Week 6 Assignment 2 Application: Chi-Square Study: Intelligence and Self-Esteem Previously in this course, you worked with parametric statistics like t tests, ANOVAs, and correlations. In order to use parametric procedures, your dependent variables must be measured on either an interval or a ratio scale. For this Assignment you will examine the nonparametric procedure called chi-square, which allows you to analyze nominal data compared to parametric tests that allow you to analyze interval and ratio data. Consider this example: You are curious whether males report that they like statistics more frequently than females report that they like statistics. You decide you will ask them a yes-or-no question, and that involves nominal data. You would then count the numbers of responses of yes and no for males and for females. Nonparametric procedures allow you to compare the male responses to the female responses and determine if gender and enjoyment of statistics are independent from each other (not related). Understanding chi-square will help you to more fully understand research studies that utilize nominal variables. Scenario: To prepare for this Assignment, imagine that you have information about 30 other participants’ self-esteem and intelligence, but for these individuals you only have data on whether they have average or above average intelligence, and whether they have high or low self-esteem. You do not have their actual scores for each variable. The observed frequencies are reported here: Intelligence Average Above Average Self-Esteem Low 7 8 High 5 10 Assignment: To complete this Assignment, submit by Day 7 your answers to the following. Based on the scenario, use SPSS to determine if intelligence is related to self-esteem in your sample by computing the appropriate chi-square test. Save and submit both the SPSS data and output files. · Explain what scale of measurement is used to measure intelligence in this example. How do you know? · Explain what scale of measurement is used to measure self-esteem. How do you know? · Before computing the chi-square, state your null and alternative hypotheses in words (not formulas). · State whether this scenario requires a one-way or two-way chi-square test. Explain your answer. · Identify the obtained χ2. · Identify the degrees of freedom and explain how it is calculated. · Identify the p value. · Explain whether you should retain or reject the null hypothesis and why. · Explain what you can determine about the relationship between self-esteem and intelligence, based on this set of data. · Submit three documents for grading: your text (Word) document with your answers and explanations to the application questions, your SPSS Data file, and your SPSS Output file. Week 6 Learning Resources This page contains the Learning Resources for this week. Be sure to scroll down the page to see all of this week's assigned Learning Resources. Required Resources Readings · Heiman,.

1. Week 6 Assignment 2
Application: Chi-Square Study: Intelligence and Self-Esteem
Previously in this course, you worked with parametric statistics
like t tests, ANOVAs, and correlations. In order to use
parametric procedures, your dependent variables must be
measured on either an interval or a ratio scale. For this
Assignment you will examine the nonparametric procedure
called chi-square, which allows you to analyze nominal data
compared to parametric tests that allow you to analyze interval
and ratio data. Consider this example: You are curious whether
males report that they like statistics more frequently than
females report that they like statistics. You decide you will ask
them a yes-or-no question, and that involves nominal data. You
would then count the numbers of responses of yes and no for
males and for females.
Nonparametric procedures allow you to compare the male
responses to the female responses and determine if gender and
enjoyment of statistics are independent from each other (not
related). Understanding chi-square will help you to more fully
understand research studies that utilize nominal variables.
Scenario:
To prepare for this Assignment, imagine that you have
information about 30 other participants’ self-esteem and
intelligence, but for these individuals you only have data on
whether they have average or above average intelligence, and
whether they have high or low self-esteem. You do not have

2. their actual scores for each variable. The observed frequencies
are reported here:
Intelligence
Average
Above Average
Self-Esteem
Low
7
8
High
5
10
Assignment:
To complete this Assignment, submit by Day 7 your answers to
the following. Based on the scenario, use SPSS to determine if
intelligence is related to self-esteem in your sample by
computing the appropriate chi-square test. Save and submit both
the SPSS data and output files.
· Explain what scale of measurement is used to measure
intelligence in this example. How do you know?
· Explain what scale of measurement is used to measure self-
esteem. How do you know?
· Before computing the chi-square, state your null and
alternative hypotheses in words (not formulas).
· State whether this scenario requires a one-way or two-way
chi-square test. Explain your answer.
· Identify the obtained χ2.
· Identify the degrees of freedom and explain how it is
calculated.
· Identify the p value.

