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DISCOVERING STATISTICS USING SPSS T H I R D E D I T I O N ANDY FIELD Andy Field’s easy-to-understand and entertaining writing style has won him many plaudits, and now an award from the British Psychological Society (2007), for his bestselling Discovering Statistics Using SPSS.

In response to feedback the Third Edition now includes new content to make the book more accessible to the introductory student at the very beginning of their statistical journey while at the same time including more material for higher level courses. Key updates in the new edition: • Fully compliant with SPSS version 16. Users of earlier versions can find the Second Edition chapter on ‘The SPSS Environment’ on the Companion Website. • Expanded and re-organised first 6 chapters to more ‘gently’ introduce basic level concepts to the beginning student. • An additional chapter on Multilevel Modelling to strengthen its use for advanced level students. • The incorporation of playful ‘real world’ examples to go alongside those made up by the author. • New textbook features and characters designed to offer reassurance to those new to statistics, as well as direction for those who wish to advance their knowledge further. • The new Companion Website also includes assessment materials and datasets pertinent to lecturers/instructors in Business and Management and Health Sciences; and a suite of other materials for lecturers/instructors and students as per the Second Edition. Visit the demo Companion website at www.sagepub.co.uk/field3edemo. Introducing Statistical Methods series March ]NN 2009 • 880 pages

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Cloth (978-1-84787-906-6) • £90.00 Paper (978-1-84787-907-3) • £34.99

(TRUFSNTS\JGXNYJ Be sure to visit the companion website at http://www.sagepub.co.uk/discoveringstatisticsusingspss.html to find a range of teaching and learning material for both lecturers and students including the following:

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Instructor’s notes: A password protected Instructors’ Manual is provided on the website with teaching notes, including: — A tutors’ guide indicating how the subject might best be taught with insights into debriefing the exercise and cases studies found within the text book. — Assessment resources: A wide range of multiple choice, short and long answer assessment questions with test generation capabilities. This section also includes model answers for long and short answer questions. — Teaching resources: An array of extra case studies, and inclass exercises with methods and debriefing sheets to aid in the quality of the learning experience for students. PowerPoint slides: PowerPoint slides for each chapter for use in class are also provided in the Instructors’ Manual on the website. The slides can be edited by instructors to suit teaching styles and needs. Teacher interaction portal: A portal direct to the textbook authors for feedback on text book related feedback, continuous improvement, recommendations, case contributions and general Q&A.

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SAGE Online readings: Full access is provided to selected Sage journal articles related to each chapter, summaries of which are given on the website and at the end of each chapter. Links to relevant websites: Direct links to related websites for each chapter are provided as appropriate. Online glossary: A searchable glossary of key concepts in the text is also available to students online. Test bank: A Sample of selective multiple choice and essay questions with model answers are available for students to test and challenge themselves. Interaction portal: Direct access to the authors to offer your own pictures that you believe represent key concepts, to communicate your glowing praise, or suggestions for improvements, and general Q&A

PREFACE Introduction What’s New? Goodbye HOW TO USE THIS BOOK What Background Knowledge Do I Need? Do The Chapters Get More Difficult As I Go Through The Book? Why Do I Keep Seeing Stupid Faces Everywhere? What Is On The CD-Rom? Acknowledgements Dedication Symbols Used In This Book Mathematical Operators Greek Symbols English Symbols Some Maths Revision WHY IS MY EVIL LECTURER FORCING ME TO LEARN STATISTICS? What Will This Chapter Tell Me? What The Hell Am I Doing Here? I Don’t Belong Here Initial Observation: Finding Something That Needs Explaining Generating Theories And Testing Them Data Collection Part 1: What To Measure Data Collection Part 2: How To Measure ANALYZING DATA EVERYTHING YOU EVER WANTED TO KNOW ABOUT STATISTICS (WELL, SORT OF) What Will This Chapter Tell Me? Building Statistical Models Populations And Samples Simple Statistical Models Going Beyond The Data Using Statistical Models To Test Research Questions THE SPSS ENVIRONMENT What Will This Chapter Tell Me? Versions Of SPSS Getting Started The Data Editor The SPSS Viewer The SPSS Smartviewer The Syntax Window Saving Files Retrieving A File EXPLORING DATA WITH GRAPHS What Will This Chapter Tell Me? The Art Of Presenting Data The SPSS Chart Builder Histograms: A Good Way To Spot Obvious Problems Boxplots (Box-Whisker Diagrams) Graphing Means: Bar Charts And Error Bars Line Charts Graphing Relationships: The Scatterplot Editing Graphs EXPLORING ASSUMPTIONS What Will This Chapter Tell Me? What Are Assumptions? Assumptions Of Parametric Data

