Main description:

In
medical and health care the scientific method is little used, and statistical
software programs are experienced as black box programs producing lots of
p-values, but little answers to scientific questions. The pocket calculator
analyses appears to be, particularly, appreciated, because they enable medical
and health professionals and students for the first time to understand the
scientific methods of statistical reasoning and hypothesis testing. So much so,
that it can start something like a new dimension in their professional world. In
addition, a
number of statistical methods like power calculations and required sample size
calculations can be performed more easily on a pocket calculator, than using a
software program. Also, there are some specific advantages of the
pocket calculator method. You better understand what you are doing. The pocket
calculator works faster, because far less steps have to be taken, averages can
be used. The current nonmathematical book is complementary to the
nonmathematical "SPSS for Starters and 2nd Levelers" (Springer Heidelberg
Germany 2015, from the same authors), and can very well be used as its daily
companion.

Contents:

Preface

I Continuous Outcome Data

1 Data Spread, Standard Deviations

2 Data Summaries: Histograms, Wide and Narrow Gaussian Curves

3 Null-Hypothesis Testing with Graphs

4 Null-Hypothesis Testing with the T-table

5 One-Sample Continuous Data (One-Sample T-Test, One-Sample Wilcoxon

6 Paired Continuous Data (Paired T-Test, Two-Sample Wilcoxon Signed Rank
Test)

7 Unpaired Continuous Data (Unpaired T-Test, Mann-Whitney)

8 Linear Regression (Regression Coefficients, Correlation Coefficients,
and their
Standard Errors)

9 Kendall-Tau Regression for Ordinal Data

10 Paired Continuous Data, Analysis with Help of Correlation
Coefficients

11 Power Equations

12 Sample Size Calculations

13 Confidence Intervals

14 Equivalence Testing instead of Null-Hypothesis Testing

15 Noninferiority Testing instead of Null-Hypothesis Testing

16 Superiority Testing instead of Null-Hypothesis Testing

17 Missing Data Imputation

18 Bonferroni Adjustments

19 Unpaired Analysis of Variance (ANOVA)

20 Paired Analysis of Variance (ANOVA)

21 Variability Analysis for One or Two Samples

22 Variability Analysis for Three or More Samples

23 Confounding

24 Propensity Score and Propensity Score Matching for Multiple
Confounders

25 Interaction

26 Accuracy and Reliability Assessments

27 Robust Tests for Imperfect Data

28 Non-linear Modeling on a Pocket Calculator

29 Fuzzy Modeling for Imprecise and Incomplete Data

30 Bhattacharya Modeling for Unmasking Hidden Gaussian Curves

31 Item Response Modeling instead of Classical Linear Analysis of Questionnaires

32 Meta-Analysis 1

33 Goodness of Fit Tests for Identifying Nonnormal Data

34 Non-Parametric Tests for Three or More Samples (Friedman and
Kruskal-Wallis)

II Binary Outcome Data

35 Data Spread: Standard Deviation, One Sample Z- Test, One Sample
Binomial

Test

36 Z-Tests

37 Phi Tests for Nominal Data

38 Chi-Square Tests

39 Fisher Exact Tests Convenient for Small Samples

40 Confounding

41 Interaction

42 Chi-square Tests for Large Cross-Tabs

43 Logarithmic Transformations, a Great Help to Statistical Analyses

44 Odds Ratios, a Short-Cut for Analyzing Cross-Tabs

45 Logodds, the Basis of Logistic Regression

46 Log Likelihood Ratio Tests for the Best Precision

47 Hierarchical Loglinear Models for Higher Order Cross-Tabs

48 McNemar Tests for Paired Cross-Tabs

49 McNemar Odds Ratios

50 Power Equations

51 Sample Size Calculations

52 Accuracy Assessments

53 Reliability Assessments

54 Unmasking Fudged Data

55 Markov Modeling for Predictions outside the Range of Observations

56 Binary Partitioning with CART (Classification and Regression Tree)
Methods

57 Meta-Analysis

58 Physicians' Daily Life and the Scientific Method

59 Incident Analysis and the Scientific Method

60 Cochran Tests for Large Paired Cross-Tabs

Index