This document summarizes key chapters from a research methodology textbook. It discusses data preparation, including editing collected data, coding responses, and handling missing data. It also covers exploring and examining data through various visualizations and statistical techniques, such as frequency tables, histograms, cross-tabulations, and boxplots. The goal is to prepare and understand data in order to gain insights.
Anybody, who is reading the research report, must necessarily be conveyed enough about the study so that he can place it in its general scientific context, judge the adequacy of its methods and thus form an opinion of how seriously the findings are to be taken. For this purpose there is the need of proper layout of the report. The layout of the report means as to what the research report should contain. A comprehensive layout of the research report should comprise preliminary pages, the main text and the end matter.
Data Mining: Concepts and Techniques (3rd ed.)- Chapter 3 preprocessingSalah Amean
the chapter contains :
Data Preprocessing: An Overview,
Data Quality,
Major Tasks in Data Preprocessing,
Data Cleaning,
Data Integration,
Data Reduction,
Data Transformation and Data Discretization,
Summary.
Anybody, who is reading the research report, must necessarily be conveyed enough about the study so that he can place it in its general scientific context, judge the adequacy of its methods and thus form an opinion of how seriously the findings are to be taken. For this purpose there is the need of proper layout of the report. The layout of the report means as to what the research report should contain. A comprehensive layout of the research report should comprise preliminary pages, the main text and the end matter.
Data Mining: Concepts and Techniques (3rd ed.)- Chapter 3 preprocessingSalah Amean
the chapter contains :
Data Preprocessing: An Overview,
Data Quality,
Major Tasks in Data Preprocessing,
Data Cleaning,
Data Integration,
Data Reduction,
Data Transformation and Data Discretization,
Summary.
A questionnaire is a research tool that consists of a series of questions. It is used to gather qualitative and quantitative information from respondents.
Unleashing the Power of Analytics: Driving Performance at the Intersection of...Human Capital Media
Performance analytics is a data-driven approach used to optimize how organizations manage and deploy learning and development that supports execution of business strategy. How are learning organizations today using data? How do they define and determine relevant analytics? Is there a critical shortage of analytics skills in the learning function? What outcomes are organizations realizing from their efforts when deploying performance analytics to drive learning and development (L&D)? Finally, how do we link learning analytics and business analytics to produce performance data that really deliver results?
In this session, we will share the results of a proprietary research conducted with hundreds of CLOs , in partnership with Human Capital Media, that delved into these questions… and more. We will shed light on best practices being employed and the challenges for learning leaders.
Learning Objectives:
Identify best practices and gaps in the adoption of analytics by learning organizations
Understand the difference and impact of learning analytics vs Performance analytics
Examine how analytics can transform strategy and outcomes for the learning function
Presented the hands-on session on “Introduction to Big Data Analysis” at Dayananda Sagar University. Around 150+ University students benefitted from this session.
Drifting Away: Testing ML Models in ProductionDatabricks
Deploying machine learning models has become a relatively frictionless process. However, properly deploying a model with a robust testing and monitoring framework is a vastly more complex task. There is no one-size-fits-all solution when it comes to productionizing ML models, oftentimes requiring custom implementations utilising multiple libraries and tools. There are however, a set of core statistical tests and metrics one should have in place to detect phenomena such as data and concept drift to prevent models from becoming unknowingly stale and detrimental to the business.
Combining our experiences from working with Databricks customers, we do a deep dive on how to test your ML models in production using open source tools such as MLflow, SciPy and statsmodels. You will come away from this talk armed with knowledge of the key tenets for testing both model and data validity in production, along with a generalizable demo which uses MLflow to assist with the reproducibility of this process.
Data Effectiveness: How to build a Data Driven and Reporting infrastructureAndrew Patricio
All organizations are increasingly data-hungry but often have to deal with very poor data quality. At DC Public Schools we took a realistic, pragmatic approach to building a data reporting infrastructure that met the organization where it was today and built a pathway to where it needs to be tomorrow
How Minitab Revolutionize Statistical Analysis in Social Science Researchcharlessmithshd
Minitab is a powerful statistical software designed specifically for data analysis. It offers a comprehensive suite of tools for statistical analysis, process control, and data visualization, making it ideal for both academic and professional use. Students always look for top-notch MINITAB homework help for a good academic record.
The Tipu approach to ITSM improvement, what ITIL calls CSI.
Tipu says we are doing CSI wrong.
An ITIL process is not a unit of work.
And we need to relax our CSI approach and accept a little risk. CSI can't be done as a formal project(s).
Find out more at http://www.basicsm.com/tipu
Improving the Customer's Experience through Data Analytics IQPC Exchange
Wells Fargo's presentation, "Improving the Customer's Experience through Data Analytics," gives Contact Center executives insight into data analytics, a case management/service team comparison and an action plan for incremental improvements.
