Assessment 2 ContextIn many data analyses, it is desirable.docx

Assessment 2 Context
In many data analyses, it is desirable to compute a coefficient
of association. Coefficients of association are quantitative
measures of the amount of relationship between two variables.
Ultimately, most techniques can be reduced to a coefficient of
association and expressed as the amount of relationship between
the variables in the analysis. There are many types of
coefficients of association. They express the mathematical
association in different ways, usually based on assumptions
about the data. The most common coefficient of association you
will encounter is the Pearson product-moment correlation
coefficient (symbolized as the italicized r), and it is the only
coefficient of association that can safely be referred to as
simply the "correlation coefficient". It is common enough so
that if no other information is provided, it is reasonable to
assume that is what is meant.
Correlation coefficients are numbers that give information about
the strength of relationship between two variables, such as two
different test scores from a sample of participants. The
coefficient ranges from -1 through +1. Coefficients between 0
and +1 indicate a positive relationship between the two scores,
such as high scores on one test tending to come from people
with high scores on the second. The other possible relationship,
which is every bit as useful, is a negative correlation between -1
and 0. A negative correlation possesses no less predictive power
between the two scores. The difference is that high scores on
one measure are associated with low scores on the other.
An example of the kinds of measures that might correlate
negatively is absences and grades. People with higher absences

will be expected to have lower grades. When a correlation is
said to be significant, it can be shown that the correlation is
significantly different form zero in the population. A correlation
of zero means no relationship between variables. A correlation
other than zero means the variables are related. As the
coefficient gets further from zero (toward +1 or -1), the
relationship becomes stronger.Interpreting Correlation:
Magnitude and Sign
Interpreting a Pearson's correlation coefficient (rXY) requires
an understanding of two concepts:
· Magnitude.
· Sign (+/-).
The magnitude refers to the strength of the linear relationship
between Variable X and Variable
The rXY ranges in values from -1.00 to +1.00. To determine
magnitude, ignore the sign of the correlation, and the absolute
value of rXY indicates the extent to which Variable X and
Variable Y are linearly related. For correlations close to 0, there
is no linear relationship. As the correlation approaches either -
1.00 or +1.00, the magnitude of the correlation increases.
Therefore, for example, the magnitude of r = -.65 is greater than
the magnitude of r = +.25 (|.65| > |.25|).
In contrast to magnitude, the sign of a non-zero correlation is
either negative or positive.
These labels are not interpreted as "bad" or "good." Instead, the
sign represents the slope of the linear relationship between X
and Y. A scatter plot is used to visualize the slope of this linear
relationship, and it is a two-dimensional graph with dots
representing the combined X, Y score. Interpreting scatter plots
is necessary to check assumptions of correlation discussed
below.
A positive correlation indicates that, as values of X increase,

the values of Y also increase (for example, grip strength and
arm strength). You may wish to view examples of positive and
negative correlations as viewed by a scatter plot.
Assumptions of Correlation
All inferential statistics, including correlation, operate under
assumptions that are checked prior to calculating them in SPSS.
Violations of assumptions can lead to erroneous inferences
regarding a null hypothesis. The first assumption is
independence of observations for X and Y scores. The
measurement of individual X and Y scores should not be
influenced by errors in measurement or problems in research
design (for example, a student completing an IQ test should not
be looking over the shoulder of another student taking that test;
his or her IQ score should be independent). This assumption is
not statistical in nature; it is controlled by using reliable and
valid instruments and by maintaining proper research
procedures to maintain independence of observations.
The second assumption is that, for Pearson's r, X and Y are
quantitative, and that each variable is normally distributed.
Other correlations discussed below do not require this
assumption, but Pearson's r is the most widely used and reported
type of correlation. It is therefore important to check this
assumption when calculating Pearson's r in SPSS. This
assumption is checked by a visual inspection of X and Y
histograms and calculations of skew and kurtosis values.
The third assumption of correlation is that X, Y scores are
linearly related. Correlation does not detect strong curvilinear
relationships. This assumption is checked by a visual inspection
of the X, Y scatter plot.
The fourth assumption of correlation is that the X, Y scores
should not have extreme bivariate outliers that influence the
magnitude of the correlation. Bivariate outliers are also detected
by a visual examination of a scatter plot. Outliers can

