Qualitative Research Methods

Department of Informatics Engineering UNIVERSITY OF COIMBRA

© A. Dias de Figueiredo, 2010 PhD Program in Information Sciences & Technologies - Research Methods

A CONCISE INTRODUCTION TO

QUALITATIVE RESEARCH METHODS IN INFORMATION SCIENCES & TECHNOLOGIES

University of Coimbra, Ph.D. in Information Sciences & Technologies Research Methods 2009/2010 © A. Dias de Figueiredo, 2010 Slide 2

1. QUANTITATIVE vs QUALITATIVE METHODS

2. A TYPICAL QUALITATIVE RESEARCH APPROACH

3. QUALITATIVE RESEARCH METHODS

4. DATA COLLECTION TECHNIQUES

5. DATA ANALYSIS TECHNIQUES: CODING 6. TRIANGULATION AND MIXED METHODS RESEARCH

7. RELIABILITY AND VALIDITY


Research in Information Sciences and Technologies is traditionally supported by two radically distinct categories of methods:

1. QUANTITATIVE VS QUALITATIVE METHODS

Quantitative Methods

Qualitative Methods

Although they are sometimes said to be incompatible (namely by members of the quantitative camp),

they should be seen as complementary to each other.


The articulation between the two visions – quantitative and qualitative – is actually leading to a ‘third major research paradigm’:

Mixed Methods



Quantitative methods are traditionally used in the natural sciences to study natural phenomena with mathematical rigor, namely using statistical analysis.

A limited amount of variables is always assumed, and these variables are seen as independent from external factors, measurable,

and holding mathematical relationships between each other.

Quantitative research generally emphasizes planning, hypotheses, large random samples, and objective measures.

It assumes the existence of a distinction between researcher and subjects, and aims at ‘generalizing’, i. e. at producing laws applicable to much broader realities.



Qualitative methods are being used increasingly in the social and human sciences for the study of highly complex and contingent phenomena, where the numbers of variables is too high to be handled by quantitative methods.

The growing attraction of Information Sciences and Technologies toward qualitative methods results, to a large extent, from the increasingly

complex, social and human, nature of the phenomena they deal with.

In the past, the social and human sciences, for fear of looking less respectable, tended to resort mainly to the

quantitative methods of the natural sciences.

Today, they are putting increasing emphasis on qualitative research and, as a consequence, obtaining much richer results.



The Information Systems discipline is considered, today, a socio-technical discipline, founded on the need to

reconcile technological solutions with the social and human dimensions of business and the organizational reality.

The main international Information Systems journals, such as MIS Quarterly, clearly show this tendency, and so does

The Communications of the ACM, one of the most popular Information Science and Technologies journals.

The same happens with most journals and conferences devoted to Information Systems.



The field of Human-Computer Interaction (HCI) is also receiving similar influences from the social and human dimensions of technology use.

A few years ago, user interfaces tended to be developed from rather mechanistic perceptual and cognitive models. Today, social

and organizational models of interaction have become essential.

Most usability studies are conducted today on the basis of socio-technical approaches inspired by those used in the social sciences.

In the meantime, we witness a shift of interest from usability – seen as just effectiveness, flexibility, and satisfaction – toward a

concern with the human experience of living with technology.



A similar evolution is occurring in Software Engineering. In fact, it started almost thirty years ago:

New software development approaches, such as Agile Programming and Extreme Programming, have in common the characteristic of putting

social and human issues at the center of the development process.

Concepts such as social norms, values, beliefs, symbolisms, representations, patterns of behavior, which were looked with suspicion in the past,

are considered today as essential to the success of technological projects.

“Personnel attributes and human relations activities provide by far the largest source of opportunity for improving software productivity” (Boehm, 1981)



Certainty - cause and effect linkages can be determined

PLOTTING THE QUANTITATIVE vs QUALITATIVE CAMPS IN STACEY’S CERTAINTY & AGREEMENT REFERENCIAL

Certainty Uncertainty

Agre

emen

t Di

sagr

eem

ent

Uncertainty - cause and effect linkages cannot

be determined and situations

are unique.

Agreement – the level of agreement between

all the parts involved varies along the axis.



Ideas Literature Review

Design Data Collection Analysis Dissemi-

nation

Berg, B. L. (2007)





nation

•  Research begins with rough Ideas that turn slowly into research questions.

Berg, B. L. (2007)





nation


•  The Literature Review gradually builds up knowledge about the research ideas.

Berg, B. L. (2007)





nation



•  The Design is the plan to be followed in order to carry out the research. It must include strategies for the selection of the samples.

