Reflections of a Woman in Science

Guide and Inspire

2011-11-28T01:27:00.001-08:00

I am back blogging again after a break.

Last week I arrived back from a trip to the

and in the pack of held mail left by the Post Office I found myNovember Toastmasters magazine. In it was an article called ‘Dare to Delegate’by Judith E. Pearson. The first thing that struck me in this article was thesentence

‘Since Toastmasters leaders cannot hire orfire, they must instead guide and inspire.’

Toastmasters is avoluntary organisation so indeed leaders cannot hire and fire but manyuniversities are in a similar position in that it is difficult to compelacademics to do anything.

The second is the importance of appealingto people’s needs and values.

Some academics may see gender equality asan important value in itself, consistent with a commitment to fairness, othersmay see it as benefitting their institution, and there are some for whom theonly effective appeal is to self-interest, for example, not following the rulescan lead to wasting time, energy and money in sorting out the consequent mess.

You do not have to be in a recognisedleadership position to promote gender equality but if you want to be effectivethen “guide and inspire” and tailor your message to the recipients actual needsand values and not to what you think they ought to be.

Transforming Organizations

2011-05-26T17:30:00.000-07:00

In a previous post I wrote about Teresa Rees’s description of three approaches to gender equality: tinkering, tailoring and transforming. Transforming is the process of modifying structures and processes to embrace diversity. Embedding change frequently requires us to address the unspoken assumptions and beliefs that underlay previous behaviour. This process is sometimes called ‘culture change’. There are layers of culture (see, for example, Cathy Trower’s presentation at the Third Annual Georgia Tech NSF ADVANCE Conference 2004:

- Structures and processes, with the additional complication that what actually happens may differ from what is laid down in an organisation’s policies and procedures.

- Rhetoric – what people say.

- Underlying assumptions and beliefs.

The first step in transforming an organisation is to reflect on what sort of organisation you have. An initiative that may have been very successful in one organisation may be a complete flop in another. In Understanding Organizations Charles Handy identifies four cultures: power, role, task and people oriented. This is not a unique way of classifying organizations but it has the merit of being simple and easy to relate to personal experience. Each culture has its strengths and weaknesses. Whether a culture is appropriate or not depends on the environment the organisation is in.

The power culture depends on a small group or a single person who controls a central source of power. Organizations with this culture can react quickly but may move in the wrong direction. They are often vulnerable to the loss of a key individual. These organisations tend to have few rules and procedures. According to Handy an organisation of this type ‘depends on trust and empathy for its effectiveness and on telepathy and personal conversation for communication.’

The role culture is more commonly referred to as a bureaucracy. Bureaucracy has negative connotations. As Handy notes: “No one, it seems, approves of bureaucracy except, interestingly, lots of people in organisations who like to know where they stand, what they have to do, who is in charge and what the rules are.” Role cultures are characterized by procedures, e.g. job descriptions and procedures for communications, and rules. Such organizations do well in stable environments but are slow to react to change. They tend to value roles and processes over the individuals who perform them but do offer security and predictability.

The task culture is focussed on completing a particular job or task. The strategy is to bring together teams of people with appropriate expertise and resources to accomplish a specific task. Individuals have control over their work and are judged by results. Overall control largely lies in allocating projects and resources as day-to day decisions have to be made by the individuals concerned. Organisations of this type tend to be flexible.

In the person culture the individual is central, as Handy puts it: “If there is a structure or an organization it exists only to serve and assist the individuals within it.” Handy’s metaphor for its structure is “a galaxy of individual stars”. Although few organizations retain such a culture since organizational goals are eventually imposed on individuals, there are individual people with this orientation, usually people with highly specialised expertise. Handy notes that such people “often feel little allegiance to the organization but regard it rather as a place to do their thing with some accruing benefit to the main employer.” Managing such individuals is not easy as many of the sources of power are ineffective.

The point of looking at a classification such as this is not to shoehorn any particular organisation into one particular category but to provide a basis for understanding what ways of influencing people are likely to be effective. To people in a role culture the fact that someone perceived to be in authority has made up a rule that something should be done is seen as sufficient reason for doing it. To someone who is oriented towards a person culture the fact that someone generally perceived to be in authority has made up a rule that something should be done may be seen as irrelevant. What are the preferred means of communication? There is little point in sending out a memo if people prefer personal communication.

Tensions can arise within organizations such as universities when different groups have different cultures. University bureaucracies (e.g. finance, human resources) inevitably tend to be bureaucratic while many academics incline to the person or ‘galaxy of individual stars’ culture. Individual research groups may have a power culture with the PI who brings in the money exercising the power. Each of these cultures has their own, usually unspoken, underlying beliefs. Efforts to bring about change in the way a university handles diversity fail if they do not take these differing beliefs into account.

Inappropriate Behaviour

2011-04-18T15:50:00.000-07:00

Recently Athene Donald blogged about two situations: a male colleague speaking to her on the phone told her that he could hear she was getting emotional and her experience at a reception after an event when a complete stranger put his arm around her waist when a photographer appeared. FemaleScienceProfessor also blogged about her experience as the only female member of a working group when a senior professor in the group mentioned, twice, that she was only included in the group as a gesture to diversity. FemaleScienceProfessor described her response, which was to ignore the remark and move the business of the group forward. There were a range of responses to these posts, many offering suggestions for ripostes in these situations, ranging from the manipulative to 'forgive and forget'.

I am from a cultural background that does not encourage physical contact. I think my own reaction to arm-draping behaviour would have depended on the stage of my career. As a very early career scientist I think my reaction would have been 'So this is what people do at this type of event'. That may also have been the conclusion drawn by my male collaegues. Later on, pre-assertiveness training, I think I would have thought something along the lines of 'I am not comfortable with this but I'll probably never see the guy again so why make a fuss'. After five years exhorting other people to use assertive responses I hope my response would be to move away, saying something like 'I would prefer you didn't do that. It makes me feel uncomfortable'. Assertive responses are not always appropriate. They rely on the person you are interacting with reacting rationally. If they are focussed on 'winning' the encounter this may not be the case. (The Harvard Business Review, May 2008, has an interesting study by Deepak Malhotr, Gillian Ku and J.Keith Murnighan on how competitive arousal leads to poor decision making.)

In general, how you respond to instances of remarks along the lines of 'you are getting emotional' or 'you are only on this committee because we needed a woman' depends on the context. If you suspect that the speaker is trying to provoke a reaction then it may well be the best strategy to ignore his remarks. A humorous remark, if you can think of one, may well be a good response if you think the speaker is just being thoughtless. 'I'll ignore that remark' may be a way of taking control without making too much fuss. Clearly, no one should be expected to object to every single belittling remark. There are circumstances in which it is better to stay focussed on the matter in hand.

In a comment on Athene's post, Dorothy Bishop mentioned 'an incident where everything hinged on whether or not a male academic had sleazy intent in an interaction with a female colleague', (more information on her blog).Here is a definition of inappropriate behaviour taken from the dignity@work policy of the University of Cambridge:

...behaviour is defined as inappropriate if it is:
Unwanted by the recipient.
Perceived by the recipient as violating his or her dignity and/or creating an intimidating, hostile, degrading, humiliating or offensive environment.
Having regard to all the circumstances, including the recipient's perception, the behaviour could reasonably be considered as having that effect.

There is nothing in this definition about the intentions of the person whose behaviour is in question. The focus is entirely on its effect on the recipient. Of course, the intentions of the person accused of inappropriate behaviour are relevant to what actions are taken to resolve the issue but they are not relevant to the question of whether inappropriate behaviour has, in fact, occurred. What sort of behaviour might be regarded as inappropriate? One scenario, from diversity awareness training material, involves a group of three people, two men and one woman. One of the men tells a joke that hinges on a comparison between a woman with pre-menstrual tension and a terrorist.(I don't remember the joke. It wasn't particularly funny, though I didn't think it was offensive either.) One of the implications of this definition of inappropriate behaviour is that there are bound to be grey areas where some people see behaviour as violating someone's dignity and others do not. This is why people should be encouraged to be able to say when someone's behaviour makes them feel uncomfortable and people who find they have inadvertently given offence to apologise and stop doing it rather than become defensive. No one wants to invoke their institution's formal dignity@work process every time someone makes an unfortunate remark.

What can institutions do to help? We should not be relying on the recipients of inappropriate behaviour or the efforts of a few individuals to address this issue. A common intervention is compulsory training. The problem with this option is that it can end up being counter-productive leading to a culture in which treating people with respect is perceived as enforced political correctness and making inappropriate behaviour more likely and harder for the recipients to deal with as they have to transgress group norms to do so. Macho 'zero tolerance' policies have the disadvantage that often no one is clear what it is that is not tolerated. Also, the problem tends to be framed as identifying and punishing offenders, which can be a barrier to people seeking help with a difficult situation if what they want to achieve is a resolution that is acceptable to everyone.

Institutions should recognize that staff and students come from a variety of different backgrounds and encourage departments to find ways of promoting appropriate behaviour. It could be as simple as the PI of a group sitting down at morning tea and saying 'I heard about this incident' (making sure it is fictional or anonymised), asking for suggestions on how to deal with it and making sure people understand why someone might find a particular behaviour inappropriate in a professional context, what they should do if they are the recipient of inappropriate behaviour, what they should do if someone suggests their behaviour is inappropriate, and why it is important. Apart from the fact that most of us would prefer workplaces in which people are treated with respect, tolerating inappropriate behaviour leads to lost productivity, attrition of good staff and students, and, if allowed to escalate, to the loss of large amounts of time and energy in pursuing formal complaints procedures.

Finally, do professional bodies have a role in promoting appropriate behaviour and appropriate responses to inappropriate behaviour? Following two high profile cases of data fabrication in 2002, the American Physical Society surveyed (Physics Today, November 2004) its members to find out the state of ethics education and how ethical issues were addressed in practice within physics. A clear majority of members who had held a Ph.D. for less than three years felt that American Physical Society ethics statements should be broadened to include treatment of subordinates. In response the American Physical Society added a statement to its Code of Ethics that subordinates should be treated with respect and with concern for their well−being. This includes the responsibility of supervisors to mentor students, postdoctoral researchers, and employees with respect to intellectual development, professional and ethical standards, and career guidance. Could other professional bodies do more to promote high standards of behaviour in the workplace?

The Big Picture

2011-03-17T18:39:00.000-07:00

The obstacles that women face in pursuing a career in science can conveniently be divided into three groups:

Structural issues such as the long training period, the predominance of insecure posts at early career level and that the period when people establish themselves as independent scientists coincides with the period of family formation.
Pragmatic issues: availability of affordable, high-quality childcare, difficulty of finding a job in the same geographic region as a partner, problems with travelling to attend conferences.
Cultural: stereo-typing, unconscious bias, family expectations.

These are not independent factors. In fact they interweave and feedback on each other. Family expectations can exacerbate pragmatic issues. Unconscious bias can magnify structural obstacles.

Not only are these factors inextricably linked but individually small disadvantages accumulate leading to lower overall success rates for women. The combination of circumstances that lead individual women to leave science will be unique for each woman. This means that attempts to isolate a single cause for the scarcity of women in science, for example, bias in appointments procedures, are unlikely to be successful.

It is apparent that the situation is different in different sciences. Very few women do physics, engineering and computer science in the first place (for UK figures see the UKRC Statistics Guide 2010) whereas in the UK over half of people attaining a postgraduate qualification by research in biological science are women (HESA, Table 7, Qualifications obtained by level, gender and subject area). It seems likely that the low numbers of women physics, engineering and computer science are predominantly due to cultural factors operating within schools. However, the example of the biological sciences shows that recruiting at the student level while clearly a necessary condition for increasing the number of women is not a sufficient condition. Structural and pragmatic issues must also be addressed if women are to be retained in STEM.

More on Quantitative vs Qualitative Evidence

2011-02-21T22:02:00.000-08:00

One perceived disadvantage of qualitative studies is that analyzing the data, for example, responses to interviews, is inevitably done within some particular interpretative framework. In fact, quantitative data is also interpreted within some framework but it is less usual to state what it is.

For example, the Asset 2010 survey of academic staff in SET departments found that 17% of women compared with 14% of men had no provision for appraisals, 9% of women compared with 7% of men could have an appraisal on request, and 74% of women compared with 80% of men had appraisals as a matter of routine. These differences are statistically significant.

There are a number of ways of interpreting this:

There are departments which discriminate against women by providing men with appraisals but not women.
Women are not uniformly distributed across disciplines. Perhaps some disciplines are more likely to operate appraisal schemes than others.
Perhaps men in departments with routine appraisals were more likely to have been encouraged to complete the survey.
The appraisal process itself discriminates against women so departments with routine appraisals are less likely to attract and retain women.
If men are more likely to receive routine appraisals, it is they who are discriminated against since they have to spend time filling in appraisal forms while their female colleagues get on with their jobs. (Presumably the slightly over a quarter of academic staff who reported that they did not find their appraisal to be either useful or valuable might concur with this interpretation.)

My point is not that any of these interpretations is particularly likely. It is that the same quantitative data can be viewed in different ways depending on your perspective. Those who are keen to establish that women are hard done by might incline to interpretation 1. Those who believe that universities are meritocracies and hence fair to all might incline to interpretation 2 or interpretation 3. Those with a less than positive experience of appraisal might incline to interpretation 4 or interpretation 5.

What then should the response be to findings such as these? The standard scientific response of wanting to eliminate incorrect explanations in order to isolate a single best explanation has the disadvantage that it could mean that action to correct an unfair situation is delayed, possibly for years. The other problem with this response is that the holders of strong views, wherever they lie on the spectrum between 'an appraisal is a good thing and everyone should have one whether they want it or not' and 'appraisals are a bureaucratic waste of time foisted on us by HR', rarely base their beliefs on rational analysis of evidence. Consequently finding more evidence is unlikely to change their minds. The standard top-down response of asserting one interpretation to be correct and labelling anyone who disagrees as 'obstructionist' or 'a dinosaur' has the disadvantage of being ineffective. Academics either ignore top-down initiatives or find ways of getting around them. My own view is that, at around a quarter, the proportion of academic staff finding their appraisals to be neither useful nor valuable is unacceptable high. The course of action I would favour is:

Explicitly recognise that people have different beliefs and experiences. Expecting someone whose experience of appraisal has been negative to be enthusiastic about a new appraisal policy is counter-productive.
Articulate what a departmental staff appraisal policy is supposed to achieve
Ask staff for suggestions for how best to achieve it
Give staff feedback on their suggestions. (Too often, people make suggestions only for them to apparently disappear. For example, a suggestion may be perceived as being too resource intensive to implement. If the person making the suggestion is given this feedback they may well be able to think of ways of achieving the same result more efficiently.)
Formulate a policy that is grounded in reality and recognises the constraints on people's time. (In an ideal world everyone would attend training courses in how to appraise/be appraised. In real life they do not, unless they are compelled to do so, in which case they turn up, resent being there and don't learn anything.)
Monitor your procedures, not just by ticking off whether everyone has completed an appraisal but by seeking feedback on whether the procedures are achieving the desired results.

Disclaimer: My own experience of appraisal has been positive, though personally I would not rate appraisal as having been particularly useful to my career development, which may have more to do with my moves between New Zealand and the UK than the process itself.

When does evidence become evidence?

2011-02-15T01:14:00.000-08:00

There are four broad categories of evidence that can be used to inform actions to improve the position of women in science.

The first category is institutional and national statistics, for example, what proportion of undergraduates, graduate students, post-docs, staff by grade are women? Such statistics are essential. You cannot even identify that you have a problem without them, or, indeed, that you have not. There are a number of difficulties with these data:

The categories that were used to present the data may not be helpful. For example, physics and chemistry are often combined as 'physical sciences' although the participation rate of women in physics is much lower than that in chemistry.
Aggregating data may obscure issues specific to a particular area or unit but reporting data at too fine a level makes it difficult to distinguish effect from random fluctuation. For example, I would be surprised if many individual departments (units of 20-50 academics) could make meaningful comparisons between the rate at which men are promoted and the rate at which women are promoted. Observations made with a sample size of three are effectively anecdotes, even if they are converted to a percentage and plotted on a graph.
Snapshot data can be difficult to interpret. As Gillian Gehring put it in a comment on the interaction between gender, level of qualification and pay in Physics World in September 2001: “We need to think like an astronomer here: the women of 50+ graduated from an “earlier universe”.” (If you are not an astronomer the reference is to the fact that, when we observe an object in the universe now, the further away it is the longer ago that the light we now see was emitted.) Women who are now 50+ were 20+ thirty years ago. Things were different then.
Even if you can demonstrate that there is a problem, the statistics by themselves throw no light on what has caused it. One approach is to try varying some practice while hoping that other factors remain the same and monitoring the statistics to see if they change. The disadvantages of this approach are that other factors do not normally remain the same, it can be difficult to ensure a uniform change in practice, especially in universities, and it can take an unfeasibly long time to have any confidence that you are observing a genuine change rather than a random fluctuation.

The second category is surveys. Surveys can be a very useful way of assessing how many women are affected by a particular issue. With the advent of tools like Survey Monkey they are technically very easy to set up though it still takes some effort to write good questions. Results can often be presented in numerical form and analyzed with conventional statistical tools, which is an advantage for those who are uncomfortable with qualitative data. The disadvantages are:

Sample sizes may be small making it difficult to achieve statistical significance.
It is difficult to assess how representative the sample is of the overall population. Respondents to surveys are often atypical at least in the respect that they have bothered to complete it.
You only get responses to the questions that you thought of when you designed the survey. If someone raises a new issue in a free text response you have no way of knowing how many others might have agreed, unless you run a follow-up survey.

The third category is existing research. Usually you don't start a scientific project from scratch. You review the literature to see what is already known. Adopting a similar approach to tackling issues for women in STEM would be both more efficient and more effective than continually starting from scratch. Furthermore, some issues can only be identified via research projects. For example, the evidence for unconscious bias largely comes from published research studies. Research projects involving multi-variate analysis of reams of data can offer useful information about what issues affect the recruitment and retention of women but require considerable time and resources. The principal disadvantage with trying to find out what is already known is lack of time. Also, much experience is recorded in non-peer-reviewed reports published by organizations, for example the reports produced by the Royal Society of Chemistry, and can be hard to discover. We need more books like Virginia Valian's 'Why so Slow? The advancement of women' that pull the research together and present it in an accessible way.

