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Hiya, Can you help me with DISCUSSION section for my survey…
Hiya,
Can you help me with DISCUSSION section for my survey report.
Here’s what I have so far:
Introduction
The mental health of individuals is an important factor in their overall wellbeing. Social support is one of the key factors that contribute to mental health, and it has been suggested that individuals with higher levels of social support have better mental health outcomes. This study seeks to investigate the relationship between social support and mental wellbeing by examining the effects of social support on mental health outcomes. The research question for this study is: How does the level of social support affect mental wellbeing?
This study will draw on three psychological theories/models in order to examine the relationship between social support and mental wellbeing. These theories/models are the Social Support and Buffering Hypothesis, the Social Network Theory, and the Social Ecological Model. The Social Support and Buffering Hypothesis suggests that social support can buffer the negative effects of stress on mental health, while the Social Network Theory suggests that individuals with strong social networks have better mental health outcomes. Finally, the Social Ecological Model suggests that social support is one of the key factors influencing mental health at multiple levels, including the individual, family, community, and society.
This study will use a survey to examine the relationship between social support and mental wellbeing. The survey will be administered to a sample of individuals and will investigate the effects of social support on mental health outcomes. The results of this study will help to shed light on the importance of social support in promoting mental wellbeing.
Method
This study used multiple linear regression analysis to estimate the relationship between the level of social support and mental wellbeing. The regression model will include two or more independent variables and a quantitative dependent variable. The purpose of this analysis is to identify the extent to which social support affects mental wellbeing.
To recruit participants for the study, an online recruitment strategy was developed. Recruitment materials has been posted on social media platforms to allow friends and family to take part, online forums on The Open University website, and SONA. Interested participants will be screened through an online survey. The sample size was 97 participants. 68 participants were recruited through SONA, and 29 participants were recruited from sending out link to friends and family.
The survey was administered to participants through an online platform, Qualtrics. The survey included demographic questions and standardised measures of social support and mental wellbeing. The survey was designed to elicit detailed information about participants’ experiences with social support. The survey data collection process began on the 31st March and finished on the 11th April as the sample size was reached.
To examine the relationship between social support and mental wellbeing, the collected data was analysed using multiple linear regression analysis. The regression model was estimated using software such as SPSS. The model was checked for assumptions, and regression coefficients was interpreted to identify the strength and direction of the relationship between social support and mental wellbeing.
A report was written to describe the main findings and their implications for mental wellbeing policy and practice. The report includes tables, figures, and narratives to present the results. The report is structured to include an introduction, methods, results, and discussion sections. The results section provides a detailed analysis of the data, including the regression model and the significance of the results.
The study provides new insights into the relationship of social support in mental wellbeing and the need for interventions that address the barriers to accessing social support. The conclusion section summarises the key findings and their implications for mental wellbeing policy and practice. The limitations of the study were also discussed, and recommendations for future research were provided.
The study was conducted in accordance with relevant ethical principles and guidelines. Participants were informed of the purpose of the study, and their confidentiality and anonymity is protected. Informed consent was obtained from all participants, and the study was approved by the relevant ethics committee. The data will be stored securely in accordance with relevant data protection laws and regulations.
Overall, this study used multiple linear regression analysis to estimate the relationship between social support and mental wellbeing. The study recruited a diverse sample of 97 participants from the general population and collected data using an online survey. The collected data was analysed using multiple linear regression analysis, and the results are presented in a report. The study provides new insights into the relationship of social support in mental wellbeing and the need for interventions that address the barriers to accessing social support.
Results
The results section of a research report summarises the findings of the study based on the data collected and analysed. In this case, the study investigated the relationship between social support and mental wellbeing using multiple linear regression analysis.
Based on the results presented, the mean mental wellbeing score was 8.58 with a standard deviation of 2.51. The sample size was 106 participants who had complete data for all variables.
The multiple linear regression analysis examined the relationship between mental wellbeing and demographic variables, and social support. The analysis found that ‘belongingsupport’ was a significant predictor of mental wellbeing (ß = .25, t(102) = 2.41, p = .018).
The R-squared value for the model was .08, indicating that ‘belongingsupport’ explained 8% of the variance in mental wellbeing. The regression equation was significant, F(4, 101) = 2.26, p = .069.
The results of the study suggest that having support in terms of belongings can have a positive impact on mental wellbeing. These findings have implications for mental wellbeing policy and practice, as interventions that address the barriers to accessing support in this area could have a significant impact on mental health outcomes.
Overall, the results of the study provide new insights into the relationship between social support and mental wellbeing and highlight the importance of targeted interventions to address specific areas of support that may be particularly important for individuals experiencing mental health issues.
