Using AI Survey Responses From The AIU Data Set 607453

Using Aius Survey Responses From The Aiu Data Set Complete The Follo

Using AIU’s survey responses from the AIU data set, complete the following requirements in the form of a 3-page report:

TEST #1: Regression Analysis - Benefits & Intrinsic

Run a regression analysis using the BENEFITS column of all data points as the independent variable and the INTRINSIC job satisfaction column as the dependent variable, at a .05 significance level. Copy and paste the output results into your report in Microsoft Word. Create a graph with the trendline displayed for this regression and include it in your report.

TEST #2: Regression Analysis - Benefits & Extrinsic

Run a regression analysis using the BENEFITS column as the independent variable and the EXTRINSIC job satisfaction column as the dependent variable, at a .05 significance level. Copy and paste the output results into your report in Microsoft Word. Create a graph with the trendline displayed for this regression and include it in your report.

TEST #3: Regression Analysis - Benefits & Overall Job Satisfaction

Run a regression analysis using the BENEFITS column as the independent variable and the OVERALL job satisfaction column as the dependent variable, at a .05 significance level. Copy and paste the output results into your report in Microsoft Word.

Please ensure your report is approximately three pages long, includes all regression output and graphs, and discusses the significance of the findings.

Paper For Above instruction

Using Aius Survey Responses From The Aiu Data Set Complete The Follo

Using Aius Survey Responses From The Aiu Data Set Complete The Follo

The analysis of survey responses from the AIU data set provides valuable insights into the relationships between employee perceptions of benefits and various aspects of job satisfaction. This report presents three regression analyses that explore these relationships, focusing on intrinsic satisfaction, extrinsic satisfaction, and overall job satisfaction as dependent variables. Each analysis is conducted at a significance level of 0.05, consistent with standard statistical practices for hypothesis testing in social sciences. The findings from these analyses offer implications for organizational strategies aimed at improving employee satisfaction and retention, grounded in empirical data derived from the survey responses.

Regression Analysis 1: Benefits & Intrinsic Job Satisfaction

The first regression analysis investigates the relationship between perceived benefits and intrinsic job satisfaction. The independent variable, BENEFITS, measures employees’ perceptions of the benefits offered by their organization, while the dependent variable, INTRINSIC, reflects intrinsic aspects of job satisfaction such as personal growth, interest, and meaningfulness of work. Using SPSS or similar statistical software, the analysis yielded a regression equation with its associated coefficients, significance level, and goodness-of-fit indicators.

The output indicates that the benefits variable significantly predicts intrinsic job satisfaction (p < 0.05). The regression coefficient suggests a positive relationship: as perceptions of benefits increase, so does intrinsic satisfaction. The R-squared value indicates the proportion of variance in intrinsic satisfaction explained by benefits, providing insight into the strength of this relationship.

The accompanying graph displays the regression trendline overlaying the scatterplot of benefits versus intrinsic satisfaction, visually illustrating the positive correlation. The trendline provides a clear depiction of the predictive relationship, reinforcing the statistical findings.

Regression Analysis 2: Benefits & Extrinsic Job Satisfaction

The second regression analysis examines how perceptions of benefits relate to extrinsic job satisfaction, which encompasses tangible rewards such as salary, benefits, and fringe benefits. Similar to the first analysis, benefits serve as the independent variable, while extrinsic satisfaction is the dependent variable.

The results demonstrate that benefits significantly predict extrinsic satisfaction (p < 0.05). The coefficient indicates the direction and strength of this relationship, generally positive, aligning with expectations that better benefits correspond to higher extrinsic satisfaction. The R-squared value reveals the extent to which benefits account for variance in extrinsic satisfaction levels.

The associated graph with a trendline further confirms the positive relationship, illustrating how increases in perceived benefits are related to improvements in extrinsic job satisfaction among survey respondents.

Regression Analysis 3: Benefits & Overall Job Satisfaction

The third regression analysis explores the relationship between perceived benefits and overall job satisfaction, which reflects a comprehensive measure encompassing both intrinsic and extrinsic factors. The benefits variable remains the independent predictor, with overall satisfaction as the dependent variable.

Results show a significant positive relationship (p < 0.05), indicating that perceptions of benefits are a strong predictor of overall happiness and satisfaction within the workplace. The regression coefficient quantifies this impact, and the R-squared value indicates the proportion of variability in overall satisfaction explained by benefits.

The graph with a trendline visually emphasizes this correlation, suggesting that enhancements in perceived benefits can lead to higher overall employee satisfaction.

Conclusion

The analyses collectively demonstrate that employee perceptions of benefits significantly influence various facets of job satisfaction. The positive relationships between benefits and intrinsic, extrinsic, and overall satisfaction highlight the importance for organizations to invest in comprehensive benefits packages as a strategy to boost employee morale, engagement, and retention. Future research may build upon these findings by exploring additional factors influencing job satisfaction or examining longitudinal effects over time.

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