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7/30/2019 Dataprocessing Report
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Alexis V. Larosa
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DataDataThe word data is derived from Latin language. It is pluralof Datum (But Data is usually used as a singular term.)Datum (singular) Data (plural). Data is any collectionof facts of figures. The data is the raw material to be
processed by a computer.Example
Names of students, marks obtained in the examination,designation of employees, addresses, quantity, rate,sales figures or anything that is input to the computer isdata. Even pictures, photographs, drawings, charts andmaps can be treated as data. Computer processes the
data and produces the output or result
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Types of Data
Mainly Data is divided into two types:1. Numeric Data2. Character Data
1. Numeric Data
The data which is represented in the form ofnumbers is known as Numeric Data. Thisincludes 0-9 digits, a decimal point (.), +,/,sign and the letters E or D.
2. Character DataCharacter data falls into two groups.i. String Dataii. Graphical Data
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String DataString data consists of the sequence ofcharacters. Characters may be English alphabets,numbers or space. The space, which separatestwo words, is also a character. The string data is
further divided into two types.a. Alphabetic Datab. Alphanumeric Data
Graphical DataIt is possible that pictures, charts and maps can
be treated as data. The scanner is normally usedto enter this type of data. The common use of thisdata is found in the National Identity Card.
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Information
A collection of data which conveys somemeaningful idea is information. It mayprovide answers to questions like who,which, when, why, what, and how.
or
The raw input is data and it has no
significance when it exists in that form.When data is collated or organized intosomething meaningful, it gainssignificance. This meaningful organizationis information
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or
Observations and recordings are done
to obtain data, while analysis is doneto obtain information
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Data Processing
Data processing:
Any operation or set of operationsperformed upon data, whether or notby automatic means, such ascollection, recording, organization,storage, adaptation or alteration to
convert it into useful information.
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Data Processing Cycle
Once data is collected, it is processed to convertit into useful information. The data is processedagain and again until the accurate result isachieved. This is called data processing cycle.
The data processing is very important activityand involves very careful planning. Usually, dataprocessing activity involves three basic activities.
1. Input2. Processing
3. Output
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Data Processing CycleStep-1
1. Input
It is the process through which collected datais transformed into a form that computer canunderstand. It is very important step becausecorrect output result totally depends on the input
data. In input step, following activities can beperformed.i) Verification
The collected data is verified to determinewhether it is correct as required. For example, the
collected data of all B.Sc. students that appeared infinal examination of the university is verified. Iferrors occur in collected data, data is corrected or itis collected again.will be given to the program as input for processing.
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ii) CodingThe verified data is coded or
converted into machine readable form
so that it can be processed throughcomputer.iii) Storing
The data is stored on the
secondary storage into a file. Thestored data on the storage media
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Data Processing CycleStep-2
2.ProcessingThe term processing denotes the actual data
manipulation techniques such as classifying, sorting,calculating, summarizing, comparing, etc. thatconvert data into information.
i) ClassificationThe data is classified into different groups and subgroups,so that each group or sub-group of data can be handledseparately.opeii) StoringThe data is arranged into an order so that it can beaccessed very quickly as and when required.
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iii) CalculationsThe arithmetic rations are performed on thenumeric data to get the required results. Forexample, total marks of each student are
calculated.iv) SummarizingThe data is processed to represent it in asummarized form. ft means that the summary of
data is prepared for top management. Forexample, the summary of the data of student isprepared to show the percentage of pass and failstudent examination etc.
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Data Processing CycleStep-3
3. OutputAfter completing the processing step, output is generated.
The main purpose of data processing is to get the required result.Mostly, the output is stored on the storage media for later user. Inoutput step, following activities can be performed.i) Retrieval
Output stored on the storage media can be retrieved at anytime. For example, result of students is prepared and stored on thedisk. This result can be retrieved when required for differentpurposes.ii).
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ConversionThe generated output can be converted into
different forms. For example, it can be representedinto graphical form.
iii) CommunicationThe generated output is sent to differentplaces. For example, weather forecast is preparedand. sent to different agencies and newspapers etc.where it is required
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Types of Data Processing
1. Manual Data Processing:This method ofdata processing involves
human intervention. The manual process of dataentry implies many opportunities for errors, suchas delays in data capture, as every single datafield has to be keyed in manually, a high amountof operator misprints or typos, high labor costsfrom the amount of manual labor required.
