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- Herausgeber: John Wiley & Sons
- Kategorie: Ratgeber
- Sprache: Englisch
- Veröffentlichungsjahr: 2012

The introduction to statistics that psychology students can't afford to be without Understanding statistics is a requirement for obtaining and making the most of a degree in psychology, a fact of life that often takes first year psychology students by surprise. Filled with jargon-free explanations and real-life examples, Psychology Statistics For Dummies makes the often-confusing world of statistics a lot less baffling, and provides you with the step-by-step instructions necessary for carrying out data analysis. Psychology Statistics For Dummies: * Serves as an easily accessible supplement to doorstop-sized psychology textbooks * Provides psychology students with psychology-specific statistics instruction * Includes clear explanations and instruction on performing statistical analysis * Teaches students how to analyze their data with SPSS, the most widely used statistical packages among students

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Table of Contents

Introduction

About This Book

What You’re Not to Read

Foolish Assumptions

How this Book is Organised

Icons Used in This Book

Where to Go from Here

Part I: Describing Data

Chapter 1: Statistics? I Thought This Was Psychology!

Know Your Variables

What is SPSS?

Descriptive Statistics

Central tendency

Dispersion

Graphs

Standardised scores

Inferential Statistics

Hypotheses

Parametric and non-parametric variables

Research Designs

Correlational design

Experimental design

Independent groups design

Repeated measures design

Getting Started

Chapter 2: What Type of Data Are We Dealing With?

Understanding Discrete and Continuous Variables

Looking at Levels of Measurement

Measurement properties

Types of measurement level

Determining the Role of Variables

Independent variables

Dependent variables

Covariates

Chapter 3: Inputting Data, Labelling and Coding in SPSS

Variable View Window

Creating variable names

Deciding on variable type

Displaying the data: The width, decimals, columns and align headings

Using labels

Using values

Dealing with missing data

Assigning the level of measurement

Data View Window

Entering new data

Creating new variables

Sorting cases

Recoding variables

Output Window

Using the output window

Saving your output

Chapter 4: Measures of Central Tendency

Defining Central Tendency

The Mode

Determining the mode

Knowing the advantages and disadvantages of using the mode

Obtaining the mode in SPSS

The Median

Determining the median

Knowing the advantages and disadvantages to using the median

Obtaining the median in SPSS

The Mean

Determining the mean

Knowing the advantages and disadvantages to using the mean

Obtaining the mean in SPSS

Choosing between the Mode, Median and Mean

Chapter 5: Measures of Dispersion

Defining Dispersion

The Range

Determining the range

Knowing the advantages and disadvantages of using the range

Obtaining the range in SPSS

The Interquartile Range

Determining the interquartile range

Knowing the advantages and disadvantages of using the interquartile range

Obtaining the interquartile range in SPSS

The Standard Deviation

Defining the standard deviation

Knowing the advantages and disadvantages of using the standard deviation

Obtaining the standard deviation in SPSS

Choosing between the Range, Interquartile Range and Standard Deviation

Chapter 6: Generating Graphs and Charts

The Histogram

Understanding the histogram

Obtaining a histogram in SPSS

The Bar Chart

Understanding the bar chart

Obtaining a bar chart in SPSS

The Pie Chart

Understanding the pie chart

Obtaining a pie chart in SPSS

The Box and Whisker Plot

Understanding the box and whisker plot

Obtaining a box and whisker plot in SPSS

Part II: Statistical Significance

Chapter 7: Understanding Probability and Inference

Examining Statistical Inference

Looking at the population and the sample

Knowing the limitations of descriptive statistics

Aiming to be 95 per cent confident

Making Sense of Probability

Defining probability

Considering mutually exclusive and independent events

Understanding conditional probability

Knowing about odds

Chapter 8: Testing Hypotheses

Understanding Null and Alternative Hypotheses

Testing the null hypothesis

Defining the alternative hypothesis

Deciding whether to accept or reject the null hypothesis

Taking On Board Statistical Inference Errors

Knowing about the Type I error

Considering the Type II error

Getting it right sometimes

Looking at One- and Two-Tailed Hypotheses

Using a one-tailed hypothesis

Applying a two-tailed hypothesis

Confidence Intervals

Defining a 95 per cent confidence interval

Calculating a 95 per cent confidence interval

Obtaining a 95 per cent confidence interval in SPSS

Chapter 9: What’s Normal about the Normal Distribution?

