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Electronic Theses, Treatises and Dissertations The Graduate School
2006
An Analysis of Safe Driving Behaviors:
Before, during, and after Two "Click It or Ticket" Model Interventions
Marco D. Tomasi
THE FLORIDA STATE UNIVERISTY COLLEGE OF ARTS AND SCIENCES
AN ANALYSIS OF SAFE DRIVING BEHAVIORS: BEFORE, DURING, AND AFTER TWO “CLICK IT OR TICKET” MODEL INTERVENTIONS
By
MARCO D. TOMASI
A Thesis submitted to the Department of Psychology in partial fulfillment of the requirements for the degree of
Master of Science
Degree Awarded:
Fall Semester, 2006
The members of the Committee approve the thesis of Marco D. Tomasi defended on October 10, 2006.
__________________________________
Jon S. Bailey
Professor Directing Thesis
__________________________________
Ellen Berler
Committee Member
__________________________________
Mark Licht
Committee Member
The Office of Graduate Studies has verified and approved the above named committee members.
ACKNOWLEDGEMENTS
I would like to thank a gentleman from southern Georgia, who shall remain nameless, whose use of a cellular phone while driving inspired me to engage in this line of research. Sara Pawelkoski, Stephanie Tolken, Nicole Muscanell, Crystal Mayo, Sara Olsen, Gina Bowers, Nicole Delano, Gretchen Mathews, Amber Watts, Nicole Cambridge, Jennifer Bryan, Marisa Snow, Stephanie Perrino, Lindsay Harrington, Chrystal Durnan, A.J. Mobley, Angela Buchanio, Anne Potteiger, and Sallie Weaver also deserve my thanks, as this study would not have been possible without efforts as research assistants. Not only did they supply their own vehicles and fuel without monetary compensation during a period of peak petroleum prices, they also performed their duties in an exemplary manner. The expertise, advice, support, and patients of my advisor, Jon S. Bailey, was critical to the initial hurdles and final completion of this study.
Last, but certainly not least, I must thank Jessica Tomasi. Without her support as a colleague, a senior research assistant, a data collector, and a loving, supportive, and understanding wife none of this research would have been possible.
TABLE OF CONTENTS
List of Tables v
List of Figures vi
INTRODUCTION 1
METHOD 9
RESULTS 16
DISCUSSION 37
APPENDIX A: 42
APPENDIX B: 44
APPENDIX C: 46
APPENDIX D: 49
REFERENCES 51
BIOGRAPHICAL SKETCH 56
LIST OF TABLES
1. States with primary and secondary safety belt laws 5
2. Observation vehicles 43
LIST OF FIGURES
1. Map of targeted intersections 9
2. Red light durations of targeted intersections 10
3. Percent of drivers unbuckled: Click It or Ticket 16
4. Percent of drivers unbuckled: B.E. A.L.I.V.E. 17
5. Percent of drivers on cell phones: Click It or Ticket 18 6. Percent of drivers on cell phones: B.E. A.L.I.V.E. 19
7. Vehicle types observed: Click It or Ticket 20
8. Vehicle types observed: B.E. A.L.I.V.E. 21
9. Gender of drivers: Click It or Ticket 22
10. Gender of drivers: B.E. A.L.I.V.E. 22
11. Percent of male and female drivers unbuckled: Click It or Ticket 23 12. Percent of male and female drivers unbuckled: B.E. A.L.I.V.E. 24 13. Percent of male and female drivers on cellular phones: Click It or Ticket 25 14. Percent of male and female drivers on cellular phones: B.E. A.L.I.V.E. 26 15. Rates of male-driven vehicle types: Click It or Ticket 27 16. Rates of male-driven vehicle types: B.E. A.L.I.V.E. 28 17. Rates of female-driven vehicle types: Click It or Ticket 29 18. Rates of female-driven vehicle types: B.E. A.L.I.V.E. 30
19. Percent of drivers unbuckled by vehicle type: Click It or Ticket 31 20. Percent of drivers unbuckled by vehicle type: B.E. A.L.I.V.E. 32 21. Percent of drivers using cellular phones by vehicle type: Click It or Ticket 33 22. Percent of drivers using cellular phones by vehicle type: B.E. A.L.I.V.E. 34
23. Interobserver agreement: Click It or Ticket 35
24. Interobserver agreement: B.E. A.L.I.V.E. 36
25. Frequency of intersections observed 50
INTRODUCTION
The leading cause of death as a result of injuries is from automobile accidents (National Center for Health Statistics, 2005). Over 32,000 people died in the United States as a result of a vehicular collision in 2001; sixty percent of these people were not wearing a seatbelt (National Center for Statistics and Analysis, 2002). In Florida, traffic fatalities resulted in the loss of 3,242 lives in Florida during 2004, up from 3,196 the previous year (Fatality Analysis Reporting System, 2004). The steady increase in vehicle fatality rates have been blamed on an increase in population, but also added distractions from electronic devices such as cellular phones and global positioning systems (Florida Highway Patrol, 2006). In 2003 the National Highway
Transportation Safety Board, or NHTSA, reported that Florida ranked 18th in seatbelt usage out of the 28 states where buckling up is a secondary offense. Florida was also one of the two states out of the 28 that did not show improvement in its rate of seatbelt usage over the previous year.
