For this assignment you will undertake an analysis based on a self-designed fictitious study that utilizes statistical analyses

Activity Description
For this assignment you will undertake an analysis based on a self-designed fictitious study that utilizes statistical analyses. You will first develop a fictitious problem to examine. It can be anything. For example, maybe you want to look at whether scores on a standardized college placement test (such as the SAT) are related to the level of income a person makes 10 years after college, or whether those who participate in a Leadership Training program were later rated as better managers compared to those who did not take the training, or whether political affiliation is related to gender. These are just a few examples. Be creative and think about what piques your interest. You might also address a problem that you may want to examine in future research for a thesis or dissertation.
You will use Excel to conduct the analysis. Write an analysis report in which you include the following:
1. Describe your research study.
2. State a hypothesis.
3. List and explain the variables you would collect in this study. There must be a minimum of three (3) variables and two (2) must meet the assumptions for a correlational analysis.
4. Create a fictitious data set that you will analyze. The data should have a minimum of 30 cases, but not more than 50 cases.
5. Conduct a descriptive data analysis that includes the following: a) a measure of central tendency; b) a measure of dispersion and c) at least one graph.
6. Briefly interpret the descriptive data analysis.
7. Conduct the appropriate statistical test that will answer your hypothesis. It must be a statistical test covered in this course such as regression analysis, single t-test, independent t-test, cross-tabulations, Chi-square, or One-Way ANOVA. Explain your justification for using the test based on the type of data and the level of measurement that the data lends to for the statistical analysis.
8. Report and interpret your findings. Use APA style and include a statement about whether you reject or fail to reject the null hypothesis.
9. Copy and paste your Excel data output to include it as an appendix to your document submission.
10. Remember, the goal of this project is to show what you have learned in the course. Therefore, this project becomes a cumulative learning project where you can demonstrate what you have learned through all the previous assignments, readings and video presentations that you have watched.
Support your paper with a minimum of five (5) scholarly resources. In addition to these specified resources, other appropriate scholarly resources, including older articles, may be included.

Length: 10-12 pages not including title and reference pages, may include spreadsheets

USE 2 articles included & 3 additional

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Introduction:
Signature Assignment: A Statistical Study
The signature assignment, as the cliché goes, is where “rubber meets the road.” Throughout this course, you were exposed to several
statistical theories and methods to evaluate hypotheses. It is now time to display your competence of the knowledge you have acquired. The
signature assignment for this course provides an opportunity for you to apply your skills and creativity to a self­designed fictitious study that
employs statistical analyses and requires you to use your computational, analytical, and interpretive skills.
Review the resources listed in the Books and Resources area below to prepare for this week’s assignments.

Books and Resources for this Week:

Books

Reference
Statistical reasoning for everyday life.

Instruction
Review Chapters
as needed

Document/Other

Reference
Kahn, J. (2010). Reporting Statistics in APA Style.
http://my.ilstu.edu/~jhkahn/apastats.html

#1 Article
Reporting Statistics in APA Style
Dr. Jeffrey Kahn, Illinois State University

The following examples illustrate how to report statistics in the text of a
research report. You will note that significance levels in journal articles-especially in tables–are often reported as either “p > .05,” “p < .05,” “p < .
01,” or “p < .001.” APA style dictates reporting the exact p value within the
text of a manuscript (unless the p value is less than .001).
Please pay attention to issues of italics and spacing. APA style is very precise
about these. Also, with the exception of some p values, most statistics should
be rounded to two decimal places.

Mean and Standard Deviation are most clearly presented in parentheses:
The sample as a whole was relatively young (M = 19.22, SD = 3.45).
The average age of students was 19.22 years (SD = 3.45).
Percentages are also most clearly displayed in parentheses with no decimal
places:

Instruction
Read Article

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Nearly half (49%) of the sample was married.
Chi-Square statistics are reported with degrees of freedom and sample size
in parentheses, the Pearson chi-square value (rounded to two decimal places),
and the significance level:
The percentage of participants that were married did not differ by
gender, ?2(1, N = 90) = 0.89, p = .35.
T Tests are reported like chi-squares, but only the degrees of freedom are in
parentheses. Following that, report the t statistic (rounded to two decimal
places) and the significance level.
There was a significant effect for gender, t(54) = 5.43, p < .001, with men
receiving higher scores than women.
ANOVAs (both one-way and two-way) are reported like the t test, but there
are two degrees-of-freedom numbers to report. First report the betweengroups degrees of freedom, then report the within-groups degrees of freedom
(separated by a comma). After that report the F statistic (rounded off to two
decimal places) and the significance level.
There was a significant main effect for treatment, F(1, 145) = 5.43, p = .02,
and a significant interaction, F(2, 145) = 3.24, p = .04.
Correlations are reported with the degrees of freedom (which is N-2) in
parentheses and the significance level:
The two variables were strongly correlated, r(55) = .49, p < .01.
Regression results are often best presented in a table. APA doesn’t say much
about how to report regression results in the text, but if you would like to
report the regression in the text of your Results section, you should at least
present the unstandardized or standardized slope (beta), whichever is more
interpretable given the data, along with the t-test and the corresponding
significance level. (Degrees of freedom for the t-test is N-k-1 where k equals
the number of predictor variables.) It is also customary to report the
percentage of variance explained along with the corresponding F test.
Social support significantly predicted depression scores, ??= -.34, t(225) =
6.53, p < .001. Social support also explained a significant proportion of
variance in depression scores, R2 = .12, F(1, 225) = 42.64, p < .001.

