
questions could be difficult), effect on subsequent questions,
logical sequence, etc.1. Recognize the form and layout of the
questionnaire-This is very essential for self-administered
questionnaire. The questions should be numbered and pre-coded. The
layout should be such that it appears to be neat and orderly, and
not clattered.1. Reproduce the questionnaire-Paper quality should
be good. Questionnaire should appear to be professional. The
required space for the answers to the question should be
sufficient. The font type and size should be appropriate. Vertical
response questions should be used, for example:Do you use brand X
of shampoo?. Yes. No Pre-test the questionnaire-The questionnaire
should be pre-tested on a small number of respondents to identify
the likely problems and to eliminate them. Each and every dimension
of the questionnaire should be pre-tested. The sample respondents
should be similar to the target respondents of the survey. Finalize
the questionnaire-Check the final draft questionnaire. Ask yourself
how much will the information obtained from each question
contribute to the study. Make sure that irrelevant questions are
not asked. Obtain feedback of the respondents on the
questionnaire.Secondary DataSecondary data is the data that have
been already collected by and readily available from other sources.
Such data are cheaper and more quickly obtainable than the primary
data and also may be available when primary data cannot be obtained
at all.Advantages of Secondary data1. It is economical. It saves
efforts and expenses.2. It is time saving.3. It helps to make
primary data collection more specific since with the help of
secondary data, we are able to make out what are the gaps and
deficiencies and what additional information needs to be
collected.4. It helps to improve the understanding of the
problem.5. It provides a basis for comparison for the data that is
collected by the researcher.Disadvantages of Secondary Data1.
Secondary data is something that seldom fits in the framework of
the marketing research factors. Reasons for its non-fitting are:-a.
Unit of secondary data collection-Suppose you want information on
disposable income, but the data is available on gross income. The
information may not be same as we require.b. Class Boundaries may
be different when units are same.Before 5 YearsAfter 5 Years
2500-50005000-6000
5001-75006001-7000
7500-100007001-10000
c. Thus the data collected earlier is of no use to you.2.
Accuracy of secondary data is not known.3. Data may be
outdated.Evaluation of Secondary DataBecause of the above mentioned
disadvantages of secondary data, we will lead to evaluation of
secondary data. Evaluation means the following four requirements
must be satisfied:-1. Availability-It has to be seen that the kind
of data you want is available or not. If it is not available then
you have to go for primary data.2. Relevance-It should be meeting
the requirements of the problem. For this we have two criterion:-a.
Units of measurement should be the same.b. Concepts used must be
same and currency of data should not be outdated.3. Accuracy-In
order to find how accurate the data is, the following points must
be considered: -a. Specification and methodology used;b. Margin of
error should be examined;c. The dependability of the source must be
seen.4. Sufficiency-Adequate data should be available.Robert W
Joselyn has classified the above discussion into eight steps. These
eight steps are sub classified into three categories. He has given
a detailed procedure for evaluating secondary data.1. Applicability
of research objective.2. Cost of acquisition.3. Accuracy of
data.
Measurement scalesA topic which can create a great deal of
confusion in social and educational research is that of types of
scales used in measuring behaviour.It is critical because it
relates to the types of statistics you can use to analyse your
data. An easy way to have a paper rejected is to have used either
an incorrect scale/statistic combination or to have used a low
powered statistic on a high powered set of data. Nominal Ordinal
Interval Ratio
NominalThe lowest measurement level you can use, from a
statistical point of view, is a nominal scale.A nominal scale, as
the name implies, is simply some placing of data into categories,
without any order or structure.A physical example of a nominal
scale is the terms we use for colours. The underlying spectrum is
ordered but the names are nominal.In research activities a YES/NO
scale is nominal. It has no order and there is no distance between
YES and NO.and statisticsThe statistics which can be used with
nominal scales are in the non-parametric group. The most likely
ones would be:modecrosstabulation - with chi-squareThere are also
highly sophisticated modelling techniques available for nominal
data.
OrdinalAn ordinal scale is next up the list in terms of power of
measurement.The simplest ordinal scale is a ranking. When a market
researcher asks you to rank 5 types of beer from most flavourful to
least flavourful, he/she is asking you to create an ordinal scale
of preference.There is no objective distance between any two points
on your subjective scale. For you the top beer may be far superior
to the second prefered beer but, to another respondant with the
same top and second beer, the distance may be subjectively small.An
ordinal scale only lets you interpret gross order and not the
relative positional distances.and statisticsOrdinal data would use
non-parametric statistics. These would include:Median and moderank
order correlationnon-parametric analysis of varianceModelling
techniques can also be used with ordinal data.
IntervalThe standard survey rating scale is an interval
scale.When you are asked to rate your satisfaction with a piece of
software on a 7 point scale, from Dissatisfied to Satisfied, you
are using an interval scale.It is an interval scale because it is
assumed to have equidistant points between each of the scale
elements. This means that we can interpret differences in the
distance along the scale. We contrast this to an ordinal scale
where we can only talk about differences in order, not differences
in the degree of order.Interval scales are also scales which are
defined by metrics such as logarithms. In these cases, the
distances are note equal but they are strictly definable based on
the metric used.and statisticsInterval scale data would use
parametric statistical techniques:Mean and standard
deviationCorrelation - rRegressionAnalysis of varianceFactor
analysisPlus a whole range of advanced multivariate and modelling
techniquesRememberthat you can use non-parametric techniques with
interval and ratio data. But non-paramteric techniques are less
powerful than the parametric ones.
