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SPSS 14.0 Statistical Procedures Companion
Marija Norusis

ISBN-10: 0131995278
ISBN-13: 9780131995277

Publisher: Prentice Hall
Copyright: 2006
Format: Paper Bound with Disk
Status: Out of Print

Suggested retail price: $63.60
This item is out of print and is no longer available for purchase.



The SPSS 14.0 Statistical Procedures Companion covers many of the more advanced statistical procedures in SPSS, which are not discussed in the SPSS 14.0 Guide to Data Analysis.  This book is intended as the continuation of the GDA.  The audience is no longer the beginning student but is instead the data analyst, either an advanced student or a professional. The Companion offers practical suggestions and emphasizes topics that arise when analyzing real data for presentations, reports, and dissertations, instead of the pristine data of homework assignments.  A data CD is included with this book.

For additional information, go to<http://www.norusis.com/>  This site offers a detailed Table of Contents, features, examples included in the book, and a sample chapter for download.


SPSS 14.0 Statistical Procedures Companion

 

Description:

You have all of the essential components: software with enough options to make a whirling dervish take pause; a data set of questionable virtue cast in a starring role; and a plot waiting to unfold. Directing this production to a satisfying conclusion isn't easy. The goal of the SPSS 14.0 Statistical Procedures Companion is to point you in the right direction.

 

Features:
  • Chapters start with concise overviews and examples of the use of the procedure
  • Tips and warnings help you to avoid common mistakes and work efficiently
  • Practical discussions explain the statistical background for each procedure
  • Instructions make it easy to obtain the output in the book
  • Examples are from diverse disciplines, including psychology, sociology, education, archaeology, medicine, library science, nursing and journalism
  • Reviewed by SPSS Inc. staff

SPSS 14.0 Statistical Procedures Companion: Chapters

1.       Introduction. An overview of the statistical procedures described in the book.

2.       Getting to Know SPSS. Tutorials; windows; dialog boxes; the Help system; Pivot Table Editor; Chart Editor; using command syntax.

3.       Introducing Data. Planning the data file; getting data into SPSS; the Text Wizard; creating new variables; selecting cases.

4.       Preparing Your Data. Checking variable definitions; case counts; data values.

5.       Transforming Your Data. Computing new variables; changing coding schemes; ranking.

6.       Describing Your Data. Taking a first look at your data with tables, charts, and descriptive statistics.

7.       Testing Hypotheses. Samples and populations; missing values; steps in testing a hypothesis; calculating confidence intervals; reporting your results correctly; commonly used tests for popular hypotheses.

8.       T-tests. One-sample, paired-samples, and independent-samples t-tests; data setups for the different t-tests; interpreting the output.

9.       One-way Analysis of Variance. Preparing the data file; examining the data; checking assumptions; interpreting the output; atoning for multiple comparisons; setting up contrasts.

10.   Crosstabulation. Chi-square tests; McNemar's test; measures of association and agreement; measures of risk; testing hypotheses about odds ratios.

11.   Correlation. Plotting the data; scatterplot matrices; correlation coefficients based on ranks; partial correlation coefficients; identifying points in a scatterplot.

12.   Bivariate Linear Regression. Least squares regression line; measures of fit; assumptions and transformations; looking for unusual points.

13.   Multiple Linear Regression. Formulating the problems; interpreting the coefficients; including categorical variables; comparing models; automated model building; checking for violations of assumptions; residuals; unusual observations.

14.   Discriminant Analysis. Calculating the functions; testing hypotheses; classifying cases into groups; automated model building; analyzing more than two groups; classification function coefficients.

15.   Logistic Regression Analysis. Basics of the model; predicted probabilities; coefficients; testing hypotheses; categorical variables; interaction terms; evaluating linearity; automated model building; diagnostics; model calibration; model discrimination; diagnostics for individual cases.

16.   Cluster Analysis. Hierarchical clustering; k-means clustering; two-step clustering; distance and similarity measures; interpreting the results.

17.   Factor Analysis. Basics of the model; determining the number of factors; goodness-of-fit tests; methods for factor extraction and rotation; computing factor scores.

18.   Reliability Analysis. Reliability coefficients: Cronbach's alpha, split-half reliability, Guttman's lower bounds; testing hypotheses about scales: parallel and strictly parallel models; Cochran's Q; intraclass correlation coefficients.

19.   Nonparametric Tests. One-sample tests: chi-square, binomial, runs; two related groups: sign test, Wilcoxon test; two independent groups: Wilcoxon, Wald-Wolfowitz runs test; three or more groups: Kruskal-Wallis, median, Friedman, Kendall's W, Cochran's Q.

20.   General Loglinear Analysis. Basic model; fitting a saturated model; fitting an unsaturated model; goodness-of-fit tests; models for ordinal data; incomplete tables; tests for square tables; Poisson regression; standardizing tables.

21.   Univariate General Linear Model. Regression; two-way ANOVA; randomized complete block design; randomized complete block design with empty cells; analysis of covariance; mixed effects nested designs; split-plot designs.

22.   Multivariate General Linear Model. Multivariate two-way fixed-effects model with interaction; profile analysis; setting up custom linear hypotheses.

23.   Repeated Measures Designs. Checking assumptions; testing hypotheses; doubly multivariate repeated measures analysis of variance.

 

    You have all of the essential components: software with enough options to make a whirling dervish take pause; a data set of questionable virtue cast in a starring role; and a plot waiting to unfold. Directing this production to a satisfying conclusion isn't easy. The goal of the SPSS 15.0 Statistical Procedures Companion is to point you in the right direction.

     

    See the SPSS 15.0 Advanced Statistical Procedures Companion for statistical introductions to some of the more advanced procedures in SPSS, including loglinear and logit analysis for categorical data,  multinomial, two-stage and weighted least-squares regression, Generalized Estimating Equations, Generalized Linear Model, Kaplan-Meier, actuarial and Cox models for analysis of time-to-event data, variance components analysis, and ALSCAL.


    For additional information, go to<http://www.norusis.com/>  This site offers a detailed Table of Contents, features, examples included in the book, and a sample chapter for download.
  • 0131594176SPSS 15.0 Statistical Procedures Companion
    Norusis
    © 2007 | Prentice Hall | Paper; 632 pages | Instock
    ISBN-10: 0131594176 | ISBN-13: 9780131594173
    Brief Description | Buy from myPearsonStore

    • SPSS® 16.0: Statistical Procedures Companion contains tips, warnings, and examples that will help you take advantage of SPSS to analyze data better. This book is a basic review of the underlying statistical concepts, with an emphasis on the practice of analyzing data. Ideal for both new and experienced users, this companion offers suggestions and strategies for handling the issues that arise when analyzing real data.

     

    SPSS 16.0: Statistical Procedures Companion covers all the statistical procedures in the Base system, the Regression Models, and Advanced Models modules. This book also contains introductory chapters on using the software, creating and cleaning data files, testing hypotheses, and describing data.

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    © 2009 | Prentice Hall | Paper; 648 pages | Instock
    ISBN-10: 0136061397 | ISBN-13: 9780136061397
    Brief Description | Buy from myPearsonStore

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