Operational definition

Operational definition

An operational definition is a statement that describes how a particular variable is to be measured, or how an objector condition is to be recognized. Operational definitions tell you what to do or what to observe. (The word “operational” means “describing what to do.”)

Operational definitions need to be clear and precise so that a reader knows exactly what to observe or measure.

Constructs:

A construct is an obstruction that cannot be observed directly. It is a concept inverted to explain behaviour. i.e. Intelligence, personality, teacher effectiveness, creativity, ability, achievement and motivation etc. To be measurable construct must be operationally defined. It is define in terms of operation of process that can be observed and measured. To measure a construct it is necessary to identify the scores of values it can assume. For example- the construct “personality” can be made measurable by defining two personality types, introverts and extroverts, as measured by scores on a 30-items questionnaire, with a high score indication a more introverted personality and a low score indicating a more extroverted personality. Similarly, the construct “teacher effectiveness” may be operationally defined by observing a teacher in action and judging effectiveness based on four levels: unsatisfactory, marginal, adequate, and excellent. When constructs are operationally defined, they become variables,

What to Do: Writing Operational Definitions

· Write an operational definition for each variable on your list of independent and dependent variables.

· Write an operational definition for each term (such as greater than, less than, increased, and significant) used to indicate the nature of the relationship between variables.

· For each definition you write, ask yourself:

• Are the rules and procedures for measuring the variables clear?

• Have mutually exclusive and totally inclusive categories for the variables been established?

• Is the standard of comparison clear for each term?

· Look over the written plan for carrying out an investigation, or write up a plan.

· Identify and list any variables or terms that do not have a single, clear, obvious meaning.

· If there are several reasonable ways to make an observation or to perform an action, choose one that suits the purpose of the investigation.

· Write a clear, complete definition of what the researcher should do or measure. Check your definition by asking yourself, Will this definition tell another person what to observe or how to measure? If necessary, revise your definition before starting your investigation

Distinguishing Operational Definitions, Variables, And Values

Throughout this book we will be referring to psychological variables. Some students do not have a grasp of the simple term variable, so this might be a good time to clarify the concept. A variable is some characteristic of the world that can vary or change. It is something that can be measured or detected. Therefore operational definitions , which define a word by telling how to measure or detect something, always define a variable.

Variables are distinct from values -numbers or scores. Variables can take on many possible values, depending on what is measured. The following table shows examples of variables, operational definitions, and values.

The last definition in the table (number of hairs on the left thumb) reminds us that operational definitions are not always good or valid. They are simply descriptions of measurement actions. We would argue that the last definition is an operational definition. It implies a set of measurement oper­ations (look at left thumb, count hairs) but it is not a good definition if you want to measure intelligence. The second and third definitions also are poor because they are poorly specified (which questionnaire? what repair?) Do not assume an operational definition is valid, just because a researcher has generated a number with it.

Examples of Operational Definitions

The best way to illustrate the process of developing operational definitions for variables is to identify several theoretical constructs and develop multiple operational definitions of each. This not only illustrates how the process is done, but also shows that most constructs can be measured in more than one way. The research literature also shows that it is common for different operational definitions to tap different aspects of a construct and thus react differently to experimental manipulations.

Anxiety

Anxiety is a concept that most of us are all too familiar with. It is an unpleasant feeling that occurs in certain situations. It can disrupt our functioning if it is excessive, but it also motivates behavior.

So how do you measure anxiety? How do you operationally define anxiety? Now this is a problem that has challenged researchers for years, and many fine operational definitions of anxiety are already available for our use. For the sake of this exercise, however, we will assume that we have to develop our own measure without benefit of much of this existing research.

Since this is a concept that we have first-hand knowledge of, we might start the process of operationally defining anxiety by asking ourselves what it is like. What do we feel? How do we react? How do others react? What features in other people would suggest to us that they are anxious? These are all excellent ways to start this process. It is especially useful to focus on factors that indicate anxiety in others, because those are likely to be more objective and observable factors and would provide higher reliability.

When we think of anxiety, we think first about the "feeling" of being anxious. We know what it is like and we can easily tell when we are experiencing it. It is less clear whether others would be able to tell that we are anxious just by looking at us. If fact, our own experience suggests that we may be effectively hiding our anxiety, because some people have told us they were impressed with how calm we were at a time when we felt anything but calm. Furthermore, others have told us they were very anxious in a situation in which we had observed them and they did not look anxious to us. Nevertheless, the feeling of anxiety is distinctive, even if it is not always public, so it provides one way of measuring anxiety.

