IBM SPSS Bootstrapping 19

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IBM SPSS Bootstrapping 19

Note: Before using this information and the product it supports, read the general informationunder Notices on p. 39.This document contains proprietary information of SPSS Inc, an IBM Company. It is providedunder a license agreement and is protected by copyright law. The information contained in thispublication does not include any product warranties, and any statements provided in this manualshould not be interpreted as such.When you send information to IBM or SPSS, you grant IBM and SPSS a nonexclusive rightto use or distribute the information in any way it believes appropriate without incurring anyobligation to you.

© Copyright SPSS Inc. 1989, 2010.

Preface

IBM® SPSS® Statistics is a comprehensive system for analyzing data. The Bootstrappingoptional add-on module provides the additional analytic techniques described in this manual.The Bootstrapping add-on module must be used with the SPSS Statistics Core system and iscompletely integrated into that system.

About SPSS Inc., an IBM Company

SPSS Inc., an IBM Company, is a leading global provider of predictive analytic softwareand solutions. The company’s complete portfolio of products — data collection, statistics,modeling and deployment — captures people’s attitudes and opinions, predicts outcomes offuture customer interactions, and then acts on these insights by embedding analytics into businessprocesses. SPSS Inc. solutions address interconnected business objectives across an entireorganization by focusing on the convergence of analytics, IT architecture, and business processes.Commercial, government, and academic customers worldwide rely on SPSS Inc. technology asa competitive advantage in attracting, retaining, and growing customers, while reducing fraudand mitigating risk. SPSS Inc. was acquired by IBM in October 2009. For more information,visit http://www.spss.com.

Technical support

Technical support is available to maintenance customers. Customers may contactTechnical Support for assistance in using SPSS Inc. products or for installation helpfor one of the supported hardware environments. To reach Technical Support, see theSPSS Inc. web site at http://support.spss.com or find your local office via the web site athttp://support.spss.com/default.asp?refpage=contactus.asp. Be prepared to identify yourself, yourorganization, and your support agreement when requesting assistance.

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© Copyright SPSS Inc. 1989, 2010 iii

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Additional Publications

The SPSS Statistics: Guide to Data Analysis, SPSS Statistics: Statistical Procedures Companion,and SPSS Statistics: Advanced Statistical Procedures Companion, written by Marija Norušis andpublished by Prentice Hall, are available as suggested supplemental material. These publicationscover statistical procedures in the SPSS Statistics Base module, Advanced Statistics moduleand Regression module. Whether you are just getting starting in data analysis or are ready foradvanced applications, these books will help you make best use of the capabilities found withinthe IBM® SPSS® Statistics offering. For additional information including publication contentsand sample chapters, please see the author’s website: http://www.norusis.com

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http://www.norusis.com

Contents

Part I: User’s Guide

1 Introduction to Bootstrapping 1

2 Bootstrapping 3

Procedures That Support Bootstrapping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5BOOTSTRAP Command Additional Features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

Part II: Examples

3 Bootstrapping 10

Using Bootstrapping to Obtain Confidence Intervals for Proportions . . . . . . . . . . . . . . . . . . . . . . . 10Preparing the Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10Running the Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11Bootstrap Specifications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14Frequency Table . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

Using Bootstrapping to Obtain Confidence Intervals for Medians . . . . . . . . . . . . . . . . . . . . . . . . . 16Running the Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16Descriptives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

Using Bootstrapping to Choose Better Predictors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19Preparing the Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19Running the Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20Parameter Estimates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

Recommended Readings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

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Appendices

A Sample Files 30

B Notices 39

Bibliography 41

Index 42

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Part I:User’s Guide

Chapter

1Introduction to Bootstrapping

When collecting data, you are often interested in the properties of the population from which youtook the sample. You make inferences about these population parameters with estimates computedfrom the sample. For example, if the Employee data.sav dataset that is included with the productis a random sample from a larger population of employees, then the sample mean of $34,419.57for Current salary is an estimate of the mean current salary for the population of employees.Moreover, this estimate has a standard error of $784.311 for a sample of size 474, and so a 95%confidence interval for the mean current salary in the population of employees is $32,878.40to $35,960.73. But how reliable are these estimators? For certain “known” populations andwell-behaved parameters, we know quite a bit about the properties of the sample estimates, andcan be confident in these results. Bootstrapping seeks to uncover more information about theproperties of estimators for “unknown” populations and ill-behaved parameters.

Figure 1-1Making parametric inferences about the population mean

How Bootstrapping Works

At its simplest, for a dataset with a sample size of N, you take B “bootstrap” samples of size Nwith replacement from the original dataset and compute the estimator for each of these B bootstrapsamples. These B bootstrap estimates are a sample of size B from which you can make inferencesabout the estimator. For example, if you take 1,000 bootstrap samples from the Employee data.savdataset, then the bootstrap estimated standard error of $776.91 for the sample mean for Currentsalary is an alternative to the estimate of $784.311.

Additionally, bootstrapping provides a standard error and confidence interval for the median, forwhich parametric estimates are unavailable.

© Copyright SPSS Inc. 1989, 2010 1

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Figure 1-2Making bootstrap inferences about the sample mean

Support for Bootstrapping in the Product

Bootstrapping is incorporated as a subdialog in procedures that support bootstrapping.See Procedures That Support Bootstrapping for information on which procedures supportbootstrapping.

When bootstrapping is requested in the dialogs, a new and separate BOOTSTRAP commandis pasted in addition to the usual syntax generated by the dialog. The BOOTSTRAP commandcreates the bootstrap samples according to your specifications. Internally, the product treats thesebootstrap samples like splits, even though they are not explicitly shown in the Data Editor. Thismeans that, internally, there are effectively B*N cases, so the case counter in the status bar willcount from 1 to B*N when processing the data during bootstrapping. The Output ManagementSystem (OMS) is used to collect the results of running the analysis on each “bootstrap split”.These results are pooled, and the pooled bootstrap results displayed in the Viewer with the restof the usual output generated by the procedure. In certain cases, you may see a reference to“bootstrap split 0”; this is the original dataset.

Chapter

2Bootstrapping

Bootstrapping is a method for deriving robust estimates of standard errors and confidenceintervals for estimates such as the mean, median, proportion, odds ratio, correlation coefficientor regression coefficient. It may also be used for constructing hypothesis tests. Bootstrappingis most useful as an alternative to parametric estimates when the assumptions of those methodsare in doubt (as in the case of regression models with heteroscedastic residuals fit to smallsamples), or where parametric inference is impossible or requires very complicated formulas forthe calculation of standard errors (as in the case of computing confidence intervals for the median,quartiles, and other percentiles).

Examples. A telecommunications firm loses about 27% of its customers to churn each month. Inorder to properly focus churn reduction efforts, management wants to know if this percentagevaries across predefined customer groups. Using bootstrapping, you can determine whether asingle rate of churn adequately describes the four major customer types. For more information,see the topic Using Bootstrapping to Obtain Confidence Intervals for Proportions in Chapter 3 inIBM SPSS Bootstrapping 19.

