The Article by Grumet, Article Review Example

Important Insights:

• An important insight gained from this article is that statistics in Psychology, or any other social science, can often ignore outliers. Moreover, it can be often assumed incorrectly that these outliers are the result of methodological problems with the study rather than individual differences or interesting phenomena.
• Researchers can use different statistical tests to find significance in results where other statistical tests have already demonstrated or found that there was no significance.
• Another important insight from this article is that significance does not always imply that the results are significant.
• On a similar note, statistical data and the conclusions drawn from this data can result in the meaning and power of the findings can be removed from the context in which the research was conducted.
• On the same line, the use of statistical testing can dehumanize the individuals under investigation, which also removes meaning from the results. Moreover, such a problem can remove any real usefulness from the research findings for these individuals.
• Social sciences, particularly Psychology, view the use of statistical testing as a means through which the discipline can gain scientific credentials like the hard sciences of physics for example.
• A very important insight gained from this article is that statistical testing can sometimes lead research rather than the research question. As such, important research questions may be ignored in favour for research questions that can be tested through the use of statistical testing.
• In a similar sense, statistical testing can be wholly inappropriate to the phenomenon under investigation. However, statistical testing is used in order to make the results seems more powerful, even if rich, qualitative is more appropriate.
• Another important insight gained from this article is that experiments or other psychological research that makes use statistical testing or analysis requires individuals/participants to be categorised in relation to some pre-judged set of criteria, which removes any possibility of fluidity of movement, opinion or individual differences.
• Finally, an important insight highlighted by this article is that theory-driven researchers can often use statistical analysis in order to support theories by removing results that are not in keeping with the theory the research is based on. Furthermore, researchers can use methods in order to remove any such problems, such as increasing the number of participants or trials, for example.

“To extent do you think psychology would actually benefit if, as of today, we were to eliminate the use of all statistical analysis from our research work?”

My personal reflection of this question, particularly in light of the article by Grumet (1993), would be that certain aspects of psychology, such as those that require interviews as research methods, would probably benefit from the elimination of the use of all statistical analysis from research work. For example, such branches of psychology like critical social psychology and social constructivism would be able to flourish and be given more credit for the rich data it produces. Furthermore, such branches would be able to demonstrate the usefulness of qualitative data in explaining human cognition and behaviour. In addition, such branches of psychology would not be marred by problems of reliability or lack of scientific reasoning.

However, on the other hand, other aspects or branches of psychology would suffer, such as experimental psychology or cognitive psychology. The discipline would suffer because it would be unable to test particular research questions or large amounts of data, which could be quickly and easily analysed through use of statistical analysis. What’s more, it would lead to more lengthy analysis via other means, particularly researcher interpretation that could result in bias or misinterpretation of the data. As such, research findings could become more unreliable or less useful for the general population. Moreover, psychology would be consider less scientific if it were to remove all statistical analysis from its research. Then again, should psychology be considered a hard science, particularly in light of its subject matter?

In conclusion, I would say that it would be unwise to completely remove all statistical analysis from our research work, as statistical analysis is required for certain research questions and research methods. However, psychology should not use statistical analysis as if it were the Holy Grail of all research practices as qualitative analysis can produce rich and detailed results that are just as important that those gained from quantitative methods.

Grumet’s Central Message

The central message of Grumet’s (1993) article is that statistical analysis is not always as important to psychological research as it has been purported. Furthermore, the central message of this article is that statistical analysis is not always suitable for psychological research due to the very varied nature of psychology and human behaviour. Grumet highlights that psychology’s blind adherence to scientific principles can lead to errors in findings and in the direction that psychological research has taken. Moreover, psychology cannot function completely through use of statistical analysis as there is need for qualitative research just as much as quantitative.

Works Cited

Grumet, Gerald. W. “The Soft Underbelly of Statistics”. Perceptual and Motor Skills, 76(1993): 515-529.