Every scientific discipline is determined by the object of measurement and the selection of appropriate methods of data collection and statistical analysis. Faulty methodology can lead to incorrect information in the results, without the researcher being aware of this. Taking incorrect knowledge as correct into account while conducting further research has far-reaching negative consequences. One of these errors present, to some degree, in every single research is bias. It is a particularly dangerous one, because it usually goes undetected by the researcher. But if you are aware of its threat there are ways to avoid it. In research, it occurs when systematic error is introduced into sampling or testing by selecting or encouraging one outcome or answer over others. It comes in numerous ways and forms. The rest of this post will focus on causes of bias in the field of gender studies.
Bias is defined as any tendency which prevents unprejudiced conside ration of a question. Gender bias occurs because researchers’ stereotypes and prejudices about gender become implicitly, unknowingly, but systematically implemented in the research process. It could be defined as a systematically erroneous gender dependent approach related to a social construct, which incorrectly regards women and men as similar/different (Ritchie, 2009).
This type of cognitive bias can occur at any phase of research, including planning, data collection and analysis. The following section sheds light into the typical research stages and the respective threats that occur in each of them, as well as providing advice on how to deal with the different flaws or threats to thorough research practice.
- Hypothesis and conceptual frameworks have to be based on reality, not on assumptions about gender characteristics, roles, and cultural values. If you want to be more objective while conducting research in order to minimise the impact of your own maybe unfounded beliefs (don’t feel bad, we all have them) you have to acknowledge your own bias; e.g. prejudices and stereotypes. It means you have to engage in introspection, and grasp the preconceived notions about what men and women are like, and what they should ideally be like. Afterwards, you should try to accept them as something that may or may not be true or right; just don’t let them lead your research project and be open to new solutions. In essence, keep an open mind concerning your assumptions and question them at every opportunity.
- Furthermore, you should avoid trying to understand the problem from a single perspective. When you make a decision about your topic of the interest you are responsible for reviewing literature relevant to the research question. That means going beyond your own point of view and reading-up thoroughly the literature on different perspectives which may propose different answers to your question. You are free to choose any theory you consider relevant, and the same theory will probably guide you in formulation of the hypothesis.
- Another problem is androcentrism. It is “the practice of giving overriding importance to men human beings or to the masculine point of view on the world, its culture and its history”. It is a by-product of male majority in science (today thankfully less than before, but still existing). Therefore, an androcentric view holds men and masculine characteristic as the norm; a standard for comparing all people (Ruiz-Cantero et al., 2007). It is clear that it can lead to prejudice or discrimination based on sex (in this case against women), also known as sexism. I think it should be clear to all why it is undesired. That means it is useful to have the perspective of female scientist on the research problem framework. This can be accomplished by building a gender balanced research team, or having it peer reviewed by a female scientist. Besides, if you want to have a representative sample of a larger population and results you can generalise to both men and women make sure you choose a gender heterogeneous sample.
- Develop a gender sensitive methodology. Gender sensitive methodology takes into account gender differences e.g. intelligence tests that measures only special intelligence (in which man are, on average, better) are not gender sensitive because they put men at an advantage and may lead to incorrect conclusion that men are more intelligent than women. Psychometric Item Response Theory offers techniques for indicating which items cause bias. One of them is Differential Item Functioning. It gives insight into items on which examinees with equal level of knowledge or skill, have different probabilities of success and failure, depending on a group which they belong to such as gender (Ritchie, 2009).
- Standardized protocols for data collection, which include training of study personnel can minimise inter-observer variability (Milas, 2005). The training should, among other things, include educating study personnel about gender biases. Another solution is to use double-blind procedure, an experimental practice where the researcher doesn’t know the critical aspects of the experiment, and therefore his lack of expectations dissolve biases and ensure this doesn’t confound the results of the study.
- Some variables function differently for men and women. Check whether measures of central tendency of men and women differ at a level with is statistically significant. Thereby you will be sure if you can generalize results across gender.
- It is not enough to conclude that men and women differ in a statistically significant way. Very small differences may have no practical significance. The statistical significance is usually calculated as p-value; the probability that a difference is caused by chance. The p-value depends on the effect size as well as on the sample size. So even the smallest differences are statistically significant if the number of participants is large enough. Use effect size measures that complement your statistical procedure in question. Cohen’s D is an effect size measure equivalent to a Z-score. It tells us how relevant the difference between the means of two groups is. In other words, it tells us how significant the overlap among the distributions of the different groups is.
- Make sure to conclude only what your research results indicate; don’t be seduced by your personal, prior believes and possible expectations.
- Also, keep in mind that no research is perfect or optimal. Be aware of potential flaws in your research and possible omissions you have made.
- Use gender sensitive language. American Psychological Associations guidelines suggest the following. Write clearly and concisely. Use sex only when it is thought on biological differences and gender when you talk about men and women as parts of social groups. Avoid the terms male and female (that implicate only biological differences), and replace them with men/boy or women/girl. To fight androcentrism eliminate the generic use of ‘man’ for ‘man’, substitute ‘person’/’people’, ‘individual(s)’, ‘human(s)’, ‘human being(s)’; for ‘mankind’, substitute ‘humankind’, ‘humanity’, ‘the human race’; for ‘manhood’, substitute ‘adulthood’, ‘maturity’; and delete unnecessary references to generic ‘man’. Try to eliminate sexual stereotyping of roles by using the same term (which avoids the generic ‘man’) for both women and men (e.g., ‘department chair’ or ‘chairperson’), or by using the corresponding verb (e.g., ‘to chair’), not calling attention to irrelevancies (e.g., ‘lady lawyer’, ‘male nurse’) (American Psychological Association, 1994).
