# 8210 week 5 assignment

For this Assignment, you will examine statistical significance and meaningfulness based on sample statements.

To prepare for this Assignment:

• Review the Week 5 Scenarios found in this week’s Learning Resources      and select two of the four scenarios for this Assignment.
• For additional support, review the Skill Builder:      Evaluating P Values and the Skill Builder: Statistical      Power, which you can find by navigating back to your Blackboard Course      Home Page. From there, locate the Skill Builder link in the left      navigation pane.

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8210 Week 5 Assignment:

Evaluating Significance of Findings

Part of your task as a scholar-practitioner is to act as a critical consumer of research and ask informed questions of published material. Sometimes, claims are made that do not match the results of the analysis. Unfortunately, this is why statistics is sometimes unfairly associated with telling lies. These misalignments might not be solely attributable to statistical nonsense, but also “user error.” One of the greatest areas of user error is within the practice of hypothesis testing and interpreting statistical significance. As you continue to consume research, be sure and read everything with a critical eye and call out statements that do not match the results.

For this Assignment, you will examine statistical significance and meaningfulness based on sample statements.

To prepare for this Assignment:

· Review the Week 5 Scenarios found in this week’s Learning Resources and select two of the four scenarios for this Assignment.

· For additional support, review the Skill Builder: Evaluating P Values and the Skill Builder: Statistical Power, which you can find by navigating back to your Blackboard Course Home Page. From there, locate the Skill Builder link in the left navigation pane.

Critically evaluate the two scenarios you selected based upon the following points:

· Critically evaluate the sample size.

· Critically evaluate the statements for meaningfulness.

· Critically evaluate the statements for statistical significance.

· Based on your evaluation, provide an explanation of the implications for social change.

Use proper APA format and citations, and referencing.

Learning Resources

Frankfort-Nachmias, C., Leon-Guerrero, A., & Davis, G. (2020). Social statistics for a diverse society (9th ed.). Thousand Oaks, CA: Sage Publications.

· Chapter 8, “Testing Hypothesis: Assumptions of Statistical Hypothesis Testing” (pp. 241-242)

Wagner, III, W. E. (2020). Using IBM® SPSS® statistics for research methods and social science statistics (7th ed.). Thousand Oaks, CA: Sage Publications.

· Chapter 6, “Testing Hypotheses Using Means and Cross-Tabulation”

Warner, R. M. (2012). Applied statistics from bivariate through multivariate techniques (2nd ed.). Thousand Oaks, CA: Sage Publications.

Applied Statistics From Bivariate Through Multivariate Techniques, 2nd Edition by Warner, R.M. Copyright 2012 by Sage College. Reprinted by permission of Sage College via the Copyright Clearance Center.

· Chapter 3, “Statistical Significance Testing” (pp. 81–124)

Applied Statistics From Bivariate Through Multivariate Techniques, 2nd Edition by Warner, R.M. Copyright 2012 by Sage College. Reprinted by permission of Sage College via the Copyright Clearance Center.

Magnusson, K. (n.d.). Welcome to Kristoffer Magnusson’s blog about R, Statistics, Psychology, Open Science, Data Visualization [blog]. Retrieved from http://rpsychologist.com/index.html

As you review this web blog, select [Updated] Statistical Power and Significance Testing Visualization link, once you select the link, follow the instructions to view the interactive for statistical power. This interactive website will help you to visualize and understand statistical power and significance testing.

Note: This is Kristoffer Magnusson’s personal blog and his views may not necessarily reflect the views of Walden University faculty.

American Statistical Association (2016). American Statistical Association Releases Statement on Statistical Significance and P-Values. Retrieved from http://www.amstat.org/newsroom/pressreleases/P-ValueStatement.pdf

As you review this press release, consider the misconceptions and the misuse of p-values in quantitative research.

Document: Week 5 Scenarios (PDF)

Use these scenarios to complete this week’s Assignment.

Datasets

Your instructor will post the datasets for the course in the Doc Sharing section and in an Announcement. Your instructor may also recommend using a different dataset from the ones provided here.

Required Media

Walden University, LLC. (Producer). (2016f). Meaningfulness vs. statistical significance [Video file]. Baltimore, MD: Author.

