This document discusses principles and methods of research data interpretation. It describes how data is organized, analyzed, and interpreted to draw meaningful inferences. Specifically, it outlines various methods of data interpretation including direct observation, tables, graphs, numerical/statistical methods, and mathematical modeling. It emphasizes that interpretation establishes relationships within data and relates results to existing knowledge to further research. Proper interpretation requires avoiding biases and false generalizations.

1. RESEARCH DATA
INTERPRETATION
BY
PROF A. BALASUBRAMANIAN
CENTRE FOR ADVANCED STUDIES IN EARTH
SCEINCE
UNIVERSITY OF MYSORE, INDIA

2. 2
‘All meanings, we know, depend on
the key of interpretation.’
-George Eliot

3. Principles of Analysis and Interpretation
 Data, as used in behavioral research, means
research results from which inferences are drawn:
usually numerical results, like scores of tests and
statistics such as means, percentages, and
correlation coefficients.
 Analysis means the categorizing, ordering,
manipulating, and summarizing of data to obtain
answers to research questions.
 Interpretation takes the results of analysis, makes
inferences pertinent to the research relations
studied, and draws conclusions about these
relations.

4. Methods of data interpretation
 Direct visual observations of raw data
 After organizing the data in tables
 After making Graphical representations
 After calculations using numerical /
statistical methods
 After mathematical modelling

5. DATA
 Data is known to be crude information
and not knowledge by itself.
 The sequence from data to knowledge
is:
 from Data to Information,
 from Information to Facts, and finally,
 from Facts to Knowledge.

6. DATA
 Data becomes information, when it becomes
relevant to your decision problem.
 Information becomes fact, when the data can
support it.
 Facts are what the data reveals.
 However the decisive instrumental (i.e., applied)
knowledge is expressed together with
some statistical degree of confidence.

7. Fact becomes knowledge, when it is
used in the successful completion
of a decision process.
massive amount of facts are
integrated as knowledge.

9.  Usefulness and utility of research findings lie in proper
interpretation.
 Interpretation is a basic component of research.
 After collecting and analyzing the data, the researcher
has to accomplish the task of drawing inferences
followed by report writing.
 This has to be done very carefully, otherwise mis
conclusions may be drawn and the whole purpose of
doing research may get vitiated.
 It is only through interpretation that the researcher
can expose relations and processes that underlie his
findings.

10. 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.,
 the effort to establish continuity in research through
linking the results of a given study with those of
another, and the establishment of some explanation
concepts.

11.  “In one sense, interpretation is concerned with
relationships within the collected data, partially
overlapping analysis.
 Interpretation also extends beyond the data of
the study to inch the results of other research,
theory and hypotheses.”

12. Interpenetration is the device
 Thus, interpenetration 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.

13. Why Interpretation?
 Interpretation is essential for the simple
reason that the usefulness and utility of
research findings lie in proper
interpretation.
 It is being considered a basic component of
research process because of the following
reasons:

14. Through interpretation
 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, and thereby can predict about the
concrete world of events. Fresh inquiries can test
these predictions later on.
 This way the continuity in research can be
maintained.

15. Interpretation leads to establishment
 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.
 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.

16. Interpretation of the findings
 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.
 Since an exploratory study does not have a
hypothesis to start with, the findings of such a
study have to be interpreted on a post factum
basis in which case the interpretation is
technically described as ‘post factum’
interpretation.

17. 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 researcher may, at times, seek the guidance
from experts for accomplishing the task of
interpretation.

18. The technique of interpretation
 The technique of interpretation often involves the
following steps:
1. Researcher must give reasonable explanations
of the relations which he/she has found and
he/she 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.
In fact, this is the technique of how generalization
should be done and concepts be formulated.

19. 2. 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.

20. 3. 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.
4. Researcher must accomplish the task of interpretation only
after considering all relevant factors affecting the problem
to avoid false generalization. He /she 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.

21. 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.

22. For correct interpretation
 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.

23. Errors can arise due to false
generalization
 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.
 Another major pitfall is the tendency to affirm that
definite relationships exist on the basis of
confirmation of particular hypotheses.

24. Researcher must remain vigilant
 In fact, the positive test results accepting the
hypothesis must be interpreted as “being in
accord” with the hypothesis, rather than as
“confirming the validity of the hypothesis”.
 The researcher must remain vigilant about all
such things so that false generalization may not
take place.
 He/she should be well equipped with and must
know the correct use of statistical measures for
drawing inferences concerning his study.

