Research method

Ch Muhammad Irfan +92-345-4426176 Facebook.com/chmuhammedirfan skype: ch.irfan786
Ch Muhammad Irfan +92-345-4426176
RESEARCH
MEHTOD
MBA ACCOUNTING &
FINANCE
2ND
QUARTER

Topic: Difference b/w Applied and Basic Research types of business research:
Definition:
Research is the process of finding solutions to a problem after a thorough study and analysis of the situational
factors.
OR
The systematic investigation into and study of materials and sources in order to establish facts and reach new
conclusions.
 BASIC RESEARCH:
The research which is done for knowledge enhancement, the research which does not have immediate
commercial potential. The research which is done for human welfare, animal welfare and plant kingdom
welfare. It is called basic, pure, fundamental research. The main motivation is to expand man's knowledge,
not to create or invent something. There is no obvious commercial value to the discoveries that result from
basic research. Basic research lay down the foundation for the applied research. Dr. G. Smoot says “people
cannot foresee the future well enough to predict what is going to develop from the basic research”. For
example:-how did the universe begin?
 APPLIED RESEARCH:
Applied research is one type of research that is used to answer a specific question that has direct applications
to the world. This is the type of research that solves a problem.
As mentioned above applied research is something that we can use. Here is a simple question: 'How should a
student study?' There are many ways to go about answering this question, and the ones we will look at have
a direct and applicable finding. For example, what can research tell us about how a student studies?
Most people like to study in their bedroom, laying on their bed in some weird posture. They collect all their
notes and spread them haphazardly across the bed. Just reading is boring, so they may have the radio on.
Some people have both the radio and the television on. Then people have to talk to their friends so their
phone isn't far off. And pets are usually somewhere in the paperwork. However, research has found that a
quiet room, without music, animals or television improves concentration.
Sitting like you will take the test creates a state of consciousness similar to taking the test. And instead of
taking all the notes and trying to cram before the test, it should be spaced out. When proper study habits are
applied, they can increase scores on tests and allow a person to retain the information longer. In other
words, we researched the best way to study and will now apply our findings - this is applied research.
Distinction b/w Applied and Basic Research:
The main distinction between applied and basic business research is:
 Applied research is specifically aimed at solving a currently experienced problem.
 Basic research has a broader objective of generating knowledge and understanding of phenomena
and problems that occur in various organizational settings.
Both types of research follow the same steps of systematic inquiry to arrive at solutions to problems.
Topic: The Hallmarks of Scientific Research
Main characteristics which will distinguish scientific research from common research.

 Purposiveness:
Research should have a definite aim or purpose. The focus is on increasing the
commitment of employees to the organization, as this will be a beneficial in many ways.
 Rigor:
A good theoretical base and sound methodological design give rigor to the research. Rigor
indicates carefulness and degree of exactitude in research.
 Testability:
Scientific research lends itself to testing logically developed hypothesis to see whether
or not data support the educated conjecture or hypothesis.
 Replicability:
The results of the test of hypothesis should be supported again and again when same
type of research is conducted in other similar circumstances.
 Precision and Confidence:
Accuracy refers to the closeness of the findings to reality based on a sample. Accuracy
reflects the degree of exactness and accuracy of the results on the basis of samples. Also known as
confidence interval in statistics. Confidence refers to the probability that our estimation is correct so that we
can confidently claim that 95% of the time our results will be true and there is only 5% chance of our results
being false.”
 Objectivity:
The conclusion drawn through the interpretation of the results of data analysis should
be objective that is, they should be based on facts of the findings derived from actual data and not on our
own subjective or emotional values.”
 Generalizability
It refers to the scope of Replicability of the research findings in one organizational
setting to others, the wider the range of Replicability of the solution generated by the research the more
useful the research is to the user.
 Parsimony
Simplicity in explaining the phenomenon or problem that occurs in generating the solutions of the problem is
preferred as compared to complex research frame work.
Topic:
Scientific Research / Scientific Method:
The Scientific Method is a logical and rational order of steps by which scientists come to conclusions about
the world around them. The Scientific Method helps to organize thoughts and procedures so that scientists
can be confident in the answers they find. Scientists use observations, hypotheses, and deductions to make
these conclusions, just like you will use the Scientific Method in your science fair project. You will think
through the various possibilities using the Scientific Method to eventually come to an answer to your original
question.
 OBSERVATION:

