Testing Equivalence in Communication Research: Theory and Application

By René Weber, L. Popova. 2012

139 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Sociology and Political Science, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/f616d530b21fdf8a892e68b37d01510554528ec6

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. Edward Maibach
2. Rachel S. Shinnar
3. B. Powell
4. Haibo Zhou
5. Dan K. Hsu

In Sociology and Political Science

1. Emily K. Vraga
2. Edward Maibach
3. Rachel S. Shinnar
4. Rick T. Wilson
5. B. Powell
6. Daniel W. Baack
7. Teresa A. Myers
8. Haibo Zhou

In Statistics and Probability

1. Edward Maibach
2. Rachel S. Shinnar
3. B. Powell
4. Teresa A. Myers
5. Haibo Zhou
6. Dan K. Hsu
7. M. B. Nielsen
8. T. Selfa

Citations in the corpus

• Does Engagement in Advocacy Hurt the Credibility of Scientists? Results from a Randomized National Survey Experiment (2017, methodology): “This represents the average effect size r for the middle third of effects (a “medium” effect) found in the communication literature in a recent review of 112 meta-analyses7 (Weber & Popova, 2012).”
• Creativity, attention and the memory for brands: an outdoor advertising field study (2015, result): “Drawing on the effect size reported for media effects (r D 0.52; Table 3; Weber and Popova 2012), coupled with the effect size from Pieters and Wedel’s (2004) study (r D 0.471), the balanced approach produced a custom D of 0.35.”
• Entrepreneurial intentions and start-ups: Are women or men more likely to enact their intentions? (2018, methodology): “Hence, we set the effect size at 0.40 as a conservative test (Weber and Popova, 2012).”
• Combatting climate change misinformation: Evidence for longevity of inoculation and consensus messaging effects (2020, unknown): “Meta-analyses have demonstrated that this method is generally effective, with effects (compared to no-treatment control groups) averaging on d = 0.43 (95% CI = [.39, .48]), which can be regarded as a strong effect size in persuasion research (Banas & Rains, 2010; Weber & Popova, 2012).”
• Equivalence Tests (2017, unknown): “This is a chicken–egg problem, since using equivalence tests will likely stimulate researchers to specify which effect sizes are predicted by a theory (Weber & Popova, 2012).”