25.  Researcher must always keep in view that the
task of interpretation is very much intertwined
with analysis and cannot be distinctly separated.
 As such he must take the task of interpretation as
a special aspect of analysis and accordingly must
take all those precautions that one usually
observes while going through the process of
analysis viz., precautions concerning the
reliability of data, computational checks,
validation and comparison of results.

26.  Researcher 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.
 Broad generalization should be avoided as most
research is not amen- able to it because the coverage
may be restricted to a particular time, a particular area
and particular conditions.
 Such restrictions, if any, must invariably be specified
and the results must be framed within their limits.

27.  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.”
 Researcher must pay special attention to this aspect
while engaged in the task of interpretation.

28. Data Interpretation Methods
 Data interpretation may be the most important key in
proving or disproving your hypothesis.
 It is important to select the proper statistical tool to
make useful interpretation of your data.
 If you pick an improper data analysis method, your
results may be suspect and lack credibility.

29. Visually scanning the data
 Before doing any statistical analyses of
the data you have collected, look closely
at the data to determine the best
method of organizing it .
 By visually scanning the data and
reorganizing it, you may be able to spot
trends or other anomalies that may help
you in your analysis of the data.

30. STATISTICS
 Statistics is a science assisting you to
make decisions under
uncertainties (based on some numerical
and measurable scales).
 Decision making process must be based on
data neither on personal opinion nor on any
belief.

31. What is Statistical Data Analysis?
Data are not information! To
determine what statistical data
analysis is, one must first define
statistics.
Statistics is a set of methods that
are used to collect, analyze, present,
and interpret data.

32. Statistical methods
 Statistical methods are used in a wide
variety of occupations and help people
identify, study, and solve many complex
problems.
 In the business and economic world,
these methods enable decision makers
and managers to make informed and
better decisions about uncertain
situations.

33. Review-Statistics:
 “We can think of statistics as a group of
computational procedures that allow us to find
meaning in numerical data”.
 Descriptive statistics provide a description of
what the data look like.
 They provide a means to describe the points of
central tendency (mean, mode, median, etc.) and
dispersion (standard deviation, variance, inter-
quartile range, etc.).

34. Inferential statistics allow the
researcher to make
 inferences about populations from smaller samples of the
population.
 Statistics of the sample are used to estimate parameters of
the population.
 A parameter is a constant value representative of the
population (such as population mean and standard
deviation) while a statistic is any calculation performed on
the sample being tested.
 Inferential statistics also allow the researcher to test their
research hypotheses.
 Some measures used in inferential statistics include the
standard error of the mean, estimators, and the p-value.

35.  The way that the data is interpreted can have
varying effects on your conclusions.
 Absolute honesty in recording and interpreting
data is required to maintain the credibility of
research.
 All of the conditions of a situation should be
considered and that we make inferences in strict
accordance with the data obtained.

36.  Using statistics to determine relationships is
paramount to the success of good research.
 Using tools such as ANOVA, correlations, Fisher
Exact Tests, regression, etc. can predict whether or
not your research hypothesis is satisfied.
 But, REMEMBER to select your p-value before you
begin your research project.
 Doing this will add credibility to your research.

37.  One other important point to remember when
doing data analysis is to use parametric statistics
instead of nonparametric statistics whenever
possible.
 Remember that parametric statistics relies on the
assumptions of normality, which gives greater
power than nonparametric testing.
 Both parametric and nonparametric statistical
tests are used for interpretation.

38. Interpreting Qualitative Data:
 Qualitative data interpretation tends to be more
subjective in nature and many times can be influenced
by the researcher’s biases.
 Effort must be put into the data collection process to
eliminate bias including collecting more than one kind
of data, get many different kinds of perspectives on the
events being studied, purposely look for contradicting
information, and acknowledging your biases that
relate to your research report.

39. Qualitative data analysis
 Qualitative data analysis is time consuming and
complex because a lot of data can be created that is
both useful and not useful.
 There is no “correct way” to analyze qualitative data.
 Efforts can be made to make your data presentation
and interpretation more credible and less biased by
using the above methods.

40. Statistics consists of
Statistics consists of the principles and methods for
 Designing studies
 Collecting data
 Presenting and analysing data
 Interpreting the results
 Statistics has been described as
 Turning data into information
 Data-based decision making.

