Joseph J. Sudano, Jr., PhD Center for Health Care Research and Policy

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Joseph J. Sudano, Jr., PhD Center for Health Care Research and Policy

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Applied Structural Equation Modeling for Dummies, by DummiesFebruary 22, 2013Indiana University, Bloomington

Joseph J. Sudano, Jr., PhD

Center for Health Care Research and Policy

Case Western Reserve University at The MetroHealth System

Adam T. Perzynski, PhD

Center for Health Care Research and Policy

Case Western Reserve University at The MetroHealth System

- Thanks Joe.
- Thanks to Bill Pridemore and all of you here at IU.
- Thanks to Doug Gunzler.
- Thanks to Kyle Kercher.

Rejected Titles for this TalkFebruary 22, 2013Indiana University, Bloomington

Joseph J. Sudano, Jr., PhD

Center for Health Care Research and Policy

Case Western Reserve University at The MetroHealth System

Adam T. Perzynski, PhD

Center for Health Care Research and Policy

Case Western Reserve University at The MetroHealth System

Structural Equation Modeling for Fashion Week

Structural Equation Modelin’ fer Pirates

Structural Equation Modelin’ fer Pirates

SEM be a statistical technique for testin' and estimatin' causal relations usin' a combination o' statistical data and qualitative causal assumptions

*From Wikipedia

- I do not actually assume you are dummies
- Feel free to assume what you want about me
- I do not assume you will be experts in SEM after this presentation
- I assume you know something about means and regression (hopefully)

- Important SEM Resources
- Measurement (and measurement error)
- Examples
- Measurement Invariance
- Latent Class Analysis
- Latent Growth Mixture Modeling

- Model Specification

- Important SEM Resources
- Measurement (and measurement error)
- Examples
- Measurement Invariance
- Latent Class Analysis
- Latent Growth Mixture Modeling

- Model Specification

- Important SEM Resources
- Measurement (and measurement error)
- Examples
- Measurement Invariance
- Latent Class Analysis
- Latent Growth Mixture Modeling

- Model Specification

- A special type of causal models
- Survey items are assumed to have measurement error
- Each question has its own amount of error

- Your answer to a survey question is causally related to a latent, unobserved variable.

health

Self-rated health

1.0?

- In general, how would you describe your health?
- We assume that every individual varies along an infinite continuum from best possible health to worst possible health.
- When any given individual answers this question, they are approximating their position on this latent continuum.

health

Self-rated health

e4

1.0

Variance > 0

< 1.0

- Single items are unreliable
- Single cases prevent generalizability
- Use multiple indicators and large samples to estimate the values of the latent, unobservered variables or factors
- The SF36 uses multiple indicators describing multiple factors in order to measure health more reliably.

- Important SEM Resources
- Measurement (and measurement error)
- Examples
- Measurement Invariance
- Latent Class Analysis
- Latent Growth Mixture Modeling

- Model Specification

Measuring Disparities: Bias in Self-reported Health Among Spanish-speaking PatientsJ.J. Sudano1,2, A.T. Perzynski1,2, T.E. Love2, S.A. Lewis1,B. Ruo3, D.W. Baker31 The MetroHealth System, Cleveland, OH; 2 Case Western Reserve University School of Medicine, Cleveland, OH; 3 Northwestern University Feinberg School of Medicine

- Do observed differences in SRH reflect true differences in health?
- Cultural and language differences may create measurement bias
- If outcomes aren’t measuring the same thing in different groups, we have a problem

- It is only possible to properly interpret group differences after measurement equivalence has been established (Horn & McArdle, 1992; Steenkamp & Baumgartner, 1998).
- “It may be the case that the groups differ … but it also may be the case that extraneous influences are giving rise to the observed difference.” Meredith & Teresi (2006 p. S69)
- The external validity of any conclusion regarding group differences rests securely on whether the measurement equivalence of the scale has been established (Borsboom, 2006).

- N= 1281
- Medical patients categorized into four groups:White, Black, English-speaking Hispanic and Spanish-speaking Hispanic.
- Multigroup Confirmatory Factor Analysis (MGCFA)

- Metric (Weak) Invariance
- Are the item factor loadings equivalent across groups?
- Is a one unit change in the item equal to a one unit change in the factor score for all groups?

