Description of the Process - Using MCA
A researcher who wants to use MCA obtains from the research subject(s) a statement which tells about time spent in an activity relating to, or having an influence on, the event being analyzed.
For example, a write-up on a passenger's experience of an airplane flight (for the purpose of finding ways to improve passenger satisfaction) or an interview with a farmer (for the purpose of investigating the lack of increased productivity following the application of new farming methods).
Next, the text is segmented into phrases which are then assigned specific qualities in four different ways. A special form of tabulating these qualities provides a clear insight into the researched questions.
The software, MINERVA, simplifies the process by automating functions, generating charts, and providing moving tabulations for rapid interpretations.
The actual process is described below.
Meaning Constitution Analysis is made up of a set of procedures which are completed in three phases.
The first thing that the researcher has to do is to formulate a question relating to the issue being investigated and obtain a statement from the subject of the research.
For example, "Please tell us about your thoughts, reflections, feelings about your work", or "please narrate one day of work at the growing fields".
The statements are obtained in the form of written text, or a recorded interview. The important thing is that the text end up in written (Microsoft Word) format.
Step 1: Segmenting the text into Meaning Units
The text (in MCA referred to as the Protocol), obtained from the research subject, is sequentially broken down into segments, called Meaning Units. These Meaning Units are listed downward in the leftmost column of a table (Using MINERVA all the researcher has to do to complete this step is to click at the break points for segmenting the text).
Step 2: Assigning Values to Meaning Units
Different sets of values are assigned to each Meaning Unit. The Modalities are characteristics which define specific aspects of the Units such as the Time Occurrence – past, recurrent, future etc.
For example, the Time Modality of "We will buy new machines" is Future.
Partial Intentions define all that is implied by the Meaning Unit.
For example, "We will buy new machines" implies the existence of such a thing called machine, also implies the action of buying, and so on.
From Partial Intentions one now derives two other types of values (called Entities and Predicates) which relate to each Meaning Unit. This completes the Analysis Phase.
In Phase 2, the Analysis Phase, one has, in effect, recreated the life/world of the subject under research. In a sense one has built a replica of that subject, into which the researcher can now peer from different angles, that is, vantage points.
The researcher uses the tabulation of the values of the Meaning Units to derive conclusions. These tabulations are called Views. One looks at the occurrence of a type of value, given a specific value of another type.
For example, the researcher observes which Partial Intentions occur given a certain Modality.
We can compare surveys/questionnaires to MCA using the example of diagnosing the illness of a patient. A survey is similar to diagnosing based on a series of vital data such as the blood profile heart rate data etc. of the patient sent to a doctor. MCA, on the other hand, is like the recreation of a copy of the functioning body of the patient with the use of measurements, x-rays, blood samples and other information. Once a model has been built the physician can now peer into the body and take any additional measurements, make whatever tests that may be necessary to arrive at a conclusion.
Questionnaires provide for specific answers with no need or room for interpretation. The only answers that one gets, however, are those to questions that the researcher thought of asking. Even then, the answers are restricted to those made available to the subject of the inquiry. There is no open ended flow of information; there is very little left to interpretation.
MCA, on the other hand, provides a view of the whole object. The researcher therefore has the possibility of looking infinitely for answers but at the same time must use some skill to know where to look.
This search is not complicated. The quality of the results will rely to some extent on the ability and experience of the researcher.
Referring to the comparison made above, using the diagnosis of a patient’s condition, a questionnaire will be just that, requiring the diagnosis with a set of data. The diagnosis could just as well be programmed into a computer. MCA brings the actual patient; skill and experience will guide the research and hence the quality of the results.