In a review of employee records, management is interested in the previous work experience ofemployees. Work experience is right skewed, which makes the mean a less desirable estimate ofthe “typical” previous work experience among employees than the median. However, parametricconfidence intervals are not available for the median in the product. For more information, seethe topic Using Bootstrapping to Obtain Confidence Intervals for Medians in Chapter 3 in IBMSPSS Bootstrapping 19.

Management is also interested in determining what factors are associated with employee salaryincreases by fitting a linear model to the difference between current and starting salaries.When bootstrapping a linear model, you can use special resampling methods (residual andwild bootstrap) to obtain more accurate results. For more information, see the topic UsingBootstrapping to Choose Better Predictors in Chapter 3 in IBM SPSS Bootstrapping 19.

Many procedures support bootstrap sampling and pooling of results from analysis of bootstrapsamples. Controls for specifying bootstrap analyses are integrated directly as a commonsubdialog in procedures that support bootstrapping. Settings on the bootstrap dialog persist acrossprocedures so that if you run a Frequencies analysis with bootstrapping through the dialogs,bootstrapping will be turned on by default for other procedures that support it.

To Obtain a Bootstrap Analysis

E From the menus choose a procedure that supports bootstrapping and click Bootstrap.


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Figure 2-1Bootstrap dialog box

E Select Perform bootstrapping.

Optionally, you can control the following options:

Number of samples. For the percentile and BCa intervals produced, it is recommended to use atleast 1000 bootstrap samples. Specify a positive integer.

Set seed for Mersenne Twister. Setting a seed allows you to replicate analyses. Using this controlis similar to setting the Mersenne Twister as the active generator and specifying a fixed startingpoint on the Random Number Generators dialog, with the important difference that setting theseed in this dialog will preserve the current state of the random number generator and restore thatstate after the analysis is complete.

Confidence Intervals. Specify a confidence level greater than 50 and less than 100. Percentileintervals simply use the ordered bootstrap values corresponding to the desired confidenceinterval percentiles. For example, a 95% percentile confidence interval uses the 2.5th and 97.5thpercentiles of the bootstrap values as the lower and upper bounds of the interval (interpolatingthe bootstrap values if necessary). Bias corrected and accelerated (BCa) intervals are adjustedintervals that are more accurate at the cost of requiring more time to compute.

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Sampling. The Simple method is case resampling with replacement from the original dataset. TheStratified method is case resampling with replacement from the original dataset, within the stratadefined by the cross-classification of strata variables. Stratified bootstrap sampling can be usefulwhen units within strata are relatively homogeneous while units across strata are very different.

Procedures That Support Bootstrapping

The following procedures support bootstrapping.

Note:Bootstrapping does not work with multiply imputed datasets. If there is an Imputation_variable in the dataset, the Bootstrap dialog is disabled.Bootstrapping uses listwise deletion to determine the case basis; that is, cases with missingvalues on any of the analysis variables are deleted from the analysis, so when bootstrappingis in effect, listwise deletion is in effect even if the analysis procedure specifies anotherform of missing value handling.

Statistics Base Option

Frequencies

The Statistics table supports bootstrap estimates for the mean, standard deviation, variance,median, skewness, kurtosis, and percentiles.The Frequencies table supports bootstrap estimates for percent.

Descriptives

The Descriptive Statistics table supports bootstrap estimates for the mean, standard deviation,variance, skewness, and kurtosis.

Explore

The Descriptives table supports bootstrap estimates for the mean, 5% Trimmed Mean,standard deviation, variance, median, skewness, kurtosis, and interquartile range.The M-Estimators table supports bootstrap estimates for Huber’s M-Estimator, Tukey’sBiweight, Hampel’s M-Estimator, and Andrew’s Wave.The Percentiles table supports bootstrap estimates for percentiles.

Crosstabs

The Directional Measures table supports bootstrap estimates for Lambda, Goodman andKruskal Tau, Uncertainty Coefficient, and Somers’ d.The Symmetric Measures table supports bootstrap estimates for Phi, Cramer’s V, ContingencyCoefficient, Kendall’s tau-b, Kendall’s tau-c, Gamma, Spearman Correlation, and Pearson’s R.The Risk Estimate table supports bootstrap estimates for the odds ratio.The Mantel-Haenszel Common Odds Ratio table supports bootstrap estimates and significancetests for ln(Estimate).

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Means

The Report table supports bootstrap estimates for the mean, median, grouped median, standarddeviation, variance, kurtosis, skewness, harmonic mean, and geometric mean.

One-Sample T Test

The Statistics table supports bootstrap estimates for the mean and standard deviation.The Test table supports bootstrap estimates and significance tests for the mean difference.

Independent-Samples T Test

The Group Statistics table supports bootstrap estimates for the mean and standard deviation.The Test table supports bootstrap estimates and significance tests for the mean difference.

Paired-Samples T Test

The Statistics table supports bootstrap estimates for the mean and standard deviation.The Correlations table supports bootstrap estimates for correlations.The Test table supports bootstrap estimates for the mean.

One-Way ANOVA

The Descriptive Statistics table supports bootstrap estimates for the mean and standarddeviation.The Multiple Comparisons table supports bootstrap estimates for the mean difference.The Contrast Tests table supports bootstrap estimates and significance tests for value ofcontrast.

GLM Univariate

The Descriptive Statistics table supports bootstrap estimates for the Mean and standarddeviation.The Parameter Estimates table supports bootstrap estimates and significance tests for thecoefficient, B.The Contrast Results table supports bootstrap estimates and significance tests for thedifference.The Estimated Marginal Means: Estimates table supports bootstrap estimates for the mean.The Estimated Marginal Means: Pairwise Comparisons table supports bootstrap estimatesfor the mean difference.The Post Hoc Tests: Multiple Comparisons table supports bootstrap estimates for the MeanDifference.

Bivariate Correlations

The Descriptive Statistics table supports bootstrap estimates for the mean and standarddeviation.The Correlations table supports bootstrap estimates for correlations.

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Bootstrapping

Note: If nonparametric correlations (Kendall’s tau-b or Spearman) are requested in addition toPearson correlations, the dialog pastes CORRELATIONS and NONPAR CORR commands with aseparate BOOTSTRAP command for each. The same bootstrap samples will be used to computeall correlations.

Partial Correlations

The Descriptive Statistics table supports bootstrap estimates for the mean and standarddeviation.The Correlations table supports bootstrap estimates for correlations.

Linear Regression

The Descriptive Statistics table supports bootstrap estimates for the mean and standarddeviation.The Correlations table supports bootstrap estimates for correlations.The Model Summary table supports bootstrap estimates for Durbin-Watson.The Coefficients table supports bootstrap estimates and significance tests for the coefficient, B.The Correlation Coefficients table supports bootstrap estimates for correlations.The Residuals Statistics table supports bootstrap estimates for the mean and standard deviation.

Ordinal Regression

The Parameter Estimates table supports bootstrap estimates and significance tests for thecoefficient, B.

