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Dr. C. Lee Giles David Reese Professor, College of Information Sciences and Technology Professor of Computer Science and Engineering Professor of Supply Chain and Information Systems The Pennsylvania State University, University Park, PA, USA [email protected] http://clgiles.ist.psu.edu IST 511 Information Management: Information and Technology Digital Humanities and Research Methods Special thanks to V. Ryabov,
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Dr. C. Lee Giles David Reese Professor, College of Information Sciences and Technology

Jan 15, 2016

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IST 511 Information Management: Information and Technology Digital Humanities and Research Methods. Dr. C. Lee Giles David Reese Professor, College of Information Sciences and Technology Professor of Computer Science and Engineering Professor of Supply Chain and Information Systems - PowerPoint PPT Presentation
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Page 1: Dr. C. Lee Giles David Reese Professor, College of Information Sciences and Technology

Dr. C. Lee GilesDavid Reese Professor, College of Information Sciences

and Technology

Professor of Computer Science and Engineering

Professor of Supply Chain and Information Systems

The Pennsylvania State University, University Park, PA, USA

[email protected]

http://clgiles.ist.psu.edu

IST 511 Information Management: Information and Technology

Digital Humanities and Research Methods

Special thanks to V. Ryabov,

Page 2: Dr. C. Lee Giles David Reese Professor, College of Information Sciences and Technology

Today

What are the digital humanitiesWhat are research methods

– Qualitative– Quantitative– Computational

Last time:• Digital libraries• Scientometics and bibliometrics

Page 3: Dr. C. Lee Giles David Reese Professor, College of Information Sciences and Technology

Tomorrow

Your research presentations

Page 4: Dr. C. Lee Giles David Reese Professor, College of Information Sciences and Technology

Digital Humanities

Other names for the digital humanities

• Computational humanities• Computational archaeology• Computational history• etc

• Cultural informatics

Page 5: Dr. C. Lee Giles David Reese Professor, College of Information Sciences and Technology

Digital Humanities

Page 6: Dr. C. Lee Giles David Reese Professor, College of Information Sciences and Technology

Humanities

What are the humanities?

Wikipedia

Stanford

National Endowment for the Humanities (NEH)

Page 7: Dr. C. Lee Giles David Reese Professor, College of Information Sciences and Technology

History of the Digital Humanities

Not that old – 1940’s – start of digitization

Page 8: Dr. C. Lee Giles David Reese Professor, College of Information Sciences and Technology

“Digitus Dei est hic!”

http://www.corpusthomisticum.org/it/index.age

Page 9: Dr. C. Lee Giles David Reese Professor, College of Information Sciences and Technology

Hockey’s Consolidation

1970’s –mid-1980’s

Page 10: Dr. C. Lee Giles David Reese Professor, College of Information Sciences and Technology

New Developments

Mid 1980’s –early 1990’s

http://www.tei-c.org/index.xml http://www.tei-c.org/index.xml

Page 11: Dr. C. Lee Giles David Reese Professor, College of Information Sciences and Technology

http://www.perseus.tufts.edu/hopper/

Page 12: Dr. C. Lee Giles David Reese Professor, College of Information Sciences and Technology

Web/Humanities 1.0: From the few to the many

Web/Humanities 2.0: From the many to the many

Page 13: Dr. C. Lee Giles David Reese Professor, College of Information Sciences and Technology

http://vos.ucsb.edu/

http://nines.org/

Page 14: Dr. C. Lee Giles David Reese Professor, College of Information Sciences and Technology

http://www.manovich.net/

Film and Media and Communication

StudiesFilmMediaCommunicationCulturalFeministSTS

Page 15: Dr. C. Lee Giles David Reese Professor, College of Information Sciences and Technology

Explosion of new groups, communities, subjects

Page 16: Dr. C. Lee Giles David Reese Professor, College of Information Sciences and Technology

The Disciplines

Is this all?

