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SPSS for Starters and 2nd Levelers
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-assessmen...
| Clasificación: | Libro Electrónico |
|---|---|
| Autores principales: | , |
| Autor Corporativo: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Cham :
Springer International Publishing : Imprint: Springer,
2016.
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| Edición: | 2nd ed. 2016. |
| Temas: | |
| Acceso en línea: | Texto Completo |
Tabla de Contenidos:
- 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.