Discriminant Analysis

The Standardized Canonical Discriminant Function Coefficients table supports bootstrapestimates for standardized coefficients.The Canonical Discriminant Function Coefficients table supports bootstrap estimates forunstandardized coefficients.The Classification Function Coefficients table supports bootstrap estimates for coefficients.

Advanced Statistics Option

GLM Multivariate


Linear Mixed Models

The Estimates of Fixed Effects table supports bootstrap estimates and significance tests for theestimate.The Estimates of Covariance Parameters table supports bootstrap estimates and significancetests for the estimate.

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Generalized Linear Models


Cox Regression

The Variables in the Equation table supports bootstrap estimates and significance tests for thecoefficient, B.

Regression Option

Binary Logistic Regression

The Variables in the Equation table supports bootstrap estimates and significance tests for thecoefficient, B.

Multinomial Logistic Regression


BOOTSTRAP Command Additional Features

The command syntax language also allows you to:Perform residual and wild bootstrap sampling (SAMPLING subcommand)

See the Command Syntax Reference for complete syntax information.

Part II:Examples

Chapter

3Bootstrapping

Bootstrapping is a method for deriving robust estimates of standard errors and confidenceintervals for estimates such as the mean, median, proportion, odds ratio, correlation coefficientor regression coefficient. It may also be used for constructing hypothesis tests. Bootstrappingis most useful as an alternative to parametric estimates when the assumptions of those methodsare in doubt (as in the case of regression models with heteroscedastic residuals fit to smallsamples), or where parametric inference is impossible or requires very complicated formulas forthe calculation of standard errors (as in the case of computing confidence intervals for the median,quartiles, and other percentiles).

Using Bootstrapping to Obtain Confidence Intervals for Proportions

A telecommunications firm loses about 27% of its customers to churn each month. In order toproperly focus churn reduction efforts, management wants to know if this percentage variesacross predefined customer groups.This information is collected in telco.sav. For more information, see the topic Sample Files in

Appendix A on p. 30. Use bootstrapping to determine whether a single rate of churn adequatelydescribes the four major customer types.

Note: This example uses the Frequencies procedure and requires the Statistics Base option.

Preparing the Data

You must first split the file by Customer category.

E To split the file, from the Data Editor menus choose:Data > Split File...


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Figure 3-1Split File dialog box

E Select Compare groups.

E Select Customer category as the variable on which to base groups.

E Click OK.

Running the Analysis

E To obtain bootstrap confidence intervals for proportions, from the menus choose:Analyze > Descriptive Statistics > Frequencies...

Figure 3-2Frequencies main dialog

E Select Churn within last month [churn] as a variable in the analysis.

E Click Statistics.

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Figure 3-3Statistics dialog box

E Select Mean in the Central Tendency group.

E Click Continue.

E Click Bootstrap in the Frequencies dialog box.

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E To replicate the results in this example exactly, select Set seed for Mersenne Twister and type9191972 as the seed.

E Click Continue.

E Click OK in the Frequencies dialog box.

These selections generate the following command syntax:

SORT CASES BY custcat.SPLIT FILE LAYERED BY custcat.PRESERVE.SET RNG=MT MTINDEX=9191972.SHOW RNG.BOOTSTRAP

/SAMPLING METHOD=SIMPLE/VARIABLES INPUT=churn/CRITERIA CILEVEL=95 CITYPE=PERCENTILE NSAMPLES=1000/MISSING USERMISSING=EXCLUDE.

FREQUENCIES VARIABLES=churn/STATISTICS=MEAN/ORDER=ANALYSIS.

RESTORE.

The SORT CASES and SPLIT FILE commands split the file on the variable custcat.

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The PRESERVE and RESTORE commands “remember” the current state of the random numbergenerator and restore the system to that state after bootstrapping is over.The SET command sets the random number generator to the Mersenne Twister and the indexto 9191972, so that the bootstrapping results can be replicated exactly. The SHOW commanddisplays the index in the output for reference.The BOOTSTRAP command requests 1,000 bootstrap samples using simple resampling.The variable churn is used to determine the case basis for resampling. Records with missingvalues on this variable are deleted from the analysis.The FREQUENCIES procedure following BOOTSTRAP is run on each of the bootstrapsamples.The STATISTICS subcommand produces the mean for variable churn on the original data.Additionally, pooled statistics are produced for the mean and the percentages in the frequencytable.

Bootstrap SpecificationsFigure 3-5Bootstrap specifications

The bootstrap specifications table contains the settings used during resampling, and is a usefulreference for checking whether the analysis you intended was performed.

StatisticsFigure 3-6Statistics table with bootstrap confidence interval for proportion

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The statistics table shows, for each level of Customer category, the mean value for Churn withinlast month. Since Churn within last month only takes values 0 and 1, with 1 signifying a customerwho churned, the mean is equal to the proportion of churners. The Statistic column shows thevalues usually produced by Frequencies, using the original dataset. The Bootstrap columns areproduced by the bootstrapping algorithms.

Bias is the difference between the average value of this statistic across the bootstrap samplesand the value in the Statistic column. In this case, the mean value of Churn within last monthis computed for all 1000 bootstrap samples, and the average of these means is then computed.Std. Error is the standard error of the mean value of Churn within last month across the1000 bootstrap samples.The lower bound of the 95% bootstrap confidence interval is an interpolation of the 25th and26th mean values of Churn within last month, if the 1000 bootstrap samples are sorted inascending order. The upper bound is an interpolation of the 975th and 976th mean values.

The results in the table suggest that the rate of churn differs across customer types. In particular,the confidence interval for Plus service customers does not overlap with any other, suggestingthese customers are, on average, less likely to leave.

When working with categorical variables with only two values, these confidence intervalsare alternatives to those produced by the One-Sample Nonparametric Tests procedure or theOne-Sample T Test procedure.

Frequency TableFigure 3-7Frequency table with bootstrap confidence interval for proportion

The Frequency table shows confidence intervals for the percentages (proportion × 100%) for eachcategory, and are thus available for all categorical variables. Comparable confidence intervals arenot available elsewhere in the product.

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Chapter 3

Using Bootstrapping to Obtain Confidence Intervals for Medians

In a review of employee records, management is interested in the previous work experience ofemployees. Work experience is right skewed, which makes the mean a less desirable estimate ofthe “typical” previous work experience among employees than the median. However, withoutbootstrapping, confidence intervals for the median are not generally available in statisticalprocedures in the product.This information is collected in Employee data.sav. For more information, see the topic Sample

Files in Appendix A on p. 30. Use Bootstrapping to obtain confidence intervals for the median.

Note: this example uses the Explore procedure, and requires the Statistics Base option.


E To obtain bootstrap confidence intervals for the median, from the menus choose:Analyze > Descriptive Statistics > Explore...

Figure 3-8Explore main dialog

E Select Previous Experience (months) [prevexp] as a dependent variable.

E Select Statistics in the Display group.

E Click Bootstrap.