Since feelings are internal events, apparently without consistent external features, we will have to rely on self-reports to find out whether a person is feeling anxious. We could simply ask people to rate their level of anxiety on a 100-point scale, a technique that is commonly used. These are often referred to as SUDS (subjective units of distress) ratings. We could instead ask people a number of questions about their feelings, questions that tap elements of anxious feelings. These might include things like "I am worried about what might happen." or "I can feel my heart pound." The number of such items endorsed by the person would likely indicate the level of anxiety. With mild anxiety, a few might be endorsed, but as the anxiety became more intense, more and more of the items would be endorsed, because more of the anxiety symptoms would be intense enough that the person noticed them.

We just mentioned something that probably resonated with many of you. When you are anxious, your heart feels as if it is pounding, and when you are very anxious, you almost always experience this sensation. This is a real effect. Anxiety is not just a feeling; it is also a physiological response. When we are anxious, our heart beats faster and stronger, our muscles tense and we shake, our palms sweat and sometimes even our face sweats, our voice may crack or our face flush. Sometimes these effects are visible to others; often they are not unless the anxiety is very strong.

We all have witnessed someone giving a talk in class who was visibly shaking, whose voice was cracking, and whose face lit up the entire room with a red glow. We can use these responses to provide another set of ways of operationally defining anxiety. We can measure the physiological changes in people as an indication of their anxiety. If their heart rate increases, we would take that as a sign of anxiety. If their palm sweats, that is another sign of anxiety. Without going into the complexities of how one measures each of these things, we will just say that it is relatively easy to do so, and that these measures have often been used to index the anxiety level of participants in studies. With modern telemetry, it is even possible to monitor many of these physiological responses while the person is carrying out everyday activities in his or her natural environment.

Most of the people who were obviously nervous about giving a talk in school somehow got through the talks, but a few quit in the middle, sometimes even leaving the room. This is yet another indicator of anxiety--in this case, the behavior of fleeing the situation. We do not see it often in classroom situations, but people who are anxious of snakes will often run away or at least step back from the object of their fear. Furthermore, we often see avoidance of situations that produce anxiety. Someone who has been very anxious giving talks in public may chose to only take classes that do not require a presentation. He or she may even chose jobs later that are unlikely to require a presentation, even though it may mean making considerably less or having a less prestigious job. So behavior, both escape and avoidance, is yet another indicator of anxiety.

We have outlined three separate strategies for operationally defining anxiety. They include (1) asking people how anxious they are feeling, (2) measuring their physiological response, and (3) observing their behavior, especially their escape and avoidance behaviour. The natural question for most students is which of these the BEST measure of anxiety is. In essence, which of the measures captures true anxiety most precisely?

The answer to this question for anxiety is often frustrating to students, but reflects the complex reality of human emotions. The answer is "It depends." Most students seem to prefer the physiological measures, because they seem more "basic." Certainly, the physiological measures have the advantage that we cannot deliberately lie about them. If we are anxious and we don't want people to know that we are anxious, we can always lie about how we feel, provided our anxiety is not so obvious that everyone can see signs of it. We can also stay in situations in spite of intense anxiety to avoid losing face or to do something that we feel is critical. Many nervous parents have spoken up at PTO meetings, because they thought it was important to the well being of their children.

But physiological measures also have their problems. The heart rate will indeed go up when we are anxious, but it also goes up for lots of other reasons as well. Walk up a flight of stairs and your heart rate will have increased several beats a minute to meet the aerobic demand. Your palms will sweat from nervousness, but they also sweat, along with the rest of your body, when you are hot. The same is true of face flushing. Your muscles will tighten when nervous, but they also tighten when you are expecting to act or are engaged in physical action. So none of our measures of anxiety is ideal.

If none of our measures of anxiety is ideal, which one should we use. The best answer is "as many as we can." The truth is that each of these measures capture a different aspect of the construct of anxiety, and therefore they do not always agree with one another. For example, people can avoid a situation without showing visible signs of anxiety, but the avoidance is a strong indicator of their feeling about the situation. Even though there may be little physiological arousal and they may claim to not be anxious, their avoidance is telling another story. The validity of that other story can often be confirmed if the person is required to face what they have been avoiding.