RatioA ratio scale is the top level of measurement and is not
often available in social research.The factor which clearly defines
a ratio scale is that it has a true zero point.The simplest example
of a ratio scale is the measurement of length (disregarding any
philosophical points about defining how we can identify zero
length).The best way to contrast interval and ratio scales is to
look at temperature. The Centigrade scale has a zero point but it
is an arbitrary one. The Farenheit scale has its equivalent point
at -32o. (Physicists would probably argue that Absolute Zero is the
zero point for temperature but this is a theoretical concept.) So,
even though temperture looks as if it would be a ratio scale it is
an interval scale. Currently, we cannot talk aboutno temperature-
and this would be needed if it were a ration scale.and
statisticsThe same as for Interval dataComparative scaling
techniques Pairwise comparisonscale a respondent is presented with
two items at a time and asked to select one (example: Do you prefer
Pepsi or Coke?). This is an ordinal level technique when a
measurement model is not applied. Krus and Kennedy (1977)
elaborated the paired comparison scaling within their
domain-referenced model. TheBradleyTerryLuce (BTL) model(Bradley
and Terry, 1952; Luce, 1959) can be applied in order to derive
measurements provided the data derived from paired comparisons
possess an appropriate structure. Thurstone'sLaw of comparative
judgmentcan also be applied in such contexts. Rasch modelscaling
respondents interact with items and comparisons are inferred
between items from the responses to obtain scale values.
Respondents are subsequently also scaled based on their responses
to items given the item scale values. The Rasch model has a close
relation to the BTL model. Rank-ordering a respondent is presented
with several items simultaneously and asked to rank them (example:
Rate the following advertisements from 1 to 10.). This is an
ordinal level technique. Bogardus social distance scale measures
the degree to which a person is willing to associate with a class
or type of people. It asks how willing the respondent is to make
various associations. The results are reduced to a single score on
a scale. There are also non-comparative versions of this scale.
Q-Sort Up to 140 items are sorted into groups based on rank-order
procedure. Guttman scale This is a procedure to determine whether a
set of items can be rank-ordered on a unidimensional scale. It
utilizes the intensity structure among several indicators of a
given variable. Statements are listed in order of importance. The
rating is scaled by summing all responses until the first negative
response in the list. The Guttman scale is related to Rasch
measurement; specifically, Rasch models bring the Guttman approach
within a probabilistic framework. Constant sum scale a respondent
is given a constant sum of money, script, credits, or points and
asked to allocate these to various items (example: If you had 100
Yen to spend on food products, how much would you spend on product
A, on product B, on product C, etc.). This is an ordinal level
technique. Magnitude estimation scale In apsychophysicsprocedure
invented byS. S. Stevenspeople simply assign numbers to the
dimension of judgment. The geometric mean of those numbers usually
produces apower lawwith a characteristic exponent. Incross-modality
matchinginstead of assigning numbers, people manipulate another
dimension, such as loudness or brightness to match the items.
Typically the exponent of the psychometric function can be
predicted from the magnitude estimation exponents of each
dimension.Non-comparative scaling techniques[edit] Continuous
rating scale(also called the graphic rating scale) respondents rate
items by placing a mark on a line. The line is usually labeled at
each end. There are sometimes a series of numbers, called scale
points, (say, from zero to 100) under the line. Scoring and
codification is difficult. Likert scale Respondents are asked to
indicate the amount of agreement or disagreement (from strongly
agree to strongly disagree) on a five- to nine-point response scale
(not to be confused with a Likert scale). The same format is used
for multiple questions. It is the combination of these questions
that forms the Likert scale. This categorical scaling procedure can
easily be extended to amagnitude estimationprocedure that uses the
full scale of numbers rather than verbal categories. Phrase
completion scales Respondents are asked to complete a phrase on an
11-point response scale in which 0 represents the absence of the
theoretical construct and 10 represents the theorized maximum
amount of the construct being measured. The same basic format is
used for multiple questions. Semantic differential scale
Respondents are asked to rate on a 7 point scale an item on various
attributes. Each attribute requires a scale with bipolar terminal
labels. Stapel scale This is a unipolar ten-point rating scale. It
ranges from +5 to 5 and has no neutral zero point. Thurstone scale
This is a scaling technique that incorporates the intensity
structure among indicators. Mathematically derived scale
Researchers infer respondents evaluations mathematically. Two
examples aremulti dimensional scalingandconjoint analysis.Types of
Sampling DesignsWhen conducting research, it is almost always
impossible to study the entire population that you are interested
in. For example, if you were studying political views amongcollege
students in the United States, it would be nearly impossible to
survey every single college student across the country. If you were
to survey the entire population, it would be extremely timely and
costly. As a result, researchers usesamplesas a way to gather
data.A sample is a subset of thepopulationbeing studied.It
represents the larger population and is used to draw inferences
about that population. It is a research technique widely used in
the social sciences as a way to gather information about a
population without having to measure the entire population.There
are several different types and ways of choosing a sample from a
population, from simple to complex.Non-probability Sampling
TechniquesNon-probability samplingis 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.Reliance On Available Subjects.Relying on available
subjects, such as stopping people on a street corner as they pass
by, is one method of sampling, although it is extremely risky and
comes with many cautions. This method, sometimes referred to as
aconvenience sample, does not allow the researcher to have any
control over the representativeness of the sample. It is only
justified if the researcher wants to study the characteristics of
people passing by the street corner at a certain point in time or
if other sampling methods are not possible.The researcher must also
take caution to not use results from a convenience sample to
generalize to a wider population.Purposive or Judgmental Sample.A
purposive, or judgmental, sample is one that is selected based on
the knowledge of a population and the purpose of the study. For
example, if a researcher is studying the nature of school spirit as
exhibited at a school pep rally, he or she might interview people
who did not appear to be caught up in the emotions of the crowd or
students who did not attend the rally at all. In this case, the
researcher is using a purposive sample because those being
interviewed fit a specific purpose or description.Snowball Sample.A
snowball sample is appropriate to use in research when the members
of a population are difficult to locate, such as homeless
individuals, migrant workers, orundocumented immigrants. A snowball
sample is one in which the researcher collects data on the few
members of the target population he or she can locate, then asks
those individuals to provide information needed to locate other
members of that population whom they know. For example, if a
researcher wishes to interview undocumented immigrants from Mexico,
he or she might interview a few undocumented individuals that he or
she knows or can locate and would then rely on those subjects to
help locate more undocumented individuals. This process continues
until the researcher has all the interviews he or she needs or
until all contacts have been exhausted.Quota Sample.A quota sample
is one in which units are selected into a sample on the basis of
pre-specified characteristics so that the total sample has the same
distribution of characteristics assumed to exist in the population
being studied. For example, if you a researcher conducting a
national quota sample, you might need to know what proportion of
the population is male and what proportion is female as well as
what proportions of each gender fall into different age categories,
race or ethnic categories, educational categories, etc. The
researcher would then collect a sample with the same proportions as
the national population.Probability Sampling TechniquesProbability
sampling is a sampling technique where the samples are gathered in
a process that gives all the individuals in the population equal
chances of being selected.Simple Random Sample.Thesimple random
sampleis the basic sampling method assumed instatistical methodsand
computations. To collect a simple random sample, each unit of the
target population is assigned a number. A set ofrandom numbersis
then generated and the units having those numbers are included in
the sample. For example, lets say you have a population of 1,000
people and you wish to choose a simple random sample of 50 people.
