Daniel J. Bauer & Patrick J. Curran

Transcription

1 GET FILE='C:\Users\dan\Dropbox\SRA\antisocial.sav'. >Warning # Command name: GET FILE >SPSS Statistics is running in Unicode encoding mode. This file is encoded in >a locale-specific (code page) encoding. The defined width of any string >variables are automatically tripled in order to avoid possible data loss. Yo u >can use ALTER TYPE to set the width of string variables to the width of the >longest observed value for each string variable. DATASET NAME DataSet1 WINDOW=FRONT. * Restructuring the data. VARSTOCASES /MAKE age FROM age86 age88 age90 age92 /MAKE anti FROM anti86 anti88 anti90 anti92 /INDEX=Index1(4) /KEEP=id homecog age86 age88 age90 age92 /NULL=KEEP. Variables to Cases [DataSet1] C:\Users\dan\Dropbox\SRA\antisocial.sav Generated Variables Name Index1 age anti Label <none> <none> <none> Processing Statistics Variables In Variables Out 11 6 * Recode age so 0 is age 6. COMPUTE =age - 6. EXECUTE. *optimally fitting model was linear with homoscedastic errors over time. MIXED anti WITH /FIXED= /PRINT=G SOLUTION TESTCOV /RANDOM=INTERCEPT SUBJECT(id) COVTYPE(UN). Page 1 1

2 Mixed Model Analysis Model Dimension a Levels Covariance Structure Parameters Subject Variables Random Effects + b 2 Unstructured 3 id Total b. As of version 11.5, the syntax rules for the RANDOM subcommand have changed. Your command syntax may yield results that differ from those produced by prior versions. If you are using version 11 syntax, please consult the current syntax reference guide for more information. Information Criteria a -2 Restricted Log Likelihood Akaike's Information Criterion (AIC) Hurvich and Tsai's Criterion (AICC) Bozdogan's Criterion (CAIC) Schwarz's Bayesian Criterion (BIC) The information criteria are displayed in smaller-is-better form. Type III Tests of a Source Numerator df Denominator df F Sig Page 2 2

3 Estimates of a Parameter Estimate Std. Error df t Sig. Lower Bound Upper Bound Covariance Parameters Estimates of Covariance Parameters a Parameter Estimate Std. Error Wald Z Sig. Lower Bound Upper Bound [subject = UN (1,1) id] UN (2,1) UN (2,2) Random Effect Covariance Structure (G) a id id id id Unstructured * New multilevel model with as a predictor. MIXED anti WITH /FIXED= * /PRINT=G SOLUTION TESTCOV /RANDOM=INTERCEPT SUBJECT(id) COVTYPE(UN) /SAVE=PRED RESID. Mixed Model Analysis Page 3 3

4 Model Dimension a Levels Covariance Structure Parameters Subject Variables Random Effects * + b 2 Unstructured 3 id Total b. As of version 11.5, the syntax rules for the RANDOM subcommand have changed. Your command syntax may yield results that differ from those produced by prior versions. If you are using version 11 syntax, please consult the current syntax reference guide for more information. Information Criteria a -2 Restricted Log Likelihood Akaike's Information Criterion (AIC) Hurvich and Tsai's Criterion (AICC) Bozdogan's Criterion (CAIC) Schwarz's Bayesian Criterion (BIC) The information criteria are displayed in smaller-is-better form. Type III Tests of a Source Numerator df Denominator df F Sig * Page 4 4

5 Estimates of a Parameter Estimate Std. Error df t Sig. Lower Bound Upper Bound * Covariance Parameters Estimates of Covariance Parameters a Parameter Estimate Std. Error Wald Z Sig. Lower Bound Upper Bound [subject = UN (1,1) id] UN (2,1) UN (2,2) Random Effect Covariance Structure (G) a id id id id Unstructured * New multilevel model with as a predictor. MIXED anti WITH /FIXED= * /PRINT=G SOLUTION TESTCOV /RANDOM=INTERCEPT SUBJECT(id) COVTYPE(UN) /TEST 'inter fe' intercept 1 0 /TEST 'slope fe' 1 * 0 /TEST 'inter ' intercept 1 1 /TEST 'slope ' 1 * 1. Mixed Model Analysis Page 5 5