Looking at it from another perspective, we often see people with considerable anxiety, as measured by their physiological responses, performing all of the things required of them. Golfers might calmly sink a 10-foot putt to win a tournament, even though their heart might be racing and their palms are dripping wet. So are they anxious or not? Scientifically, the fact that these various measures of anxiety do not always agree has led to a much more thorough understanding of anxiety. We now know that it is not a single construct, but rather represents a complex collection of responses, and that the pattern that we will see will depend on the situation that the person is in. We would never have been able to recognize that if we had not operationally defined anxiety in several different ways and used all of those various definitions in our research studies

Conclusion

The Perfect Operational Definition

One must accept that there is no perfect operational definition for a given construct. Each operational definition will have advantages and disadvantages. A self-report measure is often quick and easy, but it is subject to presentational biases by the participants who take it. Actual counts of behavior are less affected by presentational biases, but they are much more time consuming and often miss critical behavior that is private. Physiological measures can tap some constructs, but physiological changes occur for many different reasons; therefore, it is hard to know if observed physiological changes are an indication of the construct of interested. Laboratory analogues have the advantage of experimental control, but there is always the question of how closely they relate to real world behavior.

Because no single operational definition is likely to provide the perfect measure of the construct of interested, it is wise to consider using more than one operational definition in a given research study. If you randomly select a dozen research studies from the best journals, you may be surprised to see how often this approach is used. Multiple operational definitions help us to zero in on the constructs that we are studying, and they often give us insights into the complexity of those constructs. We already discussed how anxiety researchers now recognize that the feelings, behavior, and physiology of anxiety are not just alternate ways of tapping anxiety, but represent distinctly different aspects of anxiety. By recognizing this basic fact, we can begin to identify how these various aspects of anxiety fit together. This is science at its best--a concerted effort at zeroing in on the workings of nature.

References

Creswell, J. W. (2008). Educational Research (3rd ed.). Upper Saddle River: Pearson Education, Inc.

Kerlinger, F. N. (1979). Behavioral research: A conceptual approach. New York: Holt, Rinehart, & Winston

Gay, L.R. (2009). Educational Research: competencies for analysis and application/L.R. Gay, Geoffrey E. Mills, Peter W. Airasin.-9^th ed.

Jhon W. Best and James V. Khan (2009). Research in Education, 10^th Ed

http://www.psywww.com/intropsych/ch01_psychology_and_science/variables_and_values.html

http://www.mikeraulin.org/graziano7e/default.htm

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GROUNDED THEORY

What Is Grounded Theory

Grounded theory is a relatively new approach to research originally defined as “the discovery of theory from data” (Glaser & Strauss, 1967, p. XX). In their seminal work The Discovery of Grounded Theory, the originators of grounded theory, Barony Glaser and Anselm Strauss, described the research process as the discovery of theory through the rigors of social research. A more detailed definition forwarded by Strauss and Corbin (1990) is as follows:

“A grounded theory is one that is inductively derived from the study of the phenomenon it represents. That is, it is discovered, developed and provisionally verified through systematic data collection and analysis of data pertaining to that phenomenon. Therefore, data collection, analysis and theory stand in reciprocal relationship to one another.” (p. 23)

Grounded theory research is discovered empirically, through induction, not deduction. The focus of grounded theory research, on support from evidence, promises to develop theories that minimally fit the immediate situation being addressed. The responsiveness of grounded theory research is aimed at contextual values and not merely the values of the investigator. Grounded theory research involves the formulation of local understandings that without inquiry by the researcher remain implicit and unexplained (Lincoln & Guba, 1985).

The Application of grounded theory

Given the differences in approaches to the method, most texts and articles on the subject advocate

reading the original 'Discovery' as a starting point. Whilst it may have dated somewhat since its

publication, the guiding principles and procedures are explained in detail and endure as the essential

guidelines for applying the method. It is also important to note that its original intent was a

methodology specifically for sociologist. In recent years, the diffusion across a number of disciplines

such as social work, health studies, psychology and more recently management, has meant the

adaptation of the method in ways that may not be completely congruent with all of the original

principles. However, despite conflicting perceptions over methodological transgressions and implementation, there remain a set of fundamental homothetic principles associated with the method.