These data were based on observational studies of drivers conducted by the NHTSA in all 50 states, Washington D.C., and Puerto Rico (Solomon, Chaudhary, & Cosgrove, 2004). Currently 75% of American motorists wear their seatbelts (NHTSA, National Occupant Protection Use Survey, 2002). Many of the lives lost every year in motor vehicle collisions could be saved if occupants would buckle up. These numbers could be further reduced by decreasing distractions to drivers, which is responsible for 22.7% of automobile crashes (Hendricks, Fell, & Freedman, 2001)
Over the past two decades cell phone usage has grown at an exponential rate in the United States. In 1985 it was estimated that over 100,000 Americans owned cellular phones;
nineteen years later that number just exceeded 159,000,000 Americans (AEI-Brookings Joint Center for Regulatory Studies, 2004). As costs associated with usage continue to decrease and coverage continues to increase, more and more Americans are using cell phones. As cell phone usage in general increases, the usage of cell phones while driving is also increasing. Studies in the 1990’s began demonstrating diminished driving ability due to cell phone usage (Goodman,
Bents, Tijerina, Wierwille, Lemer, & Benel, 1997; McKnight & McKnight, 1993). Sparked by these studies, a movement was made to investigate further, as well as prove statistically, the logical connection between automobile accidents and cell phone use. However the problems in accurately reporting the rate of traffic accidents due, either wholly or in part, to cellular phone usage by drivers creates a weakness in the argument to ban talking on a cellular phone while driving. Redelmeier and Weinstein (1999) estimated 730 fatal crashes per year resulted from drivers on cell phones. In contrast Hahn, Tetlock, and Burnett (2000) reported an estimate of 300 fatal crashes annually. Both of these estimates pale in comparison to the figures reported by Clayton, Archuleta, Helms, and Vallejo in 2004; their estimate was over 2,550 fatal traffic accidents a year. The discrepancies in these figures may be attributed to the exponential growth of cellular phone use in the United States, heightened attention by the media to cell phones as a contributing factor to motor vehicle accidents, or differences in sampling methodologies.
Estimates of phone usage by drivers also vary greatly. Eby and Vivoda reported in 2002 that 2.7% of Michigan motorists on the roads during daylight hours were on the phone at any given moment. In 2004 Clayton, Archuleta, Helms, and Vallejo estimated that as many as 85%1 of American cell phone users were concurrently driving and talking on the phone. Goodman, Bents, Tijerina, Wierwelle, Lerner, and Benel (1999) also estimated 85% of American motorists were on the phone while driving. However, it should be noted that Goodman et al.’s estimate was based on data collected though a self-report survey instead of direct observation. In a 2005 study, the NHTSA estimated 10% of drivers talk on a cell phone while driving, up from 8% the previous year. The same study also estimated 6% of motorists were using hand-held units, an increase from 5% in 2004 (Glassbrenner, 2005). These figures are primarily based on self-report surveys, generalizing samples from observational studies to the population at large, or a
combination of the two. The vast discrepancy in results illustrates the need for further study.
Researchers and safety experts do agree that the distractions created by using a cell phone increase the risk of an accident while driving. The use of “hands-free” devises does not appear to adequately decrease the distractions caused by cell phones. Hancock, Lesch, and Simmons (2003) tested the ability of 42 drivers to make decisions vital to the avoidance of accidents while carrying on a hands-free cellular phone conversation on a closed test track. All drivers showed a
1Clayton, Archuleta, Helms, and Vallejo (2004) did not report the methodology used to generate their stated estimate of cellular phone use by automobile drivers.
reduction in response time and an increase in pressure applied to the brake pedal. Hancock, Lesch, and Simmons also demonstrated a 15% increase in the number of red lights and stop signs ignored by the participants while they were on the phone. Strayer, Frank, and Drews (2003) showed that even with a hands-free device, participants’ reaction time in a driving simulator was significantly impaired. An analysis using eye-tracking technology attributed the slowed reaction time to inattention to information in the visual field. The authors continued by reporting that cell phone conversations “impaired implicit perceptual memory for items presented at fixation.”
Further support for the distractive nature of “hands-free” cellular phone devices was added by Treffner and Barnett (2004). Their study tested participants’ ability to maneuver an automobile around closed-circuit road course while either operating a “hands-free” cellular phone or not using any type of phone. The authors analyzed factors related to the control of the vehicle, such as eye movement and braking time. The results showed that the group using cell phones were less sensitive to driving-related stimuli, had slower response times to stimuli, and had erratic braking patterns.
In a 2003 study a direct comparison of driving performance while using three types of cell phone devices was tested. Matthews, Legg, and Charlton measured the mental demand, physical demand, overall driving performance, effort, and frustration of drivers while using either no cell phone at all, a hand-held cell phone, a personal hands-free unit, or a cell phone with external speaker and microphones. All drivers agreed that use of any cell phone while driving, regardless of technology, was more demanding than without. When using a cell phone, driver self-report and intelligibility tests showed that a personal hands-free unit was the least demanding, while the unit with the external speaker was the most. This suggests that, while both hands-free units such as earpieces and hand-held units are both distracting to a driver, the hands- free unit may be slightly less distracting in a relative sense.
In response to criticisms of the use of simulated cellular phone calls or calls containing memory tasks being used in this line of research, Rakauska, Gugerty, and Ward (2004) tested driving performance with varying intensities of naturalistic conversations. All participants in the study showed an increased variability in their driving speeds, a decrease in their overall velocity, and greater variability in the location of their feet in relation to the pedals, regardless of how mentally demanding the conversation they were having. Drivers experiencing the conversations
categorized as “difficult” showed a greater magnitude of change than those with “easy”
conversations across all targets.
The increasing number of empirical studies linking cell phones to distracted drivers and automobile accidents has begun to impact governmental policy in some parts of the world. Due to public safety concerns on the roadways, England, Italy, and Australia all have laws limiting the use of phones by drivers (Goodman, Bent, Tijerina, Wierwikke, Lerner, & Benel, 1997).
However, as is often the case in politics, emerging research is not significantly influencing public policy. While research linking cellular phone usage to automobile accidents continues to be published, only three states in the U. S. have banned their use by drivers. The city of Chicago passed an initiative in July 2005 banning the use of handheld phones while driving. However the ban only applies within the city limits and does not extend to the greater Chicagoland area.
