Social Anxiety And Living Accommodation Impact On Academic Motivation In Undergraduate Students Assignment Sample

Introduction of Social Anxiety And Living Accommodation Impact On Academic Motivation In Undergraduate Students Assignment

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Social anxiety is one of the most common diseases among students aged 18-25. One of the main reasons behind this problem is the negative impact of social behaviours on the students. According to the report, 11.9% of college students are facing the problem of social anxiety. Nowadays most college students are facing social anxiety disorder, which is not treated like other diseases. The report is based on the study of social anxiety and its impact on those stu8dents who are living outside their home for the study purpose. Parents, guardians, and the college professors are not paying attention to this disorder as a result the amount of social anxiety is increasing day by day. According to the survey, students who are living in the accommodation are facing the more impact of this “Social Anxiety Disorder” rather than the students who are studying from their own house. 42.3% of the students showed moderate to severe symptoms which is indicating the fact most of the students who are living under accommodation are the victims of this SAD. The main aim of this research is to protect the students from social anxiety. It will help them to achieve academic achievements and awards.

 Method

The research method is completely based on different methods. The research process is progressed with different kinds of methods.

Research Question

The complete research is based on the questions and answers. The problems in response to social anxiety are the focus which was discussed through the question answers type. The research questions were completely based on the topic-related matter (Dempsey et al. 2019). The questions were associated with the problems during the living in accommodation. Social anxiety and their opinion about this topic were also a part of this research question.

Study design

The study or the research was completely done with the help of a “Correlational questionnaire”. This method is based on survey type of questions. Generally, this indicates a special type of research where the researchers mainly focus on finding the relationship between the different variables. The topic is based on social anxiety and its impact on the students. SAAD and the students both are the variable things in this society, which indicates “Correlational questionnaire” was the suitable method to conduct this research.

Measuring the problem

The research was based on the questionnaires but it was not enough to find the data for the analysis from the collected data. For the proper measuring “Social interaction anxiety scale” was used for this research. The scores were given to the respondents on a scale from 0 to 80 to understand the negative effect of social anxiety on them (Farrer et al. 2019). High scoring on this scale mainly indicates that the person or the student is facing more amount of social anxiety than the other students are. In this research, 36 scores were selected as the cut-off in this scale, which helped to find out the proper students who were dealing with social anxiety. The scale was mainly used as the diagnostic tool in this research.

Regression Analysis

The regression analysis is mainly based on the statistical analysis of the collected data. This analysis method generally focuses on the statistical analysis of the collected data to find out the relationship between different variables. In this research, two variable topics were selected as the main point. Social anxiety and the students aged 18-25 are the most variable topic of this research and the collected data from those students through the “Correlational questionnaire” were gone through the regression analysis (England et al. 2019). The outcome of the analysis ultimately gave the important data for this research method. Statistical analysis for this research was done under the different statistical processes.

Secondary data

The secondary data collection is also done for this research method. Different kinds of journals were used for the data collection. The unavailability of the data on this topic forced the researchers to find some other evidence like the government documents or old newspapers to compare the effect of the social anxiety in the previous days and recent situations. The secondary data were mainly collected to understand the role of social anxiety on the students and in which way it affects them the most rather than the other people of different age groups (Brigati et al. 2020). Finding out the different data about the students who lived in accommodation helped a lot to understand the structure of accommodation living.

Survey Platform

Technological help was also taken for the proper analysis of the collected data. The software, which was used for the analysis of the data, is Qualtrics. The software helped in this study by modification of the surveys. The text responses are analysed with this tool to get the proper result. This software supported the regression analysis because it includes different kinds of step analysis. It also helped to create the reports without any coding system.

 Results

Model of Coefficient

The research result is mainly focused on the model of coefficient. The term coefficient here mainly represents the changing variables in the mean value. The other values of the result in this model remain the same. According to this result, three predictors are selected Intercept, accommodation, and the anxiety total. On this basis, the model of efficiency is mainly made and the "P-Value" in the case of the intercept is “<0.001”, the coefficient value of the accommodation according to this chart is 0.958, and at the end, the correlation value of anxiety total score is "< 0.001".

Model Fit measures

According to the data and figure number 2, the model of fit measures can be described. This model is mainly based on the fitness of the collected data with the observations. There are two different kinds of values present in this chart one is representing the expected value and another one is representing the p-value of the model of the fit theory. Here the p-value of the model of fit is <0.001. The adjusted value of this model is 0.15.