dramatically influence the magnitude of the correlation, which
sometimes leads to errors in null hypothesis testing. Bivariate
outliers are particularly problematic when a sample size is small
and suggests an N of at least 100 for studies that report
correlations.
The fifth assumption of correlation is that the variability in Y
scores is uniform across levels of X. This requirement is
referred to as the homogeneity of variance assumption, which is
usually difficult to assess in scatter plots with a small sample
size. This assumption is typically emphasized when checking
the homogeneity of variance for a t-test or analysis of variance
(ANOVA) studied later in the course.
Hypothesis Testing of Correlation
The null hypothesis for correlation predicts no significant linear
relationship between X and Y, or H0: rXY = 0. A directional
alternative hypothesis for correlation is either an expected
significant positive relationship (H1: rXY > 0) or significant
negative relationship (H1: rXY < 0). A non-directional
alternative hypothesis would simply predict that the correlation
is significantly different from 0, but it does not stipulate the
sign of the relationship (H1: rXY ≠ 0). For correlation as well
as t-tests and ANOVA studied later in the course, the standard
alpha level for rejecting the null hypothesis is set to .05. SPSS
output for a correlation showing a p value of less than .05
indicates that the null hypothesis should be rejected; there is a
significant relationship between X and Y. A p value greater than
.05 indicates that the null hypothesis should not be rejected;
there is not a significant relationship between X and Y.Effect
Size in Correlation
Even if the null hypothesis is rejected, how large is the
association between X and Y? To provide additional context,
the interpretation of all inferential statistics, including
correlation, should include an estimate of effect size. An effect

size is articulated along a continuum from "small," to
"medium," to "large."
An effect size for correlation is an estimate of the strength of
association between X and Y in unit-free terms (that is, effect
size estimation is independent of how X and Y are originally
measured). Another advantage is that an effect size is calculated
independently from the sample size of the study, as any non-
zero correlation will be significant if the sample size is large
enough. The effect size for correlation is calculated as r2
(pronounced "r-square"), and it is simply the squared value of r.
For example, r = .50 results in an effect size r2 = .25 (.52 =
.25).
Roughly speaking, a correlation less than or equal to .10 is
"small," a correlation between .10 and .25 is "medium," and a
correlation above .25 is "large" (Warner, 2013).Alternative
Correlation Coefficients
The most widely used correlation is referred to as Pearson's r.
Pearson's r is calculated between X and Y variables that are
measured on either the interval or ratio scale of measurement
(for example, height and weight). There are other types of
correlation that depend on other scales of measurement for X
and Y. A point biserial (rpb) correlation is calculated when one
variable is dichotomous (for example, gender) and the other
variable is interval/ratio data (for example, weight). If both
variables are ranked (ordinal) data, the correlation is referred to
as Spearman's r (rs). Although the underlying scales of
measurement differ from the standard Pearson's r, rpb and rs
values are both calculated between -1.00 and +1.00 and are
interpreted similarly.
If both variables are dichotomous, the correlation is referred to
as phi (ɸ). A final test of association is referred to as chi-
square. Phi and chi-square are studied in Advanced Inferential

Statistics.
Correlation – Application
We will apply our understanding of correlation in the third IBM
SPSS assessment. For the remaining data analysis assessments
in this course, we will use the Data Analysis and Application
(DAA) template. The DAA is separated into five
sections:Section 1: Data File Description
· Describe the context of the data set.
· Specify the variables used and their scale of measurement.
· Specify sample size (N).Section 2: Testing Assumptions
· Articulate the assumptions of a statistical test.
· Provide SPSS output that tests assumptions and interpret
them.Section 3: Research Question, Hypotheses, and Alpha
Level
· Articulate a research question relevant to the statistical test.
· Articulate the null hypothesis and alternative hypothesis.
· Specify the alpha level.Section 4: Interpretation
· Provide SPSS output for an inferential statistic and report it.
· Interpret statistical results against the null hypothesis.
· State conclusions.
· Analyze strengths and limitations of the statistical test.
Section 5: Conclusions
· State conclusions.

