PSYC 320 Week 3 Behavior Modification Project: Research Paper Example

Behavior Modification Project Research Paper: Increasing Daily Study Time Through Self-Monitoring, Goal Setting and a Contingency Contract

Abstract

This paper reviews research supporting a self-management program to increase a college student's daily minutes of focused study. Procrastination is common among students and is predicted most strongly by task aversiveness, delay, low self-efficacy and impulsiveness. Interventions for procrastination are moderately effective, and research shows that monitoring progress and forming implementation intentions both promote goal attainment. The planned treatment combines daily self-monitoring with a public graph, a specific study plan stated as if-then intentions and a contingency contract that makes a preferred evening activity contingent on reaching a daily goal.

Introduction

As a full-time student who also works twenty hours a week, I have long put off studying until the night before deadlines. For this project, I defined the target behavior as minutes of focused study per day: time seated at my desk with course material open and my phone in another room, ending when I stand up, check my phone or turn to unrelated material. I will record duration with a kitchen timer and log the total each night. Miltenberger (2015) emphasizes that behavior modification begins with a clear operational definition and a reliable recording method, because the effect of a treatment can be judged only against accurate baseline data. My goal is to raise my average from what I expect to be about 30 minutes a day to at least 90 minutes on weekdays. I chose study time because it matters for my grades, because I can observe and record it every day without special equipment, and because a change should be visible within the eight weeks of the course. A roommate agreed to check my log against the timer on two evenings a week, which gives a simple check of reliability.

Research on the Target Behavior

Procrastination, which Steel describes as putting off something one meant to do even while expecting the delay to make things worse, is the main obstacle to my study time. Steel (2007) reviewed the research and meta-analyzed 691 correlations on its possible causes and effects. Some traits often blamed for procrastination, such as neuroticism and rebelliousness, were only weakly related to it. The strongest and most consistent predictors were task aversiveness, task delay, low self-efficacy and impulsiveness, along with low conscientiousness and its facets of self-control, distractibility and organization. Steel integrated these findings in temporal motivation theory, which holds that people put off tasks whose rewards are distant and whose costs are immediate. The theory also predicts that procrastination increases as a task becomes more unpleasant and as confidence in completing it falls, which explains why the hardest courses are often the ones students delay most.

That account fits my experience closely. Studying for an exam two weeks away offers a delayed reward, while my phone offers an immediate one. In behavioral terms, the competing behavior is reinforced right away and studying is reinforced late, if at all. Miltenberger (2015) explains that immediate consequences control behavior more effectively than delayed ones, which suggests that a treatment must bring the rewards of studying closer in time. It also suggests reducing the immediate rewards of the competing behavior, for example by keeping the phone out of reach while studying, and raising self-efficacy by breaking study into manageable blocks with visible progress.

Research on the Treatment

Research suggests that procrastination can be reduced. A meta-analysis of intervention studies found that interventions reduced procrastination overall, with cognitive behavioral approaches among the most effective (van Eerde & Klingsieck, 2018). Their review also noted wide variation in how procrastination was measured, which supports my decision to measure a specific behavior, minutes of study, rather than a general trait.

The first component of my treatment is self-monitoring with a visible graph. Harkin et al. (2016) meta-analyzed 138 randomized studies with 19,951 participants and found that interventions that increased the monitoring of goal progress improved goal attainment, with an average effect of d = 0.40. Effects were larger when progress was physically recorded and when it was reported or made public. I will therefore graph my daily minutes on paper taped to my door and share a weekly total with a study partner. The graph serves two purposes: it gives me immediate feedback each night, and it provides the data the project's later reports require.

The second component is a set of implementation intentions, specific if-then plans that link a situation to a response. Gollwitzer and Sheeran (2006) meta-analyzed 94 independent tests and concluded that spelling out when, where and how one will act gave goal pursuit a sizable boost over merely resolving to act (d = 0.65), across many kinds of goals. My plan will state, for example, "If I finish dinner on a weekday, then I will put my phone in the kitchen and start the timer at my desk," which turns a vague intention into a cue for action. Implementation intentions work, the authors argue, because the situation named in the plan comes to trigger the response automatically, which reduces the need for willpower in the moment. This component also uses stimulus control, which Miltenberger (2015) describes as bringing a behavior under the influence of specific antecedents; studying at the same desk at the same time should make that setting a cue for studying.

The third component is a contingency contract based on the Premack principle, in which a high-probability behavior is made contingent on a low-probability one (Miltenberger, 2015). I will allow myself an hour of video games each evening only after recording at least 90 minutes of study. Because the reward is immediate and contingent, it addresses the delay that Steel (2007) identified as central to procrastination. My study partner will witness the contract, adding social accountability. The contract will state the behavior, the criterion, the reinforcer and the consequence of not meeting the goal, which is simply that the evening hour of games is not earned that day. Miltenberger (2015) notes that reinforcers should be delivered consistently, so I will not make exceptions during the treatment phase.

Conclusion

The research suggests that my procrastination reflects a familiar pattern: studying offers delayed rewards, while distractions offer immediate ones. That pattern is common among students, but it is not fixed. Two limitations deserve mention. Self-monitoring can itself change behavior, so some improvement may appear before the full treatment begins, and a self-managed contract depends on my honesty in delivering or withholding the reward. My roommate's reliability checks and my study partner's weekly review are meant to guard against both problems, and the graph will make any slippage visible. The treatment brings consequences closer through self-monitoring, if-then plans, stimulus control and a contingency contract. In the next part of the project, I will record two weeks of baseline data without intervention, then introduce the treatment and compare the phases on a graph. If the program works, the graph should show a clear rise in daily study minutes after treatment begins, and the level should hold steady rather than fading after the first few days. If it does not, the baseline and treatment data will show which component needs adjustment, such as a smaller daily goal or a different reinforcer.

References

Gollwitzer, P. M., & Sheeran, P. (2006). Implementation intentions and goal achievement: A meta-analysis of effects and processes. Advances in Experimental Social Psychology, 38, 69-119. https://doi.org/10.1016/S0065-2601(06)38002-1

Harkin, B., Webb, T. L., Chang, B. P. I., Prestwich, A., Conner, M., Kellar, I., Benn, Y., & Sheeran, P. (2016). Does monitoring goal progress promote goal attainment? A meta-analysis of the experimental evidence. Psychological Bulletin, 142(2), 198-229. https://doi.org/10.1037/bul0000025

Miltenberger, R. G. (2015). Behavior modification: Principles and procedures (6th ed.). Cengage Learning.

Steel, P. (2007). The nature of procrastination: A meta-analytic and theoretical review of quintessential self-regulatory failure. Psychological Bulletin, 133(1), 65-94. https://doi.org/10.1037/0033-2909.133.1.65

van Eerde, W., & Klingsieck, K. B. (2018). Overcoming procrastination? A meta-analysis of intervention studies. Educational Research Review, 25, 73-85. https://doi.org/10.1016/j.edurev.2018.09.002