SPSS for Starters, Part 2

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Springer Science & Business Media, Jul 9, 2012 - Medical - 104 pages
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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.

 

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Contents

1 Introduction
1
2 Multistage Regression 35 Patients
3
3 Multivariate Analysis Using Path Statisticspath statistics 35 Patients
7
4 Multivariate Analysis of Variance 35 and 30 Patients
13
5 Categorical Data 60 patients
20
6 Multinomial Logistic Regression 55 Patients
25
7 Missing Data Imputation 35 Patients
29
8 Comparing the Performance of Diagnostic Tests 650 and 588 Patients
35
13 Log Rank Testing 60 Patients
61
14 Segmented Cox Regression 60 Patients
65
15 Curvilinear Estimation 20 Patients
69
16 Loess and Spline Modeling 90 Patients
74
17 Assessing Seasonality 24 Averages
81
18 Monte Carlo Tests and Bootstrapsbootstraps for Analysis of Complex Data 10 20 139 and 55 Patients
87
19 Artificial Intelligence 90 Patients
91
20 Robust Testing 33 Patients
95

9 MetaRegression 20 and 9 Studies
38
10 Poisson RegressionPoisson Regression 50 and 52 Patients
43
11 Confounding 40 patients
49
12 Interaction Random Effect Analysis of Variancerandom effect analysis of variance 40 Patients
55
Final Remarks
99
Index
101
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