Note: The approximate length of this media piece is 4 minutes.

In this media program, Dr. Matt Jones discusses the differences in meaningfulness and statistical significance. Focus on how this information will inform your Discussion and Assignment for this week.

Accessible player

Walden University, LLC. (Producer). (2016n). Halfway point [Video file]. Baltimore, MD: Author.

Note: The approximate length of this media piece is 2 minutes.

In this media program, Dr. Annie Pezalla, Associate Director of Curriculum and Assessment with the Office of Research and Doctoral Studies at Walden University, discusses what you have learned so far in the course. She also discusses what you have to look forward to as well as things to look out for in the remainder of the course.

Accessible player

Optional Resources

Skill Builders:

· Evaluating P Values

· Statistical Power

To access these Skill Builders, navigate back to your Blackboard Course Home page, and locate “Skill Builders” in the left navigation pane. From there, click on the relevant Skill Builder link for this week.

You are encouraged to click through these and all Skill Builders to gain additional practice with these concepts. Doing so will bolster your knowledge of the concepts you’re learning this week and throughout the course.

© 2016 Laureate Education, Inc. Page 1 of 2

Week 5

Scenarios

1. The p-value was slightly above conventional threshold, but was described as
“rapidly approaching significance” (i.e., p =.06).

An independent samples t test was used to determine whether student satisfaction
levels in a quantitative reasoning course differed between the traditional classroom
and on-line environments. The samples consisted of students in four face-to-face
classes at a traditional state university (n = 65) and four online classes offered at
the same university (n = 69). Students reported their level of satisfaction on a five-
point scale, with higher values indicating higher levels of satisfaction. Since the
study was exploratory in nature, levels of significance were relaxed to the .10 level.
The test was significant t(132) = 1.8, p = .074, wherein students in the face-to-face
class reported lower levels of satisfaction (M = 3.39, SD = 1.8) than did those in the
online sections (M = 3.89, SD = 1.4). We therefore conclude that on average,
students in online quantitative reasoning classes have higher levels of satisfaction.
The results of this study are significant because they provide educators with
evidence of what medium works better in producing quantitatively knowledgeable
practitioners.

2. A results report that does not find any effect and also has small sample size
(possibly no effect detected due to lack of power).

A one-way analysis of variance was used to test whether a relationship exists
between educational attainment and race. The dependent variable of education
was measured as number of years of education completed. The race factor had
three attributes of European American (n = 36), African American (n = 23) and
Hispanic (n = 18). Descriptive statistics indicate that on average, European
Americans have higher levels of education (M = 16.4, SD = 4.6), with African
Americans slightly trailing (M = 15.5, SD = 6.8) and Hispanics having on average
lower levels of educational attainment (M = 13.3, SD = 6.1). The ANOVA was not
significant F (2,74) = 1.789, p = .175, indicating there are no differences in
educational attainment across these three races in the population. The results of
this study are significant because they shed light on the current social conversation

3. Statistical significance is found in a study, but the effect in reality is very small (i.e.,
there was a very minor difference in attitude between men and women). Were the
results meaningful?

An independent samples t test was conducted to determine whether differences
exist between men and women on cultural competency scores. The samples
consisted of 663 women and 650 men taken from a convenience sample of public,
private, and non-profit organizations. Each participant was administered an
instrument that measured his or her current levels of cultural competency. The

© 2016 Laureate Education, Inc. Page 2 of 2

cultural competency score ranges from 0 to 10, with higher scores indicating higher
levels of cultural competency. The descriptive statistics indicate women have
higher levels of cultural competency (M = 9.2, SD = 3.2) than men (M = 8.9, SD =
2.1). The results were significant t (1311) = 2.0, p <.05, indicating that women are
more culturally competent than are men. These results tell us that gender-specific
interventions targeted toward men may assist in bolstering cultural competency.

4. A study has results that seem fine, but there is no clear association to social
change. What is missing?

A correlation test was conducted to determine whether a relationship exists
between level of income and job satisfaction. The sample consisted of 432
employees equally represented across public, private, and non-profit sectors. The
results of the test demonstrate a strong positive correlation between the two
variables, r =.87, p < .01, showing that as level of income increases, job
satisfaction increases as well.