Methods of Experimental Research

course code 200800180

2008-09, period 1, September-November


  1. [2008.10.30] Added Gelman & Hill (2007) to Further Reading section; added hyperlink to Online Course Evaluation.
  2. [2008.10.23] Replaced assignment for session 6.
  3. [2008.10.15] Added additional readings: session 2 (design), session 5 (effect size).
  4. [2008.09.27] Added Statistics in Linguistics to the Additional Reading section, with thanks to John_seb.
  5. [2008.09.22] Classes will start on Thursdays at 9:30.
  6. [2008.09.15] Change of venue: The venue for the remainder of the course has been changed to:
    University College Utrecht campus, Building U, room 2.08
    street address: Kriekenpitplein 18, Utrecht (entrance to campus is at Prins Hendriklaan, Utrecht, more about visiting UCU).
  7. [2008.09.01] We will use a Yahoo bulletin board to exchange information in this course. The bulletin board or 'group' for this course is at Please subscribe to (join) this group, by visiting the group webpage and following instructions given there. More instructions are available below, see the first session in the schedule. PS: This is a members-only group, meaning that only members can access its contents. The teacher has to approve your subscription for membership.



Hugo Quené
e-mail hugo dot quene AT let uu nl,
Trans 10, room 1.17
office hours Tue 14:00-15:30 and by appointment



Thu9:30-13:00 UCU, Bldg U, room 2.08


This course will have only one class meeting per week, on Thursday morning. The focus is on independent study, assignments, and peer review.
The course will be taught in English.

Before each class meeting you'll have to do the following:

  1. make assignments about the topics covered in the last meeting;
  2. hand in your assignments (see below), by Monday 18:00h at the latest;
  3. review and judge the assignments of a fellow student, by Wednesday 12:00h at the latest;
  4. read and study new materials.

During a class meeting we will discuss your assigments, using your mutual reviews and judgements. After that, new topics will be introduced.

After each class meeting, assignments have to be handed to the group bulletin board (at, so all information is available to all.
Put your work in one document per week, in PDF format.
Place your document in a folder on your personal webpage at UU — check the CIM help pages for instructions. Make sure your document is accessible over the web. Now send a short message to the group bulletin board, with a short description of your work and with a working hyperlink (URI) to your document on your webpage. (You don't have to enclose the URI in tags, just copying the right http://server/username/folder/weekX.pdf string into the message should suffice).
An easier solution is to upload your PDF document to the group bulletin board; make sure it gets into the Files section under the appropriate subsection (week one, week two, etc).
All this should be done by Monday 18:00h at the latest.
Retrieve the document of your victim for this week, and write a review of her/his work in a separate document. Place your review on your personal webpage, and again announce its location on the group bulletin board, by Wednesday 12:00h at the latest.
Between Wednesday noon and Thursday class session, you should read the peer review of your assignment. Notice that everybody's cooperation is required to make this schedule work! Failure to meet deadlines will cause problems "downstream", so make sure to finish and upload your work on time.

Peer review, commenting the work of a peer or colleague, is a serious business. You can learn more about it through these web pages:


Your final grade is determined by the weekly assigments (80%) and the final assigment (20%). This final grade will only be available after the end of the course.
Your assignments and reviews will not be graded week-by-week! Students should therefore assess their own work, with the help of their fellow students' assignments, the peer reviews, and class discussions.


session 1: Thu 11 Sept

Experimentation. General methodology. Design of experiment. Refresher on inferential statistics. How to peer-review.

