SPSS for Starters and 2nd Levelers

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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 i

mportant 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.

Author(s): Ton J. Cleophas, Aeilko H. Zwinderman (auth.)
Edition: 2
Publisher: Springer International Publishing
Year: 2016

Language: English
Pages: XXV, 375
Tags: Biomedicine general; Computer Applications; Biometrics; Statistical Theory and Methods; Statistics and Computing/Statistics Programs

Front Matter....Pages i-xxv
Front Matter....Pages 1-1
One-Sample Continuous Data (One-Sample T-Test, One-Sample Wilcoxon Signed Rank Test, 10 Patients)....Pages 3-6
Paired Continuous Data (Paired T-Test, Wilcoxon Signed Rank Test, 10 Patients)....Pages 7-10
Paired Continuous Data with Predictors (Generalized Linear Models, 50 Patients)....Pages 11-15
Unpaired Continuous Data (Unpaired T-Test, Mann-Whitney, 20 Patients)....Pages 17-21
Linear Regression (20 Patients)....Pages 23-28
Multiple Linear Regression (20 Patients)....Pages 29-34
Automatic Linear Regression (35 Patients)....Pages 35-40
Linear Regression with Categorical Predictors (60 Patients)....Pages 41-45
Repeated Measures Analysis of Variance, Friedman (10 Patients)....Pages 47-51
Repeated Measures Analysis of Variance Plus Predictors (10 Patients)....Pages 53-57
Doubly Repeated Measures Analysis of Variance (16 Patients)....Pages 59-65
Repeated Measures Mixed-Modeling (20 Patients)....Pages 67-73
Unpaired Continuous Data with Three or More Groups (One Way Analysis of Variance, Kruskal-Wallis, 30 Patients)....Pages 75-78
Automatic Nonparametric Testing (30 Patients)....Pages 79-84
Trend Test for Continuous Data (30 Patients)....Pages 85-88
Multistage Regression (35 Patients)....Pages 89-93
Multivariate Analysis with Path Statistics (35 Patients)....Pages 95-100
Multivariate Analysis of Variance (35 and 30 Patients)....Pages 101-107
Missing Data Imputation (35 Patients)....Pages 109-114
Meta-regression (20 and 9 Studies)....Pages 115-119
Front Matter....Pages 1-1
Poisson Regression for Outcome Rates (50 Patients)....Pages 121-125
Confounding (40 Patients)....Pages 127-133
Interaction, Random Effect Analysis of Variance (40 Patients)....Pages 135-141
General Loglinear Models for Identifying Subgroups with Large Health Risks (12 Populations)....Pages 143-149
Curvilinear Estimation (20 Patients)....Pages 151-157
Loess and Spline Modeling (90 Patients)....Pages 159-164
Monte Carlo Tests for Continuous Data (10 and 20 Patients)....Pages 165-169
Artificial Intelligence Using Distribution Free Data (90 Patients)....Pages 171-174
Robust Testing (33 Patients)....Pages 175-179
Nonnegative Outcomes Assessed with Gamma Distribution (110 Patients)....Pages 181-189
Nonnegative Outcomes Assessed with Tweedie Distribution (110 Patients)....Pages 191-197
Validating Quantitative Diagnostic Tests (17 Patients)....Pages 199-201
Reliability Assessment of Quantitative Diagnostic Tests (17 Patients)....Pages 203-205
Front Matter....Pages 207-207
One-Sample Binary Data (One-Sample Z-Test, Binomial Test, 55 Patients)....Pages 209-211
Unpaired Binary Data (Chi-Square Test, 55 Patients)....Pages 213-216
Logistic Regression with a Binary Predictor (55 Patients)....Pages 217-220
Logistic Regression with a Continuous Predictor (55 Patients)....Pages 221-223
Logistic Regression with Multiple Predictors (55 Patients)....Pages 225-228
Logistic Regression with Categorical Predictors (60 Patients)....Pages 229-231
Trend Tests for Binary Data (106 Patients)....Pages 233-237
Front Matter....Pages 207-207
Paired Binary (McNemar Test) (139 General Practitioners)....Pages 239-242
Paired Binary Data with Predictor (139 General Practitioners)....Pages 243-247
Repeated Measures Binary Data (Cochran’s Q Test), (139 Patients)....Pages 249-252
Multinomial Regression for Outcome Categories (55 Patients)....Pages 253-257
Random Intercept for Categorical Outcome and Predictor Variables (55 Patients)....Pages 259-264
Comparing the Performance of Diagnostic Tests (650 and 588 Patients)....Pages 265-272
Poisson Regression for Binary Outcomes (52 Patients)....Pages 273-277
Ordinal Regression for Data with Underpresented Outcome Categories (450 Patients)....Pages 279-285
Probit Regression, Binary Data as Response Rates (14 Tests)....Pages 287-296
Monte Carlo Tests for Binary Data (139 Physicians and 55 Patients)....Pages 297-301
Loglinear Models, Logit Loglinear Models (445 Patients)....Pages 303-309
Loglinear Models, Hierarchical Loglinear Models (445 Patients)....Pages 311-319
Validating Qualitative Diagnostic Tests (575 Patients)....Pages 321-326
Reliability Assessment of Qualitative Diagnostic Tests (17 Patients)....Pages 327-329
Front Matter....Pages 331-331
Log Rank Testing (60 Patients)....Pages 333-337
Cox Regression With/Without Time Dependent Variables (60 Patients)....Pages 339-346
Segmented Cox Regression (60 Patients)....Pages 347-351
Assessing Seasonality (24 Averages)....Pages 353-358
Interval Censored Data Analysis for Assessing Mean Time to Cancer Relapse (51 Patients)....Pages 359-363
Polynomial Analysis of Circadian Rhythms (1 Patient with Hypertension)....Pages 365-371
Back Matter....Pages 373-375