Introduction to Research Methodology
Research Problem
Formulation: The
research problem undertaken for study must be carefully selected. A problem
must spring from the researcher’s mind like a plant springing from its own
seed.
If our eyes need glasses,
it is not the optician alone who decides about the number of the quantity we
require. A research guides can at the most only help a researcher choose a
subject.
The following points may be
observed by a researcher in selecting a research problem or a subject for
research.
a) Subject which is overdone
should not be normally choosen , for it will be a difficult task to throw any
new light in such a case.
b) Controversial subject
should not become the choice of an average researcher.
c) Too narrow or too vague
problem should be avoided.
d) The subject selected for
research should be familiar and feasible so that the related research
material or sources of research are within one’s reach.
e) The importance of the
subject, the qualifications and the training of a researcher, the costs
involved, the time factor are few other criteria that must also be considered
in selecting a problem.
f) The selection of a problem must be preceded by a preliminary study. This may not be necessary when the problem requires the conduct of a research closely similar to one that has already been done.
Techniques involved in
defining a problem:
a. Statement of the problem in
a general way
b. Understanding the nature of
the problem
c. Surveying the available
literature
d. Developing the ideas through discussions
e. Rephrasing the research problem
Unit II
Research
Design:
"A research design is the arrangement of conditions for collection and
analysis of data in a manner that aims to combine relevance to the
research purpose with economy in procedure."
Research designs also include the elements of data collection,
measurement of data with the respective tools, and the analysis of the data. As
a rule of thumb, the research problem a company chooses to work on, is the
determining factor of the research design chosen by the researcher instead of
the other way round.
Research design is
the conceptual structure within which research is conducted; it constitutes
the blueprint for the collection, measurement and analysis of data.
The designing decisions
happen to be in respect of:
(i) What is the study
about?
(ii) Why is the study being
made?
(iii) Where will the study be
carried out?
(iv) What type of data is
required?
(v) Where can the required data
be found?
(vi) What periods of time will
the study include?
(vii) What will be the
sample design?
(viii) What techniques of data collection will be
used?
(ix) How will the data be
analysed ?
(x) In what style will the report be prepared?
Preparation of the research design should be done with great care as any error in it may upset the entire project.
Research design, in fact, has a great bearing on the reliability of the results arrived at and as such constitutes the firm foundation of the entire edifice (Bhawan) of the research work.
Important concept relating to research design:
1. Dependent
and independent variables: A concept which can take on different
quantitative value is called a variable. As such the concepts like weight,
height, income are all examples of variables.
If one variable depends upon or is a consequence of the other variable, it is termed as an dependent variable and the variable that is antecedent to the independent variable is termed as independent variable.
2. Extraneous variable: Independent variables that are not related to the purpose of study, but may affect the dependent variable are termed as ' extraneous variables'.
3. Control: One important characteristics of a good Research Design is to minimise the influence or effect meaning of extraneous variables. The technical term ' control' is used when we design the study minimizing the effects of extraneous independent variables. i.e., to restrain experimental conditions.
4. Confounded relationship: when the dependent variable is not free from the influence of extraneous variable, the relationship between the dependent and independent variables is said to be confounded by an extraneous variables.
5. Research hypothesis: when the prediction or a hypothesised relationship is to be tested by scientific methods it is termed as research hypothesis. Usually a research hypothesis must contain, at least one independent and one dependent variable.
6.
Experimental and non experimental hypothesis testing research: when the purpose of
research is to test a research hypothesis, it is termed as hypothesis testing
research.
Research in which the independent variable is manipulated is termed ' experimental hypothesis testing research' and a research in which an independent variable is not manipulated is called ' non experimental hypothesis testing research'.
7.
Experimental and control groups: In an experimental hypothesis
testing research when a group is exposed to usual conditions, it is termed as
'Control group'.
But when the group is exposed to some novel or a special condition, it is termed as experimental group.
8.
Treatments:
The different conditions under which experimental and control groups are put
are usually referred to as 'Treatment'.
9. Experiment: The process of examining the truth of a statistical hypothesis, relating to some research problem, is known as experiment.
10. Experimental units: The predetermined plots or blocks where different treatments are used are known as experimental units.
Different
Research Design: Different
research designs can be conveniently described if we categorised them as:
a) Research design in case of
exploratory research studies: Exploratory research studies are also
termed as formulative research studies. The main purpose of such studies is
that of formulating a problem for more precise investigation or of developing
the working hypothesis from an operational point of view.
b) Descriptive research
studies & diagnostic research studies: Descriptive research
studies are those studies which are concerned with describing the
characteristics of a particular individual or of a group whereas diagnostic
research studies determine the frequency with which something occurs or its
association with something else.
c) Hypothesis research
studies: Hypothesis
testing research studies are those where the researcher tests the hypothesis of
casual relationship between variables. Such studies require procedures that
will not only reduce bias and increase reliability, but will permit drawing
inferences about causality. Usually experiments meet this requirement.
Important
Experimental Designs: Experimental
designs refers to the framework or structure of an experiment and as such there
are several experiment designs.