This step could also be called "research." It is the first stage in understanding the problem you have chosen.
After you decide on your area of science and the specific question you want to ask, you will need to research
everything that you can find about the problem. You can collect information on your science fair topic from
your own experiences, books, the internet, or even smaller "unofficial" experiments. This initial research
should play a big part in the science fair idea that you finally choose.
Let's take the example of the tomatoes in the garden. You like to garden and notice that some tomatoes are
bigger than others and wonder why. Because of this personal experience and an interest in the problem, you
decide to learn more about what makes plants grow.
 HYPOTHESIS
The next stage of the Scientific Method is known as the "hypothesis." This word basically means "a possible
solution to a problem, based on knowledge and research." The hypothesis is a simple statement that defines
what you think the outcome of your experiment will be.
Using the example of the tomato experiment, here is an example of a hypothesis:
TOPIC: "Does the amount of sunlight a tomato plant receives affect the size of the tomatoes?"HYPOTHESIS: "I
believe that the more sunlight a tomato plant receives, the larger the tomatoes will grow. This hypothesis is
based on:
(1) Tomato plants need sunshine to make food through photosynthesis, and logically, more sun means more
food, and;
(2) Through informal, exploratory observations of plants in a garden, those with more sunlight appear to
grow bigger.
 Prediction
The hypothesis is your general statement of how you think the scientific phenomenon in question works.
Your prediction lets you get specific -- how will you demonstrate that your hypothesis is true? The
experiment that you will design is done to test the prediction.
An important thing to remember during this stage of the scientific method is that once you develop a
hypothesis and a prediction, you shouldn't change it, even if the results of your experiment show that you
were wrong.
An incorrect prediction does NOT mean that you "failed." It just means that the experiment brought some
new facts to light that maybe you hadn't thought about before. The judges at your science fair will not take
points off simply because your results don't match up with your hypothesis.
Continuing our tomato plant example, a good prediction would be: Increasing the amount of sunlight tomato
plants in my experiment receive will cause an increase in their size compared to identical plants that received
the same care but less light.
 EXPERIMENT
This is the part of the scientific method that tests your hypothesis. An experiment is a tool that you design to
find out if your ideas about your topic are right or wrong. It is absolutely necessary to design a science fair
experiment that will accurately test your hypothesis. The experiment is the most important part of the
scientific method. It's the logical process that lets scientists learn about the world. On the next page, we'll
discuss the ways that you can go about designing a science fair experiment idea.
 CONCLUSION

The final step in the scientific method is the conclusion. This is a summary of the experiment's results, and
how those results match up to your hypothesis. You have two options for your conclusions: based on your
results, either
(1) you CAN REJECT the hypothesis, or
(2) you CAN NOT REJECT the hypothesis.
This is an important point. You cannot PROVE the hypothesis with a single experiment, because there is a
chance that you made an error somewhere along the way. What you can say is that your results SUPPORT the
original hypothesis.
If your original hypothesis didn't match up with the final results of your experiment, don't change the
hypothesis. Instead, try to explain what might have been wrong with your original hypothesis. What
information did you not have originally that caused you to be wrong in your prediction? What are the reasons
that the hypothesis and experimental results didn't match up?
Remember, a science fair experiment isn't a failure if it proves your hypothesis wrong or if your prediction
isn't accurate. No one will take points off for that. A science fair experiment is only a failure if its design is
flawed. A flawed experiment is one that (1) doesn't keep its variables under control, and (2) doesn't
sufficiently answer the question that you asked of it.
Topic: Variables and Its Kind
Any characteristic which is subject to change and can have more than one value such as age, intelligence,
motivation, gender, etc.
 Dependent Variable
A dependent variable is presumed to be affected by one or more independent variable. The dependent
variable is often called an outcome variable
Example: Gender of teacher and subject matter taught.
 Independent Variable
An independent variable is presumed to influence other variables. Sometimes independent variables are
called manipulated variables or experimental variables.
For Example amount of reinforcement
A selected variable is one that already exists and that the researcher locates and then chooses to study.
 Moderating Variable
A moderator variable is a secondary independent variable that the researcher selects because he or she
thinks it may affect the relationship between the primary independent variable and the dependent variable.
 Intervening / Mediator Variable
A mediator variable is a variable that attempts to explain the relationship between the 2 variables.
Example: “Does anxiety affect test performance and, if so, does it depend on test-taking experience?”
OR
Topic: Contrived & Non Contrived Study (THIS
QUESTION IS NOT CONFIRMED YET)