Berg, B. L. (2007)





nation



•  The Design is the plan to be followed in order to carry out the research. It must include strategies for the selection of the samples.

•  The Data Collection and Organization phase is devoted to gathering the data for the research and organizing it, so that it can be properly analyzed. These are difficult

tasks, since the volume of data collected in qualitative research can be enormous.

Berg, B. L. (2007)





nation

•  The Analysis includes three concurrent flows of action:

•  Data Reduction, which focuses, simplifies, and transforms raw data into more manageable forms;

•  Data Display, which presents the data as organized and compressed assemblies of information that permit conclusions to be analytically drawn; and

•  Conclusions & Verification, where the researchers review and finalize all their conclusions and make sure that they satisfy the requirements of validity.

Berg, B. L. (2007)





nation

•  Dissemination takes the form of very well written and detailed documents, so that other researchers

can evaluate the analysis and conclusions obtained and decide if they trust the results and want

to use them to feed their own research.

Berg, B. L. (2007)



From a large variety of qualitative research methods used in the social and human sciences, five stand out as more relevant

in Information Sciences and Technologies research:

Case Studies

Ethnographic Research

Grounded Theory

Action-Research


Design-Based Research


Case studies

Case studies are the most common kind of qualitative method used in Information Sciences and Technologies research.

They let us study a phenomenon in its real context, specially when the boundaries between phenomenon and context are not clear.

Case Studies


Grounded Theory

Action-Research

Typically, the researcher studies a case or variety of cases of real-world organizations where information sciences and technologies are being

used and concludes about its impacts on the organizational context.

Case studies can also be used for quantitative research, in which case they tend to follow a positivist approach. One of the best known books on case studies (Yin, 1994) corresponds to this option. Other authors,

on the contrary, take constructivist and interpretivist approaches.

Design-based Research



Ethnographic research

Ethnographic research is inspired by the practices of cultural and social anthropology, where the researcher integrates for a period of time the community where the study is taking place.

This practice is common, not just in Information Sciences and Technologies research, but also in Human-Computer Interaction, in projects that try to

understand the behavior of the users, so that better interfaces can be developed.

The approach is very common when developing and assessing information systems. E.g., to understand how the 4200 workers of a company react to the

setting up of an CRM solution so as to improve that solution and make sure that future solutions do not suffer from similar problems.

Case Studies


Grounded Theory

Action-Research



It is also being used to understand the behavior of Software Engineering teams (namely large, complex, distributed, and multi-

national teams) and improve their performance.


Grounded Theory

Grounded Theory is a research approach proposed the sociologists Barney Glaser and Anselm Strauss, who claim that research should

depart “from the ground”. The researcher categorizes empirically collected data in order to build a general theory that fits the data.

In essence, it is based on the generation of theory from data.

This approach radically defies the traditional positivist approaches, which claim that the researcher must depart from a

theory, establish hypotheses that conform to the theory, and than get to the field to confirm the hypotheses in light of the theory.

The models developed by using Grounded Theory are quite distinctive from the traditional ones in that they reveal a strong foundation on the concrete.

Case Studies


Grounded Theory

Action-Research




Action-research

Action-Research is today one of the more promising qualitative research approaches in Information Sciences and Technologies research.

In essence, it consists of repeatedly going through the cycle: Planning => Action => Reflection

Action-research corresponds to what John Dewey called the Principle of Intelligent Action.

Case Studies


Grounded Theory

Action-Research



We start by making a plan of our action in a crude first approximation, we act following that plan, and we then reflect on the results obtained.

From this reflection, we correct our previous plan, act in agreement with the new plan, and reflect on the results we have now obtained.

The cycles go on, repeatedly, until we are happy with the results.


Design-based Research Case Studies


Grounded Theory

Action-Research



Design-based Research is a research method where knowledge is built in successive approximations while

designing, building, and evaluating an artifact.

The successive improvements introduced in the artifact as it is put to test represent opportunities for consolidating the

knowledge that emerges from its design and application.

The artifact may be almost anything: a piece of equipment, a software application, the solution to a social or technical

problem, a theoretical framework, or even a whole theory.

In essence, it consists of repeatedly going through the cycle: Awareness of Problem => Suggestion =>

Development => Evaluation => Conclusion


Four data collection techniques are more relevant in Information Sciences and Technologies research:

Document Analysis

Interviews

Participant Observation

Surveys



Document Analysis

Document Analysis covers a broad range of techniques devoted to the analysis and interpretation of the

documents used as primary data sources.

