Slide share presentation of Research design and its types stated in simple and easy words and includes Definitions,Terms, Examples and types in order to have a basic concept of research design that plays a key role to conduct a research report.
Slide share presentation of Research design and its types stated in simple and easy words and includes Definitions,Terms, Examples and types in order to have a basic concept of research design that plays a key role to conduct a research report.
A Research Design is a procedural plan that is adopted by the researcher to answer questions validly, objectively, accurately and economically.
Research Design is considered as a “blueprint” for research, dealing with at least four problems: (According to Philiber, Schwab, & Samsloss, 1980) 1) Which questions to be studied, 2) Which data are relevant, 3) What data to collect, and 4) How to analysis the result.
Defining research design, classification of research design, parts of a research design, concept of research design, importance of research design, functions of research design
A Research Design is a procedural plan that is adopted by the researcher to answer questions validly, objectively, accurately and economically.
Research Design is considered as a “blueprint” for research, dealing with at least four problems: (According to Philiber, Schwab, & Samsloss, 1980) 1) Which questions to be studied, 2) Which data are relevant, 3) What data to collect, and 4) How to analysis the result.
Defining research design, classification of research design, parts of a research design, concept of research design, importance of research design, functions of research design
Research Methodology of different data analysis slides.pptxtalhachemist222
General. All solvents were reagent grade or HPLC grade. Unless otherwise noted, all materials
were obtained from commercial suppliers and used without further purification. Melting points
were obtained on a Mel-Temp apparatus and are uncorrected. 1
H NMR spectra were recorded at
400 MHz. 13C NMR spectra were recorded at 100 MHz. Flash column chromatography was carried
out by Biotage Isolera One using ISCO RediSep silica gel cartridges. Analytical HPLC was
performed on an Agilent 1200 series HPLC system equipped with an Agilent G1315D DAD
detector (detection at 220 nm) and an Agilent 6120 quadrupole MS detector using an Agilent
Eclipse Plus C18 column (2.1 mm × 50 mm, 3.5 μm) at a flow rate of 1.25 mL/min. The HPLC
solvent system consisted of deionized water and acetonitrile, both containing 0.1% formic acid.
The mobile phase in HPLC consisted of 5% acetonitrile/95% water for 0.25 min followed by a
gradient to 40% acetonitrile/60% water over 1.5 min and then a gradient to 85% acetonitrile/15%
water over 2.25 min. Unless otherwise noted, all final compounds biologically tested were
confirmed to be of ≥95% purity by the HPLC methods described above. No unexpected or
unusually high safety hazards were encountered during the course of the experiments described
below.
To a solution of 6-aminonicotinic acid 3 (100 mg, 0.72
mmol) and K2CO3 (150 mg, 1.09 mmol) in DMF (5 mL) was added chloromethyl acetate (79 mg,
0.72 mmol). After stirring at 50 °C for 5 h, DMF was removed in vacuo and the residue was
purified using a Biotage Isolera One flash purification system with a silica gel cartridge
(30→100% EtOAc in Hexanes) to give 99.7 mg (66% yield) of compound 5 d as a white solid.
To a solution of 6-aminonicotinic acid 3 (100
mg, 0.72 mmol) and K2CO3 (150 mg, 1.09 mmol) in DMF (5 mL) was added chloromethyl
isobutyrate (99 mg, 0.72 mmol). After stirring at 50 °C for 5 h, DMF was removed in vacuo and
the residue was purified using a Biotage Isolera One flash purification system with a silica gel
cartridge (30→100% EtOAc in Hexanes) to give 136.9 mg (79% yield) of compound 5e as a white
To a solution of 6-aminonicotinic acid 3 (100 mg,
0.72 mmol) and K2CO3 (150 mg, 1.09 mmol) in DMF (5 mL) was added chloromethyl butyrate
(99 mg, 0.72 mmol). After stirring at 50 o
C for 5 h, DMF was removed in vacuo and the residue
was purified using a Biotage Isolera One flash purification system with a silica gel cartridge
(30→100% EtOAc in Hexanes) to give 152.9 mg (89% yield) of compound 5f as a white solid.
To a solution of 6-aminonicotinic acid 3 (100 mg,
0.72 mmol) and K2CO3 (150 mg, 1.09 mmol) in DMF (5 mL) was added chloromethyl butyrate
(99 mg, 0.72 mmol). After stirring at 50 o
C for 5 h, DMF was removed in vacuo and the residue
was purified using a Biotage Isolera One flash purification system with a silica gel cartridge
(30→100% EtOAc in Hexanes) to give 152.9 mg (89% yield) of compound 5f as a white solid.
To a solution of 6-aminonicotine
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...pchutichetpong
M Capital Group (“MCG”) expects to see demand and the changing evolution of supply, facilitated through institutional investment rotation out of offices and into work from home (“WFH”), while the ever-expanding need for data storage as global internet usage expands, with experts predicting 5.3 billion users by 2023. These market factors will be underpinned by technological changes, such as progressing cloud services and edge sites, allowing the industry to see strong expected annual growth of 13% over the next 4 years.
Whilst competitive headwinds remain, represented through the recent second bankruptcy filing of Sungard, which blames “COVID-19 and other macroeconomic trends including delayed customer spending decisions, insourcing and reductions in IT spending, energy inflation and reduction in demand for certain services”, the industry has seen key adjustments, where MCG believes that engineering cost management and technological innovation will be paramount to success.
MCG reports that the more favorable market conditions expected over the next few years, helped by the winding down of pandemic restrictions and a hybrid working environment will be driving market momentum forward. The continuous injection of capital by alternative investment firms, as well as the growing infrastructural investment from cloud service providers and social media companies, whose revenues are expected to grow over 3.6x larger by value in 2026, will likely help propel center provision and innovation. These factors paint a promising picture for the industry players that offset rising input costs and adapt to new technologies.
