國立虎尾科技大學 |

SPSS for Starters and 2nd Levelers

Record Type:	Language materials, printed : Monograph/item
Title/Author:	SPSS for Starters and 2nd Levelers/ by Ton J. Cleophas, Aeilko H. Zwinderman.
Author:	Cleophas, Ton J.
other author:	Zwinderman, Aeilko H.
Description:	XXV, 375 p. 148 illus., 30 illus. in color.online resource. :
Contained By:	Springer Nature eBook
Subject:	Medicine. -
Online resource:	https://doi.org/10.1007/978-3-319-20600-4
ISBN:	9783319206004

SPSS for Starters and 2nd Levelers
Cleophas, Ton J.

SPSS for Starters and 2nd Levelers[electronic resource] /by Ton J. Cleophas, Aeilko H. Zwinderman. - 2nd ed. 2016. - XXV, 375 p. 148 illus., 30 illus. in color.online resource.

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 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.

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. .

ISBN: 9783319206004

Standard No.: 10.1007/978-3-319-20600-4doiSubjects--Topical Terms:

644133
Medicine.

LC Class. No.: R-RZ

Dewey Class. No.: 610

SPSS for Starters and 2nd Levelers
LDR:06866nam a22003975i 4500 001 982789
003 DE-He213
005 20200704082934.0
007 cr nn 008mamaa
008 201211s2016 gw | s |||| 0|eng d
020 $a 9783319206004 $9 978-3-319-20600-4
024 7 $a 10.1007/978-3-319-20600-4 $2 doi
035 $a 978-3-319-20600-4
050 4 $a R-RZ
072 7 $a MBGR $2 bicssc
072 7 $a MED000000 $2 bisacsh
072 7 $a MBGR $2 thema
082 0 4 $a 610 $2 23
100 1 $a Cleophas, Ton J. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 785606
245 1 0 $a SPSS for Starters and 2nd Levelers $h [electronic resource] / $c by Ton J. Cleophas, Aeilko H. Zwinderman.
250 $a 2nd ed. 2016.
264 1 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2016.
300 $a XXV, 375 p. 148 illus., 30 illus. in color. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
505 0 $a 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 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.
520 $a 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. .
650 0 $a Medicine. $3 644133
650 0 $a Application software. $3 528147
650 0 $a Biometrics (Biology). $3 1249740
650 0 $a Statistics . $3 1253516
650 1 4 $a Biomedicine, general. $3 1253757
650 2 4 $a Computer Applications. $3 669785
650 2 4 $a Biometrics. $3 677095
650 2 4 $a Statistical Theory and Methods. $3 671396
650 2 4 $a Statistics and Computing/Statistics Programs. $3 669775
700 1 $a Zwinderman, Aeilko H. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 785607
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9783319205991
776 0 8 $i Printed edition: $z 9783319206011
776 0 8 $i Printed edition: $z 9783319342504
856 4 0 $u https://doi.org/10.1007/978-3-319-20600-4
912 $a ZDB-2-SBL
912 $a ZDB-2-SXB
950 $a Biomedical and Life Sciences (SpringerNature-11642)
950 $a Biomedical and Life Sciences (R0) (SpringerNature-43708)