**Psyc611**

**Assignments**

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1. Assignments parallel the lectures.
The graduate assistant will assign deadlines to which you should adhere, and
will adjust grades for lateness as appropriate. Be advised that the graduate assistant will be required to
submit grades for assignments 1-7 by the time of the midterm exam, and for
assignments 8-12 by the time of the endterm examination

2. A maximum of 2 points for *each
question* will be assigned (i.e. 1a,
1b, 1c counts as a single question).
The point scheme is 0 = incomplete/missing, 1 = late/minor errors, 2
perfect. The assignment grade
comprises 10% of the final grade, and will derive from the sum of these
points. The maximum points are 64.

3. Be advised that these assignments are
not intended as collaborative exercises.
Your work should be your own.
If you hit a snag, you are encouraged to solicit the assistance of the
graduate assistant.

4. Unless otherwise noted, use the main
data set.

**Assignment #1 Data Entry and Variable
Naming**

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1. Enter
the main data set (posted on the web) and run descriptives to see if data look
"clean." That is,are any data outside the minimum or maximum for the
scale? (Make sure to include a label for gender, so that you remember -1 is
male)

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**Assignment #2 Descriptive Statistics and
Data Transformations**

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1a. Examine
the skew of RT.

1b. Transform
RT with log10 and with the reciprocal, then reassess skew (Don't lose the
original data!!)

1c. Assume
that you recheck your raw data, and find that 65.50 should be 6.50 (replace and
save correct score in the data file).
Reassess skew.

2a. Reverse
score eval_5. Save it under the
same name

2b. Assess
alpha for the 5 item evaluation scale

2c. Compute
a new variable, evalscal, from the 5 items

2d. Imagine
that in version 1 of the survey, frstr is a rating of Mary and sectry is a
rating of Mark. In version -1, the opposite is true
(i.e., target gender was counterbalanced). Create new variables called Mary and
Mark.

**Assignment #3 Quick-and-Dirty ANOVAs**

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1a. Compute
a 1-way between groups ANOVA using manip as IV and rt as DV Remember: You
corrected the rt data file in Assignment #2

1b. Show
Levene's test for homogeneity of variance

1c. What
other evidence might you use for homogeneity?

2a. Compute
a 2-way between groups ANOVA using manip and gender as IVs and eval_1 as the DV

2b. Show
the 6 cell means and standard deviations in an APA style table

3a. Show
the 2-way mixed ANOVA (S/AxB) using target gender (Mary and Mark) within and
subject gender between.

3b. Graph
the 2-way interaction from 3a

**Assignment #4 Repeated Measures ANOVA and
Epsilon**

1a. Compute
1-way within subjects ANOVA using eval_1, eval_2, eval_3, eval_4, and (reverse-scored) eval_5.

1b. Find
the variance-covariance matrix for the 5 levels of eval (from 1a).

1c. Compute
the Geisser-Greenhouse estimate for epsilon by hand and also compute the
minimum by hand. Compare to the
SPSS output from 1a. Compute the Huynh-Feldt estimate for epsilon by hand and
compare to the SPSS output from 1a.

2. Using
2-way mixed ANOVA (S/AxB) show how you would test whether the counterbalancing
of Mary and Mark produced a carry-over effect

**Assignment #5 Power, Effect Sizes,
Agreement Indices**

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1.
Using
gender as the IV and rt as the DV:

1a.
find power using SPSS

1b.
find power using psylib

1c.
find power using f and the corresponding power table

1d.
using Dunlap's shortcut, what n would give 80-90% power?

2.
Using
gender as the IV and rt as the DV (as above)

2a.
show h^{2}
both by hand and in SPSS

2b.
show w^{2}
by hand, using the SPSS ANOVA output

2c.
show the 95% confidence interval around the difference, by hand and on SPSS

3. jcateg
and kcateg are two judges' categorization of participants' verbal responses
into 1 of 3 mutually exclusive and exhaustive categories. Show k by hand and using SPSS

4. now
imagine that jcateg and kcateg are interchangeable judges' ratings on a 1-3
scale. Show the intraclass
correlation coefficient both in SPSS and by hand, and its test of significance

**Assignment #6 Tests Subsequent to ANOVA--
Main Effects**

1a. Compute
a 1-way between groups ANOVA using manip as IV and rt as DV (from assignment
#3)

1b. Show
Tukey HSD post-tests

2a. Show
the orthogonal contrasts of M1 vs M2 as well as M3 vs (M1+M2)/2 Use the one-way command so that you can
find SSs for the contrasts. Compute the SS for the first contrast by hand, and
compare to the SPSS output

2b. Show
the linear and quadratic trends.

2c. What
would the polynomial coefficients for the linear and quadratic trends be if you
had 5 groups?

