Research Design notes Unit 2 for MBA students

Unit2
1.Research Design, Feature of a Good Research Design
Research Design is defined as a framework of methods and techniques chosen by a researcher to
combine various components of research in a reasonably logical manner so that the research
problem is efficiently handled. It provides insights about “how” to conduct research using a
particular methodology.
Types of Research Design
A researcher must have a clear understanding of the various types of research design to select
which type of research design to implement for a study. Research design can be broadly
classified into quantitative and qualitative research design.
1. Qualitative Research Design
Qualitative research is implemented in cases where a relationship between collected data and
observation is established on the basis of mathematical calculations. Theories related to a
naturally existing phenomenon can be proved or disproved using mathematical calculations.
Researchers rely on qualitative research design where they are expected to conclude “why” a
particular theory exists along with “what” respondents have to say about it.
2. Quantitative Research Design
Quantitative research is implemented in cases where it is important for a researcher to have
statistical conclusions to collect actionable insights. Numbers provide a better perspective to
make important business decisions. Quantitative research design is important for the growth of
any organization because any conclusion drawn on the basis of numbers and analysis will only
prove to be effective for the business.

Further, research design can be divided into five types :
(I) Descriptive Research Design: In a descriptive research design, a researcher is solely
interested in describing the situation or case under his/her research study. It is a theory-based
research design which is created by gather, analyze and presents collected data. By implementing
an in-depth research design such as this, a researcher can provide insights into the why and how
of research.
(II) Experimental Research Design: Experimental research design is used to establish a
relationship between the cause and effect of a situation. It is a causal research design where the
effect caused by the independent variable on the dependent variable is observed. For example,
the effect of an independent variable such as price on a dependent variable such as customer
satisfaction or brand loyalty is monitored. It is a highly practical research design method as it
contributes towards solving a problem at hand. The independent variables are manipulated to
monitor the change it has on the dependent variable. It is often used in social sciences to observe
human behavior by analyzing two groups – effect of one group on the other.
(III) Correlational Research Design: Correlational research is a non-experimental research
design technique which helps researchers to establish a relationship between two closely
connected variables. Two different groups are required to conduct this research design method.
There is no assumption while evaluating a relationship between two different variables and
statistical analysis techniques are used to calculate the relationship between them.
Correlation between two variables is concluded using a correlation coefficient, whose value
ranges between -1 and +1. If the correlation coefficient is towards +1, it indicates a positive
relationship between the variables and -1 indicates a negative relationship between the two
variables.
(IV) Diagnostic Research Design: In the diagnostic research design, a researcher is inclined
towards evaluating the root cause of a specific topic. Elements that contribute towards a
troublesome situation are evaluated in this research design method.
There are three parts of diagnostic research design:

 Inception of the issue
 Diagnosis of the issue
 Solution for the issue
(V) Explanatory Research Design: In exploratory research design, the researcher’s ideas and
thoughts are key as it is primarily dependent on their personal inclination about a particular topic.
Explanation about unexplored aspects of a subject is provided along with details about what,
how and why related to the research questions.
Features of a Good Research Design
The features of good research design is often characterized by adjectives like flexible,
appropriate, efficient, economical and so on. Generally, the design which minimizes bias and
maximizes the reliability of the data collected and analyzed is considered a good design. The
design which gives the smallest experimental error is supposed to be the best design in many
investigations. Similarly, a design which yields maximal information and provides an
opportunity for considering many different aspects of a problem is considered most appropriate
and efficient design in respect of many research problems. Thus, the question of good design is
related to the purpose or objective of the research problem and also with the nature of the
problem to be studied. A design may be quite suitable in one case, but may be found wanting in
one respect or the other in the context of some other research problem. One single design cannot
serve the purpose of all types of research problems.
A research design appropriate for a particular research problem, usually involves the
consideration of the following factors:
1. The means of obtaining information;
2. The availability and skills of the researcher and his staff, if any;
3. The objective of the problem to be studied;
4. The nature of the problem to be studied; and
5. The availability of time and money for the research work.
The important features of Research Design may be outlined as follows:

i. It constitutes a plan that identifies the types and sources of information required for the
research problem;
ii. It constitutes a strategy that specifies the methods of data collection and analysis which would
be adopted; and
iii. It also specifies the time period of research and monetary budget involved in conducting the
study, which comprise the two major constraints of undertaking any research
2.Use of a Good Research Design – Qualitative and Quantitative Approach
When to use qualitative vs. quantitative research
Quantitative data can help you see the big picture. Qualitative data adds the details and can also
give a human voice to your survey results.
Let’s see how to use each method in a research project.
 Formulating hypotheses: Qualitative research helps you gather detailed information on
a topic. You can use it to initiate your research by discovering the problems or
opportunities people are thinking about. Those ideas can become hypotheses to be proven
through quantitative research.
 Validating your hypotheses: Quantitative research will get you numbers that you can
apply statistical analysis to in order to validate your hypotheses. Was that problem real or
just someone’s perception? The hard facts obtained will enable you to make decisions
based on objective observations.
 Finding general answers: Quantitative research usually has more respondents than
qualitative research because it is easier to conduct a multiple-choice survey than a series
of interviews or focus groups. Therefore it can help you definitely answer broad
questions like: Do people prefer you to your competitors? Which of your company’s
services are most important? What ad is most appealing?

