unit4 rm research methodology .pdf

Types of Analysis
• Analysis, in case of experimental data, involves estimating the values of
unknown parameters of the population and testing of hypotheses for
drawing inferences.
• Analysis may, therefore, be categorized as descriptive analysis and
inferential analysis
• Descriptive analysis is largely the study of distributions of one variable.
❑ This study provides us with profiles of companies, work groups, persons
and other subjects on any of a multiple of characteristics such as size.
Composition, efficiency, preferences, etc.
❑ This sort of analysis may be in respect of one variable (described as
unidimensional analysis), or in respect of two variables (described as
bivariate analysis) or in respect of more than two variables (described as
multivariate analysis).
• Descriptive analysis may also involve the study of measuring
relationships between two or more variables.

Types of Analysis
• Measuring relationships between two or more variables can be done
with correlation analysis and causal analysis
❑ Correlation analysis studies the joint variation of two or more variables for
determining the amount of correlation between two or more variables.
❑ Causal analysis is concerned with the study of how one or more variables
affect changes in another variable.
• Usually the following analyses* are involved when we make a reference
of multivariate analysis:
❑ Multiple regression analysis: This analysis is adopted when the researcher
has one dependent variable which is presumed to be a function of two or
more independent variables. The objective of this analysis is to make a
prediction about the dependent variable based on its covariance with all the
concerned independent variables.
❑ Multiple discriminant analysis: This analysis is appropriate when the
researcher has a single dependent variable that cannot be measured, but
can be classified into two or more groups on the basis of some attribute.

Types of Analysis
❑ Multivariate analysis of variance (or multi-ANOVA): This analysis is an
extension of two-way ANOVA, wherein the ratio of among group variance
to within group variance is worked out on a set of variables
❑ Canonical analysis: This analysis can be used in case of both measurable
and non-measurable variables for the purpose of simultaneously predicting
a set of dependent variables from their joint covariance with a set of
independent variables.
• Inferential analysis is concerned with the various tests of significance
for testing hypotheses in order to determine with what validity data can
be said to indicate some conclusion or conclusions.
❑ It is also concerned with the estimation of population values.
❑ It is mainly on the basis of inferential analysis that the task of
interpretation (i.e., the task of drawing inferences and conclusions) is
performed.

Statistics in Research
• The role of statistics in research is to function as a tool in designing
research, analyzing its data and drawing conclusions there from.
• Most research studies result in a large volume of raw data which must
be suitably reduced so that the same can be read easily and can be used
for further analysis.
• There are two major areas of statistics viz., descriptive statistics and
inferential statistics.
❑ Descriptive statistics concern the development of certain indices from the
raw data,
❑ Inferential statistics is about the process of generalization.
❑ Inferential statistics are also concerned with two major type of problems:
(i) the estimation of population parameters, and (ii) the testing of statistical
hypotheses.
• The important statistical measures that are used to summarize the
research data are:
(1) measures of central tendency or statistical averages; (2) measures of
dispersion; (3) measures of asymmetry (skewness); (4) measures of relationship;
and (5) other measures.

Measures of Central Tendency
• Measures of central tendency (or statistical averages) tell us the point
about which items have a tendency to cluster.
❑ Such a measure is considered as the most representative figure for the
entire mass of data.
• Measure of central tendency is also known as statistical average.
• Mean, median and mode are the most popular averages.
❑ Mean, also known as arithmetic average, is the most common
measure of central tendency and may be defined as the value which
we get by dividing the total of the values of various given items in a
series by the total number of items. we can work it out as under:

❑ In case of a frequency distribution, we can work out mean in this way:
❑ Advantages:
i) Mean is the simplest measurement of central tendency and is a widely
used measure.
ii) It is amenable to algebraic treatment and is used in further statistical
calculations.
iii) It is a relatively stable measure of central tendency
❑ Limitations:
i) It is unduly affected by extreme items;
ii) it may not coincide with the actual value of an item in a series, and
iii) it may lead to wrong impressions, particularly when the item values are
not given with the average

• Median is the value of the middle item of series when it is arranged in
ascending or descending order of magnitude.
❑ It divides the series into two halves; in one half all items are less than
median, whereas in the other half all items have values higher than median.
❑ If the values of the items arranged in the ascending order are: 60, 74, 80,
90, 95, 100, then the value of the 4th item viz., 85 is the value of median.
❑ We can also write thus:
❑ Median is a positional average and is used only in the context of qualitative
phenomena, for example, in estimating intelligence, etc., which are often
encountered in sociological fields