· Analyze strengths and limitations of the statistical test.Proper
Reporting of Correlations
Reporting a correlation in proper APA style requires an
understanding of the following elements, including the
statistical notation for a Pearson's correlation ( r ), the degrees
of freedom, the correlation coefficient, the probability value,
and the effect size. Consider the following example:
Only the correlation between organizational commitment (OC)
and organizational citizenship behavior (OCB) was statistically
significant, r(110) = +.22, p < .05 (two-tailed). The r2 was .05;
thus, only about 5% of the variance in OC scores could be
predicted from OCB scores; this is a weak positive
relationship.r, Degrees of Freedom, and Correlation Coefficient
The statistical notation for Pearson's correlation is r, and
following it is the degrees of freedom for this statistical test
(for example, 110). The degrees of freedom for Pearson's r is N
– 2, so there were 112 participants in the sample cited above
(112 – 2 = 110). Note that SPSS output for Pearson's r provides
N, so you must subtract 2 from N to correctly report degrees of
freedom. Next is the actual correlation coefficient including the
sign. After the correlation coefficient is the probability
value.Probability Values
Prior to the widespread use of SPSS and other statistical
software programs, p values were often calculated by hand. The
convention in reporting p values was to simply state, "p < .05"
to reject the null hypothesis and "p > .05" to not reject the null
hypothesis. However, SPSS provides an "exact" probability
value, so it should be reported instead.
Hypothetical examples would be "p = .02" to reject the null
hypothesis and "p = .54" to not reject the null hypothesis (round
exact p values to two decimal places). One confusing point of
SPSS output is that highly significant p values are reported as
".000," because SPSS only reports probability values out to

three decimal places. Remember that there is a "1" out there
somewhere, such as p = .000001, as there is always some small
chance that the null hypothesis is true. When SPSS reports a p
value of ".000," report "p < .001" and reject the null hypothesis.
The "(two-tailed)" notation after the p value indicates that the
researcher was testing a nondirectional alternative hypothesis
(H1: rXY ≠ 0). He or she did not have any a priori justification
to test a directional hypothesis of the relationship between
commitment and length of the relationship. In terms of alpha
level, the region of rejection was therefore 2.5% on the left side
of the distribution and 2.5% on the right side of the distribution
(2.5% + 2.5% = 5%, or alpha level of .05). A "(one-tailed)"
notation indicates a directional alternative hypothesis. In this
case, all 5% of the region of rejection is established on either
the left side (negative; (H1: rXY < 0) or right side (positive;
(H1: rXY > 0) of the distribution. A directional hypothesis must
be justified prior to examining the results. In this course, we
will always specify a two-tailed (non-directional) test, which is
more conservative relative to a one-tailed test. The advantage is
that a nondirectional test detects relationships or differences on
either side of the distribution, which is recommended in
exploratory research.Effect Size
Effect sizes provide additional context for the strength of the
relationship in correlation. Effect sizes are important because
any non-zero correlation will be statistically significant if the
sample size is large enough. After the probability value is
stated, provide the r2 effect size and interpret it as small,
medium, or large. It is good form to report the effect size for
both significant and nonsignificant statistics for meta-analyses
(that is, statistical studies that combine the results across
multiple independent research studies), but in journal articles
where space is limited, authors will often just report effect sizes
for statistics that reject the null hypothesis.ReferencesLane, D.
M. (2013). HyperStat online statistics textbook. Retrieved from
http://davidmlane.com/hyperstat/index.htmlWarner, R. M.
(2013). Applied statistics: From bivariate through multivariate

techniques (2nd ed.). Thousand Oaks, CA: Sage Publications.
1
3
Print Copy/Export Output Instructions
SPSS output can be selectively copied and pasted into Word by
using the Copy command:
1. Click on the SPSS output in the Viewer window.
2. Right-click for options.
3. Click the Copy command.
4. Paste the output into a Microsoft Word document.
The Copy command will preserve the formatting of the SPSS
tables and charts when pasting into Microsoft Word.
An alternative method is to use the Export command:
1. Click on the SPSS output in the Viewer window.
2. Right-click for options.
3. Click the Export command.
4. Save the file as Word/RTF (.doc) to your computer.
5. Open the .doc file.
Data Set Instructions
The grades.sav file is a sample SPSS data set. The fictional data
represent a teacher’s recording of student demographics and
performance on quizzes and a final exam across three sections
of the course. Each section consists of about 35 students (N =
105).Software Installation
Make sure that IBM SPSS Statistics Standard GradPack is fully
licensed, installed on your computer, and running properly. It is
important that you have either the Standard or Premium version
of SPSS that includes the full range of statistics. Proper
software installation is required in order to complete your first
SPSS data assignment in Assessment 1.

