SlideShare une entreprise Scribd logo
1  sur  14
Télécharger pour lire hors ligne
Manoj Patel
Assistant Professor
Jhunjhunwala Business School
Data Collection
{Tools, Techniques, Methods}
What is DATA?
Informatio
n
Statistics
Figures
Numbers
Facts
Records
What does web say about data??
 a collection of facts from which conclusions may be
drawn; "statistical data”
(wordnetweb.princeton.edu/perl/webwn)
 The term data means groups of information that
represent the qualitative or quantitative attributes of
a variable or set of variables(en.wikipedia.org/wiki)
 information; A collection of object-units that are
distinct from one another. (en.wiktionary.org/wiki/data)
 Data is Information that has been organised and
categorised for a pre-determined purpose.
(news.miuegypt.edu.eg/index.php)
What does web say about data??
 In computer science, data is anything in a form
suitable for use with a computer. Data is often
distinguished from programs. A program is a set of
instructions that detail a task for the computer to perform.
In this sense, data is thus everything that is not program
code. (en.wikipedia.org/wiki/Data_(computing))
 are the smallest units of measure. The word is
technically the plural of datum but often used as a
singular. Data are the components of information. They
may be the 1's and 0's of computer memory, names and
addresses in a demographic file, or the raw facts and
figures before interpretation.
(home.earthlink.net/~ddstuhlman/defin1.htm)
Types of Data
PRIMARY
 Collected by
researcher first hand
 Demands efforts and
resources
 Depends upon the
researcher’s ability
and clarity of
purpose
SECONDARY
 Collected by someone else
but used by researcher
second hand
 Cheaper and quicker
 Needs lesser resources
 Have to ascertain accuracy of
content/time/sources/
purpose/methods/
adequacy/ credibility
 Various sources/forms
Data Collection…steps
 Construction of tools for data collection
 Decision about techniques of data collection
 Testing the tool/technique by Pilot study or
Pre-testing of tool/technique
 Finalization of tool/technique
 Ascertaining reliability and validity of
tools/techniques to be used for data collection
 Actual collection of data
Data collection tools & techniques
TOOLS
 Questionnaire
 Interview schedule
 Observation schedule
 Scales
 Tests
 Inventory, Checklist,
Opinionnaire
 Sociogram/Sociometry
TEHNIQUES
 Questioning: Written,
Oral
 Interviewing: Face to
face, Telephonic,
Electronic/Net, Group,
Video
 Observation
 Projective Techniques
 Panel Methods (Diary,
Checklist, Logs etc.)
Factors influencing decision about data
collection Tool/Technique/Method
 Scale and magnitude of the study
 Characteristics of the respondents
 Unit of inquiry and analysis
 Availability of resources: Money, Time,
Human, Technical, Competence
 Field Conditions
 Subject under study
 Expected outcome
 Degree of precision/reliability required
Decisions about data collection Method
 Settings: Natural – Contrived/Artificial
 Inquiry: Obstructive/Undisguised –
Unobstructive/Disguised
 Nature: Qualitative – Quantitative
 Structure:
Structured – Semi structured – Unstructured
 Questions: Open ended – Closed ended
 Administration: Human – Mechanical
 Analysis: Pre coded – Not coded
Data comes through….
Tools & Techniques
METHOD
Procedure
Framing of Questions…
 Length of a question/tool
 Language, Sequence, Style
 Objective of asking (measuring what?)
 Structural issues:
Embarrassing/personalisation,
Leading/Directive, Assumptions/presumptions,
Hypothetical/ambiguous, median replies,
Loaded/ Inbuilt coercion-forced, Double
barreled, Double negatives
When you collect Data…YOU must-
Record time (time of the day/date/
month/year) when you collected it
Total time (number of days/months/
years) it took to collect it along with field
note for each response
Procedure you followed to collect it
Average time per respondent/unit
Experiences you had collecting it
Right Question…?!
United Nations conducted a Worldwide survey. The
question asked was:
"Would you please give your honest opinion about
solutions to the food shortage in the rest of the world?"
The survey was a huge failure.
Africa didn't know what 'food' meant, India didn't
know what 'honest' meant, Europe didn't know what
'shortage' meant, China didn't know what 'opinion'
meant, the Middle East didn't know what 'solution'
meant, South America didn't know what 'please' meant,
And in the USA they didn't know what 'the rest of the
world' meant !!
Good DATA depends upon…
 Clarity of purpose/objectives of the study
 Appropriateness of tool/technique
 Sharpness of the tool and abilities of
investigator/researcher in using the
techniques
 Cooperation/rapport with the
respondents
 Decisions about utilization at analysis
stage

Contenu connexe

Tendances

Research skills = life skills
Research skills = life skillsResearch skills = life skills
Research skills = life skillsKristy Nelson
 