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E To obtain more accurate intervals (at the cost of more processing time), select Bias corrected

accelerated (BCa).

E Click Continue.

E Click OK in the Explore dialog box.


PRESERVE.SET RNG=MT MTINDEX=592004.SHOW RNG.BOOTSTRAP

/SAMPLING METHOD=SIMPLE/VARIABLES TARGET=prevexp/CRITERIA CILEVEL=95 CITYPE=BCA NSAMPLES=1000/MISSING USERMISSING=EXCLUDE.

EXAMINE VARIABLES=prevexp/PLOT NONE/STATISTICS DESCRIPTIVES/CINTERVAL 95/MISSING LISTWISE/NOTOTAL.

RESTORE.

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The PRESERVE and RESTORE commands “remember” the current state of the random numbergenerator and restore the system to that state after bootstrapping is over.The SET command sets the random number generator to the Mersenne Twister and the indexto 592004, so that the bootstrapping results can be replicated exactly. The SHOW commanddisplays the index in the output for reference.The BOOTSTRAP command requests 1000 bootstrap samples using simple resampling.The VARIABLES subcommand specifies that the variable prevexp is used to determine thecase basis for resampling. Records with missing values on this variable are deleted from theanalysis.The CRITERIA subcommand, in addition to requesting the number of bootstrap samples,requests bias-corrected and accelerated bootstrap confidence intervals instead of the defaultpercentile intervals.The EXAMINE procedure following BOOTSTRAP is run on each of the bootstrap samples.The PLOT subcommand turns off plot output.All other options are set to their default values.

DescriptivesFigure 3-10Descriptives table with bootstrap confidence intervals

The descriptives table contains a number of statistics and bootstrap confidence intervals forthose statistics. The bootstrap confidence interval for the mean (86.39, 105.20) is similar to theparametric confidence interval (86.42, 105.30) and suggests that the “typical” employee hasroughly 7-9 years of previous experience. However, Previous Experience (months) has a skeweddistribution, which makes the mean a less desirable indicator of “typical” current salary than themedian. The bootstrap confidence interval for the median (50.00, 60.00) is both narrower andlower in value than the confidence interval for the mean, and suggests that the “typical” employeehas roughly 4-5 years of previous experience. Using bootstrapping has made it possible to obtaina range of values that better represent typical previous experience.

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Bootstrapping

Using Bootstrapping to Choose Better Predictors

In a review of employee records, management is interested in determining what factors areassociated with employee salary increases by fitting a linear model to the difference betweencurrent and starting salaries. When bootstrapping a linear model, you can use special resamplingmethods (residual and wild bootstrap) to obtain more accurate results.This information is collected in Employee data.sav. For more information, see the topic

Sample Files in Appendix A on p. 30.

Note: this example uses the GLM Univariate procedure, and requires the Statistics Base option.

Preparing the Data

You must first compute the difference between Current salary and Beginning salary.

E From the menus choose:Transform > Compute Variable...

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Figure 3-11Compute Variable dialog box

E Type diff as the target variable..

E Type salary-salbegin as the numeric expression.

E Click OK.


To run GLM Univariate with wild residual bootstrapping, you first need to create residuals.

E From the menus choose:Analyze > General Linear Model > Univariate...

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Figure 3-12GLM Univariate main dialog

E Select diff as the dependent variable.

E Select Gender [gender], Employment Category [jobcat], and Minority Classification [minority]as fixed factors.

E SelectMonths since Hire [jobtime] and Previous Experience (months) [prevexp] as covariates.

E Click Model.

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Figure 3-13Model dialog box

E Select Custom and select Main effects from the Build Terms dropdown.

E Select gender through prevexp as model terms.

E Click Continue.

E Click Save in the GLM Univariate dialog box.

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Figure 3-14Save dialog box

E Select Unstandardized in the Residuals group.

E Click Continue.

E Click Bootstrap in the GLM Univariate dialog box.

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The bootstrap settings persist across dialogs that support bootstrapping. Saving new variablesto the dataset is not supported while bootstrapping is in effect, so you need to make certain it isturned off.

E If needed deselect Perform bootstrapping.

E Click OK in the GLM Univariate dialog box. The dataset now contains a new variable, RES_1,which contains the unstandardized residuals from this model.

E Recall the GLM Univariate dialog and click Save.

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E Deselect Unstandardized, then click Continue and click Options in the GLM Univariate dialog box.

Figure 3-16Options dialog box

E Select Parameter estimates in the Display group.

E Click Continue.

E Click Bootstrap in the GLM Univariate dialog box.

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E There are no options for performing wild bootstrapping through the dialogs, so click Continue,then click Paste in the GLM Univariate dialog box.



/SAMPLING METHOD=SIMPLE/VARIABLES TARGET=diff INPUT=gender jobcat minority jobtime prevexp/CRITERIA CILEVEL=95 CITYPE=PERCENTILE NSAMPLES=1000/MISSING USERMISSING=EXCLUDE.

UNIANOVA diff BY gender jobcat minority WITH jobtime prevexp/METHOD=SSTYPE(3)/INTERCEPT=INCLUDE/PRINT=PARAMETER/CRITERIA=ALPHA(.05)/DESIGN=gender jobcat minority jobtime prevexp.

RESTORE.

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Bootstrapping

In order to perform wild bootstrap sampling, edit the METHOD keyword of the SAMPLINGsubcommand to read METHOD=WILD(RESIDUALS=RES_1).

The “final” set of command syntax looks like the following:


/SAMPLING METHOD=WILD(RESIDUALS=RES_1)/VARIABLES TARGET=diff INPUT=gender jobcat minority jobtime prevexp/CRITERIA CILEVEL=95 CITYPE=PERCENTILE NSAMPLES=1000/MISSING USERMISSING=EXCLUDE.

UNIANOVA diff BY gender jobcat minority WITH jobtime prevexp/METHOD=SSTYPE(3)/INTERCEPT=INCLUDE/PRINT=PARAMETER/CRITERIA=ALPHA(.05)/DESIGN=gender jobcat minority jobtime prevexp.

RESTORE.

The PRESERVE and RESTORE commands “remember” the current state of the random numbergenerator and restore the system to that state after bootstrapping is over.The SET command sets the random number generator to the Mersenne Twister and the indexto 9191972, so that the bootstrapping results can be replicated exactly. The SHOW commanddisplays the index in the output for reference.The BOOTSTRAP command requests 1000 bootstrap samples using wild sampling and RES_1as the variable containing the residuals.The VARIABLES subcommand specifies that the diff is the target variable in the linear model;it and the variables gender, jobcat, minority, jobtime, and prevexp are used to determine thecase basis for resampling. Records with missing values on these variables are deleted fromthe analysis.The CRITERIA subcommand, in addition to requesting the number of bootstrap samples,requests bias-corrected and accelerated bootstrap confidence intervals instead of the defaultpercentile intervals.The UNIANOVA procedure following BOOTSTRAP is run on each of the bootstrap samplesand produces parameter estimates for the original data. Additionally, pooled statistics areproduced for the model coefficients.