 Incorporating the human element: Qualitative research can also help in the final stages
of your project. The quotes you obtained from open-ended questions can put a human
voice to the objective numbers and trends in your results. Many times it helps to hear
your customers describe your company in their own words to uncover your blind spots.
Qualitative data will get you that.
How to balance qualitative and quantitative research?
These two research methods don’t conflict with each other. They actually work much better as a
team. In a world of Big Data, there’s a wealth of statistics and figures that form the strong
foundation on which your decisions can rest. But that foundation is incomplete without the
information collected from real people that gives the numbers meaning.
So how do you put these two forms of research together? Qualitative research is almost always
the starting point when you seek to discover new problems and opportunities–which will help
you do deeper research later. Quantitative data will give you measurements to confirm each
problem or opportunity and understand it.
How about an example? Let’s say you held a conference and wanted feedback from your
attendees. You can probably already measure several things with quantitative research, such as
attendance rate, overall satisfaction, quality of speakers, value of information given, etc. All
these questions can be given in a closed-ended and measurable way.
But you also may want to provide a few open-ended, qualitative research questions to find out
what you may have overlooked. You could use questions like:
 What did you enjoy most about the conference?
 How could we improve your experience?
 Is there any feedback on the conference you think we should be aware of?
If you discover any common themes through these qualitative questions, you can decide to
research them more in depth, make changes to your next event, and make sure to add quantitative
questions about these topics after the next conference.

For example, let’s say several attendees said that their least favorite thing about the conference
was the difficult-to-reach location. Next time, your survey might ask quantitative questions like
how satisfied people were with the location, or let respondents choose from a list of potential
sites they would prefer.
Open-ended vs. close-ended questions. A good way of recognizing when you want to switch
from one method to the other is to look at your open-ended questions and ask yourself why you
are using them.
For example, if you asked: “What do you think of our ice cream prices?”, people would give you
feedback in their own words and you will probably get some out-of-the-box answers.
If that’s not what you’re looking for, you should consider using an easily quantifiable response.
For example:
Relative to our competitors, do you think our ice cream prices are:
 Higher
 About the same
 Lower
This kind of question will give your survey respondents clarity and in turn it will provide you
with consistent data that is easy to analyze.
How to get qualitative data?
There are many methods you can use to conduct qualitative research that will get you richly
detailed information on your topic of interest.
 One-on-one conversations that go deep into the topic at hand.
 Case studies. Collections of client stories from in-depth interviews.
 Expert opinions. High-quality information from well-informed sources.

 Focus groups.In-person or online conversation with small groups of people to listen to
their views on a product or topic.
 Open-ended survey questions.A text box in a survey that lets the respondent express
their thoughts on the matter at hand freely.
 Observational research.Observing people during the course of their habitual routines to
understand how they interact with a product, for example.
However, this open-ended method of research does not always lend itself to bringing you the
most accurate results to big questions. And analyzing the results is hard because people will use
different words and phrases to describe their points of view, and may not even talk about the
same things if they find space to roam with their responses.
In some cases, it may be more effective to go ‘full quantitative’ with your questions.
Why Collect Quantitative Data?
Qualitative survey questions can run the risk of being too vague. To avoid confusing your
respondents, you may want to eschew questions like, “What do you think about our internet
service?” Instead you could ask a closed-ended, quantitative question like in the following
example.
The internet service is reliable:
 Always
 Most of the time
 About half the time
 Once in a while
 Never
Qualitative questions take longer to answer. Survey respondents don’t always have the
patience to reflect on what they are being asked and write long responses that accurately express
their views. It’s much faster to choose one of several pre-loaded options in a questionnaire.

Using quantitative questions helps you get more questions in your survey and more responses out
of it.
Quantitative survey questions are just more… quantifiable. Even word responses in closed-
ended questionnaires can be assigned numerical values that you can later convert into indicators
and graphs. This means that the overall quality of the data is better. Remember that the most
accurate data leads you to the best possible decisions.
Quantitative questions:
How long have you been a customer of our company?
 This is my first purchase
 Less than six months
 Six months to a year
 1-2 years
 3 or more years
 I haven’t made a purchase yet
How likely are you to purchase any of our products again?
 Extremely likely
 Very likely
 Somewhat likely
 Not so likely
 Not at all likely
Qualitative follow-up question:
Do you have any other comments, questions, or concerns?
Quantitative questions:

When you make a mistake, how often does your supervisor respond constructively?
 Always
 Most of the time
 About half of the time
 Once in a while
 Never
Qualitative question:
What does your supervisor need to do to improve his/her performance?
3. Comparison Pros and Cons of Qualitative and Quantitative Research
In the scientific community, there is great debate between qualitative and quantitative research
methods. Despite the criticism that qualitative methods are interpretive and invalid as scientific
evidence, the real discrepancy lies within the types of data that each method produces.
Quantitative data measures quantifiable terms, such as “how much,” “how long” and “how
many,” while qualitative data measures the reasons behind behavior, such as the “how” and
“why.” While neither method is “better” than the other, there are advantages and disadvantages
to both.
Qualitative: Pros
Qualitative research allows one to explore topics in more depth and detail than quantitative
research. Also, qualitative research is often less expensive than quantitative research, because
you don’t need to recruit as many participants or use extensive methods. Another pro of
qualitative research is that it offers flexibility as far as locations and timing because you don’t
need to interview a large number of people at once.

