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Writing up results from Structural Equation Models What to Report, What to Omit 1.

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Presentation on theme: "Writing up results from Structural Equation Models What to Report, What to Omit 1."— Presentation transcript:

1 Writing up results from Structural Equation Models What to Report, What to Omit 1

2 Writing up results from Structural Equation Models Reference: Hoyle and Panter chapter in Hoyle. Important to note that there is a wide variety of reporting styles (no one “standard”). 2

3 Writing up results from Structural Equation Models A Diagram – Construct Equation Model – Measurement Equation model Some simplification may be required. Adding parameter estimates may clutter (but for simple models helps with reporting). Alternatives exist (present matrices). 3

4 Reporting Structural Equation Models “Written explanation justifying each path and each absence of a path” (Hoyle and Panter) (just how much journal space is available here? ) It might make more sense to try to identify potential controversies (with respect to inclusion, exclusion). 4

5 Controversial paths? 5

6 What to report and what not to report….. Present the details of the statistical model – Clear indication of all free parameters – Clear indication of all fixed parameters  It should be possible for the reader to reproduce the model 4.Describe the data 1.Correlations and standard errors (or covariances) for all variables ?? Round to 3-4 digits and not just 2 if you do this 6

7 What to report and what not to report… 4. Describing the data (continued) – Distributions of the data Any variable highly skewed? Any variable only nominally continuous (i.e., 5-6 discrete values or less)? Report Mardia’s Kurtosis coefficient (multivariate statistic) Dummy exogenous variables, if any 5. Estimation Method If the estimation method is not ML, report ML results. 7

8 What to report and what not to report… 6. Treatment of Missing Data – How big is the problem? – Treatment method used? Pretend there are no missing data Listwise deletion Pairwise deletion FIML estimation (AMOS, LISREL, MPlus, EQS) Nearest neighbor imputation (LISREL) EM algorithm (covariance matrix imputation ) (SAS, LISREL/PRELIS) 8

9 What to report and what not to report… 7. Fit criterion – Hoyle and Panter suggest “.90; justify if lower”. – Choice of indices also an issue. There appears to be “little consensus on the best index” (H & P recommend using multiple indices in presentations) Standards: Bollen’s delta 2 (IFI) Comparative Fit Index RMSEA 9

10 Fit indices Older measures: – GFI (Joreskog & Sorbom) – Bentler’s Normed Fit index – Model Chi-Square 10

11 What to report & what not to report…. 8. Alternative Models used for Nested Comparisons (if appropriate) 11

12 9. Plausible explanation for correlated errors [“these things were just too darned big to ignore”] Generally assumed when working with panel model with equivalent indicators across time: 12

13 What to report 10. Interpretation of regression-based model – Present standardized and unstandardized coefficients (usually) – Standard errors? (* significance test indicators?) – R-square for equations Measurement model too? (expect higher R-squares) 13

14 What to report. Problems and issues – Negative error variances or other reasons for non-singular parameter covariance matrices How dealt with? Does the final model entail any “improper estimates”? – Convergence difficulties, if any LISREL: can look at F ml across values of given parameter, holding other parameters constant – Collinearity among exogenous variables – Factorially complex items 14

15 What to report & what not to report…. General Model Limitations, Future Research issues: – Where the number of available indicators compromised the model 2-indicator variables? (any constraints required?) Single-indicator variables? (what assumptions made about error variances?) Indicators not broadly representative of the construct being measured? – Where the distribution of data presented problems Larger sample sizes can help 15

16 What to report & what not to report…. General Model Limitations, Future Research issues: – Missing data (extent of, etc.) – Cause-effect issues, if any (what constraints went into non-recursive model? How reasonable are these?) 16


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