Use the case study attached that focuses on the academic procrastination for your course project. Next, analyze the methods that were applied to modify the target behavior and evaluate whether or not these methods would be useful in your own self-management project.
Be sure to address the following:
§ Training goals: summarize ways to increase and decrease target behaviors using operant conditioning.
§ Consequences: examine applications of reinforcers and punishments.
§ Schedules of reinforcement: identify as continuous or intermittent (if intermittent, is the reinforcement based on a ratio or interval schedule?)
§ What aspects of culture may impact the behavior that was identified in the case study?
§ Were there any ethical boundaries that were crossed or mentioned in the case study? What might one consider (ethically) when conducting a case study such as the one you selected?
Your case study should be a minimum of two pages, not including the title and reference pages. You must use a minimum of three sources, which should be properly cited. All references should be formatted in APA style.
1 source is attached, need 2 more for a total of 3 sources.
An Investigation of the Efficacy of Acceptance-Based Behavioral Therapy for Academic Procrastination Debra M. Glick and Susan M. Orsillo Suffolk University Procrastination among college students is both prevalent and troublesome, harming both academic performance and physical health. Unfortunately, no “gold standard” intervention exists. Research suggests that psychological inflexibility may drive procrastination. Accordingly, interventions using acceptance and mindfulness methods to increase psychological flexibility may decrease procrastination. This study compared time management and acceptance-based behavioral interventions. College students’ predictions of how much assigned reading they should complete were compared to what they did complete. Procrastination, anxiety, psychological flexibility, and academic values were also measured. Although a trend suggested that time management intervention participants completed more reading, no group differences in procrastination were revealed. The acceptance-based behavioral intervention was most effective for participants who highly valued academics. Clinical implications and future research are discussed. Keywords: academic procrastination, psychological inflexibility, acceptance, time management, academic values On average, college students report that they engage in aca- demic procrastination between 30 and 60% of the time (Rabin, Fogel, & Nutter-Upham, 2011). This high frequency is concerning given its widespread negative consequences. Not surprisingly, procrastination is negatively associated with grades on papers (Tice & Baumeister, 1997), exams (Steel, Brothen, & Wambach, 2001;Tice & Baumeister, 1997), and final course grades (Steel et al., 2001). It is also associated with poorer mental and psycholog- ical functioning. For example, students scoring higher on a self- report measure of procrastination report more stress, physical illness, and visits to the health center than do those reporting lower levels of procrastination (Tice & Baumeister, 1997). In addition, procrastination has been linked with poor mental health (Stead, Shanahan, & Neufeld, 2010), a failure to seek mental health services (Stead et al., 2010), and suicide proneness (Klibert, Langhinrichsen-Rohling, & Saito, 2005). Given these adverse ef- fects, it is not surprising that the majority of students report a desire to reduce their procrastination (Solomon & Rothblum, 1984). Unfortunately, despite its prevalence and negative impact, a “gold standard” intervention for procrastination has not yet been developed. Several studies have demonstrated the benefits of time management (TM) strategies, such as setting deadlines (e.g., Ari- ely & Wertenbroch, 2002), monitoring and reporting compliance to deadlines (Roberts, Fulton, & Semb, 1988), creating specific plans for completing goals (e.g., Gollwitzer & Brandstätter, 1997; Häfner, Oberst, & Stock, 2014), and learning study skills (Tuck- man & Schouwenberg, 2004) for task completion. However, con- siderable differences in how procrastination has been operationally defined, methodological differences across studies, and shortcom- ings to the study designs, limit the interpretability and generaliz- ability of the findings. Studies have varied with respect to how they have defined procrastination. One generally accepted defini- tion is that procrastination is the voluntarily delay of an intended course of action that occurs despite expectations that one will be worse off for the delay (Steel, 2007). Some debate exists as to whether procrastination is best understood as a behavior elicited in response to certain tasks (i.e., dilatory behavior) or as a trait characteristic that manifested across a range of situations (Schou- wenberg, 2004). In the present study, procrastination was viewed as a behavioral manifestation of a trait, rather than a trait charac- teristic itself. In addition, it was viewed as a discrepancy between the intended and the actual time frame for starting or completing work. Specifically, procrastinatory behavior was defined as . . . the delay of a task or assignment that is under one’s control. The delay should be under the control of the individual, and the task should be one that needs to be done. Procrastination involves knowing that one needs to perform an activity or attend to a task, and perhaps even wanting to do so, yet failing to motivate oneself to perform within the desired or expected time frame. (Ackerman & Gross, 2005, p. 5) Perhaps due to the differences in how procrastination has been defined, studies on the behavior have utilized a range of method- ologies. For example, Ariely and Wertenbroch (2002) assessed the efficacy of TM strategies incorporated into an academic class, rather than assessing the impact of a standalone TM training. Their study design and findings suggest that professors need to alter their class plan in order to reduce student procrastination, which they may not be willing to do. Further, the outcomes used to determine This article was published