Research Plan Development Workshop, Mario Tabucanon and Philip Vaughter, UNU-IAS
Research Plan Development Workshop, Mario Tabucanon and Philip Vaughter, UNU-IASResearch Plan Development Workshop, Mario Tabucanon and Philip Vaughter, UNU-IAS
Research Plan Development Workshop, Mario Tabucanon and Philip Vaughter, UNU-IASHanna Stahlberg
 
Analytical Methods for Systematic Review Support
Analytical Methods for Systematic Review SupportAnalytical Methods for Systematic Review Support
Analytical Methods for Systematic Review SupportDouglas Joubert
 
Methods and Data Collection
Methods and Data CollectionMethods and Data Collection
Methods and Data CollectionEverettProgram
 
Lessons from the 2013 IOM Evaluation of PEPFAR: Use of Case-Based Approaches
Lessons from the 2013 IOM Evaluation of PEPFAR: Use of Case-Based ApproachesLessons from the 2013 IOM Evaluation of PEPFAR: Use of Case-Based Approaches
Lessons from the 2013 IOM Evaluation of PEPFAR: Use of Case-Based ApproachesJSI
 
Merging the ideal with the real
Merging the ideal with the realMerging the ideal with the real
Merging the ideal with the realJisc
 
Basic research & documentation skills
Basic research & documentation skillsBasic research & documentation skills
Basic research & documentation skillsIndraneel Bhowmik
 
Selecting a Research Topic
Selecting a Research TopicSelecting a Research Topic
Selecting a Research Topicjamieduic
 
Open from beginning to end: addressing barriers to open research - a personal...
Open from beginning to end: addressing barriers to open research - a personal...Open from beginning to end: addressing barriers to open research - a personal...
Open from beginning to end: addressing barriers to open research - a personal...UoLResearchSupport
 
Research in language and literature, karpagam university, conference ppt
Research in language and literature, karpagam university, conference pptResearch in language and literature, karpagam university, conference ppt
Research in language and literature, karpagam university, conference pptvijay kumar
 
Advances in qualitative and quantitative fieldwork - Microcon fafo june 2011
Advances in qualitative and quantitative fieldwork - Microcon fafo june 2011Advances in qualitative and quantitative fieldwork - Microcon fafo june 2011
Advances in qualitative and quantitative fieldwork - Microcon fafo june 2011freida_m
 
Designing impactful research in social sciences
Designing impactful research in social sciencesDesigning impactful research in social sciences
Designing impactful research in social sciencesvijay kumar
 
Case study approach as a pedagogy in management
Case study approach as a pedagogy in managementCase study approach as a pedagogy in management
Case study approach as a pedagogy in managementsmitaj
 

Tendances (15)

Research skills = life skills
Research skills = life skillsResearch skills = life skills
Research skills = life skills
 
Research Plan Development Workshop, Mario Tabucanon and Philip Vaughter, UNU-IAS
Research Plan Development Workshop, Mario Tabucanon and Philip Vaughter, UNU-IASResearch Plan Development Workshop, Mario Tabucanon and Philip Vaughter, UNU-IAS
Research Plan Development Workshop, Mario Tabucanon and Philip Vaughter, UNU-IAS
 
Analytical Methods for Systematic Review Support
Analytical Methods for Systematic Review SupportAnalytical Methods for Systematic Review Support
Analytical Methods for Systematic Review Support
 
Methods and Data Collection
Methods and Data CollectionMethods and Data Collection
Methods and Data Collection
 
Lessons from the 2013 IOM Evaluation of PEPFAR: Use of Case-Based Approaches
Lessons from the 2013 IOM Evaluation of PEPFAR: Use of Case-Based ApproachesLessons from the 2013 IOM Evaluation of PEPFAR: Use of Case-Based Approaches
Lessons from the 2013 IOM Evaluation of PEPFAR: Use of Case-Based Approaches
 
Merging the ideal with the real
Merging the ideal with the realMerging the ideal with the real
Merging the ideal with the real
 
Basic research & documentation skills
Basic research & documentation skillsBasic research & documentation skills
Basic research & documentation skills
 
Selecting a Research Topic
Selecting a Research TopicSelecting a Research Topic
Selecting a Research Topic
 
Open from beginning to end: addressing barriers to open research - a personal...
Open from beginning to end: addressing barriers to open research - a personal...Open from beginning to end: addressing barriers to open research - a personal...
Open from beginning to end: addressing barriers to open research - a personal...
 
