# Assessing Fit. 09SEM5a

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1 Assessing Fit Evaluating models χ 2 Absolute and incremental fit indices Fit index formulae Indices based on residuals Hu & Bentler Downsides and caveats Comparing nested models Mathematically equivalent models 1

2 Evaluating Models: The First Step First, evaluate theory, constructs, and method Do the paths make theoretical sense?» have any links been supported in the literature?» have some links never been tested?» have some links previously been disconfirmed? What variables have been left out?» assume C causes both A and B but is omitted from the model» then links from A to B or B to A are suspect Are measures reliable and valid? Any problems in procedure/administration? Only then consider model fit» χ2 and fit indices 2

3 Fit: χ 2 Most common index of fit is χ 2 goodness-of-fit index Derived directly from the fitting function (F)» fitting function is one number that represents the fit between the implied and observed covariance matrices» usually it is the sum of squared differences between the matrices (least squares method), but other functions exist χ 2 = F*(N-1)» where F = the value of the fitting function» N = sample size (number of participants) this is where sample size is used Called χ 2 because it s distributed as χ 2 if» model is correct» endogenous variables have multivariate normal distribution 3

4 More About χ 2 Really a badness-of-fit index» so, small numbers mean better fit (0 would be best of all) Small p values (e.g. <.05) are bad» p value tells us whether we should reject hypothesis that χ 2 = 0» we don t want to reject that hypothesis» ideally, would like to see p =.30,.40,.50 Recall that χ 2 depends on sample size» more participants means χ 2 will be bigger» so, more likely to reject hypothesis that observed and implied covariance matrices are the same χ 2 has some drawbacks» greatly affected by sample size» assumption of mv normality often violated 4

5 Still More About χ 2 Recall that more constraints means good fit is harder to achieve» solving for 2 variables with 3 equations is likely to produce better fit than solving for 2 vars with 20 equations» more places where misfit can occur To adjust or account for this, some people recommend dividing χ 2 by df» called normed chi-square (NC)» χ 2 /df <= 2.0 (or 3.0 or even 5.0) considered acceptable» remember, df = # of observations - # parameters, NOT number of participants or cases» some say to only do this correction if number of participants is large» doesn't completely correct for influence of sample size 5

6 Example Consider two models with the same χ 2 More parsimonious model (model with fewer parameters to estimate, therefore more df for error) is acceptable by this criterion Less parsimonious model not acceptable χ df χ 2 /df

7 Beyond χ 2 : Fit Indices Descriptive; intuitively interpreted Not testing a null hypothesis Many different indices» different programs print different ones Good to look at (and report) several» but cf. McDonald & Moon Ho: "It is sometimes suggested that we should report a large number of these indices, apparently because we do not know how to use any of them." Some caveats:» they measure overall or average fit; can be good even if fit in one portion of the model is quite bad» good values do not guarantee that the model makes theoretical sense» good values do not guarantee or prove that the model is correct 7

8 Categories of Fit Indices Two main types 1) Absolute and incremental (or relative) fit indices» rough guideline: values above.90 usually considered adequate» some suggest we should require >.95 2) Indices based on residuals matrix» rough guideline: values below.10 or.05 usually considered adequate Kline also discusses predictive fit indices» e.g. Akaike information criterion and its derivatives» useful for comparing non-nested models» see Kline p. 142, pp

9 Absolute and Incremental Indices Represent how much of the variance in the covariance matrix has been accounted for Not testing a null hypothesis Examples:» comparative fit index (CFI)» normed fit index (NFI)» non-normed fit index (NNFI) (aka Tucker- Lewis index, TLI)» incremental fit index (IFI)» goodness-of-fit index (GFI)» adjusted goodness-of-fit index (AGFI) 9

10 Absolute vs. Incremental Absolute indices are computed using formulae that include discrepancies (matrix of residuals) and df and sample size» not comparing this model to any model; hence the label "absolute" Formulae for incremental or relative indices also include discrepancies from a "null" model» hence the labels "incremental" or "relative" or "comparative" 10

11 Formula elements Will present formulae for some fit indices First, define δ as the degree of misspecification of the model» if δ = 0, model is not misspecified Subtract df for the model from the χ 2 for the model» but don't let δ be less than zero So, not requiring fit to be perfect (i.e., χ 2 = zero) ( ) δ ˆ = max χ 2 df,0 M M M 11

12 CFI: Comparative Fix Index CFI =1 ˆ δ M ˆ δ B Compares performance on your model to performance on baseline ("null" or "independence") model» baseline model assumes zero correlation between all observed variables Proposed by Bentler» hence, it first appeared in EQS, but is printed by Lisrel and Amos now, also Recommended by Kline and Hu & Bentler» better than some other indices for small samples» criticism: baseline model implausible 12

13 NFI: Normed Fix Index NFI =1 χ M 2 χ B 2 Also comparing model to baseline Not adjusting for df of either model» i.e., no bonus for parsimony» sensitive to sample size (N) Proposed by Bentler & Bonett 13

14 NNFI: Non-normed Fix Index χ M 2 NNFI =1 χ B 2 df M df B Proposed by Bentler & Bonett» also called the Tucker-Lewis index Still comparing model to baseline Does adjust for df Can fall outside range Recommended by Hu & Bentler 14

15 Absolute Fit Indices: GFI & AGFI Other indices do not compare to a baseline model -- "absolute" indices Jöreskog proposed two measures based on % variance explained GFI =1 v residual v total v residual = residual variance in covariance matrix (variance that can't be explained by the model) v total = total variance in the covariance matrix GFI: goodness of fit AGFI adjusts for number of parameters 15