• Mode is the most commonly or frequently occurring value in a series.
❑ The mode in a distribution is that item around which there is maximum
concentration.
❑ Advantages:
i) Like median, mode is a positional average and is not affected by the values
of extreme items. it is, therefore, useful in all situations where we want to
eliminate the effect of extreme variations.
ii) Mode is particularly useful in the study of popular sizes. For example, a
manufacturer of shoes is usually interested in finding out the size most in
demand so that he may manufacture a larger quantity of that size.
• Geometric mean is also useful under certain conditions. It is defined as
the nth root of the product of the values of n times in a given series.
• Symbolically, we can put it thus:

❑ F or instance, the geometric mean of the numbers, 4, 6, and 9 is worked out
as

• Harmonic mean is defined as the reciprocal of the average of
reciprocals of the values of items of a series.
❑ Symbolically, we can express it as under:

❑ For instance, the harmonic mean of the numbers 4, 5, and 10 is worked out
as

Measures of Dispersion
• An averages can represent a series only as best as a single figure can,
but it certainly cannot reveal the entire story of any phenomenon under
study.
• Specially it fails to give any idea about the scatter of the values of items
of a variable in the series around the true value of average.
❑ In order to measure this scatter, statistical devices called measures
of dispersion are calculated.
❑ Important measures of dispersion are (a) range, (b) mean deviation,
and (c) standard deviation.
• Range is the simplest possible measure of dispersion and is defined as
the difference between the values of the extreme items of a series. Thus,
Range = (Highest value of an item in a series) − (Lowest value of an
item in a series )
❑ The utility of range is that it gives an idea of the variability very quickly
❑ The drawback is that range is affected very greatly by fluctuations of
sampling.

• Mean deviation is the average of difference of the values of items from
some average of the series.
❑ Such a difference is technically described as deviation.
❑ In calculating mean deviation we ignore the minus sign of deviations while
taking their total for obtaining the mean deviation.
❑ Mean deviation is, thus, obtained as under:

❑ When mean deviation is divided by the average used in finding out the
mean deviation itself, the resulting quantity is described as the coefficient of
mean deviation.
❑ It is a better measure of variability than range as it takes into consideration
the values of all items of a series.

• Standard deviation is most widely used measure of dispersion of a
series and is defined as the square-root of the average of squares of
deviations, when such deviations for the values of individual items in a
series are obtained from the arithmetic average.
❑ It is worked out as under:
Or
Sometimes, we work out the square of standard deviation, known as
variance, which is frequently used in the context of analysis of
variation.

Measures of Asymmetry (Skewness)
• When the distribution of item in a series happens to be perfectly
symmetrical, we then have the following type of curve for the
distribution:
❑ Such a curve is technically described as a normal curve and the relating
distribution as normal distribution.

• A normal curve is perfectly bell shaped curve in which case the value of
X or M or Z is just the same and skewness is altogether absent.
• But if the curve is distorted (whether on the right side or on the left
side), we have asymmetrical distribution which indicates that there is
skewness.
❑ If the curve is distorted on the right side, we have positive skewness
❑ when the curve is distorted towards left, we have negative skewness
❑ This is shown in the figure below:

• Skewness is, thus, a measure of asymmetry and shows the manner in
which the items are clustered around the average.
❑ In a symmetrical distribution, the items show a perfect balance on either
side of the mode, but in a skew distribution the balance is thrown to one
side.
❑ The amount by which the balance exceeds on one side measures the
skewness of the series.
❑ The difference between the mean, median or the mode provides an easy
way of expressing skewness in a series.
❑ In case of positive skewness, we have Z < M < X and in case of negative
skewness we have X < M < Z.

Measures of Relationship
• In case of bivariate or multivariate populations, we often wish to know
the relation of the two and/or more variables in the data to one another.
❑ We may like to know, for example, whether the number of hours students
devote for studies is somehow related to their family income, to age, to sex
or to similar other factor.
• We have to answer two types of questions in bivariate or multivariate
populations viz.,
❑ Does there exist association or correlation between the two (or more)
variables? If yes, of what degree?
❑ Is there any cause and effect relationship between the two variables in case
of the bivariate population or between one variable on one side and two or
more variables on the other side in case of multivariate population? If yes,
of what degree and in which direction?
• The first question is answered by the use of correlation technique and
the second question by the technique of regression.