Page 17: Dr. C. Lee Giles David Reese Professor, College of Information Sciences and Technology

Archeology

http://www.u.arizona.edu/~mlittler/ http://www.u.arizona.edu/~mlittler/ http://www.cast.uark.edu/ http://www.cast.uark.edu/

http://www.cast.uark.edu/other/nps/nadb/ http://www.cast.uark.edu/other/nps/nadb/

Page 18: Dr. C. Lee Giles David Reese Professor, College of Information Sciences and Technology

Art History and The Arts

http://www.vraweb.org/ http://www.vraweb.org/

http://users.ecs.soton.ac.uk/km/projs/vasari/ http://users.ecs.soton.ac.uk/km/projs/vasari/

http://www.getty.eduhttp://www.getty.edu

Page 19: Dr. C. Lee Giles David Reese Professor, College of Information Sciences and Technology

Classical Studies

http://www.romereborn.virginia.edu/http://www.romereborn.virginia.edu/

http://scriptorium.lib.duke.edu/papyrus/ http://scriptorium.lib.duke.edu/papyrus/

http://nolli.uoregon.edu/rioni.html http://nolli.uoregon.edu/rioni.html Problematics

• Obsolescence and Preservation

Page 20: Dr. C. Lee Giles David Reese Professor, College of Information Sciences and Technology

History

http://ashp.cuny.eduhttp://ashp.cuny.edu

http://valley.vcdh.virginia.edu/ http://valley.vcdh.virginia.edu/

Accessibility

Page 21: Dr. C. Lee Giles David Reese Professor, College of Information Sciences and Technology

Teaching and Learning

New Learning Environments

New Subjects

New Pedagogies

Digital Disconnect

Page 22: Dr. C. Lee Giles David Reese Professor, College of Information Sciences and Technology

Literary Studies

http://ted.streamguys.net/ted_rives_mockingbirds_2006.mp3http://ted.streamguys.net/ted_rives_mockingbirds_2006.mp3

http://www.rossettiarchive.org/http://www.rossettiarchive.org/http://www.emilydickinson.org/ http://www.emilydickinson.org/

What happens to lit and “literary” in the age of digital tech?

Page 23: Dr. C. Lee Giles David Reese Professor, College of Information Sciences and Technology

Thematic of Textuality vs. Visuality

Jerome McGann‘: digital technology and literary studies written (and published) between 1993 and 2001. Episodes in the history of McGann's engagement with the intellectual opportunities offered by the interaction between computer power, digital technology and literary studies.

Richard Mayer: For hundreds of years verbal messages have been the primary means of explaining ideas to learners. Although verbal learning offers a powerful tool for humans, this book explores ways of going beyond the purely verbal

Page 24: Dr. C. Lee Giles David Reese Professor, College of Information Sciences and Technology

Teaching and LearningDigital Disconnect

ElectracyOralPrint Electronic

Page 25: Dr. C. Lee Giles David Reese Professor, College of Information Sciences and Technology

Examples

http://www.vectorsjournal.org/issues/index.php?issue=5http://www.vectorsjournal.org/issues/index.php?issue=5

http://www.vectorsjournal.org/index.php?page=7&projectId=86http://www.vectorsjournal.org/index.php?page=7&projectId=86

http://vectors.usc.edu/issues/05_issue/bluevelvet/http://vectors.usc.edu/issues/05_issue/bluevelvet/

Page 26: Dr. C. Lee Giles David Reese Professor, College of Information Sciences and Technology

Wayne State Digital

http://www.lib.wayne.edu/resources/digital_library/index.php http://www.lib.wayne.edu/resources/digital_library/index.php

Page 27: Dr. C. Lee Giles David Reese Professor, College of Information Sciences and Technology

Social Networks

Page 28: Dr. C. Lee Giles David Reese Professor, College of Information Sciences and Technology

From Remediation to Convergence and Intermediation

Page 29: Dr. C. Lee Giles David Reese Professor, College of Information Sciences and Technology

HASTAC

http://www.hastac.org/ http://www.hastac.org/

Page 30: Dr. C. Lee Giles David Reese Professor, College of Information Sciences and Technology

Digital Antiquity - Mission

Organization devoted to enhancing preservation and access to digital records of archaeological investigations – to permit scholars to more effectively create and

communicate knowledge of the long-term human past;

– to enhance the management of archaeological resources; and

– to provide for the long-term preservation of irreplaceable records of archaeological investigations.

