Brian S. Everitt A Handbook of Statistical Analyses using SPSS

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Display 1.19 Syntax Editor showing command syntax for the dialogue box in Display 1.8. options is pasted into a Syntax Editor window. Should such a window not exist at the time, SPSS will automatically create one. For example, selecting Paste on the dialogue box shown in Display 1.8 produces the Syntax Editor window given in Display 1.19. If a Syntax Editor window already exists, then SPSS will append the latest command to the contents of the window. The commands contained in a Syntax Editor window can be executed by selecting the command All… from the Run drop-down menu on the editor’s menu bar or by selecting from the toolbar. This generates output in the Output Viewer in the usual way. It is also possible to execute only selected commands in the syntax window by highlighting the relevant commands and then using the command Selection from the Run drop-down menu or by clicking the run symbol on the toolbar. The contents of the Syntax Editor window can be saved as a syntax file by using the Save or Save As command from the File drop-down menu of the Syntax Editor. The extension used by SPSS to indicate syntax is *.sps. A syntax file can be opened again by using the commands File – Open – Syntax… from the Data Editor, Output Viewer, or Syntax Editor menu bar. More than one Syntax Editor can be open at one time. In that case, SPSS executes the commands of the designated Syntax Editor window. By default, the window opened last is the designated window. The designation is indicated and can be changed in the same way as that of the Output Viewer windows (see Section 1.6). © 2004 by Chapman & Hall/CRC Press LLC
Chapter 2 Data Description and Simple Inference for Continuous Data: The Lifespans of Rats and Ages at Marriage in the U.S. 2.1 Description of Data In this chapter, we consider two data sets. The first, shown in Table 2.1, involves the lifespan of two groups of rats, one group given a restricted diet and the other an ad libitum diet (that is, “free eating”). Interest lies in assessing whether lifespan is affected by diet. The second data set, shown in Table 2.2, gives the ages at marriage for a sample of 100 couples that applied for marriage licences in Cumberland County, PA, in 1993. Some of the questions of interest about these data are as follows: How is age at marriage distributed? Is there a difference in average age at marriage of men and women? How are the ages at marriage of husband and wife related? © 2004 by Chapman & Hall/CRC Press LLC
Page 2 and 3: A Handbook of Statistical Analyses
Page 4 and 5: Preface SPSS, standing for Statisti
Page 6 and 7: Distributors The distributor for SP
Page 8 and 9: 3 Simple Inference for Categorical
Page 10 and 11: 10 Survival Analysis: Sexual Milest
Page 12 and 13: 1. SPSS Base (Manual: SPSS Base 11.
Page 14 and 15: Display 1.1 Initial SPSS for Window
Page 16 and 17: Display 1.3 Variable View window of
Page 18 and 19: three-periods symbol and filling in
Page 20 and 21: Display 1.6 Opening an existing SPS
Page 22 and 23: Display 1.8 Typical dialogue box. s
Page 24 and 25: Transpose… opens a dialogue for s
Page 26 and 27: Display 1.11 Selecting subsets of c
Page 28 and 29: A large number of functions are sup
Page 30 and 31: Display 1.15 Graph procedures demon
Page 32 and 33: More than one Output Viewer can be
Page 34 and 35: The Chart Editor facilities are des
Page 38 and 39: Table 2.1 Lifespans of Rats (in Day
Page 40 and 41: The test-statistic is where - y 1 a
Page 42 and 43: For small samples, p-values for the
Page 44 and 45: Display 2.1 Data View spreadsheet f
Page 46 and 47: lifespan in days diet Restricted di
Page 48 and 49: Lifespan in days 1600 1400 1200 100
Page 50 and 51: Display 2.6 Settings for controllin
Page 52 and 53: Display 2.8 Generating an independe
Page 54 and 55: Display 2.10 Generating a Mann-Whit
Page 56 and 57: Display 2.13 Generating descriptive
Page 58 and 59: simply because ages were measured o
Page 60 and 61: Display 2.18 Generating a Wilcoxon
Page 62 and 63: Husbands' ages (years) 80 70 60 50
Page 64 and 65: Display 2.24 Generating correlation
Page 66 and 67: Display 2.26 Fitting a simple linea
Page 68 and 69: Table 2.4 Motor Vehicle Theft in th
Page 70 and 71: 2.4.5 More on Husbands and Wives: E
Page 72 and 73: Table 3.1 Belief in the Afterlife B
Page 74 and 75: make up the table. Most commonly, a
Page 76 and 77: This is known as a hypergeometric d
Page 78 and 79: (2) Odds ratio The odds of Variabl
Page 80 and 81: Display 3.2 Cross-classifying two c
Page 82 and 83: Odds Ratio for diet (Restricted die
Page 84 and 85: 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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Measure: MEASURE_1 Within Subjects
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e used, or for a conservative appro
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Table 7.2 Visual Acuity and Lens St
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Chapter 8 Analysis of Repeated Meas
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Table 8.1 A Subset of the Data from
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Table 8.1 (continued) A Subset of t
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Box 8.1 Random Effects Models Supp
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0 10 20 30 Response Average group 1
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95% CI for mean score 30 20 10 0 N
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a) TAU group Baseline depression sc
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Display 8.6 Part of Data View sprea
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Display 8.7 Defining the variables
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variables; and the variance paramet
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Fixed Effects Random Effects Residu
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different post-treatment time point
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Iteration 0 1 2 3 4 5 6 7 8 Update
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a) Estimates of error term (residua
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Table 8.3 Depression Ratings for Su
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Constant error variance across repe
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Display 9.1 Characteristics of 21 T
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Competing models in a logistic regr
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Survived? Total Survived? Total no
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Display 9.4 Defining a five-way tab
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Display 9.6 Defining a logistic reg
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Step 1 a Step 1 PCLASS PCLASS(1) PC
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Table 9.1 Unadjusted Effects of Cat
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of all explanatory variables simult
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Step 1 a Table 9.2 LR Test Results
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log-odds of survival log-odds of su
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9.4 Exercises 9.4.1 More on the Tit
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Chapter 10 Survival Analysis: Sexua
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Table 10.1 (continued) Times to Fir
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Table 10.2 Times to Completion of t
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To compare the survivor functions b
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10.3 Analysis Using SPSS 10.3.1 Sex
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Display 10.2 Generating Kaplan-Meie
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Survival Analysis for AGESEX Age of
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Display 10.6 Requesting tests for c
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to complete the task. Children rema
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Cases available in analysis Cases d
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Display 10.10 Plotting the predicte
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Display 10.12 Saving results from a
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The smooth lines in the respective
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EFT AGE Display 10.15 Selected outp
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Table 10.3 Heroin Addicts Data Subj
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Table 10.3 (continued) Heroin Addic
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Table 11.1 Crime Rates in the U.S.
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Table 11.2 AIDS Patient’s Evaluat
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that need to be considered. If the
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the analysis is based on the correl
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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. (
Page 338:
Stevens, J. (1992) Applied Multivar
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Brian S. Everitt A Handbook of Statistical Analyses using SPSS

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