Assumptions:

· The aim of grounded theory research is to generate or discover a theory;

· the researcher has to set aside theoretical ideas to allow a “substantive” theory to emerge;

· theory focuses on how individuals interact in relation to the phenomenon under study;

· theory asserts a plausible relation between concepts and sets of concepts;

· theory is derived from data acquired through fieldwork, interviews, observations, and documents;

· data analysis is systematic and begins as soon as data become available;

· data analysis proceeds through identifying categories and connecting them;

· further data collection (or sampling) is based on emerging concepts;

· these concepts are developed through constant comparison with additional data;

· data collection can stop when new conceptualizations emerge;

· data analysis proceeds from “open coding” (identifying categories, properties, and dimensions) through “axial coding” (examining conditions, strategies, and consequences) to selective coding around an emerging story line; and

· the resulting theory can be reported in a narrative framework or as a set of propositions.

Features of Grounded Theory

· simultaneous collection and analysis of data.

· creation of analytic codes and categories developed from data and not by pre-existing conceptualisations (theoretical sensitivity).

· discovery of basic social processes in the data.

· inductive construction of abstract categories.

· theoretical sampling to refine categories.

· writing analytical memos as the stage between coding and writing .

· the integration of categories into a theoretical framework.

Methods

The basic idea of the grounded theory approach is to read (and re-read) a textual database (such as a corpus of field notes) and "discover" or label variables (called categories, concepts and properties) and their interrelationships. The ability to perceive variables and relationships is termed "theoretical sensitivity" and is affected by a number of things including one's reading of the literature and one's use of techniques designed to enhance sensitivity.

Open Coding

Open coding is the part of the analysis concerned with identifying, naming, categorizing and describing phenomena found in the text. Essentially, each line, sentence, paragraph etc. is read in search of the answer to the repeated question "what is this about? What is being referenced here?" These labels refer to things like schools, information gathering, friendship, social loss, etc. They are the nouns and verbs of a conceptual world. Part of the analytic process is to identify the more general categories that these things are instances of, such as institutions, work activities, social relations, social outcomes, etc.

We also seek out the adjectives and adverbs --- the properties of these categories. For example, about a friendship we might ask about its duration, and its closeness, and its importance to each party. Whether these properties or dimensions come from the data itself, from respondents, or from the mind of the researcher depends on the goals of the research. It is important to have fairly abstract categories in addition to very concrete ones, as the abstract ones help to generate general theory. the most grounded theorists believe they are theorizing about how the world *is* rather than how respondents see it. The process of naming or labeling things, categories, and properties is known as coding. Coding can be done very formally and systematically or quite informally. In grounded theory, it is normally done quite informally. For example, if after coding much text, some new categories are invented, grounded theorists do not normally go back to the earlier text to code for that category. However, maintaining an inventory of codes with their descriptions (i.e., creating a codebook) is useful, along with pointers to text that contain them. In addition, as codes are developed, it is useful to write memos known as code notes that discuss the codes. These memos become fodder for later development into reports.

Axial Coding

Axial coding is the process of relating codes (categories and properties) to each other, via a combination of inductive and deductive thinking. To simplify this process, rather than look for any and all kind of relations, grounded theorists emphasize causal relationships, and fit things into a basic frame of generic relationships. The frame consists of the following elements:

Element	Description
Phenomenon	This is what in schema theory might be called the name of the schema or frame. It is the concept that holds the bits together. In grounded theory it is sometimes the outcome of interest, or it can be the subject.
Causal conditions	These are the events or variables that lead to the occurrence or development of the phenomenon. It is a set of causes and their properties.
Context	Hard to distinguish from the causal conditions. It is the specific locations (values) of background variables. A set of conditions influencing the action/strategy. Researchers often make a quaint distinction between active variables (causes) and background variables (context). It has more to do with what the researcher finds interesting (causes) and less interesting (context) than with distinctions out in nature.
Intervening conditions	Similar to context. If we like, we can identify context with moderating variables and intervening conditions with mediating variables. But it is not clear that grounded theorists cleanly distinguish between these two.
Action strategies	The purposeful, goal-oriented activities that agents perform in response to the phenomenon and intervening conditions.
Consequences	These are the consequences of the action strategies, intended and unintended.

Selective Coding

Selective coding is the process of choosing one category to be the core category, and relating all other categories to that category. The essential idea is to develop a single storyline around which all everything else is draped. There is a belief that such a core concept always exists.

I believe grounded theory draws from literary analysis, and one can see it here. The advice for building theory parallels advice for writing a story. Selective coding is about finding the driver that impels the story forward.

Memos

Memos are short documents that one writes to oneself as one proceeds through the analysis of a corpus of data. There are two kinds of memos-(1) field note and (2) the code note. Equally important is the theoretical note. A theoretical note is anything from a post-it that notes how something in the text or codes relates to the literature, to a 5-page paper developing the theoretical implications of something. The final theory and report is typically the integration of several theoretical memos. Writing theoretical memos allows you to think theoretically without the pressure of working on "the" paper.

