I disagree with Bewildered's view about the teaching posts. They can be a great way to enhance your CV, ESPECIALLY if you have the opportunity to do a Postgraduate Certificate in Higher Education (or whatever that particular institution calls it).

I'm on a full time (fixed term) teaching fellowship at a Russell Group uni and I have a lot of support in developing my research career while doing a heck of a lot of teaching. I even put in a grant bid last month and am being fully supported in completing my PhD. 20 percent of my time is for 'scholarship' and 80 percent is accounted by teaching and admin duties. And it genuinely does work out that way. That's not to say that all institutions will support research time for teaching-focused academics, but it does happen and it can definitely be a good step up in your career.

One of my mentors has been encouraging me to actually forego the postdoc route after my PhD because I have so much teaching/admin experience. He says that when they're hiring new lecturers, if they're comparing inexperienced people they know that at least 1 arm of the lectureship will need extra support. For post docs, they need more support in teaching and admin, and less support in research. For me, I'd need support in research and less in teaching and admin as I've lots of experience there.

Anyways, back to the question at hand. What would your notice period be at the new job? And when would you start? Is there any way you can put off the start date until after the interviews for this other position? That way, you could withdraw without starting the job if you got the others. Academia is a small community and reputations matter. I think the worst thing you could do would be to start the non-teaching job and then hand in your notice immediately (or very soon after). That would screw them over and waste their money and reflect badly on you.

Out of interest... why? From a visual design/readability perspective I disagree, so am always intrigued when people say this.

I think typeface/fonts would be my specialist subject on Mastermind, I'm a bit of a geek about it. My PhD has been about typing and I have colleagues here working on readability and accessibility of text on screen, so it's just a weird thing I geek out on a bit :-)

Personally, I think Calibri is fine. Garamond... Hmz. I've just tried using it in my document and I do find it a bit hard to read, for me the regular width of the lines is slightly too thin meaning that the difference in widths between the thinner parts and the wider parts is emphasised which adds a strange dimension. It also seems to be smaller and lighter than other serif fonts and is particularly hard to read if you're using italics for anything (e.g. statistics). Like I say, for my serif font I'm using Georgia. Edit: But now after looking at fonts for you I'm changing that to Constantia using the lining option for numbers and in tables having the tabular proportions set. Palantino isn't a bad shout either...

Absolutely not. In fact I think it's a good thing. Tables and figures aren't typically going to be 1.5 spaced so a sans serif font makes sense. Plus, it's often for content that is quite precise and regular, especially if it's graphs. I mean, I wouldn't advise having a sans serif font on graph axes, for example. But, for the main text on paper, I wouldn't consider a sans serif as serif is objectively easier to read. (I've read papers on it, but can't remember the references off the top of my head).

I tend to prefer sans serif in headings, tables and figures and a serif font for text. So, if it were me I'd make the change to a serif font for the text (I'm using Georgia). Your Uni may have specific guidelines but mine just specifies the minimum height of a character.

Oh and from a readability perspective if your thesis is going to be printed I'd absolutely use a serif font for the main text (and left justified). There's evidence serif/sans serif doesn't make much difference on screen. But, on paper, serif is definitely easier to read. With the clean lines and regularity of formatting for tables and figures sans serif makes more sense.

From a readability perspective I think the spaces help. The publication manual states that not using spaces in stats presentation is the same as not using spaces in words from a readability perspective, and I do agree. It's easier to read with the spaces than without (especially if, like in my work, you have things like M=-2.00, which is a lot easier to read as M = -2.00). But, it's not useful for word counts!

How did you bring a 20k chapter down to 12k? You must be *ruthless*. I am very impressed.

I'm going to have to cut out lots of words too. My supervisor keeps telling me not to worry about it at the moment, just get the last 2 experiments written up and then look at the word/page count.

But, a fairly recent discovery that my department's official word count for PhDs *includes* everything (appendices, references, contents etc. etc. etc. ) has caused a bit of a panic. My usual approach to word counts is to "shove things in an appendix" but that's no good here as that still counts! Agh.

Oh, and we've decided to appoint a Psychologist as one of my external examiners (I'm interdisciplinary) which means I ought to use APA style. Well that means reporting stats like: F(1, 110) = 5.402, p = 0.023, np2 = 0.302. I have a *lot* of stats in my thesis, and all those spaces between numbers and equals signs increases the words count a tonnne. In one experiment chapter alone, there are 540 instances of " = ". And i have 5 experiments.

Sigh. I'm considering replacing all of those with versions without spaces for the final word count. Which is naughty, but the mechanism for counting words is stupid. As is the idea that the Appendices have to be included.

So yeh, if anyone has any sensible strategies for cutting words, then I'd love to hear them. One of my supervisors has helpfully replied that he thinks my sentences are too long. But, as the ideas I'm trying to convey are so complex he doesn't know how he'd rewrite them to be shorter. Useful.