Finally, there are qualitative methods such as interviews and focus groups. Natural scientists tend to be uncomfortable with qualitative data dismissing it as anecdotal. This is not surprising. You can't run a focus group for electrons to gather their experiences of being accelerated by an electromagnetic field or interview proteins about how they fold (though think how many person-hours of effort would be saved if you could). However, women are intelligent, articulate human beings. You can save a lot of time and effort by simply asking them what is important to them. All too often women's lived experiences are dismissed as irrelevant. There is an unfortunate connotation to ignoring qualitative data. Doing so sends the message: ‘Women are not capable of understanding or articulating their concerns. We need our armoury of experimental tools and statistical analysis to determine what is best for them.’

If the purpose of gathering the data is to identify the factors that affect women's progress in a particular institution and find ways of ameliorating their effects we do not need to apply the same criteria of rejection/provisional acceptance that we would apply if we were seeking a general predictive theory of why women are not thriving as well as might have been expected within the institution, assuming such a theory to exist, which I doubt. Decisions have to be made using the best information available now, not put off until sufficient data have been accumulated for a result to be considered statistically significant at some conventional level of significance. That does not mean we should ignore statistical significance. Devoting resources to fixing a random fluctuation is a waste of time and effort, though, of course, likely to appear to be successful.

Quantitative and qualitative methods are complementary. Quantitative methods are good for demonstrating that there is a problem: qualitative methods are good for generating insights into what might be causing the problem. Decisions should be made on the basis of all the available data.

Unconscious Bias

2011-01-20T20:06:00.000-08:00

This is another long post. The essential points are:

There is a body of evidence that women in STEM are adversely affected by unconscious bias.
These implicit assumptions about women's roles (gender schemas) are a cognitive necessity for dealing with the social world but they can lead to inaccurate judgements.
Men and women share these implicit assumptions.
There are effective interventions that mitigate the effects of unconscious bias.

Way back in August I mentioned that there were two big ideas that made an impact on me when I started working for the Women in Science, Engineering and Technology Initiative at the University of Cambridge. The one I discussed in August was Teresa Rees's description of different approaches to gender equality as 'Tinkering, Tailoring and Transforming' – basically these correspond to removing discrimination, adapting women to fit the system and adapting the system to enable everyone to contribute to their full potential. The other big idea is the concept of unconscious bias.

I’ll start with a story. A few years ago when I was travelling on a train I glanced across the carriage and noticed that one of the passengers was reading ‘An Introduction to Electronics’. ‘Ah’, I thought to myself,’ a young man using his commuting time to catch up on some study.’ A bit later I took a better look around the carriage and realised that the reader of ‘An Introduction to Electronics’ was, in fact, a young woman. I had spent around twenty-five years doing physics. Part of my job at the University of Cambridge was to run workshops on unconscious bias and women in science. To add to the irony the reason I was on the train was that I was returning from a meeting about a mentoring project for female undergraduate engineers organised by a female professor of electrical engineering. And my initial assumption was that someone reading a book about electronics would be male.

Unconscious bias refers to implicit expectations we all have about the roles and behaviour of members of particular groups. The reason this idea had a great impact on me was that it made sense of my experience that people who professed a belief in equality, indeed were genuinely committed to achieving equality, nevertheless acquiesced in practices that unintentionally made it more difficult for women to progress in science and engineering.

I learnt about unconscious bias affecting women in professional careers from Virginia Valian’s book Why so Slow? The Advancement of women. Valian uses the phrase ‘gender schemas’ to describe the implicit hypotheses that we hold about sex differences. The content of these gender schemas does not depend on gender: men and women have the same beliefs. For example, a study of the relationship between the strength of an implicit association between ‘male’ and ‘science’ and a measure of achievement in science found that men and women had equally strong associations of ‘science’ with ‘male’ [Nosek et al, 'National differences in gender-science stereotypes predict national sex differences in science and math achievement'. Proceedings of the National Academy of Sciences, 106, 10593-10597 (2009) ]. Valian emphasizes that gender schemas are a cognitive necessity for making sense of the social world. However, in some contexts, they can lead to lower expectations for women.

Subsequently there have been two frequently cited studies reinforcing the points made by Valian. One, Steinpreis et al, 'The Impact of Gender on the Review of the Curricula Vitae of Job Applicants and Tenure Candidates: A National Empirical Study' Sex Roles 41, 718 (1999) (pdf), reports a study in which CVs that differed only in whether the name was given as Karen Miller or as Brian Miller. The study found that both men and women were more likely to vote to hire a male job applicant than a female job applicant with an identical record. In the other study Trix and Psenka, 'Exploring the Color of Glass: Letters of Recommendation for Female and Male Medical Faculty' Discourse and Society 14, 191 (2003) found that letters of recommendation for women were shorter, contained twice as many ‘doubt raisers’ (‘she has a somewhat challenging personality’), more ‘grindstone adjectives’ (conscientious, diligent) and fewer ‘stand-out adjectives (superb, outstanding). ( Similar results have been obtained by Schmader et al in a study of letters of recommendation for faculty positions in biochemistry and chemistry,' A Linguistic Comparison of Letters of Recommendation for Male and Female Chemistry and Biochemistry Job Applicants', Sex Roles 57, 509 (2007).)

The Project Implicit website has on-line tests that measure the strength of implicit associations such as that between ‘male’ and ‘science’.

What can we do about it?

The Project Implicit FAQ suggests that individuals who find that they have an implicit bias that they would rather not have could try seeking experiences that contradict their implicit bias, being conscious of their bias and its potential effect on their judgements and consciously planning actions that compensate for their known unconscious preference.

Valian suggests:

Learning about gender schemas, accepting that we may have expectations that we are unaware of that may contradict what we consciously believe and consciously changing our behaviour.
Challenging implicit hypotheses, for example, by imagining our response to someone’s behaviour if the person concerned was male rather than female.
Reducing reliance on gender schemas by

Spending more time on decisions. People use gender schemas to make automatic responses. If more time is available there is less need to rely on an automatic response.
Giving decisions our full attention. Making judgements while distracted by another task increases the reliance on gender schemas.
Holding decision makers accountable. People are more likely to form accurate judgements if they know there decisions will be reviewed.
Increasing the number of women in the candidate pool. Gender schemas become less important when women form a reasonable proportion of the group being evaluated.
Avoiding errors of reasoning such as:

Failure to appreciate covariation, where for example, an apparent variation with sex in some ability is actually due to some other variable.
Blocking, where the fact that data are consistent with a prevailing schema prevents evaluators from noticing other factors that have affected performance.
Illusory correlation, where people perceive a causal link between rare events such as incompetence and being female, simply because both events are rare.

At an institutional level

Committed leaders and leaders who are ready to legitimize woman leaders.
Objective performance criteria.

For women – how can they increase the chances of being perceived as competent?

Be where women are well represented, which isn’t a lot of help to physicists, mathematicians, engineers and computer scientists.
Be impersonal, friendly and respectful. This minimizes the negative effects of being perceived as competent.
Build power
Seek information.
Become an expert.
Get endorsed by a legitimate authority.
Negotiate
Overcome internal barriers. One of the effects of gender schemas is that women may attribute lack of reward of their efforts to their lack of ability. A better understanding of gender schemas and how they work.
(For practical suggestions on how to accomplish the above, see Suzanne Doyle-Morris’s book, Beyond the Boys’ Club.)

The STRIDE programme at the University of Michigan is an example of a comprehensive, successful intervention.

Useful resources:
1. Virginia Valian’s ‘Tutorials for Change'

2. The University of Michigan’s STRIDE Faculty Recruitment Presentation, available from the STRIDE website

3. The report of the US National Academies 'Beyond Bias and Barriers: Fulfilling the potential of women in science and engineering' National Academies Press (2007).

4. WISELI (Women in Science and Engineering Leadership Institute), University of Wisconsin-Madison Reports and Publications – A particularly intriguing report listed here is ‘Interventions That Affect Gender Bias in Hiring: A Systematic Review’ by Isaac, Lee and Carnes Academic Medicine 84, 1440 (2009).This report analysed 27 previous studies of interventions that affect gender differences in the evaluation of job applicants. The authors found that the studies showed a negative bias in evaluations of women for positions in areas traditionally or predominantly held by men and also that the assessments by men and women rarely differed. Interventions that were effective in mitigating the effects of unconscious bias were:
-    Providing assessors with clear evidence of job-related competencies, with the proviso that additional evidence of ‘communal qualities’ was provided for women.
-    A commitment to the value of credentials before reviewing the applicants.
-    Women’s presence at greater than 25% of the applicant pool.
Two studies found unconscious resistance to anti-bias training. And, according to the abstract:

‘Explicit employment equity policies and an attractive appearance benefited men more than women, whereas repeated employment gaps were more detrimental to men. Masculine-scented perfume favoured the hiring of both sexes.’ (My emphasis).

Videos to catch

2010-12-27T21:05:00.000-08:00

In the absence of anything to watch on TV over the holidays, I've been watching videos from the internet. Here are three that I've found particularly interesting:

'Why we have too few women leaders' by Sheryl Sandberg, Chief Operating Officer at Facebook (approx 15 min). While she acknowledges that changes in business practices are necessary, for example, flexible hours, in this talk she focuses on what individuals can do. Her three points are:

‘Sit at the table’ – Women tend to underestimate their own abilities, they don't negotiate higher salaries and while men attribute success their abilities women tend cite external factors. One of the issues is that while successful men are perceived as likeable successful women are not.
‘Make your partner a real partner’ – sharing the chores is good for your marriage.
‘Don't leave before you leave’ – Don’t turn down that promotion or that new project because you are planning on having a child, especially not if you don’t even currently have a boyfriend.

Women need to watch this video. So do managers.

Margaret Wertheim on the beautiful math of coral in which Margaret Wertheim reveals the link between crochet, coral and hyperbolic geometry (approx 15min). She makes an interesting point about our tendency to value symbolic knowledge, marks on paper or on a computer screen, over embodied knowledge, things we can touch and feel.

The truth about denial by Naomi Oreskes, Professor of History and Science Studies at the University of California San Diego. This is a video of a talk given in 2007. The first part covers the history of the science of global warming from Tyndall to the Fourth Assessment Report of the IPCC. The second part addresses the question, why, if the science is uncontroversial, do so many Americans think it is still doubtful? In 1992 US President George H. W. Bush signed the United Nations Framework Convention on Climate Change. Why in 2007 were we still arguing about whether global warming was even happening? (approx 1 hour)

Simpson's Paradox

2010-12-13T13:53:00.000-08:00

Suppose there are 300 students doing a Maths exam, 180 men and 120 women, and another 200 students doing an English exam, 60 men and 140 women. Suppose 30% of the Maths students and 20% of the English students get an A. If the performance of male and female students is exactly the same then the results will be as shown in the Table:

	Proportion of men getting an A	Proportion of women getting an A	Overall Proportion getting an A
Maths	54/180 = 0.3	36/120 = 0.3	90/300 = 0.3
English	12/60 = 0.2	28/140 = 0.2	40/200 = 0.2
Combined	66/240 = .2750	64/260 = 0.2462	130/500 = 0.2600

So, despite the fact that there is no difference in men's and women's performance on the individual exams, overall 27.5% of men got an A and only 24.62% of women got an A. This phenomenon in which the trend in amalgamated data is different from the trends in the individual groups is known as Simpson's Paradox. A famous example is graduate admissions at UC Berkeley in 1973: 44% of the 8442 male applicants were admitted compared with 35% of the 4321 female applicants, a difference that was too large to be due to chance. However, when the data were disaggregated by decision making unit a different picture emerged: few units showed a statistically significant difference between the rates of male and female admissions and there were just as many units that appeared to favour women as to favour men. The reason for the discrepancy in the overall admission rates was that women were proportionately more likely to apply to units with low admission rates. In fact, when the data were pooled taking this into account there was a small but statistically significant bias in favour of women. [P.J.Bickel, E.A. Hammel and J.W.O'Connor Science, vol 187 pp 398-404 (1975)].

The Berkeley example dates from over thirty-five years ago. Do we still see this type of error in the analysis of gender statistics? Yes, we do.

I speculate that the reasons for this include:

A preference for results that confirm our existing beliefs. If we believe that women are hard done by we are less likely to question results that support that belief.
A fear that questioning specific results will be perceived as questioning the principle of equality or will be interpreted as a reason for failing to support an otherwise useful initiative.
Lack of knowledge of statistics and its pitfalls.

Additional Sources: I first came across the Berkeley example in a video of a lecture 'Lies, Damned Lies and Statistics: The misapplication of statistics in everyday life' by Dr Talithia D. Williams, a lecture in the Distinctive Voices series of the US National Academy of Sciences. It's well worth watching.

Equal Pay

2010-11-25T21:00:00.000-08:00

This is, once again, a somewhat technical post. It highlights some of the shortcomings of focussing on gender pay gaps as a means of identifying biases in pay. It also raises the question: what is a practically important gap? I do not have a good answer to that question. It would be nice to see some discussion of what makes a pay gap important for practical purposes. A related question is: how much effort should employers put into addressing a pay gap that is in some sense ‘large’ but that has a high probability of being due to chance (i.e. is not statistically significant), bearing in mind that resources that are used for one activity are not available for other activities? Again, there is probably not a unique answer to this question but it would be nice to see it discussed.

The principle of equal pay is equal pay for:

•    Equal work – work that is the same or broadly similar.
•    Equivalent work – work that has been rated as equivalent by a job evaluation scheme.
•    Work of equal value – work that places similar demands on those performing it.

As noted by the Equality and Human Rights Commission (EHRC) the overall gender pay gap is not an indicator of unequal pay. They suggest that it should be seen as an ‘equal opportunity gap’. The analysis in my earlier post suggests that it is not a particularly useful measure of that either. Nevertheless, it is popular.

The tool to check for equal pay is an equal pay audit. Equal pay audits should be carried out for ethnicity and disability as well as gender but gender is often easier since the quality of the data is better. Basically the steps in an equal pay audit are:
1. Determine which people are doing equal work, equivalent work or work of equal value. An organisation that has a job evaluation scheme may assume that everyone with a job evaluated to be in the same grade is doing equivalent work or they may split people on the same grade into groups doing similar work. The latter procedure is preferable if there are enough employees since aggregating over all the employees in a grade could mask problems that were specific to one group.

2. Assess whether men and women are equally paid. There are two situations that might apply: there is systematic bias against women (or men) or a few individual women (or men) are disadvantaged, for example, the way in which starting salary is determined may disadvantage people returning from a career break which tends to disadvantage some, but not all, women. Although carrying out an equal pay audit may uncover instances of the latter it is more usual to concentrate on the former, though both are illegal. The EHRC suggests that the first step is to calculate the average basic pay for men and women and the average total pay for men and women. The EHRC recommends that if the difference between the average pay for men and the average pay for women is greater than 5% or if the difference between the average pay for men and the average pay for women is greater than 3% and there is a pattern of gaps favouring one sex over the other then further investigation is required.

Most scientists asked to determine whether the difference of two means indicated the presence of systematic bias would probably start by trying a t-test. There are some problems with this approach, especially if the means are calculated from a small number of employees, because t-tests are based on the assumption that the estimate of the mean has a normal distribution. Tests of statistical significance, calculate the probability of observing a difference of at least the size you did observe on the assumption that there is no difference. The larger the value of this probability the more likely it is that the observed effect could have arisen just by chance even if, in fact, there is no difference. It is not necessary to adopt the convention that a result is significant if the probability of getting a result at least that big is less than 0.05 and not otherwise. Depending on the cost it might be reasonable to take action even if the probability that the observed difference is due to chance is 0.3, or even more, depending on the circumstances. Note that observing an effect that is not statistically significant does not imply that there is no effect. It implies that there is not enough data to say whether or not there is an effect.

As noted by the EHRC, statistical significance should not be confused with effect size (see Technical Note 3.5). A large difference can fail to be statistically significant if there are a small number of employees, or a small number of employees of one sex. A small difference can be statistically significant if there are a large number of employees. So, for example, an institution might be more worried about a 15% gender pay gap that had a 10% probability of being exceeded due to chance than about a 1% gender pay gap that had a 4% probability of being exceeded due to chance. In the latter case the institution is saying that they are willing to accept a low probability that the observed result is due to chance since they believe the bias to be too small to be of practical importance. What constitutes a large difference? What is practical importance?

The EHRC criteria are that if, on average, one sex earns more than 5% more than the other for doing particular equivalent jobs, or more than 3% more if there is a pattern, then there is a problem that needs investigating. These criteria could lead to anomalies depending on how pay is determined. Organizations that rely on negotiation by individuals to set pay or on pay schemes with a significant component dependent on performance evaluation are open to inadvertent discrimination leading to systematic discrepancies that would be evident in gender pay gaps. Other organizations use a system in which the person identifying the need for a position writes a job specification that is used by a professional job evaluator to assign a grade to the job with the person appointed to the position being assigned to a point within the grade on the basis of their qualifications and experience and then progressing by automatic annual increments to a point at which he or she needs to apply for promotion to discretionary or contribution points of the grade. Under this system there is much less scope for discrimination. Possible ways in which bias can occur are:
1.    There could be a tendency for women to be appointed at a lower point in the grade.
2.    Women could be less likely to apply for promotion to discretionary or contribution points.
3.    The job evaluation scheme could result in jobs predominantly done by men being graded higher than jobs predominantly done by women.
4.    Women could be less likely to receive, or receive lower amounts of, additional payments such as allowances, payment for additional responsibilities, recruitment incentives or market supplements.
5.    There could be differences in the contractual hours of different occupational groups in jobs evaluated to be at the same grade.
6.    There could be differences in pension entitlements or retirement ages between different groups with different representations of men and women.

Two factors which could influence gender pay gaps but which are not equal pay issues are:
1. Women might be more likely to leave giving a greater proportion of women on lower points in the grade or men might be more likely to leave, for example, for higher graded positions, leaving proportionately more women at the top of the grade.
2. Women may have been entering jobs at this level in increasing numbers in recent years leading to a clustering of women at lower points in the grade.

We thus have factors which are equal pay related that will be reflected in gender pay gaps, such as lower starting salaries, factors which are equal pay related that will not be reflected in equal pay gaps, such as biases in the job evaluation scheme, and factors that are not equal pay related but will affect the gender pay gap.