(a)
Informal experiment designs:
(i) Before-and-after
without control design
(ii) After-only with control design
(iii) Before-and-after with control design
(b)
Formal experiment design:
(i) Completely randomised
design (C.R. Design)
(ii) Randomised block design (R.B. Design)
(iii) Latin-square design (L-S Design)
(iv) Factorial designs
(i) Before-and-after without control design: In such a design a single test group
or area is selected and the dependent variable is
measured before the introduction of the treatment. The treatment is then
introduced and the dependent variable is measured again after the treatment has
been introduced.
(ii)
After-only with control design: In this design two groups or area are
selected and the treatment is introduced into the test area only. The dependent
variable is then measured in both the areas at the same time. Treatment impact
is assessed by subtracting the value of the dependent variable in the control area
from its value in the test area.
(iii) Before-and-after with
control design: In
this design two areas are selected and the dependent variable is measured
in both the areas for an identical time-period before the treatment. The
treatment is then introduced into the test area only, and the dependent
variable is measured in both for an identical time-period after the introduction
of the treatment. The treatment effect is determined by subtracting the change
in the dependent variable in the control area from the change in the dependent
variable in test area.
i)
Completely
randomised design (C.R. Design): Involves only two principles viz.,
the principle of replication and the principle of randomisation of
experimental designs.
(a) Two group simple randomised
design:
(b) Simple replications design:
(ii) Randomised block
design: R.B
design is an improvement over the C.R. Design. In R.B design, subjects are
first divided into groups, known as blocks, such that within each group the subjects
are relatively homogenous in respect to some selected variable.
(iii)Latin Square design (L.S.Design): L.S design is an experimental design very frequently used in agricultural research.The conditions under which agricultural investigations are carried out are different from those in other studies for nature plays an important role in agriculture.
(iv) Factorial designs: Factorial designs are used in experiments where the effects of varying more than one factor are to be determined. They are specially important in several economic and social phenomena where usually a large number of factors are affect a particular problem.
Sampling Design: All items in any field of enquiry constitute a ‘Universe’ or ‘Population’. A complete enumeration of all items in the ‘population’ is known as a census enquiry.
A sample plan is a definite plan for obtaining a sample from a given population. It refers to the technique of the procedure the researcher would adopt in selecting items for the sample. Sample design may as well lay down the number of items to be included in the sample i.e., the size of the sample. Sample design is determined before data are collected. Researcher must select/prepare a sample design which should be reliable and appropriate for his research study.
Steps in Sample Design: The researcher must pay attention of the following:
(i) Types of universe: The first step in developing any sample design is to clearly define the set of objects, technically called the ‘Universe, to be studied. The universe can be finite or infinite.
(ii) Sampling Unit: Sampling unit may be a geographical one such as state, district, village, etc., or construction unit such as house, flat, etc., or it may be a social unit such as family, club, school, etc., or it may be an individual.
(iii) Source List: It is also known as ‘sampling frame’ from which sample is to be drawn. It contains the names of all items of a universe. If source list is not available, researcher has to prepare it. Such a list should be comprehensive, correct, reliable and appropriate.
(iv) Size of sample: This refers to the number of items to be selected from the universe to constitute a sample. The size of sample should neither be excessively large, nor too small. It should be optimum.
(v) Parameters of interest: In determining the sample design, one must consider the question of the specific population parameters which are of interest. There may also be important sub-groups in the population about whom we would like to make estimates.
(vi) Budgetary constraint: Cost consideration, from practical point of view, have a major impact upon decision relating to not only the size of the sample but also to the type of sample.
(vii) Sampling procedure: The researcher must decide the type of sample he will use i.e., this technique or procedure stands for the sample design itself. There are several sample designs out of which the researcher must choose one for study. He must select that design which, for a given sample size and for a given cost, has a smaller sampling error.
Types
of samples: Sample selection methodologies with examples
The process of deriving a sample is called a
sampling method. Sampling forms an integral part of the research design as this
method derives the quantitative data and the qualitative data that can be
collected as part of a research study. Sampling methods are
characterized into two distinct approaches: probability sampling and
non-probability sampling.
Probability sampling methodologies with examples
Probability
Sampling is
a method of deriving a sample where the objects are selected from a population-based
on the theory of probability. This method includes everyone in the population,
and everyone has an equal chance of being selected. Hence, there is no bias
whatsoever in this type of sample. Each person in the population can
subsequently be a part of the research. The selection criteria are decided at
the outset of the market research study and form an important component of
research.
Probability sampling can be further
classified into four distinct types of samples. They are:
·
Simple random sampling: The most straightforward way of
selecting a sample is simple random sampling. In this method, each member has
an equal chance of being a part of the study. The objects in this sample
population are chosen purely on a random basis, and each member has the same
probability of being selected. For example, if a university dean would
like to collect feedback from students about their perception of the teachers
and level of education, all 1000 students in the University could be a part of
this sample. Any 100 students can be selected at random to be a part of this
sample.
·
Cluster sampling: Cluster sampling is a type of
sampling method where the respondent population is divided into equal clusters.