The Assumption Of Normality Testing Whether A Distribution Is Normal Testing For Homogeneity Of Variance Correcting Problems In The Data CORRELATION What Will This Chapter Tell Me? Looking At Relationships How Do We Measure Relationships? Data Entry For Correlation Analysis Using SPSS Bivariate Correlation Partial Correlation Comparing Correlations Calculating The Effect Size How To Report Correlation Coefficents What Have I Discovered About Statistics? REGRESSION What Will This Chapter Tell Me? An Introduction To Regression Doing Simple Regression On SPSS Interpreting A Simple Regression Multiple Regression: The Basics How Accurate Is My Regression Model? How To Do Multiple Regression Using SPSS Interpreting Multiple Regression What If I Violate An Assumption? How To Report Multiple Regression Categorical Predictors And Multiple Regression LOGISTIC REGRESSION What Will This Chapter Tell Me? Background To Logistic Regression What Are The Principles Behind Logistic Regression? Assumptions And Things That Can Go Wrong Binary Logistic Regression: An Example That Will Make You Feel Eel Interpreting Logistic Regression How To Report Logistic Regression Testing Assumptions: Another Example Predicting Several Categories: Multinomial Logistic Regression COMPARING TWO MEANS What Will This Chapter Tell Me? Looking At Differences The T-Test The Dependent T-Test The Independent T-Test Between Groups Or Repeated Measures? The T-Test As A General Linear Model What If My Data Are Not Normally Distributed? COMPARING SEVERAL MEANS: ANOVA What Will This Chapter Tell Me? The Theory Behind ANOVA Running One-Way ANOVA On SPSS Output From One-Way ANOVA Calculating The Effect Size Reporting Results From One-Way Independent ANOVA Violations Of Assumptions In One-Way Independent ANOVA ANALYSIS OF COVARIANCE, ANCOVA What Will This Chapter Tell Me? What Is ANCOVA?

Assumptions And Issues In ANCOVA Conducting ANCOVA On SPSS Interpreting The Output From ANCOVA ANCOVA Run As A Multiple Regression Testing The Assumption Of Homogeneity Of Regression Slopes Calculating The Effect Size Reporting Results What To Do When Assumptions Are Violated In ANCOVA FACTORIAL ANOVA What Will This Chapter Tell Me? Theory Of Factorial ANOVA (BetweenGroups) Factorial ANOVA Using SPSS Output From Factorial ANOVA Interpreting Interaction Graphs Calculating Effect Sizes Reporting The Results Of Two-Way ANOVA Factorial ANOVA As Regression What To Do When Assumptions Are Violated In Factorial ANOVA REPEATED MEASURES DESIGNS What Will This Chapter Tell Me? Introduction To Repeated Measures Designs Theory Of One-Way Repeated-Measures ANOVA One-Way Repeated Measures ANOVA Using SPSS Output For One-Way Repeated Measures ANOVA Effect Sizes For Repeated Measures ANOVA Reporting One-Way Repeated Measures ANOVA Repeated Measures With Several Independent Variables Output For Factorial Repeated Measures ANOVA Effect Sizes For Factorial Repeated Measures ANOVA Reporting The Results From Factorial Repeated Measures ANOVA What To Do When Assumptions Are Violated In Repeated Measures ANOVA MIXED DESIGN ANOVA What Will This Chapter Tell Me? Mixed Designs What Do Men And Women Look For In A Partner? Mixed ANOVA On SPSS Output For Mixed Factorial ANOVA: Main Analysis Calculating Effect Sizes Reporting The Results Of Mixed ANOVA NONPARAMETRIC TESTS What Will This Chapter Tell Me? When To Use Non-Parametric Tests Comparing Two Independent Conditions: The Wilcoxon Rank Sum Test And MannWhitney 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

MULTIVARIATE ANALYSIS OF VARIANCE (MANOVA) What Will This Chapter Tell Me? When To Use MANOVA Introduction: Similarities And Differences To ANOVA Theory Of MANOVA Practical Issues When Conducting MANOVA MANOVA On SPSS Output From MANOVA Reporting Results From MANOVA Following Up MANOVA With Discriminant Analysis Output From The Discriminant Analysis Reporting Results From Discriminant Analysis Some Final Remarks What To Do When Assumptions Are Violated In MANOVA EXPLORATORY FACTOR ANALYSIS What Will This Chapter Tell Me? When To Use Factor Analysis Factors Discovering Factors Research Example Running The Analysis Interpreting Output From SPSS How To Report Factor Analysis Reliability Analysis How To Report Reliability Analysis CATEGORICAL DATA What Will This Chapter Tell Me? Analysing Categorical Data Theory Of Analysing Categorical Data Assumptions Of The Chi-Square Test Doing Chi-Square On SPSS Several Categorical Variables: Log-Linear Analysis Assumptions In Loglinear Analysis Loglinear Analysis Using SPSS Output From Loglinear Analysis Following Up Loglinear Analysis Effect Sizes In Loglinear Analysis Reporting The Results Of Loglinear Analysis MULTILEVEL LINEAR MODELS What Will This Chapter Tell Me? Hierarchical Data Theory Of Multilevel Linear Models The Multilevel Model Some Practical Issues Multilevel Modelling On SPSS Growth Models How To Report A Multilevel Model Epilogue Glossary APPENDICES Table Of The Standard Normal Distribution Critical Values Of The T-Distribution Critical Values Of The F-Distribution Critical Values Of The Chi-Square Distribution References

