Effects of Sleep on GPA - Research Paper Example

? Effects of Sleep on GPA Nickie Rodriguez Effects of Sleep on GPA Results and Discussion This chapter aims at providing the results obtained in the study, analysis of such results as well as discussions into the topic or problem of the study. This study aimed at establishing the relationship between sleep and GPA of a student. Amongst the main objectives in this study was to identify how amount of sleep affects the GPA of different students across varied levels (per class). In order to obtain effective data to enhance discussions and making conclusions, the study interviewed juniors, seniors, freshmen, and sophomores. From the findings it was obvious that amount of sleep affects the GPA of students across different classes. The sample size used that represented the total population under study was 50. The following discussion provides the results as well as the analysis of different results to confirm the objectives of the study. Different tests were used such as t-test, chi-square test, ANOVA, and correlations. In addition, descriptive statistics provided some insight into the aim of the study. In order to obtain sufficient data, this research aimed at answering three main questions. The research questions included: 1. Is there any relationship between GPA and the hours of sleep a student gets? 2. What is the relationship between the amount of sleep and the GPA per class year? 3. What is the relationship between sleep habits, amount of sleep and GPA? Answering the above questions relied on testing different hypotheses. The hypotheses tested using data or results obtained from the study are discussed in the following paragraphs. H1: There is a relationship between the level of sleep and GPA (i.e., too much sleep low GPA/ Too little sleep low GPA/ between 7and 9 hrs sleep better GPA 3.00 or better) H2: Juniors may have better sleep habits as well as get better sleep and may have a decent GPA due to being accustomed to college life and having a normalized routine. Freshmen are still adjusting and may feel stressed in their new environment and seniors may be stressed and may overload their schedules in a rush to graduate, find jobs and/or get into a graduate school. H3: healthier one’s habits are the more likely they are to get the required amount of sleep and may therefore have better GPA scores. The following information represents the findings or results obtained from the study, which made it possible to have the hypotheses tested hence making conclusions and recommendations with respect to the topic of the study. Findings The following table provides a summary of some of the data collected in respect to different aspects of the participants. Gender Age Race Class Standings Course Load N Valid Missing Mean Median Mode Std. Deviation 50 0 0.38 0.00 0 0.490 50 0 22.26 20.00 19 4.852 50 0 2.46 2.00 2 1.164 50 0 1.42 1.00 1 0.906 50 0 0.94 1.00 1 0.512 Breaking down the data into specific components, the following results provide data on specific headings such as gender, age, and race amongst others. It is important to break down the data into their components and subcomponents in order to be able to critically and effectively analyze every component in respect to the research aims and objectives. 1. Gender: The following data provides a description of the participants in the study with respect to gender: Frequency Percent Valid Percent Cumulative Percent N Valid Missing Freshman Sophomore Total 50 0 31 18 50 50 0 62.0 36.0 100.0 50 0 62.0 36.0 100.0 50 0 62.0 100.0 Amongst the participants, the majority were female (62.0%) and male comprised 36% of the total number of participants. 2. Race: In terms of race, the following results/findings were obtained from the study: Frequency Percent Valid Percent Cumulative Percent N Valid Missing African America Caucasian Asian or Pacific Islander Latino Other Total 50 0 8 27 2 10 3 50 50 0 16.0 54.0 4.0 20.0 6.0 100.0 50 0 16.0 54.0 4.0 20.0 6.0 100.0 50 0 16.0 70.0 74.0 94.0 100.0 Amongst the participants, Caucasian formed the biggest proportion, 54% with the other races Asian of Pacific Islander forming the smallest proportion, 4%. This has a significant impact in discussing the results later on in this paper. 