First, each person is numbered 1 through 1,000. Then, you generate
a list of 50 random numbers (typically with a computer program) and
those individuals assigned those numbers are the ones you include
in the sample.Systematic Sample.In asystematic sample, the elements
of the population are put into a list and then everykth element in
the list is chosen (systematically) for inclusion in the sample.
For example, if the population of study contained 2,000 students at
a high school and the researcher wanted a sample of 100 students,
the students would be put into list form and then every 20th
student would be selected for inclusion in the sample. To ensure
against any possible human bias in this method, the researcher
should select the first individual at random. This is technically
called asystematic sample with a random start.Stratified
Sample.Astratified sampleis a sampling technique in which the
researcher divided the entire target population into different
subgroups, or strata, and then randomly selects the final subjects
proportionally from the different strata. Thistype of samplingis
used when the researcher wants to highlight specificsubgroupswithin
the population. For example, to obtain a stratified sample of
university students, the researcher would first organize the
population by college class and then select appropriate numbers of
freshmen, sophomores, juniors, and seniors. This ensures that the
researcher has adequate amounts of subjects from each class in the
final sample.Cluster Sample.Cluster samplingmay be used when it is
either impossible or impractical to compile an exhaustive list of
the elements that make up the target population. Usually, however,
thepopulation elementsare already grouped into subpopulations and
lists of those subpopulations already exist or can be created. For
example, lets say the target population in a study was church
members in the United States. There is no list of all church
members in the country. The researcher could, however, create a
list of churches in the United States, choose a sample of churches,
and then obtain lists of members from those churches.10
Interviewing RulesIn the current job market, you'd better have your
act together, or you won't stand a chance against the competition.
Check yourself on these 10 basic points before you go on that
all-important interview.1. Do Your ResearchResearching the
companybefore the interview and learning as much as possible about
its services, products, customers and competition will give you an
edge in understanding and addressing the company's needs. The more
you know about the company and what it stands for, the better
chance you have ofselling yourself in the interview. You also
should find out about thecompany's cultureto gain insight into your
potential happiness on the job.2. Look SharpSelectwhat to wear to
the interview. Depending on the industry and position, get out your
best interview clothes and check them over for spots and wrinkles.
Even if the company has a casual environment, you don't want to
look like you slept in your outfit. Above all, dress for
confidence. If you feel good, others will respond to you
accordingly.3. Be PreparedBring along a folder containing extra
copies of your resume, a copy of yourreferencesand paper to take
notes. You should also have questions prepared to ask at the end of
the interview. For extra assurance, print a copy of Monster's
handyinterview take-along checklist.4. Be on TimeNever arrive late
to an interview. Allow extra time to arrive early in the vicinity,
allowing for factors like getting lost. Enter the building 10 to 15
minutes before the interview.
5. Show EnthusiasmA firmhandshakeand plenty of eye contact
demonstrate confidence. Speak distinctly in a confident voice, even
though you may feel shaky.6. ListenOne of the most
neglectedinterview skillsislistening. Make sure you are not only
listening, but also reading between the lines. Sometimes what is
not said is just as important as what is said.7. Answer the
Question AskedCandidates often don't think about whether they are
actually answering the questions their interviewers ask. Make sure
you understand what is being asked, and get further clarification
if you are unsure.8. Give Specific ExamplesOne specific example of
your background is worth 50 vague stories. Prepare your stories
before the interview.Give examplesthat highlight your successes and
uniqueness. Your past behavior can indicate your future
performance.9. Ask QuestionsMany interviewees don't ask questions
and miss the opportunity to find out valuable information.
Thequestions you askindicate your interest in the company or
job.10. Follow UpWhetherit's through email or regular mail,
theinterview follow-upis one more chance to remind the interviewer
of all the valuable traits you bring to the job and company. Don't
miss this last chance to market yourself.It is important to appear
confident and cool for the interview. One way to do that is to be
prepared to the best of your ability. There is no way to predict
what an interview holds, but by following these important rules you
will feel less anxious and will be ready to positively present
yourself.UNIT-3Data processing
Data processingis, broadly, "thecollectionand manipulation of
items of data to producemeaningfulinformation."[1]In this sense it
can be considered a subset ofinformation processing, "the change
(processing) of information in any manner detectable by an
observer."[note 1]The term is often used more specifically in the
context of a business or other organization to refer to the class
of commercial data processing applications.[2]ContentsData
processing functions[edit]Data processing may involve various
processes, including: Validation Ensuring that supplied data is
"clean, correct and useful" Sorting "arranging items in some
sequence and/or in different sets." Summarization reducing detail
data to its main points. Aggregation combining multiple pieces of
data. Analysis the "collection, organization, analysis,
interpretation and presentation of data.". Reporting list detail or
summary data or computed information. Classification separates data
into various categories.HistoryTheUnited States Census
Bureauillustrates the evolution of data processing from manual
through electronic procedures.Manual data processing[edit]Although
widespread use of the termdata processingdates only from the
nineteen-fifties[3]data processing functions have been performed
manually for millennia. For examplebookkeepinginvolves functions
such as posting transactions and producing reports like thebalance
sheetand thecash flow statement. Completely manual methods were
augmented by the application ofmechanicalor electroniccalculators.
