**GAF, Consumer Satisfaction, and Type of Clinical Agency
(Public or Private)**

A researcher wants to know if mental health clients
of private versus public service agencies differ on Global Assessment of
Functioning (GAF) scores and on Satisfaction with Services (Satisfaction). She
has collected data for 34 clients from a private agency and for 47 clients of a
public agency.

**Directions:**

Use the SPSS data file for Module 3 (located in Topic
Materials) to answer the following questions:

1. What
is the independent variable in this study? What are the dependent variables?

2. The
first step for the researcher will be to clean and screen the data. Please
do this for the researcher and report your findings. Be sure to check it
for possible coding errors, as well as complete the screening of the data to
see if the data meet assumptions for parametric tests. Did you find any errors
that the researcher made when setting up the SPSS data file (check the variable
view)? If so, what did you find? How did you correct it?

HINT:

Yes, one of
the variables is incorrectly listed as scale.

3. Were
there missing values on any of the variables? If so, what might you do for
those for the independent variable? What about those for each of the
dependent variables? Explain your reasoning.

HINTS:

·
Yes, each variable has some missing data.
Describe how many (and % of all) are missing on each variable.

·
When considering what to do about the missing
values on each variable, consider if you really can guess what agency a person
came from. Next, for the continuous
variables, consider (1) what % of values are missing (if more than 5% are
missing, what might this mean?); (2) is there a pattern to the missing
scores? Include information from the
Output file of your SPSS Explore analyses to provide specific number and % of
missing values on each of the dependent variables. Based on this, what recommendation would you
make for what to do about the missing values?

4. Did
you find any outliers on the dependent variables that were due to errors of
coding? If so, what and why? How would you correct an error of coding?3

HINT:

One of the outliers on one continuous variable
clearly is a coding error. Which one is that? What would be the best way to
handle that outlier?

5. How
might you deal with outliers that are not due to coding errors? Explain
your reasoning.

HINT:

Use the information you have from your Output file
from your Explore analyses to describe the outliers (e.g. how many outliers are
there on each continuous variable; do they fall above and/or below the mean).
What are ways to handle outliers on the continuous variables? Might there be
some arguments against deleting outliers? What are these?

6.
Check the descriptive statistics, histograms,
stem-and-leaf plots, and the tests for normality that you obtained from your
analyses (see box to check in "Plots" when using Explore to analyze
descriptive statistics of your data). Considering the skewness and kurtosis
values, as well as the Shapiro-Wilk's results (preferred for small sample
sizes), did the distribution of scores on either of the dependent variables
violate the assumption of normality? How can you tell from the information
you obtained from your analyses?

HINTS:

·
First, you can look at your histograms and
stem-and-leaf plots to see if you observe marked skewness or other indicators
of differences between the distribution of scores from the normal distribution.

·
Next, you can inspect the computed values for
skewness and kurtosis for your variables from your analyses. Report these
values in your answer for the continuous dependent variables? Which ones are
greater than__+__1.0? What does having a skewness or kurtosis value that
is greater than__+__ 1.0 tell you about normality? Then, discuss what
having these kinds of values tell you about the normality of the distribution
of scores on that variable.

·
Next, look at the Shapiro-Wilks’ tests of
normality that you ran. Results with p < .001 or less indicate a violation
of the normality assumption using this type of evaluation.

7.
If in #6, you identified any distributions that violate
the assumption of normality, what are some options you might use to try to
correct the distribution to get closer to normality? (You do not need to do
these steps. Just describe them.)

8.
Write a sample result section, discussing your data
screening activity.