Primary research wod
Primary research wodPrimary research wod
Primary research wod
 
Research in language and literature, karpagam university, conference ppt
Research in language and literature, karpagam university, conference pptResearch in language and literature, karpagam university, conference ppt
Research in language and literature, karpagam university, conference ppt
 
Advances in qualitative and quantitative fieldwork - Microcon fafo june 2011
Advances in qualitative and quantitative fieldwork - Microcon fafo june 2011Advances in qualitative and quantitative fieldwork - Microcon fafo june 2011
Advances in qualitative and quantitative fieldwork - Microcon fafo june 2011
 
Designing impactful research in social sciences
Designing impactful research in social sciencesDesigning impactful research in social sciences
Designing impactful research in social sciences
 
Case study approach as a pedagogy in management
Case study approach as a pedagogy in managementCase study approach as a pedagogy in management
Case study approach as a pedagogy in management
 
Anonymisation 101
Anonymisation 101Anonymisation 101
Anonymisation 101
 

En vedette

A NEW APPROACH TOWARDS INFORMATION SECURITY BASED ON DNA CRYPTOGRAPHY
A NEW APPROACH TOWARDS INFORMATION SECURITY BASED ON  DNA CRYPTOGRAPHY A NEW APPROACH TOWARDS INFORMATION SECURITY BASED ON  DNA CRYPTOGRAPHY
A NEW APPROACH TOWARDS INFORMATION SECURITY BASED ON DNA CRYPTOGRAPHY Abhishek Majumdar
 
DNA based Cryptography_Final_Review
DNA based Cryptography_Final_ReviewDNA based Cryptography_Final_Review
DNA based Cryptography_Final_ReviewRasheed Karuvally
 
Purposes of observation
Purposes of observationPurposes of observation
Purposes of observationNelyloves Yap
 
A new DNA encryption technique for secure data transmission with authenticati...
A new DNA encryption technique for secure data transmission with authenticati...A new DNA encryption technique for secure data transmission with authenticati...
A new DNA encryption technique for secure data transmission with authenticati...Sajedul Karim
 
Grouping students; important facts about the chapter
Grouping students; important facts about the chapter Grouping students; important facts about the chapter
Grouping students; important facts about the chapter chus1987
 

En vedette (7)

A NEW APPROACH TOWARDS INFORMATION SECURITY BASED ON DNA CRYPTOGRAPHY
A NEW APPROACH TOWARDS INFORMATION SECURITY BASED ON  DNA CRYPTOGRAPHY A NEW APPROACH TOWARDS INFORMATION SECURITY BASED ON  DNA CRYPTOGRAPHY
A NEW APPROACH TOWARDS INFORMATION SECURITY BASED ON DNA CRYPTOGRAPHY
 
Dna cryptography
Dna cryptographyDna cryptography
Dna cryptography
 
DNA based Cryptography_Final_Review
DNA based Cryptography_Final_ReviewDNA based Cryptography_Final_Review
DNA based Cryptography_Final_Review
 
Purposes of observation
Purposes of observationPurposes of observation
Purposes of observation
 
A new DNA encryption technique for secure data transmission with authenticati...
A new DNA encryption technique for secure data transmission with authenticati...A new DNA encryption technique for secure data transmission with authenticati...
A new DNA encryption technique for secure data transmission with authenticati...
 
Event sampling
Event samplingEvent sampling
Event sampling
 
Grouping students; important facts about the chapter
Grouping students; important facts about the chapter Grouping students; important facts about the chapter
Grouping students; important facts about the chapter
 

Similaire à Data collection

T3 data collecting techniques
T3 data collecting techniquesT3 data collecting techniques
T3 data collecting techniqueskompellark
 
Tenc Winterschool09 Davinia Slideshare
Tenc Winterschool09 Davinia SlideshareTenc Winterschool09 Davinia Slideshare
Tenc Winterschool09 Davinia Slideshareguest94c824
 
Articulation
Articulation Articulation
Articulation butest
 
Quantitative search and_qualitative_research by mubarak
Quantitative search and_qualitative_research by mubarakQuantitative search and_qualitative_research by mubarak
Quantitative search and_qualitative_research by mubarakHafiza Abas
 
Tools and techniques in qualitative and quantitative research
Tools and techniques in qualitative and quantitative researchTools and techniques in qualitative and quantitative research
Tools and techniques in qualitative and quantitative researchDeepikakohli10
 
Data Interview and Data Management Plans
Data Interview and Data Management PlansData Interview and Data Management Plans
Data Interview and Data Management PlansJulie Goldman
 
Assistive Technology Webquest - Whitlow
Assistive Technology Webquest - WhitlowAssistive Technology Webquest - Whitlow
Assistive Technology Webquest - Whitlowjwhitlow6
 
Collaborative work part IV DATA COLLECTION INSTRUMENTS
Collaborative work part IV DATA COLLECTION INSTRUMENTSCollaborative work part IV DATA COLLECTION INSTRUMENTS
Collaborative work part IV DATA COLLECTION INSTRUMENTSandrevallejo1217
 