Understanding the Normal Distribution

Defining the normal distribution

Determining whether a distribution is approximately normal

Determining Skewness

Defining skewness

Assessing skewness graphically

Obtaining the skewness statistic in SPSS

Looking at the Normal Distribution and Inferential Statistics

Making inferences about individual scores

Considering the sampling distribution

Making inferences about group scores

Chapter 10: Standardised Scores

Knowing the Basics of Standardised Scores

Defining standardised scores

Calculating standardised scores

Using Z Scores in Statistical Analyses

Connecting Z scores and the normal distribution

Using Z scores in inferential statistics

Chapter 11: Effect Sizes and Power

Distinguishing between Effect Size and Statistical Significance

Exploring Effect Size for Correlations

Considering Effect Size When Comparing Differences Between Two Sets of Scores

Obtaining an effect size for comparing differences between two sets of scores

Interpreting an effect size for differences between two sets of scores

Looking at Effect Size When Comparing Differences between More Than Two Sets of Scores

Obtaining an effect size for comparing differences between more than two sets of scores

Interpreting an effect size for differences between more than two sets of scores

Understanding Statistical Power

Seeing which factors influence power

Considering power and sample size

Part III: Relationships between Variables

Chapter 12: Correlations

Using Scatterplots to Assess Relationships

Inspecting a scatterplot

Drawing a scatterplot in SPSS

Understanding the Correlation Coefficient

Examining Shared Variance

Using Pearson’s Correlation

Knowing when to use Pearson’s correlation

Performing Pearson’s correlation in SPSS

Interpreting the output

Writing up the results

Using Spearman’s Correlation

Knowing when to use Spearman’s correlation

Performing Spearman’s correlation in SPSS

Interpreting the output

Writing up the results

Using Kendall’s Correlation

Performing Kendall’s correlation in SPSS

Interpreting the output

Writing up the results

Using Partial Correlation

Performing partial correlation in SPSS

Interpreting the output

Writing up the results

Chapter 13: Linear Regression

Getting to Grips with the Basics of Regression

Adding a regression line

Working out residuals

Using the regression equation

Using Simple Regression

Performing simple regression in SPSS

Interpreting the output

Writing up the results

Working with Multiple Variables: Multiple Regression

Performing multiple regression in SPSS

Interpreting the output

Writing up the results

Checking Assumptions of Regression

Normally distributed residuals

Linearity

Outliers

Multicollinearity

Homoscedasticity

Type of data

Chapter 14: Associations between Discrete Variables

Summarising Results in a Contingency Table

Observed frequencies in contingency tables

Percentaging a contingency table

Obtaining contingency tables in SPSS

Calculating Chi-Square

Expected frequencies

Calculating chi-square

Obtaining chi-square in SPSS

Interpreting the output from chi-square in SPSS

Writing up the results of a chi-square analysis

Understanding the assumptions of chi-square analysis

Measuring the Strength of Association between Two Variables

Looking at the odds ratio

Phi and Cramer’s V Coefficients

Obtaining odds ratio, phi coefficient and Cramer’s V in SPSS

Using the McNemar Test

Calculating the McNemar test

Obtaining a McNemar test in SPSS

Part IV: Analysing Independent Groups Research Designs

Chapter 15: Independent t-tests and Mann–Whitney Tests

Understanding Independent Groups Design

The Independent t-test

Performing the independent t-test in SPSS

Interpreting the output

Writing up the results

Considering assumptions

Mann–Whitney test

Performing the Mann–Whitney test in SPSS

Interpreting the output

Writing up the results

Considering assumptions

Chapter 16: Between-Groups ANOVA