Qualitative: Cons
One major disadvantage of qualitative research is that it cannot quantify how many of your
audience answer one way or another. This makes it extremely difficult to create any type of solid
statistic. Another con is that you cannot generalize your findings. As opposed to quantitative
surveys, qualitative research does not allow you to use your findings as a basis for a broader
audience or the public in general.
Quantitative: Pros
One of the pros to quantitative research involves the fast speed that data can be collected. This
data can also be analyzed fairly quickly. In addition, using statistically valid random samples, a
survey can quickly be generalized to the entire population. Another advantage involves the
planning process for programs and messages. With the reliable, repeatable information that
quantitative surveys can provide, a trusted set of statistics can give confidence when making
future plans. Quantitative research can also be anonymous, which is useful when dealing with
sensitive topics. Another major pro of quantitative research is that it allows you to generalize
your findings beyond the participant group.
Quantitative: Cons
One cons of quantitative research is the limited ability to probe answers. Also, people who are
willing to respond may share characteristics that don’t apply to the audience as a whole, creating
a potential bias in the study. In addition, quantitative research experiments can be costly.

Exploratory Research – Concept, Types
4. Exploratory Research is defined as a research used to investigate a problem which is
not clearly defined. It is conducted to have a better understanding of the existing problem,
but will not provide conclusive results. For such a research, a researcher starts with a
general idea and uses this research as a medium to identify issues that can be the focus
for future research. An important aspect here is that the researcher should be willing to
change his/her direction subject to the revelation of new data or insight. Such a research
is usually carried out when the problem is at a preliminary stage. It is often referred to as
grounded theory approach or interpretive research as it used to answer questions like
what, why and how.
For example: Consider a scenario where a juice bar owner feels that increasing the variety of
juices will enable increase in customers, however he is not sure and needs more information. The
owner intends to carry out an exploratory research to find out and hence decides to do an

exploratory research to find out if expanding their juices selection will enable him to get more
customers of if there is a better idea.
Types of Exploratory Research
While it may sound a little difficult to research something that has very little information about
it, there are several methods which can help a researcher figure out the best research design, data
collection methods and choice of subjects. There are two ways in which research can be
conducted namely primary and secondary.. Under these two types, there are multiple methods
which can used by a researcher. The data gathered from these research can be qualitative or
quantitative. Some of the most widely used research designs include the following:
1. Primary Research Methods
Primary research is information gathered directly from the subject. It can be through a group of
people or even an individual. Such a research can be carried out directly by the researcher
himself or can employ a third party to conduct it on their behalf. Primary research is specifically
carried out to explore a certain problem which requires an in-depth study.
(I) Surveys/polls: Surveys/polls are used to gather information from a predefined group of
respondents. It is one of the most important quantitative method. Various types of surveys or
polls can be used to explore opinions, trends, etc. With the advancement in technology, surveys
can now be sent online and can be very easy to access. For instance, use of a survey app through
tablets, laptops or even mobile phones. This information is also available to the researcher in real
time as well. Nowadays, most organizations offer short length surveys and rewards to
respondents, in order to achieve higher response rates.
For example: A survey is sent to a given set of audience to understand their opinions about the
size of mobile phones when they purchase one. Based on such information organization can dig
deeper into the topic and make business related decision.
(II) Interviews: While you may get a lot of information from public sources, but sometimes an
in person interview can give in-depth information on the subject being studied. Such a research is

a qualitative research method. An interview with a subject matter expert can give you
meaningful insights that a generalized public source won’t be able to provide. Interviews are
carried out in person or on telephone which have open-ended questions to get meaningful
information about the topic.
For example: An interview with an employee can give you more insights to find out the degree
of job satisfaction, or an interview with a subject matter expert of quantum theory can give you
in-depth information on that topic.
(III) Focus groups: Focus group is yet another widely used method in exploratory research. In
such a method a group of people is chosen and are allowed to express their insights on the topic
that is being studied. Although, it is important to make sure that while choosing the individuals
in a focus group they should have a common background and have comparable experiences.
For example: A focus group helps a research identify the opinions of consumers if they were to
buy a phone. Such a research can help the researcher understand what the consumer value while
buying a phone. It may be screen size, brand value or even the dimensions. Based on which the
organization can understand what are consumer buying attitudes, consumer opinions, etc.
(IV) Observations: Observation research can be qualitative observation or quantitative
observation. Such a research is done to observe a person and draw the finding from their reaction
to certain parameters. In such a research, there is no direct interaction with the subject.
For example: An FMCG company wants to know how it’s consumer react to the new shape of
their product. The researcher observes the customers first reaction and collects the data, which is
then used to draw inferences from the collective information.
2. Secondary Research Methods
Secondary research is gathering information from previously published primary research. In such
a research you gather information from sources likes case studies, magazines, newspapers,
books, etc.