Next, click grades.sav in the Assessment 1 Resources to
download the file to your computer.
· You will use grades.sav throughout the course.
The definition of variables in the grades.sav data set are found
in the Assessment 1 Context. Understanding these variable
definitions is necessary for interpreting SPSS output.
In Assessment 1, you will define values and scales of
measurement for all variables in your grades.sav file.
Verify the values and scales of measurement assigned in the
grades.sav file using information in the Data Set on page 2 of
this document.
Data Set
There are 21 variables in grades.sav,. Open your grades.sav file
and go to the Variable View tab. Make sure you have the
following values and scales of measurement assigned.
SPSS variable
Definition
Values
Scale of measurement
id
Student identification number
Nominal
lastname
Student last name
Nominal
firstname
Student first name
Nominal
gender
Student gender
1 = female; 2 = male
Nominal
ethnicity

Student ethnicity
1 = Native; 2 = Asian; 3 = Black;
4 = White; 5 = Hispanic
Nominal
year
Class rank
1 = freshman; 2 = sophomore;
3 = junior; 4 = senior
Scale
lowup
Lower or upper division
1 = lower; 2 = upper
Ordinal
section
Class section
Nominal
gpa
Previous grade point average
Scale
extcr
Did extra credit project?
1 = no; 2 = yes
Nominal
review
Attended review sessions?
1 = no; 2 = yes
Nominal
quiz1
Quiz 1: number of correct answers
Scale
quiz2

Scale
quiz3
Scale
quiz4
Scale
quiz5
Scale
final
Final exam: number of correct answers
Scale
total
Total number of points earned
Scale
percent
Final percent
Scale
grade
Final grade
Nominal
passfail
Passed or failed the course?
Nominal

2
Assessment 2 Context
In many data analyses, it is desirable to compute a coefficient
of association. Coefficients of association are quantitative
measures of the amount of relationship between two variables.
Ultimately, most techniques can be reduced to a coefficient of
association and expressed as the amount of relationship between
the variables in the analysis. There are many types of
coefficients of association. They express the mathematical
association in different ways, usually based on assumptions
about the data. The most common coefficient of association you
will encounter is the Pearson product-moment correlation
coefficient (symbolized as the italicized r), and it is the only
coefficient of association that can safely be referred to as
simply the "correlation coefficient". It is common enough so
that if no other information is provided, it is reasonable to
assume that is what is meant.
Correlation coefficients are numbers that give information about
the strength of relationship between two variables, such as two
different test scores from a sample of participants. The
coefficient ranges from -1 through +1. Coefficients between 0
and +1 indicate a positive relationship between the two scores,
such as high scores on one test tending to come from people
with high scores on the second. The other possible relationship,
which is every bit as useful, is a negative correlation between -1
and 0. A negative correlation possesses no less predictive power
between the two scores. The difference is that high scores on
one measure are associated with low scores on the other.
An example of the kinds of measures that might correlate
negatively is absences and grades. People with higher absences

techniques (2nd ed.). Thousand Oaks, CA: Sage Publications.
1
3
Running head: DATA ANALYSIS AND APPLICATION
TEMPLATE 1
DATA ANALYSIS AND APPLICATION TEMPLATE 4
Data Analysis and Application (DAA) TemplateLearner
NameCapella University
Data Analysis and Application (DAA) Template
Use this file for all assignments that require the DAA Template.
Although the statistical tests will change from week to week,
the basic organization and structure of the DAA remains the
same. Update the title of the template. Remove this text and
provide a brief introduction.Section 1: Data File Description
Describe the context of the data set. You may cite your previous
description if the same data set is used from a previous
assignment.
Specify the variables used in this DAA and the scale of
measurement of each variable.
Specify sample size (N).Section 2: Testing Assumptions
1. Articulate the assumptions of the statistical test.
Paste SPSS output that tests those assumptions and interpret
them. Properly integrate SPSS output where appropriate. Do not
string all output together at the beginning of the section.
Summarize whether or not the assumptions are met. If
assumptions are not met, discuss how to ameliorate violations
of the assumptions.Section 3: Research Question, Hypotheses,
and Alpha Level
1. Articulate a research question relevant to the statistical test.