One-Way Between-Groups ANOVA

Seeing how ANOVA works

Calculating a one-way between-groups ANOVA

Obtaining a one-way between-groups ANOVA in SPSS

Interpreting the SPSS output for a one-way between-groups ANOVA

Writing up the results of a one-way between-groups ANOVA

Considering assumptions of a one-way between-groups ANOVA

Two-Way Between-Groups ANOVA

Understanding main effects and interactions

Obtaining a two-way between-groups ANOVA in SPSS

Interpreting the SPSS output for a two-way between-groups ANOVA

Writing up the results of a two-way between-groups ANOVA

Considering assumptions of a two-way between-groups ANOVA

Kruskal–Wallis Test

Obtaining a Kruskal–Wallis test in SPSS

Interpreting the SPSS output for a Kruskal–Wallis test

Writing up the results of a Kruskal–Wallis test

Considering assumptions of a Kruskal–Wallis test

Chapter 17: Post Hoc Tests and Planned Comparisons for Independent Groups Designs

Post Hoc Tests for Independent Groups Designs

Multiplicity

Choosing a post hoc test

Obtaining a Tukey HSD post hoc test in SPSS

Interpreting the SPSS output for a Tukey HSD post hoc test

Writing up the results of a post hoc Tukey HSD test

Planned Comparisons for Independent Groups Designs

Choosing a planned comparison

Obtaining a Dunnett test in SPSS

Interpreting the SPSS output for a Dunnett test

Writing up the results of a Dunnett test

Part V: Analysing Repeated Measures Research Designs

Chapter 18: Paired t-tests and Wilcoxon Tests

Understanding Repeated Measures Design

Paired t-test

Performing a paired t-test in SPSS

Interpreting the output

Writing up the results

Assumptions

The Wilcoxon Test

Performing the Wilcoxon test in SPSS

Interpreting the output

Writing up the results

Chapter 19: Within-Groups ANOVA

One-Way Within-Groups ANOVA

Knowing how ANOVA works

The example

Obtaining a one-way within-groups ANOVA in SPSS

Interpreting the SPSS output for a one-way within-groups ANOVA

Writing up the results of a one-way within-groups ANOVA

Assumptions of a one-way within-groups ANOVA

Two-Way Within-Groups ANOVA

Main effects and interactions

Obtaining a two-way within-groups ANOVA in SPSS

Interpreting the SPSS output for a two-way within-groups ANOVA

Interpreting the interaction plot from a two-way within-groups ANOVA

Writing up the results of a two-way within-groups ANOVA

Assumptions of a two-way within-groups ANOVA

The Friedman Test

Obtaining a Friedman test in SPSS

Interpreting the SPSS output for a Friedman test

Writing up the results of a Friedman test

Assumptions of the Friedman test

Chapter 20: Post Hoc Tests and Planned Comparisons for Repeated Measures Designs

Why do you need to use post hoc tests and planned comparisons?

Why should you not use t-tests?

What is the difference between post hoc tests and planned comparisons?

Post Hoc Tests for Repeated Measures Designs

The example

Choosing a post hoc test

Obtaining a post-hoc test for a within-groups ANOVA in SPSS

Interpreting the SPSS output for a post-hoc test

Writing up the results of a post hoc test

Planned Comparisons for Within Groups Designs

The example

Choosing a planned comparison

Obtaining a simple planned contrast in SPSS

Interpreting the SPSS output for planned comparison tests

Writing up the results of planned contrasts

Examining Differences between Conditions: The Bonferroni Correction

Chapter 21: Mixed ANOVA

Getting to Grips with Mixed ANOVA

The example

Main Effects and Interactions

Performing the ANOVA in SPSS

Interpreting the SPSS output for a two-way mixed ANOVA

Writing up the results of a two-way mixed ANOVA

Assumptions

Part VI: The Part of Tens

Chapter 22: Ten Pieces of Good Advice for Inferential Testing

Statistical Significance Is Not the Same as Practical Significance

Fail to Prepare, Prepare to Fail

Don’t Go Fishing for a Significant Result

Check Your Assumptions

My p Is Bigger Than Your p

Differences and Relationships Are Not Opposing Trends

Where Did My Post-hoc Tests Go?

