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About this document
- Pages
- 3 pages
- Price
- $3 one-time, yours to keep
- Membership
- Included in every plan
- Format
- Subject
- Psychology, Statistics
- Module
- Module 7
This document describes a study on optimism and longevity in cancer patients. It guides students through a multiple regression analysis using SPSS, with socioeconomic status, age, and optimism as independent variables, and longevity as the dependent variable. It is for Module 7.
What's inside
- Study description: cancer patients' optimism and longevity
- Independent variables: socioeconomic status, age, optimism
- Dependent variable: longevity
- Multiple regression analysis using SPSS
- Results section composition
Who it is for: For students of PSY 870, Module 7. (Postgraduate)
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Summary
Optimism and longevity in cancer patients are studied using multiple regression. A student learns how to perform this statistical analysis and report the findings.
Background and Data
A cancer specialist rated optimism in male patients with incurable cancer. Data on socioeconomic status, age, optimism and longevity were gathered. These data are used for a statistical analysis.
Multiple Regression Analysis
A student calculates a simultaneous multiple regression. Socioeconomic status, age and optimism are the independent variables. Longevity is the dependent variable.
Interpreting the Results
A student checks for statistical significance and practical importance of the independent variables. The student also checks for collinearity between the independent variables. The student calculates and interprets R and adjusted R-square.
Reporting the Findings
A student writes a results section. This section reports the unique contributions of the independent variables.
Key terms
- Multiple regression
- A statistical method for predicting a dependent variable from several independent variables.
- Independent variables
- Variables that are used to predict the outcome. Here, they are socioeconomic status, age and optimism.
- Dependent variable
- The outcome that is being predicted. Here, it is longevity.
- Collinearity
- A situation where independent variables are highly correlated with each other.
- R and adjusted R-square
- Measures of how well the independent variables predict the dependent variable.
- Unique contribution
- The amount of variance in the dependent variable that is explained by an independent variable.
Questions this document helps with
- How do you perform a multiple regression analysis?
- What do you look for when interpreting the results?
- Which variables significantly predict longevity in cancer patients?
Summary written with AI from the document's text.
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