Chapter IV. DATA COLLECTION INSTRUMENTS
Chapter IV. DATA COLLECTION INSTRUMENTS Chapter IV. DATA COLLECTION INSTRUMENTS
Chapter IV. DATA COLLECTION INSTRUMENTS Cristina Tamayo
 
Research and advocacy by Seetal Daas
Research and advocacy by Seetal DaasResearch and advocacy by Seetal Daas
Research and advocacy by Seetal DaasSeetal Daas
 
ICT in education
ICT in educationICT in education
ICT in educationsneha1112
 
Technology Motivators and Usage in Non-Profit Arts Organizations
Technology Motivators and Usage in Non-Profit Arts OrganizationsTechnology Motivators and Usage in Non-Profit Arts Organizations
Technology Motivators and Usage in Non-Profit Arts OrganizationsCAMT
 

Similaire à Data collection (20)

Data collection
Data collectionData collection
Data collection
 
Data analysis
Data analysisData analysis
Data analysis
 
T3 data collecting techniques
T3 data collecting techniquesT3 data collecting techniques
T3 data collecting techniques
 
Tenc Winterschool09 Davinia Slideshare
Tenc Winterschool09 Davinia SlideshareTenc Winterschool09 Davinia Slideshare
Tenc Winterschool09 Davinia Slideshare
 
Articulation
Articulation Articulation
Articulation
 
Quantitative search and_qualitative_research by mubarak
Quantitative search and_qualitative_research by mubarakQuantitative search and_qualitative_research by mubarak
Quantitative search and_qualitative_research by mubarak
 
Ecer 2011
Ecer 2011Ecer 2011
Ecer 2011
 
Ecer 2011
Ecer 2011Ecer 2011
Ecer 2011
 
Tools and techniques in qualitative and quantitative research
Tools and techniques in qualitative and quantitative researchTools and techniques in qualitative and quantitative research
Tools and techniques in qualitative and quantitative research
 
Data Interview and Data Management Plans
Data Interview and Data Management PlansData Interview and Data Management Plans
Data Interview and Data Management Plans
 
Assistive Technology Webquest - Whitlow
Assistive Technology Webquest - WhitlowAssistive Technology Webquest - Whitlow
Assistive Technology Webquest - Whitlow
 
Collaborative work part IV DATA COLLECTION INSTRUMENTS
Collaborative work part IV DATA COLLECTION INSTRUMENTSCollaborative work part IV DATA COLLECTION INSTRUMENTS
Collaborative work part IV DATA COLLECTION INSTRUMENTS
 
SETT
SETTSETT
SETT
 
The scientific method
The scientific methodThe scientific method
The scientific method
 
Chapter IV. DATA COLLECTION INSTRUMENTS
Chapter IV. DATA COLLECTION INSTRUMENTS Chapter IV. DATA COLLECTION INSTRUMENTS
Chapter IV. DATA COLLECTION INSTRUMENTS
 
Research and advocacy by Seetal Daas
Research and advocacy by Seetal DaasResearch and advocacy by Seetal Daas
Research and advocacy by Seetal Daas
 
Whole
WholeWhole
Whole
 
ICT in education
ICT in educationICT in education
ICT in education
 
Descriptive Method
Descriptive MethodDescriptive Method
Descriptive Method
 
Technology Motivators and Usage in Non-Profit Arts Organizations
Technology Motivators and Usage in Non-Profit Arts OrganizationsTechnology Motivators and Usage in Non-Profit Arts Organizations
Technology Motivators and Usage in Non-Profit Arts Organizations
 

Plus de Research Scholar - HNB Garhwal Central University, Srinagar, Uttarakhand.

Plus de Research Scholar - HNB Garhwal Central University, Srinagar, Uttarakhand. (20)

Sbi history and merger
Sbi history and mergerSbi history and merger
Sbi history and merger
 
Uint 2 learning (o.b)
Uint  2 learning (o.b)Uint  2 learning (o.b)
Uint 2 learning (o.b)
 
Tourism scope in India
Tourism scope in IndiaTourism scope in India
Tourism scope in India
 
OB Introduction, Scope, Challenges and Opportunities, goal and OB Model
OB Introduction, Scope, Challenges and Opportunities, goal and OB Model OB Introduction, Scope, Challenges and Opportunities, goal and OB Model
OB Introduction, Scope, Challenges and Opportunities, goal and OB Model
 
NOTES RETAIL AND DISTRIBUTION MANAGEMENT
NOTES RETAIL AND DISTRIBUTION MANAGEMENT NOTES RETAIL AND DISTRIBUTION MANAGEMENT
NOTES RETAIL AND DISTRIBUTION MANAGEMENT
 
NOTES RETAIL AND DISTRIBUTION MANAGEMENT
NOTES RETAIL AND DISTRIBUTION MANAGEMENT NOTES RETAIL AND DISTRIBUTION MANAGEMENT
NOTES RETAIL AND DISTRIBUTION MANAGEMENT
 