The grounded theory process

1. The identification of an area of interest and data collection

Initially, as with any piece of research, the process starts with an interest in an area one wishes to explore further. Usually researchers adopt grounded theory when the topic of interest has been relatively ignored in the literature, or has been given only superficial attention. Consequently, the researcher's mission is to build his/her own theory from the ground. However, most researchers will have their own disciplinary background which will provide a perspective from which to investigate the problem. Nobody starts with a totally blank sheet. A sociologist will be influenced by a body of sociological thought, a psychologist will perceive the general phenomenon from either a cognitive, behavioural, or social perspective, and a business academic may bring to bear organisational, marketing, economic, or systems concepts which have structured their analysis of managerial behaviour. These theories provide sensitivity and focus which aid the interpretation of data collected during the research process. The difficulty in applying grounded theory comes when the area of interest has a long, credible and empirically based literature. Grounded theory may still be used, but literature in the immediate area should be avoided so as not to prejudice or influence the perceptions of the researcher.

Here the danger lies in entering the field with a prior disposition, whether conscious of it or not, of testing such existing work rather than developing uncoloured insights about the area of study. In order to avoid this, it is generally suggested that the researcher enter the field at a very early stage and collect data in whatever form appropriate. Unlike other qualitative methodologies which acknowledge only one source of data, for example the words of those under study as in the case of phenomenology, grounded theory research may be based on single or multiple sources of data. These might include interviews, observations, focus groups, life histories, and introspective accounts of experiences. With grounded theory, researchers should also avoid being too structured in their methods of collecting information. For example, an interview should not be conducted using a prescribed formal schedule of questions. This would defeat the objective which is to attain first hand information from the point of view of the informant. Nonetheless, this is easier in theory than in practice. Informants usually want some guidance about the nature of the research and what information is sought. Totally unstructured interviews therefore cause confusion, incoherence, and result in meaningless data. Structured interviews, on the other hand, may be merely an extension of the researcher's expectations. The art lies therefore in finding a balance which allows the informant to feel comfortable enough to expand on their experiences, without telling them what to say.

2. Interpreting the data and further data collection

As the data are collected they should be analysed simultaneously by looking for all possible interpretations. This involves utilising particular coding procedures which normally begins with open coding. Open coding is the process of breaking down the data into distinct units of meaning. As a rule, this starts with a full transcription of an interview, after which the text is analysed line by line in an attempt to identify key words or phrases which connect the informant's account to the experience under investigation. This process is associated with early concept development which consists of "identifying a chunk or unit of data (a passage of text of any length) as belonging to, representing, or being an example of some more general phenomenon". In addition to open coding, it is important to incorporate the use of memos. Memos are notes written immediately after data collection as a means of documenting the impressions of the researcher and describing the situation. These are vital as they provide a bank of ideas which can be revisited in order to map out the emerging theory. Essentially, memos are ideas which have been noted during the data collection

process which help to reorientate the researcher at a later date.

3. Theoretical sampling

A further feature of the method relates to the sampling of informants. Sampling is not determined to begin with, but is directed by the emerging theory. Initially, the researcher will go to the most obvious places and the most likely informants in search of information. However, as concepts are identified and the theory starts to develop, further individuals, situations and places may need to be incorporated in order to strengthen the findings. This is known as 'theoretical sampling' which is "the process of data collection for generating theory whereby the analyst jointly collects, codes and analyses the data and decides what data to collect next and where to find it, in order to develop the theory as it emerges. This process of data collection is 'controlled' by the emerging theory" (Glaser, 1978 p.36).

In addition to theoretical sampling, a fundamental feature of grounded theory is the application of the 'constant' comparative method. As the name implies, this involves comparing like with like, to look for emerging patterns and themes. "Comparison explores differences and similarities across incidents within the data currently collected and provides guidelines for collecting additional data...........Analysis explicitly compares each incident in the data with other incidents appearing to belong to the same category, exploring their similarities and differences". This process facilitates the identification of concepts. Concepts are a progression from merely describing what is happening in the data, which is a feature of open coding, to explaining the relationship between and across incidents. This requires a different, more sophisticated, coding technique which is commonly referred to as 'axial coding' and involves the process of abstraction onto a theoretical level.

