1. DATA ANALYSIS IN RESEARCH
Dr. N. NAGESHA
Professor
Dept. of Industrial & Production Engineering
University BDT College of Engineering
(A Constituent College of VTU, Belagavi)
DAVANAGERE –

2. Presentation Plan Introduction (Definition of Research)
Classification of Research
Research Methodology Vs Methods
Process of Research
Research Design
Data analysis
(Univariate, Bivariate, and Multivariate)
18 March 2015
Dr.NN, VTU_3DWSRML_ROB

3. This is a general definition which applies to all disciplines
What is Research?
“Research is the systematic approach to obtaining and confirming new and reliable knowledge”
Systematic and orderly (following a series of steps)
Purpose is new knowledge, which must be reliable
This is a general definition which applies to all disciplines
18 March 2015
Dr.NN, VTU_3DWSRML_ROB

4. Research is not …… Accidental Discovery Mere Data Collection
Accidental discovery may occur in structured research process
Usually takes the form of a phenomenon not previously noticed
Mere Data Collection
An intermediate step to gain reliable knowledge
Collecting reliable data is part of the research process
Searching out Published Research results in Libraries/Internet
This is an important early step of research
The research process always includes synthesis and analysis
But, just reviewing of literature is not research
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Dr.NN, VTU_3DWSRML_ROB

5. But, Research is… Searching for Explanation of Events,
Phenomena, Relationships and Causes:
What, how, and why things occur
Are there interactions
A Process:
Planned and managed – to make the information generated credible
The process is creative
3. All well designed and conducted research has potential application.
4. Researchers are also responsible to help users understand research implications
18 March 2015
Dr.NN, VTU_3DWSRML_ROB

6. Basic Vs Applied Research
Types of Research
Basic Vs Applied Research
Basic – to determine or establish fundamental facts and relationships within a discipline or field of study. It develop theories.
Applied – undertaken specifically for the purpose of obtaining information to help resolve a particular problem.
The distinction between them is the Application
Basic has little application to real world policy and management but could be
done to guide applied research.
18 March 2015
Dr.NN, VTU_3DWSRML_ROB

7. Classification of Research
Descriptive Research – the attempt to determine, describe, or identify something
The intent is often synthesis, which pulls knowledge or information together
Analytic – the attempt to establish why something occurs or how it came to be.
All disciplines generally engage in both
18 March 2015
Dr.NN, VTU_3DWSRML_ROB

8. Methodology Vs Methods of Research
Methodology and Method are often (incorrectly) interchangeably
used
Methodology – the study of the general approach to inquiry in a given field.
Method – the specific techniques, tools or procedures applied to achieve a given objective
Research methods include regression analysis, mathematical analysis, OR, DOE, surveys, data gathering, etc.
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Dr.NN, VTU_3DWSRML_ROB

9. The Process of Research
1. Selection of topic 
2. Reviewing the literature
3. Development of theoretical and conceptual frameworks
4. Clarification of research question/hypothesis 
5. Research design
6. Data collection
7. Data analysis
8. Drawing conclusions
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Dr.NN, VTU_3DWSRML_ROB

10. 1 2 3 4 5
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11. THEORY Examine a social relationship, study the relevant literature
Asking the Research Question
Formulating the
Hypotheses
Evaluating the Hypotheses
Analyzing Data
Develop a research design
Contribute new evidence to literature and begin again
THEORY
Examine a social relationship, study the relevant literature
Collecting Data
18 March 2015
Dr.NN, VTU_3DWSRML_ROB

12. The Process of Research
Research is a creative process
“…research includes far more than mere logic … It includes insight, genius, groping, pondering – ‘sense’ … The logic we can teach; the art we cannot”
Research requires (or at least works best) with imagination, initiative, intuition, and curiosity.
There are different types of creativity, characteristic of different situations – “applied” and “theoretical” most closely associate with research
18 March 2015
Dr.NN, VTU_3DWSRML_ROB

13. Fostering Creativity (Ladd 1987)
Gather and use previously developed knowledge
Exchange ideas
Apply deductive logic
Look at things alternate ways
Question or challenge assumptions
Search for patterns or relationships
Take risks
Cultivate tolerance for uncertainty
Creativity may provide the difference between satisfactory and outstanding research
18 March 2015
Dr.NN, VTU_3DWSRML_ROB