Page 31: Dr. C. Lee Giles David Reese Professor, College of Information Sciences and Technology

We’re Losing the Archaeological RecordExplosion of Digital Information

– >50,000 field projects/year, 1000s of databases– Primary archaeological data is now “born digital”

Absence of Trusted Repositories– Few institutions capable of long-term data curation – Media on which data resides is treated as an artifact– Standard work flows do not move digital data into trusted repositories

Fragility of Digital Data– Media degradation & software obsolescence– Loss of data semantics (metadata)

We need a trusted digital repository for archaeological documents and data

Page 32: Dr. C. Lee Giles David Reese Professor, College of Information Sciences and Technology

Digital Antiquity’s Repository: tDAR - the Digital Archaeological

RecordOn-line, trusted digital repository for archaeological data and

documents that– financially and socially sustainable, – long-term preservation of data & metadata– on-line discovery, and access for data and documents produced by

archaeological projects. – web ingest interface: acquire metadata and user upload of data

Scope– targets digital products of ongoing research & legacy data– focus on archival data (not continuously updated databases such as site

files)– Work of scholars in the US and the Americas more broadly

Page 33: Dr. C. Lee Giles David Reese Professor, College of Information Sciences and Technology

Digital Antiquity Builds on the ADS Model

The Archaeology Data Service (ADS) in the UK has a 10 year track record of success– ADS is heavily staffed (ca 10FTE), provides a high level of curation

and high quality archive

– ADS provides a refined presentation layer for its projects

– ADS processes a relatively small number of projects (ca 200) each year at a high unit cost

Page 34: Dr. C. Lee Giles David Reese Professor, College of Information Sciences and Technology

Digital Antiquity Diverges from ADS In Order to Scale to the US Situation

50,000 federally mandated cultural resource field projects conducted each year in the US. – tDAR aspires to capture the digital data and documents from a substantial

fraction

Implies a different business modelDemands much heavier reliance on users to provide metadata that

make their data meaningfulRequires a user-friendly ingest interface for metadata acquisition

and data upload

Page 35: Dr. C. Lee Giles David Reese Professor, College of Information Sciences and Technology

Prototype Ingest Interface

Page 36: Dr. C. Lee Giles David Reese Professor, College of Information Sciences and Technology

Preservation and Access RequirementsTo maintain the utility of data, we must preserve the data

(bits) on a sustainable media, in a sustainable format, along with their semantics– Existing coding keys and manuals are inadequate

Cannot require universal coding schemes– We must employ ontologies to allow naive users to locate

relevant resources.

We must plan for integration of data that employ different systematics.– We must collect detailed database metadata (e.g., at the table,

column, and value level)

Need persistent URIs, DOIs

Page 37: Dr. C. Lee Giles David Reese Professor, College of Information Sciences and Technology

Metadata & Database SemanticsStandardization of original data on deposit is

unacceptable– We must capture, not transform, original semantics– Digital coding sheets at dataset registration time

Our representation is not highly abstract but structured by archaeological practice

On registration, the dataset creator – associates database codes with dataset labels through a

coding sheet – and maps coding sheet labels to default (and possible

alternate) ontologies created by material class experts

Page 38: Dr. C. Lee Giles David Reese Professor, College of Information Sciences and Technology

Modeling Global Societal Evolution Over a Half-Century: Modeling Global Societal Evolution Over a Half-Century: Petascale Humanities ComputingPetascale Humanities Computing

Institute for Computing in the Humanities, Arts, and Social Institute for Computing in the Humanities, Arts, and Social Science at the University of Illinois and Center Affiliate of the Science at the University of Illinois and Center Affiliate of the

National Center for Supercomputing ApplicationsNational Center for Supercomputing Applications

Page 39: Dr. C. Lee Giles David Reese Professor, College of Information Sciences and Technology
Page 40: Dr. C. Lee Giles David Reese Professor, College of Information Sciences and Technology

Research DirectionsResearch Directions

Forecast global stability

Model social group interactions

Gain a better understanding of the underpinnings of global unrest and how society functions

Quantify the flow of information across the world and how human societies produce and consume realtime information

Gain new understanding of the evolution of the civil war discourse

Page 41: Dr. C. Lee Giles David Reese Professor, College of Information Sciences and Technology

The Digital Humanities

Very large field, encompasses a tremendous variation in applications

Focus on the textually-driven humanities, such as history, journalism, etc

Page 42: Dr. C. Lee Giles David Reese Professor, College of Information Sciences and Technology

Quantitative Qualitative Computation

Digital humanities requires “Quantitative Qualitative Computation” – find ways of converting the “latent” aspects of language into computable numeric indicators

Historically have focused on facts and discarded the rest as “uncomputable”

More recently, dimensions such as “tone” have become booming industries (brand mining)

Page 43: Dr. C. Lee Giles David Reese Professor, College of Information Sciences and Technology

Quantitative Qualitative Computation

VERY computationally expensive

Easy to take Google Ngram dataset and plot frequency of “democrat” vs “republican” in time to see who gets more book coverage each year

Gauging which one gets the most POSITIVE coverage, however, and WHERE that coverage comes from requires a LOT of computation

Page 44: Dr. C. Lee Giles David Reese Professor, College of Information Sciences and Technology

Building a Global Map

The map at the start of this presentation visualizes a geographic cross-section through a much larger dataset: a petascale network

What does a digital humanities pipeline look like?

