METHODS OF DATA COLLECTION BY ADEDOYIN SAMUEL ADEBAYO INTERNATIONAL BLACK SEA UNIVERSITY TBILISI - GEORGIA MA IN EDUCATION STUDENT NO:12500151 LECTURER: PROF. IRINA BAKHTADZE METHODS OF COLLECTING DATA Introduction: Data Collection is an important aspect of any type of research study. Inaccurate data collection can impact the results of a study and ultimately lead to invalid results. Data collection methods for impact evaluation vary along a continuum. At the one end
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Data & Knowledge Engineering Introduction Database Systems and Knowledgebase Systems share many common principles. Data & Knowledge Engineering (DKE) stimulates the exchange of ideas and interaction between these two related fields of interest. DKEreaches a world-wide audience of researchers‚ designers‚ managers and users. The major aim of the journal is to identify‚ investigate and analyze the underlying principles in the design and effective use of these systems.DKE achieves this aim
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known as the “Cybercrime Prevention Act of 2012″. SEC. 2. Declaration of Policy. — The State recognizes the vital role of information and communications industries such as content production‚ telecommunications‚ broadcasting electronic commerce‚ and data processing‚ in the nation’s overall social and economic development. The State also recognizes the importance of providing an environment conducive to the development‚ acceleration‚ and rational application and exploitation of information and communications
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Factors that influence the selection of data collection instruments Data Collection is an important aspect of any type of research study. Inaccurate data collection can impact the results of a study and ultimately lead to invalid results. Data collection methods for impact evaluation vary along a continuum. At the one end of this continuum are quantitative methods and at the other end of the continuum are Qualitative methods for data collection. A data collection instrument is a tool for monitoring
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Data Depth and Optimization Komei Fukuda fukuda@ifor.math.ethz.ch Vera Rosta rosta@renyi.hu In this short article‚ we consider the notion of data depth which generalizes the median to higher dimensions. Our main objective is to present a snapshot of the data depth‚ several closely related notions‚ associated optimization problems and algorithms. In particular‚ we briefly touch on our recent approaches to compute the data depth using linear and integer optimization programming. Although
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Basic Business Statistics 11th Edition Chapter 1 Introduction and Data Collection Basic Business Statistics‚ 11e © 2009 Prentice-Hall‚ Inc. Chap 1-1 Learning Objectives In this chapter you learn: How Statistics is used in business The sources of data used in business The types of data used in business The basics of Microsoft Excel The basics of Minitab Basic Business Statistics‚ 11e © 2009 Prentice-Hall‚ Inc.. Chap 1-2 Why Learn Statistics? So you are
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Chapter 5: The Data Link Layer Our goals: ❒ understand principles behind data link layer services: ❍ ❍ ❍ ❍ ❒ error detection‚ correction sharing a broadcast channel: multiple access link layer addressing reliable data transfer‚ flow control: done! instantiation and implementation of various link layer technologies 5: DataLink Layer 5-1 Link Layer ❒ ❒ ❒ ❒ ❒ 5.1 Introduction and services 5.2 Error detection and correction 5.3Multiple access protocols 5
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System Based On Web Data Mining for Personalized E-learning Jinhua Sun Department of Computer Science and Technology Xiamen University of Technology‚ XMUT Xiamen‚ China jhsun@xmut.edu.cn Yanqi Xie Department of Computer Science and Technology Xiamen University of Technology‚ XMUT Xiamen‚ China yqxie@xmut.edu.cn Abstract—In this paper‚ we introduce a web data mining solution to e-learning system to discover hidden patterns strategies from their learners and web data‚ describe a personalized recommender
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communication system in which hosts are connected through a network 2-20 Computer Science CS677: Distributed OS Lecture 1‚ page 3 Persistence • Persistent communication – Messages are stored until (next) receiver is ready – Examples: email‚ pony express Computer Science CS677: Distributed OS Lecture 1‚ page 4 Transient Communication • Transient communication – Message is stored only so long as sending/receiving application are executing – Discard message if it can’t
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Transforming Logical Data Models into Physical Data Models Susan Dash Ralph Reilly IT610-1404A-01 According to an article written by Tom Haughey the process for transforming a logical data model into a physical data model is: The business authorization to proceed is received. Business requirements are gathered and represented in a logical data model which will completely represent the business data requirements and will be non-redundant. The logical model is then transformed into a first cut physical
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