Figure 1 shows a distribution of women on a nine point scale, perhaps arising as a combination of women ending up with lower starting salaries, being less likely to apply for promotion and men leaving for better paid positions. The trend line has a slope of -0.0425, so the proportion of women falls by about 4 percentage points per scale point. If there are the same number of people on each salary point and each salary point is 2.5% higher than the one below then this distribution leads to a gender pay gap of 2.8% and no investigation is required. If, however, each salary point is 5% higher than the one below the gender pay gap is 5.7% and further investigation is required although the underlying biases that led to this situation would be the same in both cases. Both gaps are statistically significant if there are twenty or more people on each salary point.

Figure 2 shows a similar distribution on a fourteen point scale. The trend line has a slope of -0.0312. In this case the gender pay gap is 5.1% when the increment from one scale point to the next is 2.5%. This gap is highly statistically significant if there are at least twenty people on each scale point. Note that if this grade was split into two grades of seven scale points the gender pay gaps would be 1.6% for each grade though the men and women would be being paid the same salaries as before.

These examples show that the same underlying biases can give rise to gaps that may or may not be regarded as practically important depending on the particular salary structure.

As another example, suppose you have 100 men doing a particular job at a particular grade with an average salary of £25,000 and 100 women doing the same job at the same grade who would have the same average salary except that a policy of taking existing salary into account when determining the starting point in the grade has led to twenty women who returned from a career break being paid 6% less than similarly qualified men or women who had not taken a career break. The average salary for all the women is £24717, a gap of 1.2%. This is not likely to be statistically significant (on a ten point scale with an average of twenty people per scale point with a 3% increment there is a 15% chance of men’s average pay exceeding that of women by at least 1.2%) and nor is it large, though the twenty affected women might disagree.

This example shows that relying on the gender pay gap to identify anomalies could result in failing to detect substantial biases.

This still leaves the question: what is a practically important gap? There does not seem to be a good answer to this question. Is it acceptable for women to paid one scale point less than comparable men as long as the gap between scale points is less than 3% but not acceptable if the gap between scale points is greater than 3%? Is exactly the same bias acceptable if it occurs over two grades with a small number of steps but not if it occurs over one grade with a larger number of steps? Is it acceptable for 50% of women to be paid one scale point less than comparable men but not for 100% of women? What are the criteria for practical importance? Practically important to whom? The women earning 6% less than they might have been? The employer who might face an equal pay claim?

Does this mean carrying out an equal pay audit is a waste of time? No, it does not. An organization that measures its gender pay gaps for groups identified as doing the same or equivalent work or doing work of equal value is more likely to identify anomalies than one that does not. What it does mean is that organizations should examine their pay schemes and identify how anomalies could occur, for example, that people returning from career breaks tended to be placed on lower starting salaries thus tending to disadvantage women, and monitor those points directly regardless of whether or not they have observed substantial gaps.

An effective equal pay audit would:
•    Describe the way the institution sets pay or, at the very least, refer to another document that does so.
•    Identify the processes where bias could occur, e.g. setting starting salaries.
•    Monitor those processes.

It could be that in a system where individuals negotiate their own pay or individuals’ line managers have a large say in setting performance pay that the best way of monitoring is to measure the gender pay gap. In institutions with job evaluation schemes and set pay scales it would be better to monitor starting salaries and progression directly to avoid potential bias being masked by other factors that affect the pay gap.

Processes that could introduce bias include the job evaluation scheme, if one is in place. The EHRC has pertinent advice on how to check that a job evaluation scheme does not itself inadvertently discriminate against women.

Has your institution carried out an equal pay audit? Does it meet the above criteria? Do you think it should? Is there anything you can do about it if it doesn’t?

Women's Networks

2010-11-23T13:49:00.000-08:00

I have been involved in women's networks in science either passively or actively for about twenty-five years. These are my thoughts on women's networks.

Why do we need them? What are the benefits?
Women's networks can:
•    Provide a safe and supportive environment for women to exchange experiences.
•    Provide a means for women to exchange information.
•    Build women's skills by giving them the opportunity to take on various roles within the network.
•    Increase women's visibility.
•    Enable women to push for change more effectively.

How can they be effective?
•    Provide a regular programme of events to maintain momentum. It does not matter if attendance at some events is low. Just getting the email saying that something is happening reminds people that the network is active.
•    The best publicity is word of mouth. Women who have had a good experience tell their friends.
•    Maintain a positive focus.
There are many types of events that women's networks can run, for example, speaker events, career-focussed events and social events. Generally the types of events a network runs will depend on the interests and enthusiasms of its members. Although, in principle, women's networks provide a way for women to make their views on existing policies or proposed changes to policies known, in practice, whether or not this happens depends on whether an individual feels strongly enough about the issue, and has the time and energy, to do something about it.

Structures
In my experience members of women's networks are not interested in formal structures. They prefer informal arrangements to prescriptive specifications. It is better to make things happen and then worry about structures. Nevertheless, in my experience there are minimum requirements if the network is to be more than a handful of friends who happen to meet fairly regularly. There needs to be someone who is visibly responsible for the network. This person will often be known as the Chair of the network. There also needs to be a treasurer, someone to keep records and someone who is responsible for communicating with members, for example, via a newsletter. These responsibilities do not need to be held by different people but I think it helps if there are identifiable people taking responsibility for these areas. It gives people a point of contact if they have a query or suggestion or if they want to invite a representative of the network to an event. It is also important that the network does not become reliant on one or two people otherwise it can collapse if one of them gets a new job, has a baby or moves away. This means there has to be a way of ensuring new people take up positions of responsibility. Having an ‘incoming president’ or ‘incoming chair’ position is useful for ensuring continuity.

Resources
In order to function effectively a network needs some resources. Obviously the time and energy of those who organise events are essential. Women's networks within organisations need to be properly resourced either with money or with in-kind assistance such as free meeting rooms. The work that people put into such networks needs to be recognised as part of their job and not seen as an optional extra. A women's network can increase productivity, for example, by helping women find effective solutions to difficulties they may be experiencing. For women's networks operating outside of a single employer the situation is more difficult, although they play a very important role in broadening the range of experiences available to their members. Such networks have to have sufficient resources to pay for venue hire and refreshments as well as, in some cases, expenses for speakers. These resources have to found either from members, potentially deterring some women from participating, or through sponsorship, which can be time-consuming to find and is especially difficult for women with day jobs who do not necessarily have the time to contact and follow-up potential sponsors.

Exclusive or Inclusive
Every women's network I have been involved with has, at some point, discussed the issue: is it just for women or can men join too? This has usually been a question of principle rather than practice since men usually self-exclude anyway. One one hand women's networks need to provide a safe and supportive environment for women. On the other many of the issues that constrain women's full participation in employment are never going to be resolved by groups of women talking among themselves. We need men to get involved. In my view a safe and supportive environment means one in which the discourse is set and shaped by women not one from which men are excluded.

What would help?
•    Money – of course. Ideally support would be aimed at helping the network achieve its objectives but inevitably a sponsor tends to want the network to help achieve the sponsor’s objectives.
•    Recognition - line managers making sure women know about opportunities to participate in women's networks and senior managers and influential members of the business community promoting the benefits of women's networks.
•    Capacity building in skills such as effective use of the web from on-line booking to running a discussion forum, running effective meetings, and fund-raising.

The Pace of Change

2010-11-17T19:02:00.000-08:00

One of the indicators of the position of women in academic science is the proportion of women among academic staff in STEM departments. How fast can this indicator change?

The first constraint is the number of vacancies available for women to be appointed to. For example, if a department has a turnover of 5% per year and the number of academic staff is growing at 2% per year then the overall vacancy rate is 7% per year.

The next most important constraint is the proportion of women in the pool of potential applicants. If the pool of potential applicants is 50% women and no women leave the department then a department with a vacancy rate of 7% could achieve an increase in the proportion of women of 3.5 percentage points per year. In these circumstances a department could get from 25% to 50% women in 7-8 years. However, if turnover was around 3% and the number of academics was static then the best the department could achieve, if no women leave, is a growth in the proportion of women of 1.5 percentage points per year. At that rate it would take 17 years to get from 25% to 50%. Of course, this might well be an underestimate since it is unlikely that no women would leave over a seventeen year period.

There is an additional complication. Suppose a department is able to make six appointments over a three year period and it makes the appointments from a pool that is 50% women. If recruitment is fair with respect to gender then the probability distribution for the number of women appointed will be a binomial distribution with N=6 and p=0.5. This gives a probability of 0.31 of appointing exactly three women, a probability of 0.34 of appointing two or fewer women and a probability of 0.34 of appointing four or more women (probabilities do not add to one due to rounding). Hence for time periods in which a small number of appointments are made fair recruitment processes could easily result in apparent growth rates between 2/3 and 4/3 times the expected rate.

Conclusions:
1. There are limits to how fast the proportion of women among academic staff in STEM can increase. Growth rates of a few percentage points per year are not unreasonable.
2. Estimating the long-term growth rate from measurements made over short periods is futile.

The second conclusion implies that simply collecting data on the proportion of women among new appointees is unlikely to reveal inadvertent bias in the recruitment process. While these data are necessary it is also necessary to assess the recruitment process against best practice established by large studies, such as that described in the US National Academies Report Gender Differences at Critical Transitions in the Careers of Science, Engineering and Mathematics Faculty. (A briefing on the report is available from the pages of the National Academies Committee on Women in Science, Engineering and Medicine.)

Communicating

2010-11-08T21:28:00.000-08:00

I have just read two very different books: Randy Olson's 'Don't be such a scientist' and 'Healing our History: the challenge of the Treaty of Waitangi ' by Robert and Joanna Consedine.

Olson's book is an easy read - short, breezy, anecdotal but making the very important point that if we want to engage non-scientists with science we need to have good stories while retaining factual accuracy. Anyone who is involved in science communication should read this book but for me it also sparked some thoughts about how we communicate about women in science. For example, much of our communication is directed to scientists, people to whom accuracy matters, the sort of people who feel the need to point out to an actor speaking enthusiastically about spotting whales that there are no gray whales in the Atlantic or who are worried by the fact that in James Cameron’s original version of Titanic the ship sinks under southern hemisphere stars. Are we careful to make accurate statements? Do we play to the strengths of academics or do we regard them as problems? A male participant in the University of Michigan’s STRIDE programme commented on its style saying it followed ‘pure academic principles of engagement … It was clear that they wanted you to study, work, read, form opinions, validate or invalidate current approaches … to become educated.’ (Reference 1) Are our efforts concerned with engaging scientists as scientists or are we uncomfortable with argument and dissent?

'Healing our History' was a difficult book for me as a Pakeha to read. Some background: in 1840 the British government signed a treaty with the Maori people of New Zealand. The representatives of the British government and successive New Zealand governments then spent the next 140 years ignoring the provisions of the treaty and exploiting the Crown’s position as the sole purchaser of Maori land, while promoting the view that if Maori were disadvantaged then it was their own fault and the solution was for them to become Europeans. Requests for restitution for the failure to abide by the treaty are still spun as ‘demands for handouts’.

I would hesitate to draw parallels between the situation of Maori in New Zealand and women in science. I think the histories and consequences are quite different. Nevertheless, I believe there are lessons from the Consedines’ book that can be applied to thinking about women in science.

Firstly, I now understand the attraction of ‘wilful ignorance’. If you take no steps to understand the facts then you can comfortably deny that there is a problem. Those who refuse to collect data on the grounds that they already know that their workplace is fair may fall into this camp. Knowing that there is a problem demands a response, even if that response is to decide to do nothing.

Secondly, Robert Consedine runs workshops on Treaty issues. The initial workshops are run in parallel: one for Maori with a Maori facilitator and one for Pakeha with a Pakeha facilitator. Prior to attending a workshop, many people are uncomfortable with this arrangement, which they feel smacks of separatism. However, experience has shown that it is useful for Pakeha to be able to express their fears and misconceptions in a safe environment and it is useful for Maori to be able to explore their identity as Maori without being regarded as representative of all Maori. Before attending a parallel workshop only 16% of participants believed they were needed. After attending a workshop 90% thought they were needed. The need for women to have a safe environment in which to express their concerns has long been recognized. What about men? Do they have a safe space in which to express their concerns and fears?

A common reaction to the workshops was puzzlement that a group of Pakeha were teaching the Treaty of Waitangi. The idea that Pakeha would take responsibility for learning and teaching Treaty commitments was mystifying. How many men are willing to take responsibility for promoting gender equality? How often is it seen as a women’s issue? What does ‘take responsibility’ mean? The Consedines quote Jorge Rosner: ‘Responsibility literally means “the ability to respond”. You only respond when you are fully aware of you behaviour and your choices, then, on the basis of your awareness, you can freely choose what to do.’ (Reference 2) Taking responsibility does not mean accepting guilt for what has happened in the past. It means looking at the current situation, asking what needs to be done, and doing it.

Robert Consedine also writes ‘I encourage people to live with the questions, as a ‘solution focus’ is often a barrier to change in this arena.’ This is a difficult concept for those of us in a ‘What’s the question? Here’s the answer. Move on’ culture, especially those of us trained in using reductionist techniques to solve problems.

So, two very different, but challenging and thought-provoking books.

Boundaries

2010-11-04T16:56:00.000-07:00

Men do not let anyone seize their estates, and if there is the slightest dispute about their boundaries they rush to stones and arms; but they allow others to encroach on their lives - why, they themselves even invite in those willing to take over their lives. You will find no one willing to share out his money; but to how many does each of us divide up his life!

De Brevitate Vitae (On the Shortness of Life) Lucius Annaeus Seneca (c. 3 BC - 65 AD) Roman philosopher, statesman and dramatist Translated by C. D. N. Costa (1997), Penguin Books - Great Ideas

How good are you at setting boundaries on other people's access to your time?

The picture shows two kereru (New Zealand wood pigeons) in a tree. It was taken from our living-room window. If you want a link to the subject of the post I guess it is a reminder to look out the window from time to time.

2010-10-29T02:13:00.000-07:00

Earlier this month there was a furore here in New Zealand that resulted in one of the hosts of a popular television breakfast programme resigning after firstly suggesting in an interview with the Prime Minister that the next Governor-General of New Zealand should look more like a New Zealander than the current Governor-General, Sir Anand Satyanand, who was born in New Zealand and is of Indian ethnic origin, and secondly creating a diplomatic incident by making jokes about the Delhi Chief Minister Sheila Dikshit’s name.

Among the many opinion pieces that appeared was one by Karlo Mila, who is a New Zealander of Tongan/Pakeha (European) descent. She recounts the story of how she was welcomed into the coolest group of girls in the class at secondary school only to have one of its ringleaders say ‘I’m so glad there are no Maoris in our class’, then turn to her and add, ‘Sorry, Karlo, if I offended you? I didn’t mean you, of course.’ Karlo describes how quickly her thirteen year old self responded ‘I don’t mind. Why would I care? I’m not Maori.’

It reminded me of a quote from Elizabeth Blackburn :

"Someone once asked me how I did it as a woman," Blackburn recalls. "I said something that surprised even me at the time: 'I disguised myself as a man.' I had not really realized until that conversation that that's what I was doing. At the time, I didn't think of it as a sad thing, but it is sad."

Elizabeth Blackburn, UCSF
DISCOVER Vol. 23 No. 11 (November 2002)

And, indeed of my own response on being asked how I had coped with a predominantly male work environment: ‘Once I had been accepted as a good bloke there were no problems.’

Clearly, this is not as straightforward as denying that you are a woman, although one may hear the occasional approving comment ‘But you are not one of those women.’ I believe it has more to do with a tacit acceptance of male as norm, for example, an uncritical acceptance of the proposition that you have to stay late to do science, a proposition described by Blackburn in the same article as ‘the biggest pile of crap’.

I suspect that some of the antipathy that some women express towards organizations or events with the word ‘women’ in the title stems from having gone to considerable effort to fit in their male dominated workplace and not wanting to blow their cover.

I do not see a problem with adopting a more masculine style if that is more effective. I do not see that as being any different from saying ‘S’il vous plaît’ and ‘Merci’ when in France. I think it is more about having to behave in ways that are not comfortable. As one of the post-docs quoted in the Discover article put it "It's just, you've got to be this person that I don't want to be in order to be successful as a scientist."

This is why programmes such as Springboard and, at a different level, Suzanne Doyle-Morris’s “Beyond the Boys’ Club” are so important: they are about being successful and a woman rather than being successful despite being a woman.

We also need to articulate what needs to change and engage men in conversations about how to bring change about.

Gender Pay Gaps - Myths

2010-09-27T23:00:00.001-07:00

Myths about the Gender Pay Gap

Warning: this post contains mathematics. I apologise to those of you who find this intimidating but I make statements that follow mathematically from the definition of the gender pay gap and it is important that I give my reasoning so that those who are not put off by a bit of straightforward algebra can check it. If you can not read the equations and want to, try this link.

The important conclusion is that an institutional gender pay gap is an incomplete and ambiguous measure of inequality. It is incomplete because the gender pay gap can be small or zero even when the overall proportion of women in the workplace is low. We therefore need to know the proportion of women in the workplace as well as the gender pay gap. It is ambiguous because while if the proportion of women in each salary interval falls as the salary increases then the pay gap is non-zero it is possible for the pay gap to be small or zero and the proportion of women by salary interval to still have undesirable features such as a lack of women at the highest levels. In addition, because the gender pay gap compounds structural factors that are common to both men and women, namely, the salary scale and the number of people in each salary interval, with an inequality factor, namely, how the proportion of women varies with salary, it is difficult to compare different workplaces unless the structural factors are similar.

For these reasons the minimum information required to make sense of a gender pay gap is

the number of women and the number of men (number rather than proportion since the proportion can be calculated from the numbers and the numbers give an idea of whether the measured gap reflects underlying inequality or just a fluctuation or contingency)
the average salary for women
the average salary for men
the proportion of women by grade or salary

Myth 1: The gender pay gap measures the extent to which women are paid less than men for doing the same job.

There are three contributions to the gender pay gap:
1. Occupational segregation: there are more women in low paid occupations and occupations in which women predominate attract lower pay.
2. Vertical segregation: within an occupation there are more women at lower levels.
3. In some cases women are paid less than men for doing the same job or for work of equivalent value, which is illegal.