3. · Explain whether you should retain or reject the null hypothesis
and why.
· Explain what you can determine about the relationship
between self-esteem and intelligence, based on this set of data.
· Submit three documents for grading: your text (Word)
document with your answers and explanations to the application
questions, your SPSS Data file, and your SPSS Output file.
Week 6 Learning Resources
This page contains the Learning Resources for this week. Be
sure to scroll down the page to see all of this week's assigned
Learning Resources.
Required Resources
Readings
· Heiman, G. (2015). Behavioral sciences STAT 2 (2nd ed).
Stamford, CT: Cengage.
· Chapter 10, “Describing Relationships Using Correlation and
Regression” (pp.162-181)
· Chapter 13, “Chi Square and Nonparametric Procedures”
(pp.218-229 only)
· Chapter 10 Review Card (p. 10.4)
· Chapter 13 Review Card (p. 13.4)
Media
· Bjorkman, S. (2014). SPSS tutorial - Chi-square. Retrieved
from http://screencast.com/t/mJsqW8p7
Note: The approximate length of this media piece is 5 minutes.
This video demonstrates calculating and interpreting chi-square
analyses in SPSS.

4. · Ludwig, T. E. (n.d.a). Correlation [Interactive media].
Retrieved June 11, 2013, from
http://bcs.worthpublishers.com/WebPub/Psychology/psychsim5/
PsychSim5%20Tutorials/Correlation/Correlation.htm
Note: This site offers additional information about correlations,
including interactive media examples.
· Son, J. (2011). Statistics – Regression [Video file]. Retrieved
from
http://www.youtube.com/user/EducatorVids2?v=51JcydfYaTYH
YPERLINK
"http://www.youtube.com/user/EducatorVids2?v=51JcydfYaTY
&feature=pyv"&HYPERLINK
"http://www.youtube.com/user/EducatorVids2?v=51JcydfYaTY
&feature=pyv"feature=pyvNote: The approximate length of this
media piece is 5 minutes.
This video explains linear regression, including predictor and
response variables.
· StatsLectures. (2011d). SPSS - Pearson's r (+hypothesis test)
[Video file]. Retrieved from
http://www.youtube.com/watch?v=jexXeAoymh4Note: The
approximate length of this media piece is 4 minutes.
This video shows how to calculate Pearson’s r in SPSS. In
addition, a hypothesis test is conducted to determine if the
Pearson’s r is significant.
Optional Resources
· BBC (Producer). (2010). The joy of stats [Video series].
Retrieved from http://www.bbc.co.uk/programmes/p00cgkfk
· “Hans Rosling’s 200 Countries, 200 Years, 4 Minutes”
· University of South Carolina. (n.d.a). Regression applet.
Retrieved June 11, 2013, from

5. http://www.stat.sc.edu/~west/javahtml/Regression.html
· University of South Carolina. (n.d.b). Understanding
correlation. Retrieved June 11, 2013, from
http://www.stat.tamu.edu/~west/applets/rplot.html
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Week 6 Assignment 1
Application: Correlation Study: Intelligence and Self-Esteem
In Chapter 1 you read about the differences between
experimental and observational research and read that
correlational studies are one type of observational research. In
an experiment, the researcher manipulates a variable to
determine differences between two or more levels of that
variable. In an observational study, the researcher looks at
patterns of relationships without manipulating variables. In
correlational studies, you cannot show that one variable causes
a change in another variable. However, you can demonstrate
that as one variable increases, another increases. You also may
find that as one variable increases, the other decreases. You
may even find that there is no relationship at all. Use your
understanding of correlations to work through the following

6. scenario.
Scenario:
To prepare for this Assignment, recall that in Week 1 you
imagined you were a researcher interested in determining if
student intelligence is related to self-esteem. Now imagine that
10 individuals participated in your study and the raw data are
given here:
Participant
Self-Esteem Score
IQ
1
3.2
100
2
4.1
140
3
2.2
95
4
3.0
112
5
2.6
130
6
2.0
99
7
5.0
118
8
4.8
121
9
3.7

7. 129
10
4.4
138
Assignment:
To complete this Assignment, submit by Day 7 your answers to
the following. Based on the scenario, use SPSS to determine if
self-esteem is related to intelligence in your sample by
computing a correlation. Save and submit both the SPSS data
and output files.
· Before computing the correlation, state either a one-tailed or
two-tailed, alternative hypothesis and the corresponding null
hypothesis in words (not formulas).
· Based on the hypotheses you stated, explain whether you
should conduct a one-tailed or two-tailed test. Provide a
rationale for your choice.
· Identify what the correlation coefficient (r) is for this data set.
· State the degrees of freedom and explain how it is calculated.
· Identify the p value.
· Explain whether you should retain or reject the null
hypothesis. Provide a rationale for your decision.
· Describe the direction and strength of the relationship between
self-esteem and intelligence.
· Submit three documents for grading: your text (Word)
document with your answers and explanations to the application
questions, your SPSS Data file, and your SPSS Output file.
Week 6 Learning Resources

