Subjective Interpretation of Data

Interpretation of data does imply subjectivity. In fact, it does two-fold: first, one must consider bias in interpretation stemming from the analyst; second, one must consider individual subjectivity during personal analysis of the results.  However, this is not a negative quality.  It is through subjectivity that one can develop objective findings.  Subjective nature allows one to consider and reach an ultimate conclusion, or purpose for the raw data.  When I teach inferencing to my students, I stress that inferencing is not a guess or postulation unto itself; it is rooted in truth, and bolstered by experience.  Research is grounded by data, but subjective questioning led to the design, the methodology, and the interpretation.  Authors “speculate about [the] implications” of data-based results (McMillan, 2007, p. 361).  Of course, even though the speculation is subjective, it is also “reasoned” – there must be data to back up the conclusions (McMillan, 2007, p. 362).

A third type of subjectivity related to interpretation of data stems from subjective data itself.  To some, subjectivity might arise from factors leading to the data (such as an aforementioned conversation had about ill-timed pretests to boost post-test scores).  Other emphasis should be placed on subjective questioning.  Research which relies on opinions, self-ratings, or other such measurements is inherently subjective and dependent upon personal biases of the study participants in addition to interpretative biases of the researcher.  This does not mean that the results are any less important, simply that data itself often lacks true objectivity.  Nothing can be viewed outside of its context.

McMillan, J. H. (2007). Educational Research: Fundamentals for the Consumer 6th ed. Pearson.

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