A person whose job it was to perform calculations manually or using
a calculator was called a "computer."The1850 United States
Censusschedule was the first to gather data by individual rather
thanhousehold. A number of questions could be answered by making a
check in the appropriate box on the form. From 1850 through 1880
the Census Bureau employed "a system of tallying, which, by reason
of the increasing number of combinations of classifications
required, became increasingly complex. Only a limited number of
combinations could be recorded in one tally, so it was necessary to
handle the schedules 5 or 6 times, for as many independent
tallies."[4]"It took over 7 years to publish the results of the
1880 census"[5]using manual processing methods.Automatic data
processing[edit]The termautomatic data processingwas applied to
operations performed by means ofunit record equipment, such
asHerman Hollerith's application ofpunched cardequipment for
the1890 United States Census. "Using Hollerith's punchcard
equipment, the Census Office was able to complete tabulating most
of the 1890 census data in 2 to 3 years, compared with 7 to 8 years
for the 1880 census. ... It is also estimated that using Herman
Hollerith's system saved some $5 million in processing costs"[5](in
1890 dollars) even with twice as many questions as in
1880.Electronic data processing[edit]Computerized data processing,
orElectronic data processingrepresents the further evolution, with
the computer taking the place of several independent pieces of
equipment. The Census Bureau first made limited use ofelectronic
computersfor the1950 United States Census, using aUNIVAC
Isystem,[4]delivered in 1952.Further evolution[edit]"Data
processing (DP)" has also previously been used to refer to the
department within an organization responsible for the operation of
data processing applications.[6]The termdata processinghas mostly
been subsumed under the newer and somewhat more general
terminformation technology(IT).[citation needed]"Data processing"
has acquired a negative connotation, suggesting use of older
technologies. As an example, in 1996 theData Processing Management
Association(DPMA) changed its name to theAssociation of Information
Technology Professionals. Nevertheless, the terms are roughly
synonymous.Applications[edit]Commercial data processing[edit]Main
article:Electronic data processingCommercial data processing
involves a large volume of input data, relatively few computational
operations, and a large volume of output. For example, an insurance
company needs to keep records on tens or hundreds of thousands of
policies, print and mail bills, and receive and post payments.Data
analysis[edit]Main article:Data analysisIn a science or engineering
field, the termsdata processingandinformation systemsare considered
too broad, and the more specialized termdata analysisis typically
used. Data analysis makes use of specialized and highly accurate
algorithms and statistical calculations that are less often
observed in the typical general business environment.One divergence
of culture between data processing and data analysis is shown by
the numerical representations generally used; In data processing,
measurements are typically stored
asintegers,fixed-pointorbinary-coded decimalrepresentations of
numbers, whereas the majority of measurements in data analysis are
stored asfloating-pointrepresentations of rational numbers.For data
analysis, packages likeSPSSorSAS, or their free counterparts such
asDAP,gretlorPSPPare often used.RESEARCH METHODOLOGY
Processing and Analysis of Data
The data, after collection, has to be processed and analysed in
accordance with the outline laid down for the purpose at the time
of developing the research plan. This is essential for a scientific
study and for ensuring that we have all relevant data for making
contemplated comparisons and analysis. Technically speaking,
processing implies editing, coding, classification and tabulation
of collected data so that they are amenable to analysis. The term
analysis refers to the computation of certain measures along with
searching for patterns of relationship that exist among
data-groups. Thus, in the process of analysis, relationships or
differences supporting or conflicting with original or new
hypotheses should be subjected to statistical tests of significance
to determine with what validity data can be said to indicate any
conclusions.1 But there are persons (Selltiz, Jahoda and others)
who do not like to make difference between processing and analysis.
They opine that analysis of data in a general way involves a number
of closely related operations which are performed with the purpose
of summarising the collected data and organising these in such a
manner that they answer the research question(s). We, however,
shall prefer to observe the difference between the two terms as
stated here in order to understand their implications more
clearly.PROCESSING OPERATIONS1.
Editing:Editing of data is a process of examining the collected
raw data (specially in surveys) to detect errors and omissions and
to correct these when possible. As a matter of fact, editing
involves a careful scrutiny of the completed questionnaires and/or
schedules. Editing is done to assure that the data are accurate,
consistent with other facts gathered, uniformly entered, as
completed as possible and have been well arranged to facilitate
coding and tabulation.With regard to points or stages at which
editing should be done, one can talk of field editing and central
editing. Field editing consists in the review of the reporting
forms by the investigator for completing (translating or rewriting)
what the latter has written in abbreviated and/or in illegible form
at the time of recording the respondents responses. This type of
editing is necessary in view of the fact that individual writing
styles often can be difficult for others to decipher. This sort of
editing should be done as soon as possible after the interview,
preferably on the very day or on the next day. While doing field
editing, the investigator must restrain himself and must not
correct errors of omission by simply guessing what the informant
would have said if the question had been asked.Central editing
should take place when all forms or schedules have been completed
and returned to the office. This type of editing implies that all
forms should get a thorough editing by a single editor in a small
study and by a team of editors in case of a large inquiry.
Editor(s) may correct the obvious errors such as an entry in the
wrong place, entry recorded in months when it should have been
recorded in weeks, and the like. In case of inappropriate on
missing replies, the editor can sometimes determine the proper
answer by reviewing the other information in the schedule. A t
times, the respondent can be contacted for clarification. The
editor must strike out the answer if the same is inappropriate and
he has no basis for determining the correct answer or the response.
In such a case an editing entry of no answer is called for. All the
wrong replies, which are quite obvious, must be dropped from the
final results, especially in the context of mail surveys.Editors
must keep in view several points while performing their work:They
should be familiar with instructions given to the interviewers and
coders as well as with the editing instructions supplied to them
for the purpose.While crossing out an original entry for one reason
or another, they should just draw a single line on it so that the
same may remain legible. They must make entries (if any) on the
form in some distinctive colur and that too in a standardised form.
They should initial all answers which they change or supply.