Reading: Before: Assignments:
Your elaborations on the questions below have to be placed on your personal webpage, and announced on the group webpage, as described above. Write clearly, correctly, and concisely. Make a document in PDF format with a maximum length of about 2000 words. (I've made a short explanation about how to make a non-proprietary document, in Dutch.)
  1. Visit your institutional library (e.g. Letterenbibliotheek). Take a recent printed issue (2006 or 2007) of an experimental journal (in phonetics, psycholinguistics, speech pathology, etc.), such as Journal of Phonetics, Journal of Memory and Language, Phonetica, JLSHR, and select an article that reports an experimental study.
    (a) Which questions does the study attempt to answer?
    (b) Which independent and dependent variables are involved in the study?
    (c) Describe the design of the experiment.
  2. A researcher wants to know whether the vowel duration in stressed vowels is longer than in unstressed vowels. There are two groups of participants, and the researcher is interested in their difference (e.g. L1 and L2 speakers). The target vowels occur in the first vs. the third syllable of three-syllable words. To prevent strategic behavior (what's that?), a speaker may not produce words with different stress patterns: all words produced by a single speaker need to have the same stress pattern.
    Provide a possible design for this experiment. Indicate which factors are between or within subjects, dependent or independent, etc. Make a graph or table to illustrate your design.
  3. Answer the following questions in RvH section 2.9: Exercises 2, 3, 4, 5, 6, 8.
  4. In preparation for next week, also read RvH Chapter 3.
  5. This last assignment is not for peer review but for independent study. Now is the perfect time to brush up your statistical skills. Answer the tentamina of my Statistics course (see above). Afterwards, check your answers with those provided on the course webpage. Determine what parts of your statistics proficiency are still deficient. Design a plan of action, to remedy your shortcomings during this teaching period.

session 2: Thu 18 Sept

Experimental design.


Additional readings:


For this assignment you have to provide the experimental design of a prospective (future) study of your own. You could, for example, select an idea for your masters thesis, or a research project for one of your classes, or a follow-up study building on a previous experiment. Your prospective study should in principle be suitable for publication in a top peer-reviewed journal in your field; this means that not only the question being addressed, but the design and methodology need to be very good too! Your experimental design and methods should be adequate to provide answers to your question.

Give a brief introduction about the issues your study attempts to answer, and describe and motivate the experimental design and methods. Which are the dependent and independent variables? Discuss the construct validity of your manipulations (treatments) and observations. Describe and classify your design according to the schemes in the reading materials (within-subject, split-plot, etc). Can you give some estimate of the expected effect size? And if so, what would be the power of your study? How many units (children, participants, sentences, items) do you need to achieve that power? Think about plausible alternative explanations, and other threats to the validity of your study, and how to neutralize these threats in your design.

As before, your elaborations have to result in a PDF document to be placed (or announced) on the group webpage (see above). Write clearly, correctly, and concisely (you'll probably need about 2 or 3 pages of text).

session 3: Thu 25 Sept

ANOVA: general principles, one-way, post-hoc test, power.

Readings: RvH: Chapters 3 and 4.

Your answers and solutions to the questions below have to be handed in as described above. As always, write clearly, correctly, and concisely.
  1. Answer the following questions in RvH, section 3.10: Exercises 2, 5.
  2. Answer the following questions in RvH, section 4.11: Exercises 1, 2, 3, 4, 7, 8.

session 4: Thu 2 Oct

ANOVA: multifactorial designs, interaction, fixed vs random factors, error terms.

Readings: RvH: Chapters 5 and 6.

Your answers and solutions to the questions below have to be handed in as described above. As always, write clearly, correctly, and concisely.
  1. Answer the following questions in RvH, section 5.13: Exercises 1, 3, 5, 6.
  2. Answer the following questions in RvH, section 6.9: Exercises 3, 4, 5.
  3. In a study of cardiovascular risk factors, joggers who run at least 15 miles per week were compared with a control group described as "generally sedentary". Both men and women participated in this study. The design is a 2×2 between-subjects ANOVA, with Group and Sex as factors. There were 200 participants for each combination of factors. One of the dependent variables is the rate of heartbeat of a participant, after 6 minutes on a treadmill, expressed in beats per minute.
    Data from this study are available here in SPSS format, or as plain text (the latter file contains variable names in the first line).
    (a) What do you think of the construct validity? Please comment.
    (b) Is is allowed to conduct an analysis of variance on these data? Motivate your answer with relevant statistical considerations.
    (c) Conduct a two-way ANOVA on these data.
    (d) Write a summary of the results of this study, including the (partial) effect size η and η2. Draw your conclusions clearly.
    (e) From each cell (combination of factors), draw a random sample of n=20 individuals, out of the 200 in that cell. Explain how you have performed the random sampling. Repeat the two-way ANOVA on this smaller data set.
    (f) Discuss the similarities and differences in results between (b) and (d).
    This exercise is adapted from: Moore, D.S., & McCabe, G.P. (2003). Introduction to the Practice of Statistics (4th ed.). New York: Freeman. Example 13.8, pp.813-816.

session 5: Thu 9 Oct

ANOVA: Repeated Measures, post-hoc tests.

