Methodology tutorial - qualitative data analysis

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Qualitative data analysis

This is part of the methodology tutorial (see its table of contents).

Learning goals

Understanding the purpose of code data and creating code books

Learn about some descriptive analysis of variables techniques (including situations and roles)

Learn about some causal analysis techniques

Prerequisites

• Methodology tutorial - theory-finding research designs
• Methodology tutorial - qualitative data acquisition methods

Level

• It aims at beginners. As with any method, you will have to practise and you likely will need some (human) guidance or at least serious additional reading. The purpose of this tutorial is to help you understand what qualitative data analysis could mean, not to present ready-made solutions.

Quality

• Slide style, should be expanded

Introduction: classify, code and index

In short, qualitative data analysis usually implies to related and iterative steps. See Methodology tutorial - theory-finding research designs for the general principle.

(1) Data needs to be coded and indexed so that you can find it for data analysis. More particularly:

• Information coding allows to identify variables and values, therefore allows for systematic analysis of data (and therefore reliability)
• ensures enhanced construction validity, i.e. that you look at things that will allow you to measure concept

(2) You then can do visualizations, matrices, grammars, etc.

(3) Interpret these

Before we start - Keep your documents and ideas safe !

(1) Write memos (conservation of your thoughts). It is useful to write short memos (vignettes) when an interesting idea pops up, when you looked at something and want to remember your thoughts

(2) Create contact sheets that allow you remember your field work. After each contact (telephone, interviews, observations, etc.), make a short data sheet that should include:

• A clear tag for indexing purposes (filename or tag on paper), e.g. CONTACT_senteni_2005_3_25.doc.
• Type of contact, date, place, and a link to the interview notes, transcripts.
• Principal topics discussed and research variables addressed (or pointer to the interview sheet).
• Initial interpretative remarks, new speculations, things to discuss next time.

(3) Index your interview notes:

• Put your transcription (or audio/video files or audio tapes) in a safe place
• Assign a code to each "text", e.g. INT-1 or INTERVIEW_senteni_3_28-1. The same code could be used as filename.
• You also may insert the contact sheet (see above)
• Number the pages if you take notes manually (they can fall down ...)

(4) Do not trust your hard-disk !

Codes and categories

First step in qualitative data analysis is coding.

A code is a “label” to tag a variable (concept) and/or a value found in a "text"

The coding principle

1. A code is assigned to each (sub)category, i.e. theoretical variable you work on
   • In other words: you must identify variable names
2. In addition, you can for each code assign a set of possible values (e.g.: “positive”/”neutral/”negative)
3. You then will systematically scan all your texts (documents, interview transcripts, dialogue captures, etc.) and tag all occurrences of variables.
4. Three very different coding strategies exist as we shall see later:
   • Code-book creation according to theory
   • Coding by induction (according to “grounded theory”)
   • Coding by ontological categories

Benefit of coding

• Coding will allow you to find all informations regarding variables of interest to your research
• Reliability will be improved

The procedure with a picture

Technical Aspects of coding

• The safest and most reliable way to code is to use some specialized software
  • e.g. Atlas or Nvivo (NuDist),
  • however, this takes a lot of time !
• For a smaller piece (e.g. a master thesis), we suggest to simply tag the text on paper
  • you can make a reduced photocopy of the texts to gain some space in the margins
  • overline or circle the text elements you can match to a variable
  • make sure to distinguish between codes and other marks you may leave.
• Don’t use "flat" and long code-books, introduce hierarchy (according to dimensions identified)
• Each code should be short but also mnemonic (optimize)
  • e.g. to code according to a schema “principal category” - “sub-category” (“value”) use a code like:

CE-CLIM(+)

instead of:

external_context -climate (positive)

• Don’t start coding before you have good idea on your coding strategy !
  • either your code book is determined by you research questions and associated theories, frameworks, analysis grids
  • or you really learn how to use an inductive strategy like "grounded theory".

Coding reliability

Assigning a code to a "text" segment is not always obvious and coding similar passages exactly the same way even less. In other words, we have a reliability problem.

There are two ways of improving reliability:

• Use clear and operational categories
• Use two or three coders (yourself and a friend) and compute intercoding index. If it is low, you will have to revise your coding scheme.

