Appraising Findings of a Quantitative Study

1. Were the primary hypotheses addressed in the introduction?

No hypotheses were stated for the article by Sigala et al. (2022). Instead, the introduction reviewed relevant literature and stated its purpose, which was to provide the foundation for rigorous evaluations of the impact of restaurant menu added-sugar warning labels on consumer behavior (Sigala et al., 2022, p. 2). Specifically, the authors aimed to better understand the relative performance of multiple added-sugar warning label designs while establishing whether restaurant menu added-sugar warning labels could change consumer perception and knowledge outcomes on the causal pathway between warning-label exposure and behavior change (Sigala et al., 2022, p. 2). With this research intention, the authors proceeded in explaining the methodology of the study, leaving no indication of how they expected the results to play out.

2. How were participants selected and assigned to groups and were groups similar at the start (before the intervention)?

Participants were selected and assigned to groups through a process of recruitment. The researchers recruited 1327 U.S. adults matching 2018 American Community Survey (ACS) 5-year estimates (20132018 American Community Survey, 2021) for age (1834, 3554, ?55 years), gender, race and ethnicity (Hispanic [any race], non-Hispanic White, non-Hispanic Black, and non-Hispanic Asian), and education (?highschool diploma/GED, some college, ?bachelor's degree) from an online sample provided by Dynata. Participants gave their informed consent and participants were assigned to view restaurant menu excerpts with 1 of 25 labels. A simple allocation ratio (via Qualtrics Randomizer) was used for assignment.

3. Did the researcher discuss how the size of the sample was determined?

The researchers did not discuss how the size of the sample was determined, but they did identify it as one of the studys strengths that...

When determining sample size for a study, there are a number of factors to consider. The first is the type of data that will be collected. For example, if the data is dichotomous (i.e. it can only take two values), then a smaller sample size may be sufficient. However, if the data is continuous (i.e. it can take any value), then a larger sample size will be needed. The second factor to consider is the level of precision required. For example, if the study is looking at a small effect size, then a larger sample size will be needed in order to detect this effect. Finally, the type of statistical analysis that will be used should also be taken into account. For example, parametric tests usually require larger sample sizes than non-parametric tests. In general, therefore, there is no one-size-fits-all answer to the question of how sample size should be determined. Rather, it depends on the specific characteristics of the study in question (Adcock, 1997).

4. Were the interventions well defined and consistently delivered (fidelity to treatment)?

The experiment was well defined in terms of explaining how the study was done with participants interacting with the visual stimuli. Consistency is assumed throughout. No intervention was...

…calculated Cohen's d and examined statistical significance after using the Holm-Bonferroni procedure (not pre-registered but added based on peer-reviewer feedback) to correct for multiple comparisons within each family of outcomes and comparisons (e.g., comparison of PME among the 3 main label groups, comparisons of PME among the 6 icon-only labels) (Sigala et al., 2022, p. 5). In terms of demographics among the groups, the researchers reported no statistically significant differences.

However, when it came to perceptions of message effectiveness, the researchers found that the icon-only warning labels and the icon-plus-text warning labels were perceived as significantly more effective than the control label (Sigala et al., 2022, p. 5). They also found that the positive impact of icon-only labels and the icon-plus-text labels relative to the control label on perceived effectiveness was significantly stronger among those trying to reduce their sugar intake than those not trying (p < 0.01 and p < 0.001, respectively) (Sigala et al., 2022, p. 5). In any scientific study, it is important to ensure that the results are statistically significant. This means that the results are not due to chance or random error, but are instead meaningful and reliable. In order to be confident in the results of a study, it is essential to ensure that all tests for statistical significance have been conducted properly. Only then can researchers be sure that their results are meaningful and trustworthy. I would use the study findings in practice by making sure that educational materials make use of strong visual cues to…