Categorising Continuous Data

Be Consistent

Get Help!

Chapter 23: Ten Tips for Writing Your Results Section

Reporting the p-value

Reporting Other Figures

Don’t Forget About the Descriptive Statistics

Do Not Overuse the Mean

Report Effect Sizes and Direction of Effects

The Case of the Missing Participants

Be Careful with Your Language

Beware Correlations and Causality

Make Sure to Answer Your Own Question

Add Some Structure

Cheat Sheet

Psychology Statistics For Dummies®

by Donncha Hanna and Martin Dempster

Psychology Statistics For Dummies®

Published by John Wiley & Sons, LtdThe Atrium Southern Gate Chichester West Sussex PO19 8SQ England www.wiley.com

Copyright © 2012 John Wiley & Sons, Ltd, Chichester, West Sussex, England

Published by John Wiley & Sons, Ltd, Chichester, West Sussex, England

All Rights Reserved. No part of this publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, scanning or otherwise, except under the terms of the Copyright, Designs and Patents Act 1988 or under the terms of a licence issued by the Copyright Licensing Agency Ltd, Saffron House, 6-10 Kirby Street, London EC1N 8TS, UK, without the permission in writing of the Publisher. Requests to the Publisher for permission should be addressed to the Permissions Department, John Wiley & Sons, Ltd, The Atrium, Southern Gate, Chichester, West Sussex, PO19 8SQ, England, or emailed to [email protected], or faxed to (44) 1243 770620.

Trademarks: Wiley, the Wiley logo, For Dummies, the Dummies Man logo, A Reference for the Rest of Us!, The Dummies Way, Dummies Daily, The Fun and Easy Way, Dummies.com, Making Everything Easier, and related trade dress are trademarks or registered trademarks of John Wiley & Sons, Inc., and/or its affiliates in the United States and other countries, and may not be used without written permission. All other trademarks are the property of their respective owners. John Wiley & Sons, Inc., is not associated with any product or vendor mentioned in this book.

Limit of Liability/Disclaimer of Warranty: The publisher and the author make no representations or warranties with respect to the accuracy or completeness of the contents of this work and specifically disclaim all warranties, including without limitation warranties of fitness for a particular purpose. No warranty may be created or extended by sales or promotional materials. The advice and strategies contained herein may not be suitable for every situation. This work is sold with the understanding that the publisher is not engaged in rendering legal, accounting, or other professional services. If professional assistance is required, the services of a competent professional person should be sought. Neither the publisher nor the author shall be liable for damages arising herefrom. The fact that an organization or Website is referred to in this work as a citation and/or a potential source of further information does not mean that the author or the publisher endorses the information the organization or Website may provide or recommendations it may make. Further, readers should be aware that Internet Websites listed in this work may have changed or disappeared between when this work was written and when it is read.

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British Library Cataloguing in Publication Data: A catalogue record for this book is available from the British Library

ISBN 978-1-119-95287-9 (pbk); ISBN 978-1-119-95393-7 (ebk); ISBN 978-1-119-95394-4 (ebk); ISBN 978-1-119-95395-1 (ebk)

Printed and bound in Great Britain by TJ International, Padstow, Cornwall.

10 9 8 7 6 5 4 3 2 1

About the Authors

Donncha Hanna is, among other more interesting things, a lecturer at the School of Psychology, Queen’s University Belfast.

He has been teaching statistics to undergraduate students, postgraduate students and real professional people for over 10 years (he is not as old as Martin). His research focuses on mental health and the reasons why students do not like statistics; these topics are not necessarily related. He attempts to teach statistics in an accessible and easy to understand way without dumbing down the content; maybe one day he will succeed.

