Structural Equation Modelling (SEM)

Pataasin ang iyong marka sa homework at exams ngayon gamit ang Quizwiz!

What is SEM?

A procedure where we: Measure an important variable, like a DV Specify some theoretical relationship Estimate the variables that fit the relationship Then check how strongly the estimated variables agree with the actual variables (model fit - chi sq) It is an extension of Path Analysis

Power and Sample Size

SEM dependent upon the number of variables used. The number of subjects should exceed the number of variables (observations) N > v If N < v = singularity N greater than or equal to 50 + ( v . 8) (eight people for every variable)

Model Misspecification

Model chi square is increased by the degree the model is misspecified. Changed one line and chi square changes a lot. If theory put relationship in wrong direction then theory no longer fits.

Types of Identification

Model consists of observations (measures variables), v and parameters (q) Over-identified v > q v(v+1)/2 > q Just-identified v = q v(v+1)/2 = q Under-identified v < q v(v+1)/2 < q

Why use SEM

Allows the use of esoteric designs not available in normal techniques ie things you could not imagine doing. Its a better way to do things. Makes clearer, more pure estimates of variances, covariances and if asked, means and intercepts. Can have multiple DV's. Can include multiple intervening/mediating variables while regressing these variable onto multiple DVs. Allows researcher to hypothesise how items of scales load onto factors. Can allow researcher to explicitly model latent variables.

Power and Sample size Regression Approach Estimation techniques

Asymptotically Distribution Free Criterion: if data non-parametric and requires at least 500 subjects, better with 2000. Satorra Bentler approach for bias normal data or non-parametric data and requires at least 500 subjects. Maximum Likelihood, Ordinary Least Squares, General Lest Squares: requires at least 100 subjects.

Model Specification

Features to be estimated can include: means and intercepts 'variances & error covariances & factor loadings. Models consist of: Inferences latent variables errors, variance of observed variables disturbances, variance of latent or unobserved variables Variables may be: observed or unobserved exogenous (unexplained variables) or endogenous (explained variables)

Identification

Observations = things we measure. Parameters - things we specify (b-weights, correlations etc) If we have more parameters than observations, we are trying to measure more than we know. A model with more parameters than observations has df<=0 & cannot be measured. This model is not identified. A model requires sufficient observations to enable it to be identified. A model cannot be identified if some of its parameters cannot be uniquely estimated. One observation with two parameters to be estimated cannot be resolved.

Model Structure

The measured model - all the measured variables and their error terms. The structural model - all the latent variables and the relationships between them, and between them and the measured model ie everything inferred.

Specifying the model - relationships

Three classes of relationships exist between variables: 1)Covariances or correlations 2) Direct factor loadings (regression weights) 3) Reciprocal factor loadings

Degrees of freedom

Under-identified df < q or df < 0 Over-identified df > q or df = 1 or more Just-identified df = q or df = 0

Model Components

Unobserved-exogenous (unmeasured inferred variables: typically errors, residuals or disturbances) Observed-endogenous (measured variables that contribute to other variables: explained by reference to a exogenous variable) Unobserved-endogenous (unmeasured variables - contribute to other variables, explained by reference to a exogenous variable) Observed-exogenous (measured variables that are explained no further, for instance an IV)

When would it be used.

When we have complex questions to answer. When we want to confirm or explore a theory. How well does your model (hypotheses) fit the data collected. When we have latent variables.


Kaugnay na mga set ng pag-aaral

Academic vocabulary/analyze-define

View Set

Electricity and Magnetism (Chap 5 & 11)

View Set

Chapter 24 Management of patients with chronic pulmonary disease

View Set

Medical Microbiology Chapter 3 sample questions

View Set

The essential theater enhanced tenth edition Mid-Term

View Set

Genetics: Exam 4 Pearson Quizlet

View Set

Combo with "Muscles MA&P" and 26 others

View Set

chapter 7- dna structure and gene function

View Set