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Task 3.2
Measures
Participants and Setting
Participants will be children that ride their bicycles to school from 12 school located in three regions of the country.
Primary Measures
Adult Observers. Observers seated in vehicles parked at the exit next to the bicycle compound will record helmet use at the end of the school day at each of the participating 12 schools. Observers will record whether the helmet is on the students head and whether the helmet is worn correctly. To be scored as correctly worn, the helmet must be buckled snugly (the loop formed by the buckle must not form a loop the observer estimated would accommodate more than a few fingers), and the helmet needs to be level (if the forehead is exposed because the helmet is tipped up in the front, or the back of the head is exposed because the helmet is raked forward, it will not be scored as level).
The percentage of students wearing a bicycle helmet each day will be computed by dividing the number of children wearing a helmet by the total number of children bicycling. The percentage of helmets worn correctly will be calculated by dividing the number of children wearing a helmet correctly by the total number of children bicycling. The school supervisor will train all adult observers by illustrating each of the possible response outcomes for correct helmet use.
Peer observers. The officer or other program coordinator at each school will select and train students to observe and record bicycle helmet use, and correct bicycle helmet use. During the treatment condition, 1 or 2 peer observers will be assigned to observe helmet use each day. Helmet use will be observed and recorded the same way by student observers as it was by the adult observers. The SRO or other adult program coordinator will train observers to record helmet use by demonstrating examples of correct and incorrect helmet use and showing the children a video on correct helmet use. Student observers will then be taken outside as a group on the 1st day of the intervention and will observe and record helmet use of students departing school on bicycles; the officer will then review whether each helmet was scored correctly or incorrectly. Students will be trained to use the same definitions for target behaviors employed by adult observers.
Probe Measures
Adult observers will collect three types of probe measures. The first will involve children riding to school at a specified time (morning). The second involves children riding home from school at specified distances (approximately 0.5 mile from the school). The distance measure will be included to determine if the children removed their helmets after leaving the school area. Both measures will be included to assess whether the treatment generalized over time and was maintained over distance. Distance probe data will be collected by research assistants who will sit in a car parked along the route (to decrease the likelihood that students will notice that they were being observed). Morning data will be scored by adults parked across from the school. The third type of probe measure will be taken after school in the neighborhood to determine whether children continue to wear their helmet when they ride their bicycle after school. Adults driving through the neighborhood will obtain this measure. The presence of a special sticker that participating students in the study will earn for bringing in a pledge will help assess whether cyclists are program participants. Data will only be collected from cyclists who are program participants.
Inter-Observer Agreement
A measure of inter-observer agreement will be obtained for three to five sessions during each condition of the experiment at each school. Inter-observer agreement data for distance probes will be collected for a third of the distance measures at each school. Inter-observer agreement will be calculated for helmet use and correct helmet use (data collected by adults) for cyclists departing school and for probe data by dividing the number of agreements on the occurrence of the behavior by the number of agreements on occurrence plus the number of disagreements.
Treatment Fidelity Measures
Experimental Design
A multiple baseline design can be used when it is suspected that the treatment may produce such a robust effect that it may persist after the treatment is removed. This design requires collecting baseline data at several sites before the treatment is applied. The treatment is then introduced at a different point in time at each site. Each time a site receives the treatment additional baseline data are collected at the untreated locations. The untreated sites serve as a control for possible confounding variables since significant changes should only be detected following the introduction of the treatment at each site.
We propose using a multiple baseline across design across three regions as a control for changes in economic factors, weather, local publicity or other factors that could be confounded with the treatment. By selecting jurisdictions that implemented the program at different points in time we can demonstrate that the changes in the measures of effectiveness only occurred following the implementation of the school helmet program. We propose selecting one state from the South East, one from the Midwest, and one state from the South West or North West part of the country. At present is likely that Florida, Michigan or Illinois, and Arizona or Oregon will be the third state.
The multiple baseline design is illustrated below.
BaselineTreatment 1Treatment 2Treatment 3Follow-upRegion 1BaselineTreatmentTreatmentBaselineBaselineRegion 2BaselineBaselineTreatmentTreatmentBaselineRegion 3BaselineBaselineBaselineTreatmentBaseline
The program will implement in four schools within each region for a total of 12 schools. This will provide a large data sample from which to perform a statistical analysis of the success of the program.
Statistical Analysis
The statistical analysis used in this study will be based on the general time-series intervention regression modeling approach described in Huitema and McKean (1998, 2000a, b) and McKnight, McKean, and Huitema (2000). This approach accommodates both independent and auto correlated error structures encountered in time-series intervention designs of the type used in behavioral research. Certain variants of this approach have been developed for the analysis of both simple and complex versions of single case designs (Huitema, 2009a, b), including the multiple-baseline design proposed for this research.
Power Analysis
The purpose of this section is to provide evidence that the proposed study will be sufficiently powered (sensitive) to detect treatment effects of the size that are considered to be of practical importance. An effect is detected when a formal test of the potential effect is demonstrated to be statistically significant (assuming a conventional level for alpha). If a test applied to sample data is statistically significant under the condition that there is a true population treatment effect then a correct decision has been made using the test.
It is of interest to know the probability of identifying an intervention effect of a specified size before the experiment is carried out. If this probability is low there is little justification for carrying out the experiment. Hence, this probability should be estimated as a routine aspect of experimental design. The formal term attached to the probability of detecting an effect when it exists is statistical power. It is usually considered unwise to proceed with an experiment if the a priori power estimate is less than .80.
Although power is a function of several properties of an applied experiment, the essential preliminary issue that must be considered before power can be estimated is the size of the effect that is judged to be of practical importance. Once the minimum effect size worth detecting has been set, the power estimate can be computed. In the context of the proposed investigation this value has been set at 15 percentage points. Previous research suggests that the helmet use will probably be between 14% and 82%. Suppose it is 48%. If the intervention increases the yielding percentage to 63% that will be the minimum effect considered to be of practical importance. In previous work the average increase in helmet use averaged 30% ranging from 16% to 43%.
The choice of 15 points as the minimum value of interest was based on expert opinion and evaluations of the practical importance of prior research results. Further, this estimate is on the conservative side although such values are necessarily subjective even when based on prior results. The power question in the proposed study is as follows: If there is a true treatment effect of 15 points in the population, what is the probability that the planned experiment will yield a statistically significant result?
Overview of the power analysis
The power estimates computed for the proposed study rest on the following assumptions/conditions/estimates:
The experimental design and analysis is similar to a previous design used to study the similar behavior (Van Houten, Van Houten, & Malenfant, 2007).
The dependent variable will be measured in a manner similar to that used in previous research on the same topic.
The error variance obtained in the previous study will underestimate the error variance of the proposed study. The error variance will be approximately 20 percent higher in the proposed study because (a) a smaller intervention increase in the average percentage value is likely and (b) the variance of percentage value is a function of the average percentage value.
Alpha for the test will be set at the conventional value of .05.
The sample size relevant for the power analysis was based on the harmonic mean of the number of weeks within baseline and treatment phases in the two cities.
The non-centrality parameter associated with a 15 point effect is 2.85.
The power estimate is over .97 for each of the two main intervention tests carried out for each city; the power estimate is over .99 for the second stage test that formally combines the results of the tests on the individual cites. Hence the planned experiment is highly powered to detect the effect size of interest. In fact, the test is sufficiently powerful to detect effects considerably smaller than the minimum value judged to be of practical importance.
Revised Data Collection Research Design Middle School Level PAGE 1
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