There was some controversy in the manner in which the proposal was passed in Chicago, as the words “cell phone” or “mobile phone” never appeared in any of the legal documents. News of the approval of the measure was met with great pubic displeasure. A few other U.S. cities, such as Washington D.C., have passed similar legislature measures in place. These local attempts to stop drivers from using their phones will not succeed everywhere. Seven states, including Florida, prohibit local governments from using law enforcement to curb cell phone usage in automobiles (Glassbrenner, 2005).
Intervening on Driving Safety
The principal method of intervening upon unbuckled drivers in the United States has been through legislation and law enforcement. The U.S. Congress began requiring all
automobiles manufactured in the United States to have seatbelts (Transportation Safety Center, 2002). The progression of laws to enforce the use of seatbelts has varied by state since then.
Currently seatbelt laws can be separated into two main categories, primary and secondary offences. In states with primary safety belt laws otherwise law-abiding motorists not wearing a seatbelt can be pulled over and ticketed by law enforcement officers. In states with secondary safety belt laws unbuckled motorists must first be pulled over for a primary offense such as speeding or failing to come to a complete stop at an intersection. The law enforcement officer may then ticket the motorist for failure to wear a safety belt in addition to the primary offense.
New Hampshire is the only state that does not fall into either category, as it lacks adult safety belt laws. A list of states separated by type of safety belt legislation is displayed in Table 1.
Periodically, law enforcement agencies temporarily alter the seatbelt laws in all states for special programs. The most recognizable of these programs is “Click It or Ticket”, an annual, nation-wide campaign designed to increase safety belt usage. To achieve this, seatbelt usage is changed to a primary offences during the two week program. Extensive advertising accompanies the campaign in an attempt to educate the public. After the completion of the campaign each year the safety belt laws return to their pre-“Click It or Ticket” form. The “Click It or Ticket”
model has also been applied to various programs at the local level.
Table 1. States with primary and secondary safety belt laws.
Primary Law Secondary Law No Adult Safety Belt Law
Washington Arizona Virginia New Hampshire
Hawaii Utah Rhode Island
California Vermont West Virginia
Oregon Montana Missouri
Maryland Pennsylvania South Carolina
New Mexico Alaska Florida
Iowa Nevada Idaho
North Carolina Colorado South Dakota
New York Illinois Wisconsin
Georgia Nebraska Tennessee
Texas Delaware Kentucky
Michigan Ohio North Dakota
Indiana Arkansas Kansas
New Jersey Mississippi Massachusetts Connecticut
Alabama Oklahoma Louisiana
In 2005 the NHTSA reported $10 million was spent on national advertising for “Click It or Ticket”, and another $16 million was spent on state-based local advertising. In Florida, the campaign issued 109,139 tickets, including 37,000 for not wearing a seatbelt (Florida Highway Patrol, June 2005). Observations conducted by the NHTSB estimate that in states where the program was fully implemented, such as Florida, seatbelt usage increased 8.6% (Solomon, Ulmer, Preusser, 2002).
A variety of behavior-based safety approaches have been used to intervene on behaviors of both professional and non-professional drivers. These studies have targeted making complete stops at traffic control devices (Van Houten & Retting, 2001; Hacket, Lebbon, & Austin, 2004;
Ludwig & Geller, 2001; Ludwig, Biggs, Wagner, & Geller, 2001; Ludwig & Geller, 1997; Olson
& Austin, 2001), yielding for pedestrians (Harré & Wrapson, 2004; Nasar, 2003), turn signal usage (Ludwig & Geller, 2001; Ludwig, Biggs, Wagner, & Geller, 2001; Ludwig & Geller, 1999a; Ludwig & Geller, 1999b; Ludwig & Geller, 1997; Ludwig & Geller, 1991), and safety- belt usage (Clayton, Archuleta, Helms, & Vallejo, 2004; Gras, Cunill, Planes, Sullman, &
Oliveras, 2003; Berry, Geller, Calef, & Calef, 1992; Geller & Lehman, 1991; Berry & Geller, 1991; Geller, Patterson, & Talbott, Cox, Cox, & Cox, 2000; Ludwig & Geller, 2001; Ludwig &
Geller, 1999a; Ludwig & Geller, 1999b; Austin, Alvero, & Olson, 1998; Williams, Thyer, Bailey, & Harrison, 1989; Rudd & Geller, 1985; Streff & Geller, 1986; Engerman, Austin, &
Bailey, 1997; Ludwig & Geller, 2000; Ludwig & Geller, 1997; Ludwig & Geller, 1991; Berry, Gilmore, & Geller, 1994; Hagenzieker, 1991; Malenfant, Wells, Van Houten, & Williams, 1996;
Wells, Malenfant, Williams, & Van Houten, 2000; Thyer, Geller, Williams, & Purcell, 1987;
Geller, Bruff, & Nimmer, 1985). Barker, Bailey, and Lee (2004) even applied the procedures used with motorists to increase the frequency of buckled up children in supermarket shopping carts.
In the area of behavioral research, the most noteworthy of the studies attempting to improve seatbelt usage are the Thyer et al. (1987) and Geller et al. (1985) studies. In the Thyer et al. study, the researchers “flashed” a prompt to buckle up to drivers leaving a university parking lot from the side of the road. If drivers complied with the prompt the sign displaying the prompt was flipped to reveal a “thank you” message. This work was later replicated by Hacket, Lebbon, and Austin (2004) at a different university. The Thyer et al. methodology was also used as a template for roadside prompting procedures conducted at a retirement facility (Cox, Cox, &
Cox, 2000). In 2004 Clayton, Archuleta, Helms, and Vallejo took this methodology a step further by intervening on both seatbelt disuse and cellular phone usage by drivers leaving a university parking lot. This was the first, and to date only published study to use an applied behavior analytic intervention to attempt to decrease the frequency of drivers using cell phones.