3. Class: Since the research was aimed at establishing the correlation between amount of sleep and GPA across different classes, the participants in the classes were represented as follows: Frequency Percent Valid Percent Cumulative Percent N Valid Missing Freshman Sophomore Junior Senior Total 50 0 7 22 14 7 50 50 0 14.0 44.0 28.0 14.0 100.0 50 0 14.0 44.0 28.0 14.0 100.0 50 0 14.0 58.0 86.0 100.0 The most represented class was sophomore, 44% with the lowest being freshman and senior at 14% each. Other data describing the components or variables of the participants as obtained in the study are represented in the following table: Do u Work Where do u Live Total Commute Time Qty People live with N Valid Missing Mean Median Mode Std. Deviation 50 0 1.10 1.00 1 0.789 50 0 1.10 1.00 2 0.909 50 0 2.46 2.00 0.00 0.62479 50 0 1.42 1.00 1 1.861 Living Environment Naps per Day Naps for how Long Hrs of Sleep good 4 U N Valid Missing Mean Median Mode Std. Deviation 50 0 1.56 2.00 2 0.837 50 0 0.36 .00 2 0.485 50 0 0.3090 .00 0.00 0.60201 50 0 7.4502 .0000 1 1.27476 Descriptive Analysis The following is a descriptive analysis of the results or findings obtained in an attempt to finding the answers to the research questions, attaining research objectives, and testing hypotheses. Descriptive Results Descriptive Statistics N Minimum Maximum Mean Std. Deviation #2 Age 50 18 36 22.26 4.852 Amount of Sleep 50 4.86 9.43 7.3129 1.22380 Sleep Score Good Bad 50 15.00 55.00 37.7000 10.45935 GPA 50 2.10 3.85 3.1014 .40863 NB: The above statistics describes the mean and standard deviations for the amount of sleep, sleep score (measured as either good or bad), and the GPA scores. The above information is represented in the following graph: Mean Std. Deviation N Sleep Score Good Bad 37.7000 10.45935 50 Amount of Sleep 7.3129 1.22380 50 #13 GPA 3.1014 .40863 50 #2 Age 22.26 4.852 50 Data analysis in this study applied both descriptive and inferential statistics in a bid to analyzing collected information for purposes of answering the research questions and attaining research objectives through testing the above mentioned hypotheses. The above statistics is a descriptive analysis on the amount of sleep and frequency. From the above graph, it is evident that the amount of sleep on GPA and Age significantly differs. Descriptive statistics are usually very essential in describing general outlook of data and information obtained from a given study. Through descriptive statistics as illustrated above, it is possible for individuals to have a general understanding of some of the concepts or variables under study. Correlations Other than descriptive statistics, the study used inferential statistics to establish the correlations between different variables in order to make effective and proper inferences. Inferential statistical tools of t-test, chi-square, and Pearson Correlations were used in identifying correlations between different variables in the study. Relationships between various variables in the study were tested through correlation analysis. Correlation analysis involved finding of the Pearson Correlation across each combination of the variable being tested for correlation. The following tables illustrate some of the correlations or relationships obtained from the data analysis. Correlation between various variables and the idea of doing something (sleeping aids or works/news) before participants slept. Test Take Sleep Aids Works or news before bed Gender Pearson Correlation Sig. (2-tailed) N -0.278 0.51 50 -0.536 .000 50 Age Pearson Correlation Sig. (2-tailed) N 0.070 0.630 50 -0.462 0.001 50 Race Pearson Correlation Sig. (2-tailed) N 0.161 0.264 50 -0.226 0.115 50 Amount of sleep Pearson Correlation Sig. (2-tailed) N 0.040 0.785 50 0.312 0.027 50 Sleep Score Good Bad Pearson Correlation Sig. (2-tailed) N 0.384 0.006 50 0.319 0.240 50 GPA Pearson Correlation Sig. (2-tailed) N -0.251 0.079 50 0.206 0.151 50 Naps Pearson Correlation Sig. (2-tailed) N -0.046 0.75 50 -0.042 .772 50 Where do u live Pearson Correlation Sig. (2-tailed) N -0.083 0.565 50 -0.046 0.001 50 Rated Sleep Pearson Correlation Sig. (2-tailed) N -0.058 0.691 50 -0.133 0.356 50 Course Load Pearson Correlation Sig. (2-tailed) N -0.048 0.742 50 -0.039 0.785 50 Take Sleep Aids Pearson Correlation Sig. (2-tailed) N 1 50 -0.173 0.230 50 Work or news b 4 bed Pearson Correlation Sig. (2-tailed) N -0.173 0.230 50 1 50 Other data attained through Pearson Correlation test are illustrated in the following tables in a bid to providing a clearer understanding of how various variables correlate within each other. In summary, the following table illustrates some of the inferential test statistics that were used in testing and analyzing the collected data in line with the main aim or objective of this study. The data obtained in the study was analyzed to obtain the Pearson’s Correlation for each variable as indicated in the following table. Read More