Based on the results of the Multiple Linear Regression (MLR) analysis, we investigated whether several independent variables could significantly predict the dependent variable, overall mental wellbeing (overallMW). The results of the regression analysis are presented below:
The Model: overallMW = ß0 + ß1X1 + ß2X2 + ß3X3 + ß4X4 + ß5*X5
Where: ß0 = -2.54334 (constant) ß1 = -1.854 (coefficient for X1) ß2 = -0.001 (coefficient for X2) ß3 = 0.001 (coefficient for X3) ß4 = 0.052 (coefficient for X4) ß5 = -0.015 (coefficient for X5)
The results of the regression indicated that the model explained 38.3% of the variance and that the model was a significant predictor of overallMW, F(5,100) = 9.87, p < .001. The final predictive model was: overallMW = -2.54334 - 1.854X1 - 0.001X2 + 0.001X3 + 0.052X4 - 0.015*X5 Therefore, based on the coefficients, we can conclude that X1 has a negative effect on overallMW, while X4 has a positive effect on overallMW. X2, X3, and X5 have almost no effect on overallMW. In terms of the MLR assumptions, we tested for several assumptions, including linearity, normality, homoscedasticity, and independence of errors. Our results showed that the residuals were approximately normally distributed, as evidenced by the normal P-P plot of regression standardised residuals. Additionally, we checked for the presence of outliers using Cook's distance, Mahalanobis distance, and studentized residuals. There were some high leverage points, but none of them appeared to be overly influential, as shown by the centred leverage values. We also conducted a test for multicollinearity, and the results indicated that there was no significant multicollinearity between the independent variables. However, the assumption of homoscedasticity was violated, as evidenced by the scatterplot of the residuals and predicted values. This suggests that the variance of the residuals is not constant across all levels of the predicted values. This violation may have implications for the accuracy of our model's predictions, particularly for extreme values of the predicted variable. Further investigation may be required to address this issue. Overall, the results suggest that the model has some predictive power, although there may be limitations in its ability to accurately predict the dependent variable due to violations of the homoscedasticity assumption. The Multiple Linear Regression (MLR) model is a statistical technique used to predict the value of a dependent variable (Y) based on one or more independent variables (X1, X2, etc.). The model assumes that there is a linear relationship between the independent variables and the dependent variable. The MLR model equation takes the form: Y = ß0 + ß1X1 + ß2X2 + ... + ßnXn where ß0 is the constant, the value of Y with no contribution from any other variables, and ß1, ß2, ..., ßn are the regression coefficients that represent the increase in Y for every unit increase in X1, X2, ..., Xn, respectively. In the given example, the MLR was conducted to investigate the relationship between the overallMW (dependent variable) and several independent variables. The results of the regression analysis indicate that the model is a significant predictor of overallMW (F = 15.099, p = 0.00689), and the model explains 31% of the variance in overallMW. The final predictive model is not presented in the given information. The assumptions of MLR include linearity, independence of errors, homoscedasticity, normality of errors, and absence of multicollinearity. The assumption of linearity is met when there is a linear relationship between the independent variables and the dependent variable. Independence of errors means that the errors or residuals should not be correlated with each other or with the independent variables. Homoscedasticity means that the variance of the errors is constant across all levels of the independent variables. Normality of errors means that the distribution of the residuals should be approximately normal. Multicollinearity means that the independent variables should not be highly correlated with each other. In the given example, the assumption of normality is met as evidenced by the normal probability plot of the residuals. The assumption of homoscedasticity cannot be determined as there is no information about the plot of residuals against predicted values. The assumption of linearity, independence of errors, and absence of multicollinearity have not been tested or reported in the given information. If these assumptions are not met, it can affect the validity and reliability of the results and interpretation of the model. Linearity: A scatterplot was created to assess the linearity assumption. The plot shows a roughly linear relationship between the dependent variable "overallMW" and the independent variables. Therefore, the linearity assumption is met. Independence of observations was assumed since the data were collected independently and the sample size is relatively large. The homoscedasticity assumption was checked by plotting the residuals against the fitted values. The plot shows that the variance of residuals is roughly constant across all values of the fitted values, which suggests that the homoscedasticity assumption is met. Normality: A normal probability plot of residuals was examined to assess the normality assumption. The plot shows that the residuals are approximately normally distributed. Additionally, a Shapiro-Wilk test was conducted and the results (W = 0.986, p = 0.338) suggest that the residuals are normally distributed. Therefore, the normality assumption is met. Multicollinearity was checked by examining the correlation matrix among the independent variables. The highest correlation coefficient between any two independent variables was 0.266, which is below the threshold of 0.7. Therefore, multicollinearity is not a concern in this model. In summary, all of the assumptions of the MLR model were met, suggesting that the results of the regression analysis can be trusted. However, it is worth noting that the outliers may have influenced the results, particularly in the case of the large leverage value (observation 347) and the large deleted residual. Can you help with DISCUSSION section. Thanks