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Parameter EstimatesFigure 3-18Parameter estimates

The Parameter Estimates table shows the usual, non-bootstrapped, parameter estimates for themodel terms. The significance value of 0.105 for [minority=0] is greater than 0.05, suggestingthat Minority Classification has no effect on salary increases.

Figure 3-19Bootstrap parameter estimates

Now look at the Bootstrap for Parameter Estimates table. In the Std. Error column, you see thatthe parametric standard errors for some coefficients, like the intercept, are too small comparedto the bootstrap estimates, and thus the confidence intervals are wider. For some coefficients,like [minority=0], the parametric standard errors were too large, while the significance valueof 0.006 reported by the bootstrap results, which is less than 0.05, shows that the observeddifference in salary increases between employees who are and are not minorities is not due tochance. Management now knows this difference is worth investigating further to determinepossible causes.

29

Bootstrapping

Recommended Readings

See the following texts for more information on bootstrapping:

Davison, A. C., and D. V. Hinkley. 2006. Bootstrap Methods and their Application. : CambridgeUniversity Press.

Shao, J., and D. Tu. 1995. The Jackknife and Bootstrap. New York: Springer.

Appendix

ASample Files

The sample files installed with the product can be found in the Samples subdirectory of theinstallation directory. There is a separate folder within the Samples subdirectory for each ofthe following languages: English, French, German, Italian, Japanese, Korean, Polish, Russian,Simplified Chinese, Spanish, and Traditional Chinese.

Not all sample files are available in all languages. If a sample file is not available in a language,that language folder contains an English version of the sample file.

Descriptions

Following are brief descriptions of the sample files used in various examples throughout thedocumentation.

accidents.sav. This is a hypothetical data file that concerns an insurance company that isstudying age and gender risk factors for automobile accidents in a given region. Each casecorresponds to a cross-classification of age category and gender.adl.sav. This is a hypothetical data file that concerns efforts to determine the benefits of aproposed type of therapy for stroke patients. Physicians randomly assigned female strokepatients to one of two groups. The first received the standard physical therapy, and the secondreceived an additional emotional therapy. Three months following the treatments, eachpatient’s abilities to perform common activities of daily life were scored as ordinal variables.advert.sav. This is a hypothetical data file that concerns a retailer’s efforts to examine therelationship between money spent on advertising and the resulting sales. To this end, theyhave collected past sales figures and the associated advertising costs..aflatoxin.sav. This is a hypothetical data file that concerns the testing of corn crops foraflatoxin, a poison whose concentration varies widely between and within crop yields. A grainprocessor has received 16 samples from each of 8 crop yields and measured the alfatoxinlevels in parts per billion (PPB).anorectic.sav. While working toward a standardized symptomatology of anorectic/bulimicbehavior, researchers (Van der Ham, Meulman, Van Strien, and Van Engeland, 1997) made astudy of 55 adolescents with known eating disorders. Each patient was seen four times overfour years, for a total of 220 observations. At each observation, the patients were scored foreach of 16 symptoms. Symptom scores are missing for patient 71 at time 2, patient 76 at time2, and patient 47 at time 3, leaving 217 valid observations.bankloan.sav. This is a hypothetical data file that concerns a bank’s efforts to reduce therate of loan defaults. The file contains financial and demographic information on 850 pastand prospective customers. The first 700 cases are customers who were previously given


31

Sample Files

loans. The last 150 cases are prospective customers that the bank needs to classify as goodor bad credit risks.bankloan_binning.sav. This is a hypothetical data file containing financial and demographicinformation on 5,000 past customers.behavior.sav. In a classic example (Price and Bouffard, 1974), 52 students were asked torate the combinations of 15 situations and 15 behaviors on a 10-point scale ranging from0=“extremely appropriate” to 9=“extremely inappropriate.” Averaged over individuals, thevalues are taken as dissimilarities.behavior_ini.sav. This data file contains an initial configuration for a two-dimensional solutionfor behavior.sav.brakes.sav. This is a hypothetical data file that concerns quality control at a factory thatproduces disc brakes for high-performance automobiles. The data file contains diametermeasurements of 16 discs from each of 8 production machines. The target diameter for thebrakes is 322 millimeters.breakfast.sav. In a classic study (Green and Rao, 1972), 21 Wharton School MBA studentsand their spouses were asked to rank 15 breakfast items in order of preference with 1=“mostpreferred” to 15=“least preferred.” Their preferences were recorded under six differentscenarios, from “Overall preference” to “Snack, with beverage only.”breakfast-overall.sav. This data file contains the breakfast item preferences for the firstscenario, “Overall preference,” only.broadband_1.sav. This is a hypothetical data file containing the number of subscribers, byregion, to a national broadband service. The data file contains monthly subscriber numbersfor 85 regions over a four-year period.broadband_2.sav. This data file is identical to broadband_1.sav but contains data for threeadditional months.car_insurance_claims.sav. A dataset presented and analyzed elsewhere (McCullagh andNelder, 1989) concerns damage claims for cars. The average claim amount can be modeledas having a gamma distribution, using an inverse link function to relate the mean of thedependent variable to a linear combination of the policyholder age, vehicle type, and vehicleage. The number of claims filed can be used as a scaling weight.car_sales.sav. This data file contains hypothetical sales estimates, list prices, and physicalspecifications for various makes and models of vehicles. The list prices and physicalspecifications were obtained alternately from edmunds.com and manufacturer sites.car_sales_uprepared.sav. This is a modified version of car_sales.sav that does not include anytransformed versions of the fields.carpet.sav. In a popular example (Green and Wind, 1973), a company interested inmarketing a new carpet cleaner wants to examine the influence of five factors on consumerpreference—package design, brand name, price, a Good Housekeeping seal, and amoney-back guarantee. There are three factor levels for package design, each one differing inthe location of the applicator brush; three brand names (K2R, Glory, and Bissell); three pricelevels; and two levels (either no or yes) for each of the last two factors. Ten consumers rank22 profiles defined by these factors. The variable Preference contains the rank of the averagerankings for each profile. Low rankings correspond to high preference. This variable reflectsan overall measure of preference for each profile.