41. Data interpretation:
 Uncovering and explaining trends in the data.
 The analyzed data can then be interpreted and
explained.
 In general, when scientists interpret data, they
attempt to explain the patterns and trends
uncovered through analysis, bringing all of their
background knowledge, experience, and skills to
bear on the question and relating their data to
existing scientific ideas.

42.  Given the personal nature of the
knowledge they draw upon, this step can
be subjective, but that subjectivity is
scrutinized through the peer
review process.

43. Data collection is the systematic
recording
 Data collection is the systematic recording of
information;
 data analysis involves working to uncover
patterns and trends in datasets;
 data interpretation involves explaining those
patterns and trends.

44. Scientists interpret data based on
their background
 Scientists interpret data based on their
background knowledge and experience; thus,
different scientists can interpret the same data in
different ways.
 By publishing their data and the techniques they
used to analyze and interpret those data, scientists
give the community the opportunity to both
review the data and use them in future research.

45. Data Analysis
 Data Analysis is the process of systematically
applying statistical and/or logical techniques to
describe and illustrate, condense and recap, and
evaluate data.
 Various analytic procedures “provide a way of drawing
inductive inferences from data and distinguishing the
signal (the phenomenon of interest) from the noise
(statistical fluctuations) present in the data”..

46.  While data analysis in qualitative research
can include statistical procedures, many
times analysis becomes an ongoing iterative
process where data is continuously collected
and analyzed almost simultaneously.
 Indeed, researchers generally analyze for
patterns in observations through the entire
data collection phase.

47.  The form of the analysis is determined by
the specific qualitative approach taken (field
study, ethnography content analysis, oral
history, biography, etc) and
 the form of the data (field notes,
documents, audiotape, videotape).

48.  An essential component of ensuring data integrity
is the accurate and appropriate analysis of research
findings.
 Improper statistical analyses distort scientific
findings, mislead casual readers, and may
negatively influence the public perception of
research.
 Integrity issues are just as relevant to analysis of
non-statistical data as well.

49. Considerations/issues in data
analysis
 There are a number of issues that researchers should be cognizant of with respect to data
analysis. These include:
 Having the necessary skills to analyze
 Concurrently selecting data collection methods and appropriate analysis
 Drawing unbiased inference
 Inappropriate subgroup analysis
 Following acceptable norms for disciplines
 Determining statistical significance
 Lack of clearly defined and objective outcome measurements
 Providing honest and accurate analysis
 Manner of presenting data
 Environmental/contextual issues
 Data recording method
 Partitioning ‘text’ when analyzing qualitative data
 Training of staff conducting analyses
 Reliability and Validity
 Extent of analysis

50. Summarizing data
 Tables
 Simplest way to summarize data
 Data are presented as absolute numbers or percentages
 Charts and graphs
 Visual representation of data
 Data are presented as absolute numbers or percentages

51. Basic guidance when summarizing
data
 Ensure graphic has a title
 Label the components of your graphic
 Indicate source of data with date
 Provide number of observations (n=xx) as a reference
point
 Add footnote if more information is needed

52. Tables: Frequency distribution
Year Number of births
1900 61
1901 58
1902 75
Set of categories with numerical counts

53. Tables: Relative frequency
number of values within an interval
total number of values in the table
Year # births (n) Relative frequency (%)
1900–1909 35 27
1910–1919 46 34
1920–1929 51 39
Total 132 100.0
x 100

54. Tables
Year Number of births
(n)
Relative frequency (%)
1900–1909 35 27
1910–1919 46 34
1920–1929 51 39
Total 132 100.0
Percentage of births by decade between 1900 and 1929

55. Graphical representation
 The graphical representation of data is categorized as
basic five types.
 Graphical representation 1: Bar graph.
 Graphical representation 2: Pie graph.
 Graphical representation 3: Line graph.
 Graphical representation 4: Scatter plot.
 Graphical representation 5: Histogram.

56. Charts and graphs
 Charts and graphs are used to portray:
 Trends, relationships, and comparisons
 The most informative are simple and self-explanatory

57. Use the right type of graphic
 Charts and graphs
 Bar chart: comparisons, categories of data
 Line graph: display trends over time
 Pie chart: show percentages or proportional share

58. Charts Are Analog!
 Basic Rule: Charts show overviews - for details use tables!
 Quantitative data can be represented as charts by using the
following analog properties
 Position of graphical elements along a common scale; position of
graphical elements along identical scales at different locations (e.g.
graphs arranged in a row)
 Distances (lengths)
 Slopes and angles
 Areas (e.g. of circles, squares or other shapes)
 Lightness (grayscale) or texture gradient
 People differ in their ability to estimate physical properties: They are
best at estimating positions anddistances, but not so good at
estimating slopes, angles, and areas (in this order).
 Charts are images: Good charts enable users to easily and quickly find
relevant/critical data or recognize important relations between data.