8. This page contains the Learning Resources for this week. Be
sure to scroll down the page to see all of this week's assigned
Learning Resources.
Required Resources
Readings
· Heiman, G. (2015). Behavioral sciences STAT 2 (2nd ed).
Stamford, CT: Cengage.
· Chapter 10, “Describing Relationships Using Correlation and
Regression” (pp.162-181)
· Chapter 13, “Chi Square and Nonparametric Procedures”
(pp.218-229 only)
· Chapter 10 Review Card (p. 10.4)
· Chapter 13 Review Card (p. 13.4)
Media
· Bjorkman, S. (2014). SPSS tutorial - Chi-square. Retrieved
from http://screencast.com/t/mJsqW8p7
Note: The approximate length of this media piece is 5 minutes.
This video demonstrates calculating and interpreting chi-square
analyses in SPSS.
· Ludwig, T. E. (n.d.a). Correlation [Interactive media].
Retrieved June 11, 2013, from
http://bcs.worthpublishers.com/WebPub/Psychology/psychsim5/
PsychSim5%20Tutorials/Correlation/Correlation.htm
Note: This site offers additional information about correlations,
including interactive media examples.
· Son, J. (2011). Statistics – Regression [Video file]. Retrieved
from
http://www.youtube.com/user/EducatorVids2?v=51JcydfYaTY&
feature=pyvNote: The approximate length of this media piece is
5 minutes.

9. This video explains linear regression, including predictor and
response variables.
· StatsLectures. (2011d). SPSS - Pearson's r (+hypothesis test)
[Video file]. Retrieved from
http://www.youtube.com/watch?v=jexXeAoymh4Note: The
approximate length of this media piece is 4 minutes.
This video shows how to calculate Pearson’s r in SPSS. In
addition, a hypothesis test is conducted to determine if the
Pearson’s r is significant.
Optional Resources
· BBC (Producer). (2010). The joy of stats [Video series].
Retrieved from http://www.bbc.co.uk/programmes/p00cgkfk
· “Hans Rosling’s 200 Countries, 200 Years, 4 Minutes”
· University of South Carolina. (n.d.a). Regression applet.
Retrieved June 11, 2013, from
http://www.stat.sc.edu/~west/javahtml/Regression.html
· University of South Carolina. (n.d.b). Understanding
correlation. Retrieved June 11, 2013, from
http://www.stat.tamu.edu/~west/applets/rplot.html
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As a consumer of research, you know that relationships are of
critical importance. You must first know if a relationship exists
between two variables before you can determine if one variable
may account for another. In this week’s readings, you focused
on correlations that are used to tell you if two variables are
related to one another, but you also now know that you cannot
infer causation from a significant correlation alone. That is, you

10. might find that years of education and salary are related, but
that does not tell you if more education causes your salary to
increase. Correlations also do not allow you to predict a
participant’s score on one variable, based on his or her score on
another variable. One way to predict one score from another is
by using regression. For example, if you wanted to know what
salary you could expect in your field if you went back to school
for another 2 years, regression could help you make that
prediction.
In this Discussion you will apply regression to a research
scenario of your choosing.
To prepare: Imagine a situation in which you would like to
predict an outcome. Think about why you would choose to use
regression rather than correlation. Why is prediction more
important than simply describing a relationship?
Post by Day 3 a description of a scenario where you would like
to predict an outcome based on a predictor variable. Describe
how regression would help you make your prediction. Apply the
following terms to your scenario (making sure to fully explain
each concept in relation to your example): criterion, predictor,
linear regression line, correlation (positive or negative), and
proportion of variance accounted for (R2).
Respond by Day 6 to at least one of your peers in one of the
following ways:
· Challenge key points in the scenario in which they use
regression.
· Support key points in the scenario in which they use
regression.
References:
As a consumer of research, you know that relationships are of
critical importance. You must first know if a relationship exists
between two variables before you can determine if one variable
may account for another. In this week’s readings, you focused
on correlations that are used to tell you if two variables are
related to one another, but you also now know that you cannot
infer causation from a significant correlation alone. That is, you