6 Model Dimension a Levels Covariance Structure Parameters Subject Variables Random Effects * + b 2 Unstructured 3 id Total b. As of version 11.5, the syntax rules for the RANDOM subcommand have changed. Your command syntax may yield results that differ from those produced by prior versions. If you are using version 11 syntax, please consult the current syntax reference guide for more information. Information Criteria a -2 Restricted Log Likelihood Akaike's Information Criterion (AIC) Hurvich and Tsai's Criterion (AICC) Bozdogan's Criterion (CAIC) Schwarz's Bayesian Criterion (BIC) The information criteria are displayed in smaller-is-better form. Type III Tests of a Source Numerator df Denominator df F Sig * Page 6 6

7 Estimates of a Parameter Estimate Std. Error df t Sig. Lower Bound Upper Bound * Covariance Parameters Estimates of Covariance Parameters a Parameter Estimate Std. Error Wald Z Sig. Lower Bound Upper Bound [subject = UN (1,1) id] UN (2,1) UN (2,2) Random Effect Covariance Structure (G) a id id id id Unstructured Custom Hypothesis Test 1 (inter fe) Contrast Estimates a,b Contrast Estimate Std. Error df Test Value t Sig. Lower Bound Upper Bound L a. inter fe b. Dependent Variable: anti. Custom Hypothesis Test 2 (slope fe) Page 7 7

8 Contrast Estimates a,b Contrast Estimate Std. Error df Test Value t Sig. Lower Bound Upper Bound L a. slope fe b. Dependent Variable: anti. Custom Hypothesis Test 3 (inter ) Contrast Estimates a,b Contrast Estimate Std. Error df Test Value t Sig. Lower Bound Upper Bound L a. inter b. Dependent Variable: anti. Custom Hypothesis Test 4 (slope ) Contrast Estimates a,b Contrast Estimate Std. Error df Test Value t Sig. Lower Bound Upper Bound L a. slope b. Dependent Variable: anti. *Generating data based on the simple intercept and slope results. new file. input program. loop #i = 0 to 9. compute age = #i. compute fe = *age. compute = *age. end case. end loop. end file. end input program. formats fe (F8.4). execute. *Ploting the predicted trends from the simple slopes. Page 8 8

9 GRAPH /LINE(MULTIPLE)=MEAN(fe) MEAN() BY age. Graph fe Mean age *Selecting a subset of cases to plot individual predicted trajectories. DATASET ACTIVATE DataSet1. USE ALL. COMPUTE filter_$=(id < 100). FILTER BY filter_$. EXECUTE. *spliting the file by gender. SORT CASES BY. SPLIT FILE LAYERED BY. *Plotting trajectories. Page 9 9

10 GGRAPH /GRAPHDATASET NAME="graphdataset" VARIABLES= PRED_1 id /GRAPHSPEC SOURCE=INLINE. BEGIN GPL SOURCE: s=usersource(id("graphdataset")) DATA: =col(source(s), name("")) DATA: PRED_1=col(source(s), name("pred_1")) DATA: id=col(source(s), name("id"), unit.category()) GUIDE: axis(dim(1), label("")) GUIDE: axis(dim(2), label("predicted Values")) GUIDE: legend(aesthetic(aesthetic.color.interior), label("id")) SCALE: linear(dim(2),min(0),max(10)) ELEMENT: line(position(*pred_1), color.interior(id)) END GPL. GGraph : 0 id Predicted Values Page 10 10

11 : 1 id Predicted Values * Turn split file off. SPLIT FILE OFF. * Turn off the filter. FILTER OFF. USE ALL. EXECUTE. Page 11 11