Psychology Research Methods Introductory (Hps201) Assignment Sample

Introduction: Psychology Research Methods Introductory (Hps201)

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The information about statistical value and observations is based on the students anxiety, duration of sleep, sleeping patterns, use of social media, practices of meditation and consumption of coffee. Several tests have been performed on which the statistical data has been leveraged to answer the given questions.

Discussion

1: Student anxiety scores are significantly associated with several factors. How much of the variability in anxiety scores cannot be attributed to these factors?

Figure 1: Samples of T-Test

(Source: SPSS)

The measurably huge variety in rest length between full-time and part-time understudies, demonstrated by the t-measurement of significant worth which is - 2.05, features the way that this distinction isn't probably going to be the aftereffect of possibility. The negative typical variety of - 1.26 hours shows that part-time understudies frequently get more rest than full-time understudies do (Sen, & Yildirim, 2022). It's essential to perceive that it's a middle and that the two gatherings contain people with various qualities. A few understudies who concentrate full-time might get more rest than certain understudies who concentrate part-time. Proceeding on to student stress scores, their high correlation with a number of variables suggests that these variables account for some of the variation in student anxiety ratings.

Figure 2: Levene's test of Homogeneity

(Source: SPSS)

The invalid speculation, which expresses that the varieties of at least two gatherings are equivalent, is tried utilizing the Levene's test, a factual system. The presumption viable is that there are no distinctions in rest designs among full-and part-time understudies. It can t conclude that the invalid assumption of its p-esteem at 0.474 is higher than the genuinely critical degree of 0.05 (Rahman, & Muktadir, 2021). This demonstrates that there is insufficient data to reach the determination that part-time understudies' and full-time understudies' varieties in rest term are unique.

Figure 3: Descriptive Analysis

(Source: SPSS)

The outcomes of the regression analysis imply there exists a negative relationship between the amount of sleep and social media use. This implies that those who use social media more frequently get less sleep overall. It is crucial to remember that an association does not imply causality. The inverse relationship between online social networking use and length of sleep has a few potential causes. Social media use may contribute to sleep shortage by interfering with good sleep habits, as one theory suggests. Those who utilize social media right before bed may have a harder time falling asleep or staying asleep throughout the night (Zou, et al. 2019). Another hypothesis is that using social media might increase stress and worry, both of which may disrupt sleep.

Figure 4: Model Coefficient Test

(Source: SPSS)

Based on the variables that are predicted, the model variables are the numbers that are utilized to construct the anxiety score. Social media is an indicator variable in this situation. According to the model parameter for social media, which is -0.33, the anxiety score is predicted to decline by 0.33 units for every unit more of social media use. When all of the variables that predict are equal to zero, the anxiety score's intercept value is what it is. The intercept in this instance is 114.30, indicating that a person who does not utilize social media should have an anxiety score of 114.30 (Cronk, 2019). The statistically significant nature of the equation's coefficients is determined by the t-value. The model's predictive variable is statistically significant if the t-value is higher than 1.96 or lower than -1.96.

Figure 5: Correlation Matrix

(Source: SPSS)

An estimation of the level of importance and bearing of the relationship between two factors is the connection coefficient. Its qualities shift from - 1 to 1, where 0 signifies an absence of relationship, 1 is the most noteworthy conceivable connection among factors, and - 1 indicates the most conceivable negative connection. The connection lattice portrayed in the realistic uncovers a positive relationship between nervousness score and hours worked "(upsides of p = 0.016 and r = 0.38)", utilizations of computerized media (p 0.001 r = 0.52), and a negative relationship between uneasiness score and exercise "(p = 0.065 r = - 0.29)", and between tension score and rest "(, p = 0.014 and r = - 0.39)". The people who set forth more energy at work, invest additional time on interpersonal interaction locales, alongside practice less are anticipated to have more noteworthy tension levels (Okagbue, et al. 2021). Alternately, those with lower tension levels are likely to rest longer and exercise more.

2: Assume that the population average for anxiety levels in university students is 70 (SD = 3). Dr Inez recruits a group of students who do not meditate and compares their anxiety levels to the population of university students. Dr Inez concludes that students who do not practice meditation have significantly higher anxiety levels than university students in the population. Do you agree with this conclusion?