Editors initials and the date of editing should be placed on each
completed form or schedule.2. Coding:Coding refers to the process
of assigning numerals or other symbols to answers so that responses
can be put into a limited number of categories or classes. Such
classes should be appropriate to the research problem under
consideration. They must also possess the characteristic of
exhaustiveness (i.e., there must be a class for every data item)
and also that of mutual exclusively which means that a specific
answer can be placed in one and only one cell in a given category
set. Another rule to be observed is that of unidimensionality by
which is meant that every class is defined in terms of only one
concept.Coding is necessary for efficient analysis and through it
the several replies may be reduced to a small number of classes
which contain the critical information required for analysis.
Coding decisions should usually be taken at the designing stage of
the questionnaire. This makes it possible to precode the
questionnaire choices and which in turn is helpful for computer
tabulation as one can straight forward key punch from the original
questionnaires. But in case of hand coding some standard method may
be used. One such standard method is to code in the margin with a
coloured pencil. The other method can be to transcribe the data
from the questionnaire to a coding sheet. Whatever method is
adopted, one should see that coding errors are altogether
eliminated or reduced to the minimum level.3. Classification:Most
research studies result in a large volume of raw data which must be
reduced into homogeneous groups if we are to get meaningful
relationships. This fact necessitates classification of data which
happens to be the process of arranging data in groups or classes on
the basis of common characteristics. Data having a common
characteristic are placed in one class and in this way the entire
data get divided into a number of groups or classes. Classification
can be one of the following two types, depending upon the nature of
the phenomenon involved: Classification according to attributes: As
stated above, data are classified on the basis of common
characteristics which can either be descriptive (such as literacy,
sex, honesty, etc.) or numerical (such as weight, height, income,
etc.). Descriptive characteristics refer to qualitative phenomenon
which cannot be measured quantitatively; only their presence or
absence in an individual item can be noticed. Data obtained this
way on the basis of certain attributes are known as statistics of
attributes and their classification is said to be classification
according to attributes.Such classification can be simple
classification or manifold classification. In simple classification
we consider only one attribute and divide the universe into two
classesone class consisting of items possessing the given attribute
and the other class consisting of items which do not possess the
given attribute. But in manifold classification we consider two or
more attributes simultaneously, and divide that data into a number
of classes (total number of classes of final order is given by 2n,
where n = number of attributes considered). Whenever data are
classified according to attributes, the researcher must see that
the attributes are defined in such a manner that there is least
possibility of any doubt/ambiguity concerning the said attributes.
Classification according to class-intervals: Unlike descriptive
characteristics, the numerical characteristics refer to
quantitative phenomenon which can be measured through some
statistical units. Data relating to income, production, age,
weight, etc. come under this category. Such data are known as
statistics of variables and are classified on the basis of class
intervals. For instance, persons whose incomes, say, are within Rs
201 to Rs 400 can form one group, those whose incomes are within Rs
401 to Rs 600 can form another group and so on. In this way the
entire data may be divided into a number of groups or classes or
what are usually called, class-intervals. Each group of
class-interval, thus, has an upper limit as well as a lower limit
which are known as class limits. The difference between the two
class limits is known as class magnitude. We may have classes with
equal class magnitudes or with unequal class magnitudes. The number
of items which fall in a given class is known as the frequency of
the given class. All the classes or groups, with their respective
frequencies taken together and put in the form of a table, are
described as group frequency distribution or simply frequency
distribution. Classification according to class intervals usually
involves the following three main problems:How may classes should
be there? What should be their magnitudes?There can be no specific
answer with regard to the number of classes. The decision about
this calls for skill and experience of the researcher. However, the
objective should be to display the data in such a way as to make it
meaningful for the analyst. Typically, we may have 5 to 15 classes.
With regard to the second part of the question, we can say that, to
the extent possible, class-intervals should be of equal magnitudes,
but in some cases unequal magnitudes may result in better
classification. Hence researchers objective judgement plays an
important part in this connection. Multiples of 2, 5 and 10 are
generally preferred while determining class magnitudes. Some
statisticians adopt the following formula, suggested by H.A.
Sturges, determining the size of class interval:i = R/(1 + 3.3 log
N)wherei = size of class interval;R = Range (i.e., difference
between the values of the largest item and smallest item among the
given items);N = Number of items to be grouped.It should also be
kept in mind that in case one or two or very few items have very
high or very low values, one may use what are known as open-ended
intervals in the overall frequency distribution. Such intervals may
be expressed like under Rs 500 or Rs 10001 and over. Such intervals
are generally not desirable, but often cannot be avoided. The
researcher must always remain conscious of this fact while deciding
the issue of the total number of class intervals in which the data
are to be classified.How to choose class limits?While choosing
class limits, the researcher must take into consideration the
criterion that the mid-point (generally worked out first by taking
the sum of the upper limit and lower limit of a class and then
divide this sum by 2) of a class-interval and the actual average of
items of that class interval should remain as close to each other
as possible. Consistent with this, the class limits should be
located at multiples of 2, 5, 10, 20, 100 and such other figures.
Class limits may generally be stated in any of the following
forms:Exclusive type class intervals: They are usually stated as
follows:1020203030404050The above intervals should be read as
under:10 and under 2020 and under 3030 and under 4040 and under
50Thus, under the exclusive type class intervals, the items whose
values are equal to the upper limit of a class are grouped in the
next higher class. For example, an item whose value is exactly 30
would be put in 3040 class interval and not in 2030 class interval.