Page 45: Dr. C. Lee Giles David Reese Professor, College of Information Sciences and Technology

Petascale Networks

Start with a petascale network

10 billion actors connected by over 100 trillion relationships just from a single dataset covering only 30 years

Assuming simple tuple structure: ID,WEIGHT,ID, that’s 8b * 3 = 24 bytes * 100 trillion rows = 2.4PB

Need this all memory-resident for random access across the ENTIRE dataset

This is just a small pilot dataset

Data is XD

Page 46: Dr. C. Lee Giles David Reese Professor, College of Information Sciences and Technology

From “Big Data” to “Really Big From “Big Data” to “Really Big Data”Data”

Page 47: Dr. C. Lee Giles David Reese Professor, College of Information Sciences and Technology

Is XD really “Big Data?”Is XD really “Big Data?”

Total disk of all current production XD systems combined: 12.1PB (Gordon is 1/3 of the entire XD)

If we add all XD tape silos, we get 34.1PB

The entire national allocated research infrastructure is just 12PB of disk and 22PB of tape!

Microsoft’s Bing search engine uses 150PB of spinning disk

Biggest scientific projects will generate only 10-20TB / day of data, while Twitter alone produces 28GB of new data a day and Bing processes 2PB / day

Page 48: Dr. C. Lee Giles David Reese Professor, College of Information Sciences and Technology

““Really Big Data”Really Big Data”

Traditional sciences are “small data” compared with the information world of news and social media

200 MILLION new tweets a day

1BILLION new Facebook items a day: average person adds 3 items to Facebook every single day

Page 49: Dr. C. Lee Giles David Reese Professor, College of Information Sciences and Technology

““Really Big becomes REALLY Big”Really Big becomes REALLY Big”

Social media in particular is vastly outpacing traditional information sources

Entire New York Times 1945-2005 = 18M articles = 2.9 billion words

5 BILLION words added to Twitter each DAY (almost twice the total volume of the Times in the last 60 years)

Page 50: Dr. C. Lee Giles David Reese Professor, College of Information Sciences and Technology

And Even Bigger

HaitiTrust includes Google Books and contains 4% of all books every printed = 9.4 million digitized works = 3.3 billion pages = 2 trillion words

Estimated 49.5 trillion words ever printed in books over last 600 years

Twitter alone will reach that size in just 27 years with zero additional growth. With its current rate of tripling post volume each year, it will take just three years

Page 51: Dr. C. Lee Giles David Reese Professor, College of Information Sciences and Technology

Storing and Searching Big TextStoring and Searching Big Text

Page 52: Dr. C. Lee Giles David Reese Professor, College of Information Sciences and Technology

The BIG World of Text

With scientific datasets, you have data and then you have an index (HDF + PyTables)

With text the data IS the index

Text is vast and operates at the microlevel of the word (equivalent to every query searching every pixel of a vast image archive)

Unstructured

Tons of associated metadata

Page 53: Dr. C. Lee Giles David Reese Professor, College of Information Sciences and Technology

What is research?

Research work and comparable development work refer to systematic activity to increase the level of knowledge and the use of the knowledge to find new applications.

The essential criterion is whether the activity generates fundamental new knowledge.

Research could be: basic, applied, and developmental.

Page 54: Dr. C. Lee Giles David Reese Professor, College of Information Sciences and Technology

Basic research

This refers to such activities to gain new knowledge which do not primarily aim to practical applications.

Basic research includes, for example, analysis of qualities, structures and dependencies whose objectives are to form and test new hypotheses, theories and scientific regularities.

Furthermore, basic research can be also directed, in which the results can be expected to result in significant applications; sometimes, however, only in the long run.

Page 55: Dr. C. Lee Giles David Reese Professor, College of Information Sciences and Technology

Applied research

This refers to such activities to gain new knowledge, which primarily aim to develop specific practical application.

The purpose of applied research is to deal with questions of everyday life.