NOTES RETAIL AND DISTRIBUTION MANAGEMENT
NOTES RETAIL AND DISTRIBUTION MANAGEMENT NOTES RETAIL AND DISTRIBUTION MANAGEMENT
NOTES RETAIL AND DISTRIBUTION MANAGEMENT
 
NOTES RETAIL AND DISTRIBUTION MANAGEMENT
NOTES RETAIL AND DISTRIBUTION MANAGEMENT NOTES RETAIL AND DISTRIBUTION MANAGEMENT
NOTES RETAIL AND DISTRIBUTION MANAGEMENT
 
Liberalization, Privatization, Globalization
Liberalization, Privatization, Globalization Liberalization, Privatization, Globalization
Liberalization, Privatization, Globalization
 
Analysis of variance anova
Analysis of variance anovaAnalysis of variance anova
Analysis of variance anova
 
Industrial policy
Industrial policyIndustrial policy
Industrial policy
 
Type of economy
Type of economyType of economy
Type of economy
 
What is business environment
What is business environmentWhat is business environment
What is business environment
 
Business responsibility
Business responsibilityBusiness responsibility
Business responsibility
 
Notes unit ii
Notes unit iiNotes unit ii
Notes unit ii
 
Marketing environment.
Marketing environment.Marketing environment.
Marketing environment.
 
Indian business environment
Indian business environmentIndian business environment
Indian business environment
 
Technology Embalmed in Parle-G
Technology Embalmed in Parle-GTechnology Embalmed in Parle-G
Technology Embalmed in Parle-G
 
Unempolyement in India
Unempolyement in IndiaUnempolyement in India
Unempolyement in India
 
ECONOMIC TRENDS, Monetary policy of India,
ECONOMIC TRENDS, Monetary policy of India, ECONOMIC TRENDS, Monetary policy of India,
ECONOMIC TRENDS, Monetary policy of India,
 

Dernier

Predicting Salary Using Data Science: A Comprehensive Analysis.pdf
Predicting Salary Using Data Science: A Comprehensive Analysis.pdfPredicting Salary Using Data Science: A Comprehensive Analysis.pdf
Predicting Salary Using Data Science: A Comprehensive Analysis.pdfBoston Institute of Analytics
 
NLP Project PPT: Flipkart Product Reviews through NLP Data Science.pptx
NLP Project PPT: Flipkart Product Reviews through NLP Data Science.pptxNLP Project PPT: Flipkart Product Reviews through NLP Data Science.pptx
NLP Project PPT: Flipkart Product Reviews through NLP Data Science.pptxBoston Institute of Analytics
 
Student Profile Sample report on improving academic performance by uniting gr...
Student Profile Sample report on improving academic performance by uniting gr...Student Profile Sample report on improving academic performance by uniting gr...
Student Profile Sample report on improving academic performance by uniting gr...Seán Kennedy
 
Top 5 Best Data Analytics Courses In Queens
Top 5 Best Data Analytics Courses In QueensTop 5 Best Data Analytics Courses In Queens
Top 5 Best Data Analytics Courses In Queensdataanalyticsqueen03
 
RS 9000 Call In girls Dwarka Mor (DELHI)⇛9711147426🔝Delhi
RS 9000 Call In girls Dwarka Mor (DELHI)⇛9711147426🔝DelhiRS 9000 Call In girls Dwarka Mor (DELHI)⇛9711147426🔝Delhi
RS 9000 Call In girls Dwarka Mor (DELHI)⇛9711147426🔝Delhijennyeacort
 
ASML's Taxonomy Adventure by Daniel Canter
ASML's Taxonomy Adventure by Daniel CanterASML's Taxonomy Adventure by Daniel Canter
ASML's Taxonomy Adventure by Daniel Cantervoginip
 
Data Factory in Microsoft Fabric (MsBIP #82)
Data Factory in Microsoft Fabric (MsBIP #82)Data Factory in Microsoft Fabric (MsBIP #82)
Data Factory in Microsoft Fabric (MsBIP #82)Cathrine Wilhelmsen
 
Biometric Authentication: The Evolution, Applications, Benefits and Challenge...
Biometric Authentication: The Evolution, Applications, Benefits and Challenge...Biometric Authentication: The Evolution, Applications, Benefits and Challenge...
Biometric Authentication: The Evolution, Applications, Benefits and Challenge...GQ Research
 
Semantic Shed - Squashing and Squeezing.pptx
Semantic Shed - Squashing and Squeezing.pptxSemantic Shed - Squashing and Squeezing.pptx
Semantic Shed - Squashing and Squeezing.pptxMike Bennett
 