Manual processing also implies higher laborexpenses in regards to spending for equipmentand supplies, rent, etc.
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Types of Data Processing
EDPEDP (electronic data processing), an
infrequently used term for what is today usuallycalled "IS" (information services or systems) or
"MIS" (management information services orsystems), is the processing ofdata by a computerand its programs in an environment involvingelectronic communication. EDP evolved from "DP"(data processing), a term that was created whenmost computing input was physically put into thecomputer in punched card form or in ATM cardsform and output as punched cards or paperreports.
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Types of Data Processing
3.Real time processing
In a real time processing, there is a continualinput, process and output of data. Data has to beprocessed in a small stipulated time period (real
time), otherwise it will create problems for thesystem.For example, when a bank customer withdraws asum of money from his or her account it is vitalthat the transaction be processed and the account
balance updated as soon as possible, allowingboth the bank and customer to keep track offunds.
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Types of Data Processing
4.Batch processing
In a batch processing group oftransactions collected over a period of
time is collected, entered, processed andthen the batch results are produced. Batchprocessing requires seperate programs forinput, process and output. It is an efficient
way of processing high volume of data.For example: Payroll system, Examinationsystem and billing system.
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Computer Processing Operations
A computer can perform only thefollowing four operations which enablecomputers to carry out the various dataprocessing activities we have just
discussed.(a) Input/Output operationsA computer can accept data (input)
from and supply processed data (output)to a wide range of input/output devices.These devices such as keyboards, displayscreens, and printers make human-machine communication possible.
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Computer Processing Operations
(b) Calculation and text manipulation Operations
Computer circuits performcalculations on numbers. They arealso capable of manipulatingnumerics and other symbols used intext with equal efficiency.
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Computer Processing Operations
(c) Logic/Comparison Operations
A computer also possesses the ability to perform logicaloperations.
For example, if we compare two items represented by thesymbols A and B, there are only three possible outcomes.
A is less than B (AB).
A computer can perform such comparisons and the,depending on the result, follow a predetermined path tocomplete its work. This ability to compare is an importantproperty of computers.
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Computer Processing Operations
(d) Storage and Retrieval Operations
Both data and program instructions arestored internally in a computer.
Once they are stored in the internalmemory, they can be called up quicklyor retrieved, for further use.
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Generic algorithm in datacollection
How will the information be collected andanalysed?
What/who are the data sources?
What types of data are most appropriate?
What are the most appropriate methods of
data collection?
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How will the data be analysed andpresented in order to address the keyevaluation questions?
What ethical issues are involved in theevaluation and how will they addressed?
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Management techniques (includesproject, plant and sales engineering)
Information gathering techniques
Brainstorming
Delphi technique here a facilitator
distributes a questionnaire to experts,responses are summarized (anonymously) &re-circulated among the experts for
comments. This technique is used to achievea consensus of experts and helps to receiveunbiased data, ensuring that no one personwill have undue influence on the outcome
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Interviewing
Root cause analysis for identifying aproblem, discovering the causes that led to it
and developing preventive action Checklist analysis
Assumption analysis -this technique
may reveal an inconsistency ofassumptions, or uncover problematicassumptions.
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Diagramming techniques
Cause and effect diagrams
System or process flow charts
Influence diagrams graphical representationof situations, showing the casual influences orrelationships among variables and outcomes
SWOT analysis
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Expert judgment individuals who haveexperience with similar project in the nottoo distant past may use their
judgment through interviews or riskfacilitation workshops
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ICT Techniques
Manual Input Methods
Automated input methods
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Other techniques
Site Visits -These are used to obtainfirst-hand understanding of the processes,activities, physical environment
and working conditions
Focus Groups These are used to obtaindifferent reactions to one topic.
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Prototyping-Aprototypeis a model onwhich later stages or development isbased or judged. Prototypes are usually
primitive forms used to evaluate adesign. Prototypes may or may notactually work.
Simulation
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Data Analysis
Goal of an analysis:
* To explain cause-and-effectphenomena
* To relate research with real-worldevent
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* To predict/forecast the real-world
phenomena based on research
* Finding answers to a particularproblem
* Making conclusions about real-worldevent
based on the problem
* Learning a lesson from the problem
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An analysis must have four elements:
* Data/information (what)
* Scientific reasoning/argument (what?
who? where? how? what happens?)