Applied research includes, for example, seeking applications for findings of basic research, or creation of new methods and means to solve a specific problem .

Page 56: Dr. C. Lee Giles David Reese Professor, College of Information Sciences and Technology

Developmental research

Uses the knowledge gained from research and/or practical experience to create new materials, products, manufacturing processes, methods and systems or to improve existing ones significantly.

It includes, for example, so-called action research which produces information directly in the situation in which it is also applied, and any research and development activities taking place during R&D projects in industry.

Page 57: Dr. C. Lee Giles David Reese Professor, College of Information Sciences and Technology

Research process

Selecting a topic & research questions

Selecting research methods

Reading for research

Collecting dataAnalyzing dataWriting a research report/paper/thesis

Page 58: Dr. C. Lee Giles David Reese Professor, College of Information Sciences and Technology

When and why to write down?

Write to remember Experienced researchers never wait for the end of the project to start writing. They make a list of sources, summaries, keeping lab notes, making outlines, etc.

Write to understandWhen you arrange or rearrange the results of your research in new ways, you can discover new connections, contrasts, complications, and implications.

Write to gain perspectiveThe basic reason for writing is to get your thoughts out of your head and onto the paper, where you can see them more clearly.

Page 59: Dr. C. Lee Giles David Reese Professor, College of Information Sciences and Technology

What is not a scientific research?

Investigations, which refer to data gathering, editing, and analyzing for planning or decision processes. Investigations are usually an actual part of the planning process.

Gathering of the general information. For example: continuous observations primarily for other reason than the research, such as hydrological weather observations, the production of statistics, opinion polls, archaeological excavations obligated by the law, collecting and arranging documents, market research, inventory and charting of the natural resources.

Production of computer applications, unless they are a part of a research project.

Page 60: Dr. C. Lee Giles David Reese Professor, College of Information Sciences and Technology

Criteria of good research work

Fertility. The results of the research pose new questions, reveal new problems and directions for further research.

Relevance. Good research is significant and influential.

Objectivity. Although researchers can freely define the problems and the hypotheses, the implementation of the method, as well as the results and reporting, must be objective.

Verification. It must be possible to examine every research discovery, test result, measurement and interpreted result from the point of view of its validity and relevance.

Practicality. Every good study shows opportunities for practical applications.

Page 61: Dr. C. Lee Giles David Reese Professor, College of Information Sciences and Technology

Ethical questions of research

Research is a profoundly social activity. Reporting research connects us not just to those who will use it, but also to those whose research we used.

Do not plagiarize or claim credit for the results of others.

Do not misreport sources or invent results.

Do not submit data whose accuracy there is a reason to question, unless you raised the questions.

Do not hide objections you cannot respond to.

Do not caricature or distort opposing views.

Do not destroy or hide sources and data important for those who follow.

Page 62: Dr. C. Lee Giles David Reese Professor, College of Information Sciences and Technology

Plagiarism

Plagiarism is the worst thing that can happen to a researcher.

You plagiarize when, intentionally or not, you use someone else’s words or ideas but fail to credit that person, leading your readers to think that those words are yours.

Standards for plagiarism could be different in different fields.

Every time you use the exact words of the source:

– type quotation marks before and after them

– record the words exactly as they are in the source

– cite the source

Page 63: Dr. C. Lee Giles David Reese Professor, College of Information Sciences and Technology

Finding a research topic

Your interests

General topic

Focused topic

Research questions

Page 64: Dr. C. Lee Giles David Reese Professor, College of Information Sciences and Technology

Twelve issues to keep in mind

How much choice you have

Your motivation

Regulations and expectations

Your subject or field of study

Previous examples of research projects

The size of the topic

The time you have available

The cost of research

The resources you have available

Your need for support

Access issues

Methods for researching

Page 65: Dr. C. Lee Giles David Reese Professor, College of Information Sciences and Technology

From general to focused topic

The topic is usually too broad if you could state it in four or five words.

Examples:

– ”Evaluation of user interfaces” – a broad topic

– ”The use of cognitive models for the efficient development and evaluation of user interfaces” – a focused topic

Don’t narrow your topic so much that you can’t find enough data on it.

Page 66: Dr. C. Lee Giles David Reese Professor, College of Information Sciences and Technology

From topic to research questions

A typical mistake of beginner researchers: they rush from a topic to immediate data collection.

Readers of research reports don’t want just information – they want an answer to a question worth asking.

