RM4151 UNIT-3 - RESEARCH METHODOLOGY AND IPR
Bid data analytics
UNIT-3
DATA ANALYSIS AND REPORTING
OVERVIEW OF MULTIVARIATE ANALYSIS
Multivariate analysis (MVA) is a Statistical procedure for analysis of data involving more
than one type of measurement or observation. It may also mean solving problems
where more than one dependent variable is analyzed simultaneously with other
variables.
Multivariate analysis is based in observation and analysis of more than one statistical
outcome variable at a time. In design and analysis, the technique is used to perform
trade studies across multiple dimensions while taking into account the effects of all
variables on the responses of interest.
The purposes of multivariate data analysis is to study the relationships among the P
attributes, classify the n collected samples into homogeneous groups, and make
inferences about the underlying populations from the sample.
Most of multivariate analysis deals with estimation, confidence sets, and hypothesis
testing for means, variances, co-variances, correlation coefficients, and related, more
complex population characteristics.
Multivariate ANOVA (MANOVA) extends the capabilities of analysis of variance
(ANOVA) by assessing multiple dependent variables simultaneously. ANOVA statistically
tests the differences between three or more group means.
Overview
Multivariate analysis is conceptualized by tradition as the statistical study of
experiments in which multiple measurements are made on each experimental unit and
for which the relationship among multivariate measurements and their structure are
important to the experiment's understanding.
For instance, in analyzing financial instruments, the relationships among the various
characteristics of the instrument are critical. In biopharmaceutical medicine, the patient's
multiple responses to a drug need be related to the various measures of toxicity.
Some of what falls into the rubric of multivariate analysis parallels traditional univariate
analysis; for example, hypothesis tests that compare multiple populations. However, a
much larger part of multivariate analysis is unique to it; for example, measuring the
strength of relationships among various measurements.
1
0
HYPOTHESIS TESTING AND MEASURES OF ASSOCIATION
Hypothesis tests are statistical tools widely used for assessing whether or not there is
an association between two or more variables. These tests provide a probability of the
type 1 error (p-value), which is used to accept or reject the null study hypothesis.
Hypothesis testing is an act in statistics whereby an analyst tests an assumption
regarding a population parameter. The methodology employed by the analyst depends
on the nature of the data used and the reason for the analysis. Hypothesis testing is
used to assess the plausibility of a hypothesis by using sample data.
Hypothesis testing is an act in statistics whereby an analyst tests an assumption
regarding a population parameter. The methodology employed by the analyst depends
on the nature of the data used and the reason for the analysis.
Hypothesis testing is used to assess the plausibility of a hypothesis by using sample
data. Such data may come from a larger population, or from a data-generating
process.
Hypothesis Testing Works
In hypothesis testing, an analyst tests a statistical sample, with the goal of providing
evidence on the plausibility of the null hypothesis.
Statistical analysts test a hypothesis by measuring and examining a random sample of
the population being analyzed. All analysts use a random population sample to test two
different hypotheses: the null hypothesis and the alternative hypothesis.
The null hypothesis is usually a hypothesis of equality between population parameters;
e.g., a null hypothesis may state that the population mean return is equal to zero. The
alternative hypothesis is effectively the opposite of a null hypothesis (e.g., the
population mean return is not equal to zero). Thus, they are mutually exclusive, and
only one can be true. However, one of the two hypotheses will always be true.
Steps of Hypothesis Testing
All hypotheses are tested using a four-step process:
1. The first step is for the analyst to state the two hypotheses so that only one can
be right.
2. The next step is to formulate an analysis plan, which outlines how the data will
be evaluated.
3. The third step is to carry out the plan and physically analyze the sample data.
4. The fourth and final step is to analyze the results and either reject the null
hypothesis, or state that the null hypothesis is plausible, given the data.
Measures of association
1
0
Measures of association refers to a wide variety of statistics that quantify the strength
and direction of the relationship between exposure and outcome variables, enabling
comparison between different groups. The measure calculated depends on the study
design used to collect data. Examples of measures of association include risk ratio
(relative risk), rate ratio, odds ratio, and proportionate mortality ratio.
Measures of association are used in various fields of research but are especially
common in the areas of epidemiology and psychology, where they frequently are
used to quantify relationships between exposures and diseases or behaviours.
Measures of effect size in ANOVA are measures of the degree of association between
and effect (e.g., a main effect, an interaction, a linear contrast) and the dependent
variable. They can be thought of as the correlation between an effect and the dependent
variable.
measure of association, in statistics, any of various factors or coefficients used
to quantify a relationship between two or more variables. Measures of association are
used in various fields of research but are especially common in the areas
of epidemiology and psychology, where they frequently are used to quantify
relationships between exposures and diseases or behaviours.