Frankly, I suspect it's not going to be easy to get a PhD place with a 2:2 bachelors straight away. And I'd say that if you are looking for funding then that would be almost impossible straight away. But, it's not all doom and gloom. Your Masters is the opportunity to prove yourself. I would think your best plan might be to plan to take a year out between the Masters and PhD, perhaps getting a job that is related to the field if you can - if you do that it will mean you are applying for PhD places when you've already been awarded your Masters. If you can get distinction level marks in the Masters, then you can demonstrate that the low Bachelor's isn't a true reflection of your ability. But, if you're applying without the Masters completed (especially the final project/dissertation) then I think you'll struggle. You might be offered a place, but suspect that any option of funding would be almost impossible.

Hi Bill,

I agree with TreeOfLife, but I'm also quite intrigued (i've been having G*Power issues myself recently as my design doesn't fit well into G*Power). I'm by no means an expert on Power Analysis, but I've spent quite a lot of time recently thinking about it so if you want an opinion I'll absolutely take a look.

Drop me a PM with more info, I can send an email address if you'd like.

(Also, on a related topic - you don't know how to put power analysis into G*Power which has multiple repeated measures do you?)

Sometimes. Not always.

You not what is pretty much always bad though? Making overstated generalisations based on limited knowledge. (And yes, I see the irony that I am making a generalisation here, but my knowledge in this area is certainly not limited).



My sample size is appropriate for my broader discipline. Hell, I've seen plenty of peer reviewed published research with far smaller samples than mine. There is no other previous quantitative work which would give me the data needed to have done an a priori sample size calculation. Personally, I don't see the point in picking an arbitrary effect size, they're about as useful as a chocolate teapot. So I took a punt, which as I'm in an emerging area is a completely valid approach. I mean, someone has to collect the first data on a subject so others can make sensible sample size estimations in the future, right?

The punt didn't quite work out. So, in my thesis discussion I want to consider how many participants would have been needed to prevent a type 2 error. And then I can include this in the discussions of future work together with approaches for learning more about this thing that everyone is fascinated by, and everyone has opinions on but no one understands. But, I can't quite figure out how, and my deadline is approaching fast.

So, if anyone out there does have any ideas of resources that could help, please share!

I'm not doing corrections but have to submit by end of Sept so a similar deadline. What software are you using to do the stats? If it's SPSS I'd HIGHLY reccomend Discovering Statistics using IBM SPSS Statistics. It's a big fat book, but Andy Field really covers the basics in the most accessible way I've come across before. I've heard the R version is pretty good too, but I'm an SPSS user myself so can't really speak to that.

My PhD is very stats heavy so if you have some specific questions feel free to message me too :-)

Hi all,

I'm hoping someone may be able to help me with this. If not, doesn't matter really, but I can't figure out the answer and it's doing my nut in!

So, a typical experiment design in my PhD is a 2 by 2 repeated measures design where BOTH variables are repeated measures. So, I've got 2 IVs, both with 2 levels, both repeated measures. Sometimes I have a mixed design - 2 by 2 by 2 with 2 repeated measures IVs and 1 between participants factor.

Does anyone know how I can calculate the power in this design? I get statistically significant interactions (with pretty strong effect sizes according to partial eta squared) but the simple main effects are non-significant. So, that indicates a lack of power. I would like to know how much power it lacks, and how many participants would be needed to prevent the type 2 error that I appear to be experiencing. (Not that I'm going to go out and run more participants, but I think the argument I'm presenting would be stronger if I had the numbers).

I've been using G*Power (hello high learning curve!) and I've figured out how to do a power analysis for a one-way repeated measures ANOVA, as well as a power analysis for a Mixed ANOVA (as long as it's 1 within and 1 between factor). But, I can't figure out how to enter the data so I get the correct degrees of freedom and accommodate the correlations between my multiple repeated measures variables.

Does anyone know how to do this? Either using G*Power or with some other (Mac) software or online resource?

THANKS in advance,
Anna

How's everyone doing today?

I finished up another chapter yesterday while working from home (which I rarely do because I like to use the big screen in my office). That's 3 experiments written up and 2 left to do. 59 days and counting!

Meeting my supervisor this aft and I'm armed with pages of questions. Eek.

Wow, 5 days to go is awesome! I've got 61 days to go and the mountain to climb is still massive (I'm blogging about it here: https://annashodgepodge.com/). Yesterday, I made a day by day plan of what I had to do on each day and I think that's going to be really motivating for me. Have you got a list of what still needs to be done? Just think about how much better you'll feel going into the viva knowing you made all of the suggested corrections before you submitted. Good luck!

Final submission has to be before 30th Sept.... :-o