This document provides instructions for an assignment involving analyzing two examples using descriptive and inferential statistics. It also poses several questions about statistical concepts like nonparametric vs parametric tests, internal and external validity, sampling, standard deviation, standard error of measurement, t-tests, and examining variables for correlation analysis. Students are instructed to answer the questions, provide SPSS output when necessary, and submit their work by the specified deadline.

1. This week's assignment questions are extracted from your
Warner (2013) text. Answer each question, providing IBM SPSS
analysis when necessary to support your answer. Save your
work in a Word file named
course number_assignment number_Last Name_First Initial
, for example,
PSY8625_u01a1_Smith_K
. The deadline for submitting your work is 11:59 PM CST on
Sunday of Week 1.Given below are two applications of
statistics. Identify which one of these is descriptive and which
is inferential. Explain your decision.
Example1:
An administrator at Corinth College looks at the verbal
Scholastic Aptitude Test (SAT) scores for the entire class of
students admitted in the fall of 2005 (mean = 660) and the
verbal SAT scores for the entire class admitted in the fall of
2004 (mean = 540) and concludes that the class of students
admitted to Corinth in 2005 had higher verbal scores than the
class of students admitted in 2004.
Example 2:
An administrator takes a random sample of 45 Corinth College
students in the fall of 2005 and asks them to self-report how
often they engage in binge drinking. Members of the sample
report an average of 2.1 binge drinking episodes per week. The
administrator writes a report that says, "The average level of
binge drinking among all Corinth College students is about 2.1
episodes per week."For what types of data would you use
nonparametric versus parametric statistics?Briefly describe the
difference between internal and external validity.When a
researcher has an accidental or convenience sample, what kind
of population can he or she try to make inferences about?For
each of the following lists of scores, indicate whether the value
of
SS
will be negative, 0, between 0 and +15, or greater than +15.

2. (You do not need to actually calculate
SS.
)
Sample A:
X
= [103, 156, 200, 300, 98].Sample B:
X
= [103, 103, 103, 103, 103, 103].Sample C: X = [101, 102, 103,
102, 101].Assume that a population of thousands of people
whose responses were used to develop an anxiety test had scores
that were normally distributed with
M
= 30 and
s
= 10.
What proportion of people in this population would have
anxiety scores within each of the following ranges of scores?
Below 20.Above 30.Between 10 and 50.What is
SEM?
What does the value of SEM tell you about the typical
magnitude of sampling error?
As s increases, how does the size of SEM change (assuming that
N stays the same)?
As
N increases, how does the size of SEM change (assuming that s
stays the same)?
Under what circumstances should a
t
distribution be used rather than the normal distribution to look

3. up areas or probabilities associated with distances from the
mean?
To complete questions 9 and 10, use the
bpstudy.sav
file in the Resources.Select three variables from the dataset
bpstudy.sav.
Two of the variables should be good candidates for a
correlation, and the other variable should be a poor candidate
for a correlation. Good candidates are variables that meet the
assumptions (such as normally distributed, reliably measured,
interval-ratio level of measurement). Poor candidates are
variables that do not meet assumptions or that have clear
problems (such as restricted range, extreme outliers, gross non-
normality of distribution shape).
Use the FREQUENCIES procedure to obtain a histogram and all
univariate descriptive statistics for each of the three variables.
Create a scatter plot for the two "good candidate" variables.
Create a scatter plot for the "poor candidate" variable using one
of the two good variables. Properly embed SPSS output where
appropriate in your answer to Question 9 below. Explain which
variables are good and poor candidates for a correlation analysis
and give your rationale. Comment on empirical results from
your data screening—both the histograms and scatter plots—as
evidence that these variables meet or do not meet the basic
assumptions necessary for correlation to be meaningful and
honest. What other information would you want to have about
the variables in order to make better informed judgments?
Is there anything that could be done (in terms of data
transformations or eliminating outliers for instance) to make
your poor candidate variable better?

4. If so, what would you recommend?