· Toggle DrawerOverviewFor this assessment, you will complete .docx

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Overview
For this assessment, you will complete an SPSS data analysis
report using t-test output for assigned variables.
You will review the theory, logic, and application of t tests.
The t test is a basic inferential statistic often reported in
psychological research. You will discover that t tests, as well as
analysis of variance (ANOVA), compare group means on some
quantitative outcome variable.
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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 that includes strengths and limitations
of an independent-samples t test.
. Competency 2: Analyze the decision-making process of data
analysis.
2. Analyze the assumptions of the independent-samples t test.
. 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 output of the independent-samples t test.
. Competency 5: Apply a statistical program's procedure to data.
5. Apply the appropriate SPSS procedures to check assumptions
and calculate the independent-samples t test to generate relevant
output.
. 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.
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Context
Read Assessment 3 Context [DOC] for important information on
the following topics:
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. Logic of the t test.
. Assumptions of the t test.
. Hypothesis testing for a t test.
. Effect size for a t test.
. Testing assumptions: The Shapiro-Wilk test and Levene's test.
. Proper reporting of the independent-samples t test.
. t, degrees of freedom, and t value.
. Probability value.
. Effect size.
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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.
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Various Forms of the t Test
. In what research situations should the paired-samples t test be
used rather than the independent-samples t test?

Two Versions of the Independent-Samples t Test
. Why are there are two different versions of the t test on the
SPSS printout and how do you decide which one is more
appropriate?
Application of t Tests
. Is there a research question from your professional life or
career specialization that can be addressed by an independent-
samples t test?
. Why would a t test be the appropriate analysis for this research
question?
. What are the variables and their scale of measurement?
. What is the expected outcome? (For example, "The group 1
mean score will be significantly greater than the group 2 mean
score because….")
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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 presented for other assessments.
. 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.
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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 3 Context [DOC].
7. Read this resource for information about the statistical
terminology and concepts needed to complete this assessment.
SPSS Software and Procedures
. IBM SPSS Step-By-Step Instructions: t Tests [DOCX].
8. This course file provides instructions for conducting a t test

using SPSS.
. In your IBM SPSS Statistics Step by Step library e-book:
9. Chapter 11, "The t-Test Procedure,"
t Test
. StatSoft, Inc. (2013). Electronic statistics textbook. Tulsa,
OK: StatSoft. Retrieved from http://www.statsoft.com/textbook
10. Basic Statistics.
1. This section contains sections on t tests.
· Skillsoft. (n.d.). Introduction to hypothesis testing and tests
for means in six sigma [Video].
. View these two videos about tests for means.
1. One-Sample Tests for Means.
1. Two-Sample Tests for Means.
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
· Delphin-Rittmon, M. E., Flanagan, E. H., Bellamy, C. D.,
Diaz, A., Johnson, K., Molta, V., ... Ortiz, J. (2015). Learning
from those we serve: Piloting a culture competence intervention
co-developed by university faculty and persons in
recovery. Psychiatric Rehabilitation Journal, 39(1), 14–19.
School of Education Learners
· Stone, E. (2010). t test, independent samples. In N. J. Salkind
(Ed.), Encyclopedia of research design (pp. 1552–1556).
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
Read Assessment 3 Context (linked in the Resources) to learn
about the concepts used in this assessment.
You will use the following resources for this assessment. They

are linked in the Resources.
· Complete this 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: t Tests 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 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.
Step 1: Write Section 1 of the DAA
· Provide a context of the grades.sav data set.
· Include a definition of the specified variables (predictor,
outcome) and corresponding scales of measurement.
· Specify the sample size of the data set.
· Analyze the assumptions of the t test.
· Paste the SPSS histogram output for gpa and discuss your
visual interpretations.
· Paste SPSS descriptives output showing skewness and kurtosis
values for gpa and interpret them.
· Paste SPSS output for the Shapiro-Wilk test of gpa and
interpret it.
· Report the results of the Levene's test and interpret it.
· Summarize whether or not the assumptions of the t test are
met.
· Specify a research question related to gender and gpa.
· Articulate the null hypothesis and alternative hypothesis.

