Predicting self-reported research misconduct and questionable research practices in university students using an augmented Theory of Planned Behavior

被引:21
作者
Rajah-Kanagasabai, Camilla J. [1 ]
Roberts, Lynne D. [1 ]
机构
[1] Curtin Univ, Sch Psychol & Speech Pathol, Perth, WA 6845, Australia
来源
FRONTIERS IN PSYCHOLOGY | 2015年 / 6卷
关键词
research misconduct; data fabrication; data falsification; academic integrity; Theory of Planned Behavior; descriptive norms; justifications; questionable research practices; ACADEMIC DISHONESTY; SCIENTIFIC MISCONDUCT; RESPONSIBLE CONDUCT; NORMATIVE CONDUCT; FOCUS THEORY; NORMS; ETHICS; JUSTIFICATIONS; PERSONALITY; PREVALENCE;
D O I
10.3389/fpsyg.2015.00535
中图分类号
B84 [心理学];
学科分类号
04 ; 0402 ;
摘要
This study examined the utility of the Theory of Planned Behavior model, augmented by descriptive norms and justifications, for predicting self-reported research misconduct and questionable research practices in university students. A convenience sample of 205 research active Western Australian university students (47 male, 158 female, ages 18-53 years, M = 22, SD = 4.78) completed an online survey. There was a low level of engagement in research misconduct, with approximately one in seven students reporting data fabrication and one in eight data falsification. Path analysis and model testing in LISREL supported a parsimonious two step mediation model, providing good fit to the data. After controlling for social desirability, the effect of attitudes, subjective norms, descriptive norms and perceived behavioral control on student engagement in research misconduct and questionable research practices was mediated by justifications and then intention. This revised augmented model accounted for a substantial 40.8% of the variance in student engagement in research misconduct and questionable research practices, demonstrating its predictive utility. The model can be used to target interventions aimed at reducing student engagement in research misconduct and questionable research practices.
引用
收藏
页数:11
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