e. kevin kelloway, ph.d. canada research chair in occupational health psychology
TRANSCRIPT
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STRUCTURAL EQUATION MODELING
WITH MPLUSE. Kevin Kelloway, Ph.D.
Canada Research Chair in Occupational Health Psychology
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Overview
Day 1: Familiarization with the Mplus environment – Varieties of regression
Day 2 Introduction to SEM: Path Modeling, CFA and Latent variable analysis
Day 3 Advanced Techniques – Longitudinal data, multi-level SEM etc
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Today’s Agenda
0900 - 1000 Introduction : The Mplus Environment
1000 – 1015 Break 1015 – 1100 Using Mplus: Regression 1100 – 1200 Variations on a theme:
Categorical, Censored and Count Outcomes
1200 – 1300 Break 1300 – 1400 Multilevel models: Some theory 1400 – 1415 Break 1415 – 1530 Estimating multilevel models in
Mplus
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MPLUS
Statistical modeling program that allows for a wide variety of models and estimation techniques
Explicitly designed to “do everything” Techniques for handling all kinds of data
(continuous, categorical, zero-inflated etc),
Allows for multilevel and complex data Allows the integration of all of these
techniques
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The Mplus Framework
Observed variablesx background variables (no model structure)y continuous and censored outcome variablesu categorical (dichotomous, ordinal, nominal) andcount outcome variables• Latent variablesf continuous variables– interactions among f’sc categorical variables– multiple c’s
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Mplus Configurations
BASE MODEL – Does regression and most versions of SEM
Mixture - Adds in mixture analysis (using categorical latent variables)
Multi-level Add-on –adds the potential for multi-level analysis
Recommend the Combo Platter
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Some Characteristics of Mplus
Batch processor Text commands (no graphical interface)
and keywords Commands can come in any order in the
file Three main tasks
GET THE DATA into MPLUS and DESCRIBE IT ESTIMATE THE MODEL of INTEREST REQUEST THE DESIRED OUTPUT
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The Mplus Language
10 Commands TITLE Provides a title • DATA (required) Describes the Dataset • VARIABLE (required) Names/identifies Variables • DEFINE Computes/transforms • ANALYSIS Technical details of analysis • MODEL Model to be estimated • OUTPUT Specifies the output • SAVEDATA Saves the data • PLOT Graphical Output • MONTECARLO Monte Carlo Analysis
Comments are denoted by ! And can be anywhere in the file
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Some conventions
“is” “are” and = can generally be used interchangeably Variable: Names is Bob Variable: Names = Bob Variable: Names are Bob
“-” denotes a range Variable: Names = Bob1 – Bob5
: ends each command ; ends each line
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Getting the data into Mplus (1)
Step 1: Move your data into a “.dat” file (ASCII) – SPSS or Excel will do this
Step 2: Create the command file with DATA and VARIABLE STATEMENTS
Step 3 (Optional) I always ask for the sample statistics so that I can check the accuracy of data reading
OPEN and RUN Day1 Example 1.inp
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Example 1
TITLE: This is an example of how to read data into Mplus from an ASCII File
DATA: file is workshop1.dat; Variable: NAMES are sex age hours location TL
PL GHQ Injury; USEVARIABLES = tl – injury; Output: Sampstat;
Include the demographic variables in the analysis
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Output: Three major divisions
Repeat the input instructions – check to see if proper N, K and number of groups
Describe the analysis – describes the analysis, check for accuracy
Report the results Fit Statistics Parameter Estimates Requested information (sample statistics,
standardized parameters etc) NOTE: Not all output is relevant to your
analysis
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Getting Data into MPLUS (2)
N2Mplus – freeware program that will read SPSS or excel files
Will Create the data file Will write the Mplus syntax which can be
pasted into mplus Limit of 300 variables Watch variable name lengths (SPSS
allows more characters than does Mplus)
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MULTIPLE REGRESSIONGeneral Goal
To predict one variable (DV or criterion) from a set of other variables (IVs or Predictors). IVs may be (and usually are) intercorrelated. Minimize least squares (minimize prediction error) - Maximize R
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Bivariate Regression
Correlation is ZxZy/N Line of best fit (OLS Regression line) is
found by y = mx+b where b = Y intercept Y – bX And m = slope = r Sdy/Sdx
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Multiple Regression
Extension of Bivariate Regression to the case of multiple predictors
Predictors may be (usually are) intercorrelated so need to partial variance to determine the UNIQUE effects of X on Y
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Regression
To specify a simple linear regression you simply add a Model line to the file
Model DV on IV1 IV2 IV3….IVX You also want to specify some specific forms of
output to get the “normal” regression information
Useful options are SAMPSTAT – sample statistics for the variables STANDARDIZED – standardized parameters Savedata:Save=Cooks Mahalanobis What predicts GHQ?
