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Anonymous posted on Tuesday, April 01, 2003 - 11:10 am
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I have three annual assessments of two variables for husbands and wives from 71 couples: ratings of frequency of child problems (h8t-h10t for husbands and w8t-w10t for wives)and parenting satisfaction (h8psat-h10psat for husbands and w8psat-w10psat for wives). There are missing data. Because I know that there is no growth for either variable, I want to examine the intra- and cross-parent relation between averaged parenting satisfaction and averaged ratings of child problems for each parent over the 3 assessments. I want errors for husbands' ratings of child problems and wives' ratings of child problems to correlate. Here is my MPLUS syntax: USEVARIABLES ARE h8t-h10t h8psat-h10psat w8t-w10t w8psat-w10psat; MISSING ARE ALL (-99); ANALYSIS: TYPE = MISSING H1; ITERATIONS=5000; H1ITERATIONS=5000; MODEL: hit BY h8t-h10t@1; hipsat BY h8psat-h10psat@1; wit BY w8t-w10t@1; wipsat BY w8psat-w10psat@1; hit ON hipsat wipsat; wit ON hipsat wipsat; Here is selected output: Chi-Square Test of Model Fit Value 101.594 Degrees of Freedom 56 P-Value 0.0002 Chi-Square Test of Model Fit for the Baseline Model Value 383.150 Degrees of Freedom 66 P-Value 0.0000 CFI/TLI CFI 0.856 TLI 0.831 Loglikelihood H0 Value -1968.303 H1 Value -1917.506 Information Criteria Number of Free Parameters 34 Akaike (AIC) 4004.606 Bayesian (BIC) 4081.537 Sample-Size Adjusted BIC 3974.428 (n* = (n + 2) / 24) RMSEA (Root Mean Square Error Of Approximation) Estimate 0.107 90 Percent C.I. 0.073 0.140 Probability RMSEA <= .05 0.005 SRMR (Standardized Root Mean Square Residual) Value 0.181 MODEL RESULTS Estimates S.E. Est./S.E. HIT BY H8T 1.000 0.000 0.000 H9T 1.000 0.000 0.000 H10T 1.000 0.000 0.000 HIPSAT BY H8PSAT 1.000 0.000 0.000 H9PSAT 1.000 0.000 0.000 H10PSAT 1.000 0.000 0.000 WIT BY W8T 1.000 0.000 0.000 W9T 1.000 0.000 0.000 W10T 1.000 0.000 0.000 WIPSAT BY W8PSAT 1.000 0.000 0.000 W9PSAT 1.000 0.000 0.000 W10PSAT 1.000 0.000 0.000 HIT ON HIPSAT -1.442 0.497 -2.903 WIPSAT -0.007 0.682 -0.011 WIT ON HIPSAT -0.343 0.390 -0.881 WIPSAT -0.856 0.563 -1.519 WIT WITH HIT 13.997 5.449 2.569 WIPSAT WITH HIPSAT 1.309 0.573 2.284 Intercepts H8T 12.276 1.057 11.618 H9T 11.804 1.021 11.560 H10T 12.519 1.218 10.279 H8PSAT 17.535 0.331 52.950 H9PSAT 17.168 0.320 53.610 H10PSAT 17.272 0.307 56.250 W8T 11.343 0.773 14.665 W9T 12.915 0.958 13.483 W10T 12.392 1.076 11.520 W8PSAT 17.183 0.276 62.289 W9PSAT 17.148 0.267 64.280 W10PSAT 17.124 0.288 59.469 Variances HIPSAT 4.840 1.015 4.769 WIPSAT 2.674 0.654 4.088 Residual Variances H8T 22.196 6.302 3.522 H9T 15.799 5.485 2.881 H10T 37.567 8.939 4.202 H8PSAT 2.947 0.722 4.078 H9PSAT 1.969 0.557 3.534 H10PSAT 1.123 0.432 2.601 W8T 11.455 4.456 2.571 W9T 33.161 7.305 4.539 W10T 40.830 9.589 4.258 W8PSAT 2.729 0.638 4.279 W9PSAT 1.966 0.566 3.474 W10PSAT 2.239 0.615 3.640 HIT 41.801 9.210 4.538 WIT 23.935 5.776 4.144 My question involves the stated 56 degrees of freedom for the chi-square test of model fit. There are 12 observed scores, giving (12x13)/2 = 78 parameters. The output says I have 34 free parameters, and the tech1 output confirms this: PARAMETER SPECIFICATION NU H8T H9T H10T H8PSAT H9PSAT ________ ________ ________ ________ ________ 