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Smoking cessation A study published in 2010 in The New England Journal of Medicine investigated the effect of financial incentives on smoking cessation. As part of the study, 878 employees of a company, all of whom were smokers, were randomly assigned to one of two treatment groups. One group (442 employees) was to receive information about smoking cessation programs, while the other ( 436 employees) was to receive that same information, as well as a financial incentive to quit smoking. The primary endpoint of the study was smoking cessation status six months after the initial cessation was reported. After implementation of the program, \(14.7 \%\) of individuals in the financial incentive group reported cessation six months after the initial report, compared to \(5.0 \%\) of the information-only group. a. For this study, identify the experimental units, explanatory and response variable(s), and treatments. b. Assuming the observed difference in cessation rates between the groups \((14.7 \%-5.0 \%=9.7 \%)\) is statistically significant, is this convincing evidence that the difference was due to the effect of the financial incentive and not due to ordinary random variation?

Step by step solution

01

Identify the Experimental Units

The experimental units in this study are the 878 employees who participated in the program. In experiments, the experimental units are the subjects or objects that are observed or receive the treatments.

02

Determine the Explanatory Variable

The explanatory variable in this study is the treatment condition: whether the employees received just the information alone or the information plus a financial incentive. The explanatory variable is what you change or control in the experiment to observe an effect.

03

Define the Response Variable

The response variable is the smoking cessation status six months after the initial cessation report. It is the outcome that is measured and expected to change due to the explanatory variable.

04

Describe the Treatments

There are two treatments. The first treatment is providing only the smoking cessation information to a group of employees. The second treatment is providing both the information and a financial incentive to a different group of employees.

05

Evaluate the Statistical Significance

The study observed a difference in cessation rates of 9.7% between the two groups. If this difference is statistically significant, it suggests that the financial incentive likely had an effect beyond ordinary random variation, implying causation.

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Key Concepts

These are the key concepts you need to understand to accurately answer the question.

Understanding the Explanatory Variable

In experimental design, the explanatory variable is the factor you manipulate or vary to observe its impact on another variable. In the smoking cessation study, the explanatory variable is the type of treatment given to the employees. Specifically, it is whether the employees received just the information about smoking cessation programs or both the information and a financial incentive. By controlling this variable, researchers aim to determine if it has a direct effect on the tobacco use status of the employees. This is crucial in understanding the underlying reasons behind behavior change, because the explanatory variable represents the cause that could lead to the observed outcomes.

Defining the Response Variable

The response variable in an experiment is the outcome that researchers measure to see how it responds to changes in the explanatory variable. In the context of the smoking cessation study, the response variable is the smoking cessation status of the employees six months after the initial attempt to quit. This variable is crucial as it reflects the effectiveness of the treatments. By analyzing how the response variable changes with different levels of the explanatory variable, researchers can assess the impact of their interventions and gauge the success rate of each method in helping individuals quit smoking.

Exploring Statistical Significance

Statistical significance is a measure that determines whether the observed effects in an experiment are due to the explanatory variable or merely a result of random chance. In this study, the difference in smoking cessation rates between the two groups was 9.7%. If this difference is statistically significant, it implies that the likelihood of this outcome occurring by random variation alone is very low. Researchers use statistical tests to establish whether their findings are robust enough to suggest a true effect. Achieving statistical significance strengthens the argument that the financial incentive had a real impact on smoking cessation, rather than the difference being a fluke.

Identifying Treatment Groups

Treatment groups are the different categories or conditions that subjects in an experiment are assigned to. In the smoking cessation study, there were two primary treatment groups. One group received only information about smoking cessation programs, while the other received the same information plus a financial incentive. These groups allow for direct comparison and help isolate the effect of the financial incentive on quitting rates. By ensuring that the only major difference between the groups is the presence of the financial incentive, researchers can more confidently attribute differences in outcomes to the treatment differences, rather than extraneous factors.