Measuring the Psychometric Properties of Adolescent Pedestrian Behavior Questionnaire

Document Type : Original Article

Authors

1 Department of Public Health; Research Center for Health Sciences; Social Determinants of Health Research Center, Hamadan University of Medical Sciences, Hamadan, Iran

2 Department of Public Health, School of Public Health, Hamadan University of Medical Sciences, Hamadan, Iran

3 Department of Public Health, School of Public Health; Social Determinants of Health Research Center, Hamadan University of Medical Sciences, Hamadan, Iran

4 Department of Biostatistics, School of Public Health; Modeling of Non-Communicable Diseases Research Center, Hamadan University of Medical Sciences, Hamadan, Iran

5 Department of Emergency Medical Services, School of Nursing and Midwifery, Hamadan University of Medical Sciences, Hamadan, Iran

10.4103/atr.atr_12_22

Abstract

Background: Road traffic injuries are among the main causes of mortality in adolescents. The aim of this study was to determine the psychometric properties of an adjusted adolescent pedestrian behavior questionnaire (APBQ). Materials and Methods: Using the 29-item self-report pedestrian behavior questionnaire designed by Sadeghi-Bazargani et al. for all age groups as the framework, some of the items were removed depending on the type of behaviors among adolescents and some new questions were added. Eventually, the primary questionnaire was developed with 26 items, including 19 questions from Sadeghi-Bazargani et al.'s questionnaire and seven new questions. The tool was adjusted for adolescents and the psychometric properties were determined among a randomly selected group of 300 Junior high school students in Rasht City, Iran. After determining face validity, content validity and construct validity of the tool by experts, the reliability of the tool was examined based on explorative factor analysis (EFA) with Promax rotation and confirmatory factor analysis in AMOS. Eventually, a self-report questionnaire with 14 items was developed to assess the self-report behavior of adolescent pedestrians. Results: The mean age of the participants was 13.59 (±0.92) years. The Kasier-Meyer-Olkin value was 0.828, which confirmed the EFA. The analysis by the maximum likelihood method with Promax rotation identified four factors with eigenvalues >1 and factor loading ≥0.5. Therefore, pedestrian behaviors were categorized into four groups (unsafe road crossing behavior, distraction, positive behavior, and playing on the road). Conclusion: The APBQ can be a proper tool for self-reporting adolescent pedestrians' behaviors. It can also be used for studies on safe behaviors in adolescent pedestrians.

Keywords

References

1.
Touahmia M. Identification of risk factors influencing road traffic accidents. Eng Technol Appl Sci Res 2018;8:2417-21.  Back to cited text no. 1
    
2.
World Health Organization. Road Traffic Injuries; 2018. Available from: https://www.who.int/news-room/fact-sheets/detail/road-traffic-injuries. [Last accessed on 2022 Feb 19].  Back to cited text no. 2
    
3.
Krug E. Decade of action for road safety 2011-2020. Injury 2012;43:6-7.  Back to cited text no. 3
    
4.
Sasidharan L, Menéndez M. Application of partial proportional odds model for analyzing pedestrian crash injury severities in Switzerland. J Transp Saf Secur 2019;11:58-78.  Back to cited text no. 4
    
5.
World Health Organization. Global Status Report on Road Safety 2018: Summary. World Health Organization; 2018.  Back to cited text no. 5
    
6.
Balasubramanian V, Bhardwaj R. Pedestrians' perception and response towards vehicles during road-crossing at nighttime. Accid Anal Prev 2018;110:128-35.  Back to cited text no. 6
    
7.
Barin EN, McLaughlin CM, Farag MW, Jensen AR, Upperman JS, Arbogast H. Heads up, phones down: A pedestrian safety intervention on distracted crosswalk behavior. J Community Health 2018;43:810-5.  Back to cited text no. 7
    
8.
Pešić D, Antić B, Glavić D, Milenković M. The effects of mobile phone use on pedestrian crossing behaviour at unsignalized intersections – Models for predicting unsafe pedestrian behaviour. Saf Sci 2016;82:1-8.  Back to cited text no. 8
    