A data analyst is a professional who interprets data, analyzes trends, and extracts meaningful insights to help businesses make informed decisions. They work with large datasets, employing statistical methods, programming languages, and data visualization tools to identify patterns, correlations, and anomalies within the data.
Key responsibilities of a data analyst typically include:
Data Collection: Gathering data from various sources, such as databases, spreadsheets, and APIs.
Data Cleaning and Preprocessing: Ensuring data accuracy by removing inconsistencies, errors, and duplicates, and transforming data into a usable format.
Data Analysis: Using statistical techniques and analytical tools to explore data, uncover trends, and generate insights.
Data Visualization: Creating visual representations of data through charts, graphs, and dashboards to communicate findings effectively.
Reporting: Presenting analysis results to stakeholders in a clear and understandable manner, often through reports and presentations.
Decision Support: Providing actionable recommendations based on data insights to support business objectives and strategies.
Data analysts typically possess a strong background in mathematics, statistics, and computer science, along with proficiency in programming languages like Python, R, or SQL. They also need excellent problem-solving skills, attention to detail, and the ability to work collaboratively with cross-functional teams.
How to Review, Cleanse, and Transform Clinical Data in Oracle InFormPerficient, Inc.
When it comes to clinical trials, the consequences of bad data can be severe. Research and development becomes complicated and lives can be put at risk. The need for clinical data to be clean is critical for comprehensive reporting and analysis, ultimately enabling safer drugs and devices to be brought to market faster.
During our 30-minute, no-nonsense webinar, we discussed why and how organizations can leverage Oracle Health Sciences Data Management Workbench (DMW) to revitalize the clinical trial data captured in Oracle Health Sciences InForm.
How To Talk To a Live Person at American Airlinesflyn goo
This page by FlynGoo can become your ultimate guide to connecting with a live person at American Airlines. Have you ever felt lost in the automated maze of customer service menus? FlynGoo is here to rescue you from endless phone trees and automated responses. With just a click or a call to a specific number, we ensure you get the human touch you deserve. No more frustration, no more waiting on hold - we simplify the process, making your travel experience smoother and more enjoyable.
Assessing the Influence of Transportation on the Tourism Industry in Nigeriagsochially
This research dissertation investigates the complex interplay between transportation and the tourism industry in Nigeria, aiming to unravel critical insights that contribute to the enhancement of the overall tourist experience. The study employs a multi-faceted approach, literature review establishes a robust theoretical framework, incorporating The Service Quality and Satisfaction Theory to guide the research questions and hypotheses.
The methodology involves the distribution of a structured questionnaire, ensuring a representative sample and facilitating a comprehensive analysis of the gathered data.
Key findings include the nuanced perceptions of transportation infrastructure adequacy, safety and security concerns, financial influences on travel decisions, and the cultural and ecological impacts of transportation choices. These findings culminate in a comprehensive set of recommendations for policymakers and practitioners in the Nigerian tourism industry. The findings contribute to the existing literature by providing actionable insights for policymakers, stakeholders, and researchers in the Nigerian tourism sector.
The recommendations encompass gender-sensitive planning, infrastructure enhancements, safety measures, and strategic interventions to address financial constraints, ensuring a holistic and sustainable development of the tourism industry in Nigeria.
Author: Imafidon Osademwingie Martins
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During the coldest months, Italy transforms into a winter wonderland, providing visitors with a very unique experience. From the Settimana Bianca ski event to the lively Carnevale celebrations, Italy's winter festivities provide something for everyone. Enjoy hot cocoa, eat hearty comfort foods, and buy during winter deals. Explore the country's rich cultural past by participating in Settimana Bianca, and Carnevale, sipping hot chocolate, shopping during winter deals, and indulging in winter comfort foods. Visit our website https://timeforsicily.com/ for more information.
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Wayanad, nestled in Kerala's Western Ghats, is a lush paradise renowned for its scenic landscapes, rich biodiversity, and cultural heritage. From trekking Chembra Peak to exploring ancient Edakkal Caves, Wayanad offers thrilling adventures and serene experiences. Its vibrant economy, driven by agriculture and tourism, highlights a harmonious blend of nature, tradition, and modernity.
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Unlock the secrets to success with our comprehensive 8-Step Glamping Accelerator Go-To-Market Plan! Watch our FREE webinar, where you'll receive expert guidance and invaluable insights on every aspect of launching and growing your glamping business.
Hidden Gems of Europe - DISCOVERING THE CONTINENT'S BEST-KEPT SECRETSKamil Uğraş TÜRKOĞLU
Europe, continent rich in history, culture, and natural beauty, is often synonymous with famous cities like Paris, Rome, and London. These iconic destinations attract millions of tourists every year, captivating them with their renowned landmarks, vibrant culture, and bustling urban life. However, beyond these well-trodden paths lie countless hidden gems waiting to be discovered. These lesser-known destinations offer unique experiences, authentic encounters, and breathtaking landscapes that often surpass the allure of their famous counterparts.
