SPSS for Starters and 2nd Levelers

Häftad, Engelska, 2016

Av Ton J. Cleophas, Aeilko H. Zwinderman

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Third, the authorsfelt 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.

Produktinformation

Utgivningsdatum2016-10-29
Mått155 x 235 x 22 mm
Vikt610 g
FormatHäftad
SpråkEngelska
Antal sidor375
Upplaga2
FörlagSpringer International Publishing AG
ISBN9783319342504

Tillhör följande kategorier

Matematisk statistik inom Naturvetenskap och teknik
Systemvetenskap och AI inom Data och it
Medicinsk utrustning och medicinska tekniker inom Medicin
Affärsapplikationer inom Data och it
Tillämpad datateknik inom Data och it

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 analysisof 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 binarypredictor.- 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.- SegmentedCox regression.- Assessing seasonality.- Probability assessment of survival with interval censored data analysis.- Index.