Signup date: 12 Mar 2009 at 4:31pm
Last login: 27 Mar 2012 at 6:40pm
Post count: 60
Hi I also agree with using special interest groups as oppose to trying sites specifically for developing one to one friendships. There is a great site called meetup.com where people host informal events, which I highly recommend. In my experience, there are normally a few first timers or people going on their own, so I've never felt uncomfortable. Good luck
How are they paired? I would say your supervisor is partly right if you deliberately set out to sample your sites in these pairs by finding some kind of match for each site. If these sites are 'matched' I would take the value of one minus the other, which will give you 15 values and then see if the mean (or non-parametric equivalent) of those 15 values is zero. This would be a one-sample t-test, but you should get the same answer if you start with the paired data and do a paired t-test, or a non-parametric test given the small sample size. The result would tell you whether, overall, there was evidence for a difference between the sites within each pair.
Hope that helps
First, I would make sure that you objectively define your outliers. There is a simple rule of thumb for identifying possible outliers: anything below Q1-1.5*(Q3-Q1) and anything above Q3+1.5*(Q3-Q1), where Q1 is the 25th percentile and Q3 the 75th.
Then, I would just re-run the analyses with the identified outliers removed and refer to it as a 'sensitivity analysis' to see whether it affects your conclusions.
I've been recommended by IT that it's best to save chapters as individual files and only combine the documents at the end. It also makes it easier to send sups completed chapters as you go on. In Word 2007 you combine the files using Insert-Object. Regarding the references, before combining you can save a copy of the chapters with bibliographies removed, then combine the files, then click 'update references' on whichever CWYW software you're using, and then insert the final bibliography.
Anyway, slightly went off the original topic, but best of luck with the section breaks Sneaks.
Hi, I agree with the others, definitely PhDs are no guarantee to a good job, and won't necessarily add value to your career beyond your MSc.
I'm sorry that you're having such a hard time looking for work, but it is good that you are at least getting interviews. Have you registered with the many recruitment agencies for the pharma industry? I know that they are annoying to deal with, but many companies don't recruit directly.
What sort of skills do you think a PhD will help you develop? If you feel your academic knowledge may need to come up-to-date, have you considered doing some short courses? Shop around though as prices vary widely. Other skills you develop while doing a PhD are Presentation Skills or Academic Writing, but these can be done at a decent FE college. So, in summary I don't recommend doing a PhD at this stage and for your reasons. I myself would not have done one if I had known how little it would help me with jobs and how broke I'd be at the end of it.
It's interesting to hear the different reasons for people wanting to pursue PhDs, but this recent article made me think about why universities are keen to recruit them. Essentially, especially in science, students and postdocs are seen as cheap labour and it is easy to get a lot out of someone who is in it for very altruistic reasons or for the love of the subject. Having said that, my motivation was just to not have any barriers to maximising my income in the line of work I was already in, which seems a bit superficial compared to some of the other posts here.
Hiya, correlation doesn't assume the same number of values but the same number of individuals, i.e. everyone was asked the two questions. Though you should have at least around 10 values if you want to assume the variables are linear and use a Pearson correlation. The test you will use will also depend on the number of individuals you have.
The effect size is the beta coefficient (B) for your X variable from a regression model. So you need to do a linear regression for X on Y and report B along with its 95%CI and p-value. In the example you gave this effect size will tell you that for every, say, gram increase in chocolate consumption (X) you increase in your weight (Y) by B kgs. I hope that helps.
A two-tailed is the one that is normally always used to allow for the fact that either group 1 or group 2 has the higher mean. One-tailed tests are only used if your alternative hypothesis is that one group (i.e. you know which one) has a bigger mean, which is rarely justifiable. In whichever stats package you are using there should be an option for doing a one-tailed test instead of the default two-tailed.
Yes, you can say there is a very small, but statistically significant difference in the level of trust between the two groups. BTW, if you are doing a 2-sample t-test the effect size is just the difference of the two means. What scale did you use to quantify level of trust? To be used in a t-test it should have at least 10 values, e.g. a trust score from 0 to 10.
Those are some good tips. This site also has a list of interview questions you could prepare for.
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