There are numerous causes of the gender pay gap, for example, research commissioned by the Government Equalities Office in the UK identifies several factors including differences in years of full time work and the negative effect on wages of having worked part time or taken time out of the labour market to care for a family.

Myth 2: The gender pay gap is a useful indicator of inequality.

The gender pay gap is defined by:

gap = (average pay for men - average pay for women)/(average pay for men)

The average pay for women can be written as:

and the average pay for men as

where is the number of people with salary and is the proportion of them who are women. The number of different salaries (or salary categories) is . The symbol means add all the terms from 1 to together. These formulas work when people are paid on a salary scale or when there are enough people that it makes sense to make a histogram of the number of people in each salary interval.
The total number of people is . The total number of women is , the total number of men is and the overall proportion of women is . This implies that the difference between the average pay for men and the average pay for women can be written as

and the gender pay gap as

.

So, the difference between average pay for men and average pay for women depends on two structural factors, namely, the salary scale and the proportion of jobs at each salary scale point, and an inequality factor, namely, the way in which the proportion of women at each scale point varies with scale point. One implication is that the pay gap will be zero whenever the proportion of women is constant with scale point regardless of what that proportion is. Hence, the pay gap is an incomplete measure of inequality. A workplace with a zero pay gap that has only 10% women is hardly a shining example of gender equality.

From a mathematical point of view we have two equations

, which defines , and

which defines the gap. If we want then we have two equations in unknowns. This is an under-determined system, unless there are only two steps on the salary scale, so there is the possibility of finding other solutions that give a zero gap besides for all i. This means that while if is constant then the gap is zero and if falls systematically as i increases then the gap will be non-zero there could be solutions which have a small or zero gap that nevertheless have undesirable features such as a lack of women at the highest salary levels. The figure below shows an example, which has 220 men and 180 women (45% women among a staff of 400) on an eleven point scale where each point is has a salary 5% greater than the one below starting from £20,000. The average salary for men is £24,923.41 and the average salary for women is £24,901.72, which is a gap of 0.09%. Nevertheless, only 35% of the posts in the top three grades are held by women and only 20% of the posts in the highest grade are held by women. Click here to view the spreadsheet I used to create this figure. The spreadsheet itself is available at this link.

So, as a measure of inequality the gender pay gap is both incomplete and ambiguous.

Myth 3: The overall national pay gap will be eliminated if each workplace eliminates its own pay gap.

Suppose Employer A has a largely female, largely relatively unskilled workforce while Employer B has a largely male, largely skilled or professional workforce. Both employers could eliminate their pay gaps but Employer A would still be paying their predominantly female workforce less on average than Employer B was paying their predominantly male workforce.

Myth 4: The gender pay gap provides a means of comparing inequality across workplaces

As noted in under Myth 2, the gender pay gap depends on two structural factors and an inequality factor. Unless the workplaces have the same salary scale and the same proportion of jobs at each salary scale point it is very difficult to draw conclusions about differences in equality in different workplaces. It would also be helpful if there was agreement on whether to divide by the average salary for men or the average salary for women in the expression for the pay gap. It could also be the case that in the example discussed under Myth 3 that Employer B has a gender pay gap that is hard to eliminate due to a shortage of women with the necessary professional qualifications, for example, in engineering, while Employer A is able to eliminate their gap despite the fact that women working for Employer B have higher average salaries than women working for employer B.

Uk Equality Legislation: Specific Duties Consultation

2010-09-23T18:33:00.000-07:00

In a previous post I wrote about different approaches to equality under Teresa Rees’s headings: Tinkering, Tailoring, Transforming. Recent equality legislation in the UK has the potential to be a framework for transforming both workplaces and service delivery to incorporate genuine equality. It also has the potential to create self-sustaining bureaucracies that achieve very little. Which happens depends not on the competence of organisations’ equality and diversity personnel but on the extent to which women, and other groups, avail themselves of the opportunities presented. The consultation on the specific duties is one such opportunity. While responding to consultations can seem like a waste of time, if you do not even attempt to make your views known your voice certainly will not be heard. So, if you live in the UK, download the consultation document from the Government Equalities Office website and respond.

The Equality Act 2010 integrates the former general equality duties that applied to disability, race and sex and extends them to apply to other characteristics such as sexual orientation. The general equality duty requires public authorities, which include universities and research councils, to eliminate discrimination and harassment, advance equality of opportunity and foster good relations between members of different groups. In the context of gender, the Act makes it explicit that advancing equality of opportunity includes removing or minimising disadvantages experienced by women (or men) but not by men (or women), taking steps to meet the needs of women (or men) that are different from those of men (or women), and encouraging women (or men) to participate in public life or any other activity in which participation by women (or men) is disproportionately low. (Note: the Act frames these duties in a way that applies to all characteristics. I have used gender as an example to avoid using the jargon that is required for a more general formulation.)

The specific duties are set by regulation and are intended to provide a framework that ensures that something actually happens. Under previous legislation the specific duties varied. For example, the Race Equality Duty had a detailed prescription for data collection in Higher Education. The Gender Equality Duty required public authorities to gather and use information but had no specific requirements for data collection, other than that the requirement ‘to consider the need to include objectives to address the causes of any gender pay gap’ implies that you actually know what your pay gap is.

The previous specific duties for gender were:

To prepare and publish a gender equality scheme, showing how it will meet its general and specific duties and setting out its gender equality objectives.
In formulating its overall objectives, to consider the need to include objectives to address the causes of any gender pay gap.
To gather and use information on how the public authority's policies and practices affect gender equality in the workforce and in the delivery of services.
To consult stakeholders (i.e. employees, service users and others, including trade unions) and take account of relevant information in order to determine its gender equality objectives.
To assess the impact of its current and proposed policies and practices on gender equality.
To implement the actions set out in its scheme within three years, unless it is unreasonable or impracticable to do so.
To report against the scheme every year and review the scheme at least every three years.

[Source: Gender Equality Duty Code of Practice Gender Equality Duty Code of Practice England and Wales EOC 2006]. It was the responsibility of the Equality and Human Rights Commission (EHRC) to enforce the legislation by issuing guidance and, if necessary, through compliance orders or court orders.

The focus of the proposed new specific duties is on accountability through transparency. Public authorities will be required to publish data that will enable citizens and concerned groups to hold public authorities to account. The EHRC will determine what data should be published though the consultation document mentions the gender pay gap, the proportion of staff from ethnic minority communities and the distribution of disabled employees throughout an organisation’s structure.

Differences from the old specific duty for gender are

Public authorities will no longer be required to have an equality scheme. Consequently there will no longer be requirements to implement the scheme, to report against the scheme or to review the scheme.
There will no longer be a specific requirement for consultation but public bodies will be expected to be open about how they have engaged with people.
There will not be a specific duty requiring equality impact assessments as it is expected that equality impact assessment would form part of normal decision-making. However, the annual publication of equality information will include impact assessments.
Equality objectives should be reviewed every four years.

Differences from the proposals put forward under the previous government are:

There will be no national priorities set by the Secretary of State.
There will be no special focus on procurement as the general and specific duties already apply to all the functions of a public body.
Public bodies will no longer be required to set out the steps they propose to take in order to achieve equality objectives.

The proposed specific duties are

Workforce Transparency: Public bodies with 150 or more employees will be required to publish data, to be specified by the EHRC, on equality in their workforces. This is expected to include data on their gender pay gap, the proportion of staff from ethnic minorities and the distribution of disabled employees throughout the organisation’s structure. The data will have to be published at least annually.
Service Provision: Public bodies will be required to publish data, at least annually, that will enable people to judge how effectively they are eliminating discrimination, advancing equality and fostering good relations through the services they provide.
Setting objectives: Public bodies will be required, as part of their normal business planning process to set equality outcome objectives that are informed by evidence and that are specific, relevant and measurable. This will enable meaningful scrutiny by citizens and other interested groups. The objectives should be reviewed at least every four years.

The focus on outcomes is welcome. Far too much time and effort has gone into producing plans and then writing reports against those plans in which whatever did happen is presented as though it were what was planned. Trying to minimise the work involved in demonstrating compliance is also welcome. Partly because resources should be directed to achieving aims not demonstrating compliance and partly because equality should be embedded within normal procedures and practices not treated as an optional or externally imposed extra.

My concerns are:

Will this ‘meaningful scrutiny by citizens and other interested groups’ actually occur? Are there enough people with the time and resources to carry out this scrutiny? How is it envisaged that such people will hold an institution such as a large, research-intensive university accountable?
What data will be required? From a mathematical point of view the institutional gender pay gap is a flawed measure of inequality. However, a lot of people have invested a lot of time and effort into promoting it as a measure of inequality so we are probably stuck with it. The minimum amount of information required to make sense of a gender pay gap is the number of men, the number of women, the average salary of the men, the average salary of the women, and the proportion of women by salary band. It would also be helpful to know if women are disproportionately represented in some occupational groups and, in the context of research and academic staff in universities, whether there are differences by discipline. For workplaces that are large enough for such an exercise to be meaningful, it would be useful to know the proportion of women by salary band and age. This would help distinguish between situations where women are hard done by and something should be done and situations where women were hard done by and something has been done. There is no point in devoting time and resources to fixing something that is not broken. The Equality Challenge Unit, which provides guidance on equality for the higher education sector, has suggestions for the data that higher education institutions should use to inform setting equality objectives in their briefing ‘Revising Gender Equality Schemes’ (January 2010) and the ECU Gender Equality Scheme Self-Assessment Tool.
If there is a conflict between presenting data and maintaining the privacy of individuals then privacy should be paramount.
Objectives should be realistic and achievable as well as measurable. There is no point aiming for some arbitrary percentage of women among some particular group if that cannot be attained within a reasonable timeframe. It is often forgotten that the most important constraint on how fast the proportion of women among academics can change is the rate at which vacancies occur for them to be appointed to, unless new positions are created. Similarly there is no point aiming to train some proportion of your staff in something-or-other if the resources to deliver the training are not available.
What do we mean by measurable? For example, in 2004 women made up 49% of acceptances to Natural Sciences at Cambridge. Five years later in 2009 women made up 40% of acceptances to Natural Sciences at Cambridge (Source: Cambridge University Reporter Undergraduate Admissions Statistics Special Issue, No. 15 2009-2010 and 21 February 2005). Is this a worrying decline or a random fluctuation? Having looked at the numbers, I am inclined to the latter view, though the former is tenable depending on how much the data are tortured. It could also reflect a change in the proportion of acceptances to biological Natural Sciences. Suppose the numbers had been the other way around (i.e. 49% in 2009 and 40% in 2004). Would this be evidence that the University was meeting equality objectives?
It is hard to see how an institution could set or achieve equality objectives without consulting with relevant groups. It is very important that institutions should be required to state with whom and how they consulted.
Institutions should also be required to state what steps they took to achieve their equality objectives. This would aid the ‘citizens and other interested groups’ to assess whether an institution is building a genuinely equal environment or whether it is just managing the numbers. For example, suppose an institution has reduced its gender pay gap. It would be of interest to know if this had been achieved by making lots of catering assistants and clerical workers redundant or by waiting for other institutions to develop the careers of their female staff and then poaching them. In addition it would be useful to other institutions to help them assess what actions are effective.

The proposed specific duties should enable institutions to embed equality within their organisations. If you have a view on whether or not the proposed specific duties make it more or less likely that this will happen then you should respond to the consultation.

Christchurch Earthquake 2

2010-09-19T23:46:00.000-07:00

More on the Christchurch earthquake, also known as the Canterbury earthquake or the Darfield earthquake...

After much emailing on my husband's part, he and I travelled to Christchurch on Wednesday 8 September to help install sensors to measure accelerations caused by earthquakes in the aftershock sequence following the 7.1 earthquake on 4 September. The project is part of the Quake Catcher Network run from Stanford (qcn.stanford.edu): the Rapid Aftershock Mobilization Program in New Zealand (http://qcn.stanford.edu/ramp/). A Ph.D. Student from Stanford had arrived in Christchurch that morning with 200 sensors in her luggage.

People volunteer to have a sensor in their home for a period of 4-6 weeks. The picture shows one of the sensors. It is secured to the floor using duct tape and glue for a hard floor and duct tape and Velcro for carpet. The cable plugs into a USB port on a computer which has to have BOINC (Berkeley Open Infrastructure for Network Computing) installed to manage data transfers to the server. Between 8 September and 14 September up to five teams of people from GNS Science, Stanford and the Universities of Auckland and Wellington installed nearly all of the 200 sensors around Christchurch and the surrounding region.

Different parts of the city were affected differently by the shaking. Driving in from the south, we saw very little damage until we reached the central city area where a number of older brick or masonry buildings had been badly damaged. In fact, the three main types of damage were chimneys that either collapsed or became unsafe, older brick or masonry buildings that partially collapsed and problems due to soil liquefaction. When we arrived on 8 September many streets in the central city area were cordoned off. In fact, the serviced apartments where we were staying were inside a cordoned area and we had to be escorted to reception by a soldier. By the time we arrived, water and power had been restored over most of the affected area, though not in some of the most badly affected neighbourhoods and in rural areas. The biggest inconveniences for us were that for the first few days we were not allowed to use the lifts and the internet connections to the rooms were not working properly, possibly because aftershocks were loosening the ethernet cables. By Monday 13 September much of the city was functioning normally, though a few streets were still closed due to unsafe buildings or continuing demolition.

The response of Christchurch residents to the call for volunteers to host a sensor was amazing. Even those whose houses were undamaged had still had an extraordinarily stressful experience, plus the additional stress of on-going aftershocks, including one of magnitude 5.1 on the morning of 8 September (before we arrived) that caused additional damage.

The GeoNet website has more information about the earthquake, including a video montage of the fault trace reconnaissance and some more on aftershocks. The GNS Science website has more information as well. There is an animation of the aftershock sequence at www.christchurchquakemap.co.nz.

We also recommend the Nobanno Bengali restaurant on the corner of Armagh and Colombo Streets in Christchurch. The food is excellent.

Christchurch Earthquake

2010-09-04T01:27:00.000-07:00

The major earthquake near Christchurch , around 350km north of here, woke us at 4.35 this morning. It was strong enough here for me to be concerned about objects falling, though none did. The timing was fortunate, in that not many people were out on the streets. Had they been, there would have been many more casualties. As it is, some people had some amazing escapes. For news stories see TVNZ or BBC.

Tinkering, Tailoring , Transforming

2010-08-30T21:07:00.000-07:00

There are two 'big ideas' or explanatory frameworks that made a big impression on me when I first started working in women in SET as opposed to being a woman scientist. One was Teresa Rees's description of different approaches to gender equality as 'Tinkering, Tailoring and Transforming'.

Tinkering refers to an approach based on equal treatment. This is the approach that underlay legislation such as the Sex Discrimination Act (1975) and the Equal Pay Act (1970): it is illegal to treat someone less favourably on the grounds of sex.

The second approach recognizes that equal treatment may not be sufficient to achieve equality: deeply engrained differences make it essential to take action to tackle disadvantages. This is the ethos behind positive action and Rees refers to it as tailoring. The focus of much 'tailoring' activity is adjusting women to accommodate existing structures and processes, for example, 'Women into management' courses.

Transforming sees differences not as a problem to be overcome but as something to be embraced for mutual benefit. In this approach the focus is on adjusting structures and processes to accommodate differences or mainstreaming.

Clearly, institutions must comply with the legal requirement not to discriminate. Also, most people think that equal treatment is fair.

Positive action can be very powerful, especially initiatives that encourage women to advance in their careers while embracing their identities as women. The disadvantage is that it can lead to a focus on 'deficit model' or 'male as norm' approaches in which the problem is seen as being that women are not men with the solution being to 'fix the women' by encouraging them to act more like men. (Note that 'deficit model' is used with two quite different meanings in the literature. For example, Sonnert and Holton, American Scientist 84 (1996) 63-71, see http://www.aps.org/programs/women/workshops/upload/sonnert1.pdf, define the 'deficit model' to mean that women receive fewer chances and opportunities along their career paths as a result of legal, political or social structural obstacles. On the other hand, Carol B. Muller, founder of MentorNet refers to the 'deficit model' as the assumption that women lack something - ability, experience, interest, inspiration, motivation - that they need to succeed, see Pan-Organizational Summit on the U.S. Science and Engineering Workforce (2003)). Positive action measures can also lead to resentment both among men who feel that women are being given an unfair advantage and among women who feel that they are being labelled as in need of remedial help.

Rees describes the difference between positive action and mainstreaming as

'Rather than helping round women fit into square holes, it makes those holes more adaptable – to take all sizes and shapes.'

The advantage of mainstreaming is that it embeds equality within the organization rather than seeing it as an optional, or externally imposed, extra. The process of embedding equality may lead to resentment: some are unhappy with any measure that goes beyond equal treatment, some will interpret changes as special treatment, and some have a deeply held belief that employees ought to mould themselves to their employer's requirements. The principal disadvantage is that it is difficult to achieve. Inequality results from a large number of interacting factors. Identifying issues and appropriate actions is difficult; monitoring progress on any useful timescale near impossible.

All three approaches are necessary. It is important that people be treated fairly. It is necessary that women should be empowered to succeed within the current structures and processes. We can't wait for them to be fixed. To achieve genuine equality structures and processes have to change.

2010-08-04T20:28:00.000-07:00

Yesterday evening I went to a Dunedin AWIS meeting at which three scientists spoke about their careers. One of the things they had been asked to do was comment on things they like and things they dislike about their jobs. Listening to their remarks set me thinking about what were the things I liked and disliked about working in women in SET. So here they are:

Likes (in no particular order)

The occasions when someone told me, either via a feedback form or in person, that an event I had facilitated made a difference to her life.
The chance to meet some amazing women. Not just the high-fliers; some of the women with less stellar careers have inspirational stories of courage and persistence.
The intellectual challenge. Understanding the issues for women in SET involves thinking about interacting people which is a much more difficult problem than the interacting electrons I was accustomed to. However, extracting signal from noise is a problem both activities have in common and there are interesting experimental data and explanatory frameworks in women in SET, see, for example, Virgina Valian’s ‘Why so slow? The advancement of women?’
The variety and opportunity to develop a wide range of skills: I could be facilitating a personal development course one day, analysing data the next and representing the University at a meeting the following day.
Being a Springboard trainer.
Believing that the ultimate aim of what I was doing was to make the University a place where women have an equal opportunity with men to fulfil their potential in SET, even if they also choose to live with a partner and have children. (I also believe that many of the measures that are required for this to happen would make the University a better place for everyone.)