Serious researchers never report data for their own sake but to support the answer to research questions they formulated.

Page 67: Dr. C. Lee Giles David Reese Professor, College of Information Sciences and Technology

Identifying research questions

Research questions stemming from the topic.

Ask predictable questions about the topic, like who, what, when, where, how and why.

Examples:

– ”How cognitive models are applied to the development user interfaces?”

– ”Does the use of these model make the interface design more efficient? In which situations?”

Page 68: Dr. C. Lee Giles David Reese Professor, College of Information Sciences and Technology

Identifying research questions

For a small-scale research project 2-3 main research questions are usually enough.

When research questions are right, they should suggest not just the field of study, but also the methods for carrying out the research and the kind of analysis required.

Research questions should be motivated! You have to explain why they are important.

Page 69: Dr. C. Lee Giles David Reese Professor, College of Information Sciences and Technology

Group research

Enables you to share responsibility.

Lets you specialize in those aspects of the work to which you are best suited.

Provides you with useful experience of team working.

Allows you to take on larger-scale topics than you could otherwise manage.

Provides you with a ready made support network.

May be essential for certain kinds of research.

Page 70: Dr. C. Lee Giles David Reese Professor, College of Information Sciences and Technology

Individual research

Gives you sole ownership of the research.

Means that you are wholly responsible for the progress and success of the research.

May result in a more focused project.

The quality of research work is determined by you alone.

Means that you have to carry out all elements of the research process.

Page 71: Dr. C. Lee Giles David Reese Professor, College of Information Sciences and Technology

Dimensions of research

How research is used Basic, applied

Purpose of the study Exploratory, descriptive, explanatory, predictive

The way time enters in Cross-sectional, longitudinal (time series, panel, cohort), case study

Technique for collecting data:

For quantitative & computational data

For qualitative data

Experiments, surveys, content analysis, existing statistics, harvesting

Field research, historical comparative research

Page 72: Dr. C. Lee Giles David Reese Professor, College of Information Sciences and Technology

Purpose of study: exploratory goals of research

The goal is “to explore”.

Become familiar with the basic facts, setting, and concerns.

Create a general mental picture of conditions.

Formulate and focus questions for future research.

Generate new ideas, proposals, or hypotheses.

Determine the feasibility of conducting research.

Develop techniques for measuring and locating future data.

Page 73: Dr. C. Lee Giles David Reese Professor, College of Information Sciences and Technology

Purpose of study: descriptive goals of research

The goal is “to describe”.

Provide a detailed, highly accurate picture.

Locate new data that contradict past data.

Create a set of categories or classify types.

Clarify a sequence of steps or stages.

Document a causal process or mechanism.

Report on the background or context of a situation.

Page 74: Dr. C. Lee Giles David Reese Professor, College of Information Sciences and Technology

Purpose of study: explanatory goals of research

The goal is “to explain”.

Test a theory’s predictions or principle.

Elaborate and enrich a theory’s explanation.

Extend a theory to new issues or topics.

Support or refute an explanation or prediction.

Link issues or topics with a general principle.

Determine which of several explanations is best.

Page 75: Dr. C. Lee Giles David Reese Professor, College of Information Sciences and Technology

Types of longitudinal research

Time-series research. The same type of information is collected on a group of people or other units across multiple time periods.

Panel study. Exactly the same people, group, organizations, or other units are observed across time periods.

Cohort analysis. A category of people who share a similar life experience in a specified time period is studied. It is “explicitly marcoanalytic” meaning examining category as a whole for important features.

Page 76: Dr. C. Lee Giles David Reese Professor, College of Information Sciences and Technology

Time dimension in research:case studies

Examines in depth many features of a few cases over a duration of time.

Cases may be individuals, groups, organizations, events, or geographic units.

The data are usually more detailed, varied, and extensive. Most involve qualitative data about a few cases.

Qualitative and case study research are not identical!

Page 77: Dr. C. Lee Giles David Reese Professor, College of Information Sciences and Technology

General strategies for doing research

Quantitative vs. qualitative

Quantitative research is empirical research where the data are in the form of numbers or a database.

Computational research uses data management techniques

Qualitative research is empirical research where the data are not in the form of numbers.

Deskwork vs. fieldwork (staying in the office, library, or laboratory vs. going out to research)

Page 78: Dr. C. Lee Giles David Reese Professor, College of Information Sciences and Technology

The similarities between qualitative and quantitative research

Quantitative research are used for testing theory, but also for exploring an area and generating hypothesis and theory.