16 More on GFI & AGFI Can fall outside range Hu & Bentler» GFI and AGFI do not perform as well with latent variable models as with path models» too many Type I errors when N is large (accepting models that should be rejected) Kline» AGFI has not performed well in simulation studies GFI still often reported, perhaps because it was the first fit index proposed 16

17 Residuals-Based Indices Indices based on residuals matrix» looks at discrepancies between observed and predicted covariances» e.g., root mean square error of approximation (RMSEA), standardized root mean squared residual (SRSR)» values below.05 (or, sometimes,.10) considered adequate 17

18 RMSEA RMSEA: Root Mean Square Error of Approximation Error of Approximation» lack of fit of our model to population data, when parameters are optimally chosen vs. Error of Estimation» lack of fit of our model (with parameters chosen via fitting to the sample data) to population data (with optimally-chosen parameters) Error of estimation is affected by sample size, but error of approximation is not 18

19 RMSEA RMSEA = ˆ δ M df M N 1 ( ) Recall that δ = degree of misspecification of the model Kline:» <=.05 "close approximate fit"» >.05 but <.08 "reasonable approximate fit"» > =.10 "poor fit"» also suggests a method for using confidence intervals (see pp ) 19

20 RMR and Standardized RMR RMR = root mean square residual Simply the mean absolute value of the covariance residuals Standardized formula puts it into a metric that is easily interpretable A "badness of fit" index (in that higher numbers mean worse fit) Values of standardized RMR <.10 generally considered adequate 20

21 Hu & Bentler (1995) Reviewed research on fit indices» usually, simulation studies Found that fit statistics were not robust across» sample size» estimation method (e.g., maximum likelihood vs. generalized least squares)» distribution of latent variables (e.g., violations of multivariate normality) For maximum likelihood, large samples (N > 250) and independent latent variables, H&B recommend» NNFI, IFI, CFI, MacDonald (MFI) Argue against.90 benchmark» instead, use indices to compare/contrast competing models 21

22 Interpreting & Reporting Don't rely on just one fit index Don't pick the indices to interpret/report based on the ones that are most supportive of your model Decide on a set of fit indices you will generally rely on and go with those every time» suggestions in Kline, Hu & Bentler, Hoyle & Panter, McDonald & Moon Ho Ideally, different fit indices will point to the same conclusion If fit indices lead to different conclusion, conservative choice is to reject the model 22

23 Down Side of Fit Indices Fit indices are useful because they summarize fit» reduce it to one number The flip side of this is that the fit indices obscure the reason for good (or bad) fit Is misfit scattered about most or all of the cells in the residual matrix?» if so, this is probably the best we can do Or is misfit limited to a few cells?» if so, model may be misspecified» may be able to improve model dramatically by adding one or two paths Good overall fit may hide the fact that one or two key theoretical relationships are not being accurately reproduced Moral: always look at your residual matrix 23

24 Model Fit: Caveats Good (or perfect) fit does not ensure that model is correct, only that it is plausible» be careful of your language, when presenting results» good to test alternative models if you can reject these, the argument for your model is strengthened» good to also discuss equivalent models (more in a moment) Fit different than predictive power» the "% variance" indices represent how much of the variance/covariance matrix you can explain not how much of the variance in latent or observed endogenous variables is explained» could have perfect fit but not explain very much variance in any of the endogenous variables 24

25 Model Fit: Caveats Values of fit indices depend critically on how many degrees of freedom were available to estimate fit» no degrees of freedom available saturated model fit is always perfect (for recursive models)» few degrees of freedom available not much room for disconfirming fit is likely to be very good» many df available fit could be quite bad thus, good fit is more impressive» this is why the various "adjusted" fit indices were developed 25

26 Comparing Models: Nested Models A nested model is a logical subset of another model» obtained by estimating fewer parameters (i.e., dropping paths)» also called hierarchical models When fewer parameters are estimated (i.e., more df), fit will always be worse» the question is, how much worse?» if fit is almost as good, the nested model (with fewer parameters) is preferred because it s more parsimonious» what is almost as good? One of the few places in SEM where we do classic significance testing 26

27 Nested Models: Comparing χ 2 s Compute the difference between the χ 2 values for the two models» χ2 for the reduced model cannot be smaller than the χ2 for the model it is nested under» logically, it must be larger (or the same) because fewer parameters are being estimated -- impossible for fit to be better Compute the difference between the df for the two models Look up p value in χ2 table Example» Baseline (or full) model: χ 2 (4) = 3.21» Reduced model: χ 2 (6) = 11.41» Δχ 2 = 8.2; for 2 df, p <.05 (χ 2 for 1 df = 3.84)» fit is significantly worse without those two paths 27

28 Comparing Models: Equivalent Models There are (almost) always alternate models that are mathematically equivalent» will have identical fit There are mathematical rules for generating equivalent models» Lee-Herschberger replacing rules» e.g., X-->Z can be replaced by Z-->X D X <--> D Z X-->Z and Z-->X (with equality constraint) Equivalent models not given enough attention in the literature 28

29 Comparing Models Useful to generate at least some equivalent models» some can be rejected on basis of logic, theory or prior data» others cannot be rejected; this is good to know Non-equivalent, non-nested competing models» there may be models that are not mathematically equivalent, but have similar fit to your preferred model» if the models aren t nested, cannot compare χ2 directly (although this is often done)» rely on the art of comparing fit indices» also rely on theory, previous research, logic» also, AIC-type indices for non-nested models 29

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