Basic output through to regression models

Basic output through to regression models

Title: Stata2Mplus conversion for ego_ghq12_id.dta.dta List of variables converted shown below

ghq01 : ghq time1 item 1 ghq02 : ghq time1 item 2 ghq03 : ghq time1 item 3 ghq04 : ghq time1 item 4 ghq05 : ghq time1 item 5 ghq06 : ghq time1 item 6 ghq07 : ghq time1 item 7 ghq08 : ghq time1 item 8 ghq09 : ghq time1 item 9 ghq10 : ghq time1 item 10 ghq11 : ghq time1 item 11 ghq12 : ghq time1 item 12 f1 : Scores for factor 1 id : Data: File is "C:\work\courses\mar09_course\ego\ego_ghq12_id.dta.dat" ; listwise is on;

Variable: Names are ghq01 ghq02 ghq03 ghq04 ghq05 ghq06 ghq07 ghq08 ghq09 ghq10 ghq11 ghq12 f1 id; Missing are all (-9999) ; !usevariables = ghq01 ghq03 ghq05 ghq07 ghq09 ghq11; usevariables = ghq02 ghq04 ghq06 ghq08 ghq10 ghq12; idvariable = id;

Analysis: Type = basic ;

output: !sampstat;

plot: type is plot3;

savedata: file is "C:\work\courses\mar09_course\ego\ego_odd.dat" ;

Output

INPUT READING TERMINATED NORMALLY

Stata2Mplus conversion for ego_ghq12_id.dta.dtaList of variables converted shown belowghq01 : ghq time1 item 1ghq02 : ghq time1 item 2ghq03 : ghq time1 item 3ghq04 : ghq time1 item 4ghq05 : ghq time1 item 5ghq06 : ghq time1 item 6ghq07 : ghq time1 item 7ghq08 : ghq time1 item 8ghq09 : ghq time1 item 9ghq10 : ghq time1 item 10ghq11 : ghq time1 item 11ghq12 : ghq time1 item 12f1 : Scores for factor 1id :

SUMMARY OF ANALYSIS

Number of groups 1Number of observations 1119Number of dependent variables 6Number of independent variables 0Number of continuous latent variables 0

Observed dependent variables

Continuous GHQ02 GHQ04 GHQ06 GHQ08 GHQ10 GHQ12

Variables with special functions ID variable ID

Estimator MLInformation matrix OBSERVEDMaximum number of iterations 1000Convergence criterion 0.500D-04Maximum number of steepest descent iterations 20Maximum number of iterations for H1 2000Convergence criterion for H1 0.100D-03

Input data file(s) C:\work\courses\mar09_course\ego\ego_ghq12_id.dta.datInput data format FREE

SUMMARY OF DATA

Number of missing data patterns 1

SUMMARY OF MISSING DATA PATTERNS

MISSING DATA PATTERNS (x = not missing) 1 GHQ02 x GHQ04 x GHQ06 x GHQ08 x GHQ10 x GHQ12 x

MISSING DATA PATTERN FREQUENCIES

Pattern Frequency 1 1119

COVARIANCE COVERAGE OF DATA

Minimum covariance coverage value 0.100

PROPORTION OF DATA PRESENT

Covariance Coverage

GHQ02 GHQ04 GHQ06 GHQ08 GHQ10 GHQ12

________ ________ ________ ________ ________ ________

GHQ02 1.000 GHQ04 1.000 1.000 GHQ06 1.000 1.000 1.000 GHQ08 1.000 1.000 1.000 1.000 GHQ10 1.000 1.000 1.000 1.000 1.000 GHQ12 1.000 1.000 1.000 1.000 1.000 1.000

RESULTS FOR BASIC ANALYSIS

ESTIMATED SAMPLE STATISTICS

Means GHQ02 GHQ04 GHQ06 GHQ08 GHQ10 GHQ12 ________ ________ ________ ________ ________ ________ 1 2.161 2.123 2.060 2.195 1.987 2.223

Covariances GHQ02 GHQ04 GHQ06 GHQ08 GHQ10 GHQ12 ________ ________ ________ ________ ________ ________ GHQ02 0.768 GHQ04 0.152 0.373 GHQ06 0.350 0.229 0.653 GHQ08 0.211 0.199 0.271 0.387 GHQ10 0.387 0.264 0.439 0.305 0.873 GHQ12 0.266 0.196 0.312 0.250 0.380 0.520

Correlations GHQ02 GHQ04 GHQ06 GHQ08 GHQ10 GHQ12 ________ ________ ________ ________ ________ ________ GHQ02 1.000 GHQ04 0.284 1.000 GHQ06 0.494 0.465 1.000 GHQ08 0.387 0.525 0.538 1.000 GHQ10 0.473 0.464 0.581 0.524 1.000 GHQ12 0.421 0.445 0.535 0.556 0.564 1.000

PLOT INFORMATION

The following plots are available:

Histograms (sample values) Scatterplots (sample values)