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Categorical Outcomes
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LOGISTIC REGRESSION
Used typically with dichotomous outcome (also ordered logistic and probit models)
Similar to regression – generate an overall test of goodness of fit
Generate parameters and tests of parameters
Odds ratios When split is 50/50 then discriminant and
logistic should give the same result When split varies, then logistic is
preferred
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TESTS
Likelihood chi-squared - baseline to model comparisons
ParameterTest (B/SE) Odds ratio - increase/decrease in odds of
being in one outcome category if predictor increases by 1 unit (Log of B)
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In Mplus
Specify one outcome as categorical (can be either binary or ordered)
Default estimator is MLR which gives you a probit analysis
Changing to ML gives you a Logistic regression
RUN DAY1Example3.inp To dichotomize the outcome (from a
multi-category or continuous measure define: cut injury (1);
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COUNT DATA
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GENERIC PROBLEM – GROSSLY DISTORTED (DISTRIBUTION OF), OR VIOLATED ASSUMPTIONS FOR THE CRITERION VARIABLE
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Data from a study of metro transit bus drivers (n=174) Data on workplace violence (extent to which one has been hit/kicked; attacked by a weapon;had something thrown at you) 1 = not at all 4 = 3 or more times Data cleaning suggests highly skewed and kurtotic distribution
Descriptive Statistics
N Minimum Maximum Mean Std. DeviationSkewness Kurtosis Statistic Statistic Statistic Statistic Statistic Statistic Std. Error Statistic Std. Errorviolence170 1.00 3.00 1.2353 .37623 1.900 .186 3.677 .370Valid N (listwise) 170
An Example
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Scores pile up at 1 (Not at all)
violence
104 59.8 61.2 61.2
36 20.7 21.2 82.4
15 8.6 8.8 91.2
8 4.6 4.7 95.9
6 3.4 3.5 99.4
1 .6 .6 100.0
170 97.7 100.0
4 2.3
174 100.0
1.00
1.33
1.67
2.00
2.33
3.00
Total
Valid
SystemMissing
Total
Frequency Percent Valid PercentCumulative
Percent
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►Negative Binomial. This distribution can be thought of as the number of trials required to observe k successes and is appropriate for variables with non-negative integer values. If a data value is non-integer, less than 0, or missing, then the corresponding case is not used in the analysis. The fixed value of the negative binomial distribution's ancillary parameter can be any number greater than or equal to 0. When the ancillary parameter is set to 0, using this distribution is equivalent to using the Poisson distribution. Normal. This is appropriate for scale variables whose values take a symmetric, bell-shaped distribution about a central (mean) value. The dependent variable must be numeric. Poisson. This distribution can be thought of as the number of occurrences of an event of interest in a fixed period of time and is appropriate for variables with non-negative integer values. If a data value is non-integer, less than 0, or missing, then the corresponding case is not used in the analysis.
More Estimators
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COUNT DATA
DATA IN WHICH ONLY NON-NEGATIVE INTEGERS CAN OCCUR (0,1,2,3 ETC)
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►Counts are discrete not continuous
►Counts are generated by a Poisson distribution (discrete probability distribution)
►Poisson distributions are typically problematic because they are skewed (by definition non-normal)
are non-negative (cannot have negative predicted values)
have non constant variance– variance increases as mean
increases
BUT…
Poisson regressions also make some very restrictive assumptions about the data (i.e., the underlying rate of the DV is the same for all individuals in the population or we have measured every possible influence on the DV)
Some Observations on Count Data
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►allows for more variance than does the poisson model (less restrictive assumptions)Can fit a poisson model and calculate dispersion (Deviance/df). Dispersion close to 1 indicates no problem; if over dispersion use the negative binomialPoisson but not neg binomial is available in Mplus
The Negative Binomial Distribution
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Zero Inflated Poisson Regresson (ZIP Regression)Zero Inflated Negative Binomial Regression (ZINB Regression)
Assumes two underlying processespredict whether one scores 0 or not 0Predict count for those scoring > 0
Zero Inflated Models
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Day1 Example4
Run to obtain a Poisson Regression Outcome is specified as a count variable
To obtain a ZIP regression run Day1 Example5
Note that one can specify different models for occurrence and frequency
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MULTILEVEL MODELS IN MPLUS
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What is the correlation between X and Y?