Readings: Links:
Compare these notes from similar courses in experimental research methods, at other universities: Assignments:
Your answers and solutions to the questions below have to be handed in as described above. As always, write clearly, correctly, and concisely.
  1. Answer the following questions in RvH, section 8.15: Exercises 1, 2, 3, 4, 5.

effect size

If we are comparing two groups of means, as in a pairwise t test, then the effect size d is defined as: d = (m1-m2)/s (Cohen, 1969, p.18; m represents mean).

A value of d=.2 is regarded as small, d=.5 as medium, d=.8 as large. It is left to the researcher to classify intermediate values (ibid., p.23-25).
The difference in body length between girls of 15 and 16 years old has a small effect size, just as male-female differences in sub-tests of an IQ test. "A medium effect size is conceived as one large enough to be visible to the naked eye," e.g. the difference in body length between girls of ages 14 and 18. Large effect sizes are "grossly perceptible", e.g. the difference in body length between girls of ages 13 and 18, or the difference in IQ between PhD graduates and freshman students.

If we are comparing k groups of means, as in an F test (ANOVA), then the effect size f is defined as: f = sm/s, where sm in turn is defined as the standard deviation of the k different group means (ibid., p.268). If k=2, then d=2f (ibid., p.278). These rules apply only if all groups are of the same size; otherwise different criteria apply.

A value of f=.10 is regarded as small, f=.25 as medium, f=.40 as large. Again, it is left to the researcher to classify intermediate values (ibid., p.278-281).
Small-sized effects can also be meaningful or interesting. Large differences may correspond to small effect sizes, due to measurement error, disruptive side effects, etc. Medium effect sizes are observed in IQ differences between house painters, mechanics, carpenters, butchers. Large effect sizes are observed in IQ differences between house painters, mechanics, carpenters, (railroad) engine drivers, and lab technicians.

Adapted from: Cohen, J. (1969). Statistical Power Analysis for the Behavioral Sciences (1st ed.). New York: Academic Press.

Additional reading: Rosenthal, R., R. L. Rosnow, & Rubin, D.B. (2000). Contrasts and Effect Sizes in Behavioral Research: A correlational approach. Cambridge: Cambridge University Press. ISBN 0-521-65980-9.

Thu 16 Oct: no class

There will be no class this week, because of Fall Break (herfstvakantie). This gives you a chance to catch up on reading materials, etc. There is a refresher assignment (see below) but there will be no peer review of this assignment!

Here is one refresher assignments, outside the normal peer-review process. Your answers and solutions to the question below do NOT have to be handed in, but you're invited to submit them into folder "extra" on the group webpage. As always, write clearly, correctly, and concisely.
  1. In a fictitious study, the effect of a growing potion was investigated. The growing potion was administered in 5 different dosages (of 1, 3, 5, 10, and 20 units per day), to 10 men and to 10 women for each dosage, during 15 days. The dependent variable is the increase in body length of a participant, after 15 days, in cm.
    Data from this study are available here in SPSS format, or as plain text (the latter file contains variable names in the first line).
    (a) Import these data into SPSS or a statistical package of your choice. Make a graph of the increase in body length, for each of the 10 conditions. (Hint: In SPSS use a "clustered boxplot".) Discuss what the graph shows.
    (b) Conduct a two-way ANOVA on these data, with Sex and Dosage as two "fixed" factors. Include measures of effect size and of power in your report.
    (c) What is the range of generalisation over dosages, in the ANOVA in (b)? Discuss the external validity of the dosage factor.
    (d) Conduct a two-way ANOVA, but now with Dosage as "random" factor. (Hint: SPSS does not handle "mixed" models like this one very well. It's probably easiest to calculate the F-ratios by hand, using the ANOVA results obtained under (b) above.)
    (e) What is now the range of generalisation over dosages, in the ANOVA in (d)? Again discuss the external validity of the dosage factor.
    (f) Discuss the similarities and differences in results between the two ANOVAs in this assignment. Does the growing potion have a different effect on men and women?

session 6: Thu 23 Oct

regression, error of measurement, reliability.

Reading: Links:


Let us assume that we have 2 observations for each of 5 persons. These observations are about the perceived body weight, as judged by two 'raters' or judges, x1 and x2. The data are as follows:

person  x1  x2
 1      60  62
 2      70  68
 3      70  71
 4      65  65
 5      65  63

Because we have only two measures (variables), there is only one pair of measures to compare in this example. Very often, however, there are more than two judges involved, and hence many more pairs.