In short, this article guides you through the research process by identifying specific moments and the ways in which gender bias occurs, as well as recommends ways in which the impact of gender bias on your research results and interpretation can be reduced.
In conclusion, I offer you a checklist which you can use to make sure that you have done as much as you can to get gender bias free, and therefore more accurate, research results.
- Acknowledge your own gender bias
- Review literature relevant to the research question
- Have the perspective of female and male scientist on the research problem framework
- Develop a gender sensitive methodology
- Standardise protocols for data collection and/or use double-blind procedure
- Check whether measures of central tendency of men and women differ at a level which is statistically significant
- Use effect size measures that complement your statistical procedure
- Make sure to conclude only what your research results indicate
- Be aware of potential flaws in your research and possible omissions you have made
- Use gender sensitive language
Good luck with your research!
American Psychological Association. (1994). Publication manual of the American Psychological Association (4th ed.). Washington, DC: American Psychological Association.
Coe, R., (2002). It’s the Effect Size, Stupid. What effect size is and why it is important. Presentation to the Annual Conference of the British Educational Research Association, England 2002. Retrieved from http://www.leeds.ac.uk/educol/documents/00002182.htm
Milas, G. (2005). Survey methods in social investigation. Jastrebarsko: Naklada Slap
Ritchie, T. D. (2009). Gender bias in research. In J. O’Brien, J. Fields, & E. Shapiro (Eds.), Encyclopedia of gender and society (vol. 2, pp. 713-715). Thousand Oaks, CA: Sage Publications.
Ruiz Cantero, M.T. et al. (2007). A framework to analyse gender bias in epidemiological research. Journal of epidemiology and community health, 61(2), 46-53.
Nina Jelić is a graduate psychology student at Faculty of Humanities and Social Sciences, University of Zagreb, Croatia. Her fields of interest are social and clinical psychology.
It has been claimed by some feminist psychologists (e.g Nicolson) that psychology is biased both at a theoretical level and also in the way that psychologists carry out their research. Many of the major theories in psychology either have shown a bias against women or have ignored women specifically, preferring to assume that their development would be no different from men's. The first of these approaches is described as an alpha bias, in that some theories suggest an enduring difference between genders, an example being Freud's psychosexual development theory. Many such theories offer a view of gender differences based on the ideas of biological essentialism, treating all differences as though they follow essentially from biologicial differences between the sexes. The second is beta bias, meaning that some theories ignore any possible differences between the sexes and present a theory (often based solely on the study of men) that does not represent the two sexes equally.
An example of an alpha-biased theory is Freud's psychosexual theory. Freud represented women as being less morally mature than men, in that their super-egos were less well developed (because they had not experienced castration anxiety). By representing women in this way, Freud was legitimising the treatment of women as second-class citizens in Victorian society. It is not surprising, therefore, that women were excluded from many professions in Freud's time, and were even denied the vote. Feminist critics of Freud's theory claim that Freud was simply reflecting the sexism of his time, and was therefore building cultural sexism into his theory rather than offering an objective, scientific view of male and female development.
Another alpha-biased theory is Kohlberg's theory of moral development. Kohlberg believed that the average male would reach at least stage 4, whereas female would reach only stage 3. The difference between these stages is that one reflects social morality (e.g laws) whereas the other reflects morality of personal relationships. Kohlberg thought that the latter was less mature. This view was criticised by Gilligan who claimed that women were not morally inferior to men but spoke in a different moral voice, based on care and responsibility rather than being dominated by male notions of justice. She suggested that Kohlberg had obtained his results because of the very abstract dilemmas used in his research, but when using real life dilemmas (e.g whether to have an abortion), women showed just as much evidence of mature moral development.
Critics of Gilligans theory (e.g Unger and Crawford) have argued that Gilligan's research was not comparable with the rather more abstract dilemmas faced by Kohlberg's subjects. Gilligan was also accused of showing a failure to explore factors other than gender which could be related to differences in moral reasoning (e.g social class, education, race/ethnicity). Finally, Gilligan failed to explore the possibility that what appeared to be a sex difference in moral reasoning, might also reflect women's subordinate social position, with perhaps the ethic of "care and responsibility" being expressed by less powerful people generally, rather than just women.
Many other theories in psychology have typically ignored possible differences between men and women. For example, the lifespan theories of Levinson and Erikson tended to focus on male development with the apparent assumption that female development will follow the same pattern. This concentration on male development in these theories is an example of the androcentric bias in psychology, in that ideas of "normal" behaviour are drawn almost exclusively from studies of the development of males. The danger of this approach is that we make assumptions about what is "normal" development based on a biased sample. Any deviation from this is then seen as evidence of abnormal development, and legitimises prejudice against the "deviant" group (in this case, females).
Questions about similarities and differences between the sexes are not just scientific questions, they may also be seen as highly political. Research has attempted to answer important questions about the differences between males and females using traditional experimental designs that lack the sensitivity to explore the questions being asked. The results of such research have then been used to exclude women from some occupations or to represent women as being victims of their own bodies. Menstruation has, in particular, captured the imagination of both scientists and feminists. The 'discovery' of prementrual syndrome, during which female hormones become so unstable as to aparently render some women capable of murder, has led to a great deal of experimental research in this area (e.g Nicolson, 1997). Nicolson argues that many of the performance and mood differences traditionally identified between premenstrual women and others were products of poor research design. Several other well-designed studies, she claims, reported no differences but these were not published, as their findings were subject to doubt.
- Written by xanjalix
- Suitable for Psychology A-level
- Suitable for PYA4 or PYA5
Article by TSR User on Thursday 15 February 2018