SAVEDATA INFORMATION

Order and format of variables GHQ02 F10.3 GHQ04 F10.3 GHQ06 F10.3 GHQ08 F10.3 GHQ10 F10.3 GHQ12 F10.3 ID I5

Save file C:\work\courses\mar09_course\ego\ego_odd.dat Save file format 6F10.3 I5

Histograms and Scatterplots

Define new variables

Variable: Names are ghq01 ghq02 ghq03 ghq04 ghq05 ghq06 ghq07 ghq08 ghq09 ghq10 ghq11 ghq12 f1 id; Missing are all (-9999) ;

usevariables = sumodd sumeven; idvariable = id;

Define: sumodd = ghq01+ ghq03 +ghq05 +ghq07 +ghq09 +ghq11; sumeven = ghq02 +ghq04 +ghq06 +ghq08 +ghq10 +ghq12;

Analysis: Type = basic ;

Etc.

Histogram dialogue box

8 bins

10 bins

12 bins

multihist ghq02 ghq04 ghq06 ghq08 ghq10 ghq12 F

requ

ency

ghq02 (n=1119/1119)

ghq time1 item 21 2 3 4

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ghq04 (n=1119/1119)


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ghq06 (n=1119/1119)


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ghq08 (n=1119/1119)


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ghq10 (n=1119/1119)


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ghq12 (n=1119/1119)


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Scatterplot dialogue box

Full sample

Random sample of 250

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Sum of even items

Su

m o

f o

dd

ite

ms

Regression models

Linear RegressionVariable: Names are ghq01 ghq02 ghq03 ghq04 ghq05 ghq06 ghq07 ghq08 ghq09 ghq10 ghq11 ghq12 f1 id; Missing are all (-9999) ; usevariables = sumodd sumeven; idvariable = id;

Define: sumodd = ghq01+ ghq03 +ghq05 +ghq07 +ghq09 +ghq11; sumeven = ghq02 +ghq04 +ghq06 +ghq08 +ghq10 +ghq12;Analysis: estimator = ML;Model: sumodd on sumeven;output: sampstat cinterval;plot: type is plot3;

savedata: file is "C:\work\courses\mar09_course\ego\ego_oddeven_regress.dat" ; SAVE = MAHALANOBIS COOKS INFLUENCE;







TESTS OF MODEL FIT

Chi-Square Test of Model Fit

Value 0.000 Degrees of Freedom 0 P-Value 0.0000

Chi-Square Test of Model Fit for the Baseline Model

Value 1635.553 Degrees of Freedom 1 P-Value 0.0000

CFI/TLI CFI 1.000 TLI 1.000

Loglikelihood H0 Value -5155.247 H1 Value -5155.247

Output

Information Criteria

Number of Free Parameters 3 Akaike (AIC) 10316.495 Bayesian (BIC) 10331.555 Sample-Size Adjusted BIC 10322.027 (n* = (n + 2) / 24)

RMSEA (Root Mean Square Error Of Approximation)

Estimate 0.000 90 Percent C.I. 0.000 0.000 Probability RMSEA <= .05 0.000

SRMR (Standardized Root Mean Square Residual)

Value 0.000

MODEL RESULTS Two-Tailed Estimate S.E. Est./S.E. P-Value

SUMODD ON SUMEVEN 0.890 0.015 60.886 0.000

Intercepts SUMODD 1.941 0.193 10.051 0.000

Residual Variances SUMODD 2.868 0.121 23.654 0.000

CONFIDENCE INTERVALS OF MODEL RESULTS

Lower .5% Lower 2.5% Estimate Upper 2.5% Upper .5%

SUMODD ON SUMEVEN 0.852 0.861 0.890 0.919 0.928

Intercepts SUMODD 1.444 1.563 1.941 2.320 2.439

Residual Variances SUMODD 2.556 2.631 2.868 3.106 3.181

Compare with Stata

Source | SS df MS Number of obs = 1119-------------+------------------------------ F( 1, 1117) = 3700.56 Model | 10633.9457 1 10633.9457 Prob > F = 0.0000 Residual | 3209.82016 1117 2.87360802 R-squared = 0.7681-------------+------------------------------ Adj R-squared = 0.7679 Total | 13843.7659 1118 12.3826171 Root MSE = 1.6952

------------------------------------------------------------------------------ sumodd | Coef. Std. Err. t P>|t| [95% Conf. Interval]-------------+---------------------------------------------------------------- sumeven | .8900851 .0146318 60.83 0.000 .8613762 .9187941 _cons | 1.941059 .1933 10.04 0.000 1.561787 2.320332------------------------------------------------------------------------------

Say something about OLS / ML estimation

SAVE = MAHALANOBIS COOKS INFLUENCE;

Mahalanobis distance

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1 70 139 208 277 346 415 484 553 622 691 760 829 898 967 1036 1105