Donncha lives in Belfast with two fruit bats, a hedgehog and a human named Pamela.

Martin Dempster is a Senior Lecturer in the School of Psychology, Queen’s University Belfast. He is a Health Psychologist and Chartered Statistician who has also authored A Research Guide for Health & Clinical Psychology.

He has been teaching statistics to undergraduate psychology students for over 20 years. As a psychologist he is interested in the adverse reaction that psychology students often have to learning statistics and endeavours to work out what causes this (hopefully not him) and how it can be alleviated. He tries to teach statistics in an accessible manner (which isn’t always easy).

Martin lives in Whitehead, a seaside village in Co. Antrim, Northern Ireland, which isn’t very well-known, which is why he lives there.

Dedication

From Donncha: For my mother and father. Thank you for everything.

From Martin: For Tom, who joined the world half way through the development of this book and has been a glorious distraction ever since.

Author’s Acknowledgments

From Donncha: I’m very grateful to the team at Dummies Towers for their work and guidance in getting this book to print – particularly our editors Simon Bell and Mike Baker.

I would like to thank all the students, colleagues and teachers who have helped shape my thinking and knowledge about statistics (and apologise if I have stolen any of their ideas!). I must also acknowledge Pamela (who didn’t complain when I used the excuse of writing this book to avoid doing the dishes) and my sister, Aideen, who offered practical help as always. Thanks to my friend and colleague Martin Dorahy who put up with me in New Zealand where half of this book was written. And of course to Martin Dempster, without whom there would be no book.

From Martin: This book is the product of at least 20 years of interaction with colleagues and students; picking up their ideas; answering their questions; and being stimulated into thinking about different ways of explaining statistical concepts. Therefore, there are many people to thank – too many too list and certainly too many for me to remember (any more).

However, there are a few people who made contributions to the actual content of this book. My brother, Bob, who has a much better sense of humour than me, helped with some of the examples in the book. Noleen helped me to better formulate my thinking when I was having some difficulty and supported my decision to undertake this project in the first place. My mum and dad spurred me on with their ever-present encouragement. Finally, thanks to my colleague Donncha, who floated the idea of writing this book and asked me to collaborate with him on its development.

Publisher’s Acknowledgments

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Some of the people who helped bring this book to market include the following:

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Project Editor: Simon Bell

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Cartoons: Ed McLachlan

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Introduction

We recently collected data from psychology students across 31 universities regarding their attitudes towards statistics; 51 per cent of the students did not realise statistics would be a substantial component of their course and the majority had negative attitudes or anxiety towards the subject. So if this sounds familiar take comfort in the fact you are not alone!

Let’s get one thing out of the way right now. The statistics component you have to complete for your degree is not impossible and it shouldn’t be gruelling. If you can cope with cognitive psychology theories and understand psycho-biological models you should have no difficulty. Remember this isn’t mathematics; the computer will run all the complex number crunching for you. This book has been written in a clear and concise manner that will help you through the course. We don’t assume any previous knowledge of statistics and in return we ask you relinquish any negative attitudes you may have!

The second point we need to address is why, when you have enrolled for psychology, are you being forced to study statistics? You need to know that statistics is an important and necessary part of all psychology courses. Psychology is an empirical discipline, which means we use evidence to decide between competing theories and approaches. Collecting quantitative information allows us to represent this data in an objective and easily comparable format. This information must be summarised and analysed (after all, pages of numbers aren’t that meaningful) and this allows us to infer conclusions and make decisions. Understanding statistics not only allows you to conduct and analyse your own research, but importantly it allows you to read and critically evaluate previous research.

Also, statistics are important in psychology because psychologists use their statistical knowledge in their day-to-day work. Consider a psychologist who is working with clients exhibiting depression, anxiety and self-harm. They must decide which therapy is most useful for particular conditions, whether anxiety is related to (or can predict) self harm, or whether clients who self harm differ in their levels of depression. Statistical knowledge is a crucial tool in any psychologist’s job.