Figure 6: T-Test of one sample

(Source: SPSS)

The total number of students is assumed to be 70. The group of students is based on the anxiety levels of students. It shows that the mean value in anxiety shows about 62.79. The value of SD is 13.95. The median is observed as 65 where the total number is observed as 19. It can be stated that the conclusion for Dr. Inez s students who are not habituated with the practice of meditation. This states that the level of anxiety will increase. The figure of level in anxiety shows the portion of time consumed in social media. It provides that the students who do like to spend time on social media will face the level of anxiety (Aithal, & Aithal, 2020). The anxiety level will lead the students to enhanced isolation and loneliness. It is true that the assumption of Dr Inez is correct and true. The less exercise level will tend to move students to face more anxiety levels.

3: Are students more likely to be engaged in Casual or Part/Full time work?

Figure 7: Proportion Test

(Source: SPSS)

Students are more probable to be working casually, while the projected percentage is 0.50, the observed percentage of students working either part-time or full-time is 0.72. While the projected percentage is 0.50, the actual proportion of students working part-time is 0.28. This indicates that there are actually fewer learners working full- or part-time than anticipated while more students working casually than anticipated (Mazza, et al. 2020). The percentage test, the p-value of 0.004 indicates that the distinction is significant in statistical terms. This indicates that there is just a 0.4% possibility of accidentally seeing a difference this size or bigger.

Figure 8: Estimated Marginal means for age group and employment status

(Source: SPSS)

It is observed that students will likely be busy and engaged in casual employment. It is observed that the amount of students who are engaged in part time and full time work is 0.72. The age group of the students belongs to more than 40 years. But it is anticipated that the casual work amount is about 0.28. The expected amount of casual work is 0.28. Hence, from these parts it can be stated that the students have engaged themselves in casual work in larger portions than the part time and regular work basis. Where fewer students are engaged in part time and full time jobs (Ccanto, et al. 2020). Another explanation is that causal work is more elastic and flexible. This is significant for students who are intending to balance their work life and study.

4: Students whose anxiety levels are higher than 70% of the general population are eligible to take part in an anxiety management program. During the trimester, Amelia reports spending 160 minutes per day on social media. Based on the time spent on social media, is Amelia likely to be eligible for the program?

Figure 9: Scatterplot

(Source: SPSS)

The scatter plot reveals a relationship between social media activity and anxiety level that is favorable. As a result, those who use social media more frequently typically score higher on anxiety tests. It is crucial to remember that a relationship does not imply causality. Two things do not necessarily cause each other just because they are associated. The association between social media activity and anxiety has a few potential causes. One theory is that using social media exposes users to unachievable standards of success and beauty, which can cause anxiety. Another theory is that using social media increases isolation and a sense of feelings of isolation which can contribute to anxiety (Howitt, & Cramer, 2020). Using social media can become addicted, and addiction can cause anxiety. It is significant to notice that the scatter diagram also reveals that many individuals use social media intensively while simultaneously scoring low on anxiety. This implies that anxiety may also be influenced by other variables, such as psychology and ancestry.

It's expected that Amelia qualifies for the anxiety program for management dependent on the scatter diagram. However, without knowing her precise anxiety level, it is difficult to determine for sure. Amelia should consult a doctor or a mental health expert if she is worried about her anxiety levels. Amelia said she experienced a lot of anxiety this trimester. Her increased usage of social media or other causes, such as stress from her studies or her personal relationships, could be too responsible for this (De Winter, 2019).Only students whose levels of anxiety exceed more than 70 percent of the population as a whole are eligible for the anxiety treatment program. Amelia's physician or a psychological expert can evaluate her sense of anxiety levels to figure out whether she fulfills this criteria.

Figure 10: Chi Square Fitness Test

(Source: SPSS)

The chi-square distribution is tested for goodness of fit in the image, using a single degree of freedom and a p-value of 0.004. A statistical test called the Chi-square goodness of fit assessment is used to examine if a set of observed values corresponds to what would be predicted by a theoretical distribution. The distribution of chi-squares with one degree of freedom in this situation serves as the theoretical distribution. A chi-square statistic will be as great as or larger than the value that was observed. Statistical significance is commonly defined as a p-value 0.05.