Descriptive Statistics Mean Std. Deviation N x 8.0000 4.42396 15 y 8.0000 4.42396 15
Correlationsa
x y x Pearson Correlation 1 .912**
Sig. (2-tailed) .000 y Pearson Correlation .912** 1 Sig. (2-tailed) .000 a. Listwise N=15
An Example
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Split Sample by Group
Group 1 r = 0.0 Mean = 3 N=5 Group 2 r = 0.0 Mean = 8 N=5 Group 3 r = 0.0 Mean = 13 N=5
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Multi-level data occurs when responses are grouped (nested) within one or more higher level units of responses
E.G. Employees nested within teams/groups
Longitudinal data – observations nested within individuals
Creates a series of problems that may not be accounted for in standard techniques (e.g., regression, SEM etc)
Introduction
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Individuals within each group are more alike than individuals from different groups (variance is distorted) – violation of the assumption of independence
We may want to predict level 1 responses from level 2 characteristics (i.e., does company size predict individual job satisfaction). If we analyse at the lowest level only we under-estimate variance and hence standard errors leading to inflated Type 1 errors – we find effects where they don’t exist
Aggregation to the highest level may distort the variables of interest (or may not be appropriate)
Some Problems with MultiLevel Data
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Simpson’s – Completely erroneous conclusions may be drawn if grouped data, drawn from heterogeneous populations are collapsed and analyzed as if drawn from a single population
Ecological – The mistake of assuming that the relationship between variables at the aggregated (higher) level will be the same at the disaggregated (lower) level
Two Paradoxes
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Essentially an extension of a regression modelY = mx + b + errorMultilevel models allow for variation in the regression parameter (intercepts (b) and slopes(m)) across the groups comprising your sampleAlso allow us to predict variation ask why groups might vary in intercepts or slopesIntercept differences imply mean differences across groupsSlope differences indicate different relationships (e.g., correlations) across groups
What are multi-level models?
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Attempting to explain (partition) variance in the DV
Why don’t we all score the same on a given variable?
Simplest explanation is error – individual’s score is the grand mean + error.
If employees are in groups – then the variance of the level 1 units has at least 2 components – the variance of individuals around the group mean (within group variance) and the variance of the group means around the grand mean (between group variance)
This is known as the intercepts only or “variance components” or “unconditional” model – it is a baseline that incorporates no predictors
The Multilevel model
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Can introduce predictors either at level 1 or level 2 or both to further explain variance
Can allow the effects of level 1 predictors to vary across groups (random slopes)
Can examine interactions within and across levels
Can incorporate quadratic terms etc
The Multilevel model (cont’d)
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TECHY STUFF – GETTING THE DATA IN SHAPE
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To create level 2 observations we often need to aggregate variables to the higher level and to merge the aggregated data with our level 1 data. To aggregate you need to specify [a] the variables to be aggregated, [b] the method of aggregation (sum, mean etc) and [c] the break variable (definition for level 2)
SPSS allows you to aggregate and save group level data to the current file using the aggregate command
Mplus allows you to do this within the Mplus run
File Handling: Aggregation
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If you choose to aggregate, then there should be some empirical support (i.e., evidence for similar responses within group). Some typical measures are:
ICC – the interclass correlation. The extent to which variance is attributable to group differences. From ANOVA (MSb-MSw)/MSb+C-1(MSw) where C= average group size
ICC(2) -reliability of means(MSb – MSw)/MSb
Rwg (multiple variants) indices of agreement
MPLUS calculates the ICC for random intercept models
Notes on Aggregation
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Centering a variable helps us to interpret the effect of predictors. In the simplest sense, centering involves subtracting the mean from each score (resulting a distribution of deviation scores that have a mean of 0)Centering (among other things) helps with convergence by imposing a common scaleGRAND MEAN Centering – involves subtracting the sample mean from each scoreGROUP MEAN Centering –involves subtracting the group mean from each score – must be done manually.
Centering Predictors
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Grand mean – each score is measured as a deviation from the grand mean. The intercept is the score of an individual who is at the mean of all predictors “the average person”Group mean – each score measured as a deviation from the group mean. The intercept is the score of an individual who is at the mean of all predictors in the group “the average person in group X”Grand mean is the same transformation for all cases – for fixed main effects and overall fit will give the same results as raw dataGroup mean – different for each group – different results
Centering (cont’d)
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Grand mean – helps model fitting, aids interpretation (meaningful 0), may reduce collinearity in testing interactions, or between model parameters or squared effects – may reduce meaning if raw scores actually “mean something”Group mean – helps model fitting, can remove collinearity if you are including both group (aggregate) and individual measures of the same construct in the model (aggregate data explains between group and individual level explains within group variance).