In simple words, we can say that under exclusive type class
intervals, the upper limit of a class interval is excluded and
items with values less than the upper limit (but not less than the
lower limit) are put in the given class interval.Inclusive type
class intervals: They are usually stated as
follows:1120213031404150In inclusive type class intervals the upper
limit of a class interval is also included in the concerning class
interval. Thus, an item whose value is 20 will be put in 1120 class
interval. The stated upper limit of the class interval 1120 is 20
but the real limit is 20.99999 and as such 1120 class interval
really means 11 and under 21.When the phenomenon under
consideration happens to be a discrete one (i.e., can be measured
and stated only in integers), then we should adopt inclusive type
classification. But when the phenomenon happens to be a continuous
one capable of being measured in fractions as well, we can use
exclusive type class intervals.How to determine the frequency of
each class?This can be done either by tally sheets or by mechanical
aids. Under the technique of tally sheet, the class-groups are
written on a sheet of paper (commonly known as the tally sheet) and
for each item a stroke (usually a small vertical line) is marked
against the class group in which it falls. The general practice is
that after every four small vertical lines in a class group, the
fifth line for the item falling in the same group, is indicated as
horizontal line through the said four lines and the resulting
flower (IIII) represents five items. All this facilitates the
counting of items in each one of the class groups. An illustrative
tally sheet can be shown as under:An Illustrative Tally Sheet for
Determining the Number of 70 Families in Different Income
Groups
Alternatively, class frequencies can be determined, specially in
case of large inquires and surveys, by mechanical aids i.e., with
the help of machines viz., sorting machines that are available for
the purpose. Some machines are hand operated, whereas other work
with electricity. There are machines which can sort out cards at a
speed of something like 25000 cards per hour. This method is fast
but expensive.4. Tabulation:When a mass of data has been assembled,
it becomes necessary for the researcher to arrange the same in some
kind of concise and logical order. This procedure is referred to as
tabulation. Thus, tabulation is the process of summarising raw data
and displaying the same in compact form (i.e., in the form of
statistical tables) for further analysis. In a broader sense,
tabulation is an orderly arrangement of data in columns and rows.
Tabulation is essential because of the following reasons.4. It
conserves space and reduces explanatory and descriptive statement
to a minimum.4. It facilitates the process of comparison.4. It
facilitates the summation of items and the detection of errors and
omissions.4. It provides a basis for various statistical
computations.Tabulation can be done by hand or by mechanical or
electronic devices. The choice depends on the size and type of
study, cost considerations, time pressures and the availaibility of
tabulating machines or computers. In relatively large inquiries, we
may use mechanical or computer tabulation if other factors are
favourable and necessary facilities are available. Hand tabulation
is usually preferred in case of small inquiries where the number of
questionnaires is small and they are of relatively short length.
Hand tabulation may be done using the direct tally, the list and
tally or the card sort and count methods. When there are simple
codes, it is feasible to tally directly from the questionnaire.
Under this method, the codes are written on a sheet of paper,
called tally sheet, and for each response a stroke is marked
against the code in which it falls. Usually after every four
strokes against a particular code, the fifth response is indicated
by drawing a diagonal or horizontal line through the strokes. These
groups of five are easy to count and the data are sorted against
each code conveniently. In the listing method, the code responses
may be transcribed onto a large work-sheet, allowing a line for
each questionnaire. This way a large number of questionnaires can
be listed on one work sheet. Tallies are then made for each
question. The card sorting method is the most flexible hand
tabulation. In this method the data are recorded on special cards
of convenient size and shape with a series of holes. Each hole
stands for a code and when cards are stacked, a needle passes
through particular hole representing a particular code. These cards
are then separated and counted. In this way frequencies of various
codes can be found out by the repetition of this technique. We can
as well use the mechanical devices or the computer facility for
tabulation purpose in case we want quick results, our budget
permits their use and we have a large volume of straight forward
tabulation involving a number of cross-breaks.Tabulation may also
be classified as simple and complex tabulation. The former type of
tabulation gives information about one or more groups of
independent questions, whereas the latter type of tabulation shows
the division of data in two or more categories and as such is
deigned to give information concerning one or more sets of
inter-related questions. Simple tabulation generally results in
one-way tables which supply answers to questions about one
characteristic of data only. As against this, complex tabulation
usually results in two-way tables (which give information about two
inter-related characteristics of data), three-way tables (giving
information about three interrelated characteristics of data) or
still higher order tables, also known as manifold tables, which
supply information about several interrelated characteristics of
data. Two-way tables, three-way tables or manifold tables are all
examples of what is sometimes described as cross tabulation.Data
analysisResearch ProcessDefinition:Researchers who are attempting
to answer a research question employ the research process. Though
presented in a liner format, in practice the process of research
can be less straightforward. This said, researchers attempt to
follow the process and use it to present their research findings in
research reports and journal articles.Identifying research
problemsResearch problems need to be researchable and can be
generated from practice, but must be grounded in the existing
literature. They may be local, national or international problems,
that need addressing in order to develop the existing evidence
base.Searching the existing literature baseA thorough search of the
literature using data bases, internet, text and expert sources
should support the need to research the problem. This should be
broad and in depth, showing a comprehensive search of the problem
area.Critical appraisal of the literatureA critical appraisal
framework should be employed to review the literature in a
systematic way.Developing the questions/ and or hypothesisA more
specific research question and /or hypothesis may be developed from
the literature review, that provides the direction for the
research, which aims to provide answers to the question /hypothesis
posed.Theoretical baseThe research may employ a theoretical base to
examining the problem, especially seen in masters level research
and in many research studies. In the health and social care field
this might come from the social sciences, psychology or
anthropology.Sampling strategiesSampling is the method for
selecting people, events or objects for study in research.
Non-probability and probability sampling strategies enable the
researcher to target data collection techniques. These may need to
be of a specific size (sometimes determined by a power calculation)
or composition.Data collection techniquesThese are the tools and
approaches used to collect data to answer the research question
/hypothesis. More than one technique can be employed, the commonest
are questionnaires and interviews.Approaches to qualitative and
quantitative data analysisThis component is more fully explored in
the site, but can involve qualitative and quantitative approaches,
dependent on the type of data collected.Interpretation of
resultsThe results are interpreted, drawing conclusions and
answering the research question /hypothesis. Implications for
practice and further research are drawn, which acknowledge the
limitations of the research.Dissemination of researchThe research
and results can be presented through written reports, articles,
papers and conferences, both in print and electronic forms.5 Steps
To Data ProcessingData is an integral part of all business
processes. It is the invisible backbone that supports all the
operations and activities within a business. Without access to
relevant data, businesses would get completely paralyzed. This is
because quality data helps formulate effective business strategies
and fruitful business decisions.
Therefore, the quality of data should be maintained in good
condition in order to facilitate smooth business proceedings. In
order to enhance business proceedings, data should be made
available in all possible forms in order to increase the
accessibility of the same.