• There are several methods of applying the two techniques, but the
important ones are as under:
❑ In case of bivariate population: Correlation can be studied through (a)
cross tabulation; (b) Charles Spearman’s coefficient of correlation; (c) Karl
Pearson’s coefficient of correlation; whereas cause and effect relationship
can be studied through simple regression equations
❑ In case of multivariate population: Correlation can be studied through (a)
coefficient of multiple correlation; (b) coefficient of partial correlation;
whereas cause and effect relationship can be studied through multiple
regression equations.
• Cross tabulation: This approach is specially useful when the data are in
nominal form.
❑ Under it we classify each variable into two or more categories and then
cross classify the variables in these sub-categories.
❑ Then we look for interactions between them which may be symmetrical,
reciprocal or asymmetrical.

❑ A symmetrical relationship is one in which the two variables vary together,
but we assume that neither variable is due to the other.
❑ A reciprocal relationship exists when the two variables mutually influence
or reinforce each other.
❑ Asymmetrical relationship is said to exist if one variable (the independent
variable) is responsible for another variable (the dependent variable).
• Charles Spearman’s coefficient of correlation (or rank correlation): It
is the technique of determining the degree of correlation between two
variables in case of ordinal data where ranks are given to the different
values of the variables.
❑ The main objective of this coefficient is to determine the extent to which the
two sets of ranking are similar or dissimilar.
❑ This coefficient is determined as under:

• Karl Pearson’s coefficient of correlation (or simple correlation): It is
the most widely used method of measuring the degree of relationship
between two variables.
• This coefficient assumes the following:
(i) that there is linear relationship between the two variables;
(ii) that the two variables are casually related which means that one of the
variables is independent and the other one is dependent; and
(iii) a large number of independent causes are operating in both variables so
as to produce a normal distribution.

❑ It

Simple Regression Analysis
• In simple regression, we have only two variables, one variable (defined
as independent) is the cause of the behavior of another one (defined as
dependent variable).
• The basic relationship between X and Y is given by
❑ This equation is known as the regression equation of Y on X (also
represents the regression line of Y on X when drawn on a graph) which
means that each unit change in X produces a change of b in Y, which is
positive for direct and negative for inverse relationships.
❑ Thus, the regression analysis is a statistical method to deal with the
formulation of mathematical model depicting relationship amongst
variables which can be used for the purpose of prediction of the values of
dependent variable, given the values of the independent variable.

Methods of communicating and displaying
analysed data
• Broadly, there are four ways of communicating and displaying the
analyzed data. These are:
❑ Text
❑ Tables
❑ graphs; and
❑ statistical measures
• Text: Text, by far, is the most common method of communication in
both quantitative and qualitative research studies and, perhaps, the
only method in the latter.
❑ It is, therefore, essential that you know how to communicate effectively,
keeping in view the level of understanding, interest in the topic and need
for academic and scientific rigour of those for whom you are writing.
❑ Your style should be such that it strikes a balance between academic and
scientific rigour and the level that attracts and sustains the interest of your
readers.

analyzed data
• Tables: Tables offer a useful means of presenting large amounts of
detailed information in a small space
• Figure below shows the structure of a table.

analysed data
• A table has five parts:
1. Title – This normally indicates the table number and describes the type of
data the table contains.
❑ The description accompanying the table number must clearly specify the
contents of that table.
❑ In the description identify the variables about which information is
contained in the table, for example ‘Respondents by age’ or ‘Attitudes
towards uranium mining’.
❑ If a table contains information about two variables, the dependent variable
should be identified first in the title, for example ‘Attitudes towards
uranium mining [dependent variable] by gender [independent variable]’.
2. Stub - The stub, usually the first column on the left, lists the items about
which information is provided in the horizontal rows to the right.
3. Column headings – The subcategories of a variable, listed along the x-axis
(the top of the table).