Qualitative research can be used for testing hypotheses and theories, even though it is mostly used for theory generation.

Qualitative data often include quantification (e.g. statements such as more than, less than, most, etc.) and can be quite large.

Quantitative approaches (e.g. large-scale surveys) can also collect qualitative (non-numeric) data.

The underlying philosophical positions are not necessarily as distinct as the stereotypes suggest.

Page 79: Dr. C. Lee Giles David Reese Professor, College of Information Sciences and Technology

Questions leading to quantitative research

Quantitative research is used when it is possible to specify variables which can be measured or tested or indicated as numbers by using some other method.

Examples of questions:

– How much of something occurs in the phenomenon X?

– How often something occurs in the phenomenon X?

– Is the occurrence of Y and X statistically significant?

– Can we classify Y?

Page 80: Dr. C. Lee Giles David Reese Professor, College of Information Sciences and Technology

Questions leading to qualitative research

The objective of qualitative research is usually to build a new construct from observed points or from existing issues.

This new construct should be clearer that the previous one or it should emphasise some points so that they can be understood better.

Examples of questions:

– What is the phenomenon like?

– What kind of qualities the phenomenon has?

Page 81: Dr. C. Lee Giles David Reese Professor, College of Information Sciences and Technology

Deductive Theory

Theory

Hypotheses

Data Collection

Findings

Hypotheses Confirmed or Rejected

Revision of Theory

Page 82: Dr. C. Lee Giles David Reese Professor, College of Information Sciences and Technology

Induction

[General research question]

Observation

Theory Formulation

Page 83: Dr. C. Lee Giles David Reese Professor, College of Information Sciences and Technology

Quantitative and Qualitative Methods

Quantitative:

Deductive (and inductive)

Tests hypotheses

Positivism

Objectivism

Employs measurement

Macro

Detached researcher

Qualitative:

Inductive

Produces theories

Phenomenology

Constructionism

Usually does not employ measurement

Micro

Involved researcherOld ideas? Some research is now both!Examples?

Page 84: Dr. C. Lee Giles David Reese Professor, College of Information Sciences and Technology

Main Steps in Quantitative Research:

1. Theory2. Hypothesis3. Research design4. Devise measures of concepts5. Select research site(s)6. Select research subjects/respondents7. Administer research instruments/ collect data8. Process data9. Analyse data10. Write up findings and conclusions

Page 85: Dr. C. Lee Giles David Reese Professor, College of Information Sciences and Technology

Main Steps in Qualitative Research:

1. General research question

2. Select relevant site(s) and subjects

3. Collection of relevant data

4. Interpretation of data

5. Conceptual and theoretical work

6. Tighter specification of the research question

7. Collection of further data

8. Conceptual and theoretical work

9. Write up findings

Page 86: Dr. C. Lee Giles David Reese Professor, College of Information Sciences and Technology

Examples of Quantitative Research Methods:

Experiments

Social surveys – Cross-sectional

– Comparative (cross-national)

– Longitudinal

Content Analysis

Secondary Statistical Analysis

Official Statistics– Demography

– Epidemiology

Field stimulations– Structured Interviews and Observation.

Page 87: Dr. C. Lee Giles David Reese Professor, College of Information Sciences and Technology

Examples of Qualitative Research:

In-depth Interviews

Focus Groups

Ethnography/Field Research

Historical-Comparative Research

Discourse Analysis

Narrative Analysis

Media Analysis

Page 88: Dr. C. Lee Giles David Reese Professor, College of Information Sciences and Technology

Combine both

Quantitative and qualitative research are often cast as opposing fields.

But sometimes they blur - qualitative research may employ quantification in their work or may be positivist in their approach. Some quantitative may employ phenomenology.

Both can be also be combined in a project– Qualitative can facilitate quantitative research (1) can provide hypotheses

(2) fill in the gaps, help interpret relationships

– Quantitative can facilitate qualitative through locating interviewees and help with generalising findings

– Together they can give you a micro and macro level versions and so you can examine the relationships between the two levels. They can complement each other.

Page 89: Dr. C. Lee Giles David Reese Professor, College of Information Sciences and Technology

What we covered

• Research methods

• Digital humanities

Page 90: Dr. C. Lee Giles David Reese Professor, College of Information Sciences and Technology

Questions

• Role in the information science?

• Examples of qualitative/quantitative/computational methods?