Embed Size (px)
Citation preview
RESEARCH DATA INTERPRETATION
BY
PROF A. BALASUBRAMANIAN
CENTRE FOR ADVANCED STUDIES IN EARTH SCEINCE
UNIVERSITY OF MYSORE, INDIA
2
‘All meanings, we know, depend on the key of interpretation.’
-George Eliot
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.
Methods of data interpretationDirect visual observations of raw data
After organizing the data in tables
After making Graphical representations
After calculations using numerical / statistical methods
After mathematical modelling
DATAData 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.
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.
Fact becomes knowledge, when it is used in the successful completion of a decision process.
massive amount of facts are integrated as knowledge.
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 misconclusions 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.
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.
“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.”
Interpenetration is the deviceThus, 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.
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:
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.
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.
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.
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.
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.
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.
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.
Precautions in InterpretationOne 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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.).
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.
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.
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.
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.
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.
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.
Statistics consists ofStatistics 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.
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.
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.
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.
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.
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”..
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.
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).
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.
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
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
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
Tables: Frequency distribution
Year Number of births
1900 61
1901 58
1902 75
Set of categories with numerical counts
Tables: Relative frequencynumber 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
TablesYear 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
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.
Charts and graphs Charts and graphs are used to portray:
Trends, relationships, and comparisons
The most informative are simple and self-explanatory
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
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.
Chart Types and Their Uses
Pie chartContribution 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
Pie Chart
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
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
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
AREA CHARTS
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
Column/Bar Chart
Use it to…
Present few data; useful for comparisons of data
DO NOT USE IT FOR…
Larger data sets: Use line charts
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
Segmented Column/Bar Chart
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.
Frequency Polygon, Histograms
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
Line Chart/Graph
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
Scatterplot
Use it to… Show measurements over time (one-dimensional
scatterplot)
Convey an overall impression of the relation between two variables (Two-dimensional scatterplot)
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
Bar chartComparing categories
0
1
2
3
4
5
6
Quarter 1 Quarter 2 Quarter 3 Quarter 4
Site 1
Site 2
Site 3
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
% o
fn
ew
en
rolle
es
test
ed
fo
r H
IV
Months
Site 1
Site 2
Site 3
Q1 Jan–Mar Q2 Apr–June Q3 July–Sept Q4 Oct–Dec
Has the program met its goal?
0%
10%
20%
30%
40%
50%
60%
Quarter 1 Quarter 2 Quarter 3 Quarter 4
% o
f n
ew e
nro
llees
tes
ted
fo
r H
IV
Site 1
Site 2
Site 3
Percentage of new enrollees tested for HIV at each site, by quarter
Target
Stacked bar chartRepresent 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
Line graph
0
1
2
3
4
5
6
Year 1 Year 2 Year 3 Year 4
Nu
mb
er
of
clin
icia
ns
Clinic 1
Clinic 2
Clinic 3
Number of Clinicians Working in Each Clinic During Years 1–4*
Displays trends over time
Line graph
0
1
2
3
4
5
6
Year 1 Year 2 Year 3 Year 4
Nu
mb
er o
f cl
inic
ian
s
Clinic 1
Clinic 2
Clinic 3
Number of Clinicians Working in Each Clinic During Years 1-4*
Y1 1995 Y2 1996 Y3 1997 Y4 1998
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
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
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?
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
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
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
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
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?
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
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}
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
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
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..
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).
Measures of Central Tendency and Variability Mean, median, mode
Standard deviation, range
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.
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.
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.
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.
Multivariate Analysis Multiple regression
Canonical correlation
Discriminant analysis
Factor analysis
Cluster analysis
Path analysis
Analysis of covariance structures
Log-linear models
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.
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.
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.
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.
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
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.
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.
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.
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.
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
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
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
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
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)
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)
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
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
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
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
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
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
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
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
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
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
Add Interpretation of Analysisof Data Include your interpretation:
What does the data MEAN with regards to that theme?
The “So what?” of the theme and/or data.
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
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.
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.
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).
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.
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."
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.
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
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
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.
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.
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
Representation v/s interpretation Tables/charts/graphs
Diagrams
Maps/ atlases/ sketches
Profiles/ cross-sections
Plots
3-D views
Animations
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.
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.
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.
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.
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.
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.
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.”
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.
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.
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.
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.
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.
Thank You
