Brian S. Everitt A Handbook of Statistical Analyses using SPSS

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Table 9.1 Unadjusted Effects of Categorical Predictor Variables on Survival Obtained from Logistic Regressions Categorical Predictor LR Test OR for Survival 95% CI for OR Passenger class (pclass) First vs. third class Second vs. third class Gender (sex) Female vs. male Number of siblings/spouses aboard (sibsp1) 0 vs. 3 or more 1 vs. 3 or more 2 vs. 3 or more Number of parents/children aboard (parch1) 0 vs. 3 or more 1 vs. 3 or more 2 vs. 3 or more X 2 (2) = 127.8, p < 0.001 4.743 2.197 X 2 (1) = 231.2, 3.581–6.282 1.641–2.941 p < 0.001 8.396 6.278–11.229 X 2 (3) = 14.2, p < 0.001 X 2 (3) = 46.7, p < 0.001 2.831 5.571 4.405 1.225 3.467 2.471 1.371–5.846 2.645–11.735 1.728–11.23 0.503–2.982 1.366–8.802 0.951–6.415 standard deviations (14.41, 302.87, and 11565.19) simply to avoid very small regression coefficients due to very large variable values. Inclusion of all three age terms under the Covariates list in the Logistic Regression dialogue box gives the results shown in Display 9.10. We find that the combined age terms have a significant effect on survival (LR: X 2 (3) = 16.2, p = 0.001). The single parameter Wald tests show that the quadratic and cubic age terms contribute significantly to explaining variability in survival probabilities. These results confirm that a linear effect of age on the logodds scale would have been too simplistic a model for these data. Having found that all our potential predictors are associated with survival when considered singly, the next step is to model their effects simultaneously. In this way, we will be able to estimate the effect of each adjusted for the remainder. A logistic regression model that accounts for the effects of all five explanatory variables can be fitted by including the four categorical variables and the three age terms under the Covariates list of the Logistic Regression dialogue box (see Display 9.6). (The Categorical… button was again employed to define categorical variables.) Part of the resulting output is shown in Display 9.11. We note from the “Case Processing Summary” table that the number of cases included in the analysis is reduced to 1046 because information on at least one explanatory variable (age) is missing for 263 passengers. The SPSS output does not supply an LR test for the effect of each variable when added to a model containing all the other explanatory variables, rather the table of Omnibus Tests provides a test for the effect © 2004 by Chapman & Hall/CRC Press LLC
Step 1 a C_AGE C_AGE2 C_AGE3 Constant Step 1 Omnibus Tests of Model Coefficients Step Block Model Chi-square df Sig. 16.153 3 .001 16.153 3 .001 16.153 3 .001 Variables in the Equation 95.0% C.I.for EXP(B) B S.E. Wald df Sig. Exp(B) Lower Upper .098 .113 .742 1 .389 1.102 .883 1.376 .235 .074 10.155 1 .001 1.265 1.095 1.461 -.339 .128 6.999 1 .008 .713 .555 .916 -.501 .079 40.372 1 .000 .606 a. Variable(s) entered on step 1: C_AGE, C_AGE2, C_AGE3. Display 9.10 LR test and odds ratios for logistic regression of survival on three age terms. Step 1 a PCLASS PCLASS(1) PCLASS(2) SEX(1) C_AGE C_AGE2 C_AGE3 SIBSP1 SIBSP1(1) SIBSP1(2) SIBSP1(3) PARCH1 PARCH1(1) PARCH1(2) PARCH1(3) Constant Unweighted Cases a Selected Cases Unselected Cases Total Case Processing Summary Included in Analysis Missing Cases Total N Percent 1046 79.9 263 20.1 1309 100.0 0 .0 1309 100.0 a. If weight is in effect, see classification table for the total number of cases. Variables in the Equation 95.0% C.I.for EXP(B) B S.E. Wald df Sig. Exp(B) Lower Upper 85.200 2 .000 2.159 .234 85.195 1 .000 8.661 5.476 13.699 .874 .206 17.950 1 .000 2.397 1.600 3.591 2.550 .176 210.422 1 .000 12.812 9.077 18.083 -.413 .156 6.955 1 .008 .662 .487 .899 .195 .106 3.363 1 .067 1.215 .987 1.497 -.200 .165 1.474 1 .225 .819 .592 1.131 18.725 3 .000 2.166 .510 18.031 1 .000 8.722 3.210 23.700 2.008 .516 15.152 1 .000 7.449 2.710 20.475 1.541 .653 5.562 1 .018 4.668 1.297 16.798 5.525 3 .137 .518 .552 .880 1 .348 1.679 .569 4.955 1.031 .583 3.126 1 .077 2.805 .894 8.801 .844 .623 1.838 1 .175 2.326 .686 7.884 -4.989 .755 43.661 1 .000 .007 a. Variable(s) entered on step 1: PCLASS, SEX, C_AGE, C_AGE2, C_AGE3, SIBSP1, PARCH1. Display 9.11 Selected output from logistic regression of survival on all potential explanatory variables. © 2004 by Chapman & Hall/CRC Press LLC
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A Handbook of Statistical Analyses
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Preface SPSS, standing for Statisti
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Distributors The distributor for SP
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3 Simple Inference for Categorical
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10 Survival Analysis: Sexual Milest
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1. SPSS Base (Manual: SPSS Base 11.
