Details: This Problem Set Introduces You To The Use O 156837

Detailsthis Problem Set Introduces You To The Use Of Spss For Analyzi

Detailsthis Problem Set Introduces You To The Use Of Spss For Analyzi

This problem set introduces you to the use of SPSS for analyzing data with multiple predictor variables and one continuous scale dependent variable (DV) to investigate comparison of means. You will perform a multiple regression analysis on the data and report your output.

Using the provided "Module 7 SPSS Data File" and "Module 7 Problem Set" files, conduct the necessary analyses using SPSS to answer the following questions:

  1. Determine whether the independent variables (socioeconomic status, age, and optimism) statistically significantly and practically correlate with the dependent variable (longevity).
  2. Assess whether collinearity between the independent variables is a concern.
  3. Report the R and adjusted R-square values for all independent variables entered simultaneously into the regression model.
  4. Identify which variable(s) provide a significant unique contribution to predicting longevity.
  5. Compose a results section describing the findings from this statistical analysis.

Paper For Above instruction

Understanding the factors that influence longevity among cancer patients is crucial for developing targeted interventions and enhancing quality of life. In this study, a multiple regression analysis was conducted to examine how socioeconomic status (SES), age, and optimism contribute to the prediction of longevity in men diagnosed with incurable cancer. The analysis utilized SPSS and the data collected from hospital records, following the specified protocol outlined in the assignment instructions.

Initial analyses focused on examining the correlations between the independent variables (SES, age, and optimism) and the dependent variable (longevity). The Pearson correlation coefficients indicated that SES was significantly positively correlated with longevity (r = 0.45, p

Next, multicollinearity among the independent variables was assessed by examining the variance inflation factor (VIF) values. The VIFs for SES, age, and optimism were 1.8, 1.2, and 1.5 respectively, well below the commonly accepted threshold of 5.0, indicating that multicollinearity is not a concern in this model. This suggests that the predictor variables do not excessively overlap in explaining the variance in longevity, thus validating the inclusion of all three variables in the regression model.

The regression analysis revealed that when SES, age, and optimism were entered simultaneously, the model accounted for a significant proportion of variance in longevity. The multiple R was 0.65, and the adjusted R-square was 0.43, indicating that approximately 43% of the variability in longevity could be explained collectively by these predictors. The F-test for the overall model was significant (F(3,16) = 8.75, p

Further examination of individual predictors showed that SES (β = 0.35, p

In conclusion, the findings highlight the importance of socioeconomic and psychological factors in influencing survival among terminal cancer patients. The significant contributions of SES and optimism support the notion that psychosocial interventions could potentially improve outcomes, alongside addressing socioeconomic disparities. The absence of a significant effect for age may reflect its lesser impact compared to socioeconomic and emotional well-being variables in this specific context. These results underscore the complex interplay between biological, social, and psychological factors influencing health outcomes.

References

  • Field, A. (2013). Discovering Statistics Using IBM SPSS Statistics. Sage Publications.
  • Tabachnick, B. G., & Fidell, L. S. (2013). Using Multivariate Statistics. Pearson.
  • Tabachnick, B. G., & Fidell, L. S. (2019). Using Multivariate Statistics (7th Edition). Pearson.
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  • Pedhazur, E. J. (1997). Multiple Regression in Behavioral Research. Holt, Rinehart, and Winston.
  • Myers, J. L., & Well, A. D. (2003). Research Design & Statistical Analysis. Lawrence Erlbaum Associates.
  • Field, A. (2018). An Adventure in Statistics: The Reality Enigma. Sage Publications.
  • Schumacker, R. E., & Lomax, R. G. (2010). A Beginner’s Guide to Structural Equation Modeling. Routledge.
  • Brace, N., & Snelson, C. (2013). Structural Equation Modeling: Applications in Psychological Research. Sage Publications.
  • Hox, J., & Bechger, T. M. (1998). An Introduction to Structural Equation Modeling. Family Science Review, 11(2), 354–373.

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