Residual Reading Time for Self-Paced Reading Experiments

[Dwivedi Lab]

We are doing a series of self-paced reading experiments and need to
calculate the residual reading times for each individual participant
then combine all the participant data into one group file for
analysis. So far we've been importing the data into excel and
manipulating it there in order to get residual reading time values. We
have created a series of excel files to streamline this process,
however these files are prone to error and end up taking more time to
fix than they actually save. Is there a way to calculate residual
reading time data from within E-Prime? Or is there a faster, fool-
proof way to calculate residual reading time in general?

Any help is appreciated!

[Michiel Spape]

Perhaps you can start by saying what residual reading times are?
Sorry, I'm more used to cognitive fields than psycholinguistics (I assume residual reading, as a concept, is commonly known there?)
Best,
Mich

Michiel Spapé
Research Fellow
Perception & Action group
University of Nottingham
School of Psychology

Any help is appreciated!

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[Dwivedi Lab]

Residual reading time (RRT) is a way to correct for sentence length,
word length, and individual differences between participants' reading
speeds. For example, a sentence with five words is read faster than
one with 10; a sentence with 10 long words is read faster than one
with 10 short words; etc. Since we're using a self-paced design,
naturally some people will read and progress through the sentences
faster than others. By calculating RRT, we can eliminate this bias.
First thing we do is plot all of the raw reading times against the
number of characters per word for each participant and create a line
of best fit. This line represents the average speed that that
particular person reads depending on the number of characters (ie word
length). The general trend is that as the number of characters
increase, so does reading time. We do this for every participant,
resulting in average reading times that are specific to every
participant. Using the formula of the line of best fit, we can
determine the average reading time for words that have 2 characters, 3
characters, 14 characters, etc. From there we compare the actual raw
reading time to the average value. The amount by which the raw value
deviates from our calculated value (ie the line) is known as the
residual reading time. These values appear as plus or minus values
(+/-).
This way, two participants may read the same word at completely
different speeds, but now we compare that value to their own reading
pace in order to determine whether they're taking longer to read that
word or not instead of comparing it to a group average which is less
accurate.

Our method for calculating RRT was adopted from the following article,
explained in the appendix: “Semantic Influences on Parsing: Use of
Thematic Role Information in Syntactic Ambiguity Resolution” Trueswell
and Tanenhaus, 1994

Hopefully this makes sense


On Jun 29, 10:24 am, Michiel Spape <Michiel.Sp...@nottingham.ac.uk>
wrote:

> For more options, visit this group athttp://groups.google.com/group/e-prime?hl=en.

[Michiel Spape]

Hi,
I see, it sounds quite useful! Anyway, so what you need is
A) reading times for various words; I don't know how you obtain these exactly, but I guess from E-Prime, before the rest of the experiment starts or something
B) average reading times for each word-length
... such that you can calculate by means of simple linear regression the 'line', and therefore, later on, the residual.

What exactly is the problem? You just make something like an array (or a dozen of variables: sum2, sum3.. sum14) where you keep the sum of each reading time for each wordlength, then after all reading times are known, you divide them by the amount of reading times encountered, and then you calculate a linear regression. It's a bit of work, I grant you that, and if you're not brilliant with statistics, you will have to look up how these work exactly (I think there are decent articles with examples on Wikipedia), but I think you're right in saying that it makes sense to do this within E-Prime rather than afterwards. How far do you get, though, and where do you get stuck?

Best,
Michiel

[CL]

Thanks for your quick response (re: using Eprime to calculate RRTs
directly vs. Excel).
Your suggestions for using Eprime are quite comparable to procedures
in Excel --so it's
unclear if this would help to speed up the calculation.

Thanks again for your time.

--Chris L.

On Jun 30, 6:50 am, Michiel Spape <Michiel.Sp...@nottingham.ac.uk>

[Michiel Spape]

Hiya,

Note: like David mcF, I don't work for PST and perhaps can give you a quicker method.

I tend to use Excel a LOT, to the extent the data of the behavioural part of my experiments takes about as much space on my hard-drive as the physiological part. There's a few good reasons one might want to do things on-line in E-Prime, however, in particular  in the way your analysis isn't smeared all over various files (and in excel, even within files, across sheets, in various formulas and macros). The only, but rather large downside, to it is that in Excel, at least you already have the formulas for regression and whatnot built in, which saves a lot of time. Anyway, should you ever come up with a good way for calculating residual reading time in E-Prime, please post the answer here for the Greater and Common Good!

 

Cheers,

Michiel

---

Van: e-p...@googlegroups.com namens CL
Verzonden: do 1-7-2010 19:43
Aan: E-Prime
Onderwerp: Re: Residual Reading Time for Self-Paced Reading Experiments

For more options, visit this group at http://groups.google.com/group/e-prime?hl=en.

[Michiel Spape]

That should have read ‘and perhaps they can give you a quicker method’!

Cheers,

Mich

 

Michiel Spapé

Research Fellow

Perception & Action group

University of Nottingham

School of Psychology