32

Appendix A

carpet_prefs.sav. This data file is based on the same example as described for carpet.sav, but itcontains the actual rankings collected from each of the 10 consumers. The consumers wereasked to rank the 22 product profiles from the most to the least preferred. The variablesPREF1 through PREF22 contain the identifiers of the associated profiles, as defined incarpet_plan.sav.catalog.sav. This data file contains hypothetical monthly sales figures for three products soldby a catalog company. Data for five possible predictor variables are also included.catalog_seasfac.sav. This data file is the same as catalog.sav except for the addition of a setof seasonal factors calculated from the Seasonal Decomposition procedure along with theaccompanying date variables.cellular.sav. This is a hypothetical data file that concerns a cellular phone company’s effortsto reduce churn. Churn propensity scores are applied to accounts, ranging from 0 to 100.Accounts scoring 50 or above may be looking to change providers.ceramics.sav. This is a hypothetical data file that concerns a manufacturer’s efforts todetermine whether a new premium alloy has a greater heat resistance than a standard alloy.Each case represents a separate test of one of the alloys; the heat at which the bearing failed isrecorded.cereal.sav. This is a hypothetical data file that concerns a poll of 880 people about theirbreakfast preferences, also noting their age, gender, marital status, and whether or not theyhave an active lifestyle (based on whether they exercise at least twice a week). Each caserepresents a separate respondent.clothing_defects.sav. This is a hypothetical data file that concerns the quality control processat a clothing factory. From each lot produced at the factory, the inspectors take a sample ofclothes and count the number of clothes that are unacceptable.coffee.sav. This data file pertains to perceived images of six iced-coffee brands (Kennedy,Riquier, and Sharp, 1996) . For each of 23 iced-coffee image attributes, people selected allbrands that were described by the attribute. The six brands are denoted AA, BB, CC, DD, EE,and FF to preserve confidentiality.contacts.sav. This is a hypothetical data file that concerns the contact lists for a group ofcorporate computer sales representatives. Each contact is categorized by the department ofthe company in which they work and their company ranks. Also recorded are the amount ofthe last sale made, the time since the last sale, and the size of the contact’s company.creditpromo.sav. This is a hypothetical data file that concerns a department store’s efforts toevaluate the effectiveness of a recent credit card promotion. To this end, 500 cardholders wererandomly selected. Half received an ad promoting a reduced interest rate on purchases madeover the next three months. Half received a standard seasonal ad.customer_dbase.sav. This is a hypothetical data file that concerns a company’s efforts to usethe information in its data warehouse to make special offers to customers who are mostlikely to reply. A subset of the customer base was selected at random and given the specialoffers, and their responses were recorded.customer_information.sav. A hypothetical data file containing customer mailing information,such as name and address.customer_subset.sav. A subset of 80 cases from customer_dbase.sav.

33

Sample Files

debate.sav. This is a hypothetical data file that concerns paired responses to a survey fromattendees of a political debate before and after the debate. Each case corresponds to a separaterespondent.debate_aggregate.sav. This is a hypothetical data file that aggregates the responses indebate.sav. Each case corresponds to a cross-classification of preference before and afterthe debate.demo.sav. This is a hypothetical data file that concerns a purchased customer database, forthe purpose of mailing monthly offers. Whether or not the customer responded to the offeris recorded, along with various demographic information.demo_cs_1.sav. This is a hypothetical data file that concerns the first step of a company’sefforts to compile a database of survey information. Each case corresponds to a different city,and the region, province, district, and city identification are recorded.demo_cs_2.sav. This is a hypothetical data file that concerns the second step of a company’sefforts to compile a database of survey information. Each case corresponds to a differenthousehold unit from cities selected in the first step, and the region, province, district, city,subdivision, and unit identification are recorded. The sampling information from the firsttwo stages of the design is also included.demo_cs.sav. This is a hypothetical data file that contains survey information collected using acomplex sampling design. Each case corresponds to a different household unit, and variousdemographic and sampling information is recorded.dmdata.sav. This is a hypothetical data file that contains demographic and purchasinginformation for a direct marketing company. dmdata2.sav contains information for a subset ofcontacts that received a test mailing, and dmdata3.sav contains information on the remainingcontacts who did not receive the test mailing.dietstudy.sav. This hypothetical data file contains the results of a study of the “Stillman diet”(Rickman, Mitchell, Dingman, and Dalen, 1974). Each case corresponds to a separatesubject and records his or her pre- and post-diet weights in pounds and triglyceride levelsin mg/100 ml.dvdplayer.sav. This is a hypothetical data file that concerns the development of a new DVDplayer. Using a prototype, the marketing team has collected focus group data. Each casecorresponds to a separate surveyed user and records some demographic information aboutthem and their responses to questions about the prototype.german_credit.sav. This data file is taken from the “German credit” dataset in the Repository ofMachine Learning Databases (Blake and Merz, 1998) at the University of California, Irvine.grocery_1month.sav. This hypothetical data file is the grocery_coupons.sav data file with theweekly purchases “rolled-up” so that each case corresponds to a separate customer. Some ofthe variables that changed weekly disappear as a result, and the amount spent recorded is nowthe sum of the amounts spent during the four weeks of the study.grocery_coupons.sav. This is a hypothetical data file that contains survey data collected bya grocery store chain interested in the purchasing habits of their customers. Each customeris followed for four weeks, and each case corresponds to a separate customer-week andrecords information about where and how the customer shops, including how much wasspent on groceries during that week.

34

Appendix A

guttman.sav. Bell (Bell, 1961) presented a table to illustrate possible social groups. Guttman(Guttman, 1968) used a portion of this table, in which five variables describing such thingsas social interaction, feelings of belonging to a group, physical proximity of members, andformality of the relationship were crossed with seven theoretical social groups, includingcrowds (for example, people at a football game), audiences (for example, people at a theateror classroom lecture), public (for example, newspaper or television audiences), mobs (like acrowd but with much more intense interaction), primary groups (intimate), secondary groups(voluntary), and the modern community (loose confederation resulting from close physicalproximity and a need for specialized services).health_funding.sav. This is a hypothetical data file that contains data on health care funding(amount per 100 population), disease rates (rate per 10,000 population), and visits to healthcare providers (rate per 10,000 population). Each case represents a different city.hivassay.sav. This is a hypothetical data file that concerns the efforts of a pharmaceuticallab to develop a rapid assay for detecting HIV infection. The results of the assay are eightdeepening shades of red, with deeper shades indicating greater likelihood of infection. Alaboratory trial was conducted on 2,000 blood samples, half of which were infected withHIV and half of which were clean.hourlywagedata.sav. This is a hypothetical data file that concerns the hourly wages of nursesfrom office and hospital positions and with varying levels of experience.insurance_claims.sav. This is a hypothetical data file that concerns an insurance company thatwants to build a model for flagging suspicious, potentially fraudulent claims. Each caserepresents a separate claim.insure.sav. This is a hypothetical data file that concerns an insurance company that is studyingthe risk factors that indicate whether a client will have to make a claim on a 10-year termlife insurance contract. Each case in the data file represents a pair of contracts, one of whichrecorded a claim and the other didn’t, matched on age and gender.judges.sav. This is a hypothetical data file that concerns the scores given by trained judges(plus one enthusiast) to 300 gymnastics performances. Each row represents a separateperformance; the judges viewed the same performances.kinship_dat.sav. Rosenberg and Kim (Rosenberg and Kim, 1975) set out to analyze 15 kinshipterms (aunt, brother, cousin, daughter, father, granddaughter, grandfather, grandmother,grandson, mother, nephew, niece, sister, son, uncle). They asked four groups of collegestudents (two female, two male) to sort these terms on the basis of similarities. Two groups(one female, one male) were asked to sort twice, with the second sorting based on a differentcriterion from the first sort. Thus, a total of six “sources” were obtained. Each sourcecorresponds to a proximity matrix, whose cells are equal to the number of people in asource minus the number of times the objects were partitioned together in that source.kinship_ini.sav. This data file contains an initial configuration for a three-dimensional solutionfor kinship_dat.sav.kinship_var.sav. This data file contains independent variables gender, gener(ation), and degree(of separation) that can be used to interpret the dimensions of a solution for kinship_dat.sav.Specifically, they can be used to restrict the space of the solution to a linear combination ofthese variables.marketvalues.sav. This data file concerns home sales in a new housing development inAlgonquin, Ill., during the years from 1999–2000. These sales are a matter of public record.

