Courses

Course Overview

Data collection is a process by which data is gathered and measured. All this must be done before high-quality research can begin and answers to lingering questions can be found.  Data collection is usually done with software, and there are many different data collection procedures, strategies, and techniques.

So why is data collection important? It is through data collection that a business or management has the quality information they need to make informed decisions from further analysis, study, and research. Without data collection, companies would stumble around in the dark using outdated methods to make their decisions. Data collection instead allows them to stay on top of trends, provide answers to problems, and analyze new insights to great effect.

Course Duration
2 days (16 hours)

Course Fee
$ 716.90/- (GST Inclusive)

Medium of Instruction: English

Mode:
Webinar

Course Outline

MODULE 1: Data Collection and Quality Framework

Participants will learn how to identify which data sources likely match the research question, how to turn research questions into measurable pieces, and how to think about an analysis plan. This module also provides a general framework that allows participants not only to understand each step required for a successful data collection and analysis but also help to identify errors associated with different data sources. Participants will learn some metrics to quantify each potential error and can describe the quality of a data source.

• Research Designs and Data Sources

–     Types of Data

–     Data Generation

–     Access Issues

• Measurements and Analysis Plan

–     Inductive Reasoning

–     Planning on data collection

• Quality of Data

–     Types of Error

–     Inference

• Survey Life Cycle

–     Design Perspective

–     Process Perspective

–     Quality Perspective

MODULE 2: Modes of Data Collection

The module reviews a range of survey data collection methods that are both interview-based (face-to-face and telephone) and self-administered (paper questionnaires that are mailed and those that are implemented online, i.e., as web surveys). Mixed mode designs are also covered as well as several hybrid modes for collecting sensitive information.

• Classic Modes of Survey Data Collection
  • Introduction to Survey Errors
  • Variable Error and Bias
• Interviewers and Interviewing
  • Wording vs Meaning
  • Standardized Interviewing
  • Proxy Responding
• Emerging Modes, new Data Sources
  • Mobile Web Surveys
  • Text Message Surveys
  • Text vs Voice Interviews
  • Social Media Responses

MODULE 3: Qualitative Data Collection Methods

This module presents a detailed overview of qualitative methods of data collection, including observation, interviews, and focus group discussions. We will start with an in-depth overview of each method, explore how to plan for data collection, including developing data collection guides and discuss techniques for managing data collection.

• Observation Techniques
  • Structured Observation
  • Unstructured Observation
• Interviews Designs
  • Interviewing Strategies
  • Questioning Strategies
• Focus Group Discussions
  • Designing Activities
  • Mock Demonstration
• Transcription
  • Process of Data Reduction
  • De-Identification
  • Working in Multilingual Contexts

MODULE 4: Sampling and Handling Missing Data

Good data collection is built on good samples.  But the samples can be chosen in many ways. We will examine simple random sampling that can be used for sampling persons or records. This module also covers the steps used in weighting sample surveys, including methods for adjusting for nonresponse. Alternative techniques for imputing values for missing items will be discussed.

• Sampling as a Research Tool
  • Need for Sampling
  • Surveys and Sampling
• Randomization
  • Output of Randomization
• General Steps in Weighting
  • Goals of Estimation
  • Improving Precision
• Imputing for Missing Items
  • Reasons for Imputation
  • Means and Hot deck Imputation
  • Regression Imputation
  • Effect on Variances

MODULE 5: Data Mining and Analysis

In this module, participants will learn how data mining can potentially find useful patterns from huge data sets. The insights derived from Data Mining are used for marketing, fraud detection, scientific discovery, etc.

• Data Mining Implementation Process
  • Business & Data Understanding
  • Data Preparation & Transformation
  • Data Modelling & Evaluation
  • Data Deployment
• Data Mining Process
  • Data Mining Techniques
  • Challenges in Implementation
  • Benefits and Disadvantages
  • Data Mining Applications

MODULE 6: Data Mining and Business Intelligence Tools

Based on the nature of data, there is a need for businesses to decide which tool is suitable to handle the data. In this module, participants will learn what all tools are available in the market.

• Introduction to Data Mining Tools
• Business Intelligence Tools
  • Gartner Analysis Report
  • Tableau vs PowerBI vs Qlik

Who should attend?

This course is suitable for beginners as well as those that know about one particular data source, but not others, and are looking for a general framework to evaluate data products. Applicable to students, working professionals, and PMETs.

System Requirements

Windows OS installed with Microsoft Excel 2016 or above.

Mode of Assessment
Written and Practical