· Specify the alpha level.
· Paste the SPSS output of the t test.
· Report the results of the SPSS output using proper APA
guidelines. Include the following:
. t.
. Degrees of freedom.
. t value.
. p value.
. Effect size.
. Interpretation of effect size.
. Means and standard deviations for each group. Mean
difference.
. Interpret the results against the null hypothesis.
· Discuss the implications of this t test as it relates to the
research question.
· Conclude with an analysis of the strengths and limitations
of t-test analysis.
For this essay, you will analyze what makes an effective rhetor
(someone who uses
rhetoric publicly) and how specific rhetorical tactics operate.
You will select a person
(a “rhetorical superhero”), living or dead, and examine how
their language and texts
have made a difference to improve a specific community (e.g.
convinced people to
work towards change, compelled people to act, etc.). You can

define “community”
broadly (e.g. women, the American people, etc.) or more
specifically (e.g. the Bronx,
Russian LGBTQ youth, etc.).
Your essay should use rhetorical vocabulary to account for who
the rhetor is (ethos),
how the person made/makes a difference using language (in
specific incidences),
and how that difference is tangible or verifiable in the world
(audience). Your essay
should incorporate evidence from published interviews and/or
historical or reference
materials. You must include some direct quotes from words
your subject has written
or spoken aloud, and these quotes should come from at least
four distinct outside
sources (primary sources). For example, if you were writing
your essay about
Beyonce, you could quote from two of her songs, an interview,
and an award
acceptance speech. What is important is that you are able to
find sources that
demonstrate how your subject uses rhetoric. All outside sources
must be cited in-

text and in a Works Cited page, according to MLA format.
In your rhetorical analysis essay, you analyzed one “text”
(song/music video) in
depth. In this essay, you will be writing about multiple sources
and synthesizing your
analysis to draw conclusions about your subject.
Your essay should consider what specific rhetorical techniques
your subject uses
(ethos, pathos, logos, repetition, figurative language, diction,
sentence structure,
parallelism, tone, visual rhetoric, etc.). Your essay should also
explain HOW and
WHY these rhetorical techniques are effective. How do they
help your subject reach
their audience(s)? What evidence (cite outside sources) do you
have that
supports that their use of rhetoric is effective?
Your rhetorical superhero should be someone relatively well-
known (i.e. not just
someone you know personally). They can be a historical figure,
politician, scientist,
celebrity, artist, writer, activist, musician, philanthropist,
athlete, coach, etc. The only

requirement is that they be an effective rhetorician, meaning
that they are able to
reach their audience(s) by using rhetoric in a convincing and
ethical way.
In this essay, you will use some of the strategies of the personal
narrative and some
of the strategies of the rhetorical analysis essay. During the
personal narrative unit,
we explored how to use an essay to tell a story, entertain the
reader, and
characterize people. In this essay, you will be using some of
those strategies to
make a compelling argument about how a particular rhetor
engages with the world
and communicate with their audience. You can (but don’t have
to) use first person in
this essay, and you can use a more informal tone than you used
in the rhetorical
analysis essay.
Because I want us to focus on ways in which rhetoric can be
used to improve our
communities, I request that you not select a “rhetorical

superhero” who has used
rhetoric for evil purposes (i.e. don’t write about fascists,
dictators, serial killers, etc.).
The goal of this activity is for you and your classmates to think
critically about how
we can borrow strategies from others to use rhetoric effectively
in our own lives, so
let’s focus on rhetoric that is not intended to cause harm.
Your final essay will be at least 5-6 pages (not counting the
Works Cited page),
double-spaced, with 12 pt. Times New Roman font and MLA
format.
You will also be giving a short (5 minute) oral presentation on
your essay topic at
the end of the semester. This presentation is a separate grade
that is worth 5% of
your overall grade in the course.
Rubric for Essay 3: Rhetorical Profile
*Note that all of these categories are not weighted equally in
determining the final
grade