35

Sample Files

nhis2000_subset.sav. The National Health Interview Survey (NHIS) is a large, population-basedsurvey of the U.S. civilian population. Interviews are carried out face-to-face in a nationallyrepresentative sample of households. Demographic information and observations abouthealth behaviors and status are obtained for members of each household. This datafile contains a subset of information from the 2000 survey. National Center for HealthStatistics. National Health Interview Survey, 2000. Public-use data file and documentation.ftp://ftp.cdc.gov/pub/Health_Statistics/NCHS/Datasets/NHIS/2000/. Accessed 2003.ozone.sav. The data include 330 observations on six meteorological variables for predictingozone concentration from the remaining variables. Previous researchers (Breiman andFriedman, 1985), (Hastie and Tibshirani, 1990), among others found nonlinearities amongthese variables, which hinder standard regression approaches.pain_medication.sav. This hypothetical data file contains the results of a clinical trial foranti-inflammatory medication for treating chronic arthritic pain. Of particular interest is thetime it takes for the drug to take effect and how it compares to an existing medication.patient_los.sav. This hypothetical data file contains the treatment records of patients who wereadmitted to the hospital for suspected myocardial infarction (MI, or “heart attack”). Each casecorresponds to a separate patient and records many variables related to their hospital stay.patlos_sample.sav. This hypothetical data file contains the treatment records of a sampleof patients who received thrombolytics during treatment for myocardial infarction (MI, or“heart attack”). Each case corresponds to a separate patient and records many variablesrelated to their hospital stay.poll_cs.sav. This is a hypothetical data file that concerns pollsters’ efforts to determine thelevel of public support for a bill before the legislature. The cases correspond to registeredvoters. Each case records the county, township, and neighborhood in which the voter lives.poll_cs_sample.sav. This hypothetical data file contains a sample of the voters listed inpoll_cs.sav. The sample was taken according to the design specified in the poll.csplan planfile, and this data file records the inclusion probabilities and sample weights. Note, however,that because the sampling plan makes use of a probability-proportional-to-size (PPS) method,there is also a file containing the joint selection probabilities (poll_jointprob.sav). Theadditional variables corresponding to voter demographics and their opinion on the proposedbill were collected and added the data file after the sample as taken.property_assess.sav. This is a hypothetical data file that concerns a county assessor’s efforts tokeep property value assessments up to date on limited resources. The cases correspond toproperties sold in the county in the past year. Each case in the data file records the townshipin which the property lies, the assessor who last visited the property, the time since thatassessment, the valuation made at that time, and the sale value of the property.property_assess_cs.sav. This is a hypothetical data file that concerns a state assessor’s effortsto keep property value assessments up to date on limited resources. The cases correspondto properties in the state. Each case in the data file records the county, township, andneighborhood in which the property lies, the time since the last assessment, and the valuationmade at that time.property_assess_cs_sample.sav. This hypothetical data file contains a sample of the propertieslisted in property_assess_cs.sav. The sample was taken according to the design specified inthe property_assess.csplan plan file, and this data file records the inclusion probabilities

36

Appendix A

and sample weights. The additional variable Current value was collected and added to thedata file after the sample was taken.recidivism.sav. This is a hypothetical data file that concerns a government law enforcementagency’s efforts to understand recidivism rates in their area of jurisdiction. Each casecorresponds to a previous offender and records their demographic information, some detailsof their first crime, and then the time until their second arrest, if it occurred within two yearsof the first arrest.recidivism_cs_sample.sav. This is a hypothetical data file that concerns a government lawenforcement agency’s efforts to understand recidivism rates in their area of jurisdiction. Eachcase corresponds to a previous offender, released from their first arrest during the month ofJune, 2003, and records their demographic information, some details of their first crime, andthe data of their second arrest, if it occurred by the end of June, 2006. Offenders were selectedfrom sampled departments according to the sampling plan specified in recidivism_cs.csplan;because it makes use of a probability-proportional-to-size (PPS) method, there is also a filecontaining the joint selection probabilities (recidivism_cs_jointprob.sav).rfm_transactions.sav. A hypothetical data file containing purchase transaction data, includingdate of purchase, item(s) purchased, and monetary amount of each transaction.salesperformance.sav. This is a hypothetical data file that concerns the evaluation of twonew sales training courses. Sixty employees, divided into three groups, all receive standardtraining. In addition, group 2 gets technical training; group 3, a hands-on tutorial. Eachemployee was tested at the end of the training course and their score recorded. Each case inthe data file represents a separate trainee and records the group to which they were assignedand the score they received on the exam.satisf.sav. This is a hypothetical data file that concerns a satisfaction survey conducted bya retail company at 4 store locations. 582 customers were surveyed in all, and each caserepresents the responses from a single customer.screws.sav. This data file contains information on the characteristics of screws, bolts, nuts,and tacks (Hartigan, 1975).shampoo_ph.sav. This is a hypothetical data file that concerns the quality control at a factoryfor hair products. At regular time intervals, six separate output batches are measured and theirpH recorded. The target range is 4.5–5.5.ships.sav. A dataset presented and analyzed elsewhere (McCullagh et al., 1989) that concernsdamage to cargo ships caused by waves. The incident counts can be modeled as occurring ata Poisson rate given the ship type, construction period, and service period. The aggregatemonths of service for each cell of the table formed by the cross-classification of factorsprovides values for the exposure to risk.site.sav. This is a hypothetical data file that concerns a company’s efforts to choose newsites for their expanding business. They have hired two consultants to separately evaluatethe sites, who, in addition to an extended report, summarized each site as a “good,” “fair,”or “poor” prospect.smokers.sav. This data file is abstracted from the 1998 National HouseholdSurvey of Drug Abuse and is a probability sample of American households.(http://dx.doi.org/10.3886/ICPSR02934) Thus, the first step in an analysis of this data fileshould be to weight the data to reflect population trends.