Dislikes (in no particular order)

The fact that progress happens in very small incremental steps. This is true in science as well but at least you can package your small incremental step in a paper.
A preference in administration for structure and process over function and outcome.
A general tendency to keep starting again from scratch instead of learning from and building on what has gone before. In science you usually do a literature search to see what is already known before you start designing experiments. In women in SET it seems to be more normal not just to re-invent the wheel but to re-invent heptagonal wheels.
The difficulty of obtaining relevant data.
People who ignore or misinterpret data.
Poorly defined requests for data or requests that were framed in ways that may have made sense to the requester but certainly didn’t to me.
Zombie arguments – arguments that continue to surface no matter how many times they are rebutted. (See, for example, Isis on John Tierney.]
Action Plans. As far as I can tell, the appearance of an action in an ‘Action Plan’ is pretty much a guarantee that it won’t happen. (Reports on action plans become exercises in presenting the things that did happen as though they were the things that were planned to happen - No plan of operations extends with certainty beyond the first encounter with the enemy's main strength (Helmuth von Moltke the Elder).)
A variant of the Snark syndrome in which if women’s lived experience conflicts with prevailing wisdom on issues for women in SET it is the women’s experience that is discounted.

I have listed more dislikes than likes. That does not reflect my actual experience. I recently read The CEO and the Monk: One Company’s Journey to Profit and Purpose by Robert B. Catell (the CEO), Kenny Moore (the Monk) and Glenn Rifkin (business journalist) (Wiley, 2004). Moore describes sending a note of encouragement to someone who wanted to do something unusual with the quote

‘Don’t ask yourself what the world needs, ask yourself what makes you come alive. And then go do that. Because what the world needs are people who have come alive.’

(according to Wikipedia this quote is attributed to Howard Thurman).

Working in women in SET made me come alive.

The Snark Syndrome

2010-08-02T16:11:00.000-07:00

In my local public library I came across 'Women and Science: The Snark Syndrome' by Eileen Byrne, Professor of Education at the University of Queensland. It was published in 1993 and describes the results of a review of research and policy regarding women in science in Australia in in the mid-1980s. The title is taken from Lewis Carroll's 'The Hunting of the Snark':

'Just the place for a Snark!' the Bellman cried,
As he landed his crew with care;
Supporting each man at the top of the tide
By a finger entwined in his hair.

'Just the place for a Snark! I have said it twice:
That alone should encourage the crew.
Just the place for a Snark! I have said it thrice:
What I tell you three times is true'.

Having noted that a great deal of the received wisdom in the area of women in science is still based on assumptions, beliefs and prejudices operating at the level of superstition noted by Hypatia (between 350and 370 – 415 AD), Byrne defines the Snark Syndrome as the assertion of an alleged truth or belief or principle as the basis for policy or practice that neither has a basis in sound empirical research nor is consonant with established theory. She goes on to describe the Snark effect which requires firstly that the educator, teacher or policy-maker has internalized an assertion from hearing it being constantly repeated ('What I tell you three times is true') when the asserted belief is either unfounded or only occasionally and contextually true and secondly that the internalized belief is used to justify and implement major policies.

The research was focussed on the recruitment of women to undergraduate courses in science and engineering and retention to post-graduate, in particular, Ph.D. Courses. The researchers identified ten core factors:

same-sex role models for women
the mentor process
the image of different branches of science and technology (male, female or sex-neutral; socially responsible or systems- and machine-oriented)
male attitudes to females in 'non-traditional' disciplines; female attitudes (self-esteem, or towards peers)
single-sex versus co-education
prerequisites and school patterns of curricular choices as critical filters
mathematics as a negative critical filter
careers education and vocational counselling
women's support networks
affirmative action projects in science and technology

The research reported in the book covers role-modelling, mentorship, attitudes, image, mathematics as a critical filter and single-sex schooling versus co-education. As well as reviewing previous work the researchers gathered data on women in science and engineering in ten institutions in Australia and also both circulated papers to staff for their response and carried out group interviews with staff.

One of the factors that was particularly affected by the Snark syndrome was role-modelling. They distinguished two hypotheses. The first was:
• same-sex role-modelling is an important influence on breaking the stereotypes of ascribed masculinity and femininity in the vocational setting of curricular choice and of career aspiration.

They concluded that the research tended to support this hypothesis.

The second hypothesis was stronger:
• the acquisition of more female staff in a given discipline will, in itself, result in an increase in female students.

They concluded that this hypothesis is not supported by sound empirical evidence and nor is it consistent with well-grounded rigorous theory. One of their suggestions is that there needs to be a critical mass of women in a particular role for that role to be seen as 'sex-normal'.

However, when they analyzed the views of academic staff they found that, of those who said there were visible women in their discipline, almost all assumed that their mere presence would cause a same-sex modelling process to take place for female students. Furthermore some believed strongly that same sex role models were essential while others argued for the equal value of opposite-sex role-models. The researchers note:

'Both views were frequently described in terms of secure belief without any evidential basis for the belief. The strength of the convictions was inversely correlated with the presence of any factual basis.'

They also noted that role-modelling tended to be confused with mentoring.

Byrne suggests two policy consequences of the widespread but unsupported belief that the presence of women staff would increase enrollments by women. First, it provides an alibi for male inaction:

'It is significant that almost all the proposals put forward both in interviews and in writing also involved women taking on more work, but no traceable expected change on the part of men.'

Secondly, active role-modelling wastes women's scarce time. This does not mean that women should not be visible in the normal course of their work on committees, at public events and forums and as delegates to meetings and conferences. Simply that they should not be asked to participate in additional activities aimed at providing role models for secondary school girls:

'Grants and project money spent on ferrying untypical women to small functions without the context of an overall strategy to attack sex-role stereotyping in books, careers materials and the visual media is likely to be a total waste of scarce public money'.

Byrne also reports some of the reactions to the study:

'This is, of course, exactly the kind of garbage I associate the feminist movement with, and I hope you do not really expect me to waste my time reading it and trying to figure out what all these nonsensical questions mean! It is bad enough that we have to pay tax so that the government can employ people to produce this sort of rubbish; you can't expect me to also spend time on it.'

Policy based on clear definitions, logic and empirical evidence! What will these feminists want next?

The ability to judge the contents of a document without actually reading it is, of course, widespread in academia.

She finishes by telling a story about a conversation during one of the group interviews:

After reading the first four discussion papers in advance, and listening to the group discussion of the issues raised, a Professor from a discipline in which girls were well into the 'abnormal/rubric of exceptions' minority, said: 'Professor Byrne, I have a problem. You are two women directing this project. Do you not think that this invalidates the results?'

After a moment's stunned silence, I replied, 'Professor X, let me be clear what question you are asking. You are saying that because we do not have a mixed-sex research team, our research into these issues is invalid? Presumably you will accept that, then, 90% of scientific research so far is invalid because it has been conducted exclusively by men?'

He shook his head uncertainly.

'I'm sorry. You are saying that because we are women, we are less able or well qualified and need what Simone de Beauvoir termed a “male mediator between us and the Universe”?

He hastily protested that our qualifications and experience were impressive.

'I'm sorry to have misunderstood again. You are saying that because we are women, even if our research is in fact sound, no one will listen to us, simply because we are women?'

As the Professor struggled to come to terms with that, a colleague came to his rescue. 'I think what my colleague is saying, Professor Byrne, is that it would be a pity if so much wide-ranging and substantially funded research on so important an issue, were not influential because …' His voice died away.

I said quietly, 'So you are in fact saying that he believes that however right women are, they cannot be listened to with the same scholarly clout as men?'

That was in 1986, nearly a quarter of a century ago. Have things changed since then? Many policies intended to increase the numbers of women entering, staying in and progressing in SET are still based on the Snark Effect, that is, on internalized beliefs that are unsupported by evidence. In fact, these internalized beliefs are often impervious to facts. If the facts do not support the belief people look for reasons why the facts are wrong. For example, if the figures show that women are just as likely to be promoted as men it must be because the figures are based on an incorrect definition of who is eligible for promotion since it is well known that women are less likely to be promoted than men. I am not sure that the reaction that research on women in science and engineering cannot be objective if it is carried out by women, though of course, it would be objective if carried out by men, has entirely died out, either.

Let's be innovative

2010-07-26T21:44:00.000-07:00

Many awards and funding schemes in areas like women in STEM or STEM outreach include in their criteria that a project should be innovative. They will usually also require that the project should be evaluated in order to show that it is effective. Then, of course, it can no longer be funded, however effective it may have been shown to be, because it is no longer innovative. Unless, perhaps, adopting a practice because it has been shown to be effective is itself a novel idea and hence innovative. It makes sense to have funding that is targetted at innovative projects if mainstream funding favours projects which follow accepted or demonstrated best practice. It also makes sense to fund innovative projects if it is recognized that current best practice could be improved. If there is no source of mainstream funding in an area then it makes no sense at all to demand that projects be innovative and that they be evaluated to demonstrate their effectiveness and then have no way of continuing or building on the progress already achieved. I suppose from a funder's point of view having a pointless criterion helps reduce the number of proposals to consider. The downside is that much time and effort is expended by proposers on finding new angles so their proposals will be innovative that might have been better focussed on being effective. I suppose if you are making an award for outreach then 'We copied what they did in a neighbouring county because it seemed to work pretty well' might seem a bit feeble. When it comes to awards for improving the representation of women in STEM then I would prefer efficacy over innovation. For example, the 'Top Tips' [see the Athena SWAN website] for making an application for an Athena SWAN award contain the advice

'… make sure that you include policies and programmes that are innovative and different. That way, we are more likely to use them and publicise your achievements.'

I don't care whether policies and programmes are innovative and different and I care even less whether they can be used in SWAN Awards publicity. What does matter to me is whether or not they work

Discrimination

2010-07-14T20:04:00.000-07:00

In one of the conversations I had last week one person mentioned that he had heard at least two people making comments along the lines of ‘Well, we wouldn’t employ her. She’ll just go off and have babies.’

Yes, it is discrimination and yes, it is illegal. Why do people feel that they can openly state that they are willing to break the law? I believe it is because there is a widespread culture that legislation relating to employment, equality, health and safety, copyright, freedom of information and data protection is just bureaucratic interference with the right to use whatever means one sees fit to advance knowledge, make a profit, or both.

The standard reaction is to suggest that what people need are courses educating them about their responsibilities under the legislation. Unfortunately, these courses are then regarded as bureaucratic interference with the right to use whatever means one sees fit to advance knowledge, make a profit, or both.

What then can we do? Advancing knowledge and making a profit are both legitimate aims. In fact, one or the other is usually the primary reason an organization exists. Safe, fair workplaces, respect for privacy, and respect for people’s right to know what publicly funded institutions are doing are also legitimate aims. Often, achieving those subsidiary aims entails costs in money or time. If organizations genuinely want to achieve those subsidiary aims then they need to make the resources required available. For example, there are costs when someone takes maternity leave. Should it be left up to individual managers to figure out how those costs are met? My fantasy for universities is that they should each have a ‘Flexibility Fund’ which would provide funding for costs associated with maternity leave and flexible working, including training or coaching for managers. At the very least, we need to recognize that there are costs.

Insecurity and Flexible Working

2010-07-11T20:28:00.000-07:00

The New Zealand International Science Festival, held every two years in Dunedin, has just finished. Two of the themes that struck me from meetings and conversations during the week of the festival were insecurity in scientific careers and the importance of flexible working for women in science.

The theme of insecurity in scientific careers included not just insecurity of funding but also insecurity due to changes in priorities. Insecurity of funding affects almost everybody in science: post-docs on fixed-term contracts, researchers with open-ended contracts who will be paid only if they bring in sufficient funding, and group leaders with permanent positions who want to ensure continued employment for their group members. Insecurity due to changes in priorities includes insecurity due to funding being withdrawn from a particular area as distinct from a specific proposal failing to gain funding, re-structuring, and cuts to R&D to compensate for losses incurred as a result of disastrous decisions in other parts of a company. This type of insecurity is particularly difficult to deal with as high achievers are vulnerable as well as everyone else.

The festival included a ‘Women in Science Breakfast’, which aimed to give secondary school girls the chance to hear and meet women working in science. One of the speakers in her tips for a successful career in science stressed how important it is to take at least six months maternity leave and go back to work part time so you go to work refreshed and enthusiastic rather than depleted and exhausted.

Now, who is going to feel comfortable taking time off for maternity leave or working part time in a climate of insecurity? I once heard a talk in which the speaker advocated applying for every fellowship going. Afterwards one woman in the audience commented that she had been advised that she needed to strengthen her publication record, which should she do? The answer is both.

So, apply for all the funding going, keep producing publications, enhance your professional visibility, keep your options open in case funding for your speciality dries up and take at least six months off to have a baby and work part time when you go back to work. Is it really surprising that young women come to the conclusion that it is impossible to combine family life with a career in science?

We need funding policies that place as much emphasis on retaining good scientists as we have on attracting them in the first place.

Is correct interpretation of data important?

2010-07-06T16:22:00.000-07:00

To a scientist this is an incomprehensible question. How can it not be important to interpret data correctly?

However, if your intention is to motivate people to take action then it does not matter whether your interpretation of the data is correct so long as people believe it.

A number of my posts over the six months have dealt with the interpretation of data. Why do I think it is so important to interpret data correctly?

Incorrect data interpretation allows critics to discount your arguments.
Incorrect data interpretation allows critics to re-focus the discussion as an argument about what the data mean.
Incorrect data interpretation leaves you running the risk that scarce resources are directed at solving non-existent problems while leaving actual problems unaddressed.
The examples of poor data interpretation I have discussed include:

Making a stronger claim than the data support (Over-interpreted Data 1)
Failure to distinguish between effect and fluctuation (for example, in appointments statistics, see Myth of Hard Facts)
Failure to account for confounding variables (Over-interpreted Data 2)
Failure to rule out alternative hypotheses (Over-interpreted Data 3)

Do we really make the argument for more women in science by demonstrating such basic errors in analysis?

5. I have a pedantic streak.

Gender and Housework

2010-06-17T14:41:00.000-07:00

There is an article on getting help with housework on the Science Careers website. It is based on an article in the magazine of the American Association of University Professors, Academe, Housework is an Academic Issue, by Londa Schiebinger and Shannon K. Gilmartin. Schiebinger and Gilmartin's article is based on research on dual career couples in academia, Dual-Career Academic Couples: What Universities Need to Know. They analysed survey results from 910 men and 312 women with partners of the other sex. (There were not enough same-sex partners for quantitative analysis.) Among other things they found that there are no significant differences between male and female academic scientists in the number of paid hours worked (see Figure 4 of their article) but considerable differences between male and female scientists in the amount of time spent on household tasks: female scientists do twice as much housework as their male counterparts.. They also found that the task most likely to be outsourced is cleaning and the group most likely to outsource cleaning are women scientists with an academic partner. For both men and women outsourcing housework is associated with higher productivity. Schiebinger and Gilmartin suggest as a policy recommendation that institutions consider offering 'cafeteria' or 'flexistyle' benefits packages from which employees could choose benefits up to a specified amount, for example, some might choose assistance with housework, others help with child care and others help with elder care.

The Science Careers article focussed on hiring help with childcare and housework. It includes an interview with a female professor at MIT whose husband is a professor at Harvard who have a nanny, a cleaner and someone who comes in to cook twice a week as well as being able to leave the children with their grandparents if they need to be away for conferences. As an illustration of a possible solution to coping with the demands of an academic job it is excellent. However, Isisthescientist, in a post about the hoary old question of whether men and women differ in their aptitude for science and maths, mentioned the reaction of another women scientist blogger to the Science Careers article. Essentially this was that the article appeared to take for granted that women need advice on coping with housework but men do not. The editor of Science Careers, Jim Austin, responded to these remarks on the Science Careers blog leading to further discussion at Isisthescientist. I think we need to appreciate that women are not a homogenous group. Some women will have found the article helpful: others may feel that it reinforces stereotypes rather than challenging them. The head of department who advises new female assistant professors to get over people's expectations of what women should do around the house and get paid help with housework is still better than one who does not recognise the issue at all, even if the advice could equally apply to new male assistant professors. Likewise a head of department who tells female lecturers going on maternity leave about the university's flexible working policies but not male lecturers going on paternity leave is still better than one who gives the impression that anyone who works part time is lacking commitment. We should celebrate progress, even if it is tiny incremental steps.

A related post from a different blog is How did we get here?, in which a male doctor ponders how it came about that, by default, he became the primary earner while his wife stays at home. Comment 4 is particularly interesting.

I wonder if the asymmetry between maternity leave entitlement and paternity leave entitlement is a factor in women becoming the default stay at home partner? Note I am not saying the only factor. Of course factors like husband earns more, husband is at a crucial stage of career and you would quite like to have at least one income, the difficulty of finding flexible work and cultural and family expectations are important. I just wonder whether patterns who does what become established while the mother is on maternity leave and then become difficult to break when she goes back to work.

Over the years I have met quite a few women whose careers have been compromised or effectively ended by their decision to spend time with their children. I cannot recall one of them saying that they wished they had spent less time with their children. I have met men who wished they had spent more time with their children. If we want real equality then we have to acknowledge that parents want to spend time with their children, regardless of whether they are the father or the mother. Men are people too.

Academic Promotions

2010-06-09T20:59:00.000-07:00

Suppose a university, or one of its components, for example a school, faculty or department, collects data on promotions and discovers that the rate at which women are promoted is statistically indistinguishable from the rate at which men are promoted. What could this mean?

Possibilities include:

There is no difference between men's and women's experience of the promotion process.
There is a difference but it is too small to resolve with the data available. (Attempts to aggregate data from different years need to take into account that the outcomes in year N are not independent of the outcomes in years N-1, N-2,... .)
There is a difference but it does not manifest itself in differences in promotion rates but in some other way, for example, increased stress.
Men and women tend to have different expectations of a promotion procedure. For example, it may be the case that women tend to believe that if they meet the criteria then they will be promoted while men tend to believe that if they are better than the other guy then they will be promoted.
Men and women find the promotions procedure equally unfair but women interpret their experience within a paradigm of women's disadvantage while men use a different paradigm, for example, managerial incompetence.