14. THEORY Examine a social relationship, study the relevant literature
Asking the Research Question
Formulating the
Hypotheses
Evaluating the Hypotheses
Analyzing Data
Develop a research design
Contribute new evidence to literature and begin again
THEORY
Examine a social relationship, study the relevant literature
Collecting Data
18 March 2015
Dr.NN, VTU_3DWSRML_ROB

15. Research Design Decisions regarding what, where, when, how much
A research design
the arrangement of conditions for collection and analysis of data in a manner that aims to combine relevance to the research purpose with economy in procedure.
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Dr.NN, VTU_3DWSRML_ROB

16. Research Design The designing decisions happen to be in respect of:
(i) What is the study about?
(ii) Why is the study being made?
(iii) Where will the study be carried out?
(iv) What type of data is required?
(v) Where can the required data be found?
(vi) What periods of time will the study include?
(vii) What will be the sample design?
(viii) What techniques of data collection will be used?
(ix) How will the data be analyzed?
(x) In what style will the report be prepared?
18 March 2015
Dr.NN, VTU_3DWSRML_ROB

17. Research Design Research design must, at least, contain
(a) a clear statement of the research problem;
(b) procedures and techniques to be used for gathering information;
(c) the population to be studied;
(d) methods to be used in processing and analyzing data
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Dr.NN, VTU_3DWSRML_ROB

18. Data Collection Researchers must decide three things:
How to measure the variables of interest
How to select the cases for the research
What kind of data collection techniques to use
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Dr.NN, VTU_3DWSRML_ROB

19. Levels of Measurements
Not every statistical operation can be used with every variable. The type of statistical operations one can employ depends on how the variables are measured.
Measurement Scales:
Nominal, Ordinal, Interval, Ratio
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20. Measurement Scales
Nominal - Numbers or other symbols are assigned to a set of categories for the purpose of naming, labeling, or classifying the observations. (Place, Name, Religion, etc.)
Ordinal - Nominal variables that can be ranked from low to high. (Median, Percentile)
Interval -Variables where measurements for all cases are expressed in the same units and with equal interval. (Likert Scale) (Median, SD)
Ratio - Variables with a natural zero point, such as height and weight, are on ratio scale. (AM,GM,HM,SD,VA,CV, etc.)
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21. Variable Types
A concept which can take on different quantitative values is a variable
Discrete variables: Variables that have a minimum-sized unit of measurement, which cannot be sub-divided. Example: the number Refrigerators sold per month.
Continuous variables: Variables that, in theory, can take on all possible numerical values in a given interval. Example: length, volume, pressure, etc.
Dependent and independent variables
If one variable depends upon or is a consequence of the other variable ---
it is called a dependent variable
the variable that is antecedent to the dependent variable ---
is called an independent variable
18 March 2015
Dr.NN, VTU_3DWSRML_ROB

22. Variable Types Extraneous variable: Confounded relationship
Independent variables that are not related to the purpose of the study, but may affect the dependent variable.
Whatever effect is noticed on dependent variable as a result of extraneous variable (s) is technically described as an ‘experimental error’.
One important characteristic of a good research design is to minimize the influence or effect of extraneous variable (s).
Confounded relationship
When the dependent variable is not free from the influence of extraneous variable (s) the relationship between the dependent and independent variables is said to be confounded/confused by an extraneous variable (s).
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23. Sampling Population: The total set of individuals, objects, groups,
or events in which the researcher is interested.
Sample: A relatively small subset selected from a population.
Population
Sample
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24. Sampling Two basic types: probability and non-probability
Probability sampling can include random sampling, stratified random sampling, and cluster sampling
Non-probability sampling can include quota sampling, haphazard sampling, and convenience sampling
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Dr.NN, VTU_3DWSRML_ROB

25. Random Sampling/ Stratified Random Sampling
Every unit has an equal chance of selection
Although it is relatively simple, members of specific subgroups may not be included in appropriate proportions
Stratified Random Sampling
The population is grouped according to meaningful characteristics or strata
This method is more likely to reflect the general population, and subgroup analysis is possible
However, it can be time consuming and costly
18 March 2015
Dr.NN, VTU_3DWSRML_ROB

26. Cluster/ Multi Stage Sampling
Systematic Sampling
Every nth unit is selected (e.g., every 10th piece in a stock of flats may be selected)
The method is convenient and close to random sampling if the starting point is randomly chosen
Cluster/ Multi Stage Sampling
Natural groups are sampled and then their members are sampled
This method is convenient and can use existing units
18 March 2015
Dr.NN, VTU_3DWSRML_ROB