- Scalar (Strong) Invariance
- Are the item intercepts equivalent across groups?
- Unequal intercepts results in unequal scaling of factor scores

health

Self-rated health

e4

What happens to the model fit when we constrain all of these paths (loadings) to be equal across groups?

The Unconstrained Model Fits the Data Well

The Unconstrained Model fits the data well

The model with factor loadings constrained still fits the data well.

Metric (Weak) Invariance was Confirmed

- Scalar (Strong) Invariance
- Are the item intercepts equivalent across groups?

- Intercept: the intercept in a multiple regression model is the mean for the response when all of the explanatory variables take on the value 0.
- Could be called the “starting point”

The Unconstrained Model fits the data well

The model with factor loadings constrained still fits the data well.

Constraining the intercepts results in a worsening of model fit

The model with factor loadings constrained still fits the data well.

Constraining the intercepts results in a worsening of model fit

The fit is still poor if you allow intercepts for English-speaking Hispanics to vary

The model with factor loadings constrained still fits the data well.

The fit is acceptable if you allow intercepts for Spanish speaking Hispanics to vary

Scalar (Strong) Invariance is NOT Confirmed

Measurement equivalence of the SF36 does not exist for Spanish speaking Hispanics

Intercepts are lower for Spanish-speaking Hispanics on nearly all items

Measurement equivalence of the SF36 does not exist for Spanish speaking Hispanics

Use of English Rating Categories on Twiter

Using of Spanish Rating Categories on Twitter

- Important SEM Resources
- Measurement (and measurement error)
- Examples
- Measurement Invariance
- Latent Class Analysis
- Latent Growth Mixture Modeling

- Model Specification

Everywhere and Nowhere: Latent Class Analysis of Knowledge of the Spread of Hepatitis C

Adam T. Perzynski, PhD

E-mail: Adam.Perzynski@case.edu

- Hepatitis C is a widespread and serious disease that affects the liver.
- 170 million people worldwide are infected.
- 3.9 million Americans infected with HCV. (AHRQ 2003)
- More Americans die every year from Chronic HCV infection than from HIV

- Blood
- Injection Drug Use
- Blood Transfusions
- Needle Sticks
- Shared Household Items (Razor or Toothbrush)

- Sexual transmission of HCV is recognized but is infrequent.
- HCV is not transmitted by Coughing, Kissing, Sneezing, Touching, Bathrooms, Fecal Matter, or Contaminated Food

- Behavior Risk Factor Surveillance System (BRFSS), 2001, Arizona
- Conducted by the Centers for Disease Control (CDC)
- The world’s largest telephone survey
- Nearly 200,000 people participated in 2001

- Do you think hepatitis C can be spread thru?
- Sneezing or Coughing
- Kissing
- Unprotected Sex
- Food or Water
- Sharing Needles to Inject Street Drugs
- Using the Same Bathroom
- Contact with the Blood of an Infected Person

- Analyzed with Mplus
- Analysis proceeded in several stages
- Exploratory Factor Analysis
- Confirmatory Factor Analysis
- Cluster Analysis (Not reported)
- Latent Class Analysis
- Mixture Modeling

- Robust estimation for binary indicators
- Missing Values Imputation using Full Information Maximum Likelihood Estimation (FIML)

- Scree plot, Eigenvalues, and Root Mean Square Residuals more or less supported a two factor solution

- Instead of assuming that the latent variable is continuous (infinitely poor to infinitely good)
- We assume the latent variable is categorical.
- Membership in “hidden” empirical forms determines answers rather than a single latent continuum.

- The Two Category and Four Category models do not fit the data as well as as the Three Category model.
- HCV is Nowhere
- N = 1683 (The largest class!)

- Full Awareness of how HCV is Spread
- N = 930

- HCV is Everywhere
- N = 479

Figure 3: Estimated Probabilities of Knowing How HCV is Spread by Class Membership

- What predicts membership in each latent class?
- Do the relationships between variables vary inside of a particular class?
- Mixture Modeling
- Simultaneously test continuous and categorical predictors of class membership.