Clusters are identified and included in a sample based on defining demographic
parameters such as age, location, sex, etc. This makes it extremely easy for a
survey creator to derive practical inferences from the feedback. For example,
if the FDA wants to collect data about adverse side effects from drugs, they can
divide the mainland US into distinctive clusters, like states. Research studies
are then administered to respondents in these clusters. This type of generating
a sample makes the data collection in-depth and provides easy to consume and
act upon, insights.
·
Systematic sampling: Systematic sampling is a sampling method
where the researcher chooses respondents at equal intervals from a population.
The approach to select the sample is to pick a starting point and then pick
respondents at a pre-defined sample interval. For example, while selecting
1,000 volunteers for the Olympics from an application list of 10,000 people,
each applicant is given a count of 1 to 10,000. Then starting from 1 and
selecting each respondent with an interval of 10, a sample of 1,000 volunteers
can be obtained.
·
Stratified random sampling: Stratified random sampling is a method of
dividing the respondent population into distinctive but pre-defined parameters
in the research design phase. In this method, the respondents don’t overlap but
collectively represent the whole population. For example, a researcher looking
to analyze people from different socioeconomic backgrounds can distinguish
respondents into their annual salaries. This forms smaller groups of people or
samples, and then some objects from these samples can be used for the research
study.
Non-probability
sampling methodologies with examples
The Non-probability sampling method uses the
researcher’s discretion to select a sample. This type of sample is derived
mostly from the researcher’s or statistician’s ability to get to this sample.
This type of sampling is used for preliminary research where the primary
objective is to derive a hypothesis about the topic in research. Here each
member does not have an equal chance of being a part of the sample population,
and those parameters are known only post-selection to the sample.
We can classify non-probability sampling into four distinct types of samples. They are.
Convenience sampling: Convenience sampling, in easy terms, stands for the convenience of a researcher accessing a respondent. There is no scientific method of deriving this sample. Researchers have nearly no authority over selecting the sample elements, and it’s purely done on the basis of proximity and not representativeness.This non-probability sampling method is used when there are time and cost limitations in collecting feedback. For example, researchers that are conducting a mall-intercept survey to understand the probability of using a fragrance from a perfume manufacturer. In this sampling method, the sample respondents are chosen purely on their proximity to the survey desk and their willingness to participate in the research.
Judgemental/purposive sampling: The judgemental
or purposive sampling method is a method of developing a sample purely on
the basis and discretion of the researcher purely on the basis of the nature of
study along with his/her understanding of the target audience. In this sampling
method, people who only fit the research criteria and end objectives are
selected, and the remaining are kept out. For example, if the research topic is
understanding what University a student prefers for Masters, if the question
asked is “Would you like to do your Masters?” anything other than a response,
“Yes” to this question, everyone else is excluded from this study.
· Snowball sampling: Snowball sampling or
chain-referral sampling is defined as a non-probability sampling technique in
which the samples have traits that are rare to find. This is a sampling
technique, in which existing subjects provide referrals to recruit samples
required for a research study. For example, while collecting feedback about a
sensitive topic like AIDS, respondents aren’t forthcoming with information. In
this case, the researcher can recruit people with an understanding or knowledge
of such people and collect information from them or ask them to collect
information.
· Quota sampling: Quota sampling is a
method of collecting a sample where the researcher has the liberty to select a
sample based on their strata. The primary characteristic of this method is that
two people cannot exist under two different conditions. For example, when a
shoe manufacturer would like to understand from millenials their perception of
the brand with other parameters like comfort, pricing, etc. It selects only
females who are millennials for this study as the research objective is to
collect feedback about women’s shoes.
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Problems in Processing of data in Research Methodology:-
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Hypothesis
Testing:- Hypothesis testing is an act in statistics whereby
an analyst tests an assumption regarding a population parameter. The
methodology employed by the analyst depends on the nature of the data used and
the reason for the analysis.
Hypothesis testing is used to assess the plausibility of a hypothesis by
using sample data. Such data may come from a larger population, or from a
data-generating process. The word "population" will be used for both
of these cases in the following descriptions.
How Hypothesis Testing Works:- In hypothesis testing, an analyst tests a statistical sample, with the goal of providing evidence on
the plausibility of the null hypothesis.
Statistical analysts test a hypothesis by measuring and examining a
random sample of the population being analyzed. All analysts use a random
population sample to test two different hypotheses: the null hypothesis and
the alternative hypothesis.
The null hypothesis is usually a hypothesis of equality between
population parameters; e.g., a null hypothesis may state that the population
mean return is equal to zero. The alternative hypothesis is effectively the
opposite of a null hypothesis (e.g., the population mean return is not equal to
zero). Thus, they are mutually exclusive, and only one can be true.
However, one of the two hypotheses will always be true.
4 Steps of Hypothesis Testing
All hypotheses are tested using a four-step process:
- The first step is for the analyst to state the two
hypotheses so that only one can be right.
- The next step is to formulate an analysis plan,
which outlines how the data will be evaluated.
- The third step is to carry out the plan and
physically analyze the sample data.
- The fourth and final step is to analyze the results
and either reject the null hypothesis, or state that the null hypothesis
is plausible, given the data.
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