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Produktbild: Discovering Statistics Using IBM SPSS Statistics

Discovering Statistics Using IBM SPSS Statistics

225,99 €

inkl. gesetzl. MwSt., Versandkostenfrei

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Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

29.02.2024

Verlag

Sage Publications

Seitenzahl

1144

Maße (L/B/H)

27,1/20,3/4,7 cm

Gewicht

2362 g

Auflage

6 Revised edition

Sprache

Englisch

ISBN

978-1-5296-3001-5

Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

29.02.2024

Verlag

Sage Publications

Seitenzahl

1144

Maße (L/B/H)

27,1/20,3/4,7 cm

Gewicht

2362 g

Auflage

6 Revised edition

Sprache

Englisch

ISBN

978-1-5296-3001-5

EU-Ansprechpartner

Zeitfracht Medien GmbH
Ferdinand-Jühlke-Straße 7
99095 Erfurt
DE
produktsicherheit@zeitfracht.de

Herstelleradresse

SAGE Publications
1 Oliver's Yard 55 City Road
EC1Y 1SP London
GB
orders@sagepub.co.uk

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Die Leseprobe wird geladen.
  • Produktbild: Discovering Statistics Using IBM SPSS Statistics
  • Chapter 1: Why is my evil lecturer forcing me to learn statistics?
    What the hell am I doing here? I don't belong here
    The research process
    Initial observation: finding something that needs explaining
    Generating and testing theories and hypotheses
    Collecting data: measurement
    Collecting data: research design
    Reporting Data
    Chapter 2: The SPINE of statistics
    What is the SPINE of statistics?
    Statistical models
    Populations and Samples
    P is for parameters
    E is for Estimating parameters
    S is for standard error
    I is for (confidence) Interval
    N is for Null hypothesis significance testing, NHST
    Reporting significance tests
    Chapter 3: The phoenix of statistics
    Problems with NHST
    NHST as part of wider problems with science
    A phoenix from the EMBERS
    Sense, and how to use it
    Preregistering research and open science
    Effect sizes
    Bayesian approaches
    Reporting effect sizes and Bayes factors
    Chapter 4: The IBM SPSS Statistics environment
    Versions of IBM SPSS Statistics
    Windows, MacOS and Linux
    Getting started
    The Data Editor
    Entering data into IBM SPSS Statistics
    Importing Data
    The SPSS Viewer
    Exporting SPSS Output
    The Syntax Editor
    Saving files
    Opening files
    Extending IBM SPSS Statistics
    Chapter 5: Data Visualisation
    The art of presenting data
    The SPSS Chart Builder
    Histograms
    Boxplots (box-whisker diagrams)
    Graphing means: bar charts and error bars
    Line charts
    Graphing relationships: the scatterplot
    Editing graphs
    Chapter 6: The beast of bias
    What is bias?
    Outliers
    Overview of assumptions
    Additivity and Linearity
    Normally distributed something or other
    Homoscedasticity/Homogeneity of Variance
    Independence
    Spotting outliers
    Spotting normality
    Spotting linearity and heteroscedasticity/heterogeneity of variance
    Reducing Bias
    Chapter 7: Non-parametric models
    When to use non-parametric tests
    General procedure of non-parametric tests in SPSS
    Comparing two independent conditions: the Wilcoxon rank-sum test and Mann- Whitney test
    Comparing two related conditions: the Wilcoxon signed-rank test
    Differences between several independent groups: the Kruskal-Wallis test
    Differences between several related groups: Friedman's ANOVA
    Chapter 8: Correlation
    Modelling relationships
    Data entry for correlation analysis
    Bivariate correlation
    Partial and semi-partial correlation
    Comparing correlations
    Calculating the effect size
    How to report correlation coefficents
    Chapter 9: The Linear Model (Regression)
    An Introduction to the linear model (regression)
    Bias in linear models?
    Generalizing the model
    Sample size in regression
    Fitting linear models: the general procedure
    Using SPSS Statistics to fit a linear model with one predictor
    Interpreting a linear model with one predictor
    The linear model with two of more predictors (multiple regression)
    Using SPSS Statistics to fit a linear model with several predictors
    Interpreting a linear model with several predictors
    Robust regression
    Bayesian regression
    Reporting linear models
    Chapter 10: Comparing two means
    Looking at differences
    An example: are invisible people mischievous?
    Categorical predictors in the linear model
    The t-test
    Assumptions of the t-test
    Comparing two means: general procedure
    Comparing two independent means using SPSS Statistics
    Comparing two related means using SPSS Statistics
