Chapter 3 Methodology

1. STATISTICAL
TOOLS &
TREATMENT
Research and Statistics
Ryan L. Estonio
Master in Management
PRESENTED BY

2. 1 What is Statistics?
2 Introduction of Statisctical Treatment
3 Introduction of Statistical Tools
4
How to determine accurate tools and
treatment
TOPICS

3. Back to Agenda 03
STATISTICS

4. Back to Agenda 04
STATISTICS IN RESEARCH
• Statistics in research deals with basics in statistics that
provides statistical randomness and law of using large
samples.
• It teaches how choosing a sample size from a random
large pool of sample helps extrapolate statistical findings
and reduce experimental bias and errors.

5. STATISTICAL TREATMENT
Statistical treatment of data is a process used
to convert raw data into something
interpretable.

6. • In its simplest form, statistical treatment of data is taking raw data and turning it
into something that can be interpreted and used to make decisions.
• This process is important for businesses because it allows them to take customer
feedback and turn it into actionable insights.
There are many different statistical data treatment methods, but the most common
are surveys and polls.
What exactly is Statistical
Treament?

7. IMPORTANT
STATISTICAL
TOOLS IN
RESEARCH
Researchers in the biological field find
statistical analysis in research as the
scariest aspect of completing research.
However, statistical tools in research
can help researchers understand what
to do with data and how to interpret the
results, making this process as easy as
possible.

8. Back to Agenda 05
STATISCAL
TREAMENT
• Inferential
Statisctics
• Regression Analysis
• Correlation Analysis
• Factor Analysis

9. INFERENTIAL STATISTICS
• Inferential statistics are used to make predictions or inferences
about a population based on a sample. This is done by using
estimation methods (point estimates and confidence intervals) and
testing methods (hypothesis testing). Inferential statistics can be
used to understand how likely it is that a particular population
characteristic is true.
• For example, inferential statistics can be used to calculate the
probability that a customer will be satisfied with a product or
service.
• Once you have collected your data, the next step is to choose a
statistical analysis method. The most common methods are
regression, correlation, and factor analysis.

10. It is a method used to identify the
relationships between different variables.
For example, you could use regression
analysis to understand how customer
satisfaction ratings change based on the
number of support tickets they open.
Regression
Analysis

11. Correlation analysis is a method used to
understand how two variables relate. For example,
you could use correlation analysis to understand
how customer satisfaction ratings change based
on the number of support tickets they open.
Correlation analysis

12. Factor analysis is a method used to identify which variables impact a
particular outcome most. For example, you could use factor analysis
to determine which factors influence customer satisfaction ratings
most.
Once you have chosen a method of statistical treatment of data, the
next step is to apply it to your dataset. This step can be done using
Excel or another similar program. Once you have applied your
chosen method, you can interpret the results and use them to make
decisions about your business.
Factor
analysis

13. Statistical treatment of data for survey
is a necessary process that allows
businesses to take customer feedback
and turn it into actionable insights.
There are many different statistical
data treatment methods, but the most
common are surveys and polls.

14. STATISTICAL
TOOLS
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15. BASIC STATISTICAL TOOLS
• The standard deviation, often represented
with the Greek letter sigma, is the
measure of a spread of data around the
mean. A high standard deviation signifies
that data is spread more widely from the
mean, where a low standard deviation
signals that more data align with the
mean.
• In a portfolio of data analysis methods,
the standard deviation is useful for quickly
determining dispersion of data points.
• The arithmetic mean, more commonly
known as ―the average, is the sum of a
list of numbers divided by the number of
items on the list.
• The mean is useful in determining the
overall trend of a data set or providing a
rapid snapshot of your data. Another
advantage of the mean is that it‘s very
easy and quick to calculate.
STANDARD DEVIATION
MEAN

16. BASIC STATISTICAL TOOLS
• When measuring a large data set or
population, like a workforce, you don‘t
always need to collect information from
every member of that population – a
sample does the job just as well. The trick
is to determine the right size for a sample
to be accurate.
• Using proportion and standard deviation
methods, you are able to accurately
determine the right sample size you need
to make your data collection statistically
significant
• Regression models the relationships
between dependent and explanatory
variables, which are usually charted on a
scatterplot. The regression line also
designates whether those relationships are
strong or weak.
• Regression is commonly taught in high
school or college statistics courses with
applications for science or business in
determining trends over time
SAMPLE SIZE DETERMINATION
REGRESSION