First, let us calculate the correlation between these two variables x1 and x2. This can be done in SPSS with the Correlations command (Analyze > Correlate > Bivariate, check Pearson correlation coefficient). This yields r=.904, and the average r (over 1 pair of judges) is the same.

If you need to compute r manually, one method is to first convert x1 and x2 to Z-values [(x-mean)/s], yielding z1 and z2. Then r = SUM(z1×z2) / (n-1).

This value of r corresponds to Cronbach's Alpha of (2×.904)/(1+.904) = .946 (with N=2 judges). Cronbach's Alpha can be obtained in SPSS by choosing Analyze > Scale > Reliability Analysis. Select the "items" (or judges) x1 and x2, and select model Alpha. The output states: Reliability Coefficients [over] 2 items, Alpha = .9459 [etc.]
If the same average correlation r=.904 had been observed over 4 judges (i.e. over 4×3 pairs of judges), then that would have indicated an even higher inter-rater reliability, viz. alpha = (4×.904)/(1+3×.904) = .974.

Exactly the same reasoning applies if the data are not provided by 2 raters judging the same 5 objects, but by 2 test items "judging" a property of the same 5 persons. Both approaches are common in language research. Although SPSS only mentions items, and inter-item reliability; the analysis is equally applicable to raters or judges, and inter-rater reliability.

Note that both judges (items) may be inaccurate. A priori, we do not know how good each judge is, nor which judge is better. We know, however, that their reliability of judging the same thing (true body weight, we hope) increases with their mutual correlation.

Now, let's regard the same data, but in a different context. We have one measuring instrument of the abstract concept x that we try to measure. The same 5 objects are measured twice (test-retest), yielding the data given above. In this test-retest context, there is always just one correlation, and the idea of inter-rater reliability does not apply in this context. We find that rxx=.904.

This reliability coefficient r = s2T / s2x . This provides us with an estimate about how much of the total variance is due to variance in the underlying, unknown, "true" scores. In this example, 90.4% of the total variance is estimated to be due to variance of the true scores. The complementary part, 9.6% of the total variance, is estimated to be due to measurement error. If there were no measurement error, then we would predict perfect correlation (r=1); if the measurements would contain only error (and no true score component at all), then we would predict zero correlation (r=0) between x1 and x2.
In this example, we find that
se = sx × sqrt(1-.904) = sqrt(15.484) × sqrt(.096) = 1.219
check: s2x = 15.484 = s2T + s2e = s2T + (1.219)2,
so s2T = 15.484 - 1.486 = 13.997
and indeed r = .904 = s2T / s2x = 13.997 / 15.484.

Supposedly, x1 and x2 measure the same property x. To obtain s2x, the total observed variance of x (as needed above), we cannot use x1 exclusively nor x2 exclusively. The total variance is obtained here from the two standard deviations:
s2x = sx1 × sx2
s2x = 4.18330 × 3.70135 = 15.484

In general, a reliability coefficient smaller than .5 is regarded as low, between .5 and .8 as moderate, and over .8 as high.

session 6 (continued)

Your answers and solutions to the questions below have to be handed in as described above. As always, write clearly, correctly, and concisely.
  1. Answer the following questions: Ferguson & Takane, Chapter 24: Exercises 1, 2.
  2. We have constructed a test consisting of 4 items, with an average inter-item correlation of 0.4.
    a. How many inter-item correlations are there, between 4 items? (Ignore the trivial correlation of an item with itself.)
    b. Compute the Cronbach Alpha reliability coefficient of this test of 4 items.
    Now we add a new 5th item.
    c. How many new inter-item correlations are added to the correlation matrix when a 5th item is added to the test?
    Unfortunately the coding of this item happens to be incorrect, that is, the scale was reversed for this new item. The inter-item correlation of this 5th item with each of the 4 older items is -0.4 (note the negative sign).
    d. What is the average inter-item correlation after adding this 5th test item?
    e. Compute the Cronbach Alpha coefficient of the longer test of 5 items.
    f. Compare and discuss the reliability and usefulness of the shorter and of the longer test.
  3. A student weights an object 6 times. The object is known to weigh 10 kg. She obtains readings on the scale of 9, 12, 5, 12, 10, and 12 kg. Describe the systematic error and the random errors characterizing the scale's performance.
    Adapted from: R.L. Rosnow & R. Rosenthal (2002). Beginning Behavioral Research: A conceptual primer (4th ed.). Upper Saddle River, NJ: Prentice Hall. Ch.6, Q.7, p.159.
  4. Let us assume that in this course, in addition to writing a peer review, you would also have to grade each other's work as part of the peer review process. Grades would have to be on the Dutch scale from 1 (bad) to 10 (good). Discuss the reliability and validity of this method to assess student performance. What are the possible threats to reliability and validity, and how could these be reduced?