(I) Online Research: In today’s world, this is one of the fastest way to gather information on
any topic. A lot of data is readily available on the internet and the researcher can download it
whenever he needs it. An important aspect to be noted for such a research is the genuineness and
authenticity of the source websites that the researcher is gathering the information from.
For example: A researcher needs to find out what is the percentage of people that prefer a
specific brand phone. The researcher just enters the information he needs in a search engine and
gets multiple links with related information and statistics.
(II) Literature Research: Literature research is one of the most inexpensive method used for
discovering a hypothesis. There is tremendous amount of information available in libraries,
online sources, or even commercial databases. Sources can include newspapers, magazines,
books from library, documents from government agencies, specific topic related articles,
literature, Annual reports, published statistics from research organisations and so on.
However, a few things have to be kept in mind while researching from these sources.
Government agencies have authentic information but sometimes may come with a nominal cost.
Also, research from educational institutions is generally overlooked, but in fact educational
institutions carry out more number of research than any other entities.
Furthermore, commercial sources provide information on major topics like political agendas,
demographics, financial information, market trends and information, etc.
For example: A company has low sales. It can be easily explored from available statistics and
market literature if the problem is market related or organisation related or if the topic being
studied is regarding financial situation of the country, then research data can be accessed through
government documents or commercial sources.
(III) Case Study Research: Case study research can help a researcher with finding more
information through carefully analyzing existing cases which have gone through a similar
problem. Such analysis are very important and critical especially in today’s business world. The
researcher just needs to make sure he analyses the case carefully in regards to all the variables

present in the previous case against his own case. It is very commonly used by business
organizations or social sciences sector or even in the health sector.
For example: A particular orthopedic surgeon has the highest success rate for performing knee
surgeries. A lot of other hospitals or doctors have taken up this case to understand and
benchmark the method in which this surgeon does the procedure to increase their success rate.
5. Qualitative Techniques – Projective Techniques, Depth Interviews, Experience survey,
Focus groups, Observation
1. Projective Techniques
Projective Techniques are indirect and unstructured methods of investigation which have been
developed by the psychologists and use projection of respondents for inferring about underline
motives, urges or intentions which cannot be secure through direct questioning as the respondent
either resists to reveal them or is unable to figure out himself. These techniques are useful in
giving respondents opportunities to express their attitudes without personal embarrassment.
These techniques helps the respondents to project his own attitude and feelings unconsciously on
the subject under study. Thus Projective Techniques play a important role in motivational
researches or in attitude surveys.
Important Projective Techniques
(I) Word Association Test.
(II) Completion Test.
(III) Construction Techniques
(IV) Expression Techniques

(I) Word Association Test: An individual is given a clue or hint and asked to respond to the
first thing that comes to mind. The association can take the shape of a picture or a word. There
can be many interpretations of the same thing. A list of words is given and you don’t know in
which word they are most interested. The interviewer records the responses which reveal the
inner feeling of the respondents. The frequency with which any word is given a response and the
amount of time that elapses before the response is given are important for the researcher. For eg:
Out of 50 respondents 20 people associate the word “ Fair” with “Complexion”.
(II) Completion Test: In this the respondents are asked to complete an incomplete sentence or
story. The completion will reflect their attitude and state of mind.
(III) Construction Test: This is more or less like completion test. They can give you a picture
and you are asked to write a story about it. The initial structure is limited and not detailed like the
completion test. For eg: 2 cartoons are given and a dialogue is to written.
(IV) Expression Techniques: In this the people are asked to express the feeling or attitude of
other people.
Disadvantages of Projective Techniques
 Highly trained interviewers and skilled interpreters are needed.
 Interpreter’s bias can be there.
 It is a costly method.
 The respondent selected may not be representative of the entire population.
2. Depth Interviews
A qualitative data collection method, in-depth interviews offer the opportunity to capture rich,
descriptive data about people’s behaviors, attitudes and perceptions, and unfolding complex
processes. They can be used as a standalone research method or as part of a multi method design,
depending on the needs of the research.
How is an in depth interview carried out?

In depth interviews are normally carried out face to face so that a rapport can be created with
respondents. Body language is also used to add a high level of understanding to the answers.
Telephones can also be used by a skilled researcher with little loss of data and at a tenth of the
cost.
The style of the interview depends on the interviewer. Successful in-depth interviewers listen
rather than talk. They have a clear line of questioning and use body language to build rapport.
3. Experience Survey
Most often taking the form of a text box in a survey, open-ended questions allow your
respondents to provide a unique answer (as opposed to providing a list of predetermined
responses to select from). This approach gives respondents the freedom to say exactly what they
feel about a topic, which provides you with exploratory data that may reveal unforeseen
opportunities, issues, or quotes. You can then use this information to support the hard numbers
you’ve collected in the survey. Often it is these quotes or examples that create more powerful
statements than many averages and percentages.
4. Focus Groups
Usually done in person or online, a focus group asks a small group of people to discuss their
thoughts on a given subject. A focus group allows you to gauge the reactions of a small number
of your target audience in a controlled but free-flowing group discussion. This form of research
is a great way to test how your target audience would perceive a new product or marketing
strategy.
5. Observational Research
This approach involves observing customers or people in their actual element. A perfect example
would be watching shoppers while they visit your store. How long does it take them to find what
they are looking for? Do they look comfortable interacting with your staff? Where do they go
first, second? When do they leave without making a purchase? These real-world observations