17. • ALSO COMMONLY CALLED T TESTING, HYPOTHESIS TESTING
ASSESSES IF A CERTAIN PREMISE IS ACTUALLY TRUE FOR YOUR
DATA SET OR POPULATION.
• IN DATA ANALYSIS AND STATISTICS, YOU CONSIDER THE RESULT
OF A HYPOTHESIS TEST STATISTICALLY SIGNIFICANT 2 IF THE
RESULTS COULDN‘T HAVE HAPPENED BY RANDOM CHANCE.
HYPHOTHESIS TESTING

18. It is a widely used software package for human behavior research.
SPSS can compile descriptive statistics, as well as graphical
depictions of result.
STATISTICAL PACKAGE FOR SOCIAL
SCIENCE (SPSS)
1
This software package is used among human behavior research and
other fields. R is a powerful tool and has a steep learning curve.
However, it requires a certain level of coding. Furthermore, it comes
with an active community that is engaged in building and enhancing
the software and the associated plugins.
2
R FOUNDATION FOR STATISTICAL
COMPUTING
It is an analytical platform and a programming language.
Researchers and engineers use this software and create their own
code and help answer their research question. While MatLab can be
a difficult tool to use for novices, it offers flexibility in terms of
what the researcher needs.
3 MATLAB (THE MATHWORKS)

19. Not the best solution for statistical analysis in research, but MS
Excel offers wide variety of tools for data visualization and
simple statistics. It is easy to generate summary and
customizable graphs and figures. MS Excel is the most accessible
option for those wanting to start with statistics.
MICROSOFT EXCEL
4
It is a statistical platform used in business, healthcare, and human
behavior research alike. It can carry out advanced analyzes and
produce publication-worthy figures, tables and charts.
5 STATISTICAL ANALYSIS SOFTWARE (SAS)
It is a premium software that is primarily used among biology
researchers. But, it offers a range of variety to be used in various
other fields. Similar to SPSS, GraphPad gives scripting option to
automate analyses to carry out complex statistical calculations.
6
GRAPHPAD PRISM

20. USE OF STATISTICAL TOOLS IN RESEARCH AND
DATA ANALYSIS Statistical tools manage the large
data. Many biological studies use large data to analyze
the trends and patterns in studies. THEREFORE,
USING STATISTICAL TOOLS BECOMES ESSENTIAL, AS
THEY MANAGE THE LARGE DATA SETS, MAKING
DATA PROCESSING MORE CONVENIENT.

21. There are a range of statistical tools in research which can
help researchers manage their research data and improve the
outcome of their research by better interpretation of data.
You could use statistics in research by understanding the
research question, knowledge of statistics and your personal
experience in coding.
CONCLUSION

22. Conduct a thorough literature review: Before
starting a research project, it is important to conduct
a comprehensive review of the literature to identify the
most effective tools and treatments that have been
used in previous studies.
This will help to establish a baseline of knowledge and
inform the selection of appropriate tools and
treatments for the research project.
01
Consult with experts: Consultation with experts in
the field can provide valuable insights into the most
effective tools and treatments for a particular
research question. This can include clinicians,
researchers, and other professionals who have
expertise in the area of interest.
03
Consider the study design: The study design will play a critical role
in determining the appropriate tools and treatments for the research
project.
For example, if the study is a randomized controlled trial, the tools
and treatments should be carefully chosen to ensure that they are
suitable for the study population and that they are administered in a
standardized manner.
02
Pilot testing: Before using tools and treatments in a
research project, it is often beneficial to conduct pilot
testing to evaluate their feasibility and effectiveness.
This can help to identify any potential problems or
limitations before the study begins.
04
Determining accurate tools and treatments in
research requires a systematic and evidence-
based approach.

23. Pilot testing: Before using tools and treatments
in a research project, it is often beneficial to
conduct pilot testing to evaluate their feasibility
and effectiveness.
This can help to identify any potential problems
or limitations before the study begins.
05
Analyze the data: Once the data has been collected, it is
important to analyze it carefully to determine the
effectiveness of the tools and treatments that were used.
This will help to establish whether the results are reliable and
valid.
06
Determining accurate tools and treatments in
research requires a systematic and evidence-
based approach.

24. Thank You
Ryan L. Estonio
Master in Management
PRESENTED BY