4. Concept and category development

Axial coding is the appreciation of concepts in terms of their dynamic interrelationships. These should form the basis for the construction of the theory. "Abstract concepts encompass a number of more concrete instances found in the data. The theoretical significance of a concept springs from its relationship to other concepts or its connection to a broader gestalt of an individual's experience". In turn, once a concept has been identified, its attributes may be explored in greater depth, and its characteristics dimensionalised in terms of their intensity or weakness. Finally the data are subsumed into a core category which the researcher has to justify as the basis for the emergent theory. A core category pulls together all the strands in order to offer an explanation of the behaviour under study. It has theoretical significance and its development should be traceable back through the data. This is usually when the theory is written up and integrated with existing theories to show relevance and new perspective. Nonetheless, a theory is usually only considered valid if the researcher has reached the point of saturation. This involves staying in the field until no new evidence emerges from subsequent data. It is also based on the assumption that a full interrogation of the data has been conducted, and negative cases, where found, have been identified and accounted for.

WHAT GROUNDED THEORY IS NOT

misconceptions

· Grounded Theory Is Not an Excuse to Ignore the Literature: A common misassumption is that grounded theory requires a researcher to enter the field without any knowledge of prior research.

· Grounded Theory Is Not Presentation of Raw Data

· Grounded Theory Is Not Theory Testing, Content Analysis, or Word Counts

· Grounded Theory Is Not Simply Routine Application of Formulaic Technique to Data

· Grounded Theory Is Not Perfect

· Grounded Theory Is Not Easy

· Grounded Theory Is Not an Excuse for the Absence of a Methodology

Conclusion

We count ourselves among the scholar-practitioners who consider grounded theory as a “way of life.” As Glaser (1998) observed, “There are people who need a way of constantly looking at data and its realities in order to figure out what they are doing…To be sure grounded theory is non-religious, non-spiritual, non-ideological and non-requiring to join. It is free to use and see what emerges as one might wish. They do it every day”. The utility of grounded theory in an action context, or in purposeful therapy is clear, because grounded theory will have relevance and grab in multiple contexts and across a wide range of disciplines. It is powerful because it “grounds” itself in reality through systematically generated research.

Grounded learning developed out of the desire to put learning and conceptualization into the hands of every student. Surely, the rigor and discipline of field note taking, coding, and data analysis led to the emergence if creative ideas for student transformation. Solid grounded theory methodology can be most valuable for the educational practitioner as well. Sampling for what works in education, getting to the root of student needs and the discovery of learning gaps can all be possible future grounded learning action areas to consider.

References

Glaser, B. (1992). Basics of grounded theory analysis. Mill Valley, CA: Sociology Press.

Glaser, B. (2001). The grounded theory perspective: conceptualization contrasted with description. Mill Valley CA: Sociology Press

Glaser, B. (2002). The grounded theory perspective: Conceptualization contrasted with description. Mill Valley, CA.: Sociology Press.

Glaser, B. (2003). The grounded theory perspective II. Mill Valley, CA: Sociology Press.

Glaser, B. (2004). Grounded theory interview. Retrieved April 2004, 2004, from

http://www.groundedtheory.com/vidseries1.html

Krippendorff, K. (2003). Content analysis: An introduction to its methodology (2nd ed.). Thousand Oaks,CA: Sage.

Locke, K. (2001). Grounded theory in management research. London: Sage.

Sprenger, S. (2003). How to Go About Doing an Excellent "Explication de Texte". Retrieved 4/20/04, 2004, from

http://frenital.byu.edu:16080/classes/fr451/explicationtexte.html

Suber, P. (1997). Explication Assignment. Retrieved May 19, 2004, 2004, from

http://www.earlham.edu/~peters/courses/explicat.htm

Walker, G. H. (2002). Concept Mapping and Curriculum Design. Retrieved May 17, 2004, 2004, from http://www.studygs.net/mapping/mapping.htm

Methods of Data Analysis in Qualitative Research

Methods of Data Analysis in Qualitative Research

1.Typology

a classification system, taken from patterns, themes, or other kinds of groups of data. categories should be mutually exclusive and exhaustive if possible, often they aren't. Basically a list of categories. example: Lofland and Lofland's 1st edition list: acts, activities, meanings, participation, relationships, settings (in the third edition they have ten units interfaced by three aspects--see page 114--and each cell in this matrix might be related to one of seven topics--see chapter seven).