* Finding (what results?)
* Lesson/conclusion (so what? so how?
therefore,)
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Qualitative Data Analysis
Qualitative analysis The non-numericalexamination and interpretation ofobservations, for the purpose of
discovering underlying meanings andpatterns of relationship.
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Linking Theory and Analysis
Discovering Patterns
1. Frequencies
2. Magnitudes
3. Structures
4. Processes
5. Causes
6. Consequences
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Cross-case analysis An analysis thatinvolves an examination of more than onecase; this can be either a variable-oriented or
case-oriented analysis.
Variable-oriented analysis An analysis thatdescribes and/or explains a particular variable.
Case-oriented analysis An analysis that aims tounderstand a particular case or several cases bylooking closely at the details of each.
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Grounded Theory Method (GTM) Aninductive approach to research in whichtheories are generated solely from an
examination of data rather than beingderived deductively.
Constant Comparative Method A componentof the Grounded Theory Method in whichobservations are compared with one anotherand with the evolving inductive theory.
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Four Stages of Constant ComparativeMethod (Glaser and Strauss):
1. Comparing incident application to each
category
2. Integrating categories and their properties
3. Delimiting the theory
4. Writing theory
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Semiotics The study of signs and themeanings associated with them, thescience of signs.
What is the meaning of:
a blue ribbon?
say cheese?
a cross?
a horseshoe?
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Conversation Analysis A meticulousanalysis of the details of conversation,based on a complete transcript that
includes pauses, hems and haws.
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Fundamental Assumptions
1. Conversation is a socially constructed activity.
2. Conversations must be understood contextually.
3. Conversational analysis aims to understand thestructure and meaning of conversation throughexcruciatingly accurate transcripts of conversations.
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Qualitative Data Processing
Coding Classifying or categorizingindividual pieces of data, coupled withsome kind of retrieval system.
Coding Units
Coding as a Physical Act
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Creating CodesOpen Coding the initial classification and labeling of
concepts in qualitative data analysis.
Axial coding a reanalysis of the results of opencoding in the GTM, aimed at identifying the important,general concepts.
Selective coding in GTM, this builds on the results of
open coding and axial coding to identify the centralconcept that organizes the other concepts that havebeen identified in a body of textual materials.
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Memoing Writing memos that becomepart of the data for analysis in qualitativeresearch such as grounded theory. Memos
may describe and define concepts, dealwith methodologies issues, or offer initialtheoretical formulations.Code Notes
Theoretical Notes
Operational Notes
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Concept Mapping The graphic display ofconcepts and their interrelations, useful inthe formulation of theory.
Computer Programs for Qualitative
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Computer Programs for QualitativeData
QDA Programs
Example: Leviticus as Seen ThroughNUD*IST
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Example: Using NVivo to UnderstandWomen Film Directors
Evaluating the Quality of
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Evaluating the Quality ofQualitative Research
Validity
Reliability
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Questions/Assessments for Evaluating QualitativeResearch1. How credible are the findings?2. How has knowledge or understanding been extended
by the research?3. How well does the evaluation address its originalaims and purpose?
4. How well is the scope for drawing wider inferencesexplained?
5. How clear is the basis of evaluative appraisal?6. How defensible is the research design?7. How well defended are the same design/target
selection of cases/documents?
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Quantitative Data Analysis
Summarizing Data: variables; simple statistics;
effect statistics and statistical models;
complex models.
Generalizing from Sample to Population:
precision of estimate, confidence limits,
statistical significance, p value, errors.
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Common descriptive statistics
Count (frequencies) Percentage
Mean
Mode Median
Range
Standard deviation
Variance
Ranking
T l t t tit ti d t
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Spreadsheet simple to use, basic
graphs
Statistical packages, e.g. SPSS
Tools to support quantitative dataanalysis
Whi h l l ti d I ? It d d
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Which calculation do I use? It depends uponwhat you want to know.
Do you want to know how many individualschecked each answer?
Frequency
Do you want the proportion of people whoanswered in a certain way?
Percentage
Do you want the average number or averagescore?
Mean
Do you want the middle value in a range ofvalues or scores?
Median
Do you want to show the range in answers or
scores?
Range
Do you want to compare one group to another? Cross tab
Do you want to report changes from pre to post? Change score
Do you want to show the degree to which aresponse varies from the mean?
Standard deviation
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