59. Chart Types and Their Uses

60. Pie chart
Contribution to the total = 100%
59%23%
10%
8%
Percentage of All Patients Enrolled by Quarter
1st Qtr
2nd Qtr
3rd Qtr
4th Qtr
N=150

61. Pie Chart

62. Use it to…
 convey approximate proportional relationships
(relative amounts) at a point in time
 compare part of a whole at a given point in time
 emphasize a small proportion of parts

63. Do NOT use it…
 For exact comparisons of values, because estimating
angles is difficult for people
 For rank data: Use column/bar charts in this case; use
multiple column/bar charts for grouped data
 If proportions vary greatly; do not use multiple pies to
compare corresponding parts

64. Caution!
 Pie charts cannot represent values beyond 100%
 Each pie chart is valid for one point in time only
 Pie charts are only suited to presenting quite a few
percentage values
 Angles are harder to estimate for people than
distances; perspective pie charts are even harder to
interpret

65. AREA CHARTS

66. Use it to…
 Display over time (or any other dimension)
 How a set of data adds up to a whole (cumulated
totals)
 Which part of the whole each element represents

67. Column/Bar Chart

68. Use it to…
Present few data; useful for comparisons of data

69. DO NOT USE IT FOR…
 Larger data sets: Use line charts

70. Selecting Bars or Columns
 When in doubt, use columns
 Use a horizontal bar chart if the labels are too long to
fit under the columns

71. Segmented Column/Bar Chart

72. Use it to…
 Present a part-whole relation over time (with accurate
impression)
 Show proportional relationships over time
NOTE: Segmented column/bar charts are more
accurate than pie chart, because distances can be more
accurately estimated than areas.

73. Frequency Polygon, Histograms

74. Variants
 Polygon: Connects data points through straight lines or higher order
graphs
 Histogram: Columns/bars touch; useful for larger sets of data points,
typically used for frequency distributions
 Staircase Chart: Displays only the silhouette of the histogram; useful
for even larger sets of data points, typically used for frequency
distributions
 Step chart: Use it to illustrate trends among more than two members
of nominal or ordinal scales; do not use it for two or more variables or
levels of a single variable (hard to read)
 Pyramid histogram: Two mirror histograms; use it for comparisons

75. Line Chart/Graph

76. Use it…
 To display long data rows
 To extrapolate beyond known data values (forecast)
 To compare different graphs
 To find and compare trends (changes over time)
 To recognize correlations and covariations between
variables

77. Scatterplot

78. Use it to…
 Show measurements over time (one-dimensional
scatterplot)
 Convey an overall impression of the relation between
two variables (Two-dimensional scatterplot)

79. Don’t Use it for…
 Determining and comparing trends, recognition and
comparison of change rates
 More than one independent variable: Avoid
illustrating more than one independent variable in a
scatter plot

80. Bar chart
Comparing categories
0
1
2
3
4
5
6
Quarter 1 Quarter 2 Quarter 3 Quarter 4
Site 1
Site 2
Site 3

81. Percentage of new enrollees tested for HIV at each site, by
quarter
0
1
2
3
4
5
6
Quarter 1 Quarter 2 Quarter 3 Quarter 4
%ofnewenrolleestestedfor
HIV
Months
Site 1
Site 2
Site 3
Q1 Jan–Mar Q2 Apr–June Q3 July–Sept Q4 Oct–Dec

82. Has the program met its goal?
0%
10%
20%
30%
40%
50%
60%
Quarter 1 Quarter 2 Quarter 3 Quarter 4
%ofnewenrolleestested
forHIV
Site 1
Site 2
Site 3
Percentage of new enrollees tested for HIV at each site, by
quarter
Target

83. Stacked bar chart
Represent components of whole & compare wholes
3
4
6
10
0 5 10 15
Males
Females
0-14 years
15+ years
Number of months patients have been enrolled in HIV care
Number of Months Female and Male Patients Have Been
Enrolled in HIV Care, by Age Group