Data processing refers to the process of converting data from
one format to another. It transforms plain data into valuable
information and information into data. Clients can supply data in a
variety of forms, be it .xls sheets, audio devices, or plain
printed material. Data processing services take the raw data and
process it accordingly to produce sensible information. The various
applications of data processing can convert raw data into useful
information that can be used further for business processes.
Companies and organizations across the world make use of data
processing services in order to facilitate their market research
interests. Data consists of facts and figures, based on which
important conclusions can be drawn. When companies and
organizations have access to useful information, they can utilize
it for strategizing powerful business moves that would eventually
increase the company revenue and decrease the costs, thus expanding
the profit margins. Data processing ensures that the data is
presented in a clean and systematic manner and is easy to
understand and be used for further purposes.
Here are the 5 steps that are included in data processing:
EditingThere is a big difference between data and useful data.
While there are huge volumes of data available on the internet,
useful data has to be extracted from the huge volumes of the same.
Extracting relevant data is one of the core procedures of data
processing. When data has been accumulated from various sources, it
is edited in order to discard the inappropriate data and retain
relevant data.
CodingEven after the editing process, the available data is not
in any specific order. To make it more sensible and usable for
further use, it needs to be aligned into a particular system. The
method of coding ensures just that and arranges data in a
comprehendible format. The process is also known as netting or
bucketing.
Data EntryAfter the data has been properly arranged and coded,
it is entered into the software that performs the eventual cross
tabulation. Data entry professionals do the task efficiently.
ValidationAfter the cleansing phase, comes the validation
process. Data validation refers to the process of thoroughly
checking the collected data to ensure optimal quality levels. All
the accumulated data is double checked in order to ensure that it
contains no inconsistencies and is utterly relevant.
TabulationThis is the final step in data processing. The final
product i.e. the data is tabulated and arranged in a systematic
format so that it can be further analyzed.
All these processes make up the complete data processing
activity which ensures the said data is available for access.Data
Analysisis the process of systematically applying statistical
and/or logical techniques to describe and illustrate, condense and
recap, and evaluate data. According to Shamoo and Resnik (2003)
various analytic procedures provide a way of drawing inductive
inferences from data and distinguishing the signal (the phenomenon
of interest) from the noise (statistical fluctuations) present in
the data..While data analysis in qualitative research can include
statistical procedures, many times analysis becomes an ongoing
iterative process where data is continuously collected and analyzed
almost simultaneously. Indeed, researchers generally analyze for
patterns in observations through the entire data collection phase
(Savenye, Robinson, 2004). The form of the analysis is determined
by the specific qualitative approach taken (field study,
ethnography content analysis, oral history,
biography,unobtrusiveresearch) and the form of the data (field
notes, documents, audiotape, videotape).An essential component of
ensuring data integrity is the accurate and appropriate analysis of
research findings. Improper statistical analyses distort scientific
findings, mislead casual readers (Shepard, 2002), and may
negatively influence the public perception of research. Integrity
issues are just as relevant to analysis of non-statistical data as
well.Considerations/issues in data analysis
There are a number of issues that researchers should be
cognizant of with respect to data analysis. These include: Having
the necessary skills to analyze Concurrently selecting data
collection methods and appropriate analysis Drawing unbiased
inference Inappropriate subgroup analysis Following acceptable
norms for disciplines Determiningstatistical significance Lack of
clearly defined and objectiveoutcome measurements Providing honest
and accurate analysis Manner of presenting data
Environmental/contextual issues Data recording method Partitioning
textwhen analyzing qualitative data Training of staff conducting
analyses Reliability and Validity Extent of analysisHaving
necessary skills to analyze
A tacit assumption of investigators is that they have received
training sufficient to demonstrate a high standard of research
practice. Unintentional scientific misconduct' is likely the result
of poor instruction and follow-up. A number of studies suggest this
may be the case more often than believed (Nowak, 1994; Silverman,
Manson, 2003). For example, Sica found that adequate training of
physicians in medical schools in the proper design, implementation
and evaluation of clinical trials is abysmally small (Sica, cited
in Nowak, 1994). Indeed, a single course in biostatistics is the
most that is usually offered (Christopher Williams, cited in Nowak,
1994).A common practice of investigators is to defer the selection
of analytic procedure to a research team statistician. Ideally,
investigators should have substantially more than a basic
understanding of the rationale for selecting one method of analysis
over another. This can allow investigators to better supervise
staff who conduct the data analyses process and make informed
decisions
Concurrently selecting data collection methods and appropriate
analysis
While methods of analysis may differ by scientific discipline,
the optimal stage for determining appropriate analytic procedures
occurs early in the research process and should not be an
afterthought. According to Smeeton and Goda (2003), Statistical
advice should be obtained at the stage of initial planning of an
investigation so that, for example, the method of sampling and
design of questionnaire are appropriate.
Drawing unbiased inference
The chief aim of analysis is to distinguish between an event
occurring as either reflecting a true effect versus a false one.
Any bias occurring in the collection of the data, or selection of
method of analysis, will increase the likelihood of drawing a
biased inference. Bias can occur when recruitment of study
participants falls below minimum number required to demonstrate
statistical power or failure to maintain a sufficient follow-up
period needed to demonstrate an effect (Altman, 2001).
Inappropriate subgroup analysis
When failing to demonstrate statistically different levels
between treatment groups, investigators may resort to breaking down
the analysis to smaller and smaller subgroups in order to find a
difference. Although this practice may not inherently be unethical,
these analyses should be proposed before beginning the study even
if the intent is exploratory in nature. If it the study is
exploratory in nature, the investigator should make this explicit
so that readers understand that the research is more of a hunting
expedition rather than being primarily theory driven.Although a
researcher may not have a theory-based hypothesis for testing
relationships between previously untested variables, a theory will
have to be developed to explain an unanticipated finding. Indeed,
in exploratory science, there are no a priori hypotheses therefore
there are no hypothetical tests. Although theories can often drive
the processes used in the investigation of qualitative studies,
many times patterns of behavior or occurrences derived from
analyzed data can result in developing new theoretical frameworks
rather than determineda priori(Savenye, Robinson, 2004).