T FORURN CHA S O A V PT MP ER ER LE

Contents

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I was born on 21st June 1973. Like most people, I don’t remember anything about the first few years of life and like most children I did go through a phase of driving my parents mad by asking ‘Why?’ every five seconds. “Dad, why is the sky blue?”, “Dad, why doesn’t mummy have a willy?” etc. Children are naturally curious about the world. I remember at the age of 3 being at a party of my friend Obe (this was just before he left England to return to Nigeria, much to my distress). It was a hot day, and there was an electric fan blowing cold air around the room. As I said, children are natural scientists and my little scientific brain was working through what seemed like a particularly pressing question: “What happens when you stick your finger into a fan”? The answer, as it turned out, was that it fucking hurts1 My point is this, my curiousity to explain the world never went away, and that’s why I’m a scientist, and that’s also why your evil lecturer is forcing you to learn statistics. It’s because you have a curious mind and you want to answer new and exciting questions. To answer these questions we need statistics. Statistics is a bit like sticking your finger in a revolving fan blade: sometimes it’s very painful, but it does give you the power to answer interesting questions. This chapter is going to attempt to explain why statistics are an important part of doing research. We will overview the whole research process, from why we conduct research in the first place, through how theories are generated to why we need data to test these theories. If that doesn’t convince you to read on then maybe the fact that we discover whether coca cola kills sperm will. You’re probably wondering why you have bought this book. Maybe you liked the pictures, maybe you fancied doing some weight training (it is heavy), or perhaps you need to reach something in a high place (it is thick). The chances are though that given the choice of spending your hard earned cash on a statistics book or something more entertaining (a nice novel, a trip to the cinema etc.) you’d chose the later. So, why have you bought it? It’s likely that you bought it because you’re doing a course on statistics, or you’re doing some research, and you need to know how to analyse data. It’s possible that you didn’t realise when you started your course or research that you’d have to know this much about statistics but now find yourself inexplicably wading, neck high, through the Victorian sewer that is data analysis.

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8RFWY&QJ] Alex is a very important characater because he appears when things get particularly difficult. He’s basically a bit of a smart Aleck and so whenever you see his face you know that something scary is about to be explained. When the hard stuff is over he disappears to let you know that it’s safe to continue. Now, this is not to say that all of the rest of the material in the book is easy, he just let’s you know the bits of the book that you can skip if you’ve got better things to do with your life than read all 800 pages! Incidentally, any physical similarity between smart Alex and my editor is entirely coincidental! 'WNFS-FJRTWWMFLJ Brian pops up to ask questions and looks permanently confused. It’s no surprise to note, therefore, that he doesn’t look interly different from the author. As the book progresses he becomes increasingly despondent. Read into that what you will…

(ZWNTZX(FY He also pops up and asks questions (because he’s curious). Actually the only reason he’s here is because I wanted a cat in the book… and preferably one that looks like mine.

(WFRRNSL 8FR Sam hates statistics. In fact, she things it’s all a boring waste of time and she just wants to pass her exam and forget that she ever had to know anything about normal distributions. So, she appears and gives you a summary of the key points that you need to know. So, if like Sam, you’re cramming for an exam, she will tell you the essential information to save you having to trawl through hundresd of pages of my drivel.

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8FYFS¹X 5JWXTSFQ 8YFYNXYNHX 8QF[J Satan is a busy boy – he has all of the lost souls to torture in hell, then there’s the fires to keep fuelled, not to mention organising enough carnage on the planet’s surface to keep Norwegian black metal bands inspired. Like many of us, this leaves little time for him to analyse data, and this makes him very sad. So, he has his own personal save, who, also like some of us, spends all day dressed in a gimp mask and tight leather pants in front of SPSS analysing Satan’s data. Consequently, he knows a thing to two about SPSS, and when satan’s busy spanking a goat, he pops up in this book with SPSS’s tips.