Observation

Influence

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Sum of even items

Infl

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nc

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Logistic regression 1 – cts predictorVariable: Names are ghq01 ghq02 ghq03 ghq04 ghq05 ghq06 ghq07 ghq08 ghq09 ghq10 ghq11 ghq12 f1 id; Missing are all (-9999) ; usevariables = sumodd sumeven; categorical are sumodd; idvariable = id;

Define: sumodd = ghq01+ ghq03 +ghq05 +ghq07 +ghq09 +ghq11; sumeven = ghq02 +ghq04 +ghq06 +ghq08 +ghq10 +ghq12; cut sumodd (16);

Analysis: estimator = ML;

Model: sumodd on sumeven;

output: sampstat; cinterval;



Thresholds SUMODD$1 15.665 1.080 14.499 0.000




Thresholds SUMODD$1 12.882 13.547 15.665 17.783 18.448

CONFIDENCE INTERVALS FOR THE LOGISTIC REGRESSION ODDS RATIO RESULTS


Compare with Stata. gen sumodd_g = sumodd. recode sumodd_g 0/16=0 17/24=1(sumodd_g: 1119 changes made)

. tab sumodd_g

sumodd_g | Freq. Percent Cum.------------+----------------------------------- 0 | 916 81.86 81.86 1 | 203 18.14 100.00------------+----------------------------------- Total | 1,119 100.00

. logistic sumodd_g sumeven

Logistic regression Number of obs = 1119 LR chi2(1) = 666.85 Prob > chi2 = 0.0000Log likelihood = -196.45269 Pseudo R2 = 0.6292

------------------------------------------------------------------------------ sumodd_g | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval]-------------+---------------------------------------------------------------- sumeven | 2.638126 .184695 13.86 0.000 2.299868 3.026134------------------------------------------------------------------------------

Logistic regression 2 – binary predictorVariable: Names are ghq01 ghq02 ghq03 ghq04 ghq05 ghq06 ghq07 ghq08 ghq09 ghq10 ghq11 ghq12 f1 id; Missing are all (-9999) ; usevariables = sumodd sumeven; categorical are sumodd; idvariable = id;

Define: sumodd = ghq01+ ghq03 +ghq05 +ghq07 +ghq09 +ghq11; sumeven = ghq02 +ghq04 +ghq06 +ghq08 +ghq10 +ghq12; cut sumeven (16); cut sumodd (16);


Model: sumodd on sumeven;

output: sampstat; cinterval;

Don’t put sumeven here










Compare with Stata. gen sumeven_g = sumeven. recode sumeven_g 0/16=0 17/24=1(sumeven_g: 1119 changes made)

. tab sumeven_g

sumeven_g | Freq. Percent Cum.------------+----------------------------------- 0 | 957 85.52 85.52 1 | 162 14.48 100.00------------+----------------------------------- Total | 1,119 100.00

. xi: logistic sumodd_g i.sumeven_g


------------------------------------------------------------------------------ sumodd_g | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval]-------------+----------------------------------------------------------------_Isumeven_~1 | 104.2885 28.47434 17.02 0.000 61.0702 178.0917------------------------------------------------------------------------------

Logistic regression 3 – ordinal predictorVariable: Names are ghq01 ghq02 ghq03 ghq04 ghq05 ghq06 ghq07 ghq08 ghq09 ghq10 ghq11 ghq12 f1 id; Missing are all (-9999) ; usevariables = sumodd ghq02_1 ghq02_2; categorical are sumodd; idvariable = id;

Define: sumodd = ghq01+ ghq03 +ghq05 +ghq07 +ghq09 +ghq11; !sumeven = ghq02 +ghq04 +ghq06 +ghq08 +ghq10 +ghq12; cut sumodd (16); ghq02_1 = ghq02; ghq02_2 = ghq02; cut ghq02_1 (1); cut ghq02_2 (2); if ghq02_2 eq 1 then ghq02_1 = 0;


Model: sumodd on ghq02_1 ghq02_2;


SUMODD ON GHQ02_1 2.103 0.524 4.015 0.000 GHQ02_2 3.786 0.515 7.348 0.000




SUMODD ON GHQ02_1 0.754 1.076 2.103 3.129 3.452 GHQ02_2 2.459 2.776 3.786 4.796 5.113



SUMODD ON GHQ02_1 2.125 2.933 8.188 22.853 31.550 GHQ02_2 11.691 16.056 44.075 120.987 166.159

Compare with Stata

. recode ghq02 4=3(ghq02: 88 changes made)

. xi: logistic sumodd_g i.ghq02i.ghq02 _Ighq02_1-3 (naturally coded; _Ighq02_1 omitted)


------------------------------------------------------------------------------ sumodd_g | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval]-------------+---------------------------------------------------------------- _Ighq02_2 | 8.1875 4.287567 4.02 0.000 2.933598 22.85083 _Ighq02_3 | 44.07477 22.7058 7.35 0.000 16.05759 120.9761------------------------------------------------------------------------------

Documents

Basic output through to regression models