There exist sevHere is an example of codes use to analyse "types of job-related problems of Turkish computer teachers":

                                                                          %

1- Role Conflict 2- Inadequate Teacher Induction Policies 3- Lack of Required Technological Infrastructure and Technical Support 4- The Status of Computer Subject in School Curriculum 5- Lack of Appreciation and Positive Feedback from Colleagues 6- Unsupportive Administrators 7- Rapidly Changing Nature of Content Knowledge in Computer Education 8- Lack of Cohesive Computer Curriculum 9- Insufficiency of Pre-Service Teacher Training Programs 10- Large Class Sizes 11- Indifferent Students 12- Inadequate Supervision and Inspection eral formula to compute intercoder (inter-rater) reliability. The most simple one is:

 reliability =  number of agreements (same coding)
               / total codes (agreements plus disagreements)

Read this for a very good introduction.

Code-book creation and management

Code-book creation according to theory

The list of variables (and their codes), is defined through theoretical reasoning, e.g.

• analytical frameworks, analysis grids
• concepts found in the list of research questions and/or hypothesis

Example from an innovation study (about 100 codes):

Categories		Codes	Theoretical references
properties of the innovation		PI	....(fill for your own code book).....
external context		EC
	demography	CE-D
	support for the reform	CE-S
internal context		IC
adoption processes		AP
	official chronology	PA-OC
dynamics of the studied site		DS
external and internal assistance		EIA
causal links		CL

Befor you start think about your own code book, you really should go through the relevant literature and try to find existing code books (that you then may adapt). E.g. below is an an example of codes use to analyse types of job-related problems of Turkish computer teachers (Deryakulu & Olkun, 2007).

1. Role Conflict
2. Inadequate Teacher Induction Policies
3. Lack of Required Technological Infrastructure and Technical Support
4. The Status of Computer Subject in School Curriculum
5. Lack of Appreciation and Positive Feedback from Colleagues
6. Unsupportive Administrators
7. Rapidly Changing Nature of Content Knowledge in Computer Education
8. Lack of Cohesive Computer Curriculum
9. Insufficiency of Pre-Service Teacher Training Programs
10. Large Class Sizes
11. Indifferent Students
12. Inadequate Supervision and Inspection

Coding by induction (according to “grounded theory”)

Grounded theory (Glaser, Strauss) refers to a set of approaches that that focus on interpretation and theory building, i.e. it is a fully inductive approach. The researcher starts by coding a small data set and then increases the sample in function of emerging theoretical questions. Categories (codes) can be revised at any time.

“Grounded theory begins with a research situation. Within that situation, your task as researcher is to understand what is happening there, and how the players manage their roles. You will mostly do this through observation, conversation and interview. After each bout of data collection you note down the key issues: this I have labelled "note-taking".

Constant comparison is the heart of the process. At first you compare interview (or other data) to interview (or other data). Theory emerges quickly. When it has begun to emerge you compare data to theory.

The results of this comparison are written in the margin of the note-taking as coding. Your task is to identify categories (roughly equivalent to themes or variables) and their properties (in effect their sub-categories). Grounded theory: a thumbnail sketch, retrieved 12:39, 15 October 2008 (UTC))”

Typically, you'd both code phenomena in isolation and relations (so-called axial coding). On starting point for axial coding could be these big abstract observation categories:

• conditions (causes of a perceived phenomenon)
• interactions between actors
• strategies and tactics used by actors
• consequences of actions

Read more in [Introduction to Grounded Theory].

To use this approach you really should document yourself, as beginner you'd likely fall into various traps, in particular selection and confirmation biases, i.e. you only look at things that will interest you for one or another reason.

Coding by ontological categories

Instead of initially creating a code book from variables found in your research questions or "inductive" coding à la grounded theory, you also start by creating or using a vocabulary for a given domain. This strategy is a compromise between “grounded theory” and “theory driven” approaches.