can lead you to findings that more direct forms of research, like focus groups and interviews,
would miss.
6. Descriptive Research Design – Concept, Types and Uses
Descriptive Research is research used to “describe” a situation, subject, behavior, or
phenomenon. It is used to answer questions of who, what, when, where, and how associated with
a particular research question or problem. Descriptive studies are often described as studies that
are concerned with finding out “what is”. It attempts to gather quantifiable information that can
be used to statistically analyze a target audience or a particular subject.
Description research is used to observe and describe a research subject or problem without
influencing or manipulating the variables in any way. Hence, these studies are really
correlational or observational, and not truly experimental. This type of research is conclusive in
nature, rather than exploratory. Therefore, descriptive research does not attempt to answer
“why” and is not used to discover inferences, make predictions or establish causal relationships.
Descriptive research is used extensively in social science, psychology and educational research.
It can provide a rich data set that often brings to light new knowledge or awareness that may
have otherwise gone unnoticed or encountered. It is particularly useful when it is important to
gather information with disruption of the subjects or when it is not possible to test and measure
large numbers of samples. It allows researchers to observe natural behaviors without affecting
them in any way. Following is a list of research questions or problems that may lend themselves
to descriptive research:-
 Market researchers may want to observe the habits of consumers.
 A company may be wanting to evaluate the morale of the staff.
 A school district may research whether or not students are more likely to access online
textbooks than to use printed copies.
 A school district may wish to assess teachers’ attitudes about using technology in the
classroom.

 An educational software company may want to know what aspects of the software make
it more likely to be used by students.
 A researcher may wish to study the impact of hands-on activities and laboratory
experiments on students’ perceptions of science.
 A researcher could be studying whether or not the availability of hiking/biking trails
increases the physical activity levels in a neighborhood.
Types of Descriptive Research
In some types of descriptive research, the researcher does not interact with the subjects. In
other types, the researcher does interact with the subjects and collects information directly from
them. Some descriptive studies may be cross-sectional, whereby the researcher has a one-time
interaction with the test subjects. Other studies may be longitudinal, where the same test
subjects are followed over time. There are three main methods that may be used in descriptive
research:-
 Observational Method: Used to review and record the actions and behaviors of a group
of test subjects in their natural environment. The research typically does not have
interaction with the test subject.
 Case Study Method: This is a much more in-depth student of an individual or small
group of individuals. It may or may not involve interaction with the test subjects.
 Survey Method: Researchers interact with individual test subjects by collecting
information through the use of surveys or interviews.
The data collected from descriptive research may be quantitative, qualitative or both. The
quantitative data is typically presented in the form of descriptive statistics that provide basic
information such as the mean, median, and mode of a data set. Quantitative date may also be
tabulated along a continuum in numerical form, such as scores on a test. It can also be used to
describe categories of information or patterns of interactions. Such quantitative data is typically
represented in tables, graphs, and charts which makes it user-friendly and easy to interpret.
Qualitative data, such as the type of narrative data collected in a case study, may be organized
into patterns that emerge or it may be classified in some way, but requires more detailed analysis.

7.Concept of Cross Sectional and Longitudinal Research
Cross-Sectional Study is defined as an observational study where data is collected as a whole to
study a population at a single point in time to examine the relationship between variables of
interest.
In an observational study, a researcher records information about the participants without
changing anything or manipulating the natural environment in which they exist.
The most important feature of a cross-sectional study is that it can compare different samples at
one given point in time. For example, a researcher wants to understand the relationship between
joggers and level of cholesterol, he/she might want to choose two age groups of daily joggers,
one group is below 30 but more than 20 and the other, above 30 but below 40 and compare these
to cholesterol levels amongst non-joggers in the same age categories.
The researcher at this point in time can create subsets for gender, but cannot consider past
cholesterol levels as this would be outside the given parameters for cross-sectional studies.
Cross-sectional studies allow the study of many variables at a given time. Researchers can look
at age, gender, income etc in relation to jogging and cholesterol at a very little or no additional
cost involved.
However, there is one downside to cross-sectional study, this type of study is not able to provide
a definitive relation between cause and effect relation (a cause and effect relationship is one
where one action (cause) makes another event happen (effect), for example, without an alarm,
you might oversleep.)
This is majorly because cross-sectional study offers a snapshot of a single moment in time, this
study doesn’t consider what happens before or after. Therefore in this example stated above it is
difficult to know if the daily joggers had low cholesterol levels before taking up jogging or if the
activity helped them to reduce cholesterol levels that were previously high.

Longitudinal Study
Longitudinal study, like the cross-sectional study, is also an observational study, in which data is
gathered from the same sample repeatedly over an extended period of time. Longitudinal study
can last from a few years to even decades depending on what kind of information needs to be
obtained.
The benefit of conducting longitudinal study is that researchers can make notes of the changes,
make observations and detect any changes in the characteristics of their participants. One of the
important aspects here is that longitudinal study extends beyond a single frame in time. As a
result, they can establish a proper sequence of the events occurred.
Continuing with the example, in longitudinal study a researcher wishes to look at the changes in
cholesterol level in women above the age of 30 but below 40 years who have jogged regularly
over the last 10 years. In longitudinal study setup, it would be possible to account for cholesterol
levels at the start of the jogging regime, therefore longitudinal studies are more likely to suggest
a cause-and-effect relationship.
Overall, research should drive the design, however, sometimes as the research progresses it
helps determine which of the design is more appropriate. Cross-sectional studies can be done
more quickly as compared to longitudinal studies. That’s why a researcher may start off with
cross-sectional study and if needed follow it up with longitudinal studies.
Differences between Cross-Sectional Study and Longitudinal Study
Cross-sectional and longitudinal study both are types of observational study, where the
participants are observed in their natural environment. There are no alteration or changes in the
environment in which the participants exist.
Despite this marked similarity, there are distinctive differences between both these forms of
study. Let us analyze the differences between cross-sectional study and longitudinal study.
Cross-sectional Study Longitudinal Study