analysed data
❑ In univariate tables (tables displaying information about one variable) the
column heading is usually the ‘number of respondents’ and/or the
‘percentage of respondents’.
❑ In bivariate tables (tables displaying information about two variables) it is
the subcategories of one of the variables displayed in the column headings
4. Body – The cells housing the analyzed data
5. Supplementary notes or footnotes – There are four types of footnote: source
notes; other general notes; notes on specific parts of the table; and notes on
the level of probability. If the data is taken from another source, you have
an obligation to acknowledge this. The source should be identified at the
bottom of the table, and labeled by the word ‘Source:’. Similarly, other
explanatory notes should be added at the bottom of a table.
• Types of tables:
❑ Univariate (also known as frequency tables) – containing information about
one variable, for example

analysed data
❑ bivariate (also known as cross-tabulations) – containing information about
two variables, for example

analysed data
❑ Polyvariate or multivariate – containing information about more than two
variables, for example

analysed data
• Graphs: The main objective of a graph is to present data in a way that
is easy to understand and interpret, and interesting to look at.
❑ ‘A graph is based entirely on the tabled data and therefore can tell no story
that cannot be learnt by inspecting a table. However, graphic
representation often makes it easier to see the pertinent features of a set of
data’

analysed data
• Graphs can be constructed for every type of data – quantitative and
qualitative – and for any type of variable (measured on a nominal,
ordinal, interval or ratio scale).
• There are different types of graph, and your decision to use a particular
type should be made on the basis of the measurement scale used in the
measurement of a variable.
• When constructing a graph of any type it is important to be acquainted
with the following points:
❑ A graphic presentation is constructed in relation to two axes: horizontal
and vertical. The horizontal axis is called the ‘abscissa’ or, more commonly,
the x-axis, and the vertical axis is called the ‘ordinate’ or, more commonly,
the y-axis
❑ If a graph is designed to display only one variable, it is customary, but not
essential, to represent the subcategories of the variable along the x-axis and
the frequency or count of that subcategory along the y-axis.

analysed data
❑ A graph, like a table, should have a title that describes its contents. The
axes should be labeled also.
❑ A graph should be drawn to an appropriate scale. It is important to choose
a scale that enables your graph to be neither too small nor too large
• The histogram - A histogram consists of a series of rectangles drawn
next to each other without any space between them, each representing
the frequency of a category or subcategory
❑ Their height is in proportion to the frequency they represent.
❑ The height of the rectangles may represent the absolute or proportional
frequency or the percentage of the total.

analysed data
• The histogram(below) is effectively the same as the one above but is
presented in a three-dimensional style

analysed data

analysed data
• The bar chart - The bar chart or diagram is used for displaying
categorical data

analysed data
❑ A bar chart is identical to a histogram, except that in a bar chart the
rectangles representing the various frequencies are spaced, thus indicating
that the data is categorical.
❑ The bar chart is used for variables measured on nominal or ordinal scales.
❑ The discrete categories are usually displayed along the x-axis and the
number or percentage of respondents on the y-axis.

analysed data
• The stacked bar chart - A stacked bar chart is similar to a bar chart
except that in the former each bar shows information about two or
more variables stacked onto each other vertically.
❑ The sections of a bar show the proportion of the variables they represent in
relation to one another.
❑ The stacked bars can be drawn only for categorical data.

analysed data
• The pie chart -
❑ The circle or pie is divided into sections in accordance with the magnitude
of each subcategory, and so each slice is in proportion to the size of each
subcategory of a frequency distribution.
❑ The proportions may be shown either as absolute numbers or as
percentages.

analysed data
• The line diagram or trend curve - A set of data measured on a
continuous interval or a ratio scale can be displayed using a line
diagram or trend curve.
• A trend line can be drawn for data pertaining to both a specific time
(e.g. 1995, 1996, 1997) or a period (e.g. 1985–1989, 1990–1994, 1995–).
❑ If it relates to a period, the midpoint of each interval at a height
commensurate with each frequency is marked as a dot. These dots are then
connected with straight lines to examine trends in a phenomenon.
❑ If the data pertains to exact time, a point is plotted at a height
commensurate with the frequency. These points are then connected with
straight lines.
❑ A line diagram is a useful way of visually conveying the changes when
long-term trends in a phenomenon or situation need to be studied, or the
changes in the subcategory of a variable are measured on an interval.
❑ Trends plotted as a line diagram are more clearly visible than in a table.
❑ For example, a line diagram would be useful for illustrating trends in birth
or death rates and changes in population size.

analysed data
• The area chart - The area chart is plotted in the same way as a line
diagram but with the area under each line shaded to highlight the total
magnitude of the subcategory in relation to other subcategories
❑ For example, The figure below shows the number of male and female
respondents by age.

analysed data
• The scattergram - When you want to show visually how one variable
changes in relation to a change in the other variable, a scattergram is
extremely effective.
• For a scattergram, both the variables must be measured either on
interval or ratio scales and the data on both the variables needs to be
available in absolute values for each observation
❑ you cannot develop a scattergram for categorical variables.
❑ Data for both variables is taken in pairs and displayed as dots in relation to
their values on both axes.