There is huge variety between the noticed and anticipated values, as indicated by the decency of fit test utilizing a chi-square disseminations and one unit of opportunity, which delivered a p-worth of 0.004. This statistical test might be used to determine whether Amelia's claimed daily online platform usage (160 minutes) corresponds to the predicted distributions of levels of anxiety for the entire population in the circumstances of her case (Ho, et al. 2019). A chi-square goodness of fit evaluation may be used to determine whether Amelia's claimed time fits with the predicted distribution if there is a known correlation between higher levels of anxiety and increasing social media use. The statistically significant departure from the predicted distribution is indicated by a low p-value (0.004). There is evidence to support the idea that Amelia may not have used social media in the manner that is typically connected with anxiety. The goodness of fit test highlights the statistical importance of the detected deviation, giving rise to a concern about Amelia's eligibility for an anxiety management program given her stated use of social media. [Refer to appendix 1]

5: Dr Austin recruits a group of students who meditate when preparing for exams (n = 60, age range = 18-40), and a group of students who do not meditate when preparing for exams (n = 60, age range = over 40). Which group would be expected to have greater anxiety levels leading up to their exams?

Figure 11: Estimated Marginal Means

(Source: SPSS)

It is expected that students who do like to meditate are 63.11. The mean value notes that the value of anxiety of students who do not like to meditate are 59.36. The difference is measured about 9.7. The SD of anxiety is lower than those who meditate. This provides that the students who meditate on a regular basis are based on SD value of 5.34 where the students who don't like to exercise are based on 4.83 (Yu, et al. 2022). The higher value of SD shows that the value is closely situated with the mean value. It can be stated that the students who do not like to exercise have the higher possibility of anxiety scores than the students who practices meditation on regular basis.

Figure 12: Marginal Means plot

(Source: SPSS)

The students who like to meditate would face less anxiety than the students who do not exercise. This is only because of the regular basis of medication. The exercise helps to set the mind to focus on the current state and remove negative thoughts (De Winter, 2019). The students who do the exercise on a daily basis are better able to manage their level of tension and anxiety. It can help to proceed on their exams through a better approach.

6: Dr Okafor concludes that students who drink coffee sleep less hours than students who do not drink coffee. Is Dr Okafor correct?

Figure 13: Post HOC Comparisons

(Source: SPSS)

It is evident and observed that students who drink coffee often sleep less than those who don t like to have the drink. The mean difference shows the differentiation of duration of sleep for students who do not drink and like to have coffee. It has shown that the p Value is about 0.029. The differentiation demonstrated the mean duration of sleep (Aithal, & Aithal, 2020). The duration of sleep of students who have coffee is about 0.54 hours, which is lower than the sleep duration of those who do not have coffee. It can be stated that the statement of Dr. Okafor is correct indeed. It is a fact that caffeine is such a factor which compels difficulties for sleeping activity.

Figure 14: Descriptive Statistics

(Source: SPSS)

The statement of Dr. Okafor is true and correct for students. The duration of sleep of students is 5.53 hours who like coffee, while the mean duration of sleep for those who do not like to have coffee is about 6.76 hours (Cronk, 2019). It is observed that the difference in sleep is 1.23 hours.

Figure 15: Proportion Figure

(Source: SPSS)

The observed values of students who like to have coffee is about 0.47 in proportion, where the expected outcomes is 20 and its proportion is 0.50. Students who do not like to have coffee are 0.53 in proportion (Okagbue, et al. 2021). This has been measured through the observed value of students. The expected value 20 is based on the proportion of 0.50.

Conclusion

The statistical analysis “T-test” can reveal an important difference in \duration of sleep. Another analysis is based on the employment status of students who are employed on a casual and part time and full time basis. The mean value in anxiety level is lower for students who do not meditate whereas the value of SD and mean is higher where students practice meditation on a regular basis.

References

Journals

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