Brian S. Everitt A Handbook of Statistical Analyses using SPSS

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Display 1.1 Initial SPSS for Windows dialogue box. The facilities provided by the menus will be explained later in this chapter. SPSS also provides a toolbar for quick and easy access to common tasks. A brief description of each tool can be obtained by placing the cursor over the tool symbol and the display of the toolbar can be controlled using the command Toolbars… from the View drop-down menu (for more details, see the Base User’s Guide, SPSS Inc., 2001d). 1.3.1 The Data View Spreadsheet The Data Editor consists of two windows. By default the Data View, which allows the data to be entered and viewed, is shown (Display 1.2). The other window is the Variable View, which allows the types of variables to be specified and viewed. The user can toggle between the windows by clicking on the appropriate tabs on the bottom left of the screen. Data values can be entered in the Data View spreadsheet. For most analysis SPSS assumes that rows represent cases and columns variables. For example, in Display 1.2 some of five available variable values have been entered for twenty subjects. By default SPSS aligns numerical data entries to the right-hand side of the cells and text (string) entries to the left-hand side. Here variables sex, age, extrover, and car take numerical © 2004 by Chapman & Hall/CRC Press LLC
Display 1.2 Data View window of the Data Editor. values while the variable make takes string values. By default SPSS uses a period/full stop to indicate missing numerical values. String variable cells are simply left empty. Here, for example, the data for variables extrover, car, and make have not yet been typed in for the 20 subjects so the respective values appear as missing. The appearance of the Data View spreadsheet is controlled by the View drop-down menu. This can be used to change the font in the cells, remove lines, and make value labels visible. When labels have been assigned to the category codes of a categorical variable, these can be displayed by checking Value Labels (or by selecting on the toolbar).Once the category labels are visible, highlighting a cell produces a button with a downward arrow on the right-hand side of the cell. Clicking on this arrow produces a drop-down list with all the available category labels for the variable. Clicking on any of these labels results in the respective category and label being inserted in the cell. This feature is useful for editing the data. 1.3.2 The Variable View Spreadsheet The Variable View spreadsheet serves to define the variables (Display 1.3). Each variable definition occupies a row of this spreadsheet. As soon as data is entered under a column in the Data View, the default name of the column occupies a row in the Variable View. © 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 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 36 and 37: Display 1.19 Syntax Editor showing
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
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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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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. (
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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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