The term “document” is understood very broadly, including not just texts, but also sound, photos, videos, and any

materials that carry relevant messages.

Document Analysis

Interviews


Surveys

Typical varieties of document analysis include:

•  Conversational Analysis •  Discourse Analysis •  Narrative Analysis

•  Objective Hermeneutics



Interviews

Interviews are used to collect data from the subjects interactively, usually one-on-one.

Document Analysis

Interviews


Surveys

They are particularly useful to clarify the meaning of a phenomenon to the subjects and to obtain personal accounts about the development

of a process in which the subjects are engaged.

They are time consuming, but they are more flexible and adaptable than surveys. They can present various levels of structure:

•  structured interviews •  semi-structured interviews •  unstructured interviews

When many people are interviewed simultaneously and participants are free to interact with each other, we

talk about focus groups.

They are also useful to support the exploratory work that precedes a quantitative study and to clarify and enrich the results of quantitative studies.




In participant observation the researchers observe first-hand the activities of the subjects under study and

collect data from this observation.

The technique works well when the activities observed are frequent, the groups observed are small, and

there is little risk of disturbing the subjects.

Document Analysis

Interviews


Surveys There are two basic forms of participant observation:

•  overt •  covert

It is a time consuming technique, but it is generally more accurate than post-hoc self-reporting.



Surveys

In surveys the subjects complete a survey form without the intervention of the researcher.

Surveys are particularly useful when the subject population is large (or distributed geographically), majority opinions are

sought, and the subjects are motivated to respond.

Document Analysis

Interviews


Surveys

Special care must be taken to minimize the length of the survey, manage the choice of words and terminology, look after

the balance of its structure, fully plan the strategies for its subsequent analysis, and test-pilot it thoroughly.

Surveys can include closed and open questions, but the number of open questions should be reduced to a minimum.



Codes are tags that categorize the data collected during a study to assign meanings to them.

Coding makes it easier to search the data, make comparisons and identify patterns that require further investigation.

It can also be used to extract quantitative data from qualitative data.

A large variety of coding schemes exist, as well as many software tools – like NVivo and Atlas TI – used to assist

in coding and in helping to organize the resulting patterns.

Codes can be based on: themes, topics, ideas, concepts, terms, phrases, or keywords found in the data, but they can also correspond

to passages of audio or video recordings and to parts of images.

5. DATA ANALYSIS TECHNIQUES: CODING


research ideas

research findings

Research Process

6. TRIANGULATION AND MIXED METHODS RESEARCH


research ideas

research findings

With triangulation, the same issue is studied in

various perspectives that complement and

verify each other.


TRIANGULATION


Multiple Theories

research ideas

research findings Multiple Methods

Multiple Data

Multiple Researchers

TRIANGULATION 4 main kinds of triangulation



research ideas

research findings


Multiple Theories

Multiple Methods

Multiple Data

Multiple Researchers

The new research paradigm of Mixed Methods has

emerged from this kind of triangulation

TRIANGULATION


Mixed Methods Research (also called, by some authors, Multiple Method Research or Multimethod Research)

is becoming the third major research approach, along with quantitative research and qualitative research.

Janice Morse (2003) provides the following distinctions:

Multimethod design - the use of two or more research methods, each conducted rigorously and complete in itself, in one project.

The results are then triangulated to form a complete whole.

Mixed methods design - the incorporation of various qualitative and quantitative strategies within a single project, that

may have either a qualitative or quantitative theoretical drive.



Multimethod designs, according to Morse (2003)

For multimethod designs Morse (2003) defines three principles:

Principle 1: identify the theoretical drive (inductive or deductive) of the project.

Principle 2: develop overt awareness of the dominance (QUAN or QUAL, and simultaneous or sequential) of each project.

Principle 3: observe methodological integrity.



•  The inclusive trend, which argues that the credibility of qualitative research can only be widely accepted if the language of mainstream

(quantitative) research is maintained, although operationalized to meet the new conditions and circumstances.

Two major trends in the debate about rigor and validity in qualitative research:

•  The exclusive trend, for which the qualitative paradigm is so radically different from the quantitative paradigm that a new

language must be used to express its rigor and validity. (Guba & Lincoln, 1981; Guba & Lincoln, 1982; Guba & Lincoln, 1989)

(Yin, 1994; Morse, Barret, Mayan, Olson, & Spiers, 2002, Creswell, 2009)



THE EXCLUSIVE TREND

The proponents of the exclusive trend claim that the terms ‘validity’ and ‘reliability’ from qualitative research do not

make sense in qualitative research, so they should be replaced:

Quantitative Research Qualitative Research internal validity credibility external validity transferability

reliability dependability

objectivity confirmability



Credibility is achieved when the results are seen as believable by the participants in the research.