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Tables are useful if you find that a paragraph has almost as many numbers as
words. If you do use a table, do not also report the same information in the
text. It’s either one or the other.

Based on:
American Psychological Association. (2010). Publication manual of the
American Psychological Association (6th ed.). Washington, DC: Author.

Reporting Results of Common Statistical Tests in APA Format. (2010).
http://web.psych.washington.edu/writingcenter/writingguides/pdf/stats.pdf

# 2 Article
University of Washington
Psychology Writing Center
http://www.psych.uw.edu/psych.php#p=339
Box 351525
psywc@uw.edu
(206) 685-8278

Copyright 2010, University of Washington stats.pdf

Read Article

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Reporting Results of Common Statistical Tests in APA Format

The goal of the results section in an empirical paper is to report the
results of the data analysis used to test a
hypothesis. The results section should be in condensed format and
lacking interpretation. Avoid discussing why
or how the experiment was performed or alluding to whether your
results are good or bad, expected or
unexpected, interesting or uninteresting. This document is
specifically about how to report statistical results.
Refer to our handout “Writing an APA Empirical (lab) Report” for
details on writing a results section.

Every statistical test that you report should relate directly to a
hypothesis. Begin the results section by restating
each hypothesis, then state whether your results supported it, then
give the data and statistics that allowed you to
draw this conclusion.

If you have multiple numerical results to report, it’s often a good idea
to present them in a figure (graph) or a
table (see our handout on APA table guidelines).

In reporting the results of statistical tests, report the descriptive
statistics, such as means and standard deviations,

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as well as the test statistic, degrees of freedom, obtained value of the
test, and the probability of the result
occurring by chance (p value). Test statistics and p values should be
rounded to two decimal places. All
statistical symbols that are not Greek letters should be italicized (M,
SD, N, t, p, etc.).

When reporting a significant difference between two conditions,
indicate the direction of this difference, i.e.
which condition was more/less/higher/lower than the other
condition(s). Assume that your audience has a
professional knowledge of statistics. Don’t explain how or why you
used a certain test unless it is unusual.

p values
There are two ways to report p values. One way is to use the alpha
level (the a priori criterion for the
probablility of falsely rejecting your null hypothesis), which is
typically .05 or .01. Example: F(1, 24) = 44.4, p
< .01. You may also report the exact p value (the a posteriori
probability that the result that you obtained, or one
more extreme, occurred by chance). Example: t(33) = 2.10, p = .03. If
your exact p value is less than .001, it is
conventional to state merely p < .001. If you report exact p values,
state early in the results section the alpha
level used as a significance criterion for your tests. Example: “We
used an alpha level of .05 for all statistical

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tests.”

EXAMPLES

Reporting a significant single sample t-test (µ ? µ0):
Students taking statistics courses in psychology at the University of
Washington reported studying more hours
for tests (M = 121, SD = 14.2) than did UW college students in in
general, t(33) = 2.10, p = .034.

Reporting a significant t-test for dependent groups (µ1 ? µ2):
Results indicate a significant preference for pecan pie (M = 3.45, SD =
1.11) over cherry pie (M = 3.00, SD =
.80), t(15) = 4.00, p = .001.

Reporting a significant t-test for independent groups (µ1 ? µ2):
UW students taking statistics courses in Psychology had higher IQ
scores (M = 121, SD = 14.2) than did those
taking statistics courses in Statistics (M = 117, SD = 10.3), t(44) = 1.23,
p = .09.

Over a two-day period, participants drank significantly fewer drinks in
the experimental group (M= 0.667, SD =
Copyright 2010, University of Washington stats.pdf

1.15) than did those in the wait-list control group (M= 8.00, SD= 2.00), t(4) = -5.51, p=.005.
Reporting a significant omnibus F test for a one­way ANOVA:
An analysis of variance showed that the effect of noise was significant, F(3,27) = 5.94, p = .007. Post hoc