While Clayton et al. were able to demonstrate a socially and statistically significant increase in seat belt usage when the treatment was implemented, the intervention on cell phone usage was
less successful. Cell phone usage remained relatively unchanged across all phases of the study, and the number of drivers who hung up only increased from 0% to 1% when the treatment was in place. In addition, an overall limitation to this general model is that it only focuses on one interaction. This results in a smaller number of participants, with a percentage of those participants being observed multiple times.
A more mobile methodology that better lends itself to a broader sampling and use in community-based interventions was introduced by Geller, Bruff, and Nimmer (1985). In their
“Flash” for Life study the authors used a similar prompt/thank you sign used by Thyer et al.
However, instead of “flashing” from the side of a road Geller et al. presented the sign from within another vehicle to drivers stopped at a traffic signal. Data were collected by 3 to 4 person teams in the research vehicle. Each team was made up of a driver, a “flasher” seated in the front passenger seat, and an observer in the back seat. To achieve interobserver reliability a second observer would join the team in the backseat. Observers and drivers were undergraduate, graduate, and faculty researchers. Children, including Geller’s own offspring (Geller, 2001), were used as the “flashers” in the front seat. Each trial would begin when the research vehicle pulled up on the left of another motorist at a red light. If the driver of the targeted vehicle was unbuckled the “flasher” would display the sign. Observers would record whether the target driver looked at the sign and whether they complied. When the light turned green the sign was lowered out of sight and both vehicles proceeded on their way. Observations were only counted if the sign was displayed for at least 3 seconds.
The primary purpose of this study is to evaluate the effectiveness of two “Click It or Ticket” model campaigns on decreasing the percent of automobile drivers unbuckled. It is hypothesized that both campaigns will have little or no effect on the use of seatbelts by drivers.
The “Click It or Ticket” model primarily utilizes antecedent intervention components, along with negative, uncertain consequences. Daniels and Daniels (2004) used the example of quitting smoking to highlight the limitations of antecedent interventions. In their example, a New Year’s resolution to quit smoking was the antecedent intervention. However New Year’s resolutions quite often fail because they lack effective consequences. The “Click It or Ticket” model also lacks effective consequences. The consequences for driving while unbuckled during the “Click It or Ticket” campaign are a potentially higher risk of death than driving while buckled, and a higher probability of receiving a traffic citation. These consequences would be considered
negative by most people. Since not every instance of driving while unbuckled will result in death or a traffic ticket, the consequences are considered uncertain. When these negative, uncertain consequences are delivered, they usually aren’t immediately contingent upon the response. In their book, Daniels and Daniels ranked the effectiveness of interventions based on the properties of their consequences. Interventions utilizing negative, uncertain, future
consequences are ranked as the least effective type.
The second purpose of this study was to conduct a descriptive analysis of behaviors related to safe driving. Cellular phone use, a major distracter to motorists operating automobiles, will be recorded. Cellular phone use should not change over the course of the “Click It or
Ticket” model programs, as it was not targeted by the campaigns. Gender of drivers and the type of vehicle driven will also be recorded and analyzed. These variables have not previously been analyzed in this line of behavioral research. This study seeks to investigate whether differences may exist in seatbelt usage and cellular phone use between males and females, as well as between drivers of different types of vehicles. It is believed that the descriptive data, for both seatbelt and cell phone usage, will be beneficial to future research in transportation safety.
This study was based on the methodology first used by Geller, Bruff, and Nimmer (1985). It was designed to expand on the current body of research by analyzing descriptive factors associated with the safe operation of an automobile. This study also samples a larger number of intersections than Clayton et al. (2004) was able to study, providing a more robust data collection methodology. Finally, this study creates a base for future research seeking to intervene on seatbelt use or cellular phone usage, similar to Geller et al.’s “Flash for Life”
campaign (1985).
METHOD
Participants & Setting
Participants in this study were drivers of automobiles traveling along a set route during the observation sessions. More specifically, those drivers observed were located to the
immediate right of the observation vehicle while both vehicles were at a complete stop at red lights along the data collection route. The route was located in Tallahassee, FL, and ran from the intersection of Ocala and Tennessee, East to the intersection of Monroe and Tennessee, North to the intersection of Tharpe and Monroe, West to the intersection of Tharpe and Ocala, and finally South initial intersection, as shown in Figure 1. Data collection sessions took place between 4:00 PM and 6:00 PM, Monday through Friday. The route was repeated for the full duration of the data collection period. The route was selected based on the volume of traffic during the data collection sessions, the number of lanes, and the length of red lights.
Participants were not interviewed or debriefed during this study. In addition, unlike the Geller et al. (1985), license plate numbers were not collected as a means to later acquired further descriptive data related to the drivers in this study. It was felt that the threats to the privacy of participants far outweighed the contributions that could be made to the proposed research.
Therefore descriptive data such as age, marital status, home address, etc were not collected at any time.
Start
Figure 1: Map of targeted intersections.
Legend Targeted Intersection
Materials
Twenty-four observation vehicles were used during data collection (Appendix A). Two observation vehicles were sent out during each observation session. Each vehicle carried between two and four research assistants. Identical data sheets were used by the observers for recording of observation data (Appendix B).
Procedure
Each observation session began when the first observation vehicle exited the Westwood neighborhood, located south of the intersection of Tharpe and Ocala, and drove south on Ocala at 4:00 PM. The second observation vehicle followed at 4:07 PM. The data collection vehicles traveled in the left-most lane to minimize lane changes while in traffic. Research assistants collected data via observations while riding in the back seat of one of the observation vehicles when stopped at red lights on the data collection route. Observations were made of the driver of the vehicle to the immediate right of the observation vehicle when both the observation vehicle and the targeted vehicle were fully stopped at a red light. Both vehicles needed to be fully stopped and the traffic signal needed to be red for the observation to count. The mean durations of observation opportunities were recorded and are illustrated below in Figure 2. The mean duration in which to conduct an observation along the route during the data collection period was 56 seconds. Even at the intersection of Monroe and Third, the intersection with the shortest duration, data collectors had at least 12 seconds in which to observe a targeted driver.