session 7: Thu 30 Oct

Multiple regression, multivariate analyses.

Readings: Assignments:
Your answers and solutions to the questions below have to be handed in as described above. As always, write clearly, correctly, and concisely. File your work in folder "six" "seven" of the group webpage.
  1. Answer the following questions: Moore & McCabe, Chapter 11: Exercises 2, 3, 16, 33.
    Data for the last two questions are available here in plain text format (the first line of this file contains variable names).

Forward or Backward?

For questions 16 and 33 the FORWARD method is most appropriate. This means that you start with an empty model (only intercept b0) to which predictors are added step by step. After each addition of a predictor, you check whether the model performs significantly better than before (e.g. by checking whether R2 increases).
The questions are about the increment in R2 by adding a predictor. The relevant information is easier to find in the SPSS output if you specify the FORWARD method.
As a bonus, you could check what happens if you exclude case #51 from the data set, e.g. by marking it as a missing value. This is quite easy if you keep the regression command in a Syntax window for repeated use.


The chapter by Moore & McCabe draws heavily on typically American concepts. In the USA, your achievements are all that counts, in life as well as in study. The US grading system ranges from A+ (excellent) to F (fail).
For admission to a university, two things are taken into account: (a) your average grades in the final years of high school (HSM, HSS, HSE), and (b) your score in a national admissions exam, like the Dutch CITO test (Scholastic Aptitude Test, SAT). Top-class universities, like Harvard, Yale, Stanford, etc., use both parameters in selection. You have to be the best in your class (but your classmates are strongly competing for this honor), plus you need a minimal score on your SAT.
During your academic study, all your grades and results contribute to your Grade Point Average (GPA), a weighted average grade. This GPA is generally used as an indication of academic achievement and success. The authors attempt to predict the GPA from the previously obtained indicators (a) and (b).


Why is it "regression"? This has to do with heredity, the field of biology where regression was first developed by Francis Galton (cousin of Charles Darwin) in the late 19th century.
Take a sample of fathers, and note their body length (X). Wait for one full generation, and measure the body length of each father's oldest adult son (Y). Make a scattergram of X and Y. The best-fitting line throught the observations has a slope of less than 1 (typically about .65). This is because the sons' length Y tends to "regress to the mean" — outlier fathers tend to produce average sons, and average fathers also tend to produce average sons. Galton called this phenomenon "regression towards mediocrity". Thus the best-fitting line is a "regression" line because it shows the degree of regression to the mean, from one generation to the next. (Note that any slope larger than 0 suggests an hereditary component in the sons' body length, Y.)
Questions: Which variable has the larger variance, X or Y? Does the variation in body length increase or decrease (regress) over generations? Why?

partial correlation

The partial correlation between X1 and X2, with X3 removed from both, is given by:
r12.3 = ( r12-r13r23 ) / sqrt[ (1-r213)(1-r223) ]

final assignment

Your final assignment is to submit a revised or improved version of one previous assignment of this course. You're free to choose which one you want to revise.
As always, the revised paper should be (as much as possible) a running text, not a collection of incomplete sentences and statistical output.
In the revised version you have to accommodate the comments of your reviewer — if you agree of course. Also use the reading materials and hyperlinks provided.
You may discuss the reviewer's comments in the text of your revised version. But perhaps you find it easier to write a coherent (revised) text on your own, plus a second document with revision notes, in which you discuss the reviewer's comments explicitly, stating which comments you have taken into account, which comments you have ignored, and why.

Deadline is Thursday 6th Nov 2008, 23:59 h.

Please remember to evaluate this course. Go to, log in with your SolisID, and fill in the evaluation about this course.

Further Reading

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