According to M Capital Group: “Specifically, the long-term cost-saving opportunities available from the rise of remote managing will likely aid value growth for the industry. Through margin optimization and further availability of capital for reinvestment, strong players will maintain their competitive foothold, while weaker players exit the market to balance supply and demand.”
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
2. CONTENTS
Research design: Concept, Features of good research design,
Qualitative & Quantitative approaches, Pro & cons of both
approaches.
Exploratory research design: Concept, Types: Qualitative
techniques – Projective Techniques, Depth Interview, Experience
Survey, Focus Groups, Observation.
Descriptive Research design: Concept, Types & Uses
Concept of Cross-sectional & longitudinal Research
Experimental Design: Concept of causes, Causal relationships,
Concept of independent & Dependent variables, Concomitant
variable, Extraneous variable, Treatment, Control group
3. RESEARCH DESIGN
Research in the plan, structure and strategy of study conceived
in order to get answers to research questions and also to control
variance.
Kerlinger
Arrangement of conditions.
Overall framework of the projects.
Specific master plan or model.
Blueprint.
Guides the investigators.
Measurement and analysis of data.
Plan for collecting and utilizing data.
4. FEATURES OF A GOOD RESEARCH DESIGN
The nature of the problem
The availability of time & money for the research
The availability & skills of the researcher & his staff
The means of obtaining information
The objective of the problem to be studied
Freedom from bias
Freedom from confusing
Control of extraneous variables
5. NEED & IMPORTANCE
Helps to give directions.
Helps in decision making.
Stands for advance planning of the methods.
Great bearing on reliability of the results.
Helps researchers to anticipate potential
problems in collecting data.
A clear statement of the research problem.
Approach to be utilized in processing and
analyzing data.
6. TYPES OF RESEARCH DESIGN
Descriptive research
Experimental Research Design
Historical Research Design
Case Study
Causal Design
Longitudinal Research Design
Cross-Sectional Design
Correlation or Prospective Research Design
Cohort Design
Observational Design
Philosophical Design
7. TYPES OF RESEARCH DESIGN
Research
Design
Conclusive
Research
Design
CausalDescriptive
Longitudinal
Cross-
Sectional
Multiple
Cross-Section
Single Cross
Section
Exploratory
Research
Design
8. EXPLORATORY RESEARCH
Exploratory research tries to understand a subject of study in a
preliminary way. Research designs for exploratory work usually
depend on direct observation of a small selection of what is to
be studied.
Example- drinking behavior.
Experimenting refers to the process of research where one or
more variables.
This research is flexible in approach.
Purpose of the Study
Carried out when not much is known about the problem at
hand, or no details are available on how similar problems or
research issues have been solved in the past.
9. EXPLORATORY RESEARCH
OBJECTIVE
Major emphasis is on discovery of ideas & insights.
IN SUCH STUDIES
The sample size is small
Data requirement are vague.
The objective is general rather then specific.
Non-Probability sampling designs are used.
RESULT
Tentative
Generally used by further exploratory or conclusive research.
10. EXPLORATORY RESEARCH
USES
Formulate problems more precisely
Develop Hypotheses
Establish priorities for research
Clarify concepts
TYPES OF DATA
Secondary data
Focus groups
Key informant
Observation studies
Case studies
11. EXPLORATORY RESEARCH
EXAMPLE
Crime Patrol
In 1979, Xerox researched Japanese competitors to
understand how they could sell mid-size copy machines for
less than what it cost Xerox to make them. Today, a lot of
companies frequently use benchmarking as a standard
research tool.
12. CONCLUSIVE RESEARCH
conclusive research is meant to provide information that is
useful in reaching conclusions or decision-making.
It tends to be quantitative in nature.
It relies on both secondary & primary data.
The purpose of conclusive research is to provide a reliable or
representative picture of the population.
Conclusive research can be sub-divided into two major categories
Descriptive or statistical research
Causal research
13. DESCRIPTIVE RESEARCH
Is the most widely-used research design as indicated by the
Theses, dissertation and research report of institutions.
It common means of obtaining information include the use of
The questionnaire, personal interview with the aid of study
guide or interview schedule, and observation, either
participatory or not.
It describe a given state of affairs as fully and carefully as
possible
Purpose of the Study
Descriptive research seeks to tell what exists or what is about
a certain educational phenomenon.
A descriptive research design can serve as a first step that
identifies important factor, laying a foundation for more-
rigorous research.
14. DESCRIPTIVE RESEARCH
OBJECTIVE
Describing characteristics of individual or group.
To test specific hypotheses and examine relationship.
IN SUCH STUDIES
Data collected may relate to the respondents under study.
Research has specific objective.
Findings are definite.
RESULT
Conclusive
Finding used as input into decision-making.
15. CAUSAL RESEARCH
Known as “ex post facto” research. (Latin “after the fact”)
Attempt to determine the cause or consequences of
differences that already exist between or among groups of
individuals.
At least two different groups are compared on a dependent
variable or measure of performance (called the effect)
because the independent variable (called the cause) has
already occurred or cannot be manipulated.
DEPENDENT VARIABLE- The change or difference occurring
as a result of the independent variable.
INDEPENDENT VARIABLE- An activity of characteristic
believed to make a difference with respect to some behavior.
16. NEED & IMPORTANCE
• The researcher attempts to determine the cause, or reason,
for pre existing differences in group of individuals.
• Attempts to identify cause and effect relationships.
• Involve two or more group variables.
• Involve making comparison.
• Individuals are not randomly selected and assigned to two or
more group.
• Cannot manipulate the independent variable
• Less costly and time consuming