http://dx.doi.org/10.3886/ICPSR02934

37

Sample Files

stroke_clean.sav. This hypothetical data file contains the state of a medical database after ithas been cleaned using procedures in the Data Preparation option.stroke_invalid.sav. This hypothetical data file contains the initial state of a medical databaseand contains several data entry errors.stroke_survival. This hypothetical data file concerns survival times for patients exiting arehabilitation program post-ischemic stroke face a number of challenges. Post-stroke, theoccurrence of myocardial infarction, ischemic stroke, or hemorrhagic stroke is noted and thetime of the event recorded. The sample is left-truncated because it only includes patients whosurvived through the end of the rehabilitation program administered post-stroke.stroke_valid.sav. This hypothetical data file contains the state of a medical database after thevalues have been checked using the Validate Data procedure. It still contains potentiallyanomalous cases.survey_sample.sav. This data file contains survey data, including demographic data andvarious attitude measures. It is based on a subset of variables from the 1998 NORC GeneralSocial Survey, although some data values have been modified and additional fictitiousvariables have been added for demonstration purposes.telco.sav. This is a hypothetical data file that concerns a telecommunications company’sefforts to reduce churn in their customer base. Each case corresponds to a separate customerand records various demographic and service usage information.telco_extra.sav. This data file is similar to the telco.sav data file, but the “tenure” andlog-transformed customer spending variables have been removed and replaced bystandardized log-transformed customer spending variables.telco_missing.sav. This data file is a subset of the telco.sav data file, but some of thedemographic data values have been replaced with missing values.testmarket.sav. This hypothetical data file concerns a fast food chain’s plans to add a new itemto its menu. There are three possible campaigns for promoting the new product, so the newitem is introduced at locations in several randomly selected markets. A different promotionis used at each location, and the weekly sales of the new item are recorded for the first fourweeks. Each case corresponds to a separate location-week.testmarket_1month.sav. This hypothetical data file is the testmarket.sav data file with theweekly sales “rolled-up” so that each case corresponds to a separate location. Some of thevariables that changed weekly disappear as a result, and the sales recorded is now the sum ofthe sales during the four weeks of the study.tree_car.sav. This is a hypothetical data file containing demographic and vehicle purchaseprice data.tree_credit.sav. This is a hypothetical data file containing demographic and bank loan historydata.tree_missing_data.sav This is a hypothetical data file containing demographic and bank loanhistory data with a large number of missing values.tree_score_car.sav. This is a hypothetical data file containing demographic and vehiclepurchase price data.tree_textdata.sav. A simple data file with only two variables intended primarily to show thedefault state of variables prior to assignment of measurement level and value labels.

38

Appendix A

tv-survey.sav. This is a hypothetical data file that concerns a survey conducted by a TV studiothat is considering whether to extend the run of a successful program. 906 respondents wereasked whether they would watch the program under various conditions. Each row represents aseparate respondent; each column is a separate condition.ulcer_recurrence.sav. This file contains partial information from a study designed to comparethe efficacy of two therapies for preventing the recurrence of ulcers. It provides a goodexample of interval-censored data and has been presented and analyzed elsewhere (Collett,2003).ulcer_recurrence_recoded.sav. This file reorganizes the information in ulcer_recurrence.sav toallow you model the event probability for each interval of the study rather than simply theend-of-study event probability. It has been presented and analyzed elsewhere (Collett etal., 2003).verd1985.sav. This data file concerns a survey (Verdegaal, 1985). The responses of 15 subjectsto 8 variables were recorded. The variables of interest are divided into three sets. Set 1includes age and marital, set 2 includes pet and news, and set 3 includes music and live.Pet is scaled as multiple nominal and age is scaled as ordinal; all of the other variables arescaled as single nominal.virus.sav. This is a hypothetical data file that concerns the efforts of an Internet serviceprovider (ISP) to determine the effects of a virus on its networks. They have tracked the(approximate) percentage of infected e-mail traffic on its networks over time, from themoment of discovery until the threat was contained.wheeze_steubenville.sav. This is a subset from a longitudinal study of the health effects ofair pollution on children (Ware, Dockery, Spiro III, Speizer, and Ferris Jr., 1984). The datacontain repeated binary measures of the wheezing status for children from Steubenville, Ohio,at ages 7, 8, 9 and 10 years, along with a fixed recording of whether or not the mother wasa smoker during the first year of the study.workprog.sav. This is a hypothetical data file that concerns a government works programthat tries to place disadvantaged people into better jobs. A sample of potential programparticipants were followed, some of whom were randomly selected for enrollment in theprogram, while others were not. Each case represents a separate program participant.

Appendix

BNotices

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Patent No. 7,023,453

The following paragraph does not apply to the United Kingdom or any other country where suchprovisions are inconsistent with local law: SPSS INC., AN IBM COMPANY, PROVIDES THISPUBLICATION “AS IS” WITHOUT WARRANTY OF ANY KIND, EITHER EXPRESSOR IMPLIED, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIESOF NON-INFRINGEMENT, MERCHANTABILITY OR FITNESS FOR A PARTICULARPURPOSE. Some states do not allow disclaimer of express or implied warranties in certaintransactions, therefore, this statement may not apply to you.

This information could include technical inaccuracies or typographical errors. Changes areperiodically made to the information herein; these changes will be incorporated in new editions ofthe publication. SPSS Inc. may make improvements and/or changes in the product(s) and/or theprogram(s) described in this publication at any time without notice.

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COPYRIGHT LICENSE:

This information contains sample application programs in source language, which illustrateprogramming techniques on various operating platforms. You may copy, modify, and distributethese sample programs in any form without payment to SPSS Inc., for the purposes of developing,


40

Appendix B

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Collett, D. 2003. Modelling survival data in medical research, 2 ed. Boca Raton: Chapman &Hall/CRC.

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Green, P. E., and V. Rao. 1972. Applied multidimensional scaling. Hinsdale, Ill.: Dryden Press.

Green, P. E., and Y. Wind. 1973. Multiattribute decisions in marketing: A measurement approach.Hinsdale, Ill.: Dryden Press.

Guttman, L. 1968. A general nonmetric technique for finding the smallest coordinate space forconfigurations of points. Psychometrika, 33, 469–506.

Hartigan, J. A. 1975. Clustering algorithms. New York: John Wiley and Sons.

Hastie, T., and R. Tibshirani. 1990. Generalized additive models. London: Chapman and Hall.

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McCullagh, P., and J. A. Nelder. 1989. Generalized Linear Models, 2nd ed. London: Chapman &Hall.

Price, R. H., and D. L. Bouffard. 1974. Behavioral appropriateness and situational constraints asdimensions of social behavior. Journal of Personality and Social Psychology, 30, 579–586.

Rickman, R., N. Mitchell, J. Dingman, and J. E. Dalen. 1974. Changes in serum cholesterolduring the Stillman Diet. Journal of the American Medical Association, 228, 54–58.

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http://www.ics.uci.edu/%7Emlearn/MLRepository.html

Index

bootstrap specificationsin bootstrapping, 14

bootstrapping, 3, 10bootstrap specifications, 14confidence interval for median, 18confidence interval for proportion, 14–15parameter estimates, 28supported procedures, 5

confidence interval for medianin bootstrapping, 18

confidence interval for proportionin bootstrapping, 14–15

legal notices, 39

parameter estimatesin bootstrapping, 28

sample fileslocation, 30

trademarks, 40

42

IBM SPSS Bootstrapping 19

Documents

Transcript of IBM SPSS Bootstrapping 19