Consumer Satisfaction
PSY 870: Module 3 Problem Set 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. Solution: The below is the frequency of categorical
variables: There is only one continuous variable and descriptive statistics,
histograms, stem-and-leaf plots, and the tests for normality are given below:
GAF
Stem-and-Leaf Plot Frequency Stem & Leaf 1.00 Extremes
(=<16) 3.00 3 . 123 6.00 3 . 999999 11.00 4 . 13344444444 14.00 4 .
56666666667999 16.00 5 . 1111133333444444 5.00 5 . 55555 .00 6 . 4.00 6 . 9999
1.00 7 . 1 12.00 Extremes (>=76) Stem width: 10 Each leaf: 1 case(s)
From the above analysis, we can see that mean is 54.58 and
standard deviation 21.899. The standard deviation is very high in this case.
Skewness value is 4.257 which is bigger than 0, it means that data is
positively skewed, most values are concentrated on left of the mean, with
extreme
values to the right. Kurtosis value is 27.455 which is
bigger than 3, it means that the distribution is Leptokurtic, sharper than a
normal distribution, with values concentrated around the mean and thicker
tails. This means high probability for extreme values. We can see from the plot
of
Histogram that the data is not normal and there are some
extreme high values and extreme low value in the data. The same can be easily
seen from the stem and leaf plot and also from the box and whisker plot. The
normal probability plot shows that the all values are not on a straight line,
it means that the data departs from normality. The same
conclusion can be drawn from Shapiro-Wilk's test with p-value 0.0000 depicts
that the data is not normal. We can conclude that the distribution of scores on
dependent variable violate the assumption of normality. 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.
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