27. Convenience Sampling Snow ball Sampling Quota Sampling
This method uses readily available groups or units of individuals
It is practical and easy to use
However, it may produce a biased sample
Convenience sampling is acceptable if the purpose of the research is to test a hypothesis that certain variables are related to one another
Snow ball Sampling
Previously identified members identify others
This method is useful when a list of potential names is difficult to obtain
However, it may produce a biased sample
Quota Sampling
The population is divided into subgroups and the sample is selected based on the proportions of the subgroups necessary to represent the population
This method depends on reliable data about the proportions in the population
18 March 2015
Dr.NN, VTU_3DWSRML_ROB

28. How Large Should a Sample Be?
Sample size can be determined following a formula that statisticians have created. It finds a sample size depending on:
@ How confident the researcher wants to be (usually 95%),
@ How much error can be tolerated (usually between 2-5%),
@ An estimate of how much variability (or heterogeneity) exists in the The size of the actual population to be studied.
A. Required sample size may be estimated using the following formula (for finite population (Kothari, 2001)).
n  {Z2.N.p2}  {(N-1).e2  Z2.p2}
where: n = Size of the sample required for a given precision and confidence level
N = Finite population size;
Z = Std. variate at a given confidence level (1.96 for 95% & 2.57 for 99% confidence level)
e = Acceptable error or the precision required (About 5% of mean value)
p = Std. deviation of the population (estimated through pilot study or past experience)
18 March 2015
Dr.NN, VTU_3DWSRML_ROB

29. How Large Should a Sample Be?
B. Most conservative sample size for a confidence level of 90% and an error of  5%
is z σp = 0.05,
where σp = Standard error of proportions = √pq/n
n = Sample size, p = binomial probability of success and q = 1-p
For 90% confidence level z = 1.64, substituting we get 1.64 √pq ∕ n = 0.05.
p= 0.5, q = 1-p = 0.5; Thus n = 267
Another strategy considers the number of variables being examined and the number of hypotheses being tested. In general, a good rule of thumb is that the more details that are involved under the study, the larger the sample needs to be.
18 March 2015
Dr.NN, VTU_3DWSRML_ROB

30. Analysis of Data
The data may be collected by surveys, interviews, literature review, participant observation, Experiments, Simulation studies, etc.
The measurements obtained in a research study are called the data.
The goal of statistics is to help researchers organize and interpret the data.
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31. Analysis of Data THEORY
Asking the Research Question
Formulating the
Hypotheses
Evaluating the Hypotheses
Analyzing Data
Develop a research design
Contribute new evidence to literature and begin again
THEORY
Examine a social relationship, study the relevant literature
Collecting Data
18 March 2015
Dr.NN, VTU_3DWSRML_ROB

32. Analysis of Data
Descriptive statistics: Procedures that help us organize and describe data collected from either a sample or a population.
Inferential statistics: The logic and procedures concerned with making predictions or inferences about a population from observations and analyses of a sample.
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33. Overview: Quantitative Analysis
Data Analysis in Research may be Univariate, Bivariate, and Multivariate
Univariate Data Analysis comprises
Frequency Tables
Diagrams
Measures of Central Tendency
Measures of Dispersion
Measures of Skewness
Measures of Kurtosis
18 March 2015
Dr.NN, VTU_3DWSRML_ROB

34. Frequency Tables A frequency table provides the number of
frequency  of  people 
(respondents)  and  the 
percentage  belonging  to 
each  of  the  categories  for 
the variable in question. It 
can  be  used  in  relation  to 
all  of  the  different  types 
of variable. An example of 
a  frequency  table  is shown:
School
Students
Staff N % A 50
16.4 34
19.7 B 52
17.1 40
23.1 C 27
8.9 31
17.9 D 54
17.8 38
22.0 E 47
15.5 30
17.3
TOTAL
304
100
173
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Dr.NN, VTU_3DWSRML_ROB

35. Diagrams
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36. Measures of Central Tendency
• Arithmetic Mean : AM = (ΣfXi ) / (Σfi )
This  is  the  average  as  we  understand  it  in everyday use
• Geometric Mean : GM = (X1 .X2. X3………..Xn)1/n ;log G = (1/N)(Σfi logX i.)
It is  suitable when the data is in terms of ratios or it represents rates of change
Example: Rates of growth of population, Rates of growth of industrial production
• Harmonic Mean: HM = (Σfi )/ (Σfi  1/X i.)
This is  most suitable when calculating the average speed of a vehicle.
• Median
This is the midpoint in a distribution of values
• Mode
This  is  the  value  that  occurs  most  frequently in a distribution
• Partition Values: Quartiles, Deciles, Percentiles: Q = l + (((N/n) – F)/f) h
18 March 2015
Dr.NN, VTU_3DWSRML_ROB