Cross-sectional studies are quick to
conduct as compared to
longitudinal studies.
Longitudinal studies may vary
from a few years to even decades.
A cross-sectional study is
conducted at a given point in time.
A longitudinal study requires a
researcher to revisit participants of
the study at proper intervals.
Cross-sectional study is conducted
with different samples.
Longitudinal study is conducted
with the same sample over the
years.
Cross-sectional studies cannot pin
down cause-and-effect
relationship.
Longitudinal study can justify
cause-and-effect relationship.
Multiple variables can be studied at
a single point in time.
Only one variable is considered to
conduct the study.
Cross-sectional study is
comparatively cheaper.
Since the study goes on for years
longitudinal study tends to get
expensive.

Conclusion
It is true, study design greatly depends on the nature of research questions. Whenever a
researcher decides to collect data by deploying surveys to his/her participants, what matters the
most are the survey questions that are placed tactfully, so as to gather meaningful insights.
In other words, to know what kind of information a study should be able to collect is the first
step in determining how to carry out the rest of the study. What steps need to be included and
what can be given a pass.
Continuing from the example above, a researcher wants to establish a relation between the
variables, “jogging” and “cholesterol” in this case, one of the first things that a researcher would
need to establish in this kind of study is, to tell the most about the relationship. A few questions
to ask would be, whether to compare cholesterol levels among different populations of joggers,
non-joggers at the same point in time? Or to measure cholesterol levels in a single population of
daily joggers over an extended period of time?
The first approach typically requires a cross-sectional study and the second approach requires a
longitudinal study.
8.Experimental Design – Concept of Cause
The word experimental research has a range of definitions. In the strict sense, experimental
research is what we call a true experiment.
This is an experiment where the researcher manipulates one variable, and control/randomizes the
rest of the variables. It has a control group, the subjects have been randomly assigned between
the groups, and the researcher only tests one effect at a time. It is also important to know what
variable(s) you want to test and measure.
A very wide definition of experimental research, or a quasi-experiment, is research where the
scientist actively influences something to observe the consequences. Most experiments tend to
fall in between the strict and the wide definition.

Experimental research design is centrally concerned with constructing research that is high in
causal (internal) validity. Randomized experimental designs provide the highest levels of causal
validity. Quasi‐experimental designs have a number of potential threats to their causal validity.
Yet, new quasi‐experimental designs adopted from fields outside of criminology offer levels of
causal validity that rival experimental designs.
The design of research is fraught with complicated and crucial decisions. Researchers must
decide which research questions to address, which theoretical perspective will guide the research,
how to measure key constructs reliably and accurately, who or what to sample and observe, how
many people/places/things need to be sampled in order to achieve adequate statistical power, and
which data analytic techniques will be employed. These issues are germane to research of all
types (exploratory, explanatory, descriptive, evaluation research). However, the term “research
design” typically does not refer to the issues discussed above.
The term “experimental research design” is centrally concerned with constructing research that
is high in causal (or internal) validity. Causal validity concerns the accuracy of statements
regarding cause and effect relationships. For example, does variable 1 cause variation in variable
2? Or does variable 2 cause variation in variable 1? Or does variable 3 cause variation in both
variables 1 and 2? And what is the magnitude of the causal relationships among the variables?
Thus, research design as used herein is a concern of explanatory and evaluation research but
generally does not apply to exploratory or descriptive research.
Criteria for Establishing Causal Inferences
The three classic criteria necessary to support a causal inference, according to the philosopher
John Stuart Mill, are:
(1) Association (correlation),
(2) Temporal order, and
(3) No spuriousness.

The criterion of association requires that there is a systematic relationship between the cause and
effect variables. This criterion is by far the easiest to determine. The second criterion of
temporal order is a bit more complicated. The temporal order criterion requires that the cause, or
more precisely variation in the cause variable, must occur before the observed variation in the
effect variable.
The third criterion of no spuriousness is by far the most difficult to achieve. This criterion
requires that the observed relationship between the cause and the effect variables must not be due
to other omitted or unmeasured third variables. Using the relationship between delinquent peers
and offending as an example, this criterion requires that this relationship cannot be due to
homophily or any other potential explanation. Because there are usually many, many potentially
relevant third variables and many of these third variables are unobserved, the criterion of no
spuriousness can be quite difficult to achieve.
Causal Relationship
Causality is the relationship between cause and effect. Simple connections between cause and
effect are linear and unidirectional. Complex connections between cause and effect, when
organizations are thought of as systems, involve, circular causality, interdependent systems, and
non-linearity. Nonlinearity is where one variable can have a more than proportional effect on
another due to the very complex connections between cause and effect. With nonlinearity it may
become unclear what cause and effect mean, the links between cause and effect may become
distant in time and space, and the links between cause and effect may disappear for all practical
purposes.
The philosophical concept of causality or causation refers to the set of all particular “”causal“”
or “”cause-and-effect“” relations. Most generally, causation is a relationship that holds between
events, properties, variables, or states of affairs.
According to Sowa (2000), up until the twentieth century, three assumptions described by Max
Born in 1949 were dominant in the definition of causality:
1. Causality postulates that there are laws by which the occurrence of an entity B of a
certain class depends on the occurrence of an entity A of another class, where the word