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Display 1.1 Initial SPSS for Window
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Display 1.3 Variable View window of
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three-periods symbol and filling in
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Display 1.6 Opening an existing SPS
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Display 1.8 Typical dialogue box. s
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Transpose… opens a dialogue for s
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Display 1.11 Selecting subsets of c
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A large number of functions are sup
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Display 1.15 Graph procedures demon
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More than one Output Viewer can be
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The Chart Editor facilities are des
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Display 1.19 Syntax Editor showing
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Table 2.1 Lifespans of Rats (in Day
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The test-statistic is where - y 1 a
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For small samples, p-values for the
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Display 2.1 Data View spreadsheet f
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lifespan in days diet Restricted di
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Lifespan in days 1600 1400 1200 100
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Display 2.6 Settings for controllin
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Display 2.8 Generating an independe
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Display 2.10 Generating a Mann-Whit
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Display 2.13 Generating descriptive
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simply because ages were measured o
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Display 2.18 Generating a Wilcoxon
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Husbands' ages (years) 80 70 60 50
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Display 2.24 Generating correlation
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Display 2.26 Fitting a simple linea
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Table 2.4 Motor Vehicle Theft in th
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2.4.5 More on Husbands and Wives: E
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Table 3.1 Belief in the Afterlife B
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make up the table. Most commonly, a
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This is known as a hypergeometric d
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(2) Odds ratio The odds of Variabl
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Display 3.2 Cross-classifying two c
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Odds Ratio for diet (Restricted die
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Display 3.7 Data View spreadsheet f
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Display 3.10 Generating a chi-squar
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20 10 Observed number 30 0 Psychoti
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Chi-Square Tests Value McNemar Test
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Pearson Chi-Square Likelihood Ratio
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Race of defendant found guilty of m
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Estimate ln(Estimate) Std. Error of
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treatment are independent. Estimate
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Table 4.1 Cleaning Cars Sex (1 = ma
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Table 4.2 (continued) Minimum Tempe
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A measure of the fit of the model i
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Display 4.2 Generating the partial
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We specify the dependent variable a
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for the coefficient is given by [0.
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Display 4.7 Setting inclusion and e
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Model 1 2 Model 1 2 Variables Enter
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proportion of variance of the varia
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The final graph shown in Display 4.
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Display 4.12 Generating a scatterpl
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Model 1 Model 1 Model Summary .973a
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Display 4.15 Blocking explanatory v
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1. Residual plot: This is a scatter
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8 6 4 Frequency 10 2 0 2.25 1.75 1.
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a) Latitude January minimum tempera
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Display 4.22 Extending a spreadshee
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Table 4.4 Sulfur Dioxide and Indica
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Table 4.5 Body Fat Content and Age
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Table 5.1 Fecundity of Fruit Flies
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Table 5.3 Female Social Skills Anxi
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There are some advantages (and, unf
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Display 5.1 SPSS spreadsheet contai
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Display 5.3 Defining a one-way desi
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Dependent Variable: FECUNDIT Tukey
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Click Simple in the resulting Error
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finger taps per minute Between Grou
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Display 5.13 Defining a one-way des
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5.0 4.0 Mean score 6.0 3.0 2.0 B. r
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the data to assess whether the assu
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Chapter 6 Analysis of Variance II:
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Table 6.2 Data from Slimming Clinic
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When the cells of the design have d
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Dependent Variable: reaction time p
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Display 6.5 Requesting a line chart
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Display 6.8 Confidence intervals fo
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MANUAL Total manual no manual MANUA
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In a balanced design, the type I, I
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6.4 Exercises 6.4.1 Headache Treatm
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ANCOVA assumes that there is no int
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Table 7.1 Field Independence and a
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or error term. The u i are assumed
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To convey this design to SPSS, the
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LN_FN LN_FC LN_FI LN_CN LN_CC LN_CI
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Within-Subjects Factors Measure: ME
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Page 234 and 235: Display 9.4 Defining a five-way tab
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Page 252 and 253: Table 10.1 (continued) Times to Fir
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We assume that the residual terms a
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Correlation Murder Rape Robbery Agg
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Murder Rape Robbery Aggravated assa
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Display 11.6 Saving factor scores.
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Display 11.8 Requesting descriptive
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a) One-factor model Goodness-of-fit
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Raw Rescaled Factor 1 2 3 4 5 6 7 8
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friendly manner doubts about abilit
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Display 11.16 Reproduced covariance
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11.4.2 More on AIDS Patients’Eval
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Table 12.1 Tibetan Skulls Data Skul
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Euclidean distances are the startin
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Fisher showed that the coefficients
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Display 12.2 Declaring a discrimina
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Box's M F Display 12.4 (continued)
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Display 12.6 (continued) Functions
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Original Cross-validated a place wh
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Display 12.10 Requesting a dendrogr
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* * * * * * H I E R A R C H I C A L
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Display 12.14 Declaring a k-means c
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12.4 Exercises Cluster Number of Ca
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Table 12.2 (continued) SIDS Data Gr
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References Agresti, A. (1996) Intro
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Greenhouse, S. W. and Geisser, S. (
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Stevens, J. (1992) Applied Multivar
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Brian S. Everitt A Handbook of Statistical Analyses using SPSS

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