2. Taxonomy:

(See Domain Analysis - often used together, especially developing taxonomy from a single domain.) James Spradley A sophisticated typology with multiple levels of concepts. Higher levels are inclusive of lower levels. Superordinate and subordinate categories

3. Constant Comparison/Grounded Theory

(widely used, developed in late 60's) Anselm Strauss

• Look at document, such as field notes

• Look for indicators of categories in events and behavior - name them and code them on document

• Compare codes to find consistencies and differences

• Consistencies between codes (similar meanings or pointing to a basic idea) reveals categories. So need to categorize specific events

• We used to cut apart copies of field notes, now use computers. (Any good word processor can do this. Lofland says qualitative research programs aren't all that helpful and I tend to agree. Of the qualitative research programs I suspect that NUD*IST probably the best--see Sage Publishers).

• Memo on the comparisons and emerging categories

• Eventually category saturates when no new codes related to it are formed

• Eventually certain categories become more central focus - axial categories and perhaps even core category.

4. Analytic Induction

(One of oldest methods, a very good one) Look at event and develop a hypothetical statement of what happened. Then look at another similar event and see if it fits the hypothesis. If it doesn't, revise hypothesis. Begin looking for exceptions to hypothesis, when find it, revise hypothesis to fit all

examples encountered. Eventually will develop a hypotheses that accounts for all observed cases.

5. Logical Analysis/Matrix Analysis

An outline of generalized causation, logical reasoning process, etc. Use flow charts, diagrams, etc. to pictorially represent these, as well as written descriptions. Matthew Miles and Huberman gives hundreds of varieties in their huge book Qualitative Data Analysis, 2nd ed.

6. Quasi-statistics

(count the # of times something is mentioned in field notes as very rough estimate of frequency) Howard Becker Often enumeration is used to provide evidence for categories created or to determine if observations are contaminated. (from LeCompte and Preissle).

7. Event Analysis/Microanalysis

(a lot like frame analysis, Erving Goffman) Frederick Erickson, Kurt Lewin, Edward Hall. Emphasis is on finding precise beginnings and endings of events by finding specific boundaries and things that mark boundaries or events. Specifically oriented toward film and video. After find boundaries, find phases in event by repeated viewing.

8. Metaphorical Analysis

(usually used in later stages of analysis) Michael Patton, Nick Smith Try on various metaphors and see how well they fit what is observed. Can also ask participant for metaphors and listen for spontaneous metaphors. "Hallway as a highway." Like highway in many ways: traffic, intersections, teachers as police, etc. Best to check validity of metaphor with participants - "member check".

9. Domain Analysis

(analysis of language of people in a cultural context) James Spradley Describe social situation and the cultural patterns within it. Semantic relationships. Emphasize the meanings of the social situation to participants. Interrelate the social situation and cultural meanings. Different kinds of domains: Folk domains (their terms for domains), mixed domains, analytic domains (researcher's terms for domains).

• select semantic relationships

• prepare domain analysis worksheet

• select sample of field notes (statements of people studied)

• look for broad and narrow terms to describe semantic relationships

• formulate questions about those relationships

• repeat process for different semantic relationship

• list all domains discovered

10. Hermeneutical Analysis

(hermeneutics = making sense of a written text) Max Van Manen Not looking for objective meaning of text, but meaning of text for people in situation. Try to bracket self out in analysis - tell their story, not yours. Use their words, less interpretive than other approaches. Different layers of interpretation of text. Knowledge is constructed – we construct meaning of text (from background and current situation - Social construction because of influence of others - symbolic interactionism) Use context - time and place of writing - to understand. What was cultural situation? Historical context. Meaning resides in author intent/purpose, context, and the encounter between author and reader - find themes and relate to dialectical context. (Some say authorial intent is impossible to ascertain.) Videotape - probably needs to be secondary level of analysis. Get with another person who is using another method and analyze their field notes.

11. Discourse analysis

(linguistic analysis of ongoing flow of communication) James Gee Usually use tapes so they can be played and replayed. Several people discussing, not individual person specifically. Find patterns of questions, who dominates time and how, other patterns of interaction.

12. Semiotics

(science of signs and symbols, such as body language) Peter Manning Determine how the meanings of signs and symbols is constructed. Assume meaning is not inherent in those, meaning comes from relationships with other things. Sometimes presented with a postmodernist emphasis.