 Non-contrived settings:
If organizational research be done in the natural environment where work precedes
normally, the research is in no contrived settings.
 Contrived settings:
If organizational research be done in artificial environment the research is in contrived settings.
Correlation studies done in organizations are called field studies. Studies conducted to establish cause-and-
effect relationship using the same natural environment in which employees normally function are called field
experiments.
Experiments done to establish cause and affect relationship beyond the possibility of the least doubt require
the creation of an artificial, contrived environment in which all the extraneous factors are strictly controlled.
Similar subjects are chosen carefully to respond to certain manipulated stimuli. These studies are referred to
as lab experiments
 EXAMPLE FIELD STUDY:
If various factors are examined in the natural settings in which daily activities going on as normal
with minimal researcher interference, the study is field study (non contrived).
For Example A bank manager wants to analyze the relationship between interest rates and bank
deposit patterns of clients. She tries to correlate the two by looking at deposits into different kinds of
accounts (such as savings, certificates of deposit, and interest-bearing checking accounts) as interest rates
changed.
This is a field study where the bank manager has merely taken the balances in various types of accounts and
correlated them to the changes in interest rates. Research here is done in a non contrived setting with no
interference with the normal work routine.
 EXAMPLE FIELD EXPERIMENT:
If cause and effect relationships are studied with some amount of researcher interference, but
still in the natural settings where work continues in the normal environment, the study is field experiment
(contrived).
OR
Studies conducted to establish cause and effect relationship using the same natural environment in which
employees normally function are called field experiments.
For example, a manager wanting to know the effects of pay on performance would raise the salary
of employees in one unit, decrease the pay of employees in a third unit untouched. Here there is a tampering
with or manipulating of the pay system to establish a cause and effect relationship between pay and
performance, but the study is still conducted in the natural setting and hence is called a field experience.
 LAB EXPERIMENT:
If the researcher explores cause and effect relationship not only exercising a high degree of
control but in an artificial and deliberately created settings (contrived).
The banker may now want to establish the causal connection between interest rates and savings, beyond a
doubt. Because of this she wants to create an artificial environment and trace the true cause-and effect
relationship. She recruits 40 students who are all business majors in their final years of study and are more or

less of the same age. She splits them into four groups and gives each one of them chips that count for $1.000,
which they are told they might utilize to buy their needs or save for the future, or both. She offers them by
way of incentive, interest on what they save but manipulates the interest rates by offering a 6% interest rate
on savings for group 1, 8% for group 2, 9% for group 3, and keeps the interest at the low rate of 1% for group
4.
In this lab experiment with the contrived settings, the researcher interference has been maximal, in as much
as the setting is difficult, the independent variable has been manipulated, and most external contaminating
factors such as age and experience have been controlled.
Topic: Data Warehousing and Data mining
 Data Warehousing:
If you want to get information on all the techniques of designing, maintaining, building and retrieving data,
Data warehousing is the ideal method. A data warehouse is premeditated and generated for supporting the
decision making process within an organization. When the production databases are copied in the
warehouse, it becomes easier to answer all the queries without hampering the consistency of the production
system.
A data warehouse is actually a set of new concepts and important tools evolved into a technology. With the
help of data warehousing, it becomes easy for an organization to counter all the problems faced during
providing key information to concerned people.
Over the last two decades, a number of experiences and technologies incorporated together to evolve the
new field of Data warehousing. You can say it as a well organized and resourceful method of managing &
reporting data non uniform and scattered sourced throughout an organization.
Because of hundreds of gigabytes of transactions, it is necessary for a data warehouse to be vast. Therefore,
"data marts" are often designed for individual department or a product line. A data warehouse system is an
influential and necessary platform for merging data from old and new applications. You can transfer rules to a
warehouse without making many efforts. The prime features of a data warehouse are that it records, collects,
filters and provides basic data to different systems at higher levels.
 Data mining:
Data mining can be defined as a tool, helping automatically in the discovery of the various patterns and the
relationships in a data warehouse. With the help of the data mining, managers are able to find the various
relations between the elements of the business and the find out concepts of the business. Hence, it can be
said that data mining extracts trends and rules from data warehouse helping in the evaluation of the business
strategies ultimately leading to the improvement in the various business operations.
Data mining very commonly is known as the knowledge discovery database and the various important results
that it helps in achieving can be summed up as follows
Discovery of the new associations.
 Deviation detection.
 Event clustering.
 Pattern reorganization.
 Working of Data Mining
Data Mining is very empirical in nature and should provide real value to the organization by not only helping
in the analyzation of the operation but by also helping to learn from the activity. By this, one is helped at a
very wide level during the decision making processes.