In "Hidden Gems of Europe," we embark on a journey to uncover these secret spots, exploring the heart and soul of Europe through its quaint villages, charming towns, and secluded natural wonders. This book aims to inspire travelers to look beyond the obvious and venture into the lesser-explored corners of the continent, where true adventure and discovery await.
Antarctica- Icy wilderness of extremes and wondertahreemzahra82
In this presentation, we delve into the captivating realm of Antarctica, Earth's southernmost continent. This icy wilderness stands as a testament to extremes, with record-breaking cold temperatures and vast expanses of pristine ice. Antarctica's landscape is dominated by towering glaciers, colossal icebergs, and expansive ice shelves. Yet, amidst this frozen expanse, a rich tapestry of unique wildlife thrives, including penguins, seals, and seabirds, all finely attuned to survive in this harsh environment. Beyond its natural wonders, Antarctica also serves as a vital hub for scientific exploration, providing invaluable insights into climate change and the Earth's history
1. SESSION 7
DATA ANALYSIS
Chapter 15: Data Preparation and Description
Chapter 16: Exploring, Displaying, and
Examining Data
RESEARCH
METHODOLOGY
2. CHAPTER 16
Data Preparation and Description
Learning Objectives:
• The importance of editing the collected raw data to detect
errors and omissions.
• How coding is used to assign number and other symbols to
answers and to categorize responses.
• The use of content analysis to interpret and summarize open
questions.
• Problems with and solutions for “don’t know” responses and
handling missing data.
• The options for data entry and manipulation.
4. 15-4
Monitoring
Online Survey Data
Online surveys need
special editing attention.
CfMC provides software
and support to research
suppliers to prevent
interruptions from
damaging data .
6. 15-6
Field Editing
Speed without accuracy won’t help
the manager choose the right
direction.
•Field editing review
•Entry gaps identified
•Callbacks made
•Validate results
7. 15-7
Central Editing
Be familiar with instructions
given to interviewers and coders
Do not destroy the original entry
Make all editing entries identifiable and in
standardized form
Initial all answers changed or supplied
Place initials and date of editing
on each instrument completed
10. 15-10
Coding
Open-Ended Questions
6. What prompted you to purchase your
most recent life insurance policy?
_______________________________
_______________________________
_______________________________
_______________________________
_______________________________
_______________________________
_______________________________
_______________________________
15. 15-15
Open-Question Coding
Locus of
Responsibility Mentioned
Not
Mentioned
A. Company
_____________
___________
_______________
_________
B. Customer
_____________
___________
_______________
_________
C. Joint Company-
Customer
_____________
___________
_______________
_________
F. Other
_____________
___________
_______________
_________
Locus of
Responsibility
Frequency (n =
100)
A. Management
1. Sales manager
2. Sales process
3. Other
4. No action area
identified
B. Management
1. Training
C. Customer
1. Buying processes
2. Other
3. No action area
identified
D. Environmental
conditions
E. Technology
F. Other
10
20
7
3
15
12
8
5
20
16. 15-16
Handling “Don’t Know”
Responses
Question: Do you have a productive relationship with your
present salesperson?
Years of
Purchasing Yes No Don’t Know
Less than 1 year 10% 40% 38%
1 – 3 years 30 30 32
4 years or more 60 30 30
Total
100%
n = 650
100%
n = 150
100%
n = 200
19. 15-19
Key Terms
• Bar code
• Codebook
• Coding
• Content analysis
• Data entry
• Data field
• Data file
• Data preparation
• Data record
• Database
• Don’t know response
• Editing
• Missing data
• Optical character
recognition
• Optical mark recognition
• Precoding
• Spreadsheet
• Voice recognition
27. 15-27
Variable Population Sample
Mean µ X
Proportion p
Variance 2
s2
Standard deviation s
Size N n
Standard error of the mean x Sx
Standard error of the proportion p Sp
__
_
Symbols
28. 15-28
Key Terms
• Central tendency
• Descriptive statistics
• Deviation scores
• Frequency distribution
• Interquartile range (IQR)
• Kurtosis
• Median
• Mode
• Normal distribution
• Quartile deviation (Q)
• Skewness
• Standard deviation
• Standard normal
distribution
• Standard score (Z score)
• Variability
• Variance
29. CHAPTER 16
Exploring, Displaying, and Examining Data
Learning Objectives:
• That exploratory data analysis techniques provide insights and
data diagnostics by emphasizing visual representations of the
data.
• How cross-tabulation is used to examine relationships
involving categorical variables, serves as a framework for later
statistical testing, and makes an efficient tool for data
visualization and later decision-making
46. 16-46
Guidelines for Using Percentages
Averaging percentages
Use of too large percentages
Using too small a base
Percentage decreases can never
exceed 100%
49. 16-49
Exploratory Data Analysis
This Booth Research Services
ad suggests that the
researcher’s role is to make
sense of data displays.
Great data exploration and
analysis delivers insight from
data.