毕业文凭制作#回国入职#diploma#degree澳洲中央昆士兰大学毕业证成绩单pdf电子版制作修改#毕业文凭制作#回国入职#diploma#degree
毕业文凭制作#回国入职#diploma#degree澳洲中央昆士兰大学毕业证成绩单pdf电子版制作修改#毕业文凭制作#回国入职#diploma#degree毕业文凭制作#回国入职#diploma#degree澳洲中央昆士兰大学毕业证成绩单pdf电子版制作修改#毕业文凭制作#回国入职#diploma#degree
毕业文凭制作#回国入职#diploma#degree澳洲中央昆士兰大学毕业证成绩单pdf电子版制作修改#毕业文凭制作#回国入职#diploma#degreeyuu sss
 
2006_GasProcessing_HB (1).pdf HYDROCARBON PROCESSING
2006_GasProcessing_HB (1).pdf HYDROCARBON PROCESSING2006_GasProcessing_HB (1).pdf HYDROCARBON PROCESSING
2006_GasProcessing_HB (1).pdf HYDROCARBON PROCESSINGmarianagonzalez07
 
RABBIT: A CLI tool for identifying bots based on their GitHub events.
RABBIT: A CLI tool for identifying bots based on their GitHub events.RABBIT: A CLI tool for identifying bots based on their GitHub events.
RABBIT: A CLI tool for identifying bots based on their GitHub events.natarajan8993
 
20240419 - Measurecamp Amsterdam - SAM.pdf
20240419 - Measurecamp Amsterdam - SAM.pdf20240419 - Measurecamp Amsterdam - SAM.pdf
20240419 - Measurecamp Amsterdam - SAM.pdfHuman37
 
Identifying Appropriate Test Statistics Involving Population Mean
Identifying Appropriate Test Statistics Involving Population MeanIdentifying Appropriate Test Statistics Involving Population Mean
Identifying Appropriate Test Statistics Involving Population MeanMYRABACSAFRA2
 
办理学位证纽约大学毕业证(NYU毕业证书)原版一比一
办理学位证纽约大学毕业证(NYU毕业证书)原版一比一办理学位证纽约大学毕业证(NYU毕业证书)原版一比一
办理学位证纽约大学毕业证(NYU毕业证书)原版一比一fhwihughh
 
Statistics, Data Analysis, and Decision Modeling, 5th edition by James R. Eva...
Statistics, Data Analysis, and Decision Modeling, 5th edition by James R. Eva...Statistics, Data Analysis, and Decision Modeling, 5th edition by James R. Eva...
Statistics, Data Analysis, and Decision Modeling, 5th edition by James R. Eva...ssuserf63bd7
 
1:1定制(UQ毕业证)昆士兰大学毕业证成绩单修改留信学历认证原版一模一样
1:1定制(UQ毕业证)昆士兰大学毕业证成绩单修改留信学历认证原版一模一样1:1定制(UQ毕业证)昆士兰大学毕业证成绩单修改留信学历认证原版一模一样
1:1定制(UQ毕业证)昆士兰大学毕业证成绩单修改留信学历认证原版一模一样vhwb25kk
 
9711147426✨Call In girls Gurgaon Sector 31. SCO 25 escort service
9711147426✨Call In girls Gurgaon Sector 31. SCO 25 escort service9711147426✨Call In girls Gurgaon Sector 31. SCO 25 escort service
9711147426✨Call In girls Gurgaon Sector 31. SCO 25 escort servicejennyeacort
 
办理学位证中佛罗里达大学毕业证,UCF成绩单原版一比一
办理学位证中佛罗里达大学毕业证,UCF成绩单原版一比一办理学位证中佛罗里达大学毕业证,UCF成绩单原版一比一
办理学位证中佛罗里达大学毕业证,UCF成绩单原版一比一F sss
 

Dernier (20)

Predicting Salary Using Data Science: A Comprehensive Analysis.pdf
Predicting Salary Using Data Science: A Comprehensive Analysis.pdfPredicting Salary Using Data Science: A Comprehensive Analysis.pdf
Predicting Salary Using Data Science: A Comprehensive Analysis.pdf
 
NLP Project PPT: Flipkart Product Reviews through NLP Data Science.pptx
NLP Project PPT: Flipkart Product Reviews through NLP Data Science.pptxNLP Project PPT: Flipkart Product Reviews through NLP Data Science.pptx
NLP Project PPT: Flipkart Product Reviews through NLP Data Science.pptx
 
Student Profile Sample report on improving academic performance by uniting gr...
Student Profile Sample report on improving academic performance by uniting gr...Student Profile Sample report on improving academic performance by uniting gr...
Student Profile Sample report on improving academic performance by uniting gr...
 
