page_title: Equal opportunity
section_title: Difficulties with measurement
hierarchical_section_title: Equal opportunity / Practical considerations / Difficulties with measurement
caption_attribution_description: Eesti: Bari Ülikooli peahoone. Íslenska: Háskólinn í Bari. Italiano: Il palazzo dell'Ateneo dell'Università, in piazza Umberto I.
context_page_description: Equal opportunity is a state of fairness in which job applicants are treated similarly, unhampered by artificial barriers or prejudices or preferences, except when particular distinctions can be explicitly justified. The intent is that the important jobs in an organization should go to the people who are most qualified – persons most likely to perform ably in a given task – and not go to persons for reasons deemed arbitrary or irrelevant, such as circumstances of birth, upbringing, having well-connected relatives or friends, religion, sex, ethnicity, race, caste, or involuntary personal attributes such as disability, age, gender identity, or sexual orientation.
  Chances for advancement should be open to everybody interested, such that they have "an equal chance to compete within the framework of goals and the structure of rules established". The idea is to remove arbitrariness from the selection process and base it on some "pre-agreed basis of fairness, with the assessment process being related to the type of position" and emphasizing procedural and legal means.
context_section_description: The consensus view is that trying to measure equality of opportunity is difficult whether examining a single hiring decision or looking at groups over time.
  Single instance. It is possible to reexamine the procedures governing a specific hiring decision, see if they were followed and re-evaluate the selection by asking questions such as "Was it fair? Were fair procedures followed? Was the best applicant selected?". This is a judgment call and it is possible that biases may enter into the minds of decision-makers. The determination of equality of opportunity in such an instance is based on mathematical probability: if equality of opportunity is in effect, then it is seen as fair if each of two applicants has a 50 per cent chance of winning the job, that is, they both have equal chances to succeed (assuming of course that the person making the probability assessment is unaware of all variables – including valid ones such as talent or skill as well as arbitrary ones such as race or gender). However, it is hard to measure whether each applicant had in fact a 50 per cent chance based on the outcome.
  Groups. When assessing equal opportunity for a type of job or company or industry or nation, then statistical analysis is often done by looking at patterns and abnormalities, typically comparing subgroups with larger groups on a percentage basis. If equality of opportunity is violated, perhaps by discrimination which affects a subgroup or population over time, it is possible to make this determination using statistical analysis, but there are numerous difficulties involved. Nevertheless, entities such as city governments and universities have hired full-time professionals with knowledge of statistics to ensure compliance with equal opportunity regulations. For example, Colorado State University requires their director of its Office of Equal Opportunity to maintain extensive statistics on its employees by job category as well as minorities and gender. In Britain, Aberystwyth University collects information including the "representation of women, men, members of racial or ethnic minorities and people with disabilities amongst applicants for posts, candidates interviewed, new appointments, current staff, promotions and holders of discretionary awards" to comply with equal opportunity laws.
  It is difficult to prove unequal treatment although statistical analysis can provide indications of problems, but it is subject to conflicts over interpretation and methodological issues. For example, a study in 2007 by the University of Washington examined its own treatment of women. Researchers collected statistics about female participation in numerous aspects of university life, including percentages of women with full professorships (23 per cent), enrollment in programs such as nursing (90 per cent) and engineering (18 per cent). There is wide variation in how these statistics might be interpreted. For example, the 23 per cent figure for women with full professorships could be compared to the total population of women (presumably 50 per cent) perhaps using census data, or it might be compared to the percentage of women with full professorships at competing universities. It might be used in an analysis of how many women applied for the position of full professor compared to how many women attained this position. Further, the 23 per cent figure could be used as a benchmark or baseline figure as part of an ongoing longitudinal analysis to be compared with future surveys to track progress over time. In addition, the strength of the conclusions is subject to statistical issues such as sample size and bias. For reasons such as these, there is considerable difficulty with most forms of statistical interpretation.
  Statistical analysis of equal opportunity has been done using sophisticated examinations of computer databases. An analysis in 2011 by University of Chicago researcher Stefano Allesina examined 61,000 names of Italian professors by looking at the "frequency of last names", doing one million random drawings and he suggested that Italian acade
caption: A computerized statistical analysis suggested nepotism and a practice of unequal opportunity within Italy's academic community (photo: University of Bari)[93]
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