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

[email protected]

(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.

9

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.