Below is a table that lists things you could observe in an organization (Bogdan and Biklen, cited by Miles & Huberman:1994 61)

Types	Explanantion
Context/Situation	information on the context
Definition of the situation	interpretation of the analyzed situation by people
Perspectives	global views of the situation
Ways to look at people and objects	detailed perceptions of certain elements
Processes	sequences of events, flow, transitions, turning points, etc.
Activities	structures of regular behaviors
Events	specific activities (non regular ones)
Strategies	ways of tackling a problem (strategies, methods, techniques)
Relations and social structure	informal links
Methods	comments (annotations) of the researcher

In the literature, you may find several other such "accounting schemes". In educational technology, for example, there is a range of relatively simple code books for conversation and forum message analysis. As an example we produce a summary of students' messages code book by Eilon and Kliachko (2004).

"Level A" Group	These categories indicate knowledge construction and a significant contribution to peer learning
Comprehension	Shows written evidence about the comprehension of the subjects studied by the following categories:
Reproducing- 1	Reproduces the main points, ideas, arguments or messages found in the incoming information with reference to its source and with critical evaluation.
Directing	Directs others to relevant sources for the subjects studied (both printed and online).
Clarifying by questions	Locates ambiguous, difficult or problematic areas in the new material. Describes the context of the question or the reason for asking it.
Clarifying by answers	Gives correct, relevant and comprehensive explanations as answers. Bases the answers on retrieved information while citing its origin.
Reflection	Shows written evidence of metacognitive processes that the learner applies when studying new subj ects by the following categories:
Linking/extending	Links the new information with his or her own previous knowledge. Extends the new knowledge to other domains, especially to STS issues.
Critical evaluation	Evaluates the new information critically.
Transformation	Applies the new information in an original and creative way, draws infer ences, and gives original examples.
"Level B" Group	These categories indicate a probable contribution to peer learning, by the following categories:
Documenting	Documents l earning exper iences or indivi dual contr ibuti ons to the group.
Reproducing- 2	Reproduces the main points, ideas, ar guments or messages found in the incoming information without any evaluation or original input to it.
Learning outcomes	Presents group and individual learning outcomes.
Technical questions/answers	Questions or remarks about any subject that does not relate directly to understanding the subjects studied.
Personal knowledge	Presents personal knowledge or daily life experiences.
"Level C" Group	These categories indicate no contr ibution (or adverse contribution) to peer learning, by the following categories:
Irrelevant/unexplained questions	Poses questions without giving the context or reason for asking them.
Casual quotations	Includes quotations without their context and without further explanation.
Irrelevant/incorrect answers	Offers Irrelevant or incorrect answers to questions sent by other students.
Emotional/personal comments	Includes personal comments, which should have been sent by e-mail as instructed by the teacher . } Pattern codes Some researchers also code patterns (relationships). Simple encoding (above) breaks data down to atoms, categories), whereas pattern coding identifies relationships between atoms. Pattern coding is also one the steps in the inductive grounded theory approach. The ultimate goal is to detect (and code) regularities, but also variations and singularities. Some suggested operations: Detection of co-presence between two values of two variables E.g. people in favor of a new technology (e.g. ICT in the classroom) have a tendency to use it. Detection of exceptions e.g. technology-friendly teachers who don’t use it in the classroom In this case you may introduce new variable to explain the exception, e.g. the attitude of the superior., of the group culture, the administration, etc. Exceptions also may provoke a change of analysis level (e.g. from individual to organization) Attention: a co-presence does not prove causality Descriptive matrices and graphics Qualitative analysis attempts to put structure to data (as in exploratory quantitative techniques) In short: Analysis = visualization There exist 2 popular types of analysis: A matrix is a tabulation engaging at least one variable, e.g. Tabulations of central variables by case (equivalent to simple descriptive statistics like histograms) Crosstabulations allowing to analyze how 2 variables interact Graphs ( networks ) allow to visualize links: temporal links between events causal links between several variables etc. Some advice When you use these techniques always keep a