The participants decide about credibility.

Transferability exists when the results can be applied to other contexts.


Techniques for establishing credibility include: prolonged engagement, persistent observation, triangulation, peer debriefing,

negative case analysis, referential adequacy, and member checking.

The researcher should describe in detail the context and underlying assumptions of the research (thick description), so that transferability is possible, but the person who transfers the results

to a different context is responsible for the transfer.


Confirmability demonstrates that the inquiry is free of bias, values and prejudice, i.e. that the data interpretations and outcomes are rooted in

contexts and persons apart from the researcher and are not mere products of the researcher’s imagination.


The researcher must document the procedures, so that others can check and recheck the data throughout the study. Techniques that may be used

to strengthen confirmability include: prolonged engagement, persistent observation, peer debriefing, negative case analysis; and triangulation.

Dependability emphasizes the stability of the data over time.

The researcher must be able to account for the permanently changing context in which the research takes place, describing any changes that occur and how these changes affect the research. This requires what is sometimes called progressive subjectivity.

After the study, a data audit or external audit should be conducted by a researcher not involved in the research process, to examine both the process and product of the research study.


THE INCLUSIVE TREND

Many proposals exist within the inclusive trend. The one summarized here is proposed by Creswell (2009):


Qualitative validity does not have the meaning used in quantitative research: it just means that that the researcher checks for the accuracy of the findings by employing adequate procedures.

Qualitative reliability indicates that the researcher’s approach is consistent across different researchers and different projects.

Qualitative generalization is a term used in a limited way in qualitative research, since the intent is not to generalize finds, but rather to

explore particular phenomena in the context where they occur.


Qualitative Validity

Creswell (2009) proposes the use of eight primary strategies:


  Triangulation   Member checking

  Rich, thick description   Avoidance of researcher bias

  Negative case analysis   Prolonged engagement

  Peer debriefing   External auditing

Two strategies to avoid researcher bias are reflexivity (researcher self-awareness and self-reflection) and researcher journaling (detailed and

timely documentation of the researcher thoughts).


Qualitative reliability

Yin (2003) suggests that qualitative researchers should:


  document the procedures of their case studies   document as many steps of the procedures as possible

  set up a detailed case study protocol and database

Gibbs (2007) suggests several reliability procedures:   Checking transcripts for mistakes

  Check the persistence of the meaning of the codes   Coordinate communication among coders

  Cross-check codes developed by different researchers


• Berg, B. L. (2007). Qualitative Research Methods for the Social Sciences (6th Ed.). Pearson Education. • Boehm, B.W. (1981). Software Engineering Economics. Englewood Cliffs, N.J.: Prentice-Hall. • Creswell, J. W. (2007). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches (3rd Ed.), Sage. • Gibbs, G. R. (2007). Analyzing Qualitative Data. In U. Flick (Ed.). The Sage Qualitative Research Kit. Sage. • Guba, E. G. (1981). Criteria for assessing the trustworthiness of naturalistic inquiries, Educational Communication

and Technology Journal, 29 (2), 75-91. • Guba, E. G., & Lincoln, Y. S. (1981). Effective evaluation: Improving the usefulness of evaluation results through

responsive and naturalistic approaches. San Francisco, CA: Jossey-Bass. • Guba, E. G., & Lincoln, Y. S. (1982). Epistemological and methodological bases of naturalistic inquiry. Educational

Communication and Technology Journal, 30 (4), 233-252. • Guba, E. G., & Lincoln, Y. S. (1989). Fourth generation evaluation. Newbury Park, CA: Sage. • Morse, J. M., Barret, M., Mayan, M., Olson, K., & Spiers, J. (2002). Verification strategies for establishing reliability and

validity in qualitative research. International Journal of Qualitative Methods, 1(2), 1-19. • Morse, J. M. (2003). Principles of Mixed Methods and Multimethod Research Design. In Tashakkori, A, & Teddlie, C.

(Eds.). Handbook of Mixed Methods in Social and Behavioral Research. Sage. 189-208. • Yin, R. K. (1994). Case Study Research: Design and Methods (2nd Ed.). Sage • Yin, R. K. (2003). Case Study Research: Design and Methods (3rd Ed.). Sage

REFERENCES:

Qualitative Research Methods

Education

Transcript of Qualitative Research Methods