84. Line graph
0
1
2
3
4
5
6
Year 1 Year 2 Year 3 Year 4
Numberofclinicians
Clinic 1
Clinic 2
Clinic 3
Number of Clinicians Working in Each Clinic During Years 1–4*
Displays trends over time

85. Line graph
0
1
2
3
4
5
6
Year 1 Year 2 Year3 Year 4
Numberofclinicians
Clinic1
Clinic2
Clinic3
Number of Clinicians Working in Each Clinic During Years 1-4*
Y1 1995 Y2 1996 Y3 1997 Y4 1998

86. Interpreting data
 Adding meaning to information by making
connections and comparisons and exploring causes
and consequences
Relevance
of finding
Reasons for
finding
Consider
other data
Conduct
further
research

87. Interpretation – relevance of
finding
 Adding meaning to information by making
connections and comparisons and exploring causes
and consequences
Relevance
of finding
Reasons for
finding
Consider
other data
Conduct
further
research

88. Interpretation – relevance of
finding
 Does the indicator meet the target?
 How far from the target is it?
 How does it compare (to other time periods, other
facilities)?
 Are there any extreme highs and lows in the data?

89. Relevance
of finding
Reasons for
finding
Consider
other data
Conduct
further
research
Interpretation – possible causes?
• Supplement with expert opinion
• Others with knowledge of the program or target
population

90. Relevance
of finding
Reasons for
finding
Consider
other data
Conduct
further
research
Interpretation – consider other
data
Use routine service data to clarify questions
• Calculate nurse-to-client ratio, review
commodities data against client load, etc.
Use other data sources

91. Interpretation – other data
sources
 Situation analyses
 Demographic and health surveys
 Performance improvement data
Relevance
of finding
Reasons for
finding
Consider
other data
Conduct
further
research

92. Interpretation – conduct further
research
 Data gap conduct further research
 Methodology depends on questions being asked
and resources available
Relevance
of finding
Reasons for
finding
Consider
other data
Conduct
further
research

93. Data Interpretation
 Answer these four questions
 What is important in the data?
 Why is it important?
 What can be learned from it?
 So what?
 Remember
 Interpretation depends on the perspective of the
researcher.
 Why?

94. Interpretation
 One technique for data interpretation (Wolcott)
 Extend the analysis by raising questions
 Connect findings to personal experiences
 Seek the advice of “critical” friends.
 Contextualize findings in the research
 Converging evidence?
 Turn to theory

95. Frequencies and Continuous Measures
 Quantitative data come in two general forms:
frequencies and continuous measures.
 f={(x,y); where x is a member of the set X, and y is
either 1 or 0 depending on x’s possessing or not
possessing M}
 f={(x,y); x is an object, and y= any numeral}

96. Rules of Categorization
 The first setup in any analysis is categorization.
 The five rules of categorization are as follows:
 1.Categories are set up according to the research
problem and purpose.
 2.The categories are exhaustive.
 3.The categories are mutually exclusive and
independent.
 4.Each category (variable) is derived from one
classification principle.
 5.Any categorization scheme must be on one level
of discourse

97. Kinds of Statistical Analysis
 Frequency Distributions
 Graphs and Graphing
 Measures of Central Tendency and Variability
 Measures of Relations
 Analysis of Differences
 Analysis of Variance and Related Methods
 Profile Analysis
 Multivariate Analysis

98. Graphs and Graphing
 A graph is a two-dimension representation of a
relation or relations.
 Interaction means that the relation of an independent
variable to a dependent variable differs in different
groups or at different levels of another independent
variable..

99. Frequency Distributions
 Although frequency distributions are used primarily
for descriptive purposes, they can also be used for
other research purposes.
 Observed distributions can also be compared to
theoretical distributions (normal distributions).

100. Measures of Central Tendency and
Variability
 Mean, median, mode
 Standard deviation, range

101. Measures of Relations
 Ideally, any analysis of research data should include
both kinds of indices: measures of the significance of a
relation and measures of the magnitude of the relation.

102. Analysis of Differences
 1.it is by no means confined to the differences between
measures of central tendency.
 2.All analyses of differences are intended for the
purpose of studying relation. Conversely, the greater
the differences the higher the correlation, all other
things being equal.

103. Analysis of Variance and Related Methods
 A method of identifying, breaking down, and testing
for statistical significance variances that come from
different sources of variation.
 That is, a dependent variable has a total amount of
variance, some of which is due to the experimental
treatment, some to error, and some to other causes.