9.
Tapiro H, Oron-Gilad T, Parmet Y. Cell phone conversations and child pedestrian's crossing behavior; a simulator study. Saf Sci 2016;89:36-44.  Back to cited text no. 9
    
10.
Wells HL, McClure LA, Porter BE, Schwebel DC. Distracted pedestrian behavior on two urban college campuses. J Community Health 2018;43:96-102.  Back to cited text no. 10
    
11.
White M, White J, Siuhi S, Mwakalonge J. Self-reported behaviors and habits of distracted college pedestrians while walking. Transp Res Rec 2017;2661:76-83.  Back to cited text no. 11
    
12.
Swain P, Singh P. Assessment of the level of knowledge and practice towards road traffic safety among male adolescents in urban slums of Delhi. Int J Res Granthaalayah 2020;8:165-72.  Back to cited text no. 12
    
13.
Rosenbloom T, Mandel R, Rosner Y, Eldror E. Hazard perception test for pedestrians. Accid Anal Prev 2015;79:160-9.  Back to cited text no. 13
    
14.
Musselwhite C, Avineri E, Fulcher E, Goodwin P, Susilo Y. Understanding Public Attitudes to Road-User Safety – Literature Review. Road Safety Research Report; 2010.  Back to cited text no. 14
    
15.
Gitelman V, Levi S, Carmel R, Korchatov A, Hakkert S. Exploring patterns of child pedestrian behaviors at urban intersections. Accid Anal Prev 2019;122:36-47.  Back to cited text no. 15
    
16.
Granié MA. Gender differences in preschool children's declared and behavioral compliance with pedestrian rules. Transp Res Part F Traf Psychol Behav 2007;10:371-82.  Back to cited text no. 16
    
17.
Granié MA. Effects of gender, sex-stereotype conformity, age and internalization on risk-taking among adolescent pedestrians. Saf Sci 2009;47:1277-83.  Back to cited text no. 17
    
18.
Pfeffer K, Hunter E. The effects of peer influence on adolescent pedestrian road-crossing decisions. Traffic Inj Prev 2013;14:434-40.  Back to cited text no. 18
    
19.
Antić B, Pešić D, Milutinović N, Maslać M. Pedestrian behaviours: Validation of the Serbian version of the pedestrian behaviour scale. Trans Res Part F Traf Psychol Behav 2016;41:170-8.  Back to cited text no. 19
    
20.
Sadeghi-Bazargani H, Haghighi M, Heydari ST, Soori H, Rezapur Shahkolai F, Motevalian SA, et al. Developing and validating a measurement tool to self-report pedestrian safety-related behavior: The Pedestrian Behavior Questionnaire (PBQ). Bull Emerg Trauma 2020;8:229-35.  Back to cited text no. 20
    
21.
Deb S, Strawderman L, DuBien J, Smith B, Carruth DW, Garrison TM. Evaluating pedestrian behavior at crosswalks: Validation of a pedestrian behavior questionnaire for the U.S. population. Accid Anal Prev 2017;106:191-201.  Back to cited text no. 21
    
22.
Elliott MA, Baughan CJ. Developing a self-report method for investigating adolescent road user behaviour. Trans Res Part F Traf Psychol Behav 2004;7:373-93.  Back to cited text no. 22
    
23.
Granié MA, Pannetier M, Guého L. Developing a self-reporting method to measure pedestrian behaviors at all ages. Accid Anal Prev 2013;50:830-9.  Back to cited text no. 23
    
24.
McIlroy RC, Nam VH, Bunyasi BW, Jikyong U, Kokwaro GO, Wu J, et al. Exploring the relationships between pedestrian behaviours and traffic safety attitudes in six countries. Trans Res Part F Traf Psychol Behav 2020;68:257-71.  Back to cited text no. 24
    
25.
McIlroy RC, Plant KL, Jikyong U, Nam VH, Bunyasi B, Kokwaro GO, et al. Vulnerable road users in low-, middle-, and high-income countries: Validation of a Pedestrian Behaviour Questionnaire. Accid Anal Prev 2019;131:80-94.  Back to cited text no. 25
    
