Many employers offer worksite wellness programs to their employees. These programs can take different forms and focus on different health outcomes and health-related behaviors, depending upon the needs of the workforce at a particular employer. A large telecommunications company located in the upper Midwest has a worksite wellness program with different modules: a smoking session module, a stress management module, and so on. Employees are free to enroll in any or all of the modules they choose. Some do not enroll in any, others enroll in just one, and some enroll in two or more. One of the modules focuses on weight management. This module consists of an integrated web- and mobile device-based interface that offers tailored goal-setting and self-monitoring facilities. Through the website, participants enter information about themselves and work through a structured goal selection process. Once they have identified goals, they use the mobile app to monitor their behaviors (physical activity and diet) as well as their weight. The app also provides tailored feedback and motivation-boosting messages, also tailored to the individual employee.
Up to the present, the weight management module of this worksite wellness program has never been formally evaluated. No one knows for certain, therefore, whether or not that module actually helps people manage their weight successfully. Now, however, for various reasons, the company finds out: Does participating in the weight management module of the worksite wellness program actually help employees either lose weight or, at least, avoid putting on more weight? The company hires an outside evaluator, who argues that the strongest evaluation design would involve randomizing some employees to receive (or be offered) the intervention, and others not to receive (or be offered) it. Because the program has been up and running for some time, however, this turns out not to be feasible. The company and outside evaluator will have to use an observational study, rather than a randomized trial, to try to measure the effect of participation in the weight management module on change in weight. That is, they will need to compare the change in weight of employees who enroll in the weight management module to the change in weight of employees who did not enroll.
The evaluator realizes, however, that employees who enroll be systematically different from employees who do not enroll in ways that may render the simple comparison of the groups problematic. Perhaps heavier people are more likely to enroll than lighter people. Perhaps people who are more satisfied with their own bodies are less likely to enroll. Perhaps people with higher self-efficacy in the domains of physical activity and diet are more likely to enroll. Perhaps age, gender, or type of job at the company influence whether or not employees enroll in the weight management module. If so, and if these variables also exert an independent influence on weight change, they might function as confounders in the relationship between enrollment and weight change. The evaluator is able to convince the company that, if they cannot do a randomized trial, they can at least measure and control for potential confounders.
And that is what they do. The evaluator, with assistance from the employers, conducts a survey of a random sample of employees. She collects information on each employees weight, age, gender, and job category. The questionnaire also includes multi-item measures of body satisfaction (four items on a 1-to-5 scale), self-efficacy for physical activity (five items on a 1-to 4 scale), and self-efficacy for dietary behavior (five items on a 1-to-4 scale). Six months after the original data collection, she contacts the same employees again and obtains updated information on their weights. She computes each participants’ weight change but subtracting her or his baseline weight from her or his weight at the six-month follow-up assessment. Finally, from information collected automatically through the web-based component of the company’s weight management module, the evaluator is able to determine whether each employee was or was not enrolled in the weight management module at any point between the baseline and sixmonth follow-up data collections.
Data from this evaluation are available in the dataset WorkWell.sav. That dataset includes demographic variables, baseline weight, the scale scores, enrollment status, and weight change. The meaning of all variables should be clear from the codebook, WorkWellCodebook.doc. Your task now is to analyze that dataset. Ultimately your goal is to determine whether or not enrollment in the weight management module has any influence on weight change over the sixmonth period. To do this, however, you must take into account the possibility of confounding by baseline demographic and/or psychosocial factors. Additionally, the employer wishes to know if the effect (if any) of the weight management module is different for male versus female employees, or differs as a function of baseline body satisfaction. They hypothesize that the program may be more effective for female employees than for male employees, and for employees with low levels of baseline body satisfaction than for those who were more satisfied with their bodies at baseline. Please use SPSS, therefore, to carry out the following analyses.
1. Create a table of frequencies and/or descriptive statistics for all variables in the dataset (other than ID).
2. The first condition for a variable to be a confounder is that it must be associated with the independent variable of interest. Therefore, to determine whether WGTPRE, AGE, BODYSAT, SEPHYSACT, SEDIET, MALE, and/or JOBCAT are associated with ENROLL, run seven logistic regression models, each predicting ENROLL, and each containing one of the seven potential confounders.
3. As a further test of whether WGTPRE, AGE, BODYSAT, SEPHYSACT, SEDIET, MALE, and/or JOBCAT are associated with ENROLL, run a single multivariable logistic regression model using all seven of these potential confounders simultaneously to predict ENROLL.
4. The second condition for a variable to be a confounder is that it must be associated with the dependent variable. To determine whether WGTPRE, AGE, BODYSAT, SEPHYSACT, SEDIET, MALE, and/or JOBCAT are associated with WGTCHANGE, run seven linear regression models, each predicting WGTCHANGE, and each containing one of the seven potential confounders.
5. As a further test of whether WGTPRE, AGE, BODYSAT, SEPHYSACT, SEDIET, MALE, and/or JOBCAT are associated with WGTCHANGE, run a single multivariable linear regression model using all seven of these potential confounders simultaneously to predict WGTCHANGE.
6. Next, as a preliminary but naïve test of whether the weight management module has any effect, compare the mean weight change for employees who enrolled in that module to the mean weight change for employees who did not enroll in it. Do not control for any other variables
7. As a more rigorous test of whether the weight management module has any effect, compare the mean weight change for employees who enrolled in that module to the mean weight change for employees who did not enroll in it while controlling for baseline differences in any or all of the seven potential confounding variables: WGTPRE, AGE, BODYSAT, SEPHYSACT, SEDIET, MALE, and JOBCAT.
8. Next, run two linear regression models to see if the effect of enrollment in the weight management module appears to vary by gender. Do not include any control variables (other than gender) in the first model. Include all of the control variables in the second. Note that each model should include a gender-by-enrollment interaction variable.
9. Similarly, run two linear regression models to see if they effect of enrollment in the weight management model appears to vary according to baseline body satisfaction. Do not include any control variables (other than body satisfaction) in the first model. Include all of the control variables in the second. Note that these models should include a body-satisfaction-by-enrollment interaction variable, and remember the importance of mean-centering continuous variable before forming interaction terms.
Prepare a written report summarizing the results of all of these analyses. That report should include six tables: one containing results from Part 1; the second containing results from Parts 2 and 3; the third containing results for Parts 4 and 5; the fourth containing results from parts 6 and 7; the fifth containing results from Part 8; and the sixth containing results from Part 9. Make sure to relate the results to the following research questions:
There are two deliverables for this assignment:
(1) An SPSS syntax file that accomplishes Tasks 1 through 9. The file should contain all of the commands necessary for accomplishing these tasks, in the specified order, and should not contain any extraneous commands.
(2) Your written report summarizing the results.