analysed data
• Draw a histogram of data on the weekly wages of workers at a
construction site as given below

Meaning of Interpretation
• Interpretation refers to the task of drawing inferences from the
collected facts after an analytical and/or experimental study.
❑ In fact, it is a search for broader meaning of research findings.
• The task of interpretation has two major aspects viz.,
(i) the effort to establish continuity in research through linking the results of
a given study with those of another, and
(ii) the establishment of some explanatory concepts.
• Interpretation is the device through which the factors that seem to
explain what has been observed by researcher in the course of the study
can be better understood and it also provides a theoretical conception
which can serve as a guide for further researches.
WHY INTERPRETATION?
• Interpretation is essential for the simple reason that the usefulness and
utility of research findings lie in proper interpretation.

Meaning of Interpretation
• It is being considered a basic component of research process because of
the following reasons:
(i) It is through interpretation that the researcher can well understand the
abstract principle that works beneath his findings. Through this he can
link up his findings with those of other studies, having the same abstract
principle.
(ii) Interpretation leads to the establishment of explanatory concepts that can
serve as a guide for future research studies; it opens new avenues of
intellectual adventure and stimulates the quest for more knowledge.
(iii) Researcher can better appreciate only through interpretation why his
findings are what they are and can make others to understand the real
significance of his research findings.
(iv) The interpretation of the findings of exploratory research study often
results into hypotheses for experimental research and as such
interpretation is involved in the transition from exploratory to
experimental research.

Technique of Interpretation
• The task of interpretation is not an easy job, rather it requires a great
skill and dexterity on the part of researcher.
• Interpretation is an art that one learns through practice and
experience.
• The technique of interpretation often involves the following steps:
(i) Researcher must give reasonable explanations of the relations which he
has found and he must interpret the lines of relationship in terms of the
underlying processes and must try to find out the thread of uniformity
that lies under the surface layer of his diversified research findings.
(ii) Extraneous information, if collected during the study, must be considered
while interpreting the final results of research study, for it may prove to
be a key factor in understanding the problem under consideration.
(iii) It is advisable, before embarking upon final interpretation, to consult
someone having insight into the study and who is frank and honest and
will not hesitate to point out omissions and errors in logical
argumentation. Such a consultation will result in correct interpretation
and, thus, will enhance the utility of research results.

Technique of Interpretation
(iv) Researcher must accomplish the task of interpretation only after
considering all relevant factors affecting the problem to avoid false
generalization. He must be in no hurry while interpreting results, for quite
often the conclusions, which appear to be all right at the beginning, may not
at all be accurate.

Precautions in Interpretation
• One should always remember that even if the data are properly
collected and analyzed, wrong interpretation would lead to inaccurate
conclusions
❑ It is, therefore, absolutely essential that the task of interpretation be
accomplished with patience in an impartial manner and also in correct
perspective.
• Researcher must pay attention to the following points for correct
interpretation:
(i) At the outset, researcher must invariably satisfy himself that (a) the data
are appropriate, trustworthy and adequate for drawing inferences; (b) the
data reflect good homogeneity; and that (c) proper analysis has been done
through statistical methods.
(ii) The researcher must remain cautious about the errors that can possibly
arise in the process of interpreting results. Errors can arise due to false
generalization and/or due to wrong interpretation of statistical measures,
such as the application of findings beyond the range of observations,
identification of correlation with causation and the like.

Precautions in Interpretation
(iii) He must always keep in view that the task of interpretation is very much
intertwined with analysis and cannot be distinctly separated.
(iv) He must never lose sight of the fact that his task is not only to make
sensitive observations of relevant occurrences, but also to identify and
disengage the factors that are initially hidden to the eye. This will enable
him to do his job of interpretation on proper lines.
(v) The researcher must remember that “ideally in the course of a research
study, there should be constant interaction between initial hypothesis,
empirical observation and theoretical conceptions. It is exactly in this area
of interaction between theoretical orientation and empirical observation
that opportunities for originality and creativity lie.” He must pay special
attention to this aspect while engaged in the task of interpretation.

Significance of Report Writing
• Research report is considered a major component of the research study
for the research task remains incomplete till the report has been
presented and/or written.
❑ As a matter of fact even the most brilliant hypothesis, highly well designed
and conducted research study, and the most striking generalizations and
findings are of little value unless they are effectively communicated to
others.
❑ The purpose of research is not well served unless the findings are made
known to others.
❑ Research results must invariably enter the general store of knowledge.
• Writing of report is the last step in a research study and requires a set
of skills somewhat different from those called for in respect of the
earlier stages of research.