Top 5 Best Data Analytics Courses In Queens
Top 5 Best Data Analytics Courses In QueensTop 5 Best Data Analytics Courses In Queens
Top 5 Best Data Analytics Courses In Queens
 
RS 9000 Call In girls Dwarka Mor (DELHI)⇛9711147426🔝Delhi
RS 9000 Call In girls Dwarka Mor (DELHI)⇛9711147426🔝DelhiRS 9000 Call In girls Dwarka Mor (DELHI)⇛9711147426🔝Delhi
RS 9000 Call In girls Dwarka Mor (DELHI)⇛9711147426🔝Delhi
 
ASML's Taxonomy Adventure by Daniel Canter
ASML's Taxonomy Adventure by Daniel CanterASML's Taxonomy Adventure by Daniel Canter
ASML's Taxonomy Adventure by Daniel Canter
 
Data Factory in Microsoft Fabric (MsBIP #82)
Data Factory in Microsoft Fabric (MsBIP #82)Data Factory in Microsoft Fabric (MsBIP #82)
Data Factory in Microsoft Fabric (MsBIP #82)
 
Biometric Authentication: The Evolution, Applications, Benefits and Challenge...
Biometric Authentication: The Evolution, Applications, Benefits and Challenge...Biometric Authentication: The Evolution, Applications, Benefits and Challenge...
Biometric Authentication: The Evolution, Applications, Benefits and Challenge...
 
Semantic Shed - Squashing and Squeezing.pptx
Semantic Shed - Squashing and Squeezing.pptxSemantic Shed - Squashing and Squeezing.pptx
Semantic Shed - Squashing and Squeezing.pptx
 
毕业文凭制作#回国入职#diploma#degree澳洲中央昆士兰大学毕业证成绩单pdf电子版制作修改#毕业文凭制作#回国入职#diploma#degree
毕业文凭制作#回国入职#diploma#degree澳洲中央昆士兰大学毕业证成绩单pdf电子版制作修改#毕业文凭制作#回国入职#diploma#degree毕业文凭制作#回国入职#diploma#degree澳洲中央昆士兰大学毕业证成绩单pdf电子版制作修改#毕业文凭制作#回国入职#diploma#degree
毕业文凭制作#回国入职#diploma#degree澳洲中央昆士兰大学毕业证成绩单pdf电子版制作修改#毕业文凭制作#回国入职#diploma#degree
 
2006_GasProcessing_HB (1).pdf HYDROCARBON PROCESSING
2006_GasProcessing_HB (1).pdf HYDROCARBON PROCESSING2006_GasProcessing_HB (1).pdf HYDROCARBON PROCESSING
2006_GasProcessing_HB (1).pdf HYDROCARBON PROCESSING
 
RABBIT: A CLI tool for identifying bots based on their GitHub events.
RABBIT: A CLI tool for identifying bots based on their GitHub events.RABBIT: A CLI tool for identifying bots based on their GitHub events.
RABBIT: A CLI tool for identifying bots based on their GitHub events.
 
20240419 - Measurecamp Amsterdam - SAM.pdf
20240419 - Measurecamp Amsterdam - SAM.pdf20240419 - Measurecamp Amsterdam - SAM.pdf
20240419 - Measurecamp Amsterdam - SAM.pdf
 
Identifying Appropriate Test Statistics Involving Population Mean
Identifying Appropriate Test Statistics Involving Population MeanIdentifying Appropriate Test Statistics Involving Population Mean
Identifying Appropriate Test Statistics Involving Population Mean
 
办理学位证纽约大学毕业证(NYU毕业证书)原版一比一
办理学位证纽约大学毕业证(NYU毕业证书)原版一比一办理学位证纽约大学毕业证(NYU毕业证书)原版一比一
办理学位证纽约大学毕业证(NYU毕业证书)原版一比一
 
Statistics, Data Analysis, and Decision Modeling, 5th edition by James R. Eva...
Statistics, Data Analysis, and Decision Modeling, 5th edition by James R. Eva...Statistics, Data Analysis, and Decision Modeling, 5th edition by James R. Eva...
Statistics, Data Analysis, and Decision Modeling, 5th edition by James R. Eva...
 
1:1定制(UQ毕业证)昆士兰大学毕业证成绩单修改留信学历认证原版一模一样
1:1定制(UQ毕业证)昆士兰大学毕业证成绩单修改留信学历认证原版一模一样1:1定制(UQ毕业证)昆士兰大学毕业证成绩单修改留信学历认证原版一模一样
1:1定制(UQ毕业证)昆士兰大学毕业证成绩单修改留信学历认证原版一模一样
 
Call Girls in Saket 99530🔝 56974 Escort Service
Call Girls in Saket 99530🔝 56974 Escort ServiceCall Girls in Saket 99530🔝 56974 Escort Service
Call Girls in Saket 99530🔝 56974 Escort Service
 