- Important SEM Resources
- Measurement (and measurement error)
- Examples
- Measurement Invariance
- Latent Class Analysis
- Latent Growth Mixture Modeling

- Model Specification

Longitudinal Patterns of Depressive Symptoms in the Health and Retirement Study

Adam T. Perzynski, PhD & Joseph S. Sudano, Jr., PhD

Center for Health Care Research and Policy

Case Western Reserve University and MetroHealth

Presentation at the Annual Meeting of the Gerontological Society of America on November 22, 2010

- This is another measurement study
- Explore the use of Latent Class Growth Analysis to model changes in depressive symptoms over time in the Health and Retirement Study.
- Most studies compare the change in means scores between two waves.
- A small number of studies have modeled change as a single growth trajectory

- Often we simply calculate the mean depressive symptoms at Wave 1 (baseline).
- Subract it from the mean at Wave 2 (followup).

- Regrettably, the term “trajectory” has taken on multiple meanings across disciplines and research studies.
- A broad, inclusive definition of trajectory modeling is the analysis of patterns of change or stability.
- Confusion is possible between aggregate trajectories which summarize an overall average pattern of change for a population and disaggregated trajectories which examine multiple potential trajectories of different shapes (George 2006).

- LGA / LGCA
- Studies in older adults (ie George and Lynch 2003) typically find that the slope of the latent growth curve for depressive symptoms is small and positive, and that the slope of the curve is steepest in the oldest cohorts.

- Assumes that the average population starting point (intercept for the growth curve) and average amount of change (slope) are a sufficient depiction of variation over time in depressive symptoms.
- If discrete subtypes of depressive symptom trajectories exist, but are ignored (as in single latent growth curve and autoregressive models) the magnitude of associations could be grossly misestimated.

- Latent Class Growth Analysis (LCGA), also referred to as growth mixture modeling, belongs to a family of statistical techniques referred to as general latent variable modeling or GLVM.

- Studying the mean change or using a single trajectory for everyone assumes uniform heterogeneity in the population.
- Researchers use familiar methods and typically assume that the underlying (latent or real) distribution of variables is continuous.
- We have theoretical reasons to suspect that underlying distributions could be categorical.
- Life course theorists (Dannefer) specifically caution that intracohort differentiation is unlikely to be homogeneous.

- We think individuals and cohorts diverge over time
- Cumulative change differentiates individuals and cohorts.

- LCGA models and closely related Longitudinal Latent Class Analysis (LLCA) have been used to estimate models of depressive symptoms in prior studies of
- maternity (Campbell et al 2009; Mora et al 2009)
- childhood and adolescence (Meadows et al 2006)
- adolescence through young adulthood (Olino et al 2009)
- response to antidepressants among adults (Muthen et al, 2007; Hunter et al 2009)
- patients who have had a cardiovascular event (Kaptein et al 2006).

- 5,195 age-eligible respondents from the 1992 Health and Retirement Study cohort, who completed interviews in all seven waves through 2004.
- Depressive symptoms in HRS are measured using a dichotomous, 8-item version of the CES-D. Analysis begins with Wave 2 data due to a change in response categories from Wave 1.
- Using MPlus, we compared the fit of LCGA models of two to eight classes while also accounting for the HRS complex sampling design.
- We then tested the effect of a small number of covariates. This is very similar to a multinomial logistic regression.

- Gender
- 60.3% female

- Race/ethnicity
- 76.4% non-Hispanic White
- 14.4% Black
- 7.4% Hispanic
- 1.8% other racial/ethnic groups

- Age
- Median=55

- Education
- Mean=12.4 years (SD=3.0).

- “How many trajectories are there?”
- Measures of model fit including:
- Lo-Mendell-Rubin Test (LMR test)
- log-likelihood (LL)
- Bayesian Information Criteria (BIC) (Vuong, 1989; Muthen, 2004; Muthen, & Muthen, 2005; Nylund et al, 2007).

- Here we will use the LMR Test
- Where k is the number of latent classes, this test gives a p-value for the k-1 versus the k-class model when running the k-class model (Vuong, 1989; Muthen, B. 2005).
- The first time p > .05, k-1 is the preferred number of classes.

- How many classes are there?
- What do the classes look like?
- How is this different from looking at means or single trajectory?
- Are any demographic variables associated with being in a particular class?

Online at: http://spreadsheets.google.com/pub?key=0ApRkae54BRnudEYyUGdXZWlES3Z4VzZ6akNaOFFiekE&gid=5

- Females, African Americans and those with fewer years of education have a higher probability of being in the Many Symptoms trajectory.