0:00 0:14 0:28 0:43 0:57 1:12 1:26 1:40 1:55 2:09 2:24
MLK(2) Monroe Tennessee High(2) Basin Old Bainbridge E.Brevard Macomb Tharpe Duval Bronough Seventh Woodard High(1) Sixth Ocala Dewey Adams Thomasville Virginia MLK(1) Copland W.Brevard Fifth Georgia Carolina Gibbs Callark Third
Intersection
Duration (Minutes:Seconds)
Figure 2: Data illustrating the mean duration of red lights between 4:00 P.M. and 6:00 P.M. at the targeted intersections. The mean observation opportunity for all the stoplights on the data collection route is 56 seconds.
Safety Considerations
To minimize the risk to data collectors, the participants, and the general driving public, the Special Operations Division of the Tallahassee Police Department2 was contacted to confirm that the data collection procedures did not violate traffic laws or local ordinances. Furthermore, additional provisions, approved by the Florida State University Human Subjects Committee (Appendix C), were added to the data collection procedure.
All research assistants signed an informed consent form, approved by Florida State University’s Human Subject Committee. In addition, those research assistants who drove were required to have valid driver’s licenses and valid auto insurance. Observation vehicles were required to be in peak mechanical condition, including properly functioning lights and safety restraints. Everyone in the data collection vehicle was required to be buckled at all times while the vehicle was in motion. Each data collection team was required to bring at least one cellular phone for safety purposes. However, data collection teams were instructed not to use cellular phones while the data collection vehicle was in motion, regardless of where they were seated within the vehicle. This removed added distraction for the driver of the observation vehicle and modeled the preferred behavior for other drivers.
Observation teams did not encounter abnormal driver behavior during the study, but in the event it did occur, data collectors were instructed to halt data collection and leave the scene immediately. The primary researcher was to be contacted in the event of this occurance, as soon as it was safe to do so. In the event a hypothetical target driver made the observers nervous (following too closely, etc), data collection was to be discontinued, and the driver of the
observation vehicle was to safely pull off the road into a populated area. The primary researcher was to be immediately contacted by the observation team once the observation vehicle was off the main road. However, no such incidents occurred during data collection.
In the event of severe weather or a heavy downpour, data collectors were instructed to find a place to safely pull off the road and ride out the storm. Observers were instructed to call the principle investigator immediately when data collection was halted due to inclement weather.
No data collection sessions were cancelled due to severe weather.
2Lt. David Folsom of the TPD Special Operations Divisions was contacted via email. After reviewing the proposed data collection protocol and state and federal statutes, Lt. Folsom replied by stating, “I am personally unaware of any statute or ordinance that would prohibit such activity as you are suggesting.”
In the event of a traffic accident not involving the data collectors, and if the data collectors did not witness the actual accident, they were instructed to follow the directions of authorities to detour around the accident. Data collection was to halt while detouring around the accident. If the data collectors witness the accident, they were instructed to safely pull off the road and offer assistance. The principle investigator was to be called in such an event so that the data collection team’s whereabouts were known.
In the event of a traffic accident that involved the data collectors, the data collection team was instructed call the authorities first. They were to contact the principle investigator after contacting the authorities. Data collection teams were instructed to not call the principle investigator until they were out of immediate danger.
In the event the data collection team had a question, they were instructed not to use cellular phones while the data collection vehicle was in traffic. If they had a question that
needed immediate attention, data collectors were instructed to safely pull off the road and call the principle investigator.
If the observation team observed a driver consuming alcoholic beverages while operating an automobile, the research assistants were instructed to halt data collection, record a description of the driver, the vehicle, the license plate number, and safely exit traffic to notify the police.
Two instances of individuals consuming alcoholic beverages while in operation of an automobile on public roadways were observed during data collection. Both instances were reported to local law enforcement.
Target Variables
The primary target variable of this study was seatbelt usage by drivers. The secondary target was cellular phone use by drivers. Type of vehicle and driver gender and were also recorded to examine possible relationships to the primary and secondary targets. A driver was considered buckled if the shoulder strap is placed across the upper torso. Drivers with the shoulder harness placed behind their back were scored as unsafe. Cellular phone usage was defined as a driver holding a phone. Hands-free devices, such as ear pieces, were not scored as being on a cell phone, as it would be impossible to reliably record these data through
observation.
Observers also tracked the number of laps they made around the observation route, the intersection of each observation, and the color of the automobile observed. Car color was collected as an additional means during IOA sessions to verify both observers were collecting data on the same vehicle.
Evaluation of “Click It or Ticket” Model Campaigns
The “Click It or Ticket” model campaigns were the independent variables designed to intervene upon seatbelt use. The nation-wide “Click It or Ticket” campaign was implemented May 22, 2006 through June 2, 2006. “Buckle Up Education and Local Intensified Vehicle Enforcement”, or B.E. A.L.I.V.E., was a local program sponsored by the Florida State University Police Department in cooperation of the Florida Highway Patrol. B.E. A.L.I.V.E. was
implemented October 19, 2005 through November 2, 2005. B.E. A.L.I.V.E. had the included the three components of a “Click It or Ticket” model campaign, but was carried out on a smaller scale. B.E. A.L.I.V.E. included a temporary change in seatbelt enforcement, by changing secondary law to a primary law for the duration of the campaign. It also included an increase in enforcement, with an increase in the number of officers patrolling during the duration of the campaign. B.E. A.L.I.V.E. did not have frequent television and radio advertisements, banners, and billboards like those used by the national campaign. B.E. A.L.I.V.E. did include one lighted sign posted near the campus of Florida State University, radio advertisements on a local AM sports radio station, and word-of-mouth announcements made by officers sent around campus and the community to educate the public.