Introduction/Thesis: The essay has a clear thesis/line of inquiry
that is analytic,
specific, and focused. The thesis addresses how the rhetor uses
specific rhetorical
strategies to achieve a particular purpose. The essay’s
introduction clearly illustrates
the focus and purpose of the essay (rhetorical analysis),
provides necessary
social/political/historical context for the subject, and is an
appropriate length (no
more than ⅔ page).
F--------------------------------------------------C---------------------
-----------------------------A
Focus/Development: The essay is very well-developed, and
each paragraph has a
clear, specific focus that relates back to the paper’s main
argument (thesis). Each
body paragraph begins with a topic sentence that accurately
states that paragraph’s
focus. Sufficient evidence from at least four primary sources is
presented
and analyzed in relation to the essay’s main argument. The
analysis is thorough,

thoughtful, and insightful. The analysis explains how the
evidence is an example of a
specific rhetorical strategy, why the rhetor uses this strategy,
and how it helps them
reach their audience and achieve their purpose.
F--------------------------------------------------C---------------------
-----------------------------A
Conclusion: The essay includes a conclusion paragraph that
restates the thesis (in
different words/phrasing) and answers the “so what” question. It
should be clear to
the audience why your subject matters, why their rhetoric
matters, and why your
reader should care about them too.
F--------------------------------------------------C---------------------
-----------------------------A
Organization and Flow: The paragraphs are an appropriate
length. The essay has

a smooth flow, without any choppiness, and transitions are used
effectively.
F--------------------------------------------------C---------------------
-----------------------------A
Style: There are clear sentences with varied structures, and
there is an assertive,
formal, and academic tone. The writer avoids passive voice,
second person, and
vague constructions and word choices.
F--------------------------------------------------C---------------------
-----------------------------A
MLA Format and Grammar/Mechanics: The essay has one-inch
margins, correct
headings/headers, and correctly formatted in-text citations. The
essay has a
correctly formatted Works Cited page. There is a a specific
academic title that
accurately prepares the reader for the content of the essay.
There are no spelling,
grammar, and punctuation errors present.

F--------------------------------------------------C---------------------
-----------------------------A
Assessment 3 Context
You will review the theory, logic, and application of t-tests.
The t-test is a basic inferential statistic often reported in
psychological research. You will discover that t-tests, as well as
analysis of variance (ANOVA), compare group means on some
quantitative outcome variable.
Recall that null hypothesis tests are of two types: (1)
differences between group means and (2) association between
variables. In both cases there is a null hypothesis and an
alternative hypothesis. In the group means test, the null
hypothesis is that the two groups have equal means, and the
alternative hypothesis is that the two groups do not have equal
means. In the association between variables type of test, the
null hypothesis is that the correlation coefficient between the
two variables is zero, and the alternative hypothesis is that the
correlation coefficient is not zero.
Notice in each case that the hypotheses are mutually exclusive.
If the null is false, the alternative must be true. The purpose of
null hypothesis statistical tests is generally to show that the null
has a low probability of being true (the p value is less than .05)
– low enough that the researcher can legitimately claim it is
false. The reason this is done is to support the allegation that
the alternative hypothesis is true.
In this context you will be studying the details of the first type

of test. This is the test of difference between group means. In
variations on this model, the two groups can actually be the
same people under different conditions, or one of the groups
may be assigned a fixed theoretical value. The main idea is that
two mean values are being compared. The two groups each have
an average score or mean on some variable. The null hypothesis
is that the difference between the means is zero. The alternative
hypothesis is that the difference between the means is not zero.
Notice that if the null is false, the alternative must be true. It is
first instructive to consider some of the details of groups.
Means, and difference between them.
Null Hypothesis Significance Test
The most common forms of the Null Hypothesis Significance
Test (NHST) are three types of t tests, and the test of
significance of a correlation. The NHST also extends to more
complex tests, such as ANOVA, which will be discussed
separately. Below, the null hypothesis and the alternative
hypothesis are given for each of the following tests. It would be
a valuable use of your time to commit the information below to
memory. Once this is done, then when we refer to the tests later,
you will have some structure to make sense of the more detailed
explanations.
1. One-sample t test: The question in this test is whether a
single sample group mean is significantly different from some
stated or fixed theoretical value - the fixed value is called a
parameter.
· Null Hypothesis: The difference between the sample group
mean and the fixed value is zero in the population.
· Alternative hypothesis: The difference between the sample
group mean and the fixed value is NOT zero in the population.
2. Dependent samples t test (also known as correlated groups t
or repeated measures t): The question in this test is whether two