- Important SEM Resources
- Measurement (and measurement error)
- Examples
- Measurement Invariance
- Latent Class Analysis
- Latent Growth Mixture Modeling

- Model Specification

- Choosing the model that best represents the data structure and addresses the research questions of interest can be a daunting task.
- Brief overview of model specification tests and procedures.

- “First, your return to shore was not part of our negotiations nor our agreement so I must do nothing. And secondly, you must be a pirate for the pirate's code to apply and you're not. And thirdly, the code is more what you'd call ‘guidelines’ than actual rules.”
- Captain Barbossa from Pirates of the Caribbean: The Curse of the Black Pearl (2003)

- In model specification a researcher can use:
- logic, theory and prior empirical evidence to choose the initial model
- model comparison testing to compare the initial model to competing models
- a combination of theory, prior evidence, and the results of the model comparison testing to decide upon which model or models are appropriate for a given study

- The Chi-square statistic is computed and used to test whether the model does fit the data well.
- It is the basis for most other fit tests.
- Along with other fit tests we use it to evaluate whether to include or exclude model paths relating measures to each other for a given study.

- Also called the discrepancy function
- If not significant, the model is regarded as acceptable.*

- Some limitations are:
- Complex models with many parameters
- With large samples, models will most often be rejected, sometimes unfairly
- Where multivariate non-normality is present, the chi-square fit index is inaccurate.
- Modified tests (The Satorra-Bentler scaled chi-square) are available.

- Modification indices can be calculated individually for every path that is fixed to zero, by estimating a chi-square test statistic with one df.
- The higher the value of the modification index for a causal path, the better the predicted improvement in overall model fit if that path were added to the model.
- Jöreskogsuggested that a modification index should be at least five before the researcher considers adding the causal path and modifying the hypothesized model.

- In linear regression analysis, we interpret the r2 value as the amount of variation in the response that can be explained by the regressors in the model.
- In SEM, it is pretty much the same*
- *Not exactly, but that is beyond the “for dummies” version of this talk

- Bayesian Information Criterion (BIC)
- AkaikeInformation Criterion (AIC)
- Based on the chi-squared test statistic
- While the models under comparison can be nested or non-nested, in both these tests, as with all tests in this section, for a truly direct comparison, we prefer that the same observed measures are used in the models we are comparing.
- Both BIC and AIC feature the goodness-of-fit term for our model , derived directly from the discrepancy function when applicable, along with a penalty term.

- Cannot identify if a model has good fit. Only if one model fits better than another.
- The lower the value of BIC, AIC and BCC, the better the fit.
- BCC penalizes for model complexity more than AIC and BIC.
- BIC penalizes for model complexity more than AIC.

- Allows researchers to choose a model from among a number of candidates.
- Exploratory
- Should be guided by theory

- Given the model in Figure 1, with 7 unknown paths, the number of models is equivalent to 27=128 possible specifications of the model.
- 128 different possible models!

- The unconstrained model (The one with all seven ambiguous paths in the model) demonstrates satisfactory overall model fit
- CFI=.95
- TLI=.92
- RMSEA =.07
- chisq= 476.33, DF=63

Notes: Reported R2 values are for the equation in each model with the endogenous PF latent variable with the interpretation of total explained variance in physical functioning given all other paths in the model. C is the chi-squared test statistic and df are the associated degrees of freedom.

- When a number of models are plausible, specification tests can be used as evidence for verification of or improvement over an initial model.

- a researcher is ultimately left to decide if the results of the specification tests are unjustly in favor of a certain model due to complexity or sample size, rather than the meaning behind the causal paths.
- Thus the specification tests act more like guidelines, rather than strict codes dictating the “best” fitting model.

- Strengths
- Very flexible
- Estimate and correct for measurement error

- Limitations
- Large sample sizes
- Challenging to learn
- Need lots of hands-on experience to learn
- Need a strong theoretical basis
- It’s easy to mis-specify a model if you have no idea what you are doing.

Applied Structural Equation Modeling for Everyone!February 22, 2013Indiana University, Bloomington

Joseph J. Sudano, Jr., PhD

Center for Health Care Research and Policy

Case Western Reserve University at The MetroHealth System

Adam T. Perzynski, PhD

Center for Health Care Research and Policy

Case Western Reserve University at The MetroHealth System