1 1 2 3 4 5 NU H10PSAT W8T W9T W10T W8PSAT ________ ________ ________ ________ ________ 1 6 7 8 9 10 NU W9PSAT W10PSAT ________ ________ 1 11 12 LAMBDA HIT HIPSAT WIT WIPSAT ________ ________ ________ ________ H8T 0 0 0 0 H9T 0 0 0 0 H10T 0 0 0 0 H8PSAT 0 0 0 0 H9PSAT 0 0 0 0 H10PSAT 0 0 0 0 W8T 0 0 0 0 W9T 0 0 0 0 W10T 0 0 0 0 W8PSAT 0 0 0 0 W9PSAT 0 0 0 0 W10PSAT 0 0 0 0 THETA H8T H9T H10T H8PSAT H9PSAT ________ ________ ________ ________ ________ H8T 13 H9T 0 14 H10T 0 0 15 H8PSAT 0 0 0 16 H9PSAT 0 0 0 0 17 H10PSAT 0 0 0 0 0 W8T 0 0 0 0 0 W9T 0 0 0 0 0 W10T 0 0 0 0 0 W8PSAT 0 0 0 0 0 W9PSAT 0 0 0 0 0 W10PSAT 0 0 0 0 0 THETA H10PSAT W8T W9T W10T W8PSAT ________ ________ ________ ________ ________ H10PSAT 18 W8T 0 19 W9T 0 0 20 W10T 0 0 0 21 W8PSAT 0 0 0 0 22 W9PSAT 0 0 0 0 0 W10PSAT 0 0 0 0 0 THETA W9PSAT W10PSAT ________ ________ W9PSAT 23 W10PSAT 0 24 ALPHA HIT HIPSAT WIT WIPSAT ________ ________ ________ ________ 1 0 0 0 0 BETA HIT HIPSAT WIT WIPSAT ________ ________ ________ ________ HIT 0 25 0 26 HIPSAT 0 0 0 0 WIT 0 27 0 28 WIPSAT 0 0 0 0 PSI HIT HIPSAT WIT WIPSAT ________ ________ ________ ________ HIT 29 HIPSAT 0 30 WIT 31 0 32 WIPSAT 0 33 0 34 I would think this model would give me 78-34=44 degrees of freedom, but I get 56 df. P. 36 of the manual says that MEANSTRUCTURE is included by default in MISSING analyses. Am I correct in assuming that the discrepancy between the reported 56 df and the expected 44 df is that the intercept parameters (NU 1-12) are not really included as parameters? I ran the problem in LISREL 8.53 and get nearly identical results with 56 df and no intercept parameters. |
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The means are both part of your sample statistics and also part of your estimates. Note that there are twelve free parameters in the nu matrix. So your sample statistis are 78 + 12 = 90 - 34 = 56. If you ran the problem without means, you would have 78 - 22 = 56. Please let me know if this does not answer your question. |
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Anonymous posted on Wednesday, April 02, 2003 - 10:32 am
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I can easily get the analysis I want (without means) by dropping the TYPE=MISSING entry in the ANALYSIS section. How can I use the MISSING option and run the problem without means? I have tried setting the intercepts and means of the latent variables to 0, get the identification of the estimated parameters I want, but the estimates themselves are off. Is this a scaling issue? |
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You cannot run the MISSING analysis without means. Means are required for this estimation. However, having unstructured means as part of the model does not affect the results. These means are estimated as the sample values. You can try this out without missing to show yourself. Fixing the means to zero is not correct. |
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Anonymous posted on Wednesday, April 02, 2003 - 2:27 pm
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Thank you. I now see that the structure of the output from Mplus and LISREL differs because Mplus requires the means for estimation. Parameter estimates themselves are nearly identical in the two programs. |
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