Different Steps in Writing Report
• The usual steps involved in writing report are: (a) logical analysis of the
subject-matter; (b) preparation of the final outline; (c) preparation of
the rough draft; (d) rewriting and polishing; (c) preparation of the final
bibliography; and (f) writing the final draft.
❑ Logical analysis of the subject matter: It is the first step which is primarily
concerned with the development of a subject. There are two ways in which
to develop a subject (a) logically and (b) chronologically. The logical
development is made on the basis of mental connections and associations
between the one thing and another by means of analysis. The directions for
doing or making something usually follow the chronological order.
❑ Preparation of the final outline: Outlines are the framework upon which
long written works are constructed. They are an aid to the logical
organization of the material and a reminder of the points to be stressed in
the report.

Different Steps in Writing Report
❑ Preparation of the rough draft: The researcher now sits to write down what
he has done in the context of his research study. He will write down the
procedure adopted by him in collecting the material for his study along
with various limitations faced by him, the technique of analysis adopted by
him, the broad findings and generalizations and the various suggestions he
wants to offer regarding the problem concerned.
❑ Rewriting and polishing of the rough draft: While rewriting and polishing,
one should check the report for weaknesses in logical development or
presentation. He should check the mechanics of writing—grammar,
spelling and usage.
❑ Preparation of the final bibliography: The bibliography, which is generally
appended to the research report, is a list of books in some way pertinent to
the research which has been done. It should contain all those works which
the researcher has consulted.
❑ Writing the final draft: This constitutes the last step. The final draft should
be written in a concise and objective style and in simple language, avoiding
vague expressions such as “it seems”, “there may be”, and the like ones.

Layout of the Research Report
• The layout of the report means as to what the research report should
contain.
• A comprehensive layout of the research report should comprise (A)
preliminary pages; (B) the main text; and (C) the end matter. Let us
deal with them separately.
(A) Preliminary Pages: In its preliminary pages the report should carry a
title and date, followed by acknowledgements in the form of ‘Preface’
or ‘Foreword’. Then there should be a table of contents followed by
list of tables and illustrations so that the decision-maker or anybody
interested in reading the report can easily locate the required
information in the report.
(B) The main text: The main text provides the complete outline of the
research report along with all details.
• Each main section of the report should begin on a new page.

• The main text of the report should have the following sections: (i)
Introduction; (ii) Statement of findings and recommendations; (iii) The
results; (iv) The implications drawn from the results; and (v) The
summary.
(i) Introduction: The purpose of introduction is to introduce the research
project to the readers. It should contain a clear statement of the objectives
of research i.e., enough background should be given to make clear to the
reader why the problem was considered worth investigating. A brief
summary of other relevant research may also be stated so that the present
study can be seen in that context. The hypotheses of study, if any, and the
definitions of the major concepts employed in the study should be
explicitly stated in the introduction of the report.
(ii) Statement of findings and recommendations: After introduction, the
research report must contain a statement of findings and
recommendations in non-technical language so that it can be easily
understood by all concerned. If the findings happen to be extensive, at this
point they should be put in the summarized form

(iii) Results: A detailed presentation of the findings of the study, with
supporting data in the form of tables and charts together with a validation
of results, is the next step in writing the main text of the report. The result
section of the report should contain statistical summaries and reductions of
the data rather than the raw data. All the results should be presented in
logical sequence and splitted into readily identifiable sections. All relevant
results must find a place in the report.
(iv) Implications of the results: Toward the end of the main text, the
researcher should again put down the results of his research clearly and
precisely. He should, state the implications that flow from the results of the
study, for the general reader is interested in the implications for
understanding the human behaviour.
(v) Summary: It has become customary to conclude the research report with a
very brief summary, resting in brief the research problem, the
methodology, the major findings and the major conclusions drawn from the
research results.

(C) End Matter: At the end of the report, appendices should be enlisted in
respect of all technical data such as questionnaires, sample information,
mathematical derivations and the like ones. Bibliography of sources
consulted should also be given. Index (an alphabetical listing of names,
places and topics along with the numbers of the pages in a book or
report on which they are mentioned or discussed) should invariably be
given at the end of the report

unit4 rm research methodology .pdf

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unit4 rm research methodology .pdf