A measure of association may be determined by any of several different analyses,
including correlation analysis
and regression analysis.
(Although
the
terms correlation and association are often used interchangeably, correlation in a
stricter sense refers to linear correlation, and association refers to any relationship
between variables.) The method used to determine the strength of an association
depends on the characteristics of the data for each variable. Data may be measured on
an interval/ratio scale, an ordinal/rank scale, or a nominal/categorical scale. These three
characteristics can be thought of as continuous, integer, and qualitative categories,
respectively.
The measures of association refer to a wide variety of coefficients (including bivariate
correlation and regression coefficients) that measure the strength and direction of the
relationship between variables; these measures of strength, or association, can be
described in several ways, depending on the analysis.
There are certain points that a researcher should know in order to better
understand the measures of statistical association.


First, the researcher should know that measures of association are not the same
as measures of statistical significance. It is possible for a weak association to be
statistically significant; it is also possible for a strong association to not be
statistically significant.
For measures of association, a value of zero signifies that no relationship exists.
In a correlation analysis, if the coefficient (r) has a value of one, it signifies a
1
0
perfect relationship on the variables of interest. In regression analyses, if the
standardized beta weight (β) has a value of one, it also signifies a perfect
relationship on the variables of interest. The researcher should note that
bivariate measures of association (e.g., Pearson correlations) are inappropriate
for curvilinear relationships or discontinuous relationships.
PRESENTING INSIGHTS AND FINDINGS USING
WRITTEN REPORTS AND ORAL PRESENTATION
Understand . . .
• That while some statistical data may be incorporated into the text,
most statistics should be placed in tables, charts, or graphs.
• That oral presentations of research findings should be developed
with concern for organization, visual aids, and delivery in unique
communication settings.
Guidelines for Short Reports
1
0
 Tell reader why you are writing
 Remind reader of request
 Write in an expository style
 Write report and hold for review
 Attach detailed materials in appendix
The Long Research Report
Report Modules
 Prefatory Information
 Introduction
 Methodology
 Findings
 Conclusions & Recommendations
 Appendices
 Bibliography
Short Report: Memo or Letter-Style
1.Introduction
1
0
 Problem statement
 Research objectives
 Background
2.Conclusions
 Summary and conclusions
 Recommendations
Short Report: Technical
Prefatory Information (all)
Introduction (all, plus brief methodology and limitations)
Findings
Conclusions
Appendices
Long Report: Management
1. Prefatory Information (all)
2. Introduction (all, plus brief methods and limitations)
3. Conclusions and Recommendations
4.Findings
5.Appendices
1
0
Long Report: Technical
1. Prefatory Information
2.Introduction
3. Methodology (full, detailed)
4.Findings
5.Conclusions
6.Appendices
7.Bibliography
Sample Findings Page: Tabular
1
0
Prewriting Concerns
What is the report’s purpose?
Who will read the report?
What are the circumstances?
How will the report be used?
Adjusting Pace
Use ample white space
Use headings
Use visual aids
Use italics and underlining
Choose words carefully
Repeat and summarize
Use service words strategically
Considerations for Writing
Readability
Comprehensibility
Tone
Avoiding Overcrowded Text
Use shorter paragraphs
Indent or space parts of text
Use headings
Use bullets
Presentation of Statistics
1
0
Text
Semi-tabular
Tables
Graphics
THE ORAL REPORT
Opening
Findings and conclusions
Recommendations
PRESENTATION TYPE
Extemporaneous
Memorized
Speaker Characteristics
Vocal
•
Do you speak softly?
•
Do you speak too rapidly?
1
0
•
Do you vary volume, tone, and rate of speaking?
•
Do you fill pauses (e.g., you know, uhm, ah)?
Physical
•
Do you rock back and forth?
•
Do you fiddle with things?
•
Do you stare into space?
•
Do you misuse visuals?
Audiovisuals
High Tech
•
Computer-drawn visuals
•
Computer animation
•
Computer with embedded video and audio clips
Low Tech
•
Chalkboard/ Whiteboard
•
Handouts
•
Flip charts
•
Overhead transparencies
•
Slides
Key Terms
•
Area chart
•
Bar chart
•
Briefing
•
Executive summary
•
Extemporaneous presentation
•
Geographic chart
1
0
•
Letter of transmittal
•
Line graph
•
Management report
•
Pace
•
Pictograph
•
Pie chart
•
Readability index
•
Sentence outline
•
Technical report
•
3-D graphic
•
Topic outline
.,.,.,.,.,.,.,.,.,.,.,.,.,.,.,.,.,.,.,.,.,.,.,.,.,.,.,.,.,.,
1
0