2. Articulate the null hypothesis and alternative hypothesis.
3. Specify the alpha level.Section 4: Interpretation
1. Paste SPSS output for an inferential statistic. Properly
integrate SPSS output where appropriate. Do not string all
output together at the beginning of the section.
2. Report the test statistics.
3. Interpret statistical results against the null hypothesis.Section
5: Conclusion
1. State your conclusions.
2. Analyze strengths and limitations of the statistical test.
References
Provide references if necessary.
· For this two-part assessment, you will respond to a question
about interpreting correlations and use SPSS software to
complete a data analysis and application report.
You will examine three fundamental inferential statistics,
including correlation, t tests, and analysis of variance
(ANOVA). The first inferential statistic we will focus on is
correlation, denoted r, which estimates the strength of a linear
association between two variables. By contrast, t tests and
ANOVAs will examine group differences on some quantitative
dependent variable.
SHOW LESS
By successfully completing this assessment, you will
demonstrate your proficiency in the following course
competencies and assessment criteria:
· Competency 1: Analyze the computation, application,
strengths, and limitations of various statistical tests.
1. Develop a conclusion including strengths and limitations of
correlation.
. Competency 2: Analyze the decision-making process of data
analysis.
2. Analyze the assumptions of correlation.
. Competency 3: Apply knowledge of hypothesis testing.
3. Develop a research question, null hypothesis, alternative

hypothesis, and alpha level.
. Competency 4: Interpret the results of statistical analyses.
4. Interpret the correlation output.
. Competency 5: Apply a statistical program's procedure to data.
5. Apply the appropriate SPSS procedures to check assumptions
and calculate the correlations.
. Competency 6: Apply the results of statistical analyses (your
own or others) to your field of interest or career.
6. Develop a context for the data set, including a definition of
required variables and scales of measurement.
. Competency 7: Communicate in a manner that is scholarly,
professional, and consistent with the expectations for members
in the identified field of study.
7. Communicate in a manner that is scholarly, professional, and
consistent with the expectations for members in the identified
field of study.
Competency Map
CHECK YOUR PROGRESSUse this online tool to track your
performance and progress through your course.
· Toggle Drawer
Context
Read Assessment 2 Context [DOC] for important information on
the following topics:
SHOW LESS
. Interpreting correlation: Magnitude and sign.
. Assumptions of correlation.
. Hypothesis testing of correlation.
. Effect size in correlation.
. Alternative correlation coefficients.
. Correlation—application.
. Proper reporting of correlations.
. r, degrees of freedom, and correlation coefficient.
. Probability values.
. Effect size.
· Toggle Drawer
Questions to Consider

As you prepare to complete this assessment, you may want to
think about other related issues to deepen your understanding or
broaden your viewpoint. You are encouraged to consider the
questions below and discuss them with a fellow learner, a work
associate, an interested friend, or a member of your professional
community. Note that these questions are for your own
development and exploration and do not need to be completed
or submitted as part of your assessment.
SHOW LESS
. Correlation versus causation:
1. If correlation does not imply causation, what does it imply?
1. Are there ever any circumstances when a correlation can be
interpreted as evidence for a causal connection between two
variables?
1. If yes, what circumstances?
. Application of correlation:
2. Is there a research question from your professional life or
career specialization that can be addressed by a correlation?
2. Why would a correlation be the appropriate analysis for this
research question?
2. What are the variables and their scale of measurement?
2. What is the expected outcome (positive, negative, no
relationship)?
· Toggle Drawer
Resources
APA Resources
Because this is a psychology course, you need to format this
assessment according to APA guidelines. Additional resources
about APA can be found in the Research Resources in the
courseroom navigation menu. Use the resources to guide your
work.
. American Psychological Association. (2010). Publication
manual of the American Psychological Association (6th ed.).
Washington, DC: Author.
1. This resource is available from the Capella University
Bookstore.

Required Resources
The following resources are required to complete the
assessment.
Data Set Instructions
These are the same instructions provided in Assessment 1.
.
2. Data Set Instructions [DOCX].
Assessment Template and Output Instructions
. Data Analysis and Application (DAA) Template [DOCX].
3. Use this template to complete your assessment.
. SPSS Data Analysis Report Guidelines [DOCX].
4. Use this document for instructions on completing the DAA
template.
. Copy/Export Output Instructions [DOCX].
5. This document provides instructions for extracting output
from SPSS. You will insert your output into the assessment
answer template as indicated.
SPSS Software
The following statistical analysis software is required to
complete your assessments in this course:
. IBM SPSS Statistics Standard or Premium GradPack (recent
version for Windows or Mac).
6. As a Capella learner, you have access to the more robust IBM
SPSS Statistics Premium GradPack arranged at an academic
discount through a contracted vendor.
6. Please refer to the Statistical Software page on Campus for
general information on SPSS software, including the most
recent version made available to Capella learners.
SHOW LESS
Suggested Resources
The resources provided here are optional and support the
assessment. They provide helpful information about the topics.
You may use other resources of your choice to prepare for this
assessment; however, you will need to ensure that they are
appropriate, credible, and valid. The XX-FP7864 – Quantitative
Design and Analysis Library Guide can help direct your