entity means any physical object, phenomenon, situation, or event. A is called the cause,
B the effect.
2. Antecedence postulates that the cause must be prior to, or at least simultaneous with, the
effect.
3. Contiguity postulates that cause and effect must be in spatial contact or connected by a
chain of intermediate things in contact.”” (Born, 1949, as cited in Sowa, 2000)
Causality always implies at least some relationship of dependency between the cause and the
effect. For example, deeming something a cause may imply that, all other things being equal, if
the cause occurs the effect does as well, or at least that the probability of the effect occurring
increases. However, according to Sowa (2000), “”relativity and quantum mechanics have forced
physicists to abandon these assumptions as exact statements of what happens at the most
fundamental levels, but they remain valid at the level of human experience.
Expressing causal relationships:
In natural languages, causal relationships can be expressed by the following causative
expressions:-
1. A set of causative verbs [cause, make, create, do, effect, produce, occasion, perform,
determine, influence; construct, compose, constitute; provoke, motivate, force, facilitate,
induce, get, stimulate; begin, commence, initiate, institute, originate, start; prevent, keep,
restrain, preclude, forbid, stop, cease];
2. A set of causative names [actor, agent, author, creator, designer, former, originator;
antecedent, causality, causation, condition, fountain, occasion, origin, power, precedent,
reason, source, spring; reason, grounds, motive, need, impulse];
3. A set of effective names [consequence, creation, development, effect, end, event, fruit,
impact, influence, issue, outcome, outgrowth, product, result, upshot
Concept of Independent and Dependent Variable

A variable is something you’re trying to measure. It can be practically anything, such as objects,
amounts of time, feelings, events, or ideas. If you’re studying how people feel about different
television shows, the variables in that experiment are television shows and feelings. If you’re
studying how different types of fertilizer affect how tall plants grow, the variables are type of
fertilizer and plant height.
There are two key variables in every experiment
1. Independent Variables
The independent variable and the dependent variable. The independent variable is the variable
whose change isn’t affected by any other variable in the experiment. Either the scientist has to
change the independent variable herself or it changes on its own; nothing else in the experiment
affects or changes it. Two examples of common independent variables are age and time. There’s
nothing you or anything else can do to speed up or slow down time or increase or decrease age.
They’re independent of everything else.
2. Dependent Variable
The dependent variable is what is being studied and measured in the experiment. It’s what
changes as a result of the changes to the independent variable. An example of a dependent
variable is how tall you are at different ages. The dependent variable (height) depends on the
independent variable (age).
An easy way to think of independent and dependent variables is, when you’re conducting an
experiment, the independent variable is what you change, and the dependent variable is what
changes because of that. You can also think of the independent variable as the cause and the
dependent variable as the effect.
It can be a lot easier to understand the differences between these two variables with examples, so
let’s look at some sample experiments below.

Examples of Independent and Dependent Variables in Experiments
Below are overviews of three experiments, each with their independent and dependent variables
identified.
Experiment 1: You want to figure out which brand of microwave popcorn pops the most kernels
so you can get the most value for your money. You test different brands of popcorn to see which
bag pops the most popcorn kernels.
 Independent Variable: Brand of popcorn bag (It’s the independent variable because you
are actually deciding the popcorn bag brands)
 Dependent Variable: Number of kernels popped (This is the dependent variable because
it’s what you measure for each popcorn brand)
Experiment 2: You want to see which type of fertilizer helps plants grow fastest, so you add a
different brand of fertilizer to each plant and see how tall they grow.
 Independent Variable: Type of fertilizer given to the plant
 Dependent Variable: Plant height
Experiment 3: You’re interested in how rising sea temperatures impact algae life, so you design
an experiment that measures the number of algae in a sample of water taken from a specific
ocean site under varying temperatures.
 Independent Variable: Ocean temperature
 Dependent Variable: The number of algae in the sample
For each of the independent variables above, it’s clear that they can’t be changed by other
variables in the experiment. You have to be the one to change the popcorn and fertilizer brands
in Experiments 1 and 2, and the ocean temperature in Experiment 3 cannot be significantly
changed by other factors. Changes to each of these independent variables cause the dependent
variables to change in the experiments.
Concomitant Variable, Extraneous Variable, Treatment and control groups

CONCOMITANT VARIABLE
A concomitant variable, or covariate, is a variable which we observe during the course of our
research or statistical analysis, but we cannot control it and it is not the focus of our analysis.
Although concomitant variables are not given any central recognition, they may be confounding
or interacting with the variables being studied. Ignoring them can lead to skewed or biased data,
and so they must often be corrected for in a final analysis.
Examples of Concomitant Variables
Let’s say you had a study which compares the salaries of male vs. female college graduates. The
variables being studied are gender and salary, and the primary survey questions are related to
these two main topics. But, since salaries increase the longer someone has been in the workplace,
the concomitant variable ‘time out of college’ has the potential to skew our data if it is not
accounted for.
If this variable is observed, recorded for and accounted for in the final results, your conclusions
will be more valid. Typically this is done by noting the concomitant variable (here, age) in the
initial data gathering, and then running a regression to ‘equalize’ all of the data points to the
same number of years out of college.
Similarly, in a study comparing the effects of soil composition on the growth of tomatoes over
20 different locations country-wide, average temperatures and hours of sunlight available to each
tomato patch would both be concomitant variables that would need to be included in a final
analysis in order to get valid results.
EXTRANEOUS VARIABLE
An Extraneous Variable is something that the experimenter cannot control, which can have an
effect on the overall outcome of the experiment. The main four extraneous variables are demand
characteristics, experimenter effects, participant variables and situational variables.