An ABA reversal design (Bailey & Burch, 2002) was used to evaluate each of the “Click It or Ticket” model campaigns. Baseline data were collected before each program began. Data collection continued through the duration of each of the campaigns to serve as the second phase.
A return to baseline occurred when the programs were halted.
In keeping with the single-subject methodology of this study, visual analysis was the primary means of examining the data. According to Poling, Methot, and LeSage (1995), the three advantages to using visual analysis are that it is very robust in the types of data that can be analyzed, it allows the researcher to easily review the data after the addition of each datum point, and it is an effective process for determining the success of an intervention. Bailey and Burch (2002) further detailed the advantages of visual, or graphical, analysis. The visual analysis
process evaluates data just as it was collected, without hiding trends in the data and creating difficulties in evaluating the social validity of results. The three primary components of
conducting visual analysis are trend, variability, and level. Trend refers to the overall path of the data. This is sometimes illustrated by evaluating the slope of a line of best fit. “No trend” refers to data that displays a flat, or zero, slope. During baseline phases unsafe behaviors should show no trend. If the implementation of “Click It or Ticket” model programs are effective, unsafe behaviors should display a downward trend. Behaviors linked to safe driving should display trending in the opposite direction of unsafe behaviors during each experimental phase if the programs are effective. Descriptive variables, such as gender and vehicle type, should not be trending in any direction, regardless of experimental phase. Variability refers to the degree to which fluctuates in terms of values on the y-axis within a phase. Effective interventions should display the same amount, if not a reduction, in variability in the data when compared with
baseline phases. Descriptive data should not show changes in the variability of the data from one phase to the next. Level refers to the range on the y-axis in which the majority of the data points are located. Quite often the descriptive statistics of mean and standard deviation are utilized to assist in description of the level of a data set Unsafe behaviors should show a decrease in level during intervention from baseline phases. Safe behaviors should show an opposite effect during intervention. Descriptive variables should maintain a consistent level across all phases.
Interobserver Agreement
To assess for any possible subjectivity in the collection of data through observations by humans, interobserver agreement was measured. Due to the number of observations collected during the B.E. A.L.I.V.E. and the national “Click It or Ticket” campaigns, total, occurrence, and nonoccurrence agreement was calculated. Total agreement was calculated for each reliability session by dividing the number of agreements by the number of agreements plus the number of disagreements and then multiplying the quotient by 100 for all observations. Occurrence agreement was calculated by dividing the number of agreements by the number of agreements plus the number of disagreements and then multiplying the quotient by 100 for only observations in which at least one of the observers recorded that the behavior occurred. Nonoccurrence agreement was calculated by dividing the number of agreements by the number of agreements plus the number of disagreements and then multiplying the quotient by 100 for only observations
in which at least one of the observers recorded that the behavior did not occur (Poling, P., Methot, L. L., & LeSage, M. G. 1995).
Prior to the start of the B.E. A.L.I.V.E. evaluation, job-aids, sample videos, direct instruction, and practice in a parking lot were used to train observers. Prior to the evaluation of the “Click It or Ticket” campaign, the observers received the same training as the observers for the B.E. A.L.I.V.E. campaign received, plus additional week of training along the data collection route. During this additional week, new observers were joined by research assistants from the B.E. A.L.I.V.E. campaign and pilot studies. While data collected during this week of practice was not included in the analysis of the “Click It or Ticket” campaign, interobserver agreement was calculated immediately following each practice session. Observers for the “Click It or Ticket” campaign were given immediate feedback on the reliability of their observations.
RESULTS
Over the course of this study 5,537 drivers were observed at the targeted intersections (Appendix D) during data collection sessions. During the evaluation of the “Click It or Ticket,”
674 drivers were observed during baseline, 646 drivers during the nation campaign, and 695 during the return to baseline. Percent of drivers unbuckled per observation session is illustrated in Figure 3. A mean of 30 percent (SD=6.1) of the drivers during the initial baseline were unbuckled. The baseline data was relatively stable, with little to no trending. The percent of drivers unbuckled decreased to a mean of 26 percent (SD=4.6) during the enforcement of the
“Click It or Ticket” campaign. A slight downward trend is visible in the data. However, there is not an overall change in the range of the data from the previous phase. After the “Click It or Ticket” campaign ended a mean of 25 percent (SD=5.5) of drivers were observed to be unbuckled. The final phase shows no trend in the data across the full phase. The level of the data also has not changed from the previous phase.
Evaluation of Click It or Ticket on Driver Safety Belt Use
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29
Session
Percent of Drivers Unbuckled
Baseline Click It or Ticket Campaign Baseline
Figure 3: Percent of drivers unbuckled out of the total number of drivers observed per session.
Baseline, “Click It or Ticket” campaign, and return to baseline data are displayed.
The evaluation of B.E. A.L.I.V.E. had 1953 drivers observed during baseline, 623 drivers observed during the B.E. A.L.I.V.E. campaign, and 946 drivers observed during the return to baseline. A mean of 31 percent (SD=7.1) of drivers were unbuckled during the initial baseline condition. The baseline phase had only the slightest of upward trends, and from sessions 9 through 28 showed no trending. The mean increased to 33 percent (SD=5.5) of drivers
unbuckled when the B.E. A.L.I.V.E. campaign was implemented. The intervention phase had a downward trend, but data remained within the same bandwidth of the previous phase. Following the completion of B.E. A.L.I.V.E. the mean percentage of drivers unbuckled returned to 31 percent (SD=5.1). The data trended downward during the second baseline phase, but remained at the same level as the previous two phases. Percent of drivers unbuckled per observation session is illustrated in Figure 4.
Evaluation of B.E. A.L.I.V.E. on Driver Safety Belt Use
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50
Session
Percent of Drivers Unbuckled
Baseline B.E. A.L.I.V.E. Baseline
Figure 4: Percent of drivers unbuckled out of the total number of drivers observed per session.
Baseline, B.E. A.L.I.V.E. campaign, and return to baseline data are displayed.
