Discussion: Statistical Significance and Meaningfulness

Once you start to understand how exciting the world of statistics can be, it is tempting to fall into the trap of chasing statistical significance. That is, you may be tempted always to look for relationships that are statistically significant and believe they are valuable solely because of their significance. Although statistical hypothesis testing does help you evaluate claims, it is important to understand the limitations of statistical significance and to interpret the results within the context of the research and its pragmatic, “real world” application.

As a scholar-practitioner, it is important for you to understand that just because a hypothesis test indicates a relationship exists between an intervention and an outcome, there is a difference between groups, or there is a correlation between two constructs, it does not always provide a default measure for its importance. Although relationships are significant, they can be very minute relationships, very small differences, or very weak correlations. In the end, we need to ask whether the relationships or differences observed are large enough that we should make some practical change in policy or practice.

For this Discussion, you will explore statistical significance and meaningfulness.

To prepare for this Discussion:

• Review the Learning Resources related to hypothesis testing, meaningfulness, and statistical significance.
• Review Magnusson’s web blog found in the Learning Resources to further your visualization and understanding of statistical power and significance testing.
• Review the American Statistical Association’s press release and consider the misconceptions and misuse of p-values.
• Consider the scenario:
  • A research paper claims a meaningful contribution to the literature based on finding statistically significant relationships between predictor and response variables. In the footnotes, you see the following statement, “given this research was exploratory in nature, traditional levels of significance to reject the null hypotheses were relaxed to the .10 level.”

BY DAY 3

Post your response to the scenario in which you critically evaluate this footnote. As a reader/reviewer, what response would you provide to the authors about this footnote?

Learning Resources

REQUIRED READINGS

Frankfort-Nachmias, C., & Leon-Guerrero, A. (2018). Social statistics for a diverse society (8th ed.). Thousand Oaks, CA: Sage Publications.

• Chapter 8, “Testing Hypothesis” (pp. 203-204)

Wagner, W. E. (2016). Using IBM® SPSS® statistics for research methods and social science statistics (6th 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

Document: Data Set 2014 General Social Survey (dataset file)
Use this dataset to complete this week’s Discussion.
Note: You will need the SPSS software to open this dataset.

Document: Data Set Afrobarometer (dataset file)
Use this dataset to complete this week’s Assignment.
Note: You will need the SPSS software to open this dataset.

Document: High School Longitudinal Study 2009 Dataset (dataset file)
Use this dataset to complete this week’s Assignment.
Note: You will need the SPSS software to open this dataset.

REQUIRED MEDIA

Laureate Education (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.

Laureate Education (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 Center for Research Quality 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.

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.