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analyses using the Scheffé post hoc criterion for significance indicated that the average number of errors
was
significantly lower in the white noise condition (M = 12.4, SD = 2.26) than in the other two noise
conditions
(traffic and industrial) combined (M = 13.62, SD = 5.56), F(3, 27) = 7.77, p = .042.
Reporting tests of a priori hypotheses in a multi­group study:
Tests of the four a priori hypotheses were conducted using Bonferroni adjusted alpha levels of .0125 per
test
(.05/4). Results indicated that the average number of errors was significantly lower in the silence
condition (M =
8.11, SD = 4.32) than were those in both the white noise condition (M = 12.4, SD = 2.26), F(1, 27) = 8.90,
p =
.011 and in the industrial noise condition (M = 15.28, SD = 3.30), F(1, 27) = 10.22, p = .007. The pairwise
comparison of the traffic noise condition with the silence condition was non-significant. The average
number of
errors in all noise conditions combined (M = 15.2, SD = 6.32) was significantly higher than those in the
silence
condition (M = 8.11, SD = 3.30), F(1, 27) = 8.66, p = .009.
Reporting results of major tests in factorial ANOVA; non­significant interaction:
Attitude change scores were subjected to a two-way analysis of variance having two levels of message
discrepancy (small, large) and two levels of source expertise (high, low). All effects were statistically
significant
at the .05 significance level.
The main effect of message discrepancy yielded an F ratio of F(1, 24) = 44.4, p < .001, indicating that the
mean
change score was significantly greater for large-discrepancy messages (M = 4.78, SD = 1.99) than for
smalldiscrepancy
messages (M = 2.17, SD = 1.25). The main effect of source expertise yielded an F ratio of F(1, 24)
= 25.4, p < .01, indicating that the mean change score was significantly higher in the high-expertise
message
source (M = 5.49, SD = 2.25) than in the low-expertise message source (M = 0.88, SD = 1.21). The
interaction
effect was non-significant, F(1, 24) = 1.22, p > .05.
Reporting results of major tests in factorial ANOVA; non­significant interaction:
A two-way analysis of variance yielded a main effect for the diner’s gender, F(1, 108) = 3.93, p < .05,
such that
the average tip was significantly higher for men (M = 15.3%, SD = 4.44) than for women (M = 12.6%, SD
=
6.18). The main effect of touch was non-significant, F(1, 108) = 2.24, p > .05. However, the interaction
effect
was significant, F(1, 108) = 5.55, p < .05, indicating that the gender effect was greater in the touch
condition
than in the non-touch condition.
Reporting the results of a chi­square test of independence:
A chi-square test of independence was performed to examine the relation between religion and college
interest.
The relation between these variables was significant, X2 (2, N = 170) = 14.14, p <.01. Catholic teens were
less
likely to show an interest in attending college than were Protestant teens.

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Reporting the results of a chi­square test of goodness of fit:
A chi-square test of goodness-of-fit was performed to determine whether the three sodas were equally
preferred.
Preference for the three sodas was not equally distributed in the population, X2 (2, N = 55) = 4.53, p < .05.
Thanks to Laura Little, Ph.D., UW Department of Psychology, for providing the examples reported here

MGT5028-8 > Hypothesis Testing, T-Tests, Cross-Tabulations, and Chi-Square

Week 8 Assignment: Create and Analyze a Self-Designed Statistical Study

Activity Description
For this assignment you will undertake an analysis based on a self-designed fictitious study
that utilizes statistical analyses. You will first develop a fictitious problem to examine. It can be
anything. For example, maybe you want to look at whether scores on a standardized college
placement test (such as the SAT) are related to the level of income a person makes 10 years after
college, or whether those who participate in a Leadership Training program were later rated as
better managers compared to those who did not take the training, or whether political affiliation
is related to gender. These are just a few examples. Be creative and think about what piques your
interest. You might also address a problem that you may want to examine in future research
for a thesis or dissertation.
You will use Excel to conduct the analysis. Write an analysis report in which you include the
following:
1. Describe your research study.
2. State a hypothesis.
3. List and explain the variables you would collect in this study. There must be a

minimum of three (3) variables and two (2) must meet the assumptions for a
correlational analysis.
4. Create a fictitious data set that you will analyze. The data should have a minimum

of 30 cases, but not more than 50 cases.
5. Conduct a descriptive data analysis that includes the following: a) a measure of

central tendency; b) a measure of dispersion and c) at least one graph.
6. Briefly interpret the descriptive data analysis.
7. Conduct the appropriate statistical test that will answer your hypothesis. It must be

a statistical test covered in this course such as regression analysis, single t-test,independent t-test, cross-tabulations, Chi-square, or One-Way ANOVA. Explain your justification for using the test based on the type of data and the level of measurement that the data lends to for the statistical analysis.

8. Report and interpret your findings. Use APA style and include a statement about whether you reject or fail to reject the null hypothesis.
9. Copy and paste your Excel data output to include it as an appendix to your

document submission.
10. Remember, the goal of this project is to show what you have learned in the course.

Therefore, this project becomes a cumulative learning project where you can
demonstrate what you have learned through all the previous assignments, readings
and video presentations that you have watched.
Support your paper with a minimum of five (5) scholarly resources. In addition to these specified resources, other appropriate scholarly resources, including older articles, may be included.
Length: 10-12 pages not including title and reference pages, may include spreadsheets
Your response should demonstrate thoughtful consideration of the ideas and concepts that are
presented in the course and provide new thoughts and insights relating directly to this topic. Your
response should reflect scholarly writing and current APA standards where appropriate. Be sure
to adhere to Northcentral University’s Academic Integrity Policy.
Learning Outcomes
9.0 Determine alpha (p-values) values and interpret p-levels as related to statistical significance.
10.0 Utilize statistical software such as Excel to conduct data analysis.
11.0 Analyze the use and applicability of statistics in personal, professional, and academic
applications, and as a tool for research.