9711147426✨Call In girls Gurgaon Sector 31. SCO 25 escort service
9711147426✨Call In girls Gurgaon Sector 31. SCO 25 escort service9711147426✨Call In girls Gurgaon Sector 31. SCO 25 escort service
9711147426✨Call In girls Gurgaon Sector 31. SCO 25 escort service
 
办理学位证中佛罗里达大学毕业证,UCF成绩单原版一比一
办理学位证中佛罗里达大学毕业证,UCF成绩单原版一比一办理学位证中佛罗里达大学毕业证,UCF成绩单原版一比一
办理学位证中佛罗里达大学毕业证,UCF成绩单原版一比一
 

Data collection

  • 1. Manoj Patel Assistant Professor Jhunjhunwala Business School Data Collection {Tools, Techniques, Methods}
  • 3. What does web say about data??  a collection of facts from which conclusions may be drawn; "statistical data” (wordnetweb.princeton.edu/perl/webwn)  The term data means groups of information that represent the qualitative or quantitative attributes of a variable or set of variables(en.wikipedia.org/wiki)  information; A collection of object-units that are distinct from one another. (en.wiktionary.org/wiki/data)  Data is Information that has been organised and categorised for a pre-determined purpose. (news.miuegypt.edu.eg/index.php)
  • 4. What does web say about data??  In computer science, data is anything in a form suitable for use with a computer. Data is often distinguished from programs. A program is a set of instructions that detail a task for the computer to perform. In this sense, data is thus everything that is not program code. (en.wikipedia.org/wiki/Data_(computing))  are the smallest units of measure. The word is technically the plural of datum but often used as a singular. Data are the components of information. They may be the 1's and 0's of computer memory, names and addresses in a demographic file, or the raw facts and figures before interpretation. (home.earthlink.net/~ddstuhlman/defin1.htm)
  • 5. Types of Data PRIMARY  Collected by researcher first hand  Demands efforts and resources  Depends upon the researcher’s ability and clarity of purpose SECONDARY  Collected by someone else but used by researcher second hand  Cheaper and quicker  Needs lesser resources  Have to ascertain accuracy of content/time/sources/ purpose/methods/ adequacy/ credibility  Various sources/forms
  • 6. Data Collection…steps  Construction of tools for data collection  Decision about techniques of data collection  Testing the tool/technique by Pilot study or Pre-testing of tool/technique  Finalization of tool/technique  Ascertaining reliability and validity of tools/techniques to be used for data collection  Actual collection of data
  • 7. Data collection tools & techniques TOOLS  Questionnaire  Interview schedule  Observation schedule  Scales  Tests  Inventory, Checklist, Opinionnaire  Sociogram/Sociometry TEHNIQUES  Questioning: Written, Oral  Interviewing: Face to face, Telephonic, Electronic/Net, Group, Video  Observation  Projective Techniques  Panel Methods (Diary, Checklist, Logs etc.)
  • 8. Factors influencing decision about data collection Tool/Technique/Method  Scale and magnitude of the study  Characteristics of the respondents  Unit of inquiry and analysis  Availability of resources: Money, Time, Human, Technical, Competence  Field Conditions  Subject under study  Expected outcome  Degree of precision/reliability required
  • 9. Decisions about data collection Method  Settings: Natural – Contrived/Artificial  Inquiry: Obstructive/Undisguised – Unobstructive/Disguised  Nature: Qualitative – Quantitative  Structure: Structured – Semi structured – Unstructured  Questions: Open ended – Closed ended  Administration: Human – Mechanical  Analysis: Pre coded – Not coded
  • 10. Data comes through…. Tools & Techniques METHOD Procedure
  • 11. Framing of Questions…  Length of a question/tool  Language, Sequence, Style  Objective of asking (measuring what?)  Structural issues: Embarrassing/personalisation, Leading/Directive, Assumptions/presumptions, Hypothetical/ambiguous, median replies, Loaded/ Inbuilt coercion-forced, Double barreled, Double negatives
  • 12. When you collect Data…YOU must- Record time (time of the day/date/ month/year) when you collected it Total time (number of days/months/ years) it took to collect it along with field note for each response Procedure you followed to collect it Average time per respondent/unit Experiences you had collecting it
  • 13. Right Question…?! United Nations conducted a Worldwide survey. The question asked was: "Would you please give your honest opinion about solutions to the food shortage in the rest of the world?" The survey was a huge failure. Africa didn't know what 'food' meant, India didn't know what 'honest' meant, Europe didn't know what 'shortage' meant, China didn't know what 'opinion' meant, the Middle East didn't know what 'solution' meant, South America didn't know what 'please' meant, And in the USA they didn't know what 'the rest of the world' meant !!
  • 14. Good DATA depends upon…  Clarity of purpose/objectives of the study  Appropriateness of tool/technique  Sharpness of the tool and abilities of investigator/researcher in using the techniques  Cooperation/rapport with the respondents  Decisions about utilization at analysis stage