While not targeted by “Click It or Ticket” and B.E. A.L.I.V.E., cellular phone usage was evaluated across the same 3 conditions for both campaigns. Cell phone usage during “Click It or Ticket” is shown in Figure 5. The percent of drivers using cell phones was a mean of 15 percent (SD=4.7) during baseline. The data showed a downward trend during this initial phase. A mean of 14 percent (SD=4.4) of drivers were on cell phones during the campaign. Intervention data trended upward slightly, and remained within the same bandwidth of the previous phase. A mean of 14 percent (SD=5.0) of drivers were using a cell phone following the return to baseline.
The data showed a slight downward trend across the final phase, but remained at the same level as the previous two phases.
Evaluation of Click It or Ticket on Driver Cell Phone Use
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29
Session
Percent of Drivers on Cell Phones
Baseline
Baseline Click It or Ticket Campaign
Figure 5: Percent of drivers using cellular phones out of the total number of drivers observed per session. Baseline, “Click It or Ticket” campaign, and return to baseline data are displayed.
Cell phone use by drivers throughout the evaluation of B.E. A.L.I.V.E. is displayed in Figure 6. The study recorded cell phone at a mean of 16 percent (SD=3.5) with a flat trend during the initial baseline. A mean of 17 percent (SD=4.4) of drivers used their cell phones
during the campaign. There is a slight upward trend in the data, although the data remains at the same level as the previous phase. Drivers were on their cell phones a mean of 18 percent
(SD=3.5) during the final baseline. No trend was found in the data across the final phase, and it remained within the same bandwidth as the previous phase.
Evaluation of B.E. A.L.I.V.E. on Driver Cell Phone Use
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50
Session
Percent of Drivers on Cell Phones
Baseline B.E. A.L.I.V.E. Baseline
Figure 6: Percent of drivers using cellular phones out of the total number of drivers observed per session. Baseline, B.E. A.L.I.V.E. campaign, and return to baseline data are displayed.
The rates of vehicle type driven remained stable across the evaluation of “Click It or Ticket,” as shown in Figure 7. During the initial baseline of the “Click It or Ticket” evaluation, a mean of 59 percent (SD=5.0) of drivers drove cars, 3 percent (SD=2.2) of drove minivans, 39 percent (SD=6.6) drove trucks/SUVs. All three data series showed no trending. Cars and trucks/SUVs showed overlapping bandwidths, albeit only slightly. During the campaign the means were 63 percent (SD=5.1) for cars, 4 percent (SD=1.4) for minivans, and 33 percent (SD=4.1) for trucks/SUVs. Minivans and trucks/SUVs trended upward slightly across the phase, and cars trended downward across the phase. However, none of the data series showed a change
in level from the previous phase. The final phase recorded 60 percent (SD=6.3) of drivers in cars, 3 percent (SD=2.7) in minivans, and 37 percent (SD=7.2) in trucks/SUVs. All three data series showed no trending and no change in level across the final phase.
Vehicle Types Observed During Click It or Ticket Evaluation
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29
Session
Percent of Vehicles Observed Car
Baseline
Baseline Click It or Ticket Campaign
Truck/SUV
Minivan
Figure 7: Percent of drivers operating cars, minivans, or trucks/SUVs out of the total number of drivers observed per session. Baseline, “Click It or Ticket” campaign, and return to baseline data are displayed.
The rates of vehicle type driven during the evaluation of B.E. A.L.I.V.E., shown in Figure 8, was also very stable. The initial baseline of the B.E. A.L.I.V.E. evaluation resulted in a mean of 64 percent (SD=6.4) of drivers driving cars, 4 percent (SD=2.7) of driving minivans, 32 percent (SD=6.7) driving trucks/SUVs. Cars and minivans showed no trending, while
truck/SUV had a slight upward trend. All three data series were are unique levels, with no overlap of bandwidth between data series. During the campaign the means were 65 percent (SD=6.4) for cars, 4 percent (SD=3.3) for minivans, and 31 percent (SD=4.6) for trucks/SUVs.
Cars and minivans had trended upward across the phase, and trucks/SUV trended downward.
However, all three data series remained at the same level as the previous phase. The final phase
of B.E. A.L.I.V.E. resulted in 64 percent (SD=6.0) of drivers in cars, 4 percent (SD=2.5) in minivans, and 32 percent (SD=2.2) in trucks/SUVs. All three vehicle types showed no trending and remained at the same levels for the final phase.
Vehicle Types Observed During B.E. A.L.I.V.E. Evaluation
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Session
Percent of Vehicles Observed
Baseline B.E. A.L.I.V.E. Baseline
Car
Truck/SUV
Minivan
Figure 8: Percent of drivers operating cars, minivans, or trucks/SUVs out of the total number of drivers observed per session. Baseline, B.E. A.L.I.V.E. campaign, and return to baseline data are displayed.
During data collection 2822 male drivers and 2714 female drivers were observed. A mean of 52 percent (SD=5.2) of drivers in the “Click It or Ticket” evaluation were male and 48 percent (SD=5.1) were female, as shown in Figure 9. The B.E. A.L.I.V.E. evaluation had a more even distribution in regards to gender, as shown in Figure 10. During B.E. A.L.I.V.E. 50 percent of the drivers were male (SD=5.6) and 50 percent were female (SD=5.7). The gender data for both evaluations was very stable with no trending at a global level and remained in the same bandwidth for all phases.
Gender of Drivers During Click It or Ticket Evaluation
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Session
Percent of Drivers Observed
Baseline Baseline Click It or Ticket Campaign
Male
Female
Figure 9: Percent of male and female drivers out of the total number of drivers observed per session. Baseline, “Click It or Ticket” campaign, and return to baseline data are displayed.