Model of Comparisons

The model of comparison is mainly based on statistical tests. The tests are done from the collected data. This type of model is mainly used for the comparison between two types of variables. The whole research is based on social anxiety and its effect on students who are living in the accommodation (Dodd et al. 2021). From the result, it is clear that model 1 and model 2 are the two variable topics of this research and the P-value of the model of comparison is 0.79. Here df1 is representing the comparison between the cell mean and the grand mean and the evaluated value is 1. df2 is representing the single observation value in this model and the evaluated value of the trust factor is 10 for the model1 and 4 for the model 2.

Model of Coefficient

The model of coefficient generally represents the changes in the mean value. This mean value is associated with the value of terms, which is variable. The sign of the correlation is mainly used to determine the path of the research. Here the SE value of the anxiety total is 0.0 to 2.0. The p-value of the intercept is <0.001, and the anxiety total score is <0.001. The other perspective of these results is accommodation. The p-value of this accommodation is 0.789.

Descriptive

The descriptive analysis is done from this result. The mean, median, and mode values are completely analysed from this chart. The number of the total respondents according to this data chart is 108. The mean value in this chart represents the “arithmetic average” of the collected data set. Here the mean value is 58.41. The median value, which is representing the middle value of the complete data set, is 59.00. There is another term that is mentioned in this data report is the standard deviation (Fleming et al. 2018). This standard deviation is representing the dispersement in statistics. The value of the standard deviation is 15.78, which are near 16.

 Discussion

This discussion on this topic is mainly focusing on the analysis of the result. From the table of results 1, it is clear the “P-value” of the intercept is “<0.001” representing the chances are more t6han 1 in thousands. The value is also providing some good evidence for the null hypothesis because the value of p is “0.05<p<0.001” (Rice et al. 2020). Anxiety total score is also “<0.001” this is also supporting the null hypothesis. The p-value of accommodation is 0.958. This is showing the result according to Hypothesis 2 is that the higher rate of the students who are living under accommodation are getting huge motivation for the academic achievements rather than those who are living in the houses. The intercept and the anxiety total score also support the null hypothesis. The value of accommodation is 0.958 is greater than the other values.

The chart is mainly based on the “Model Fit measures”, here the R mainly represents the “proportion of the total variance”. The r-squared value in this table is completely controlling the predictors. To remove the highly increased value of the P the adjusted value of the R is taken in this research, which is 0.15 (Chronis-Tuscano et al. 2018). Here in this research two-variable models are used which is the reason the adjusted value of R is more important than the other values. Here the value is 0.15, which is reasonable according to the “Model of Fit measures”. The value of the R can be changed if the predictors are increased or decreased from the research topic. “P-value” in this research is <0.001 which shows the selected hypothesis based on this result is true.

In the third result sheet, the model of comparison is used and as a result, the comparison is done here within modules 1 and module 2. The value of the df1 is 1 which is representing the difference between the grand mean value and the cell mean value. In the result, df2 is representing the single observations. The value 10 is showing the difference between modules 1 and 4 within module 2. Here the p-value is 0.79 and 3, which is greater than the value of 0.07. That is the reason the value of significance is not approved here because the value of p should be less than 0.07 for making this model significant.

The data from table 4, is again showing the data about the intercept, accommodation, and the social anxiety score. The table is based on the model of coefficient. Regression analysis is mainly done through this table. The T-value within this chart measures the value of the ratio between the coefficient and the standard error associated with this coefficient. The P-value of this intercept and the anxiety total score is “<0.001” (Shao et al. 2020). The null value is not applicable in this research. 6.11 and -4.06 are the t values, which is showing the difference between the coefficient and the standard error value. The P-value here is representing hypothesis number 3 that the amount of social anxiety will increase the more amount of academic motivation will decrease. The reciprocal relationship between social anxiety and academic motivation is the main concern of this result chart.

In the last result, that means in the resulting chart 5 the descriptive analysis of the whole result is done. The mean and the median value are taken for the result chart top keep the whole result clean. The total respondents in this research were 108, all of them were active participants, and the whole, research is done on them. The mean value of the anxiety total score is 58.41 this is representing the interpretation that on average, about 58 students among 108 are facing the problem of social anxiety. The median value in this chart is showing the medium value of the complete collected data. The median value according to this result chart is 59. This is representing the fact that 50% of the students are not facing the problem, of social anxiety and their number is below 59, the other 50% of the students who are facing different kinds of social anxiety are more than 59. Standard deviation data is also available on this chart, which represents the measure of dispersion. The value of the standard deviation is 15.78, which is near about 16. The standard deviation value is indicating the fact that 58±16 students are facing the problem of social anxiety, which is ultimately stopping them to get academic credentials during their studies.

