SPSS for Starters and 2nd Levelers / by Ton J. Cleophas, Aeilko H. Zwinderman.

Format:

Book

Author/Creator:

Cleophas, Ton J. M., author.

Zwinderman, Aeilko H., author.

Contributor:

SpringerLink (Online service)

Language:

English

Subjects (All):

Medicine.

Biometry.

Application software.

Statistics.

Physical Description:

1 online resource

Edition:

Second edition 2016.

Contained In:

Springer eBooks

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2016.

System Details:

Mode of Access: World Wide Web.

text file PDF

Summary:

For medical and health workers this book is a must-have, because statistical methods in these fields are vital, and no equivalent work is available. For medical and health students this is equally true. A unique point is its low threshold, textually simple and at the same time full of self-assessment opportunities. Other unique points are the succinctness of the chapters with 3 to 6 pages, the presence of entire-commands-texts of the statistical methodologies reviewed, and the fact that dull scientific texts imposing an unnecessary burden on busy and jaded professionals have been left out. For readers requesting more background, theoretical and mathematical information a note section with references is in each chapter. The first edition in 2010 was the first publication of a complete overview of SPSS methodologies for medical and health statistics. Well over 100,000 copies of various chapters were sold within the first year of publication. Reasons for a rewrite were four. First, many important comments from readers urged for a rewrite. Second, SPSS has produced many updates and upgrades, with relevant novel and improved methodologies. Third, the authors felt that the chapter texts needed some improvements for better readability: chapters have now been classified according the outcome data helpful for choosing your analysis rapidly, a schematic overview of data, and explanatory graphs have been added. Fourth, current data are increasingly complex and many important methods for analysis were missing in the first edition. For that latter purpose some more advanced methods seemed unavoidable, like hierarchical loglinear methods, gamma and Tweedie regressions, and random intercept analyses. In order for the contents of the book to remain covered by the title, the authors renamed the book: SPSS for Starters and 2nd Levelers. Special care was, nonetheless, taken to keep things as simple as possible. Medical and health professionals tend to dislike software syntax. Therefore, virtually no syntax, but, rather, simple menu commands are given. The arithmetic is still of a no-more-than high-school level. Step-by-step analyses of different statistical methodologies are given with the help of 60 SPSS data files available through the internet. Because of the lack of time of this busy group of people, the authors have given every effort to produce a text as succinct as possible. .

Contents:

Preface.- Introduction

I Continuous outcome data

One sample continuous data

Paired continuous outcome data normality assumed

Paired continuous outcome data nonnormality accounted

Paired continuous outcome data with predictors

Unpaired continuous outcome data normality assumed

Unpaired continuous outcome data nonnormality accounted

Linear regression for continuous outcome data

Recoding for categorical predictor data

Repeated-measures-analysis of variance normality assumed.- Repeated-measures-analysis of variance nonnormality accounted

Doubly-repeated-measures-analysis of variance

Multilevel modeling with mixed linear models. Random multilevel modeling with generalized mixed linear models

One-way-analysis of variance normality assumed

One-way-analysis of variance nonnormality accounted

Trend tests of continuous outcome data

Multistage regression

Multivariate analysis with path statistics

Multivariate analysis of variance.- Average-rank-testing for multiple outcome variables and categorical predictors

Missing data imputation

Meta-regression

Poisson regression including a weight variable (time of observation) for rates

Confounding

Interaction

Curvilinear analysis

Loess and spline modeling for nonlinear data, where curvilinear models lack fit

Monte Carlo analysis, the easy alternative for continuous outcome data

Artificial intelligence as a distribution free alternative for nonlinear data

Robust tests for d ata with large outliers

Nonnegative outcome data using the gamma distribution

Nonnegative outcome data with a big spike at zero using the Tweedie distribution

Polynomial analysis for continuous outcome data with a sinusoidal pattern

Validating quantitative diagnostic tests

Reliability assessment of quantitative diagnostic tests

II Binary outcome data

One sample binary data

Unpaired binary data

Binary logistic regression with a binary predictor

Binary logistic regression with categorical predictors

Binary logistic regression with a continuous predictor

Trend tests of binary data

Paired binary outcome data without predictors

Paired binary outcome data with predictors

Repeated measures binary data

Multinomial logistic regression for outcome categories

Multinomial logistic regression with random intercepts for both categorical outcome and predictor data

Comparing the performance of diagnostic tests

Poisson regression for binary outcome data

Loglinear models for the exploration of multidimensional contingency tables

Probit regression for binary outcome data reported as response rates

Monte Carlo analysis, the easy alternative for binary outcomes

Validating qualitative diagnostic tests

Reliability assessment of qualitative diagnostic tests. III Survival and longitudinal data

Log rank tests

Cox regression

Cox regression with time-dependent variables

Segmented Cox regression

Assessing seasonality

Probability assessment of survival with interval censored data analysis

Index.

Other Format:

Printed edition:

ISBN:

978-3-319-20600-4

9783319206004

Access Restriction:

Restricted for use by site license.