- Häftad (Paperback)
- Antal sidor
- SAGE Publications Ltd
- 210 x 150 x 22 mm
- Antal komponenter
- 1970 g
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Discovering Statistics Using IBM SPSS Statistics
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What?s brand new:
- A radical new design with original illustrations and even more colour
- A maths diagnostic tool to help students establish what areas they need to revise and improve on.
- A revamped online resource that uses video, case studies, datasets, testbanks and more to help students negotiate project work, master data management techniques, and apply key writing and employability skills
- New sections on replication, open science and Bayesian thinking
- Now fully up to date with latest versions of IBM SPSS Statistics©.
All the online resources above (video, case studies, datasets, testbanks) can be easily integrated into your institution's virtual learning environment or learning management system. This allows you to customize and curate content for use in module preparation, delivery and assessment. For instructions on how to upload the resources you want, please visit the Instructors' page or alternatively, contact your local SAGE sales representative.
Please note that ISBN: 9781526445780 comprises the paperback edition of the Fifth Edition and the student version of IBM SPSS Statistics. More information on this version of the software's features can be found here.
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Recensioner i media
This book turned my hatred of stats and SPSS into love.
I love it! It's the first text I've come across that has been written in such a captivating way. There's humor, tons of information, and awesome resources both within and on the companion website. Kudos to Prof. Field!
Harvard University I also appreciate how the author made the text interesting to read, but the content is rich enough to provide readers good knowledge on how to draw insights from stats and data. Also, it provides a lot of practical guides for reporting results and findings for research paper. Can't wait to take a deeper dive into the text
I never thought I would find a statistics textbook amusing but somehow our text pulls it off. I also appreciated the online supplementary tools provided by the publisher. They provide a good synthesis of each of the chapters and some easy options to review
I really really love the book, it's the main reason why I'm not curled up in bed with my cats sobbing in fear at the moment. Speaking of cats, I gotta say the correcting cat/misconception mutt framing is very cute, and it almost broke my heart finding out the origin of that orange spiritual feline. I'm having a blast reading about stats, who would've thunk it?
Bloggat om Discovering Statistics Using IBM SPSS Sta...
Andy Field is Professor of Child Psychopathology at the University of Sussex. He has published over 80 research papers, 29 book chapters, and 17 books mostly on child emotional development and statistics.
He is the founding editor of the Journal of Experimental Psychopathology and has been an associate editor and editorial board member for the British Journal of Mathematical and Statistical Psychology, Cognition and Emotion, Clinical Child and Family Psychology Review and Research Synthesis Methods.
His ability to make statistics accessible and fun has been recognized with local and national teaching awards (University of Sussex, 2001, 2015, 2016; the British Psychological Society, 2007), a prestigious UK National Teaching Fellowship (2010), and the British Psychological Society book award (2006). He adores cats (and dogs), and loves to listen to and play very heavy music. He lives in Brighton with his wonderful wife Zo and their children.
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 DataChapter 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 testsChapter 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 factorsChapter 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 StatisticsChapter 5: Exploring data with graphs 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 graphsChapter 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 BiasChapter 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 ANOVAChapter 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 coefficentsChapter 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 modelsChapter 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, mediation and multicategory predictors The PROCESS tool Moderation: Interactions in the linear model Mediation Categorical predictors in regressionChapter 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 comparison