Gender of Drivers During B.E. A.L.I.V.E. Evaluation
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Session
Percent of Drivers Observed
Baseline B.E. A.L.I.V.E. Baseline
Male
Female
Figure 10: Percent of male and female drivers out of the total number of drivers observed per session. Baseline, B.E. A.L.I.V.E. campaign, and return to baseline data are displayed.
Overall a higher percentage of male drivers were unbuckled than female drivers. During the initial baseline phase of the “Click It or Ticket” evaluation, shown in Figure 11, 38 percent (SD=8.1) of male drivers and 21 percent (SD=5.3) of female drivers were unbuckled. Rates of unbuckled drivers trended upwards in both males and females, and while there was a slight overlap in the bandwidths, the level for males was higher than for females. The rates decreased to a mean of 34 percent (SD=2.8) of male drivers and 17 percent (SD=8.2) of female drivers when the campaign was enforced. The data trended downward for females and remained flat for the males. There again was a slight overlap in bandwidths between the two data series; however the percent of unbuckled males remained at a higher level than females. Both males and females remained within the same bandwidths established during baseline. Following the campaign 30 percent (SD=8.1) of male drivers and 18 percent (SD=6.3) of female drivers were unbuckled.
Both data series showed flat trends across the final phase. The final baseline also had the largest overlap between in bandwidth for males and females of any of the individual phases. The return to baseline did not demonstrate a change in the bandwidth for either data series from the previous two phases.
Percent of Male and Female Drivers Unbuckled During Click It or Ticket Evaluation
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Session
Percent of Drivers Unbuckled
Baseline Click It or Ticket Campaign Baseline
Male
Female
Figure 11: Percent of male and female drivers unbuckled out of the total number of male and female drivers observed per session. Baseline, “Click It or Ticket” campaign, and return to baseline data are displayed.
For the B.E. A.L.I.V.E. a mean of 39 percent (SD=10.8) of male drivers and 23 percent (SD=8.1) of female drivers were unbuckled during the initial baseline. A small upward trend was present in the baseline data, shown in Figure 12, for both males and females, with males having a slightly greater upward slope than females. The bandwidths for males and females showed some overlap during baseline; however the percent of unbuckled males occurred at a higher level and for females. The mean of unbuckled drivers increased to 41 percent (SD=8.0) for males and 24 percent (SD=7.1) for females during the campaign. The data for males showed a downward trend across the entire phase. It is interesting to note that during the first half of the B.E. A.L.I.V.E. phase the percent of male drivers unbuckled trended upward and then trended downward during the second half. For females the data had was nearly flat, with a slight downward trend. The bandwidth of the male and female data series only overlapped at the last point of the phase, and did not vary outside of what was established during baseline. The return to baseline resulted in a mean of 37 percent (SD=9.7) of male drivers unbuckled and 26 percent (SD=6.1) of female drivers. The data trended downward for the males and upward slightly for the females, but not enough for either of the data series to vary outside the same bandwidth as the previous two phases.
Percent of Male and Female Drivers Unbuckled During B.E. A.L.I.V.E. Evaluation
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Session
Percent of Drivers Unbuckled
Baseline B.E. A.L.I.V.E. Baseline
Male
Female
Figure 12: Percent of male and female drivers unbuckled out of the total number of male and female drivers observed per session. Baseline, B.E. A.L.I.V.E campaign, and return to baseline data are displayed.
Overall a higher percentage of female drivers were using cellular phones than male drivers. During the initial baseline phase of the “Click It or Ticket” evaluation, shown in Figure 13, 18 percent (SD=8.8) of female drivers and 12 percent (SD=5.7) of male drivers were on cell phones. Cell phone usage trended downward slightly in females during baseline, and upward in males. The rates decreased to a mean of 17 percent (SD=7.1) of female drivers and 12 percent (SD=5.1) of male drivers when the campaign was enforced. Female cell phone use displayed an upward trend across the campaign while male use trended downward slightly. Female usage did remain within the same bandwidth as in baseline, as did male usage. Following the campaign 19 percent (SD=6.8) of female drivers and 9 percent (SD=3.3) of male drivers were using cell phones. Usage of cell phones by female drivers trended upward during the final baseline. Male usage showed a flat trend in the second baseline. Both data series remained within the same their same respective bandwidths established in the previous two phases.
Cell Phone Use by Male and Female Drivers During Click It or Ticket Evaluation
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Session
Percent of Drivers Using Cell Phones
Baseline Click It or Ticket Campaign Baseline
Male Female
Figure 13: Percent of male and female drivers using cellular phones out of the total number of male and female drivers observed per session. Baseline, “Click It or Ticket” campaign, and return to baseline data are displayed.
For the B.E. A.L.I.V.E. a mean of 19 percent (SD=6.8) of female drivers and 13 percent (SD=6.0) of male drivers were unbuckled during the initial baseline, shown in Figure 14. The trend for both the male and female data was flat across baseline. The mean of drivers using cell phones increased to 21 percent (SD=9.7) for females and 14 percent (SD=7.8) for males during the campaign. Cell phone use by male drivers trended upward during the B.E. A.L.I.V.E. phase a remained within the same bandwidth established during baseline. The return to baseline resulted in a mean of 22 percent (SD=4.9) of female drivers on cellular phones and 15 percent (SD=4.4) of male drivers. Female drivers showed a slight downward trend during the final phase, while males had a flat trend. The data remained within the same bandwidths as in the previous two phases.
Cell Phone Use by Male and Female Drivers During B.E. A.L.I.V.E. Evaluation
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Session
Percent of Drivers Using Cell Phones
Baseline B.E. A.L.I.V.E. Baseline
Male
Female
Figure 14: Percent of male and female drivers using cellular phones out of the total number of male and female drivers observed per session. Baseline, B.E. A.L.I.V.E. campaign, and return to baseline data are displayed.
In addition to the disuse of seatbelts and the use of cellular phones, the type of vehicles driven by males and females was also examined. The evaluation of “Click It or Ticket” is
