ANOVA? Sum of Model Squares df Mean Square F Sig. 1 Regression 17.141 3.428 2.957 .015b Residual 134.498 116 1.159 Total 151.639 121 a. Dependent Variable: Average of items in Ought Measure b. Predictors: (Constant), How would you describe your political attitudes?, education, Average of items in Belief in Just World Scale, Number of correct items on Raven's Progressive Matrices, Average of items from Inherence Bias Measure Coefficientsa Standardized Coefficients Unstandardized Coefficients Model Std. Error Beta Sig. 1 (Constant) 4.660 .849 5.488 .000 Average of items from Inherence Bias Measure .299 .091 .306 3.270 .001 Average of items in Belief in Just World Scale -.123 .133 -.088 -.925 .357 Number of correct items -.012 .051 -.022 -.245 .807 on Raven's Progressive Matrices education -.089 .112 -.072 -.800 .425 How would you describe your political attitudes? .055 .048 .102 1.137 .258 a. Dependent Variable: Average of items in Ought Measure
Correlation
Correlation defines a relationship between two independent variables. It tells the degree to which variables move in relation to each other. When two sets of data are related to each other, there is a correlation between them.
Linear Correlation
A correlation is used to determine the relationships between numerical and categorical variables. In other words, it is an indicator of how things are connected to one another. The correlation analysis is the study of how variables are related.
Regression Analysis
Regression analysis is a statistical method in which it estimates the relationship between a dependent variable and one or more independent variable. In simple terms dependent variable is called as outcome variable and independent variable is called as predictors. Regression analysis is one of the methods to find the trends in data. The independent variable used in Regression analysis is named Predictor variable. It offers data of an associated dependent variable regarding a particular outcome.
Describe whether each predictor variable significantly predicts the outcome variable (and how) or not and indicate whether the relationship between inherence bias and ought is still strong after all of these other predictor variables are.
outcome variable: ought to
predictor variables: inherence bias, education level, Raven’s Progressive matrix score, conservatism (political), and belief in a just world
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