Objectivity is one of the most cherished ideals of the educational research community. In fact it is so important that if our work is accused of being subjective, its status as a source of knowledge sinks slowly into the horizon like a setting sun. Yet, though we use the term objective with ease in our conversations and in our literature, its meaning is not particularly clear, nor ... are the consequences of the tacit, almost unexamined assumptions upon which it rests.
(Eisner, 1992, p. 9)
It turns out, then, that what is crucial for the objectivity of any inquiry - whether it is qualitative or quantitative - is the critical spirit in which it has been carried out. And, of course, this suggests that there can be degrees; for the pursuit of criticism and refutation obviously can be carried out more or less seriously. 'Objectivity' is the label - the 'stamp of approval' - that is used for inquiries that are at one end of the continuum.
(Phillips, 1989, p. 36)
To pursue objectivity-or truth-to-nature or trained judgment-is simultaneously to cultivate a distinctive scientific self wherein knowing and knower converge. Moreover, the very point at which they visibly converge is in the very act of seeing not as a separate individual but as a member of a particular scientific community.
Daston, L. & Galison, P. (2007) Objectivity. Cambridge, Massachusetts, Zone Books.
Acknowledging the subjectivity of statistical analysis would be healthy for science as a whole for at least two reasons. The first is that the straightforward methods of subjective analysis, called Bayesian analysis, yield answers which are much easier to understand than standard statistical answers, and hence much less likely to be misinterpreted. This will be dramatically illustrated in our first example.
The second reason is that even standard statistical methods turn out to be based on subjective input - input of a type that science should seek to avoid. In particular, standard methods depend on the intentions of the investigator, including intentions about data that might have been obtained but were not. This kind of subjectivity is doubly dangerous. First, it is hidden; few researchers realize how subjective standard methods really are. Second, the subjective input arises from the producer rather than the consumer of the data - from the investigator rather than the individual scientist who reads or is told the results of the experiment.
Berger, J.O. & Berry, D.A. (1988) 'Statistical Analysis and the Illusion of Objectivity'.American Scientist, vol. 76 issue 2, p.159-165.
In order to defend the validity or objectivity of interpretation against the 'natural attitude' of the researcher, Husserl believed that any preconceptions or beliefs held by the researcher should be examined, acknowledged and then put to one side or 'bracketed'; a process also known as 'reduction' in phenomenology
Researchers subscribing to Heideggerian philosophy acknowledge that they can only interpret something according to their own beliefs, experiences and preconceptions, which are a legitimate part of the research process and should not be left out.
A defining 'quality indicator' in Heideggerian research is a detailed explication of the interviewer's preconceptions and reference to these throughout the research process. In contrast, a 'quality indicator' in Husserlian phenomenology is an account of how the interviewer's preconceptions have been treated so as not to influence the research in any way.
Lowes, L. Prowse, M.A. (2001) 'Standing outside the interview process? The illusion of objectivity in phenomenological data generation'. International Journal of Nursing Studies, vol. 38, pp. 471-480.
H809: Activity 12.7: Investigating objectivity
H809: Activity 12.7: Investigating objectivity (2 hours)
Objectivity is one of the most cherished ideals of the educational research community. In fact it is so important that if our work is accused of being subjective, its status as a source of knowledge sinks slowly into the horizon like a setting sun. Yet, though we use the term objective with ease in our conversations and in our literature, its meaning is not particularly clear, nor ... are the consequences of the tacit, almost unexamined assumptions upon which it rests.
(Eisner, 1992, p. 9)
It turns out, then, that what is crucial for the objectivity of any inquiry - whether it is qualitative or quantitative - is the critical spirit in which it has been carried out. And, of course, this suggests that there can be degrees; for the pursuit of criticism and refutation obviously can be carried out more or less seriously. 'Objectivity' is the label - the 'stamp of approval' - that is used for inquiries that are at one end of the continuum.
(Phillips, 1989, p. 36)
To pursue objectivity-or truth-to-nature or trained judgment-is simultaneously to cultivate a distinctive scientific self wherein knowing and knower converge. Moreover, the very point at which they visibly converge is in the very act of seeing not as a separate individual but as a member of a particular scientific community.
Daston, L. & Galison, P. (2007) Objectivity. Cambridge, Massachusetts, Zone Books.
Acknowledging the subjectivity of statistical analysis would be healthy for science as a whole for at least two reasons. The first is that the straightforward methods of subjective analysis, called Bayesian analysis, yield answers which are much easier to understand than standard statistical answers, and hence much less likely to be misinterpreted. This will be dramatically illustrated in our first example.
The second reason is that even standard statistical methods turn out to be based on subjective input - input of a type that science should seek to avoid. In particular, standard methods depend on the intentions of the investigator, including intentions about data that might have been obtained but were not. This kind of subjectivity is doubly dangerous. First, it is hidden; few researchers realize how subjective standard methods really are. Second, the subjective input arises from the producer rather than the consumer of the data - from the investigator rather than the individual scientist who reads or is told the results of the experiment.
Berger, J.O. & Berry, D.A. (1988) 'Statistical Analysis and the Illusion of Objectivity'. American Scientist, vol. 76 issue 2, p.159-165.
In order to defend the validity or objectivity of interpretation against the 'natural attitude' of the researcher, Husserl believed that any preconceptions or beliefs held by the researcher should be examined, acknowledged and then put to one side or 'bracketed'; a process also known as 'reduction' in phenomenology
Researchers subscribing to Heideggerian philosophy acknowledge that they can only interpret something according to their own beliefs, experiences and preconceptions, which are a legitimate part of the research process and should not be left out.
A defining 'quality indicator' in Heideggerian research is a detailed explication of the interviewer's preconceptions and reference to these throughout the research process. In contrast, a 'quality indicator' in Husserlian phenomenology is an account of how the interviewer's preconceptions have been treated so as not to influence the research in any way.
Lowes, L. Prowse, M.A. (2001) 'Standing outside the interview process? The illusion of objectivity in phenomenological data generation'. International Journal of Nursing Studies, vol. 38, pp. 471-480.