It is conceivable that multiple statistical tests could yield a
significant finding by chance alone rather than reflecting a true
effect. Integrity is compromised if the investigator only reports
tests with significant findings, and neglects to mention a large
number of tests failing to reach significance. While access to
computer-based statistical packages can facilitate application of
increasingly complex analytic procedures, inappropriate uses of
these packages can result in abuses as well.
Following acceptable norms for disciplines
Every field of study has developed its accepted practices for
data analysis. Resnik (2000) states that it is prudent for
investigators to follow these accepted norms. Resnik further states
that the norms are based on two factors:(1) the nature of the
variables used (i.e., quantitative, comparative, or
qualitative),(2) assumptions about the population from which the
data are drawn (i.e., random distribution, independence, sample
size, etc.). If one uses unconventional norms, it is crucial to
clearly state this is being done, and to show how this new and
possibly unaccepted method of analysis is being used, as well as
how it differs from other more traditional methods. For example,
Schroder, Carey, and Vanable (2003) juxtapose their identification
of new and powerful data analytic solutions developed to count data
in the area of HIV contraction risk with a discussion of the
limitations of commonly applied methods.
If one uses unconventional norms, it is crucial to clearly state
this is being done, and to show how this new and possibly
unaccepted method of analysis is being used, as well as how it
differs from other more traditional methods. For example, Schroder,
Carey, and Vanable (2003) juxtapose their identification of new and
powerful data analytic solutions developed to count data in the
area of HIV contraction risk with a discussion of the limitations
of commonly applied methods.
Determining significance
While the conventional practice is to establish a standard of
acceptability for statistical significance, with certain
disciplines, it may also be appropriate to discuss whether
attaining statistical significance has a true practical meaning,
i.e.,clinical significance. Jeans (1992) defines clinical
significance as the potential for research findings to make a real
and important difference to clients or clinical practice, to health
status or to any other problem identified as a relevant priority
for the discipline.Kendall and Grove (1988) define clinical
significance in terms of what happens when troubled and disordered
clients are now, after treatment, not distinguishable from a
meaningful and representative non-disturbed reference group.
Thompson and Noferi (2002) suggest that readers of counseling
literature should expect authors to report either practical or
clinical significance indices, or both, within their research
reports. Shepard (2003) questions why some authors fail to point
out that the magnitude of observed changes may too small to have
any clinical or practical significance, sometimes, a supposed
change may be described in some detail, but the investigator fails
to disclose that the trend is not statistically significant .
Lack of clearly defined and objective outcome measurements
No amount of statistical analysis, regardless of the level of
the sophistication, will correct poorly defined objective outcome
measurements. Whether done unintentionally or by design, this
practice increases the likelihood of clouding the interpretation of
findings, thus potentially misleading readers.
Provide honest and accurate analysis
The basis for this issue is the urgency of reducing the
likelihood of statistical error. Common challenges include the
exclusion ofoutliers, filling in missing data, altering or
otherwise changing data, data mining, and developing graphical
representations of the data (Shamoo, Resnik, 2003).
Manner of presenting data
At times investigators may enhance the impression of a
significant finding by determining how to presentderived data(as
opposed to data in its raw form), which portion of the data is
shown, why, how and to whom (Shamoo, Resnik, 2003). Nowak (1994)
notes that even experts do not agree in distinguishing between
analyzing and massaging data. Shamoo (1989) recommends that
investigators maintain a sufficient and accurate paper trail of how
data was manipulated for future review.Environmental/contextual
issues
The integrity of data analysis can be compromised by the
environment or context in which data was collected i.e., face-to
face interviews vs. focused group. Theinteractionoccurring within a
dyadic relationship (interviewer-interviewee) differs from the
group dynamic occurring within a focus group because of the number
of participants, and how they react to each others responses. Since
the data collection process could be influenced by the
environment/context, researchers should take this into account when
conducting data analysis.Data recording method
Analyses could also be influenced by the method in which data
was recorded. For example, research events could be documented
by:a. recording audio and/or video and transcribing laterb. either
a researcher or self-administered surveyc. eitherclosed ended
surveyoropen ended surveyd. preparing ethnographic field notes from
a participant/observere. requesting that participants themselves
take notes, compile and submit them to researchers.While each
methodology employed has rationale and advantages, issues of
objectivity and subjectivity may be raised when data is
analyzed.Partitioning the text
During content analysis, staff researchers or raters may use
inconsistent strategies in analyzing text material. Some raters may
analyze comments as a whole while others may prefer to dissect text
material by separating words, phrases, clauses, sentences or groups
of sentences. Every effort should be made to reduce or eliminate
inconsistencies between raters so that data integrity is not
compromised.Training of Staff conducting analyses
A major challenge to data integrity could occur with the
unmonitored supervision of inductive techniques. Content analysis
requires raters to assign topics to text material (comments). The
threat to integrity may arise when raters have received
inconsistent training, or may have received previous training
experience(s). Previous experience may affect how raters perceive
the material or even perceive the nature of the analyses to be
conducted. Thus one rater could assign topics or codes to material
that is significantly different from another rater. Strategies to
address this would include clearly stating a list of analyses
procedures in the protocol manual, consistent training, and routine
monitoring of raters.Reliability and Validity
Researchers performing analysis on either quantitative or
qualitative analyses should be aware of challenges to reliability
and validity. For example, in the area of content analysis,
Gottschalk (1995) identifies three factors that can affect the
reliability of analyzed data: stability , or the tendency for
coders to consistently re-code the same data in the same way over a
period of time reproducibility , or the tendency for a group of
coders to classify categories membership in the same way accuracy ,
or the extent to which the classification of a text corresponds to
a standard or norm statisticallyThe potential for compromising data
integrity arises when researchers cannot consistently demonstrate
stability, reproducibility, or accuracy of data analysisAccording
Gottschalk, (1995), the validity of a content analysis study refers
to the correspondence of the categories (the classification that
raters assigned to text content) to the conclusions, and the
generalizability of results to a theory (did the categories support
the studys conclusion, and was the f