scores for each participant differ significantly. It is actually a
special case of the one-sample test, where each person's score is
the difference between his or her two original scores (difference
scores). If there is no significant difference in the population,
then the mean population difference score is zero (the fixed
value).
· Null Hypothesis: The mean difference between the two scores
for each participant is zero in the population.
· Alternative hypothesis: The mean difference between the two
scores for each participant is NOT zero in the population
3. Independent samples t test (two independent groups): The
question in this test is whether or not two group means are from
the same population, or from populations with different means.
· Null Hypothesis: The difference between the two group's
means is zero in the population, or the two groups are from the
same population.
· Alternative hypothesis: The difference between the two
group's means is NOT zero in the population, or the two groups
are from different populations.Logic of the t-Test
Imagine that a school psychologist compares the mean IQ scores
of Class A versus Class B. The mean IQ for Class A is 102.0
and the mean IQ for Class B is 105.0. Is there a significant
difference in mean IQ between Class A and Class B?
To answer this question, the school psychologist conducts an
independent samples t-test. The independent samples t-test
compares two group means in a between-subjects (between-S)
design. In this between-S design, participants in two
independent groups are measured only once on some outcome
variable. By contrast, a paired samples t-test compares group
means in a within-subjects (within-S) design for one group.
Each participant is measured twice on some outcome variable,

such as a pretest-posttest design. For example, a school
psychologist could measure self-esteem for a class of students
prior to taking a public speaking course (pretest) and then
measure self-esteem again after completing the public speaking
course (posttest). The paired samples t-test determines if there
is a significant difference in mean scores from the pretest to the
posttest.
Focus on the logic and application of the independent samples t-
test. There are two variables in an independent samples t-test:
the predictor variable (X) and the outcome variable (Y). The
predictor variable must be dichotomous, meaning that it can
only have two values (for example, male = 1; female = 2).
Notice this is nominal level variable. The outcome variable
must be at the interval level or above (ratio). Group membership
is mutually exclusive. In nonexperimental designs, group
membership is based on some naturally occurring characteristic
of a group (for example, gender). In experimental designs,
participants are randomly assigned to one of two group
conditions (for example, treatment group = 1; control group =
2). In contrast to the dichotomous (nominal) predictor variable,
the outcome variable must be quantitative to calculate a group
mean (for example, mean IQ score, mean heart rate
score).Assumptions of the t-Test
All inferential statistics, including the independent samples t-
test, operate under assumptions checked prior to calculating the
t-test in SPSS. Violations of assumptions can lead to erroneous
inferences regarding a null hypothesis. The first assumption is
independence of observations. For predictor variable X in an
independent samples t-test, participants are assigned to one and
only one "condition" or "level," such as a treatment group or
control group. This assumption is not statistical in nature; it is
controlled by proper research procedures that maintain
independence of observations.

The second assumption is that outcome variable Y is
quantitative and normally distributed. This assumption is
checked by a visual inspection of the Y histogram and
calculation of skewness and kurtosis values. A researcher may
also conduct a Shapiro-Wilk test in SPSS to check whether a
distribution is significantly different from normal. The null
hypothesis of the ShapiroWilk test is that the distribution is
normal. If the Shapiro-Wilk test is significant, then the
normality assumption is violated. In other words, a researcher
wants the Shapiro-Wilk test to not be significant at p < .05.
The third assumption is referred to as the homogeneity of
variance assumption. Ideally, the amount of variance in Y
scores is approximately equal for group 1 and group 2. This
assumption is checked in SPSS with the Levene test. The null
hypothesis of the Levene test is that group variances are equal.
If the Levene test is significant, then the homogeneity
assumption is violated. In other words, a researcher wants the
Levene test to not be significant at p < .05.
SPSS output for the t-test provides two versions of the t-test:
"Equal variances assumed" and "Equal variances not assumed."
If the Levene test is not significant, researchers report the
"Equal variances assumed" version of the t-test. If the Levene
test is significant, researchers report the more conservative
"Equal variances not assumed" calculation of the t-test in the
second row of the output table.Hypothesis Testing for a t-Test
The null hypothesis for a t-test predicts no significant
difference in population means, or H0: µ1 = µ2. A directional
alternative hypothesis for a t-test is that the population means
differ in a specific direction, such as H1: µ1 > µ2 or H1: µ1 <
µ2. A non-directional alternative hypothesis simply predicts
that the population means differ, but it does not stipulate which
population mean is significantly greater (H1: µ1 ≠ µ2). For t-
tests, the standard alpha level for rejecting the null hypothesis