About This Book

The aim of this book is to provide an easily accessible reference guide, written in plain English, that will allow students to readily understand, carry out, interpret and report all types of statistical procedures required for their course. While we have targeted this book at psychology undergraduate students we hope it will be useful to all social science and health science students.

The book is structured in a relatively linear way; starting with the more basic concepts and progressing through to more complex techniques. This is the order in which the statistics component of the psychology degree is normally taught. Note, though, that this doesn’t mean you are expected to start from page one and read the book from cover to cover. Instead each chapter (and each statistical technique) is designed to be self-contained and does not necessarily require any previous knowledge. For example, if you were to look up the technique ‘partial correlation’ you will find a clear, jargon-free explanation of the technique followed by an example (with step-by-step instructions demonstrating how to perform the technique on SPSS, how to interpret the output and, importantly, how to report the results appropriately). Each statistical procedure in the book follows this same framework enabling you to quickly find the technique of interest, run the required analysis and write it up in an appropriate way.

As we know (both from research we have conducted and subjective experience of teaching courses) statistics tends to be a psychology student’s least favourite subject and causes anxiety in the majority of psychology students. We therefore deliberately steer clear of complex mathematical formulae as well as superfluous and rarely-used techniques. Instead we have concentrated on producing a clear and concise guide illustrated with visual aids and practical examples.

What You’re Not to Read

We have deliberately tried to keep our explanations concise but there is still a lot of information contained in this book. Occasionally you will see the technical stuff icon; this, as the icon suggests, contains more technical information which we regard as valuable in understanding the technique but not crucial to conducting the analysis. You can skip these sections and still understand the topic in question.

Likewise you may come across sidebars where we have elaborated on a topic. We think they are interesting, but we are biased! If you are in a hurry you can skip these sections.

Foolish Assumptions

Rightly or wrongly we have made some assumptions when writing this book. We assume that:

You have SPSS installed and you are familiar with using a computer. We do not outline how to install SPSS and we are assuming that you are familiar with using the mouse (pointing, clicking, etc.) and the keyboard to enter or manipulate information. We do not assume that you have used SPSS before; Chapter 3 gives an introduction to this programme and we provide you with step-by-step instructions for each procedure.

You are not a mathematical genius but you do have some basic understanding of using numbers. If you know what we mean by squaring a number (multiplying a number by itself; if we square 5 we get 25) or taking a square root – the opposite of squaring a number (the square root of a number is that value when squared gives the original number; the square root of 25 is 5) you will be fine. Remember the computer will be doing the calculations for you.

You do not need to conduct complex multivariate statistics. This is an introductory book and we limit out discussion to the type of analyses commonly required by undergraduate syllabuses.

How this Book is Organised

This book has been organised into six parts:

Part I of the book deals with describing and summarising data. It starts by explaining, with examples, the types of variables commonly used and level of measurement. These concepts are key in deciding how to treat your data and which statistics are most appropriate to analyse your data. We deal with the SPSS environment, so if you haven’t used SPSS before, or need a refresher, this a good place to start. We also cover the first descriptive statistics: the mean, mode and median. From there we go on to key ideas such as measures of dispersion and interpreting and producing the most commonly used graphs for displaying data.

Part II of the book focuses on some of the concepts which are fundamental for an understanding of statistics. If you don’t know the difference between a null and alternative hypothesis, unsure why you have to report the p-value and an effect size or have never really been confident of what statistical inference actually means, then this part of the book is for you!

Part III of the book deals with inferential statistics, the ones that examine relationships or associations between variables, including correlations, regression and tests for categorical data. We explain each technique clearly – what it is used for and when you should use it, followed by instructions on how to perform the analysis in SPSS, how to interpret the subsequent output and how to write up the results in both the correct statistical format and in plain English.

Part IV of the book deals with the inferential statistics that examine differences between two or more independent groups of data. In particular we address the Independent t-test, Mann-Whitney test and Analysis of Variance (ANOVA). For each technique we offer a clear explanation, show you how it works in SPSS, and how to interpret and write up the results.

