Methoden van Experimenteel Onderzoek

code 200800181

2008-09, blok 2, november-januari



Nieuws

  1. [2009.01.17] Zoals overlegd in de laatste bijeenkomst heb ik het draaiboek aangepast, zie het draaiboek hieronder.
  2. Deze pagina bevat informatie over cursus 200800181 (blok 2).
    For information about course 200800180 (period 1), follow the hyperlink.
  3. [2008.11.17] Zoals aangekondigd gebruiken we een digitale groep om informatie uit te wisselen in deze cursus. Die groep is te vinden als: groups.yahoo.com/group/meo200809blok2. Sluit je aan!

Praktisch

Docent

Hugo Quené
e-mail hugo dot quene AT let uu nl,
Trans 10, kamer 1.17
spreekuur dinsdag 14:00-15:30 en op afspraak

Leesstof

wordt nog bekendgemaakt!

Rooster

vrijdag15:15-17:00Drift 21, zaal 108

Organisatie

In deze cursus hebben we slechts één bijeenkomst per week, op vrijdagmiddag. De nadruk ligt op zelfstudie, opdrachten, en "peer review". De voertaal is Nederlands.

Voor een bijeenkomst moet je het volgende doen:

  1. opdrachten maken over de onderwerpen van de voorafgaande bijeenkomst;
  2. opdrachten inleveren (via cursuspagina), uiterlijk dinsdag 18:00u;
  3. bespreek en beoordeel de opdracht van een medestudent, uiterlijk donderdag 12:00u;
  4. nieuwe leesstof lezen en bestuderen.

Tijdens de bijeenkomsten zullen we de opdrachten bespreken, aan de hand van jullie reviews. Ook zullen we nieuwe stof behandelen en uitleggen.

Na een bijeenkomst moet je nieuwe opdrachten maken en inleveren (via groups.yahoo.com/group/meo200809blok2).
Lever je werk in in een enkel document per week, in PDF formaat.
Lever je werk in op de groepspagina voor deze cursus, onder Files sectie, in de juiste subsectie behorend bij die cursusweek. Dit moet allemaal voltooid zijn op dinsdag 18:00u.
Haal het werk op dat jij moet beoordelen (volgens schema op cursuspagina), en schrijf een beoordeling van zijn/haar werk in een afzonderlijk document. Plaats deze review ook op de cursuspagina. Dit moet voltooid zijn op donderdag 12:00u (overdag).
Voor de bijeenkomst op vrijdag dien je de review over jouw werk gelezen te hebben.

Meer over peer review (werk beoordelen van vakgenoten):

Beoordeling

nog vast te stellen

Draaiboek

college 1: 14 Nov

Experimenten. Methodologie. H0 en H1. Falsificatie. Empirische Cyclus. Over peer-review.

Lezen: Vooraf: Opdrachten:
  1. Visit your institutional library (e.g. Letterenbibliotheek). Take a recent printed issue (2007 or 2008) 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 the chapter by Maxwell & Delaney, Chapter 1: Exercises 1, 5, 6, 7, 10.
  4. 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.
Verwijzingen:

college 2: 21 Nov

Experimental design.

Leesstof:

Extra leesstof:

Opdrachten:

Geen opdracht.
Wat je geleerd en gelezen hebt moet je wel in verband brengen met en toepassen in de opzet van je eigen scriptie.

college 3: 28 Nov

regressie, meetfout, betrouwbaarheid.

Lezen: Verwijzingen:

Reliability

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.

college 3 (vervolg)

Opdrachten:
Je antwoorden en uitwerkingen op deze opdrachten moet je weer inleveren zoals hierboven beschreven. Zoals altijd moet je weer helder, correct en compact schrijven.
  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 k=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 k=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. 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.
  5. 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?

college 6: vrij 16 Jan

ANOVA en experimenteel design: recapitulatie

Bij dit ingelaste werkcollege horen geen opdrachten.

college 7: vrij 23 Jan

ANOVA: Repeated Measures, minF'

Leesstof: Verwijzingen:
Kijk ook naar deze pagina's van verwante cursussen over onderzoeksmethoden, bij andere universiteiten: Opdrachten:
  1. Beantwoord de volgende vragen uit het gelezen hoofdstuk: §8.15: Exercises 1, 2, 3, 4, 5.

college 8: 30 Jan

Multipele regressie, multivariate analyses.

Leesstof: Opdrachten:
Bij dit college zijn geen verplichte opdrachten.
Je kunt voor jezelf wel de volgende opdrachten maken en eventueel op de groepspagina plaatsen. Er zal echter geen peer review plaatsvinden.
  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.

HSS, SAT, GPA??

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).

regression

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) ]

eindopdracht

Voor de eindopdracht zijn twee mogelijkheden.

Ten eerste kan je een herziene versie inleveren van een eerdere opdracht uit deze cursus. Je mag zelf kiezen welke opdracht je wilt herzien.
Het herziene werkstuk dient zoveel mogelijk een vloeiend verhaal te zijn, dus geen verzameling van losse zinnen en statistische uitvoer.
In de herziene versie moet je de commentaren verwerken van je reviewer — als je het daarmee eens bent. Gebruik ook de leesstof en externe verwijzingen indien beschikbaar.
Je kunt de opmerkingen van de reviewer bespreken in je eigen herziene tekst. Maar misschien vind je het makkelijker om een aangepaste coherente tekst te schrijven, en een afzonderlijk document waarin je de opmerkingen van de reviewer bespreekt, welke je hebt overgenomen en welke niet, en waarom niet. (Dat heet een "cover letter").

Ten tweede mag je de opdrachten bij college 7 8 inleveren als eindopdracht. Verwerk je antwoorden in een vloeiend en coherent betoog, dus geen verzameling van losse zinnen en statistische uitvoer.


Deadline is vrijdag 30 januari 6 februari 2009, 23:59 h.


Verder Lezen


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