104. Profile Analysis
 Profile analysis is basically the assessment of the
similarities of the profiles of individuals or groups.
 A profile is a set of different measures of an individual
or group, each of which is expressed in the same unit
of measure.

105. Multivariate Analysis
 Multiple regression
 Canonical correlation
 Discriminant analysis
 Factor analysis
 Cluster analysis
 Path analysis
 Analysis of covariance structures
 Log-linear models

106. Indices Index can be defined in two related ways:
 1.An index is an observable phenomenon that is
substituted for a less-observable phenomenon. For
example, test scores indicate achievement levels,
verbal aptitudes, degrees of anxiety, and so on.
 2.An index is a number that is a composite of two or
more numbers. For example, all sums and averages,
coefficients of correlation.

107. Indices Indices are most important in research because they
simplify comparisons.
 The percentage is a good example.
 Percentages transform raw numbers into comparable
form.
 Indices generally take the form of quotients: ratios and
proportions.

108. Social Indicators
 Indicators, although closely related to indices—
indeed, they are frequently indices as defined
above—form a special class of variables.
 Variables like income, life expectancy, fertility,
quality of life, educational level (of people), and
environment can be called social indicators. Social
indicators are both variables and statistics.

109.  Unfortunately, it is difficult to define “social indicators.”
 In this book we are interested in social indicators as a class
of sociological and psychological variables that in the
future may be useful in developing and testing scientific
theories of the relations among social and psychological
phenomena.

110. The Interpretation of Research Data
 Adequacy of Research Design, Methodology,
Measurement, and Analysis
 Negative and Inconclusive Results
 Unhypothesized Relations and Unanticipated
Findings
 Proof, Probability, and Interpretation

111. Adequacy of Research Design, Methodology,
Measurement, and Analysis
 Most important, the design, methods of observation,
measurement, and statistical analysis must all be
appropriate to the research problem.

112. Negative and Inconclusive Results
 When results are positive, when the data support
the hypotheses, one interprets the data along the
lines of the theory and the reasoning behind the
hypotheses.
 If we can repeat the feat, the n the evidence of
adequacy is even more convincing.
 If we can be fairly sure that the methodology, the
measurement, and the analysis are adequate, then
negative results can be definite contributions to
scientific advancement.

113. Unhypothesized Relations and
Unanticipated Findings
 The unpredicted relation may be an important key
to a deeper understanding of the theory.
 For example, positive reinforcement strengthens
response tendencies.
 Unpredicted and unexpected findings must be
treated with more suspicion than predicted and
expected findings.
 Before being accepted, they should be
substantiated in independent research in which
they are specially predicted and tested.

114. Proof, Probability, and Interpretation
 Let us flatly assert that nothing can be “proved”
scientifically.
 All one can do is to bring evidence to bear that
such-and such a proposition is true.
 Proof is a deductive matter.
 Experimental methods of inquiry are not methods
of proof, they are controlled methods of bringing
evidence to bear on the probable truth or falsity of
relational propositions.
 In short, no single scientific investigation ever
proves anything.
 Thus the interpretation of the analysis of research
data should never use the word proof.

115. Effective Data Analysis
 Effective data analysis involves
 keeping your eye on the main game
 managing your data
 engaging in the actual process of quantitative and / or
qualitative analysis
 presenting your data
 drawing meaningful and logical conclusions

116. 116
The Big Picture
 Analysis should be approached as a critical, reflective,
and iterative process that cycles between data and an
overarching research framework that keeps the big
picture in mind

117. 117
Managing Data
 Regardless of data type, managing your data
involves
 familiarizing yourself with appropriate software
 developing a data management system
 systematically organizing and screening your data
 entering the data into a program
 and finally ‘cleaning’ your data

118. Statistics
 Being able to do statistics no longer means being able
to work with formula
 It’s much more important for researchers to be familiar
with the language and logic of statistics, and be
competent in the use of statistical software

119. Data Types
 Different data types demand discrete treatment, so it’s
important to be able to distinguish variables by
 cause and effect (dependent or independent)
 measurement scales (nominal, ordinal, interval, and
ratio)

120. Descriptive Statistics
 Descriptive statistics are used to summarize the basic
feature of a data set through
 measures of central tendency (mean, mode, and
median)
 dispersion (range, quartiles, variance, and standard
deviation)
 distribution (skewness and kurtosis)