11. might find that years of education and salary are related, but
that does not tell you if more education causes your salary to
increase. Correlations also do not allow you to predict a
participant’s score on one variable, based on his or her score on
another variable. One way to predict one score from another is
by using regression. For example, if you wanted to know what
salary you could expect in your field if you went back to school
for another 2 years, regression could help you make that
prediction.
In this Discussion you will apply regression to a research
scenario of your choosing.
To prepare: Imagine a situation in which you would like to
predict an outcome. Think about why you would choose to use
regression rather than correlation. Why is prediction more
important than simply describing a relationship?
Post by Day 3 a description of a scenario where you would like
to predict an outcome based on a predictor variable. Describe
how regression would help you make your prediction. Apply the
following terms to your scenario (making sure to fully explain
each concept in relation to your example): criterion, predictor,
linear regression line, correlation (positive or negative), and
proportion of variance accounted for (R2).
Respond by Day 6 to at least one of your peers in one of the
following ways:
· Challenge key points in the scenario in which they use
regression.
· Support key points in the scenario in which they use
regression.
As a consumer of research, you know that relationships are of
critical importance. You must first know if a relationship exists
between two variables before you can determine if one variable
may account for another. In this week’s readings, you focused
on correlations that are used to tell you if two variables are
related to one another, but you also now know that you cannot
infer causation from a significant correlation alone. That is, you
might find that years of education and salary are related, but

12. that does not tell you if more education causes your salary to
increase. Correlations also do not allow you to predict a
participant’s score on one variable, based on his or her score on
another variable. One way to predict one score from another is
by using regression. For example, if you wanted to know what
salary you could expect in your field if you went back to school
for another 2 years, regression could help you make that
prediction.
In this Discussion you will apply regression to a research
scenario of your choosing.
To prepare: Imagine a situation in which you would like to
predict an outcome. Think about why you would choose to use
regression rather than correlation. Why is prediction more
important than simply describing a relationship?
Post by Day 3 a description of a scenario where you would like
to predict an outcome based on a predictor variable. Describe
how regression would help you make your prediction. Apply the
following terms to your scenario (making sure to fully explain
each concept in relation to your example): criterion, predictor,
linear regression line, correlation (positive or negative), and
proportion of variance accounted for (R2).
Respond by Day 6 to at least one of your peers in one of the
following ways:
· Challenge key points in the scenario in which they use
regression.
· Support key points in the scenario in which they use
regression.
Week 6 Learning Resources

13. This page contains the Learning Resources for this week. Be
sure to scroll down the page to see all of this week's assigned
Learning Resources.
Required Resources
Readings
· Heiman, G. (2015). Behavioral sciences STAT 2 (2nd ed).
Stamford, CT: Cengage.
· Chapter 10, “Describing Relationships Using Correlation and
Regression” (pp.162-181)
· Chapter 13, “Chi Square and Nonparametric Procedures”
(pp.218-229 only)
· Chapter 10 Review Card (p. 10.4)
· Chapter 13 Review Card (p. 13.4)
Media
· Bjorkman, S. (2014). SPSS tutorial - Chi-square. Retrieved
from http://screencast.com/t/mJsqW8p7
Note: The approximate length of this media piece is 5 minutes.
This video demonstrates calculating and interpreting chi-square
analyses in SPSS.
· Ludwig, T. E. (n.d.a). Correlation [Interactive media].
Retrieved June 11, 2013, from
http://bcs.worthpublishers.com/WebPub/Psychology/psychsim5/
PsychSim5%20Tutorials/Correlation/Correlation.htm
Note: This site offers additional information about correlations,
including interactive media examples.
· Son, J. (2011). Statistics – Regression [Video file]. Retrieved
from
http://www.youtube.com/user/EducatorVids2?v=51JcydfYaTY&
feature=pyvNote: The approximate length of this media piece is
5 minutes.
This video explains linear regression, including predictor and

14. response variables.
· StatsLectures. (2011d). SPSS - Pearson's r (+hypothesis test)
[Video file]. Retrieved from
http://www.youtube.com/watch?v=jexXeAoymh4Note: The
approximate length of this media piece is 4 minutes.
This video shows how to calculate Pearson’s r in SPSS. In
addition, a hypothesis test is conducted to determine if the
Pearson’s r is significant.
Optional Resources
· BBC (Producer). (2010). The joy of stats [Video series].
Retrieved from http://www.bbc.co.uk/programmes/p00cgkfk
· “Hans Rosling’s 200 Countries, 200 Years, 4 Minutes”
· University of South Carolina. (n.d.a). Regression applet.
Retrieved June 11, 2013, from
http://www.stat.sc.edu/~west/javahtml/Regression.html
· University of South Carolina. (n.d.b). Understanding
correlation. Retrieved June 11, 2013, from
http://www.stat.tamu.edu/~west/applets/rplot.html
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