13. Content Analysis

(not very good with video and only qualitative in development of \categories - primarily quantitative) (Might be considered a specific form of typological analysis) R. P. Weber Look at documents, text, or speech to see what themes emerge. What do people talk about the most? See how themes relate to each other. Find latent emphases, political view of newspaper writer, which is implicit or look at surface level - overt emphasis. Theory driven - theory determines what you look for. Rules are specified for data analysis. Standard rules of content analysis include:

• How big a chunk of data is analyzed at a time (a line, a sentence, a phrase, a paragraph?) Must state and stay with it.

• What are units of meaning?, the categories used. Categories must be:

1. Inclusive (all examples fit a category) 2. Mutually exclusive

• Defined precisely: what are properties

• All data fits some category (exhaustive)

Also note context. Start by reading all way through, then specify rules. Could have emergent theory, but usually theory-driven. After determine categories, do the counting - how often do categories occur. Most of literature emphasizes the quantitative aspects. Originated with analyzing newspaper articles for bias - counting things in print. Very print oriented - can it be adapted for visual and verbal?

14. Phenomenology/Heuristic Analysis

(phenomenological emphasis - how individuals experience the world) Clark Moustakas Emphasizes idiosyncratic meaning to individuals, not shared constructions as much. Again, try to bracket self out and enter into the other person's perspective and experience. Emphasizes the effects of research experience on the researcher-personal experience of the research. How does this affect me as researcher. Much like hermeneutical analysis, but even more focused on the researcher's experience. Some use the term "phenomenology" to describe the researcher's experience and the idea that this is all research is or can ever be (see Lofland and Lofland, p. 14).

15. Narrative Analysis

(study the individual's speech) Catherine Reisman Overlaps with other approaches. (Is it distinctive?) Discourse analysis looks at interaction, narrative is more individual) The story is what a person shares about self. What you choose to tell frames how you will be perceived. Always compare ideas about self. Tend to avoid revealing negatives about self. Might study autobiographies and compare them.

• context-situation

• core plot in the story told about self

• basic actions

Narrative analysis could involve study of literature or diaries or folklore.

References

Taxonomic Analysis: James P. Spradley (1980). Participant observation. Fort Worth: Harcourt Brace.

Typological Systems: John Lofland & Lyn H. Lofland (1995). Analyzing social settings, 3rd ed. Belmont, Cal.: Wadsworth.

Constant Comparison: Anselm L. Strauss (1987). Qualitative analysis for social scientists. New York: Cambridge University Press.

Case Study Analysis: Sharon Merriam (1988). Case study research in education. Jossey-Bass.

Ethnostatistics: Robert P. Gephart (1988). Ethnostatistics: Qualitative foundations for quantitative research. Newbury Park, Cal.: Sage Publications.

Logical Analysis/Matrix Analysis: Miles, M. B., & Huberman, A. M. (1994). Qualitative data

analysis, 2nd ed. Newbury Park, Cal.: Sage. [Note: I think this may well be the best book available on qualitative data analysis.]

Phenomenological/Heuristic Research: Moustakas, C. (1990). Heuristic Research. Newbury Park, Cal.: Sage; and Moustakas, C. (1994). Phenomenological research methods. Newbury Park, Cal.: Sage.

Event Analysis/Microanalysis: Frederick Erickson (1992). Ethnographic microanalysis of interaction. In M. LeCompte, et. al. (Eds), The handbook of qualitative research in education (chapter 5). San Diego: Academic Press.

Analytic Induction: Jack Katz (1983). A theory of qualitative methodology. In R. M. Emerson (Ed.), Contemporary field research. Prospect Heights, Ill.: Waveland.

Hermeneutical Analysis: Max Van Manen (1990). Researching lived experience. New York: State University of New York Press.

Semiotics: Peter K. Manning (1987). Semiotics and fieldwork. Newbury Park, Cal.: Sage.

Discourse Analysis: James P. Gee (1992). Discourse analysis. In M. LeCompte, et. al. (Eds), The handbook of qualitative research in education (chapter 6). San Diego: Academic Press.

Narrative Analysis: Catherine K. Reisman (1993). Narrative analysis. Newbury Park, Cal.: Sage.

Content Analysis: R. P. Weber (1990). Basic content analysis. Newbury Park, Cal.: Sage.

Domain Analysis: James P. Spradley (1980). Participant observation. Fort Worth: Harcourt Brace. Also see J. P. Spradley, Ethnographic interview (1979, same publisher).

Metaphorical Analysis: Nick Smith (1981). Metaphors for evaluation. Newbury Park, Cal.: Sage.