Centering (cont’d)
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Grand mean – may be appropriate when the underlying model is either incremental (group effects add to individual level effects) or mediational (group effects exert influence through individual)Group mean – may be more appropriate when testing cross-level interactions
Hoffman & Gavin (1998) – Journal of Management
A general recommendation
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Calculations are complex, dependent on intraclass correlations, sample size, effect size etc etcIn general power at Level 1 increases with the number of observations and a Level 2 with the number of groupsHox (2002) recommends 30 observations in each of 30 groups Heck & Thomas (2000) suggested 20 groups with 30 observations in eachOthers suggest that even k=50 is too smallPractical constraints likely ruleBetter to have a large number of groups with fewer individuals in each group than a small number of groups with large group sizes
Power and Sample Size
How many subjects = how long is a piece of string?
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Occassionally (about 50% of the time) the program will not converge on a solution and will report a partial solution (i.e., not all parameters). In my experience lack of convergence is a direct function of sample size (small samples = convergence failures) The easiest fix is to ensure that this is not a scaling issue – ie that all variables are measured on roughly the same metric (standardize)The single most frustrating aspect of multi-level models
Convergence
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A PLAN OF ANALYSIS
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1. Ensure data are structured/arranged properly (aggregate, centered etc) – most of this can be done in MPLUS
2. Run a null model – The null model estimates a grand mean only model and provides a baseline for comparison
3. Run the unconditional model (grouping but no predictors) – assess ICC1 and whether varying intercepts is appropriate - a low ICC1 leads one to question the importance of a multilevel model (although this can be controversial)
A plan of analysis
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4. Incorporate level 1 predictors. Assess change in fit, level 1 variance and level 2 variance – starting to move into conditional models - this is equivalent to modeling our data as a series of parallel lines (one for each group) – slopes are the same but intercepts are allowed to vary
5. Allow slope to vary Assess fit, change in variance etc. Can now also estimate the covariance between intercept and slope effects that may be of interest
6. Incorporate level 2 predictors - explain team group but not individual level variance
A plan of analysis
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A global test of the model adequacy is given by the -2 log likelihood statistic – also known as the model deviance
We can examine the change in deviance as models are made more complex
No equivalent to the difference test in REML (Residual Max Likelihood)
Testing Models: -2 Log Likelihood
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No direct equivalent to an R-squared because there are multiple portions of varianceCan focus on explaining variance at either the group or the individual level (i.e., reducing the residual)One useful approach is to calculate the variance explained at each step of the modelVariance explained after predictor is added/variance before the addition of the predictor
Testing Models: Percentage of variance
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Statistical tests of parameters
Analagous to the tests of regression (B) coefficients in regression
Tests the null hypothesis that the parameter is 0
Testing Models: Parameter tests
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Run Day1Example 6 to read in the data.Measures include GHQ, transformational leadership and team identifierSample Total N =851 in 31 locations
Start by estimating the variance components (random intercept only) model
On the variable statement specify the usevariables=ghq team Specify cluster=teamAdd an analysis command
Analysis: Type = twolevel
Implementing the Analysis
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Implementing the Analysis (cont’d)
•Hypotheses
• GHQ varies across team
• GHQ is predicted by leadership
• Effect of leadership on stress varies by location
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Random Intercept Models
Run Day1Example6.inp – the variance components model – a random intercept only model
Add in the within group predictor TFL Need to include tfl on the use variables line Specify the centering centering=grandmean(tfl) Specify the within group model
Model %Within% GHQ on tfl
Maybe try the between group modelModel %between%
Ghq on Tfl
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Variable types
In Mplus twolevel analyses variables are specified as either Within (can only be modeled in the within group model) or Between (can only be modeled with the Between group model)
Unspecified variables will be used appropriately (if used in the between group model then MPLUS will calculate the aggregate score on the variable)
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Random Slope Models
Add “random” to the type statement Type = Twolevel random;
Specify the random slope in the within model as S| Y on X where S is the name of the slope, Y is the DV, X is the predictor e.g, %Within%
S|ghq on tfl;In the between model allow the random slope to
correlate with the random interceptGHQ with S
Predict the random slopeS GHQ on TFL
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Extensions
Can use any techniques previously discussed Specify outcomes as binary or ordered
(multilevel logistic), multilevel poisson etc etc etc
Can incorporate multilevel regressions into path or SEM analyses (More about this later)
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THANK YOU!!!!