is set to .05. SPSS output for a t-test showing a p value of less
than indicates that the null hypothesis should be rejected; there
is a significant difference in population means. A p value
greater than .05 indicates that the null hypothesis should not be
rejected; there is not a significant difference in population
means.Effect Size for a t-Test
There are two commonly reported estimates of effect size for
the independent samples t-test, including eta squared (η2) and
Cohen's d. Eta squared is analogous to r2. It estimates the
amount of variance in Y that is attributable to group differences
in X. Eta squared ranges from 0 to 1.0, and it is interpreted
similarly to r2 in terms of "small," "medium," and "large" effect
sizes. Eta squared is calculated as a function of an obtained t
value and the study degrees of freedom.
Cohen's d is an alternate effect size representing the number of
standard deviations the two population means are in the sample.
A small Cohen's d (< .20) indicates a high degree of overlap in
population means. A large Cohen's d (> .80) indicates a low
degree of overlap in population means.Testing Assumptions:
The Shapiro-Wilk Test and the Levene Test
Recall that two assumptions of the t-test are that:
4. Outcome variable Y is normally distributed.
5. The variance of Y scores is approximately equal across
groups (homogeneity assumption).The Shapiro-Wilk Test
In addition to a visual inspection of histograms and skewness
and kurtosis values, SPSS provides a formal statistical test of
normality referred to as the Shapiro-Wilk test. A perfect normal
distribution will have a Shapiro-Wilk value of 1.0. Values less
than 1.0 indicate an increasing departure from a perfect normal
shape. The null hypothesis of the Shapiro-Wilk test is that the
distribution is normal. When the Shapiro-Wilk test indicates a p

value less than .05, the normality assumption is violated.
To obtain the Shapiro-Wilk test, in SPSS select
"Analyze…Descriptive Statistics…Explore." Place the outcome
variable Y in the "Dependent List" box and select the "Plots"
option. Select the "Normality plots with tests" option. Press
"Continue" and then "Ok." SPSS provides the Shapiro-Wilk test
output for interpretation. A significant Shapiro-Wilk test ( p <
.05) suggests that the distribution is not normal and
interpretations may be affected. However, the t-test is fairly
robust to violations of this assumption when sample sizes are
sufficiently large (that is, > 100).The Levene Test
The homogeneity of variance assumption is tested with Levene
test. The Levene test is automatically generated in SPSS when
an independent samples t-test is conducted. The null hypothesis
for the Levene test is that group variances are equal. A
significant Levene test ( p < .05) indicates that the homogeneity
of variance assumption is violated. In this case, report the
"Equal variances not assumed" row of the t-test output. This
version of the t-test uses a more conservative adjusted degrees
of freedom ( df) that compensates for the homogeneity
violation. The adjusted df can often result in a decimal number
(for example, df = 13.4), which is commonly rounded to a whole
number in reporting (for example, df = 13). If the Levene test is
not significant (that is, homogeneity is assumed), report the
"Equal variances assumed" row of the t-test output.Proper
Reporting of the Independent Samples t-Test
Reporting a t-test in proper APA style requires an understanding
of the following elements, including the statistical notation for
an independent samples t-test (t), the degrees of freedom, the t
value, the probability value, and the effect size. To provide
context, provide the means and standard deviations for each
group. For example, imagine an industrial/organizational
psychologist randomly assigns 9 employees to a treatment group
(for example, team-bonding exercises) and 9 employees to a
control group (for example, no exercises) and then subsequently