link to the source (coded data) Try to fit each matrix or graph on a single page (or make sure that you can print things made by computer on a A3 page) you have to favor synthetic vision, but still preserve enough detail to make your artifact interpretable Consult specialized manuals e.g. Miles & Huberman, 1994 for recipes or get inspirations from qualitative research in the same domain as yours. In this tutorial we can not cover all possible types of analysis, but just provide a few examples of what can be done. Before you start doing any sort of analysis, think about what you need to answer your research questions ! The “context chart”,Miles & Huberman (1994:102) This technique allows to visualize relations and information flows between rôles and groups Example - Work flow for a "new pedagogies" program at some university There exist codified "languages" for this type of analysis, e.g. UML or OSSAD Once you clearly identified and clarified formal relations, you can use the graph to make annotations (like below) Check-lists, Miles & Huberman (1994:105) Check lists allow to make detailed summary for an analysis of an important variable. Example: "external support is important for succeeding a reform project" Examples for external support At counselor level At teacher level Analysis of deficiencies Fill in each cell as below Teaching training Change monitoring Incentives Group dynamics adequate: “we have met an organizer 3 times and it has helped us” (ENT-12:10) not adequate: “we just have informed” (ENT-13:20) etc. .. Such a table displays various dimensions of and important variable (external support). E.g. in the example above the values of the variable "external support" are listed in the left column In the other columns we insert summarized facts as reported by different roles. Review Question: Imagine how you would build such a grid to summarize teacher’s, student’s and assistant’s opinion about technical support for an e-learning platform Chronological tables Miles & Huberman (1994:110) Can summarize a studied object’s most important events in time Example: Task assignments for a blended project-oriented class Activity Date imposed tools (products) 1 Get familiar with the subject 21-NOV-2002 links, wiki, blog 2 project ideas, Q&R 29-NOV-2002 classroom 3 Students formulate project ideas 02-DEC-2002 news engine, blog 4 Start project definition 05-DEC-2002 ePBL, blog 5 Finish provisional research plan 06-DEC-2002 ePBL, blog 6 Finish research plan 11-DEC-2002 ePBL, blog 7 Sharing 17-DEC-2002 links, blog, annotation 8 audit 20-DEC-2002 ePBL, blog 9 audit 10-JAN-2003 ePBL, blog 10 Finish paper and product 16-JAN-2003 ePBL, blog 11 Presentation of work 16-JAN-2003 classroom This type of table is useful to identify important events. You can add other information, e.g. tools used in this example Matrices for roles (function in an organization or program) Miles & Huberman (1994:124) Crossing social roles with one or more variables The abstract principle can be summarized as follows (see below for an example): roles persons variable 1 variable 2 variable 3 role 1 person 1 cells are filled in with values (pointing to the source) person 2 ..... rôle 2 person 9 person 10 ..... ..... role n person n ..... Crossing roles with roles role 1 ... role 3 role 1 fill in all sorts of informations about interactions ... role 3 Example: Evaluation of the implementation of a help desk software Actor Evaluation assistance provided Assistance received Immediate effects Long term effects Explanation of the researcher Manager - - - demotivating threatened the program Felt threatened by new procedures Consultant + help choosing the right soft. involved himself - contributed to the start of the experiment - .... “Help-desk worker” +/- debugging of machines, little help with software better job satisfaction because of the tool slight improvement of throughput is still overloaded with work Users +/- A few users provided help to peers with the tool debugging of machines, little help with software Were made aware of the high amount of unanswered questions slight improvement of work performance .... Crossing between roles to visualize relations: role 1 trainers role 3 rôle 1 trainers “don’t coordinate very much” (1) dosn’t receive all the information (2) rôle 3 Techniques to hunt correlations Matrices ordered according to concepts (variables) Clusters (co-variances of variables, case typologies) An idea that certain values should "go together": Hunt co-occurrences in cells E.g.: “Can we observe a correlation between expressed needs for support and expressed needs for training for a new collaborative platform (data from teachers’s interviews)? case var 1 need for support need for training need for directives case 1 important important important case 2 not important not important not important case 3 important important important case 4 yyy not important not important not important case 5 ..... important important important case 6.... important not important not important This table shows e.g. that need