Evaluating a Quantitative Research Study

Evaluating a Quantitative Research Study

Introduction

The quantitative study by Whipps et al. (2018) examines the correlation between Nighttime Media Use (NMU), sleep patterns, and variation by weight and BMI factors among first-semester college students at Wyoming University. The research aimed to understand the correlation between NMU, sleep patterns, and weight factors to offer insight into how these factors affect the weight of the students. Findings from the study contribute to the knowledge base on a broad range of risk factors associated with obesity. According to the survey, poor and insufficient sleep are the obesogenic risk factors. Reduced sleep duration and poor sleep quality have been linked to weight gain (Patel & Hu, 2008; Lund et al., 2010). College students reported shorter sleep hours and poor-quality sleep. Sleep duration and quality were partly attributed to media devices such as computers, tablets, and smartphones in the students’ bedrooms.

The study also intends to fill the knowledge gap in the field that, although several studies have described the association between NMU and sleep habits, such studies do not represent the direct association between NMU and sleep behaviours. Additionally, existing studies do not explore the relationships between young adults and college students. The study hypothesized that the college students who portrayed suboptimal sleep patterns in their first semester showed an increased incidence of sleep disturbance due to nighttime media use and had a relationship with weight gain over a semester.

Methods

The study adopted a quantitative approach to collecting data, where data was obtained through questionnaires. The survey participants were Wyoming University students between 18 and 24 years old. Researchers contacted thirty-four instructors teaching first-year seminar courses. Out of the 34, six instructors allowed students from their classrooms to participate in the study. Of 142 eligible students recruited from the classes, 128 students participated in the study. All the participants were oriented on anthropometric measures, survey instruments, and human research protections. Pittsburgh Sleep Quality Index (PSQI) was used to obtain the data for the seven variables that were the metrics for the quality of sleep, which included duration of sleep, quality of sleep, disturbances in sleep, sleep efficiency habit, daytime dysfunction, and the use of medications to assist with sleep. The combined scores from the seven variables yielded a Global Sleep Quality (GSQ) Score. PSQI scores ranged from 0-21, with high scores indicating lower sleep quality. Notably, GSQ scores above five indicated good sleepers, while GSQ below five indicated poor sleepers.

Nighttime media use was assessed using seven questions, and a Likert Scale was used to quantify the responses. Anthropometric measures included height, weight, Body Mass Index (BMI), and waist circumference, measured at 10.5 ± 1.02 weeks. Data were analyzed descriptively, and independent sample t-tests for the gender groups were conducted. Frequency distributions from PSQI for the categorical scores were obtained, and responses from the questions assessing NMU were also generated. Pearson Correlation determined the relationship between the existence of media-supporting devices in the room and sleep variables such as quality of sleep, duration of sleep, sleep latency, and sleep efficiency. The analysis was performed in IBM SPSS v.23.0.

Results

Out of 142 students eligible, 114 participated in the study, with a response rate of 89.1%. The average duration of sleep self-reported by the participants was 7.26 ± 0.93 hours and 8.12 ± 0.93 hours as the average time spent in bed, with an average sleep latency of 19.6 ± 16.9 minutes. In addition, 33.3% of participants indicated they met the recommended number of sleep hours (at least eight hours), and 25.4% reported 6.5 or fewer. The global PSQI scores from PSQI responses indicated that 59.6% of participants were optimal sleepers (Optimal scores range = 1-5), while 22.8% were categorized as borderline (Borderline scores range = 6-7), with 17.5% accounting for poor sleepers (score range = >8).

From the nighttime media use questionnaire, 92.1% of participants had smartphones or tablets in their rooms most of the time or when they slept. Notably, 94.7% indicated using smartphones or tablets as their alarm. From the analysis of the anthropometric measures, students who play games frequently in bed are likely to have higher initial waist circumference (p = .006), post-waist circumference (p = .037), initial weight (p = .006), post-weight (p = .006), initial BMI (p = .029), and post-BMI (p = .035). The correlations for the other variables were insignificant. Unsurprisingly, NMU was not correlated with changes in anthropometric variables over eight weeks since the variables did not show any significant change. The table below shows Pearson’s correlation outcome.