3. Compute
a 1-way repeated measures ANOVA using eval_2, eval_3, and eval_4 as the levels
of your within subjects variable.
Show the linear and quadratic trends.

**Assignment #7 Tests Subsequent to ANOVA --
Interactions**

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1. Compute
a 2x3 between groups ANOVA using gender and manip as the IVs and rt as the
DV. Does SPSS allow you to conduct
range tests comparing the 6 means of the interaction? "Trick" the program into doing the Tukey HSD tests
for you.

2. Show
the simple effects tests of manipulation for each level of gender (DV is rt).

3. Imagine
that the quadratic trend of eval1, eval3, and eval4 is expected to vary across
gender. Show the test in SPSS.

4. Use
the Tukey HSD procedure to compare the 6 means from the S/AxB ANOVA
gender-by-evaluation item. Make
sure to use the Welch-Satterthwaite correction. (Use psylib range or compute by
hand, rather than using SPSS)

**Assignment #8 More Advanced ANOVA designs**

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1a. using
manip as the IV and attentio as the DV, prove to yourself that ANOVA is the
same in one-way designs for fixed and random effects

1b. using
manip and granny as the IVs and prejudic as the DV, conduct the 2-way ANOVA
using both as random effects IVs and then both as fixed effect IVs . Compare
the output

2a. download
the nested factors data set, and use in 2a

2b. use
score2 as the DV, and conduct an ANOVA in which schools are nested in systems

**Assignment #9 Simple Correlation and Regression**

1a. Regress
evalscal (your 5 item scale) on prejudic, saving residuals and the influence
statistics. Show the scatterplot.
Comment

1b. Show
the regression equation. Any
thoughts about the fact that R is positive and B is negative? How did SPSS find the standard
error of the estimate?

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**Assignment #10** **Multiple Regression**

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1a. You
predict that intellig and attentio predict evalscale. Look for signs of
multicollinearity, outliers, and suppressor variables. Plot the residuals

1b. Assuming
things look okay in 1b, examine the standard multiple regression output and
interpret the results

2. Add
Mary to the equation, then correct for shrinkage

3. Now
use hierarchical multiple regression, regressing evalscale on intellig in the first
step and adding attentio in the second step. Interpret your findings.

** **

**Assignment #11** **Multiple Regression**

1a. Download
the mediator/moderator data to test the hypothesis that the interaction of
pred1 and moderato predicts dvmod. Start with the usual snooping for problems.
Use these data for the entire problem #11

1b.
test the hypothesis (make sure to center continuous predictors)

1c. if
the interaction is significant (or marginal), graph the interaction.

2a. Test
whether mediator mediates the effect of pred2 on dvmed.

2b. If
mediation is apparent, follow-up with the BK modification of Sobel's test

2c. Represent
the results in a figure

3. Imagine
that you wanted to examine pred3 as a moderator of the pred1-->dvmod
relation. Imagine that the entered
codes represent three ethnic groups.
How would you include it as a predictor?

**Assignment #12** **Repeated Measures Multiple Regression**

1. Treat
dvmod and dvmed as 2 levels of a within subjects variable "item," and
run the repeated measures ANOVA. Now use repeated measures multiple regression to test the
same hypothesis (with rounding error, your F-ratio should be similar and df the
same).

2.
Center pred1, and create the pred*iteminteraction term. Conduct RMMR.

3. Run
a simple regression using centered pred 1 as the predictor and the average of
dvmod & dvmed as the criterion. Compare to its corresponding answer in #2
(with rounding error, your F-ratio should be similar and the df the same).

4. Generate
the equations for the cpred*item interaction, and produce the values that you
would use to graph it.

*******Revised #12

1. Treat
dvmod and dvmed as 2 levels of a within subjects variable "item," and
run the repeated measures ANOVA.
That's just like doing an SxA.
If it helps to think of dvmod/dvmed as time1/time2, go ahead and relabel
them as such

Now
use repeated measures multiple regression to test the same hypothesis, i.e.,
that there is a difference between levels. You have the F values already, but remember you want to
report R^{2} for the model.^{}

2. Center
pred1, and create the pred*iteminteraction term. Conduct RMMR testing the effects of "item",
"centered pred1", and the interaction term. You can do this procedure in GLM, just make sure to enter
the continuous variable as a covariate and tell the program to create the
interaction term. You are looking for the F for the change in R^{2} at
each step, the change in R^{2} for each step, and the final R^{2 }for
the equation.

3. Run
a simple regression using centered pred 1 as the predictor and the average of
dvmod & dvmed as the criterion. Compare to its corresponding answer in #2
(with rounding error, your F-ratio should be similar and the df the same).

4. Generate
the equations for the cpred*item interaction, and produce the values that you
would use to graph it.