## ❓ Küsimus
What is factor analysis?

### 🧠 Vastus
 Factor analysis is a collection of methods used to examine how underlying constructs influence the responses on a number of measured variables. It is related to principal component analysis but not identical; it estimates how much of the variability is due to common factors. It is performed by examining the pattern of correlations or covariances between the items that represent the underlying constructs within a data set. Once a number of factors have been established, interpretation is assisted through rotation of factors. This does not alter the underlying constructs, but it presents the loadings in a more interpretable manner. Oblique rotation is used when factors are highly correlated. Factor analysis can be applied to study human factors.

Reference(s):
- Pallant, T. (2001). Principal component analysis and factor analysis. Journal of Statistical Computing & Simulation, 3(3), 19-27.
- Tabachnick, B. G., & Fidell, J. P. (1996). Handbook of statistical methods for the behavioral sciences. Sage Publications Ltd.
- Guadagnoli, E. A., & Velicer, W. F. (1988). Exploratory factor analysis: An overview. Psychological Methods, 1(3), 157-164.

### 📚 Kontekstid:
- Transportation Technologies for Sustainability (leht 400)
- Driver Fatigue Systems – How do they Change Drivers’ Behaviour? (leht 237)
- Driver Fatigue Systems – How do they Change Drivers’ Behaviour? (leht 238)
- Transportation Technologies for Sustainability (leht 400)


## ❓ Küsimus
What is confirmatory factor analysis?

### 🧠 Vastus
 Confirmatory factor analysis is a statistical method that examines the pattern of correlations between observed measures and estimates how much of the variability is due to common factors. It is used in psychology to test theories such as Human Factors by analyzing the relationship between different physical and mental states with different environments, products, and services.

### 📚 Kontekstid:
- Transportation Technologies for Sustainability (leht 400)
- Driver Fatigue Systems – How do they Change Drivers’ Behaviour? (leht 237)
- Transportation Technologies for Sustainability (leht 400)
- Transportation Technologies for Sustainability (leht 400)


## ❓ Küsimus
Explain more "Once a number of factors have been established, interpretation is assisted through rotation of factors"?

### 🧠 Vastus
 Once a number of factors have been established, interpretation is assisted through rotation of factors because it allows for the presentation of loadings in a more interpretable manner. Rotation of factors involves moving the axes on the graph closer together or further apart so that the factor represented by the axis can intersect the items when keeping the axes at 90 degrees would not. This is done to help identify clusters of criteria and distinguish them according to the type of effect or the way in which the effect was measured. Clusters can be grouped, such as a group that can be expressed in monetary units, another group related to non-monetary environmental or safety effects, still another group related to the non-financial impacts of a particular criteria.

### 📚 Kontekstid:
- Driver Fatigue Systems – How do they Change Drivers’ Behaviour? (leht 238)
- Transportation Technologies for Sustainability (leht 400)
- Driver Fatigue Systems – How do they Change Drivers’ Behaviour? (leht 238)
- Infrastructure and Safety in a Collaborative World (leht 57)


## ❓ Küsimus
What is Oblique rotation?

### 🧠 Vastus
 Oblique rotation is a technique used to analyze the extent to which an item contributes to a factor. It presents the loadings in a more interpretable manner when the items are plotted on a graph and the factors are highly correlated.

### 📚 Kontekstid:
- Loghi, Kimon (leht 308)
- Loghi, Kimon (leht 319)
- Driver Fatigue Systems – How do they Change Drivers’ Behaviour? (leht 238)
- Railway Noise and Vibration (leht 107)


## ❓ Küsimus
What is principal component analysis?

### 🧠 Vastus
 Principal Component Analysis (PCA) is a statistical method used to reduce the dimensionality of a dataset by identifying a new set of orthogonal variables, called principal components, that capture as much variation in the original data as possible. These new variables can then be used in place of the original variables in further analysis, without losing much information. PCA works by finding the eigenvalues and eigenvectors of the covariance matrix of the data and selecting the top k eigenvectors with the highest eigenvalues, which correspond to the k most important principal components. The proportion of variation explained by each principal component can be calculated as the square of its corresponding eigenvalue. PCA is widely used in many fields, including image processing, bioinformatics, and social sciences, to analyze large datasets and extract meaningful patterns or features from them.

### 📚 Kontekstid:
- Road Safety Management Using Bayesian and Cluster analysis (leht 4)
-  (leht 3)


## ❓ Küsimus
What is orthogonal coding?

### 🧠 Vastus
 Orthogonal coding is a method of transforming categorical variables into numerical ones, similar to dummy and effects coding. However, it uses orthogonal polynomial contrasts instead of direct transformation to create new variables that correspond to higher order polynomial effects for the pre-transformed attribute. This technique retains orthogonality within each attribute and avoids inducing correlation within the data, unlike dummy and effects coding.

108

### 📚 Kontekstid:
- Justicia en salud y genética (leht 118)
- Justicia en salud y genética (leht 119)
- Justicia en salud y genética (leht 118)
- Justicia en salud y genética (leht 120)


## ❓ Küsimus
What is orthogonal polynomial contrasts?

### 🧠 Vastus
 Orthogonal polynomial contrasts are a technique used in statistical analysis to transform categorical variables into numerical values that can be used in linear models. They involve creating new variables by taking the sum of the products of each level of a factor with an orthogonal polynomial function. This allows for the creation of higher order effects of the original categorical variable, while retaining orthogonality within each attribute. Orthogonal contrasts are useful when multiple factors are being analyzed and can help to simplify the interpretation of results.

### 📚 Kontekstid:
- Transportation Systems Analysis (leht 551)
- Justicia en salud y genética (leht 118)
- Justicia en salud y genética (leht 119)
- Justicia en salud y genética (leht 124)


## ❓ Küsimus
What is direct transformation?

### 🧠 Vastus
 Direct transformation is not provided in the given context.

### 📚 Kontekstid:
- Hart, Henry George, (16 April 1843–12 Jan. 1921) (leht 14)
- Loghi, Kimon (leht 2445)
- Loghi, Kimon (leht 2445)
- Map algebra: one perspective (leht 7)