121. Inferential Statistics
 Inferential statistics allow researchers to assess
their ability to draw conclusions that extent
beyond the immediate data, e.g.
 if a sample represents the population
 if there are differences between two or more groups
 if there are changes over time
 if there is a relationship between two or more variables

122. Selecting Statistical Tests
 Selecting the right statistical test relies on
 knowing the nature of your variables
 their scale of measurement
 their distribution shape
 types of question you want to ask

123. Presenting Quantitative Data
 Presenting quantitative data often involves the
production of graphs and tables
 These need to be
1. selectively generated so that they make relevant
arguments
2. informative yet simple, so that they aid reader’s
understanding

124. Qualitative Data Analysis (QDA)
 In qualitative data analysis there is a common reliance
on words and images to draw out rich meaning
 But there is an amazing array of perspectives and
techniques for conducting an investigation

125. The QDA Process
 Qualitative data analysis creates new
understandings by exploring and interpreting
complex data from sources without the aid of
quantification
 Data source include
 interviews
 group discussions
 observation
 journals
 archival documents, etc

126. 126
Uncovering and Discovering Themes
 The methods and logic of qualitative data analysis
involve uncovering and discovering themes that run
through raw data, and interpreting the implication of
those themes for research questions

127. More on the QDA Process
 Qualitative data analysis generally involves
 moving through cycles of inductive and deductive
reasoning
 thematic exploration (based on words, concepts, literary
devises, and nonverbal cues)
 exploration of the interconnections among themes.

128. 128
Specialist QDA Strategies
 There are a number of paradigm and discipline
based strategies for qualitative data analysis
including
 content analysis
 discourse analysis
 narrative analysis
 conversation analysis
 semiotics
 hermeneutics
 grounded theory

129. Presenting Qualitative Data
 Effective presentation of qualitative data can be a real
challenge
 You’ll need to have a clear storyline, and selectively use
your words and/or images to give weight to your story

130. Drawing Conclusions
 Your findings and conclusions need to flow from
analysis and show clear relevance to your overall
project
 Findings should be considered in light of
 significance
 current research literature
 limitations of the study
 your questions, aims, objectives, and theory

131. Add Interpretation of Analysis
of Data
 Include your interpretation:
 What does the data MEAN with regards to that theme?
 The “So what?” of the theme and/or data.

132. WHY DO WE ANALYZE DATA
 The purpose of analysing data is to obtain usable and
useful information.
 The analysis, irrespective of whether the data is
qualitative or quantitative, may:
 •describe and summarise the data
 •identify relationships between variables
 •compare variables
 •identify the difference between variables
 •forecast outcomes

133. SCALES OF MEASUREMENT
 Many people are confused about what type of analysis
to use on a set of data and the relevant forms of
pictorial presentation or data display.
 The decision is based on the scale of measurement of
the data.
These scales are
 nominal,
 Ordinal and
 numerical.

134.  Nominal scale
 A nominal scale is where:
 The data can be classified into a non-numerical or
named categories and
 The order in which these categories can be written or
asked is arbitrary.

135.  Ordinal scale
 An ordinal scale is where:
 the data can be classified into non-numerical or
named categories -an inherent order exists among the
response categories.
 Ordinal scales are seen in questions that call for
ratings of quality (for example, very good, good, fair,
poor, very poor) and agreement (for example, strongly
agree, agree, disagree, strongly disagree).

136.  Numerical scale
 A numerical scale is:
 where numbers represent the possible response
categories
 there is a natural ranking of the categorieszero on the
scale has meaningthere is a quantifiable difference
within categories and between consecutive categories.

137. QUALITATIVE ANALYSIS
 "Data analysis is the process of bringing order,
structure and meaning to the mass of collected data. It
is a messy, ambiguous, time-consuming, creative, and
fascinating process.
 It does not proceed in a linear fashion; it is not neat.
 Qualitative data analysis is a search for general
statements about relationships among categories of
data."

138. Simple qualitative analysis
 Unstructured -are not directed by a script. Rich but
not replicable.
 •Structured -are tightly scripted, often like a
questionnaire.
 Replicable but may lack richness.
 •Semi-structured -guided by a script but interesting
issues can be explored in more depth.
 Can provide a good balance between richness and
replicability.

