Minor - SEM Flashcards
Centering predictors is recommended to aid in interpretability
Aiken and West (1991)
What are the benefits of SEM?
to estimate and remove error; triangulate multiple measures and separate unique from measurement error; propose more complicated causal models and then test those in a confirmatory nature (model testing/fitting approach)
What are rotations? Difference between orthogonal/oblique rotation?
Used in EFA and is intended to enhance the interpretability of retained factors and
yielding a simple structure where each factor explains as much variance as possible in non overlapping sets of indicators.
Orthogonal rotation (e.g., varimax rotation) - factors are all uncorrelated
Oblique rotation (e.g., promax rotation) allows for correlated factors.
oblique is preferred as some constructs may have correlated factors. Oblique also provides factor correlations.
What is covariance?
The basic unit of SEM; The strength of a linear relationship between X and Y and their variabilities with a single number; unstandardized form of a correlation
What is disturbance?
1 - R squared. Represents proportion of unexplained variance and is made up of measurement error and omitted causes.
Difference between EFA/CFA?
EFA is a factor analysis where one seeks a simple latent variable structure without causal arrows between variables. CFA on the other hand, is described as taking a specific hypothesized structure and assessing how well it accounts for the observed relationships found within the data. EFA (unrestricted model) doesn’t require a priori specification of number of factors whereas CFA does.
CFA can do anything that EFA does and doesn’t require as many decision points (PCA vs factor analysis; rotation method; number of factors to retain) which may bias your model.
What is PCA and when to use it?
Great for data reduction purposes, finding components that explain the most variance
What is latent growth curve modeling?
Newsom (2015) - method that investigates level change in a variable over time and while allowing for the investigation of linear and nonlinear trends and individual differences within these trends. LGCM’s involve two latent variables—one representing the intercept and the other representing the slope.
What is SEM?
A causal inference method that takes 3 inputs (a set of theoretically-sound hypotheses; a set of questions about causal relationships; data from studies) and produces 3 outputs (estimates of model parameters; a set of logical implications of the model; the degree to which the model are supported by the data)
What is the goal of SEM?
to discover a model that makes theoretical sense, is reasonably parsimonious, and has acceptably close correspondence to the data. Essentially you are establishing if your proposed model fits the data provided.
How to determine minimum sample size?
N:q Rule. N represent cases needed and q represents model parameters. A ratio of 20:1 is ideal, 10:1 is acceptable. A general rule is over 200.
Loehlin (2004)
Covers Wright’s tracing rules for path analysis: no loops, no going forward then backward, only on curved arrow per path.
What are the goals for model identification? 2 general rules for identifying a model?
to be able to express every free model parameter as a unique function of elements in the population covariance matrix
The models’ df must be at least 0 (counting rule); every latent variable including disturbance/error terms must be assigned to a scale
2 kinds of path model?
Recursive and Non-recursive. Recursive is straightforward. All causal effects unidirectional.
Kenny (1979) - Minimum number of indicators? Causal Inference Criteria?
3-5 per factor.
Temporal precedence (x precedes y in time) Relationship between variables (variables are orthogonal) nonspuriousness (ruling out confounds)