measures their rates of organizational citizenship behavior
(OCB) over a period of six months. The results show:
The mean OCB scores differed significantly across groups, t(16)
= -2.58, p = .02 (two-tailed). Mean OCB for the control group
(M = 67.8, SD = 8.2) was about 10 OCB points lower than mean
OCB for the treatment group (M = 77.9, SD = 8.1). The effect
size, as indexed by η2 was .30; this is a very large effect.t,
Degrees of Freedom, and t Value
The statistical notation for an independent samples t-test is t,
and following it is the degrees of freedom for this statistical
test. The degrees of freedom for t is n1 + n2 - 2, where n1
equals the number of participants in group 1 and n2 equals the
number of participants in group 2. In the example above, N = 18
(n1 = 9; n2 = 9). The t value is a ratio of the difference in group
means divided by the standard error of the difference in sample
means. The t value can be either positive or negative.Probability
Value
A researcher estimates the probability value based on a table of
critical values of t for rejecting the null hypothesis. In the
example above, with 16 degrees of freedom and alpha level set
to .05 (two-tailed), the table indicates a critical value of +/-
2.12 to reject the null hypothesis. The obtained t value above is
-2.58, which exceeds the critical value required to reject the
null hypothesis. SPSS determined the exact p value to be .02.
This p value is less than .05, which indicates that the null
hypothesis should be rejected for the alternative hypothesis
(that is, the two groups are significantly different in mean
OCB).Effect Size
A common index of effect size for the independent samples t-
test is eta squared (η2). SPSS does not provide this output for
the independent samples t-test, but it is easily calculated by
hand with the following formula: t2 ÷ (t2 + df). In the example
above, the calculation is (-2.58)2 ÷ [(-2.58)2 + 16] = 6.65 ÷

(6.65 + 16) = 6.65 ÷ 22.65 = .29. This eta squared value falls
between < .20 and > .80, and is therefore a "medium" effect
size.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
5
IBM SPSS Step-by-Step Guide: t Tests
Note: This guide is an example of creating t test output in SPSS
with the grades.sav file. The variables shown in this guide do
not correspond with the actual variables assigned in Assessment
3. Carefully follow the Assessment 3 instructions for a list of
assigned variables. Screen shots were created with SPSS 21.0.
Assumptions of t Tests
To complete Section 2 of the DAA for Assessment 3, you will
generate SPSS output for a histogram, descriptive statistics, and
the Shapiro-Wilk test. (Levene test output will appear in
Section 4 of the DAA.) Refer to the Assessment 3 instructions
for a list of assigned variables. The examplevariables lowup and
final are shown below.
Step 1. Open grades.sav in SPSS.
Step 2. Generate SPSS output for the Shapiro-Wilk test of
normality. (Refer to previous step-by-step guides for generating
histogram output and descriptives output for the Assessment 3
variables.)
On the Analyze menu, point to Descriptive Statistics and click
Explore…

In the Explore dialog box, move the assigned Assessment 3
variables into the Dependent List box. The final variable is used
as an example below.
Click the Plots… button.
In the Explore: Plots dialog box, select the Normality plots with
tests option.
Click Continue and then OK.
Step 3. Copy the Tests of Normality table and paste it into
Section 2 of the DAA Template. Interpret the output.
Note: The Levene test is also generated as part of the SPSS t-
test output for Section 4 (discussed next). You do not have to
provide the Levene test output twice. You can report and
interpret it in Section 2 and then provide the actual output in
Section 4.
Reporting of t Tests
DAA Section 4 involves generating the t-test output and
interpreting it. The example variables of lowup (lower division
= 1; upper division = 2) and final are shown below.
Step 1. Generate SPSS output for the t test.
On the Analyze menu, point to Compare Means and click
Independent-Samples T Test…
Step 2. In the Independent-Samples T Test dialog box:
First, move the Assessment 3 dependent variable into the Test
Variable(s) box.
Second, move the Assessment 3 assignment variable into the
Grouping Variable box. Notice the (? ?) after the variable. The
values of the independent variable are assigned in the next step.
Third, click the Define Groups… button.

Fourth, in the Define Groups dialog box, assign the
corresponding values: Group 1 = 1, Group 2 = 2.
Fifth, click Continue and then OK.
Step 3. Copy the output for the independent samples test and
paste it into Section 4 of the DAA Template. Then interpret it as
described in the Assessment 3 assignment instructions.
5
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
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.
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.
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.

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