Applications of data mining
 Helps in knowing the trends.
 Analyzation of the vendor.
 Customer segmentation.
 Identifying the customers.
 Customer clustering.
 Exchange schemes.
Topic: Define Scale and different types of scale for measure the
variable:
 Scales:
A scale is a tool or mechanism by which individuals are distinguished as to how they differ from one another
on the variables of interest to our study.
Following are the types of Scales:
Nominal Scale:
A scale whose numbers serve only as labels or tags for identifying and classifying objects. When used for
identification, there is strict one-to-one correspondence between the numbers and the objects.
In marketing research, nominal scale is used.
Ordinal Scale:
Ranking scale in which numbers are assigned to objects to indicate the relative extent to which some
characteristics is possessed. Thus it is possible to determine whether an object has more or less of
characteristics than some other object.
An ordinal scale indicates relative position.
Common example of ordinal scale include quality ranking, ranking if teams in a tournament, socioeconomics
classes, and occupational status.
Interval Scale:
A scale in which the numbers are used to rate objects such that numerically equal distances on the scale
represent equal distances in the characteristic being measured.
There is a constant or equal interval between scale values.
Ratio Scale:
The highest scale, it allows the researcher to identify or classify objects, and compare intervals or differences.
It is also meaningful to compute ratios of scale values. It is a possesses all the properties of the nominal,
ordinal, and interval scale and, in addition an absolute zero point.
It is also meaningful to compute ratios of scale values.
In the marketing, sales, costs, market share, and number of customers are available measure on ratio scale.
Topic: What are the main methods of data collection used in
research?
Interviewing:

One method of collecting data is to interview respondents to obtain information on the issues of interest.
Interviewing is a useful data collection method, especially during the exploratory stages of research. Where a
large number of interviews are conducted with a number of different interviewers, it is important to train the
interviewers with care in order to minimize interviewer bias manifested in such ways as voice inflections,
differences in wording, and interpretation. Good training decreases interviewer bias.
Interviews may be unstructured or structured, and conducted face to face, by telephone, or online.
Face -to -face interviews
Face to face interviews have a distinct advantage of enabling the researcher to establish rapport with
potential participants and therefore 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.
Telephone interviews
Telephone interviews are less time consuming and less expensive and the researcher has ready access to
anyone on the planet that has a telephone. Disadvantages are that the response rate is not as high as the
face-to- face interview as but considerably 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.
Questionnaires:
Paper-pencil-questionnaires can 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.
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 is in question as people might be in a hurry to complete it and so might not give
accurate responses.
Questionnaires often make use of Checklist and rating scales. These devices help simplify and quantify
people's behaviors and attitudes. A checklist is a list of behaviors, characteristics, or other entities that tee
researcher are looking for. Either the researcher or survey participant simply checks whether each item on
the list is observed, present or true or vice versa. A rating scale is more useful when a behavior needs to be
evaluated on a continuum. They are also known as Likert scales.
Topic: Importance of Literature Review and how will you do that.
Conducting the Literature Survey