(i) Demand Characteristics: Environmental clues that may tell the participant what is expected
of them, such as the environmental setting or the researches body language. This in turn can
affect their behaviour.
(ii) Experimenter Effects: When the researcher themselves affect the outcome by giving
subconscious clues about how to behave. This may involve unintentionally asking leading
questions that inform the participant of the desired result.
(iii) Participant variables: Something about the participant that is out of the researcher’s
control. For example, whilst researches may try and target individuals with a certain background
for an experiment, existing variables such as their health, or prior knowledge, could affect the
outcome. For example, a participant with prior knowledge of Milgram’s experiment would be an
extraneous variable in a reimagining of the experiment.
(iv) Situational Variables: Whilst the researcher may do their best to control an experiment (for
example, controlling the time of day), situational variables can still affect the results. For
example, a field experiment conducted at the same time of day across a week may experience
sporadic weather or unexpected noise pollution, changing the mood/actions of the participants.
TREATMENT
Treatment group is a group that receives a treatment in an experiment. The “group” is made up
of test subjects (people, animals, plants, cells etc.) and the “treatment” is the variable you are
studying. For example, a human experimental group could receive a new medication, a different
form of counseling, or some vitamin supplements. A plant treatment group could receive a new
plant fertilizer, more sunlight, or distilled water. The group that does not receive the treatment is
called the control group.
In an experiment, the factor (also called an independent variable) is an explanatory variable
manipulated by the experimenter. Each factor has two or more levels, i.e., different values of the
factor. Combinations of factor levels are called treatments.
Treatment Group Examples

Example no. 1: – You are testing to see if a new plant fertilizer increases sunflower size. You
put 20 plants of the same height and strain into a location where all the plants get the same
amount of water and sunlight. One half of the plants–the control group–get the regular fertilizer.
The other half of the plants–the experimental group–get the fertilizer you are testing.
Example no. 2: – You are testing to see if a new drug works for asthma. You divide 100
volunteers into two groups of 50. One group of 50 gets the drug; they are the experimental
group. The other 50 people get a sugar pill (a placebo); they are the
CONTROL GROUP
Control group, the standard to which comparisons are made in an experiment. Many experiments
are designed to include a control group and one or more experimental groups; in fact, some
scholars reserve the term experiment for study designs that include a control group. Ideally, the
control group and the experimental groups are identical in every way except that the
experimental groups are subjected to treatments or interventions believed to have an effect on the
outcome of interest while the control group is not. Inclusion of a control group greatly
strengthens researchers’ ability to draw conclusions from a study. Indeed, only in the presence of
a control group can a researcher determine whether a treatment under investigation truly has a
significant effect on an experimental group, and the possibility of making an erroneous
conclusion is reduced.
A typical use of a control group is in an experiment in which the effect of a treatment is
unknown and comparisons between the control group and the experimental group are used to
measure the effect of the treatment. For instance, in a pharmaceutical study to determine the
effectiveness of a new drug on the treatment of migraines, the experimental group will be
administered the new drug and the control group will be administered a placebo (a drug that is
inert, or assumed to have no effect). Each group is then given the same questionnaire and asked
to rate the effectiveness of the drug in relieving symptoms. If the new drug is effective, the
experimental group is expected to have a significantly better response to it than the control
group. Another possible design is to include several experimental groups, each of which is given
a different dosage of the new drug, plus one control group. In this design, the analyst will

compare results from each of the experimental groups to the control group. This type of
experiment allows the researcher to determine not only if the drug is effective but also the
effectiveness of different dosages. In the absence of a control group, the researcher’s ability to
draw conclusions about the new drug is greatly weakened, due to the placebo effect and other
threats to validity. Comparisons between the experimental groups with different dosages can be
made without including a control group, but there is no way to know if any of the dosages of the
new drug are more or less effective than the placebo.
It is important that every aspect of the experimental environment be as alike as possible for all
subjects in the experiment. If conditions are different for the experimental and control groups, it
is impossible to know whether differences between groups are actually due to the difference in
treatments or to the difference in environment. For example, in the new migraine drug study, it
would be a poor study design to administer the questionnaire to the experimental group in a
hospital setting while asking the control group to complete it at home. Such a study could lead to
a misleading conclusion, because differences in responses between the experimental and control
groups could have been due to the effect of the drug or could have been due to the conditions
under which the data were collected. For instance, perhaps the experimental group received
better instructions or was more motivated by being in the hospital setting to give accurate
responses than the control group.
A control group study can be managed in two different ways. In a single-blind study, the researcher
will know whether a particular subject is in the control group, but the subject will not know. In a
double-blind study, neither the subject nor the researcher will know which treatment the subject is
receiving. In many cases, a double-blind study is preferable to a single-blind study, since the
researcher cannot inadvertently affect the results or their interpretation by treating a control
subject differently from an experimental subject.

Research Design notes Unit 2 for MBA students

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Research Design notes Unit 2 for MBA students