26.
Nabipour AR, Nakhaee N, Khanjani N, Zirak Moradlou H, Sullman MJ. The road user behaviour of school students in Iran. Accid Anal Prev 2015;75:43-54.  Back to cited text no. 26
    
27.
O'Hern S, Stephens AN, Estgfaeller N, Moore V, Koppel S. Self-reported pedestrian behaviour in Australia. Trans Res Part F Traf Psychol Behav 2020;75:134-44.  Back to cited text no. 27
    
28.
Schwebel DC, Gaines J, Severson J. Validation of virtual reality as a tool to understand and prevent child pedestrian injury. Accid Anal Prev 2008;40:1394-400.  Back to cited text no. 28
    
29.
Stavrinos D, Byington KW, Schwebel DC. Distracted walking: Cell phones increase injury risk for college pedestrians. J Safety Res 2011;42:101-7.  Back to cited text no. 29
    
30.
Sullman MJ, Gras ME, Font-Mayolas S, Masferrer L, Cunill M, Planes M. The pedestrian behaviour of Spanish adolescents. J Adolesc 2011;34:531-9.  Back to cited text no. 30
    
31.
Sullman MJ, Mann HN. The road user behaviour of New Zealand adolescents. Trans Res Part F Traf Psychol Behav 2009;12:494-502.  Back to cited text no. 31
    
32.
Useche SA, Alonso F, Montoro L. Validation of the Walking Behavior Questionnaire (WBQ): A tool for measuring risky and safe walking under a behavioral perspective. J Trans Health 2020;18:100899.  Back to cited text no. 32
    
33.
Zhou R, Horrey WJ. Predicting adolescent pedestrians' behavioral intentions to follow the masses in risky crossing situations. Trans Res Part F Traf Psychol Behav 2010;13:153-63.  Back to cited text no. 33
    
34.
Hashemiparast M, Montazeri A, Nedjat S, Negarandeh R, Sadeghi R, Garmaroudi G. Pedestrian road crossing behavior (PEROB): Development and psychometric evaluation. Traffic Inj Prev 2017;18:281-5.  Back to cited text no. 34
    
35.
Twisk DA, Commandeur JJ, Vlakveld WP, Shope JT, Kok G. Relationships amongst psychological determinants, risk behaviour, and road crashes of young adolescent pedestrians and cyclists: Implications for road safety education programmes. Trans Res Part F Traf Psychol Behav 2015;30:45-56.  Back to cited text no. 35
    
36.
Wang H, Wu M, Cheng X, Schwebel DC. The road user behaviours of Chinese adolescents: Data from china and a comparison with adolescents in other countries. Ann Glob Health 2019;85:76.  Back to cited text no. 36
    
37.
Taghizadeh Z, Ebadi A, Montazeri A, Shahvari Z, Tavousi M, Bagherzadeh R. Psychometric properties of health related measures. Part 1: Translation, development, and content and face validity. Payesh (Health Monitor) 2017;16:343-57.  Back to cited text no. 37
    
38.
Tilden VP, Nelson CA, May BA. Use of qualitative methods to enhance content validity. Nurs Res 1990;39:172-5.  Back to cited text no. 38
    
39.
Lawshe CH. A quantitative approach to content validity. Pers Psychol 1975;28:563-75.  Back to cited text no. 39
    
40.
Masuwai AM, Saad NS. Evaluating the face and content validity of a Teaching and Learning Guiding Principles Instrument (TLGPI): A perspective study of Malaysian teacher educators. Geografia. 2016;12(3).  Back to cited text no. 40
    
41.
Mohammadbeigi A, Mohammadsalehi N, Aligol M. Validity and reliability of the instruments and types of measurments in health applied researches. J Rafsanjan Univ Med Sci 2015;13:1153-70.  Back to cited text no. 41
    
42.
Hair J, Anderson R, Tatham R, Black W. Multivariate Data Analysis. New Jersey, NJ: Prentivce-Hall International Inc.; 1998.  Back to cited text no. 42
    
Archives of Trauma Research
Volume 11, Issue 3
September 2022
Pages 105-112

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  • Receive Date: 19 June 2023
  • Accept Date: 19 June 2023

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