Well constructed staff surveys would help distinguish among these possibilities.

A particularly interesting situation occurs when it is widely accepted that women are less likely to be promoted than men, even though the evidence that this is the case is statistically unconvincing. Not only may it be accepted that women are less likely to be promoted but it may also be accepted that the reason is that women are less likely to apply for promotion, even though the evidence that this is the case is also unconvincing. In fact people may be so strongly convinced that women are less likely to apply for promotion that they propose research projects to investigate why this is the case. To a scientist this is an interesting concept: a proposal to investigate the reasons for an effect that you can't demonstrate actually exists.

When there is a widespread perception that women are disadvantaged by the promotions procedure then it is necessary to be seen to be addressing the problem, even if the data do not support its existence. In this case the 'women are less likely to apply' explanation has a political advantage. It throws the responsibility for the perceived situation onto the women involved which means that proposed remedies can focus on modifying the behaviour of the women rather than on altering the procedures or the way they are implemented. It is generally easier to persuade people of the case for modifying someone else's behaviour rather than their own. Why is this an advantage? Well, it is if it means something happens rather than nothing. Of course there are women who find the 'here is our special help for the ladies' approach offensive but in my experience they are out-numbered by those who will take whatever assistance is offered regardless of the motives of those who are offering it.

The Quota Question

2010-05-26T18:32:00.000-07:00

Are quotas useful? The answer to this question depends on what you are trying to achieve. If the problem is defined by the statement ‘there are too few women at a particular level or in a particular occupation’ then imposing sanctions, or threatening to impose sanctions, on those who fail to meet a quota will increase the number of women, provided there are a sufficient number of suitable qualified women to fill these positions.

In Norway companies were given six years to raise the percentage of women on boards to 40%. Some think that the Norwegian experience shows that the talent was there. It was simply a matter of identifying it. (‘Quotas for women on the board: do they work?’, The Sunday Times 8 June 2008, see also this comment at 20-First. However, a study by Amy Dittmar and Kenneth Ahern of the Ross School of Business at the University of Michigan showed that the performance of firms dropped as the percentage of women on the board increased. The study found that this was due to a shortage of appropriately qualified women so that boards had to appoint inexperienced women in order to meet their quota. (See also ‘Just Don’t Call Them Gender Targets: The Need to Move Diversity Hiring into the Open’ at www.theglasshammer.com. Thanks to Suzanne Doyle-Morris for alerting me to this post via LinkedIn.)

Another area where quotas operate is in the electoral process, for example, nine European countries have legislated candidate quotas and twenty-two have voluntary political party quotas (source: www.quotaproject.org). In an ideal world we would not have interventions such as all women candidate lists, but we do not live in an ideal world. A representative democracy is entitled to take steps to ensure that it actually is representative. What about the ‘best person for the job’ argument? There is evidence that voters use heuristics such as appearance to judge the competence of candidates and that this can lead to gender bias. There is also some evidence people rely less on gender stereotypes when they are familiar with female politicians. [‘Inferences of Competence from Faces Predict Election Outcomes’, Todorov et al. Science 10 June 2005:Vol. 308. no. 5728, pp. 1623 – 1626; ‘A neural basis for the effect of candidate appearance on election outcomes’, Spezio et al. Soc Cogn Affect Neurosci 3, 344-352; Chiao JY, Bowman NE, Gill H (2008); ‘The Political Gender Gap: Gender Bias in Facial Inferences that Predict Voting Behavior’, PLoS ONE 3(10): e3666. doi:10.1371/journal.pone.0003666; ‘Powerful Women: Does Exposure Reduce Bias?’, Beaman et al. MIT Department of Economics Working Paper No. 08-14 1 August 2008.] It seems reasonable to have some sort of intervention to accelerate progress.

Could quotas work in academia? Currently quotas are illegal in the UK under the Sex Discrimination Act (1975), since the existence of a quota implies that women would be appointed in order to meet the quota. If quotas were legal how would they operate? Would the quotas apply to staff employed or to new appointments? If the former the quotas would have to take into account the current levels of women in universities as otherwise institutions could be penalized for actions that occurred before the quotas were set, which seems unfair. If the latter, what would be the effect? Suppose turnover is 5% per year (since a career as a permanent academic lasts about thirty years, we would expect about 3% per year but there will also be contributions due to transfers between institutions and expansion). If half of new appointees are women then, assuming no women leave via retirement or transfer, the percentage of women among academics would rise at 2.5 percentage points per year, which may not sound much but a department that achieved this could get from 25% women to 50% women in ten years. Would the quotas apply to institutions as a whole, with institutions developing their own plans to meet them, which might encourage them to focus on subjects where recruiting women was easier, or on a subject by subject basis? The former seems unlikely given that 48% of lecturers in Higher Education in the UK in 2007-08 were female (HESA press release). If the quotas are set on a subject by subject basis, what form would they take – five out of ten appointments, ten out of twenty? How would the quotas be enforced? What penalties would there be for failing to comply?

What would the advantages be? Clearly, quotas could be effective over a timescale of ten years or so. What would the disadvantages be? Many women would be opposed because they want to be appointed for their ability not their sex. Many men would feel unfairly treated. Some women already find that any recognition they receive is belittled on the grounds that they were recognised for their gender not their achievements because men believe, despite the evidence, that women are unfairly advantaged. Quotas are a top-down measure, which is culturally inappropriate for universities. Many academics would devote their energies to finding ways around them.

So, quotas would be effective if the primary concern is simply to increase the numbers of women. However, there are a number of significant drawbacks.

Personally, though quotas may be useful in other areas, I am opposed to quotas in academia. I would want to be offered a job because the selection panel believed I was the best fit to the requirements of the position, not because they need a woman to meet a quota. Likewise, I think that being short-listed for interview so that some administrator can tick the ‘woman interviewed’ box is a waste of my time, and of the selection panel’s. I think there are better, less divisive ways of achieving increases in the number of women in permanent academic posts, such as the STRIDE project at the University of Michigan.

Over-Interpreted Data 3

2010-05-20T22:29:00.000-07:00

This example of data that does not support the inferences drawn from it is quite subtle. In the Campaign for Science and Engineering in the UK Policy Document Number 8, May 2008, ‘Delivering Diversity: Making Science & Engineering Accessible to All’ is the graph shown in Figure 1. It is noted that more women entered the system as researchers and were promoted at every subsequent level. However, the comment is also made that, while in an equal world the graph would be flat so that the percentage of women at higher levels matched that at lower levels, there is no sign of the rate of increasing under-representation abating, i.e. the slope in 2005/06 is pretty much the same as the slope in 1995/96. But the graph shows that the percentage of women at each level in 2005/06 matches that of the next lowest level ten years previously, which is roughly what you would expect as each cohort moves through the system. Since the percentage of women among researchers rose for the slope to flatten the percentage of women among professors, senior lecturers and lecturers would have to have risen faster. This raises the question: is the fall-off in the percentage of women with grade the result of current discrimination or disadvantage preventing women from moving through the system from researcher to professor or is it the result of discrimination or disadvantage that occurred before 1995. If the latter it is hard to see what we can do about it now.

In fact the profile in 1995/96 can be evolved to the profile in 2005/06 without making the assumption that women are disadvantaged in appointments or promotions in that the number of women appointed as lecturers or promoted to senior lecturer or professor matches the number available in the pool from which they are appointed or promoted. The results of a model in which the percentage of women among researchers rises linearly from 24% to 30% and the percentage of women among appointments and promotions matches the percentage in the pool are shown in Figure 2. The results from the simulation for 2005 match the data rather well. (A detailed description of the model I used to produce the simulated 2005/06 figures is available at Google Docs.) This does not prove that women did not experience discrimination or disadvantage in appointments or promotion during the period 1995 to 2005. What it does show is that the data in Figure 1 do not, by themselves, show whether they did or they did not. Of course, discrimination or disadvantage must have occurred before 1995 in order to have a starting profile that was skewed to lower grades in the first place, but I was not aware that there was any dispute about that.

In the US the National Academies Report: Gender Differences at Critical Transitions in the Careers of Science, Engineering, and Mathematics Faculty (National Academies Press, 2009) found that there were gender differences in hiring. Women were more likely to receive the first job offer than they were to be asked to interview and more likely to be interviewed but the proportion of women applying was smaller than the proportion among those receiving Ph.D. degrees. Women were also more likely to receive tenure but the proportion of women candidates for tenure was smaller than the proportion of women among assistant professors. Because the data are snapshot data it was not possible to distinguish between the possibility that women are more likely to leave before coming-up for tenure and the possibility that the numbers of women being hired as assistant professors had increased in recent years. There were no gender differences in promotion to full professor. Studies of this kind that examine the transition processes with large enough data sets to reveal differences are what is required to reveal whether and when women are disadvantaged.

Women's Awards

2010-05-13T22:01:00.000-07:00

One of the blogs I follow is femalescienceprofessor. Recently she posted questioning the necessity for awards recognizing the achievements of women in science, noting that she herself would have felt that the message from receiving a women-only award, had she been nominated, would have been ‘You’re a pretty good scientist, for a woman.” She does distinguish between awards intended to support women’s careers by making sure they are not at a systematic disadvantage and awards for achievement.

I have some sympathy with this view. However, some women-only awards do raise the profile of women in science.

Are women-only awards patronizing or do they serve a useful purpose? (One commenter’s response was ‘Yeah, I would take the money, wherever it came from.’)

Listening Skills

2010-05-11T15:49:00.000-07:00

There is a blog called The Bensh i by Randy Olson, a marine biologist turned filmmaker. One of his posts is about scientists’ inability to listen and why it can make science communication ineffective. I mentioned this inability to listen to another scientist who immediately thought of scientists’ behaviour in seminars, where, frequently, scientists are only listening in order to make their own points at the end.

I had been puzzled by the phenomenon where, at the end of a seminar, someone, usually a man, gets up and rambles on using up most of the question period on a subject of tangential relevance to that of the seminar, until I came across Ms. Mentor’s explanation. This phenomenon is called ‘peacocking’ and is a display behaviour. (I first read this in the report of a workshop on Gender Issues in the Sciences held at Colby College in Maine in 2003. The report of this workshop is available at http://www.colby.edu/~bbrown/2003Workshop.html. Emily Toth’s paper, ‘Successful Strategies for Advancement’ is on pages 22-23 and the longer transcript of her workshop starts at page 60.)

Our discussion turned to graduate student seminars. The other scientist described what happened in one department where, at question time, Prof. A. would ask the student giving the seminar a devastating question and Prof. B. would follow-up ‘like a hyena with a carcase after the lion has made the kill.’ The point of this style of questioning is to discover which students ‘have the balls’ to progress in science. There is no doubt that coping with hostile questioning is something that scientists have to learn. Is this really the best way to ensure graduate students acquire that skill? Could there possibly be gender-bias in which students are weeded-out?

Over Interpreted Data 2

2010-05-06T22:13:00.000-07:00

My second example of over-interpreted data (that is, facts that do not support the inferences that people attempt to draw from them) is from the Campaign for Science and Engineering in the UK Policy Document Number 8, May 2008, Delivering Diversity: Making Science & Engineering Accessible to All (available as a pdf). The statement is:

‘A 2005 survey of STEM workers in Higher Education, found that 41% of men had been interviewed by all male panels compared with 27% of women. This illustrates the importance of making sure that interview panels are appropriately diverse.’

The first question might be 41% of what and 27% of what? It makes a difference whether it is 7/17 = 41% and 3/11 = 27% or 410/1000 and 135/500.

The figures appear to be taken from the Athena Project Report Number 26: ASSET 2003: The Athena Survey of Science Engineering and Technology in Higher Education (available from www.athenaforum.org.uk/reports), which states, referring to appointment to a first lectureship:
‘

Of those appointed in the last two years, 35% of the men and 26% of women had been appointed by all male interview panels, an improvement on the past:
• 3 to 5 years ago - 41% of men and 28% of women were appointed by all male panels
• 6 to10 years ago - 55% of men and 28% of women were appointed by all male panels.’

Table A8 of the Appendix to Report 26 - Statistical Tables gives the numbers of those who reported having an all male interview panel by grade and gender and Table 4 gives the total numbers of respondents to the survey by grade. From these we can establish that out of 1,512 male survey respondents 606 (40%) reported an all male interview panel and out of 660 female survey respondents 200 (30%) reported an all male interview panel. These numbers show that it is statistically very unlikely that the categories (male, female) and (reported an all male interview panel, did not report an all male interview panel) are independent. There are at least four possible explanations for this observation:

All male interview panels exhibit bias in favour of men.
Women are likely to be put off accepting positions if they meet no or few women during the recruitment process.
Women perform less well at interview if there are no women on the interview panel.
Women are not uniformly distributed among different subjects (see Table 3 in the Statistical Tables). Subjects with few women may be more likely to have all male interview panels and a preponderance of male appointees.

In options 1-3 an all male panel affects the selection process. In option 4 the association of male interview panels with male appointees is a result of a common factor, namely the relative numbers of women in different subjects. Unfortunately the data as presented in the Athena Report do not allow us to distinguish between these possibilities.

My point here is not that the composition of interview panels is not important. My point is that the data quoted do not, as is claimed, illustrate that importance. In fact, I have not been able to find any data relating to the effect of the composition of interview panels on the outcome of the appointment process. I found numerous exhortations to ensure that interview panels are appropriately diverse but no evidence that this makes any difference. (I tried Google and Google Scholar. If anyone knows where such data are to be found, please would you let me know?)

The data on unconscious bias show that it depends on the gender of those being evaluated not on the gender of those doing the evaluating (see Why so slow? The advancement of women by Virginia Valian,MIT Press 1999 ) It might be that having a women on the interview panel reduces the effect of unconscious bias but if that woman has had to be imported from another department it may reinforce it.

There is evidence that women are put-off by a recruitment process where the interview panel is all male or they meet no or very few women while visiting the prospective employer, at least in the graduate recruitment process (S.L. Rynes, R.D. Bretz, B. Gerhart (1991), "The importance of recruitment in job choice: A different way of looking", Personnel Psychology, Vol. 44 pp.487 - 521. digitalcommons.ilr.cornell.edu).

Irrespective of whether there exist data showing that the gender composition of interview panels affects the outcome of recruitment, if we are genuinely trying to attract a diverse workforce we would ensure that both interview panels and the people that candidates meet during the recruitment process are visibly diverse. Surely we would want the people coming to interviews, our potential colleagues, to feel welcome and to see that our jobs are not restricted to white men (or, in the non-academic context, white women).

Flagstaff, Dunedin

2010-05-03T22:05:00.000-07:00

This view is from the summit of Flagstaff (668m), part of the Dunedin shield volcano, centred on Otago harbour, that was active 10-20 million years ago. My husband and I walked up there on Sunday afternoon. I realised that I had acclimatised to being away from Cambridge when we were discussing where to go for a walk and I commented that the climb was only 170m from the car park. Then I thought 'only 170m?' (To put this in context, there is an alternative route that starts about 100m from our house. From this starting point the climb is 500m.)

More pictures here.

Over Interpreted Data 1

2010-04-28T16:33:00.000-07:00

Sometimes people make statements that are based on fact but which draw inferences that are not supported by the data. My first example of this is taken from an equality and diversity training package available in the UK. The statement is

‘Women in full time employment are typically paid 17% less than male counterparts doing the same job.’

This is presumably based on the Office of National Statistics finding that, in April 2008, the mean hourly pay for women in full time employment was 17.4% less than that for men. (In April 2009 the gap in mean hourly rates for men and women in full time employment was 16.4%). However, the Office of National Statistics also reports the gap in median hourly pay for those in full time employment. It was 12.6% in 2008 and 12.2% in 2009. It is very difficult to see how women can be paid 17% less than male counterparts doing the same job when the median gap is around 13%. Had the statement stopped at ‘Women in full time employment are typically paid 17% less than men’ it would have been difficult to take exception to it, apart from quibbles about the extent to which ‘average’ is ‘typical’, see, for example, How to Lie with Statistics by Darrell Huff . Adding ‘doing the same job’ makes the statement problematic. There have been numerous reports on the gender pay gap including the Kingsmill Review (2003) and the reports of the Women and Work Commission: Shaping a Fairer Future (2006) and Shaping a Fairer Future – a review of the recommendations of the Women and Work Commission three years on (2009) (available from www.equalities.gov.uk). The Government Equalities Office also has a Factsheet Tackling the gender pay gap available from their website. The causes of the gender pay gap are complex and include vertical and horizontal segregation, gender stereotyping and difficulties in combining career and family responsibilities. (Horizontal segregation refers to the tendency for women to be in lower paying occupations and vertical segregation to the tendency for women to be in lower paying positions within a specific occupation.) So, at least some of the gender pay gap arises because women tend to be doing different jobs from men. Even the Fawcett Society claims only ‘up to 40%’ of the gap is due to direct discrimination, i.e. employers paying women less than men to do the same job.

The gender pay gap is a real and important issue. Women are disadvantaged and their skills are not fully utilised. Pay gaps persist even in populations with similar educational backgrounds and within the same employment sector (Purcell and Elias, Higher education and gendered career development (2004), see this website, and Connolly and Anderson, UKRC Research Briefing Equal Measures: Investigating University Science Pay and Opportunities for Success (2006), available from the UKRC website. It is precisely because it is an important issue that we should try to make accurate statements. In this case it is not difficult to find the correct figures. A Google search on ‘UK gender pay gap’ turns up the Office of National Statistics data as the top link. At the very least an inaccurate statement reduces credibility. At worst it leads to people dismissing the problem. And it seems to me that if you really want to tackle a problem then an accurate statement of the problem is a good place to start.

A Distraction

2010-04-08T20:32:00.000-07:00

In early March I started to follow ‘IOPgate’, an episode which caused a good deal of comment on climate science blogs and a couple of items in the Guardian (2 March 2010 and 4 March 2010). Briefly, the Institute of Physics (IOP) made a submission to the House of Commons Science and Technology Committee’s inquiry into the disclosure of climate data from the Climatic Research Unit at the University of East Anglia, which was interpreted in some quarters as implying that the IOP does not support the scientific consensus that the rising concentration of carbon dioxide in the atmosphere results in a rise in global temperature despite its official stance that the science is well-established and that there is no doubt that climate climate change is happening(IOP, 5 March 2010; IOP 31 March 2010; IOP Blog; physicsworld.com). This stimulated me to find out more about climate change, something I had always been meaning to do but had never had the time.