research, and the Supplemental Resources and Research
Resources, both linked from the left navigation menu in your
courseroom, provide additional resources to help support you.
Statistics Concepts and Terminology
. Assessment 2 Context [DOC].
7. Read this resource for information about the statistical
terminology and concepts needed to complete this assessment.
SPSS Software Procedures
. IBM SPSS Step-By-Step Instructions: Correlations [DOCX].
8. This course file provides an illustrated, step-by-step guide to
creating correlation statistics in SPSS.
. In your IBM SPSS Statistics Step by Step library e-book:
9. Chapter 10, "Bivariate Correlation."
Correlation?
. StatSoft, Inc. (2013). Electronic statistics textbook. Tulsa,
OK: StatSoft. Retrieved from http://www.statsoft.com/textbook
10. Basic Statistics.
1. This section contains information on correlations.
· Skillsoft. (n.d.). Basics of correlation, regression, and
hypothesis testing for six sigma [Video].
. ?Navigate to this course, click the Table of Contents, and open
the heading titled Using Correlation Analysis in Six Sigma.
There are five videos adding up to 25 minutes that you should
watch under this heading.
· Scott, S. (n.d.). Correlation and regression [Video]. Skillsoft.
Program-Specific Resources
These programs have opted to provide program-specific content
designed to help you better understand how the subject matter is
incorporated into your particular field of study.
School of Psychology Learners
Note: For the first article, focus on interpreting Table 1.
· Jia, Y., Konold, T. R., & Cornell, D. (2015). Authoritative
school climate and high school dropout rates. School
Psychology Quarterly, 31(2), 289–303.
· Anderson, C. A., & Bushman, B. J. (2001). Effects of violent
video games on aggressive behavior, aggressive cognition,

aggressive affect, physiological arousal, and prosocial behavior:
A meta-analytic review of the scientific
literature. Psychological Science, 12(5), 353–359.
School of Education Learners
· Walk, M. J., & Rupp, A. A. (2010). Pearson product-moment
correlation coefficient. In N. J. Salkind (Ed.), Encyclopedia of
research design (pp. 1023–1026). Thousand Oaks, CA: Sage.
Additional Resources for Further Exploration
· Khan Academy. (2013). Retrieved from
https://www.khanacademy.org
. This website offers resources covering a range of subjects,
including statistics.
· Assessment Instructions
Preparation
Read Assessment 2 Context (linked in the Resources) to learn
about the concepts used in this assessment. This assessment
contains two parts. Follow the instructions provided for each
part. Submit both parts of your assessment as Word documents.
Part 1: Interpreting Correlations
A meta-analysis (Anderson & Bushman, 2001) reported that the
average correlation between time spent playing video games (X)
and engaging in aggressive behavior (Y) in a set of 21 well-
controlled experimental studies was r+ = .19. This correlation
was judged to be statistically significant. In your own words,
what can you say about the nature of the relationship? Write a
one-page response to this question.
Part 2: Correlations
You will use the following resources for this assessment. They
are linked in the Resources.
· Complete this part of the assessment using the DAA Template.
· Read the SPSS Data Analysis Report Guidelines for a more
complete understanding of the DAA Template and how to
format and organize your assessment.
· Refer to IBM SPSS Step-By-Step Instructions:
Correlations for additional information on using SPSS for this
assessment.