    Reporting comparisons between two means
    Between groups or repeated measures?
    Chapter 11: Moderation and Mediation
    The PROCESS tool
    Moderation: Interactions in the linear model
    Mediation
    Categorical predictors in regression
    Chapter 12: GLM 1: Comparing several independent means
    Using a linear model to compare several means
    Assumptions when comparing means
    Planned contrasts (contrast coding)
    Post hoc procedures
    Comparing several means using SPSS Statistics
    Output from one-way independent ANOVA
    Robust comparisons of several means
    Bayesian comparison of several means
    Calculating the effect size
    Reporting results from one-way independent ANOVA
    Chapter 13: GLM 2: Comparing means adjusted for other predictors (analysis of covariance)
    What is ANCOVA?
    ANCOVA and the general linear model
    Assumptions and issues in ANCOVA
    Conducting ANCOVA using SPSS Statistics
    Interpreting ANCOVA
    Testing the assumption of homogeneity of regression slopes
    Robust ANCOVA
    Bayesian analysis with covariates
    Calculating the effect size
    Reporting results
    Chapter 14: GLM 3: Factorial designs
    Factorial designs
    Independent factorial designs and the linear model
    Model assumptions in factorial designs
    Factorial designs using SPSS Statistics
    Output from factorial designs
    Interpreting interaction graphs
    Robust models of factorial designs
    Bayesian models of factorial designs
    Calculating effect sizes
    Reporting the results of two-way ANOVA
    Chapter 15: GLM 4: Repeated-measures designs
    Introduction to repeated-measures designs
    A grubby example
    Repeated-measures and the linear model
    The ANOVA approach to repeated-measures designs
    The F-statistic for repeated-measures designs
    Assumptions in repeated-measures designs
    One-way repeated-measures designs using SPSS
    Output for one-way repeated-measures designs
    Robust tests of one-way repeated-measures designs
    Effect sizes for one-way repeated-measures designs
    Reporting one-way repeated-measures designs
    A boozy example: a factorial repeated-measures design
    Factorial repeated-measures designs using SPSS Statistics
    Interpreting factorial repeated-measures designs
    Effect Sizes for factorial repeated-measures designs
    Reporting the results from factorial repeated-measures designs
    Chapter 16: GLM 5: Mixed designs
    Mixed designs
    Assumptions in mixed designs
    A speed dating example
    Mixed designs using SPSS Statistics
    Output for mixed factorial designs
    Calculating effect sizes
    Reporting the results of mixed designs
    Chapter 17: Multivariate analysis of variance (MANOVA)
    Introducing MANOVA
    Introducing matrices
    The theory behind MANOVA
    MANOVA using SPSS Statistics
    Interpreting MANOVA
    Reporting results from MANOVA
    Following up MANOVA with discriminant analysis
    Interpreting discriminant analysis
    Reporting results from discriminant analysis
    The final interpretation
    Chapter 18: Exploratory factor analysis
    When to use factor analysis
    Factors and Components
    Discovering factors
    An anxious example
    Factor analysis using SPSS statistics
    Interpreting factor analysis
    Interpreting factor analysis
    Reliability analysis
    Reliability analysis using SPSS Statistics
    Interpreting Reliability analysis
    How to report reliability analysis
    Chapter 19: Categorical outcomes: chi-square and loglinear analysis
    Analysing categorical data
    Associations between two categorical variables
    Associations between several categorical variables: loglinear analysis
    Assumptions when analysing categorical data
    General procedure for analysing categorical outcomes
    Doing chi-square using SPSS Statistics
    Interpreting the chi-square test
    Loglinear analysis using SPSS Statistics
    Interpreting loglinear analysis
    Reporting the results of loglinear analysis
    Chapter 20: Categorical outcomes: logistic regression
    What is logistic regression?
    Theory of logistic regression
    Sources of bias and common problems
    Binary logistic regression
    Interpreting logistic regression
    Reporting logistic regression
    Testing assumptions: another example
    Predicting several categories: multinomial logistic regression
    Chapter 21: Multilevel linear models
    Hierarchical data
    Theory of multilevel linear models
    The multilevel model
    Some practical issues
    Multilevel modelling using SPSS Statistics
    Growth models
    How to report a multilevel model
    A message from the octopus of inescapable despair
    Chapter 22: Epilogue