SPSS for Starters, Part 2 [electronic resource] /
Cleophas, Ton J.author.
Zwinderman, Aeilko H.author.
The first part of this title contained all statistical tests that are relevant for starters on SPSS, and included standard parametric and non-parametric tests for continuous and binary variables, regression methods, trend tests, and reliability and validity assessments of diagnostic tests. The current part 2 of this title reviews multistep methods, multivariate models, assessments of missing data, performance of diagnostic tests, meta-regression, Poisson regression, confounding  and interaction, and survival analyses using log tests and segmented time-dependent Cox regression. Methods for assessing non linear models, data seasonality, distribution free methods, including Monte Carlo methods and artificial intelligence, and robust tests are also covered. Each method of testing is explained using a data example from clinical practice,including every step in SPSS, and a text with interpretations of the results and hints convenient for data reporting. In order to facilitate the use of this cookbook the data files of the examples is made available by the editor through extras.springer.com. Both part 1 and 2 of this title contain a minima amount of text and maximal technical details, but we believe that this property will not refrain students from mastering the SPSS software systematics, and that, instead, it will be a help to that aim. Yet, we recommend that it will used together with the textbook "Statistics Applied to Clinical Trials" (5th edition, Springer, Dordrecht 2012) and the e-books "Statistics on a Pocket Calculator Part 1 and 2 (Springer, Dordrecht, 2011 and 2012) from the same authors.
Description based upon print version of record.
SPSS for Starters, Part 2; Preface; Contents; 1 Introduction; 2 Multistage Regression (35 Patients); Example; Path Statistics; Two Stage Least Square Method; Conclusion; 3 Multivariate Analysis Using Path Statisticspath statistics (35 Patients); Example; Conclusion; 4 Multivariate Analysis of Variance (35 and 30 Patients); First Example; Second Example; Conclusion; 5 Categorical Data (60 patients); Example; Conclusion; 6 Multinomial Logistic Regression (55 Patients); Example; Conclusion; 7 Missing Data Imputation (35 Patients); Example; Regression Imputation; Multiple Imputations; Conclusion
8 Comparing the Performance of Diagnostic Tests (650 and 588 Patients)Example; Conclusion; 9 Meta-Regression (20 and 9 Studies); Example 1; Example 2; Conclusion; 10 Poisson RegressionPoisson Regression (50 and 52 Patients); Example 1; Example 2; Conclusion; 11 Confounding (40 patients); Example; Conclusion; 12 Interaction, Random Effect Analysis of Variancerandom effect analysis of variance (40 Patients); Example; Conclusion; 13 Log Rank Testing (60 Patients); Example; Log Rank Test; Conclusion; 14 Segmented Cox Regression (60 Patients); Example; Conclusion
15 Curvilinear Estimation (20 Patients)Example; Conclusion; 16 Loess and Spline Modeling (90 Patients); Example; Spline Modeling; Loess (Locally Weighted Scatter Plot Smoothing) Modeling; Note; Conclusions; 17 Assessing Seasonality (24 Averages); Example; Conclusions; 18 Monte Carlo Tests and Bootstrapsbootstraps for Analysis of Complex Data (10, 20, 139, and 55 Patients); Paired Continuous Datacontinuous data; Unpaired Continuous Datacontinuous data; Paired Binary Databinary data; Unpaired Binary Databinary data; Conclusion; 19 Artificial Intelligence (90 Patients); Example; Conclusion
20 Robust Testing (33 Patients)Example; Robust Testing; Conclusion; Final Remarks; Index
Includes bibliographical references and index.
The first part of this title contained all statistical tests that are relevant for starters on SPSS, and included standard parametric and non-parametric tests for continuous and binary variables, regression methods, trend tests, and reliability and validity assessments of diagnostic tests. The current part 2 of this title reviews multistep methods, multivariate models, assessments of missing data, performance of diagnostic tests, meta-regression, Poisson regression, confounding  and interaction, and survival analyses using log tests and segmented time-dependent Cox regression. Methods for assessing non linear models, data seasonality, distribution free methods, including Monte Carlo methods and artificial intelligence, and robust tests are also covered. Each method of testing is explained using a data example from clinical practice,including every step in SPSS, and a text with interpretations of the results and hints convenient for data reporting. In order to facilitate the use of this cookbook the data files of the examples is made available by the editor through extras.springer.com. Both part 1 and 2 of this title contain a minima amount of text and maximal technical details, but we believe that this property will not refrain students from mastering the SPSS software systematics, and that, instead, it will be a help to that aim. Yet, we recommend that it will used together with the textbook "Statistics Applied to Clinical Trials" (5th edition, Springer, Dordrecht 2012) and the e-books "Statistics on a Pocket Calculator Part 1 and 2 (Springer, Dordrecht, 2011 and 2012) from the same authors.
Medicine.
Biostatistics.
Statistics .
Medicine/Public Health, general.
Biostatistics.
Statistics for Life Sciences, Medicine, Health Sciences.