· If necessary, review the Copy/Export Output Instructions to
refresh your memory on how to perform these tasks. As with
your previous two assessments, your submission should be
narrative with supporting statistical output (table and graphs)
integrated into the narrative in the appropriate place (not all at
the end of the document).
You will analyze the following variables in the grades.sav data
set:
· gender.
· gpa.
· total.
· final.
Step 1: Write Section 1 of the DAA
Provide a context of the grades.sav data set. Include a definition
of the specified variables and corresponding scales of
measurement. Indicate the type of correlation for each X, Y pair
(for example, Pearson's r, Spearman's r, point-biserial r, et
cetera). Specify the sample size of the data set.
Test the assumptions of correlation for gpa and final. Paste the
SPSS histogram output for each variable and discuss your visual
interpretations. Paste SPSS descriptives output showing
skewness and kurtosis values and interpret them. Paste SPSS
scatter plot output with gpa set to the horizontal axis and final
set to the vertical axis. Conduct a visual inspection of the
scatter plot to analyze other assumptions of correlation.
Summarize whether or not the assumptions of correlation are
met.
Specify a research question related to gpa and final. Articulate
the null hypothesis and alternative hypothesis. Specify your
alpha level.
Paste the SPSS output of the intercorrelation matrix for all
specified variables.
· First, report the lowest magnitude correlation in the

intercorrelation matrix, including degrees of freedom,
correlation coefficient, p value, and effect size. Interpret the
effect size. Specify whether or not to reject the null hypothesis
for this correlation.
· Second, report the highest magnitude correlation in the
intercorrelation matrix, including degrees of freedom,
correlation coefficient, p value, and effect size. Interpret the
effect size. Specify whether or not to reject the null hypothesis
for this correlation.
· Third, report the correlation between gpa and final, including
degrees of freedom, correlation coefficient, p value, and effect
size. Interpret the effect size. Analyze the correlation in terms
of the null hypothesis.
Discuss the implications of this correlation as it relates to the
research question. Conclude with an analysis of the strengths
and limitations of correlational analysis.
Reference
Anderson, C. A., & Bushman, B. J. (2001). Effects of violent
video games on aggressive behavior, aggressive cognition,
aggressive affect, physiological arousal, and prosocial behavior:
A meta-analytic review of the scientific
literature. Psychological Science, 12(5), 353–359.
SPSS Data Analysis Report Guidelines
For the SPSS data analysis report assignments in Assessments
2, 3, and 4, you will use the Data Analysis and Application
(DAA) Template with the five sections described below. As
shown in the IBM SPSS step-by-step guides, label all tables and
graphs in a manner consistent with Capella's APA Style and
Format guidelines. Citations, if needed, should be included in
the text and references included in a reference section at the end
of the report. The organization of the report should include the
following five sections:
Section 1: Data File Description (One Paragraph)

1. Describe the context of the data set. Cite a previous
description if the same data set is used from a previous
assignment. To increase the formal tone of the DAA, avoid
first-person perspective "I." For example, do not write, "I ran a
scatter plot shown in Figure 1." Instead, write, "Figure 1 shows.
. . ."
2. Specify the variables used in this DAA and the scale of
measurement of each variable.
3. Specify sample size (N).
Section 2: Testing Assumptions (Multiple Paragraphs)
1. Articulate the assumptions of the statistical test.
2. Paste SPSS output that tests those assumptions and interpret
them. Properly embed SPSS output where appropriate. Do not
string all output together at the beginning of the section. In
other words, interpretations of figures and tables should be near
(that is, immediately above or below) where the output appears.
Format figures and tables per APA formatting. Refer to the
examples in the IBM SPSS step-by-step guides.
3. Summarize whether or not the assumptions are met. If
assumptions are not met, discuss how to ameliorate violations
of the assumptions.
Section 3: Research Question, Hypotheses, and Alpha Level
(One Paragraph)
1. Articulate a research question relevant to the statistical test.
2. Articulate the null hypothesis and alternative hypothesis for
the research question.
3. Specify the alpha level (.05 unless otherwise specified).
Section 4: Interpretation (Multiple Paragraphs)
1. Paste SPSS output for an inferential statistic and report it.
Properly embed SPSS output where appropriate. Do not string
all output together at the beginning of the section. In other
words, interpretations of figures and tables should be near (that
is, immediately above or below) where the output appears.
Format figures and tables per APA formatting.
2. Report the test statistics. For guidance, refer to the "Results"
examples at the end of the appropriate chapter of your Warner

text.
3. Interpret statistical results against the null hypothesis.
Section 5: Conclusion (Two Paragraphs)
1. Provide a brief summary (one paragraph) of the DAA
conclusions.
2. Analyze strengths and limitations of the statistical test.

Assessment 2 ContextIn many data analyses, it is desirable.docx

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