XWe have detected your location as outside the U.S/Canada, if you think this is wrong, you can choose your location.

Macmillan Higher Education Celebrating 20 years of Macmillan Study Skills

Cart

Continue Shopping
All prices are shown excluding Tax
The submitted promocode is invalid
* Applied promocode: ×

Important information on your ebook order

SPSS for Starters and 2nd Levelers (2nd Edition)

Author(s):
Publisher:

Springer

Pages: 375
Further Actions:

Recommend to library

AVAILABLE FORMATS

Paperback - 9783319342504

29 October 2016

$99.99

In stock

Hardcover - 9783319205991

23 October 2015

$109.99

Free Shipping

In stock

Ebook - 9783319206004

14 October 2015

$79.99

In stock

All prices are shown excluding Tax

A unique point of this book 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...

Show More

A unique point of this book 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, 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. 

Show Less

For medical and health workers it 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

A unique point is also the succinctness of the chapters with 3 to 6 pages

A unique point is also the entire-commands-texts of the statistical methodologies reviewed

A unique point is also, that dull scientific texts imposing an unnecessary burden on busy and jaded professionals have been left out

Preface.- Introduction
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 data 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.

Add a review

New Publications 

Best Sellers