Part V of the book deals with the inferential statistics that examine differences between two or more repeated measurements. Here we cover the Paired t-test, the Wilcoxon test and Analysis of Variance (ANOVA). We also focus on analysis of research designs that include both independent groups and repeated measurements: the Mixed ANOVA.

Part VI, the final part of the book, provides you with hints and tips on how to avoid mistakes and write up your results in the most appropriate way. We hope these pointers can save you from the pitfalls often made by inexperienced researchers and can contribute to you producing a better results section. We outline some of the common mistakes and misunderstandings students make when performing statistical analyses and how you can avoid them, and we provide quick and useful tips for writing your results section.

Icons Used in This Book

As with all For Dummies books, you will notice icons in the margin that signify there is something special about that piece of information.

This points out a helpful hint designed to save you time or from thinking harder than you have to.

This one is important! It indicates a piece of information that you should bear in mind even after the book has been closed.

This icon highlights a common misunderstanding or error that we don’t want you to make.

This contains a more detailed discussion or explanation of a topic; you can skip this material if you are in a rush.

Where to Go from Here

You could read this book cover to cover but we have designed it so you can easily find the topics you are interested in and get the information you want without having to read pages of mathematical formulae or find out what every single option in SPSS does. If you are completely new to this area we suggest you start with Chapter 1. Need some help navigating SPSS for the first time? Turn to Chapter 3. If you are not quite sure what a p-value or an effect size is, you’ll need to refer to Part II of the book. For any of the other techniques we suggest you use the table of contents or index to guide you to the right place.

Remember you can’t make the computer (or your head) explode so, with book in hand, it’s time to start analysing that data!

Part I

Describing Data

In this part . . .

We know: you’re studying psychology, not statistics. You’re not a mathematician and never wanted to be. Never fear, help is near. This part of the book covers the key concepts you need to grasp to describe statistical data accurately and successfully. We talk about the simplest descriptive statistics – mean, mode and median – and important ideas such as measures of dispersion and how to interpret and produce the graphs for displaying data.

We also introduce you to SPSS (Statistical Package for Social Sciences, to give it its full name) and walk you through the basics of using the program to produce straightforward statistics.

Lesen Sie weiter in der vollständigen Ausgabe!

Lesen Sie weiter in der vollständigen Ausgabe!

Lesen Sie weiter in der vollständigen Ausgabe!

Lesen Sie weiter in der vollständigen Ausgabe!

Lesen Sie weiter in der vollständigen Ausgabe!

Lesen Sie weiter in der vollständigen Ausgabe!

Lesen Sie weiter in der vollständigen Ausgabe!

Lesen Sie weiter in der vollständigen Ausgabe!

Lesen Sie weiter in der vollständigen Ausgabe!

Lesen Sie weiter in der vollständigen Ausgabe!

Lesen Sie weiter in der vollständigen Ausgabe!

Lesen Sie weiter in der vollständigen Ausgabe!

Lesen Sie weiter in der vollständigen Ausgabe!

Lesen Sie weiter in der vollständigen Ausgabe!

Lesen Sie weiter in der vollständigen Ausgabe!

Lesen Sie weiter in der vollständigen Ausgabe!

Lesen Sie weiter in der vollständigen Ausgabe!

Lesen Sie weiter in der vollständigen Ausgabe!

Lesen Sie weiter in der vollständigen Ausgabe!

Lesen Sie weiter in der vollständigen Ausgabe!

Lesen Sie weiter in der vollständigen Ausgabe!

Lesen Sie weiter in der vollständigen Ausgabe!

Lesen Sie weiter in der vollständigen Ausgabe!

Lesen Sie weiter in der vollständigen Ausgabe!

Lesen Sie weiter in der vollständigen Ausgabe!

Lesen Sie weiter in der vollständigen Ausgabe!

Lesen Sie weiter in der vollständigen Ausgabe!

Lesen Sie weiter in der vollständigen Ausgabe!

Lesen Sie weiter in der vollständigen Ausgabe!