I would like to share with you the websites that I have found most useful:

For a good overview: www.metoffice.gov.uk.
For climate scientists’ views on the science behind the controversies: www.realclimate.org.
For a summary of rebuttals to skeptics’ arguments: www.skepticalscience.com
For accounts of climate change controversies: laymans-guide.com. The accounts especially relevant to the work of the Climatic Research Unit at UEA are No. 4 The Temperature Record and No. 5 Temperature Reconstructions.
A good site for the history of the science of global warming is The Discovery of Global Warming at www.aip.org/history/climate.
There are lots of pictures at www.globalwarmingart.com.
Finally, The Inter-governmental Panel on Climate Change assessment reports at www.ipcc.ch review the literature and provide an authoritative overview.

Geek Advice

2010-04-04T17:15:00.000-07:00

There is a lot of good advice in this comment from Quill2006 to a post on the Geek Feminism blog at geekfeminsim.org requesting suggestions on what men can do to improve workplaces for women. Specifically:

1. Make it clear what behaviour is acceptable. Value people skills. Promoting purely on technical skills tends to lead to managers with poor managerial skills.

2. Hire and train women. Remember when hiring that women may not have had the same opportunities to learn the latest techniques, which does not mean that they are not capable of doing so.

3. Do your hiring and promotion procedures reward masculine behaviour while punishing women for displaying masculine behaviour.

4. Make sure women have the opportunity to be heard. Specifically, if a woman is interrupted by a male colleague ask to hear the rest of what she had to say.

You may want to pass these tips on to male colleagues.

Sexism and Research

2010-03-30T20:12:00.000-07:00

I was alerted to this post, 'Women and BigLaw: Yes, Virginia, it is Sexism' on The Mama Bee by my friend, Suzanne Doyle-Morris, via LinkedIn . It reminded me of a naturenews story 'Data show extent of sexism in physics' describing a study by Sherry Towers of post-docs at Fermilab (http://arxiv.org/abs/0804.2026) that showed women were only one third as likely to be allocated conference talks as their male colleagues. Two reactions particularly interested me. One was quoted in the news story and is from a male particle physicist who commented that the small numbers in the study (48 men and 9 women) were not enough to prove systematic bias, though Towers used an analysis that took into account the very small number of women. This illustrates one of the problems with investigating reasons for the slow progress of women in science: there are few women so it is difficult to obtain statistically significant results. The other was the first comment on the post which contained the observation: '...women in physics are generally harder working than male colleagues and are great co-workers in terms of encouragement, diligence, and backup support. They do not, however, contribute a great deal of original ideas and rigorous logical analysis to the research. Female judgment seems to more emotionally biased.' In a subsequent comment the same person complained 'I am amazed at the furious response to my comment.' The 'women are diligent but lack originality' is not an uncommon belief among physicists so the author of the comment may well have been surprised to discover that not everyone agrees. What I find uncomfortable is the condemnation evident in some of the responses. Most people have unconscious biases. I once found myself making the assumption that the reader of a book about electronics would be male - on the train returning from a meeting about women in engineering. Rather than attacking people for erroneous beliefs we should educate ourselves and others about the effects of unconscious bias and design our processes to minimize its effects.

The previous post on The Mama Bee, 'Wake Up, Ivory Tower: We Need You!' is also interesting. The author makes a plea for academic researchers on work-life balance to be more cognizant of the actual needs of employees who would benefit from flexible working. In particular, that people who need flexible working are often not in an ideal position to fight for it. It seems to me that a similar situation pertains to women in science. Academic researchers carry out projects that are often of limited relevance to women who are working in science and engineering while data that would be useful are not obtainable, for example, is there gender bias at the step from post-doc to independent scientist, for example, with a Royal Society University Research Fellowship? Do women who work part-time in science find it harder to progress?

A plea

2010-03-29T22:40:00.000-07:00

At the bottom of my posts you will see five boxes labelled: 'I agree', 'Interesting', 'I have no opinion', 'I have reservations', 'I disagree'. I would be very grateful if you would click on one of these reaction boxes. It only takes a second. Of course, if you disagree with me ideally I would like to know why but I recognise that you probably do not have time to frame a response.

Thanks, Esther

A Diversion

2010-03-29T18:05:00.000-07:00

Last week we spent a couple of days visiting the Catlins, an area on the south east of the South Island of New Zealand (www.catlins-nz.com). What does this have to do with science? Well, the pictures are

A petrified log from the Jurassic at Curio Bay
Lake Wilkie, which is a 'bog lake' trapped between sand dunes and a cliff since the last ice age. The vegetation around the lake a good demonstration of plant succession as the lake is gradually replaced by forest.
The Purakaunui Falls, which are photogenic.

There are more pictures at picasaweb.

A Digression on Inference

2010-03-21T20:56:00.000-07:00

In the previous post I used standard hypothesis testing techniques to show that interpreting data requires value judgements. The following are some musings set off by that exercise.

This post also contains technical material about statistical inference. Thinking about statistical inference tends to cause pain in the brain so for those who don’t want to struggle through the technical stuff the important points are:

The statement ‘The result is not statistically significant at the 0.05 level’ does not imply that there is no effect, only that the data do not rule out that there is no effect. The corollary of ‘The result is not statistically significant at the 0.05 level’ is ‘We are still not that sure whether there is an effect or not.’ (‘Absence of evidence is not evidence of absence.’)
Statements about statistical significance are statements about the data not about the effect. With enough data a tiny difference of no practical importance can be statistically significant and a practically important difference can be not statistically significant if there is only a small amount of data.

I had gained the impression from my undergraduate textbook on probability and statistics that hypothesis testing was a routine procedure based on ideas that had been around since at least the 1930s. This is true. It is also true that there is considerable controversy.

As I understand it (and I’m not a statistician, so please comment if you know about these issues) one controversy goes back to a dispute between Fisher and Neyman, basically about whether it is necessary to consider Type II errors. In a significance test you calculate the probability, p, that you would have observed an effect of at least the size that you did observe on the assumption that there is no effect. If this probability is small you reject the hypothesis that there is no effect. If you decide to reject the ‘no-effect’ (null) hypothesis whenever p is less than 0.05 then in the long run the rate at which you will incorrectly reject the null hypothesis (Type I error) is 1 in 20. Alternatively, you can tell people the actual value of p that you obtained.

A statement of statistical significance is a statement about the data not about the hypothesis. It tells us how confident you are about rejecting the null hypothesis on the basis of the data you have obtained. In some circumstances, for example, a quality controller in a factory deciding whether to reject or accept a shipment of components, you would need to know not only the probability of being fooled by fluctuations into rejecting the ‘no effect’ hypothesis when it is true (rejecting a shipment when it is OK) but also of failing to reject the ‘no-effect’ hypothesis when there actually is an effect (Type II error) (accepting a shipment with an unacceptable number of defects). The Neyman-Pearson approach allows you to construct a test that, for a given probability of rejecting the null hypothesis when it is true, minimizes the probability of accepting it when it is false. (More accurately, it minimizes the probability of incorrectly rejecting an alternative hypothesis. The assumption is that if you are going to take action on the basis of the test then you will either reject the null hypothesis, implicitly accepting the alternative, or you will reject the alternative hypothesis, implicitly accepting the null hypothesis.) You can always make the probability of Type II error smaller by accepting a higher probability of Type I error. In real world applications there will usually be arguments about relative costs and benefits to inform the choice. Fisher took the view that this procedure was incorrect in science:

“It is important that the scientific worker introduces no cost functions for faulty decisions, as it is reasonable and necessary to do with an Acceptance Procedure. To do so would imply that the purposes to which new knowledge was to be put were known and capable of evaluation. If, however, scientific findings are communicated for the enlightenment of other free minds, they may be put sooner or later to the service of a number of purposes, of which we can know nothing.”
[Statistical Methods and Scientific Inference, 1956]

In this view you either reject the null hypothesis with some probability of error or you regard the null hypothesis as not (yet) proved wrong. This is basically the logical point that you can use data only to disprove a hypothesis not to prove it. It does not matter how much data you have that are consistent with a hypothesis, there is always the possibility that you will eventually encounter data that disprove it. In this case you can’t make the error of accepting a hypothesis when it is false because you would never accept any hypothesis. However, when people do need to choose what action to take based on data they act as though they accept a particular hypothesis.

One of the points of contention has been that people don’t just need to know whether there is an effect or not they also need to know how big it might be. This is important even if you have not been able to reject the null hypothesis. For example, if you are comparing the rate of death from heart attacks for patients undergoing treatment A with that for patients undergoing treatment B it would be quite important to know that the data that gave a high (greater than 0.05) probability of error for rejecting the null hypothesis gave a not much higher probability of error for rejecting the hypothesis that one rate was twice the other. In other words, how well does the test discriminate between the null hypothesis and other hypotheses. This information should be given as a confidence interval (the interval that you expect to include the true value of a parameter in 95% of cases) or a calculation of the power, which measures how well the test discriminates between different possibilities.

Why do we tie ourselves up in logical knots calculating the probability that an effect of at least the size we have observed would have been observed if the hypothesis we are trying to show is false is true? Why don’t we just calculate the probability that, for example, two means are different, given the data we have observed? This is the heart of one of the great divides of twentieth century, and now twenty-first century, science. In the theory of classical statistical inference the probability that two means are different is not a meaningful concept. The two means are fixed; we just don’t know what they are. It is the estimates of the means that we calculate from the data that have probability distributions. There is an alternative method for statistical inference based on Bayes Theorem. There are two problems with this method. First, it requires us to interpret probability as meaning a degree of belief. This upsets many scientists because one person’s degree of belief in a statement might be different from another’s. They prefer an objective definition of probability. This is called the frequentist position because a popular interpretation of probability is as the long term frequency in a large number of trials, for example, if you keep tossing a fair coin long enough the ratio (Number of tails / Number of tosses) would be close to 1/2. Those who favour the degree of belief interpretation are known as Bayesians, for obvious reasons. The other problem with the Bayesian approach is that it involves using the data to calculate a final distribution for the parameter of interest, called the posterior distribution, from an initial assumed distribution, called the prior distribution. Unfortunately, there is no accepted method for choosing a prior distribution. This does not matter if you have lots of data because then the prior does not have much influence on the posterior. (If you are not familiar with statistical inference you may at this point wonder why people doing classical statistics do not need to assume a distribution in order to calculate p. The reason is that there is a wonderful mathematical result called the ‘Central Limit Theorem’ that implies that the sample mean must follow a normal distribution when the sample size is large.)

In the context of using statistics on academic appointments in a university department to inform action, the weaknesses of the Bayesian approach could be regarded as strengths. People act because they believe something to be true not because they have failed to reject the hypothesis that it is false. Also, in most departments there will be a range of pre-existing views on whether recruitment is biased in favour of men – a few who are convinced it is, a few who are convinced it is fair, some who have no idea and possibly a small group who believe that women are favoured over men. Some of these views will be strongly held. Why not make these differences explicit by incorporating them in different priors?

[If some of the above sounds vaguely like something you once learnt in a stats course try ‘The Cartoon Guide to Statistics’ by Larry Gonick and Woollcott Smith, Collins, 1993, ISBN-13: 978-0062731029.]

The Myth of Hard Facts

2010-03-04T15:13:00.000-08:00

This post gets a bit technical so for number-phobes the point is that interpreting numerical data involves value judgements, specifically about whether it is better to be fooled by random fluctuations into devoting time and resources to fixing a process that is not broken or to risk not fixing a process that is broken.

When scientists start thinking about the issues related to women in science their first instinct is to collect numerical data. Natural scientists tend to want to measure a baseline, make an intervention and then re-measure to see if the intervention made any difference. Social scientists tend to want to measure everything they can think of and then do a multi-variate statistical analysis. This is natural. It is what we have been trained to do. We also often hear 'We must have hard data, not just anecdote'.

Indeed, we do need good data but we should be aware of the limitations of the statistical approach. Let's take the example of a university department that wants to check whether its recruitment procedures are unbiased. If it is a life sciences department it might expect that 50% of academic appointments would be women, so in this case we can define unbiased recruitment as meaning that 50% of appointments are women. Mathematically the problem of determining whether this recruitment process is unbiased is equivalent to determining whether a coin is equally likely to land heads or tails. Here are the results of tossing a 10p piece ten times: HTTTHHTTTT. Seven of the ten tosses turned up tails. Is my coin-tossing biased? Seven out of ten seems quite large. What happens if I try tossing the coin twenty times? Here is the result of that experiment: THTHHHHTTT HHHHHHHHHH, which is five tails out of twenty tosses. At this point I wondered whether there was something about the way I toss coins that biased against tails so I tried another twenty tosses with the following results: HTHTTTHTHT TTTHHTTTTT or fourteen out of twenty. So from ten tosses there were 70% tails, from the first twenty tosses 25% tails and from the second twenty tosses 70% tails. Overall there were 26 tails out of 50 tosses or 52%. How can we decide whether or not my coin tossing is biased when the results are so variable?

Conventional (Fisher) hypothesis testing says that we should calculate the probability, p, of seeing an effect at least as large as that observed on the assumption that the null hypothesis, in this case, that my coin tossing is unbiased, is true. If p is less than some value, conventionally taken to be 0.05 (or 0.01 or 0.001), then the null hypothesis is rejected. This test reduces the risk of us being fooled by random fluctuations into thinking that my coin tossing is biased when it is not. Coin tossing follows a binomial distribution, shown in the graph for N=10 and N = 50, and the relevant probabilities are P(7 or more tails out of 10 tosses) = 0.17, P(5 or fewer tails out of 20 tosses) = 0.02, P(14 or more tails out of 20 tosses) = 0.06, P(26 or more tails out of 50 tosses) = 0.44. So, 70% of ten tosses is not statistically significant, 25% of twenty tosses is statistically significant, 70% of twenty tosses is not statistically significantly and overall 52% of fifty tosses is not statistically significant. Calculating statistical significance guards against what statisticians call a type I error, in this case, believing that my coin tossing is biased when it is not. There is another possible type of error, which statisticians call a type II error, which is concluding that my coin tossing is unbiased when, in fact, it is biased. This type of error is not particularly important in many contexts, for example, my coin-tossing. However, if we are talking about appointments in a university department we might be very concerned if it was concluded that the appointments process is unbiased when, in fact, it is biased. The Neyman-Pearson procedure divides the possible outcomes of a measurement into two regions: the rejection region where the null hypothesis is rejected and the acceptance regions where it is accepted. For example, for ten coin tosses we might reject the hypothesis that the coin is unbiased if we get two or fewer tails (P(2 or fewer tails in 10 tosses) = 0.055 or if we get eight or more tails (P(8 or more tails in 10 tosses) = 0.055. In this case the probability of rejecting the null hypothesis when it is true is 0.11. The probability of accepting the null hypothesis when it is actually false depends on what the true value of the parameter p of the binomial distribution is. In this case, the probability of accepting p=0.5 when is 0.62 when p is 0.3, 0.82 when p is 0.4, 0.89 when p is 0.5, 0.82 when p is 0.6 and 0.62 when p is 0.7. So we have a better than even chance of accepting that the coin-tossing is unbiased when in fact the probability of getting a tail on any toss is anywhere between 0.3 and 0.7. If we change the rejection criterion to three or fewer tails or seven or more tails then the probability of rejecting when the hypothesis is true is 0.34 and there is a better than even chance of accepting the unbiased hypothesis when the probability of getting a tail on any toss is anywhere between 0.4 and 0.6.

So, when we try to interpret our ‘hard fact’ that seven out of ten coin tosses came up tails in terms of making an inference about whether the coin-tossing process is unbiased the ‘hard fact’ disappears into a morass of value judgements about whether we prefer a higher probability of rejecting the hypothesis that my coin-tossing is unbiased when it actually is unbiased or a higher probability of accepting the hypothesis that my coin-tossing is unbiased when it is actually biased. In the case of academic appointments the judgements become do we prefer a higher probability that we devote time and resources to fixing a process that is not broken or is it more important that we are confident that the appointments process is not biased? We could, of course, use a larger number of tosses or appointments. The problem with this approach for academic appointments is that while it takes a few minutes to toss a coin fifty times it would take a department with fifty academic staff four years to make ten new appointments and twenty to make fifty new appointments if turnover is 5%. This does not seem a recipe for quick identification and correction of problems.

Science and Motherhood

2010-02-21T19:18:00.000-08:00

The other day I received two communications. One was an email alerting me to a new post on the ‘Women in Science’ forum on Nature Network (http://network.nature.com/groups/women_in_science/forum/topics/6635) that linked to a article on academic motherhood in the ‘Manage your Career’ section of The Chronicle of Higher Education (http://chronicle.com/article/The-Academic-Motherhood/64073/). The other was a flyer for the Anne McLaren Memorial Fund panel discussion at Christ’s College, Cambridge on 10 March on ' Which career first?-planning family and career (http://www.christs.cam.ac.uk/news/events/pg/view==month/date==01-03-2010/article==248). At first sight the two appear very different. The article in The Chronicle of Higher Education looks at the difficulties faced by mothers in academia who simply cannot produce as much as their childless or male counterparts because of pregnancy, child-birth and breast-feeding and makes a specific proposal as to how that could be countered, namely by women making the time spent on these activities visible on their vitae. The flyer promises a discussion of the decline of fertility with age and whether it can be prevented or circumvented. However, they do have a common theme. The article in The Chronicle of Higher Education talks of ‘a widely accepted practice of silence on child-related matters in hiring.’ It identifies three problems arising from this silence. First, academic women have no information on the potential effect of child-bearing on their careers. Second, it is hard for women to deal with discrimatory questions at hiring interviews because they have not been prepared for them. Third, unless the effect of child-bearing is made explicit, lower productivity looks like lesser competence. The flyer for the panel discussion promises an exploration of the medical aspects and consequences of fertility deferment amongst women. Two of the discussants are Melanie Davies and Susan Bewley whose editorial in the BMJ in 2005 (http://www.bmj.com/cgi/content/full/331/7517/588, available online with free registration) described reproductive ageing as a public health problem. The penultimate sentence of their editorial is ‘Free choices cannot be made with partial knowledge, economic disadvantage for mothers, and unsupportive workplaces.’ The common theme is women's need for knowledge: Knowledge of the consequences of deferred fertility, Knowledge of the consequences of having children on a woman’s professional or academic career. I believe that one of the important functions of women’s networks is to ensure that knowledge is shared. I also believe that another important function is to lobby for changes. Which is easier to change – the biology of reproduction or the social and workplace pressures that cause women to defer having children or not have them at all?