139. Simple qualitative analysis
 Recurring patterns or themes
 –Emergent from data, dependent on observation
framework if used
 •Categorizing data
 –Categorization scheme may be emergent or pre-
specified
 •Looking for critical incidents
 –Helps to focus in on key events

140. TOOLS TO SUPPORT DATA ANALYSIS
 •Spreadsheet –simple to use, basic graphs
 •Statistical packages, e.g. SPSS
 •Qualitative data analysis tools
 –Categorization and theme-based analysis, e.g. N6
 –Quantitative analysis of text-based data

141. Interpreting research results
 Researchers should describe their results clearly, and
in a way that other researchers can compare them with
their own results. They should also analyse the results,
using appropriate statistical methods to try to
determine the probability that they may have been
chance findings, and may not be replicable in larger
studies. But this is not enough.

142.  Results need to be interpreted in an objective and
critical way, before assessing their implications and
before drawing conclusions.
 Interpretation of research results is not just a concern
for researchers.
 Policymakers should also be aware of the possible
pitfalls in interpreting research results and should be
cautious in drawing conclusions for policy decisions.

143. Interpreting descriptive statistics
 The mean or average is only meaningful if the data fall into
a normal distribution curve, that is, they are evenly
distributed around the mean.
 The mean or average, by itself, has a limited value.
 There is an anecdote about a man having one foot on ice
and the other in boiling water; statistically speaking, on
average, he is pretty comfortable.
 The range of the data, and their distribution (expressed in
the standard deviation) must be known.
 It is sometimes more important to know the number or
percentage of subjects or values that are abnormal than to
know the mean

144. Representation v/s interpretation
 Tables/charts/graphs
 Diagrams
 Maps/ atlases/ sketches
 Profiles/ cross-sections
 Plots
 3-D views
 Animations

145.  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.

146.  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.
 All this explains the significance of writing research
report.
 There are people who do not consider writing of report
as an integral part of the research process.
 But the general opinion is in favour of treating the
presentation of research results or the writing of
report as part and parcel of the research project.

147.  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.
 This task should be accomplished by the researcher
with utmost care; he may seek the assistance and
guidance of experts for the purpose.

148. Different Steps in Writing Report
 Research reports are the product of slow, painstaking,
accurate inductive work.
 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;
(e) preparation of the final bibliography; and
(f) writing the final draft.

149.  Though all these steps are self explanatory, yet a brief
mention of each one of these will be appropriate for
better under-standing.
 Logical analysis of the subject matter.
 It is the first step which is primarily concerned with
the development of a subject.

150.  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 one
thing and another by means of analysis.
 Logical treatment often consists in developing the
material from the simple possible to the most complex
structures.

151.  Chronological development is based on a connection
or sequence in time or occurrence.
 The directions for doing or making something usually
follow the chronological order.
 Preparation of the final outline. It is the next step in
writing the research report.
 “Outlines are the framework upon which long written
works are constructed.
 They are an aid to the logical organisation of the
material and a reminder of the points to be stressed in
the report.”

152. Preparation of the rough draft
 This follows the logical analysis of the subject and the
preparation of the final outline.
 Such a step is of utmost importance for the researcher now
sits to write down what he has done in the context of his
/her research study.
 He/she will write down the procedure adopted by him/her
in collecting the material for his study along with various
limitations faced by him/her, the technique of analysis
adopted by him/her, the broad findings and
generalizations and the various suggestions he /she wants
to offer regarding the problem concerned.
 Rewriting and polishing of the rough draft. This step
happens to be the most difficult part of all formal writing.

153.  Usually this step requires more time than the writing
of the rough draft. The careful revision makes the
difference between a mediocre and a good piece of
writing.
 While rewriting and polishing, one should check the
report for weaknesses in logical development or
presentation.

154.  The researcher should also “see whether or not the
material, as it is presented, has unity and cohesion;
does the report stand upright and firm and exhibit a
definite pattern, like a marble arch? Or does it
resemble an old wall of moldering cement and loose
bricks.”
 In addition the researcher should give due attention to
the fact that in his rough draft he has been consistent
or not. He/she should check the mechanics of writing
spelling and usage.

155.  Preparation of the final bibliography. Next in order
comes the task of the 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.

156.  The bibliography should be arranged alphabetically
and may be divided into two parts; the first part may
contain the names of books and pamphlets, and the
second part may contain the names of magazine and
newspaper articles.
 Generally, this pattern of bibliography is considered
convenient and satisfactory from the point of view of
reader, though it is not the only way of presenting
bibliography.

157. Thank You