The first step in this process involves identifying the various published and unpublished materials.
The second step is gathering the relevant information from a library or online sources.
The third step is writing up the literature review.
Identifying the Relevant Sources:
Global business information, published articles in newspaper and periodicals, and conference proceedings,
are available on data bases. Computerized databases include bibliographies, abstracts, and full texts of
articles on various business topics. Statistical and financial databases are also easily accessible.
Basically, three forms of databases are used when reviewing the literature:
Bibliographic Databases display only the bibliographic citations, that is, the name of the author, the
title of the article (or book), source of publication, year, volume, and page numbers.
Abstract Databases provide an abstract or summary of the articles.
Full-text Databases provide the full text of the articles.
Online searches provide a number of advantages. Besides saving enormous amounts of time, they are
comprehensive in their listing and review of references, and the researcher can focus on materials most
central to the research effort.
Extracting the Relevant Information:
Bibliography accessing the online system and getting a printout of all the published works in the area of
interest from a bibliographical index will provide a comprehensive bibliography.
Abstracts: The abstract of such articles that seem to be relevant can then be obtained through the online
system.
Full-Text: The full text of articles can then be printed out.
While reading these articles, details can be systematically noted.
Writing up the Literature Review:
The documentation of the relevant studies citing the author and the year of the study is called literature
review or literature survey. The literature survey is a clear and logical presentation of the relevant research
work done thus far in the area of investigation. A point to note is that the literature survey should bring
together all relevant information in a clear and logical manner instead of presenting all the studies in
chronological order. A good literature survey also leads to a good problem statement. There are several
accepted methods of citing references in the literature survey.
Topic: What is meant by operational definition? When is it necessary,
and why is it necessary?
Definition:
Operationally defining a concept to render it measureable is done by looking at the behavioral dimensions,
facets, or properties denoted by the concept. These are then translated into observable and measureable
elements so as to develop an index of measurement of the concept.
An operational definition, when applied to data collection, is a clear, concise detailed definition of a
measure. The need for operational definitions is fundamental when collecting all types of data. It is

particularly important when a decision is being made about whether something is correct or incorrect, or
when a visual check is being made where there is room for confusion.
For example, data collected will be erroneous if those completing the checks have different views of what
constitutes a fault at the end of a glass panel production line. Defective glass panels may be passed and good
glass panels may be rejected. Similarly, when invoices are being checked for errors, the data collection will be
meaningless if the definition of an error has not been specified.
When collecting data, it is essential that everyone in the system has the same understanding and collects
data in the same way. Operational definitions should therefore be made before the collection of data begins.
Topic: Describe the contents of the written
report of a research study: (VVIQ)
Following are the contents of a written report of a research study:
 Title:
Titles are designed to inform readers on the content of your work. Aim for a title that is informative
and specific to your research. Make sure that your title clearly indicates and reflects the contents of the
report.
 Abstract:
The abstract is a brief summary of the study rationale, methodology and the main findings and conclusions of
the report. Make sure to highlight in which way your paper is making a contribution to the field.
 Table of contents:
The purpose of the table of contents is to give an overview of the subject matter and the structure of the
report, so that readers can easily jump to a specific part of the text containing the information they need. The
structure of the table of contents needs to be logical and transparent.
 Lists of tables, figures and graphs:
Lists of tables, figures and graphs guide readers to find the information they are looking for in the body of the
text. A list of symbols and abbreviations enables readers to quickly find the meaning of each symbol and
abbreviation. For the author, writing and updating lists of symbols and abbreviations contributes to
systematic notation and avoidance of double use of symbols.
Lists of tables, figures, graphs, symbols, formulas and abbreviations are generally provided after the table of
contents.
 Introduction:
An introduction should capture the audience's attention. Introductions generally start by identifying and
situating a problem in the existing literature. Next, introductions describe how the project or research was
conducted, formulate the purpose of the research or paper and highlight in which way it is making a new
contribution to the field. Finally, the introduction indicates the main points as well as the outline of the
report. Do not forget to mention the relevance of the work done.
 Body:

The body of the text explains in detail how the study was conducted, reports key findings and provides
evidence supporting your conclusion.
Divide the main body of the text in chapters. Chapters should follow a logical outline and usually include the
following three main parts: methodology, results and discussion and interpretation:
 Conclusion:
The main text ends with a concluding section. Remember that this section will be read by prospective readers
first; therefore it should be independent of the main body of the text.
 References:
The reference section comprises a list of all sources that were cited in the text.
The reference list should be alphabetical according to the first author's last name. If you use the number
system, then references are listed in the order that they have been cited in the text.
 Appendices:
Appendices convey important but non-essential information, such as raw data, long proofs, calculations and
extensive experimental results. If they would be included in the body of the text, these could harm the
readability of the text.
Topic: Write a note on: (VVIQ)
Mean/ Arithmetic Mean
The mean is the average of all numbers and is sometimes called the arithmetic mean. To calculate mean, add
together all of the numbers in a set and then divide the sum by the total count of numbers. For example, in a
data center rack, five servers consume 100 watts, 98 watts, 105 watts, 90 watts and 102 watts of power,
respectively. The mean power use of that rack is calculated as (100 + 98 + 105 + 90 + 102 W)/5 servers = a
calculated mean of 99 W per server. Intelligent power distribution units report the mean power utilization of
the rack to systems management software.
Median
In the data center, means and medians are often tracked over time to spot trends, which inform capacity
planning or power cost predictions. The statistical median is the middle number in a sequence of numbers. To
find the median, organize each number in order by size; the number in the middle is the median. For the five
servers in the rack, arrange the power consumption figures from lowest to highest: 90 W, 98 W, 100 W, 102
W and 105 W. The median power consumption of the rack is 100 W. If there is an even set of numbers,
average the two middle numbers. For example, if the rack had a sixth server that used 110 W, the new
number set would be 90 W, 98 W, 100 W, 102 W, 105 W and 110 W. Find the median by averaging the two
middle numbers: (100 + 102)/2 = 101
Mode
The mode is the number that occurs most often within a set of numbers. For the server power consumption
examples above, there is no mode because each element is different. But suppose the administrator
measured the power consumption of an entire network operations center (NOC) and the set of numbers is 90
W, 104 W, 98 W, 98 W, 105 W, 92 W, 102 W, 100 W, 110 W, 98 W, 210 W and 115 W. The mode is 98 W

since that power consumption measurement occurs most often amongst the 12 servers. Mode helps identify
the most common or frequent occurrence of a characteristic.
Topic: The hypothetic-deductive method:
It Is Been Proposed an Austrian Philosopher, Karl Popper. It Is A Typical Version Of Scientific Method. It Has
Seven Steps.
1 Identify a broad problem area:
If the manager notices a drop in sales, incorrect accounting results, low-yielding investment,
disinterestedness of employees in their work, and the like, could attract the attention of the manager to do a
research project.
2 Define the problem statement:
Scientific research starts with a definite aim or purpose. A problem statement states the general objective of
the research.
3 Develop hypotheses:
The network of associations between the problem and the variables that affect it is identified. A scientific
hypothesis must meet two requirements:
 The hypothesis must be testable
 The hypothesis must be falsifiable (we can only prove our hypotheses until they are disproved).
4 Determine Measures:
The variables in the theoretical framework should be measurable in some way.
Some variables cannot be measure quantitatively, such as unresponsive employees; we need to
operationalize this variable.
5 Data collection:
Data with respect to each variable in the hypothesis need to be obtained.
There are two types of data:
-Quantitative data
Qualitative data refer to information gathered through interviews and observations. These data usually for objects than
cannot be physically measured, like feelings and attitudes.
-Qualitative data
Quantitative data refer to information gathered about objects that can be physically measured. The
researcher could obtain these data through the company records, government statistics, or any formal
records.
6 Data Analysis:
In this step, the data gathered are statistically analyzed to see if the hypotheses that were generated have
been supported. Analyses of both quantitative and qualitative data can be done to determine if certain
relations are important.

7 Interpretation of data:
Now we must decide whether our hypotheses are supported or not by interpreting the meaning of the
results or the data analysis. Based on these results, the researcher would make recommendations in order to
solve the problem in hand.
Problem Statement:
A problem statement is a clear description of the issue(s), it includes a vision, issue statement, and method
used to solve the problem.
The 5 'W's can be used to spark the discussion about the problem.
A problem statement expresses the words that will be used to keep the effort focused and it should
represent a solvable problem.
How to Write a Problem Statement:
A problem statement is a clear concise description of the issue(s) that need(s) to be addressed by a problem
solving team. It is used to center and focus the team at the beginning, keeps the team on track during the
effort, and is used to validate that the effort delivered an outcome that solves the problem statement. It has
a specific form:
Vision - what does the world look like if we solve the problem?
Issue Statement - one or two sentences that describe the problem using specific issues. It is not a "lack of a
solution" statement. For example, our problem is that we don't have an ERP system.
Method - the process that will get followed to solve the problem. For example, DMAIC or Kaizen.
How to get started
The 5 'W's - Who, What, Where, When and Why - is a great tool that helps get pertinent information out for
discussion.
Who
Who does the problem affect? Specific groups, organizations, customers, etc.
What
What are the boundaries of the problem, e.g. organizational, work flow, geographic, customer, segments,
etc. - What is the issue? - What is the impact of the issue? - What impact is the issue causing? - What will
happen when it is fixed? - What would happen if we didn’t solve the problem?
When
When does the issue occur? - When does it need to be fixed?
Where
Where is the issue occurring? Only in certain locations, processes, products, etc.
Why
Why is it important that we fix the problem? - What impact does it have on the business or customer? - What
impact does it have on all stakeholders, e.g. employees, suppliers, customers, shareholders, etc. Each of the
answers will help to zero in on the specific issue(s) and frame the Issue Statement. Your problem statement