The total analysis and the discussion of the result is giving the data that students who are living in accommodation are more creative than the students who are studying from their homes but in the end, social anxiety is choosing them as their target (Kern et al. 2019). Parents, guardians, and the college professors are not paying attention to this disorder as a result the amount of social anxiety is increasing day by day. As a result, the students are getting distracted from their goals, and this social anxiety is placing negative effects on them rather than on the other students. On the other hand, the result is representing the fact that the guardians and the teachers should focus on the problem of social anxiety because the rate of students who are affected by this mental disorder is increasing with the progression of time. The negative impact of social anxiety kills the academic motivation within the students, which is the main reason behind the lack of interest in studies. Social media attraction, and different types of addiction, is the main reason behind creating this social anxiety. The report is based on the previous research on the students aged 18-25.

 Conclusion

The whole research is based on the social anxiety and the accommodation impact on the academic motivation of the students. Social anxiety is one of the biggest mental disorders, which cannot be diagnosed very easily. This mental disorder is affecting the students to get the academic achievements. As per the research and the result, it is clear that students who are living in accommodation are more creative than the students who are studying from the house. Social anxiety is targeting these students because they are far from their families and from their guardians; as a result, they are not properly following the social protocols to protect them. There are mainly three types of hypotheses used in this research to examine the current situation. According to the hypothesis, social anxiety is reciprocally related to academic motivation. The increase in social anxiety is decreasing the rate4 of academic motivation among the students. In the other hypothesis, it is mentioned that the students who are living in accommodation are facing this problem. As a result, the academic motivation is higher in the students who are studying from home. From the critical discussion of the result, it can conclude that the colleges, guardians, and society should take this disorder seriously; otherwise, it will finish the brilliant students by creating distractions and lack of motivation.

Reference

Brigati, J.R., England, B.J. and Schussler, E.E., 2020. How do undergraduates cope with anxiety resulting from active learning practices in introductory biology?. Plus one, 15(8), p.e0236558.

Chronis-Tuscano, A., Danko, C.M., Rubin, K.H., Coplan, R.J. and Novick, D.R., 2018. Future directions for research on early intervention for young children at risk for social anxiety. Journal of Clinical Child & Adolescent Psychology, 47(4), pp.655-667.

Dempsey, A.E., O'Brien, K.D., Tiamiyu, M.F. and Elhai, J.D., 2019. Fear of missing out (FoMO) and rumination mediate relations between social anxiety and problematic Facebook use. Addictive Behaviors Reports, 9, p.100150.

Dodd, R.H., Dadaczynski, K., Okan, O., McCaffery, K.J. and Pickles, K., 2021. Psychological wellbeing and academic experience of University students in Australia during COVID-19. International Journal of Environmental Research and Public Health, 18(3), p.866.

England, B.J., Brigati, J.R., Schussler, E.E. and Chen, M.M., 2019. Student anxiety and perception of difficulty impact performance and persistence in introductory biology courses. CBE—Life Sciences Education, 18(2), p.ar21.

Farrer, L.M., Gulliver, A., Katruss, N., Fassnacht, D.B., Kyrios, M. and Batterham, P.J., 2019. A novel multi-component online intervention to improve the mental health of university students: Randomised controlled trial of the Uni Virtual Clinic. Internet interventions, 18, p.100276.

Fleming, A.R., Edwin, M., Hayes, J.A., Locke, B.D. and Lockard, A.J., 2018. Treatment-seeking college students with disabilities: Presenting concerns, protective factors, and academic distress. Rehabilitation psychology, 63(1), p.55.

Kern, L., Hetrick, A.A., Custer, B.A. and Commisso, C.E., 2019. An evaluation of IEP accommodations for secondary students with emotional and behavioral problems. Journal of Emotional and Behavioral Disorders, 27(3), pp.178-192.

Rice, S., O'Bree, B., Wilson, M., McEnery, C., Lim, M.H., Hamilton, M., Gleeson, J., Bendall, S., D'Alfonso, S., Russon, P. and Valentine, L., 2020. Leveraging the social network for treatment of social anxiety: pilot study of a youth-specific digital intervention with a focus on the engagement of young men. Internet interventions, 20, p.100323.

Shao, R., He, P., Ling, B., Tan, L., Xu, L., Hou, Y., Kong, L. and Yang, Y., 2020. Prevalence of depression and anxiety and correlations between depression, anxiety, family functioning, social support, and coping styles among Chinese medical students. BMC psychology, 8(1), pp.1-19.