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Advanced Quantitative Methods
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Factor Analysis
A statistical method used to describe variability among observed, correlated variables in terms of a potentially lower number of unobserved variables, called factors.
Structural Equation Modeling (SEM)
A multivariate statistical analysis technique that is used to analyze structural relationships between measured variables and latent constructs.
Path Analysis
A subset of SEM that assesses the direct and indirect relationships between measured variables without latent variables.
Hierarchical Linear Modeling (HLM)
A statistical method for analyzing data with a hierarchical structure that can account for variability at different levels of analysis.
Multidimensional Scaling (MDS)
A set of related ordination techniques used in information visualization for exploring similarities or dissimilarities in data.
Canonical Correlation Analysis
A way of investigating the relationship between two multidimensional variables and maximizing their correlation.
MANOVA (Multivariate Analysis of Variance)
An extension of ANOVA used when there are two or more dependent variables.
Confirmatory Factor Analysis (CFA)
A type of factor analysis used to test whether measurements of a construct are consistent with a researcher's understanding of the nature of that construct, or factor.
Cluster Analysis
A statistical method used to create groups or clusters of subjects based on shared characteristics, with an aim to classify cases into homogeneous groups.
Discriminant Function Analysis
Used to determine the variables that discriminate between two or more naturally occurring groups.
Latent Growth Modeling (LGM)
A technique within SEM used to estimate growth trajectories over time. It represents the time-related change in a construct of interest.
Item Response Theory (IRT)
A theory and group of models used to analyze the relationship between latent traits (abilities or attributes) and their manifestations, such as responses in survey or test data.
Principal Components Analysis (PCA)
A statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called principal components.
Logistic Regression
A statistical model that in its basic form uses a logistic function to model a binary dependent variable, although more complex extensions exist for ordinal and multinomial outcomes.
Multilevel Modeling (MLM)
A type of regression analysis that accounts for the hierarchical structure of data, such as data collected from people within clusters or groups.
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