Week 5 Flashcards
(66 cards)
What is hierarchical multiple regression?
A type of multiple regression where predictors are entered based on previous research and their order is decided by the researcher.
This method allows for the examination of the contribution of additional variables to the model.
What is the primary purpose of multiple regression?
To explore the impact of multiple predictor variables on one outcome variable.
It allows for the testing of relationships between individual predictors and the outcome in the context of other predictors.
What is the assumption regarding variable types in multiple regression?
All predictor variables should be quantitative, while outcome variables should be quantitative and continuous.
Categorical or ordinal predictors can be included.
What does non-zero variance imply for predictor variables?
Predictor variables should have a variance and should not have a variance of zero.
This ensures variability in the predictors for analysis.
What does independence mean in the context of multiple regression?
All values of the outcome variable should be independent, meaning each value represents a separate entity.
This is crucial for the validity of the regression model.
What is the significance of linearity in multiple regression?
The relationship between predictor and outcome variables should be linear; non-linear relationships can lead to unreliable models.
This assumption is essential for accurate predictions.
What does multicollinearity refer to?
When predictor variables are too highly correlated.
This can distort the results of the regression analysis.
What is homoscedasticity?
Residuals at each level of the predictor should have the same variance.
This is assessed by analyzing residuals in SPSS.
What are independent errors in regression analysis?
For any two observations, the residual points should not correlate and should be independent.
This can be checked using Durbin-Watson statistics.
What is meant by normally distributed errors?
The residual values should be random and normally distributed with a mean of 0.
This can also be analyzed through residuals in SPSS.
What does R² value represent in regression analysis?
It indicates how much variance is accounted for by the model.
A higher R² value suggests a better fit of the model to the data.
What is the purpose of ANOVA in regression analysis?
To assess the significance of the regression model.
It helps determine whether the model explains a significant amount of variance.
What is a continuous predictor variable?
A variable that can take on an infinite number of values within a given range.
Examples include Year 1 mark and hours spent in workshops.
What is a categorical predictor variable?
A variable that represents categories or groups.
An example is the choice of statistics textbook.
How should binary predictors be coded in SPSS?
Categories must be coded as 0 and 1.
This allows for proper analysis in regression.
What is dummy coding?
A method used to convert categorical variables into a series of binary variables for regression analysis.
It typically involves creating new variables for each category except the reference category.
What steps should be taken to calculate a multiple regression?
- Calculate descriptive statistics
- Create a correlation matrix
- Calculate the regression
- Interpret model fit (R² value)
- Examine relationships (Beta values)
- Check assumptions and diagnostics
What is the importance of sample size in multiple regression?
A sufficient sample size is necessary to ensure the validity of the regression results.
Guidelines for sample size depend on the number of predictors and the expected effect size.
What is the relationship between predictor variables and outcome variables in multiple regression?
Predictor variables are used to explain variance in the outcome variable.
Each predictor’s contribution can be assessed through their coefficients.
What does the term ‘effect size’ refer to in multiple regression?
It indicates the strength of the relationship between predictors and the outcome variable.
Effect size can be assessed using R² value.
True or False: All predictors in a multiple regression must be continuous.
False.
Predictors can also be categorical or ordinal.
What is the first step in calculating a multiple regression?
Calculate the descriptive statistics
Means and SD sometimes used.
What does the correlation matrix provide?
It shows the relationships between variables.
What does the R² value indicate?
The proportion of variance accounted for in the outcome variable by the predictors.