Online First February 16, 2015. Debra M. Glick and Susan M. Orsillo, Department of Psychology, Suffolk University. Correspondence concerning this article should be addressed to Debra M. Glick, Suffolk University, 41 Temple Street, Boston, MA 02114. E-mail: Debra.m.glick@gmail.com T hi s do cu m en t is co py ri gh te d by th e A m er ic an Ps yc ho lo gi ca l A ss oc ia tio n or on e of its al lie d pu bl is he rs . T hi s ar tic le is in te nd ed so le ly fo r th e pe rs on al us e of th e in di vi du al us er an d is no t to be di ss em in at ed br oa dl y. Journal of Experimental Psychology: General © 2015 American Psychological Association 2015, Vol. 144, No. 2, 400–409 0096-3445/15/$12.00 http://dx.doi.org/10.1037/xge0000050 400 mailto:Debra.m.glick@gmail.com http://dx.doi.org/10.1037/xge0000050 the impact of TM range from scores on a trait measure of procras- tination (e.g., Tuckman & Schouwenberg, 2004) to the number of days between when students handed in papers and the final day of class (Ariely & Wertenbroch, 2002), the amount of hours students spend on an activity the week before it is due (Häfner et al., 2014), to GPA (Tuckman, 1998), which makes it difficult to integrate cross-study findings. Moreover, although these studies demonstrate a positive impact of TM strategies on average group performance, this approach was not effective for all participants. For example, the study skills intervention was least effective for students with a high tendency to procrastinate (Tuckman & Schouwenberg, 2004). Furthermore, across studies, TM strategies were not effective for all tasks. For example, setting implementation intentions appears to be more effective in helping participants complete “difficult” than “easy” goals (Gollwitzer & Brandstätter, 1997). One possible explanation for the limited impact of TM strategies on procrastination is that they may not sufficiently target other factors that play a causal or maintaining role in the behavior. For example, procrastination has been found to be associated with anxiety (e.g., Fritzsche, Young, & Hickson, 2003; Macher, Paech- ter, Papousek, & Ruggeri, 2012), fear of negative evaluation (e.g., Bui, 2007), fear of failure (e.g., Beck, Koons, & Milgrim, 2000), and problems with emotion regulation (Sirois & Pychyl, 2013), which may not be adequately addressed in TM. Fortunately, a number of cognitive–behavioral programs have been developed to target students’ negative thoughts about their ability to complete quality work (e.g., Kearns, Gardiner, & Marshall, 2008), their difficulty with self-regulating their behavior (e.g., Häfner et al., 2014), and their shame about being “procrastinators,” (e.g., Top- man, Kruise, & Beijne, 2004). Although these studies all report some positive outcomes, meth- odology limits the conclusions that can be drawn. First, few of the studies utilized a control group, which makes it difficult to attri- bute any change in procrastination to the cognitive–behavioral theory programs. Moreover, small sample sizes and low partici- pation (e.g., 2% of those who visited a study Web site; Topman et al., 2004) and completion rates (e.g., 57% Topman et al., 2004) limit the generalizability of the findings. Finally, a large variety of outcomes of interest were used, including scores on procrastination scales (e.g., Tuckman & Schouwenberg, 2004) and measures as- sessing stress and cognitions related to completing work (Kearns et al., 2008), whether students passed their courses (Van Horebeek, Michielsen, Neyskens, & Depreeuw, 2004), and time spent on an important academic assignment (Häfner et al., 2014). This variety prevents direct comparisons across studies. Another limitation of some of the previous interventions is that they required a great deal of time and/or resources. For example, Tuckman and Schouwenberg’s intervention included 90-min-long groups each week for up to one year, requiring large time com- mitments from both students and group leaders. Similarly, Topman et al.’s (2004) intervention required daily exercises for a coach to review, which is also quite resource-intensive. Given the preva- lence of procrastination among college students, it is important to develop cost and resource effective and easily accessible interven- tions. Given the prevalence of procrastination among college stu- dents, it is important to develop cost and resource effective and easily accessible interventions, which has been a focus of recent research in this area (e.g., Häfner et al., 2014; Scent & Boes, 2014). One recommendation is that online interventions might be particularly beneficial, cost-effective, and easier to implement. However, research is needed to determine whether this mode of delivery would be effective in reducing procrastination. One hindrance to the development of effective interventions for procrastination may be that there has not been a unified theory developed to explain the behavior. The development and provision of effective interventions for procrastination requires a strong, cohesive theoretical explanation of the behavior. Although there has been a recent increase in scientific research, much has yet to be learned about the causes and maintaining factors of procrastination (Steel, 2007). One explanation for why TM programs might be limited in their impact is that they may not be sufficiently targeting the constructs underlying procrastination. Recently it has been proposed that chronic procrastination may result from psychological inflexibility (Glick, Millstein, & Orsillo, 2014; Scent & Boes, 2014). Psycho- logical inflexibility is defined by six key psychological processes (i.e., the “hexaflex” model: experiential avoidance, cognitive fu- sion, dominance of the conceptualized past or future, attachment to the conceptualized self, lack of values clarity, and unworkable action/inaction; Hayes et al., 2004). Psychological inflexibility is proposed to be a process that contributes to the development and maintenance of a broad range of problematic behaviors and psy- chological distress (Hayes, Strosahl, & Wilson, 2012), and mea- sures of inflexibility have been shown to be associated with measures of depression, anxiety,