should be solvable. That is, it should take a reasonable amount of time to formulate, try and deploy a
potential solution.
Example:
Consider a software development and hosted data Services Company that supplies products and services to
wireless carriers. They had issues deploying new software releases into the production environment.
Deployment in this case is the work necessary for taking a production ready binary and installing, testing and
releasing it into the production environment. The company failed to deploy the releases on-schedule over
50% of the time.
Problem Statement:
We want all of our software releases to go to production seamlessly, without defects, where everyone is
aware and informed of the outcomes and status. (Vision)
Today we have too many release failures that result in too many rollback failures. If we ignore this problem;
resources will need to increase to handle the cascading problems, and we may miss critical customer
deadlines which could result in lost revenue, SLA penalties, lost business, and further damage to our quality
reputation. (Issue Statement)
Managerial Implication and Hypothesis:
Managers sometimes look at the symptoms in problematic situations and treat them as if they are the real
problems, getting frustrated when their remedies do not work. Understanding the antecedents–problem–
consequences sequence, and gathering the relevant information to get a real grasp of the problem go a long
way in pinpointing it.
Managers‘inputs help researchers to define the broad problem area and con- firm their own theories about
the situational factors impacting on the central problem. Managers who realize that correct problem
definition is critical to ultimate problem solution, do not grudge the time spent in working closely with
researchers, particularly at this stage. Awareness of information sources and ability to obtain access to the
requisite information at will through the Internet are great assets to the manager. Using this facility, the
manager can get to know how similar businesses the world over grapple with similar situations and get a
better handle on the issues at hand.
Topic: Briefly review the various methods in non-probability
sampling:
Answer:
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.
Types of Non-Probability Sampling
Convenience Sampling:
Sometimes known as grab or opportunity sampling or accidental or haphazard sampling. A type of non
probability sampling which involves the sample being drawn from that part of the population which is close
to hand. That is, readily available and convenient.

The researcher using such a sample cannot scientifically make generalizations about the total population
from this sample because it would not be representative enough.
For example, if the interviewer was to conduct a survey at a shopping center early in the morning on a given
day, the people that he/she could interview would be limited to those given there at that given time, which
would not represent the views of other members of society in such an area, if the survey was to be
conducted at different times of day and several times per week
Quota Sampling:
The population is first segmented into mutually exclusive sub-groups, just as in stratified sampling. Then
judgment used to select subjects or units from each segment based on a specified proportion. For example,
an interviewer may be told to sample 200 females and 300 males between the age of 45 and 60.
It is this second step which makes the technique one of non-probability sampling.
In quota sampling the selection of the sample is non-random.
Judgmental Sampling:
Judgmental sampling is more commonly known as purposive sampling. In this type of sampling, subjects are
chosen to be part of the sample with a specific purpose in mind. With judgmental sampling, the researcher
believes that some subjects are fit for the research compared to other individuals. This is the reason why they
are purposively chosen as subjects.
Write short Notes:
Primary data:
Primary data are first hand information’s. This information’s are collected directly from the source by means
of field studies. Primary data are original and are like raw materials. It is the crudest form of information. The
investigator himself collects primary data or supervises its collection. It may be collected on a sample or
census basis or from case studies.
Secondary data:
Secondary data are the Second hand information’s. The data which have already been collected and
processed by some agency or persons and are not used for the first time are termed as secondary data.
According to M. M. Blair, “Secondary data are those already in existence and which have been collected for
some other purpose.” Secondary data may be abstracted from existing records, published sources or
unpublished sources.

Research method

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