37. Measures of Dispersion
• Range: Xh- Xl
This  is  simply  the  difference  between  the maximum and minimum value in a distribution of value associated with interval or ratio scale variable. 
• Quartile Deviation: (Q3- Q1)/2
This  is  essentially  the  average  difference between third and first quartiles.
• Mean Deviation: (ΣIXi - X barI/ (Σfi ))
This  is  essentially  the  arithmetic average of the deviation around the mean.
• Standard Deviation: SQRT((Σfi (Xi - X bar)2/ (Σfi )))
This  is  essentially  the  average  amount  of variation around the mean.
• Variance :(Σfi (Xi - X bar)2/ (Σfi ))
This  is  the  average  of the square of deviations around the mean.
• Co-efficient of Variation :(S.D/A.M)
To avoid the error introduced by unit of measurement
18 March 2015
Dr.NN, VTU_3DWSRML_ROB

38. Measures of Skewness
Skewness  is  a  measure  of  asymmetricity  in  a distribution
Co-efficient of skewness = (A.M – Mode)/SD  
18 March 2015
Dr.NN, VTU_3DWSRML_ROB

39. Kurtosis is a measure of “peakedness” of a distribution
Measures of Kurtosis
Kurtosis  is  a  measure  of  “peakedness” of  a distribution 
4 =(Σ(Xi - X bar)4/ n)
4 /4 = 3 (Normal Curve: Mesokurtic)
 3 (Lepto kurtic)
 3 (Platy kurtic)
18 March 2015
Dr.NN, VTU_3DWSRML_ROB

40. Interquartile Range 25% 25% 25% of cases 0 250 500 750 1000
A quartile is the value that marks one of the divisions that breaks a series of values into four equal parts.
The median is a quartile and divides the cases in half.
25th percentile is a quartile that divides the first ¼ of cases from the latter ¾.
75th percentile is a quartile that divides the first ¾ of cases from the latter ¼.
The interquartile range is the distance or range between the 25th percentile and the 75th percentile. Shown below, is the interquartile.
25%
25% of cases
25%
18 March 2015
Dr.NN, VTU_3DWSRML_ROB

41. Boxplot Construction
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42. Bi-variate Data Analysis
Bivariate analysis is concerned with the analysis of  two  variables  at  a  time  in  order  to  uncover  whether or not the two variables are related. 
• Contingency  Tables  or  Cross  Tabulations (Tables)
• Correlations Analysis
1. Karl Pearson’s correlation coefficient,
2. Spearman’s Rank correlation coefficient, and
3. Kendal’s Rank correlation coefficient)
1. r = 1/n (Σ(X - X bar) (Y - Y bar))/ (xy))
2. rs = 1 - 6(Σdi 2)/ (n(n2-1)) ; {n: no of pairs, d: difference between ranks}
3.  = (2s)/(n(n-1)) ; {s: sum of scores, n: no. of pairs}
18 March 2015
Dr.NN, VTU_3DWSRML_ROB

43. Correlation Analysis
The goal of a correlational study is to determine whether there is a relationship between two variables and to describe the relationship.
A correlational study simply observes the two variables as they exist naturally.
18 March 2015
Dr.NN, VTU_3DWSRML_ROB

44. Beware of Spurious correlation
Correlation Analysis
Assesses the linear relationship between two variables
Example: height and weight
Strength of the association is described by a correlation coefficient - r
r = low, probably meaningless
r = low, possible importance
r = moderate correlation
r = high correlation
r = 0.8 –1.0 very high correlation
Can be positive or negative
Pearson’s, Spearman’s, and Kendal’s correlation coefficient
Correlation tells nothing about causation
Beware of Spurious correlation
18 March 2015
Dr.NN, VTU_3DWSRML_ROB

45. Correlation Analysis
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Dr.NN, VTU_3DWSRML_ROB

46. Correlation Analysis
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47. Multi-Variate Analysis
Multivariate analysis entails the simultaneous analysis of 3 or more variables
– Multiple Linear Regression
– Factor Analysis
– Cluster Analysis
– Discriminant Analysis
– Multi-criteria Analysis
18 March 2015
Dr.NN, VTU_3DWSRML_ROB

48. THANK YOU