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Exploring the Relationship Between Socioeconomic Status, Age, and Optimism on Longevity in Cancer Patients

Solved problem set using multiple regression to study socioeconomic factors and longevity in cancer patients.

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    Running head: MODULE 7 PROBLEM SET 1 Module 7 Problem Set

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    MODULE 7 PROBLEM SET 2 Module 7 Problem Set : Optimism and Longevity A cancer specialist from the Los Angeles County General Hospital (LACGH) rated patient optimism in 20 - to 40 - year - old male patients with incurable cancer in 1970. In 1990, the researcher examined hospital records to gather the following data:  Socioeconomic status (1 – 7 rating of occupation; higher ratings indicate higher levels of SES)  Age in 1970  Optimism in 1970 (1 – 100 rating, higher scores indicate higher levels of optimism)  Longevity (years lived after the 1970 diagnosis) Using the SPSS data file for Module 7 (located in Topic Materials), calculate a simultaneous multiple regression with SES, age, and optimism as the independent variables and longevity as the dependent variable. 1. Do the independent variables correlate statistically significantly and practically with the dependent variable? The ANOVA table shows that F (3, 240) = 41.684 and is significant at p < .001. This indicates that the combination of the predictor variable s of SES, A ge, a nd O ptimism significantly predicts the criterion variable L ongevity (Leech, Barrett, & Morgan, 2015). ANOVA a Model Sum of Squares df Mean Square F Sig. 1 Regression 1663.187 3 554.396 41.684 .000 b Residual 3192.026 240 13.300 Total 4855.213 243 a. Dependent Variable: Years Lived after Diagnosis b. Predictors: (Constant), Optimism, Age, Socioeconomic Status

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    MODULE 7 PROBLEM SET 3 Also, Leech and colleagues (2015) noted the importance of the Coefficients table because “it provides the standardized beta coefficients, which are interpreted similarly to correlation coefficients or factor weights. The t value and significance opposite each independent variable indicate whether that variable is sign ificantly contributing to the e q ua tion for pred ic tion of the dependent variable fr om the whole set of predictors” (p. 116) . Therefore, it can be observed that the predictor variable of Optimism is statistically significant, whereas the predictor variables of Socioeconomic Status and Age are not. Correlations Years Lived after Diagnosis Socioeconomic Status Age Optimism Pearson Correlation Years Lived after Diagnosis 1.000 .369 - .332 .573 Socioeconomic Status .369 1.000 - .290 .520 Age - .332 - .290 1.000 - .431 Optimism .573 .520 - .431 1.000 Sig. (1 - tailed) Years Lived after Diagnosis . .000 .000 .000 Socioeconomic Status .000 . .000 .000 Age .000 .000 . .000 Optimism .000 .000 .000 . N Years Lived after Diagnosis 244 244 244 244 Socioeconomic Status 244 244 244 244 Age 244 244 244 244 Optimism 244 244 244 244 Coefficients a Model Unstandardized Coefficients Standardized Coefficients t Sig. Correlations B Std. Error Beta Zero - order Partial Part 1 (Constant) 3.773 2.131 1.770 .078 Socioeconomic Status .236 .164 .089 1.441 .151 .369 .093 .075 Age - .073 .044 - .097 - 1.675 .095 - .332 - .107 - .088 Optimism .175 .023 .485 7.433 .000 .573 .433 .389 a. Dependent Variable: Years Lived after Diagnosis

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