Discussion

One strength associated with the study is that the researchers employed reliable measures to quantify the variables. The study replicates other findings on the correlation between NMU and sleep behaviours, such as studies by Curcio, Ferrara, and Degennaro (2006) and Patel and Hu (2008). These studies established an increase in weight for the participants. However, participants’ perceptions reduce the accuracy of self-reported behaviours, which acts as a confounding factor. Also, the study did not assess underlying clinical conditions that could affect sleep patterns. Other factors, such as diet and physical activities that could affect weight change were also not considered. Among the study’s limitations is that the 8-week period may not be enough to show any significant change in the anthropometric measures such as weight and other markers. Therefore, future studies can consider longer intervals between the measurement periods. Additionally, future studies should use larger sample sizes to validate the statistical significance of the results and ensure that it is not due to the chance that smaller samples may cause.

Conclusion

Inferences from the study coincide with findings from other studies related to the topic that established that the quality and amount of sleep are correlated with weight gain/loss. The study found that college students in their first semester are likely to report instances of sleep deprivation due to their nighttime media use habits. The study confirms that a relationship exists between NMU and sleep behaviours. The quantitative research was rigorous and employed reliable measurement instruments. The statistical tests used in the study, such as correlation and t-tests, will be helpful in my career in conducting studies to establish a relationship between variables. For instance, correlation can be applied in a survey to determine the correlation between family income and students’ academic performance. Critiquing quantitative studies is a valuable skill since it will enable me to identify the most suitable methods of conducting a particular research to obtain the most reliable results.

References

Curcio, G., Ferrara, M., Degennaro, L. (2006). Sleep loss, learning capacity and academic performance. Sleep Medical Reviews.10(5):323-337.

Lund, H. G. et al. (2010). Sleep patterns and predictors of disturbed sleep in a large population of college students. Journal of Adolescence Health. 46(2):124-132.

Patel, S. R., & Hu, F. B. (2008). Short sleep duration and weight gain: a systematic review. Obesity. 16(3):643-653.

Whipps et al. (2018). Evaluation of Nighttime Media Use and Sleep Patterns in First-semester College Students. American Journal of Health Behaviors. ™ 2018;42(3):47-55. DOI: https://doi.org/10.5993/AJHB.42.3.5.

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Before beginning this exam, review the course text and the learning activities you completed in Weeks 1 through 5.

In this second portion of the Final Exam, you will critically evaluate a quantitative research study on a social science topic. Your instructor will post an announcement with the reference for the article assigned for the exam. The study will be from a peer-reviewed journal and published within the last ten years.

Evaluating a Quantitative Research Study

In the body of your critique, describe the statistical approaches used, the variables included, the hypothesis(es) proposed, and the interpretation of the results. In your conclusion, suggest other statistical methods that could have been used and, if appropriate, offer alternative interpretations of the results. This process will allow you to apply the concepts learned throughout the course to interpret actual scientific research.

Your critique must include the following sections and information:

Introduction:

This section will introduce the assigned peer-reviewed quantitative study.
Identify the research questions and hypothesis(es) as well as the purpose of the study.
Methods:

Describe the procedures and methods of data collection, measures/instruments used, the participants and how they were selected, and the statistical techniques used.
Results:

Summarize the results presented in the study in this section.
Discussion:

Evaluate the efficacy of the research study by discussing the following:
Address the study’s strengths, weaknesses, and limitations and suggest future research directions.
Include additional forms of statistical analyses as suggestions for future research.
Conclusion:

Summarize the main points of your evaluation of the study.
Explain how the statistical test used in the study could be applied to your future career. Give one example.
Discuss how your ability to critique quantitative research could impact your future career.