for support and need for training seem to go together, e.g. cases 1,3,5 have association of "important", cases 2 and 4 have association of "not important". See next page how we can summarize this sort of information in a crosstab Co-variance expressed in a corresponding crosstab training needs * support needs need for support yes no need for training yes 3 1 no 1 2 We can observer a correlation here: "blue cells" (symmetry) is stronger than "magenta"! You should check the data above to see if we did this right ... Example typology with the same data: Type 1: "anxious" Type 2: "dependent" Type 3: "bureaucrats" Type 4: "autonomists" case 1 X case 2 X case 3 X case 4 X case 5 X case 6 X Total individuals per type 3 1 0 2 We can observe emergence of 3 types to which we assign "labels" Note: for more than 3 variables use a cluster analysis program Additional example The table shows co-occurrence between values of 2 variables. The idea is to find out what effect different types of pressure have on ICT strategies adopted by a school. Strategies of a school Type of pressure strategy 1:no reaction strategy 2:a task force is created strategy 3:internal training programs are created strategy 4:resources are reallocated strategy 5: ..... Letters written by parents (N=4)(p=0.8) (N=1)(p=0.2) Letters written by supervisory boards (N=2)(p=0.4) (N=3)(p=0.6) newspaper articles (N=1)(p=100%) type ... ..... .... Recall: Interpretation of crosstabulation See also: Methodology tutorial - quantitative data analysis (Cross-tabulation) We would like to estimate the probability that a given value of the independent (explaining) variable entails a given value of the dependent (explained) variable. The procedure calculate the % for each value of the independent variable Note: this can be either the line or the column depending on how you orient your table compute the % in the other direction Variable y to explain = Strategies of action Explaining variable x do nothing send a mail write a short tutorial Total Students making indirect suggestion 4 (80%) 1 (20%) 5 (100 %) Students explicitly complaining 2 (40%) 3 (60%) 5 (100%) Interpretation: “... if students explicitly complain, the tutor will react more strongly and engage in more helpful activities.” See: quantitative data analysis. Typology and causality graphs There are no limits of what you can draw. Basically such analysis just use a more or less precise language to draw concept maps. Below we just show two examples. Typology graphs Display attributes of types in a tree-based manner Example: Perception of a new program by different implementation agencies (e.g. schools) and its actors (e.g. teachers) Subjective causality graphs A simple causality graphs relates variables (concepts) with directed arrows. There exist many variants. One older method is “operational coding” (Axelrod, 1976) and is somewhat popular in political science. It allows to compute outcomes of reasoning chains Example: Teacher talking about active pedagogies, ICT connections, Forums Software Depending on your discipline of reference, you may be familiar with different software families that help drawing graphs: For concept mapping software, see Concept map For UML-supporting diagram software, see Unified modeling language We also can recommend a good general purpose free diagram software: DIA Finally, for people who hate to draw, there exist useful free visualization software, in particular: Graphviz Links Ground theory Bob Dick, Grounded theory: a thumbnail sketch Steve Borgatti, Introduction to Grounded Theory Glaser's Home page Grounded theory, Grounded theory (Glaser), Grounded theory (Strauss) (Wikipedia) Bibliography Amanda Coffey, A., Holbrook,B. Atkinson, P., Qualitative Data Analysis: Technologies and Representations, School of Social and Administrative Studies, University of Wales, Cardiff HTML. (This is an overview of the literature without any practical advice) Deryakulu, Deniz & Olkun, Sinan (2007). Analysis of Computer Teachers’ Online Discussion Forum Messages about their Occupational Problems. Educational Technology & Society, 10 (4), 131-142. PDF Eilon, Batia and Kliachko, Sarah (2004). The Contribution of a Substance-Oriented Forum to the Study of Human Biology in Science Teacher Education, Journal of Technology and Teacher Education (2004) 12(1), 5-24. Glaser, Barney G. (1992) Basics of grounded theory analysis: emergence vs forcing. Mill Valley, Ca.: Sociology Press. Lombard, Matthew; Jennifer Snyder-Duch and Cheryl Campanella Bracken (2008). Practical Resources for Assessing and Reporting Intercoder Reliability in Content Analysis Research Projects, Retrieved from http://www.temple.edu/sct/mmc/reliability/, accessed 20:59, 7 October 2008 (UTC). Miles, M. B. & Huberman, A. (1994). Qualitative Data Analysis: An Expanded Sourcebook. Sage. ISBN 0803955405 (This is still the cookbook for structured qualitative analysis.) Strauss, Anselm, and Corbin, Juliet (1990) Basics of qualitative research: grounded theory procedures and techniques. Newbury Park: Sage.