Decorative Data

2010-02-02T20:17:00.000-08:00

Decorative data are collected and presented not because any useful conclusions can be drawn from them but because they can be used to make a pretty picture for a report. The picture shows an example: all staff at a university by school and gender. (The data are based on real information but the numbers have been randomly adjusted by small amounts.) Yes, I’ve cheated and presented the data in a way that obscures the information content – the numbers are represented by the height of the different coloured sections of the cones but the eye sees the area. However, even if I had presented the data as a boring bar graph, it would still be difficult to draw conclusions from the data as the numbers are for all staff, but the gender balance of academic and research staff varies from discipline to discipline much more than the gender balance of support staff.

The Leaky Pipeline

2010-01-29T01:32:00.000-08:00

When I was first asked to take part in a discussion about women in physics, sometime in the late 1970’s when I was a graduate student, it was generally thought that the dearth of women in science was due to women not studying these subjects at school and university. This was called a pipeline problem: women were not becoming scientists because there were none in the pipeline. By the 1990’s, people noticed that while the numbers of women studying science were increasing the numbers of women becoming scientists were not increasing proportionately. In particular, the number of academic scientists at any particular stage was less than would have been expected from the number of undergraduates the appropriate time earlier, see for example figure 2.5 of the ETAN Report Science Policies in the European Union: Promoting Excellence through Mainstreaming Gender Equality, European Commission, 2000, http://cordis.europa.eu/improving/women/documents.htm. Another study in Italy looked at researchers at the Italian National research Council who all started in the same year and found that after ten years 26% of men and 12.8% of women had been promoted to research director. Staying Competitive: Patching America’s Leaky Pipeline in the Sciences, Marc Goulden, Karie Frasch and Mary Ann Mason, 2009 (http://www.americanprogress.org/issues/2009/11/pdf/women_and_sciences.pdf) documents the preferential loss of women in academic science between receiving a Ph.D. and achieving tenure. This preferential loss of women scientists is called the leaky pipeline since women started out in the pipeline but were subsequently lost. Confusingly, the phrase is sometimes also used to describe any circumstances in which there are fewer women at higher levels in an organization compared to lower levels regardless of the reasons for the situation.

The American Institute of Physics published a report in 2005, Women in Physics and Astronomy, 2005, http://www.aip.org/statistics/trends/reports/women05.pdf, which concluded that, in Physics and Astronomy in the US, ‘women are represented at about the levels we would expect based on degree production in the past.’ This may reflect the fact that women needed to be exceptionally committed to go into physics in the first place, particularly in the period 1967-1980, when the average percentage of Ph.D. awarded to women by US Physics Departments was 4%. (How accurately would you be able to measure a decline from 4%?)

So, is there data demonstrating the ‘leaky pipeline’ in the sense of demonstrating the preferential loss of women from academic science? Yes, there is. Do data of the form women are 47.9% of lecturers but 18.7% of professors (HESA Press Release 131, http://www.hesa.ac.uk/index.php/content/view/1397/161/, figures are for 2007/08) demonstrate a leaky pipeline? Not by themselves: together with the information that in 1970/71 42% of all full-time undergraduates were women, and, more relevantly for potential professors, 23% of all full-time postgraduates students and by 1980/81 these figures were 41% and 34%, respectively [Source: Social Trends 2009, http://www.statistics.gov.uk/downloads/theme_social/Social_Trends39/ST39_Ch03.pdf], they do, though perhaps not so dramatically as the contrast between 47.9% and 18.7% suggests.

Dramatized Data

2010-01-21T19:32:00.000-08:00

When I was in the sixth form, rather a long time ago, my English teacher asked the class if anyone had read Rachel Carson's Silent Spring. I turned out to be the only person who had so the teacher asked me what I thought of it. I said that I thought the data were convincing. Her response was that she had been impressed by the power of the writing. I wonder if this reflects a widespread difference in how people react to data. Are there those who ask what do these data tell me and those who ask how do I react to these data? One group is puzzled by being presented with data that seem incomplete, irrelevant or lacking sufficient context to draw conclusions and the other can’t understand why people are pedantically pointing out the limitations of the presented data when to them the statistics are obviously appalling and clearly demonstrate that something should be done.

An example of data presented to elicit an emotional response rather than to diagnose a problem is the statement that 47% of chemistry graduates are women but only 6% of chemistry professors. What does this tell us? We would have expected the percentage of women among professors to match the percentage of women among new graduates only if the system was in a steady state and had been for around forty years. This is clearly not the case. Current professors are largely drawn from a pool of people who were undergraduates 25-45 years ago, i.e. from about 1965 to 1985. With no further information we cannot tell whether these figures show that few women studied chemistry 25-45 years ago or whether the ones that did were much less likely to become professors of chemistry than their male counterparts. In fact this statement is not even very successful at eliciting an emotional response since it is unclear whether we should rejoice that the proportion of women among those studying chemistry is much higher than it used to be or bemoan that the low proportion of women among chemistry professors indicates that many women in chemistry are not fulfilling their potential.

Another example is the institutional gender pay gap. In February 2009 the University of Cambridge published an Equal Pay Review [http://www.admin.cam.ac.uk/reporter/2008-09/weekly/6141/4.html] which noted that the % Difference between average pay for women and average pay for men was 32%. Sounds terrible, doesn’t it? Especially when the national gender pay gap (calculated using mean hourly pay, excluding overtime) is 16% [http://www.statistics.gov.uk/cci/nugget.asp?id=167]. The first thing to notice is that these percentages are not calculated on the same basis. The Cambridge figure tells us the mean pay for men is 32% greater than the mean pay for women while the Office of National Statistics figure tells us that the mean pay for women is 16% less than the mean pay for men. Comparing on the same basis gives either 24% (Cambridge) and 16% (national) using the mean pay for men as a reference or 32% (Cambridge) and 20% (national) using the mean pay for women as a reference. If it were the case that women were paid 24% less than men for doing the same, or equivalent, work, this would, indeed, be appalling. But this is not the case. The analysis by grade shows minimal differences with gender. The information content of the Cambridge gender pay gap is that the University pays its 1500 or so, predominantly male, academic staff rather more than it pays its 1500 or so, predominantly female, clerical and secretarial staff. Even if the academic staff were 50:50 male and female this still would not remove the pay gap because of the very high percentage of women among lower paid support staff. Redressing this imbalance will require an intelligent analysis of the issues and a long-term, sustainable strategy, not knee-jerk reactions to statistics.

The common feature of both these examples is that they are both attempts to summarize a complex, multi-factor situation in one or two numbers. Perhaps this is necessary in some contexts but these ‘sound-bite statistics’ should be backed up by proper data and analysis.

Does it matter? After all, people do not generally take action based on a rational analysis of data. If quoting ‘dramatized data’ inspires action then surely that is a good thing. This approach is not without its dangers. One is that the response to ‘something must be done’ is ‘we must be seen to be doing something’ whether it is relevant and effective or not. The other is that those whose response to data is to ask ‘what does this tell me’ will conclude that the answer is nothing and that there is, therefore, no need to take any action. They may also conclude that if people are quoting unconvincing data it is because there are no convincing data. Which would be a pity because when you look at the percentage of women among academic staff in a school and discover that it has barely changed in a decade you can’t help thinking that there genuinely is a problem.

The Absence of Data

2010-01-14T20:35:00.000-08:00

This is the first of a series of posts on the use of data in the area of women in science. I spent thirty years working as a physicist. My first reaction to any problem is to get a feel for what it means quantitatively. For example, how many women are there studying SET subjects, how many researchers, how many lecturers, how many professors? The next question is: what can be deduced from the data? Can we identify the critical points in a career in academic science where we are most likely to lose women? What steps should we take now? What further data would help us identify issues and further steps to improve the situation? The remarks in this and the next few posts are not intended to suggest that there is no problem. I spent six years working towards the improved representation of women in SET. I would hardly have done this if I did not believe there are genuine constraints on women’s participation. My motivation for the next few posts is a strong personal belief that progress in this area depends on properly interpreting relevant and meaningful data and information.

My first comment is about the absence of data. When I started working in the area of women in science, as opposed to being a women in science, I was struck by the number of times a Head of Department would tell me that there were x% women in his department and when I checked the actual numbers I would find it was (x-10)%. It seems to me that if you are genuinely interested in improving the numbers of women in science the very first thing you would want to know is how many you actually had in each grade. Presumably in their scientific work they don't just guess when the actual figures are easily obtainable?

University Gender Pay Gaps and Women in SET

2010-01-05T20:19:00.000-08:00

I have sometimes heard the view expressed that addressing institutional gender pay gaps in universities is important for women in SET. I believe that, in fact, focusing on an institutional gender pay gap is at best peripheral and at worst actually detrimental to women in SET in universities.

A gender pay gap is generally calculated by finding the mean pay for women and the mean pay for men and calculating

( (mean pay for men)/(mean pay for women) – 1 )

or

( 1 – (mean pay for women)/(mean pay for men) )

assuming that the mean pay for women is lower. The former is larger and consequently sounds more impressive: the latter is the definition used by the Office of National Statistics in the UK (http://www.statistics.gov.uk/cci/nugget.asp?id=167). Sometimes the median is used rather than the mean. The median tends to be more typical since it is the point where half the population earn more and half less. Mean pay can be sensitive to a few individuals who earn much more than everybody else.

Mathematically, both the median and the mean depend on the relative frequency, which is the proportion of the population earning a particular salary. If analysis shows that women and men are paid the same for the same or equivalent jobs, then the gender pay gap is determined by the difference in the way men and women are distributed across grades. A gender pay gap results if women are more likely than men to be in lower paid positions and less likely than men to be in higher paid positions. The gender pay gap is independent of the overall proportion of women in the institution. An institution with a small overall proportion of women who are fairly evenly distributed between grades would have a small gender pay gap, for example, if every grade had exactly 10% women the gender pay gap would be zero.

The main contributing factors to the gender pay gaps in universities are

The large proportion of women among clerical and secretarial staff, who tend to be lower paid.
The small proportion of women among senior academic staff, for example, HESA figures show that in 2007-08, 19% of professors were women while 48% of lecturers were women (http://www.hesa.ac.uk/index.php?option=com_content&task=view&id=1397&Itemid=161).

Strategies to reduce the gender pay gap could include:

Increasing the percentage of women holding professorships, which should be good for women in SET.
Decreasing the percentage of women among clerical and secretarial staff, which would be irrelevant to women in SET. Unfortunately technicians have similar pay rates so we would also want to decrease the percentage of women among technical staff, which would be bad for women in SET.
Decreasing the percentage of women among research assistants and post-docs or decrease the total number of post-docs, which might be a good thing for women in SET or might not.

A particular concern is that parasite institutions who rely on poaching senior women from other institutions would be able to reduce their gender pay gaps at the expense of nurturing institutions who work to develop the careers of their staff.

It may also be that not all women want to progress through grades at the same rate as men. Some may prefer to postpone applying for promotion to more responsible and stressful jobs until their children are older. Too much focus on gender pay gaps could make life harder for women who are trying to balance career and family.

A further problem with any measure that reflects occupational and vertical segregation is that it is bound to change only slowly. This is partly because you can’t fire incumbent staff to make way for new staff of the other gender and partly because aspects of the distribution of women, for example, their concentration in clerical and secretarial grades, are the results of societal and cultural factors outside the control of the institution.

We do need some measure of how women are distributed within institutions. Claiming that nearly 50% of your work force are women does not mean much if they are predominantly in low paid and/or insecure positions. Perhaps rather than rely on a single ‘figure of merit’ (or demerit) such as the gender pay gap it would be better to focus on particular areas of concern. For example, if it is felt that the proportion of women among professors is an issue then measure the proportion of women among professors. Similarly, if the career progression of post-docs is an issue then monitor the destinations

A Scientific Comment

2009-12-04T01:26:00.000-08:00

The Lateral Thoughts article in the November Physics World (http://physicsworld.com/cws/article/indepth/40829) is a description by Rick Trebino, a professor of physics at the Georgia Institute of Technology in Atlanta, of the difficulties he experienced getting a comment on a paper with errors in it published. There is a longer version at http://www.scribd.com/doc/18773744/How-to-Publish-a-Scientific-Comment-in-1-2-3-Easy-Steps. And people wonder why women leave science to engage in more meaningful occupations!

My own theory on how to improve the reviewing process is that journals should employ people whose job is to go through reviewer’s comments removing the ad hominem remarks, unfounded speculation and unsupported opinions before they are transmitted to the author.

The I-Word

2009-11-26T19:57:00.000-08:00

The reviled word of the year in academic circles is, surely, impact. Is it really such a bad thing?

Firstly the Research Councils now require applicants for funding to assess the impact of the proposed research. The Research Councils invest £28 billion in research annually [RCUK Report, Excellence with Impact, http://www.rcuk.ac.uk/aboutrcuk/publications/corporate/excellenceimpact.htm]. It is reasonable to ask what benefit the UK derives from this investment. According to the guidance for applicants for funding [https://je-s.rcuk.ac.uk/jesHandbook/jesHelp.aspx?m=s&s=1202], impact is the demonstrable contribution that excellent research makes to society and the economy and can include fostering economic performance, increasing the effectiveness of public policy and enhancing the quality of life. In the impact summary applicants are asked to specify who will benefit from their research, how they will benefit, and what will be done to ensure that they have opportunity to benefit. Most of the Research Councils also require an Impact Plan that describes what the researchers will do to enhance the impact of the research. A diverse range of activities could be included in this plan, depending on what type of research is proposed. The Science and Technology Facilities Council guidance, for example, includes suggestions such as publications and publicity materials summarising the main outcomes in a way that beneficiaries will be able to understand and use or developing resources for schools or teachers.

Secondly, the current proposals for the Research Excellence Framework (REF) [http://www.hefce.ac.uk/pubs/hefce/2009/09_38/] that will replace the Research Assessment Exercise (RAE) are that research excellence, measured by peer review informed by citation analysis, will count for 60%, impact, assessed by an impact statement and case studies will count for 25% and research environment, assessed under the headings resourcing, management and engagement, will count for 15%. The draft ‘common menu’ of impact indicators includes

Employment of post-doctoral researchers in industry or spin-out companies.
Participation on public policy/advisory committees.
Changes to public attitudes to science.
Audience/participation levels at public engagement activities.
Positive reviews or participant feed back on public engagement activities.

These proposals could be very positive for women in science. My impression is that women often are concerned about the broader impact of their research. Also, explicit recognition of engagement activities is a welcome development, both in the funding process, where applicants have to assess the resource implications of their impact plan, and through the REF. In principle, these changes could encourage departments to take a broader view in their selection criteria for appointments and promotion. In addition, explicitly mentioning employment of post-doctoral workers as a criterion should encourage principal investigators to support the career development of their post-docs.

There are areas of concern. For example, will ‘hard’ measures of impact, such as research income from industry, outweigh ‘soft’ measures such as those relating to public engagement activities? Nevertheless, including an impact element both in criteria for funding and in criteria for assessment could help drive science departments to become more inclusive.

Why gender diversity?

2009-11-15T20:19:00.000-08:00

Why is it important to have more women in science? (Note: I am using science inclusively, that is, I imply ‘and engineering, technology, mathematics and medicine’.)

Broadly there are two approaches: justice and the business case.

1. Justice

In the preface to SET Fair: A Report on Women in Science, Engineering and Technology (SET Fair (2002)), Baroness Greenfield wrote

‘I have a strong personal conviction that any individual should be able to work in science to the extent, and at the level, appropriate for their personal choices, and commensurate with their abilities, but without gender as a constraining factor.’

I agree. A system that, for example, makes it much more likely that a woman with a baby or small child will be squeezed out of science than a man with similar responsibilities is simply unjust. This may appear to be an uncontroversial statement but one nevertheless hears it said that women ought to choose between having a family and having a fulfilling career commensurate with their abilities.

2. The Business Case

The SET Fair report lists four business reasons for greater gender diversity in science:

Competiveness – the UK needs the best people engaged in SET
Return on Investment – we do not want to lose expensively trained individuals
Benefit to Science – a more diverse workforce brings new perspectives and priorities
Missed markets and skills – 50% of customers are women so companies could be missing out on potential markets.

I will examine some of these benefits more closely in subsequent posts, for example, what does ‘best’ mean? However, let’s look at this just as a business case. To whom to the benefits accrue? Firstly, to the

as a whole: there are compelling demographic arguments that the scientific workforce will need to be drawn from a more diverse group than is currently the case in order to meet demand. Also, the loss of expensively trained individuals is a loss to the country as a whole. Secondly, technology-based companies, both because wider participation would widen the talent pool and through the new perspectives and potential new markets that greater participation of women in the scientific workforce would bring.

What happens, however, at the level of a research group? Suppose a principal investigator has a project with two years funding and hires a woman who subsequently wants to take six months maternity leave. In the UK most research sponsors will pay for the maternity leave, but they won’t necessarily pay for cover, even if a suitably qualified person can be found, or to extend the contact to make up the lost time. Even if the research sponsor will pay for cover or a contract extension, the research programme has been disrupted, with the risk that the principal investigator’s ability to attract continued funding has been compromised. Or consider the case of a university department. If they have a woman member of staff who wants to take maternity leave can they recover her maternity pay? Is it all of it or just the statutory component? What if they need to employ teaching cover? How will they ensure supervision for her research students? If departments have to meet these costs from their own resources then departments with more women taking maternity leave are at a financial disadvantage compared to other departments. So at the level of the research group or university department we have actual quantifiable costs and perceived risks of employing women, but vague and intangible benefits mostly accruing to other people. (I suspect, for example, that most principal investigators are more concerned about whether their own ideas are funded than with the benefits to science of incorporating a more diverse range of perspectives and priorities.)

I have used examples from the university sector but I suggest that similar considerations apply whenever short term financial performance or short term productivity have high priority.

If business case arguments are to be effective then the incentives at the level of research group and department have to be aligned with the goals from which the

as a whole will benefit.