4QN[JW9\NXYJI With apologies to Charles Dickens, Oliver, like his more famous fictional London urchin is always asking ‘Please sir, can I have some more?’ unlike master Twist though, our young master Twisted, is always wanting more statistics infrmation. Of course he does, who wouldn’t? Let us not be the ones to disappoint a young dirty, slightly smelly boy who dines on gruel, so when Oliver appears you can be certain of one thing: there is additional information to be found on the companions website. (There are literally hundreds of pages of extra material, so don’t be shy; download it a bath in the warm asps milk of additional material). /FSJ8ZUJWGWFNS Jane is the cleverest person in the whole universe (she makes Smart Alex look like a bit of an imbecile). The reason she is so clever is that she steals the brains of statisticians and eats them. Apparently they taste of sweaty tank tops, but nevertheless she likes them. As it heppens she is also able to absorb the contents of brains while she eats them. Having devoured some top statistics brains she knows all the really hard stuff and appears in boxes to tell you really advance things that are a bit tangential to the main text. (Readers should note that Jane wasn’t interested in eating my brain. Read into that what you will about my statistics ability.)

1FGHTFY1JSNLabcoat Leni is the cleverest person in the whole universe (she makes Smart Alex look like a bit of an imbecile). The reason she is so clever is that she steals the brains of statisticians and eats them. Apparently they taste of sweaty tank tops, but nevertheless she likes them. As it heppens she is also able to absorb the contents of brains while she eats them. Having devoured some top statistics brains she knows all the really hard stuff and appears in boxes to tell you really advance things that are a bit tangential to the main text. (Readers should note that Jane wasn’t interested in eating my brain. Read into that what you will about my statistics ability.)

8JQKYJXY Self-test is the cleverest person in the whole universe (she makes Smart Alex look like a bit of an imbecile). The reason she is so clever is that she steals the brains of statisticians and eats them.

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<MFY\NQQYMNXHMFUYJWYJQQRJ$ I was born on 21st June 1973. Like most people, I don’t remember anything about the first few years of life and like most children I did go through a phase of driving my parents mad by asking ‘Why?’ every five seconds. “Dad, why is the sky blue?”, “Dad, why doesn’t mummy have a willy?” etc. Children are naturally curious about the world. I remember at the age of 3 being at a party of my friend Obe (this was just before he left England to return to Nigeria, much to my distress). It was a hot day, and there was an electric fan blowing cold air around the room. As I said, children are natural scientists and my little scientific brain was working through what seemed like a particularly pressing question: “What happens when you stick your finger into a fan”? The answer, as it turned out, was that it fucking hurts1 My point is this, my curiousity to explain the world never went away, and that’s why I’m a scientist, and that’s also why your evil lecturer is forcing you to learn statistics. It’s because you have a curious mind and you want to answer new and exciting questions. To answer these questions we need statistics. Statistics is a bit like sticking your finger in a revolving fan blade: sometimes it’s very painful, but it does give you the power to answer interesting questions. This chapter is going to attempt to explain why statistics are an important part of doing research. We will overview the whole research process, from why we conduct research in the first place, through how theories are generated to why we need data to test these theories. Statistics is a bit like sticking your finger in a revolving fan blade: sometimes it’s very painful, but it does give you the power to answer interesting questions. This chapter is going to attempt to explain why statistics are an important part of doing research. We will overview the whole research process, from why we conduct research in the first place, through how theories are generated to why we need data to test these theories. We will overview the whole research process, from why we conduct research in the first place, through how theories are generated to why we need data to test these theories. We will overview the whole research process. In the 1970s fans didn’t have helpful protective cages around them to prevent idiotic 3 year olds sticking their fingers into the blades.

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<MFYYMJMJQQFR.ITNSLMJWJ$.ITS¹YGJQTSLMJWJ You’re probably wondering why you have bought this book. Maybe you liked the pictures, maybe you fancied doing some weight training (it is heavy), or perhaps you need to reach something in a high place (it is thick). The chances are though that given the choice of spending your hard earned cash on a statistics book or something more entertaining (a nice novel, a trip to the cinema etc.) you’d chose the later. M

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Group 1 (positive reinforcement): During seminars I congratulate all students in this group on their hard ork and success. — Even when people get things wrong, I am supportive and say things like ‘that was very nearly the right answer. — Your’re coming along really well’ and then give the student a nice pice of chocolate. Group 2 (negative reinforcement): This group receives seminars in which I give relentless verbal abuse to all of the students even when they give the correct anser. I demean their contribution and am patronizing and dismissive of everything they say. I tell students that they are stupid, worthless and shouldn’t be doing the course at all. Group 3 (no reinforcement): This group receives normal university style seminars (some might argue that this is the same as group 2!). Students are not praised or punished and instead I give them no feedback at all.

So, why have you bought it? It’s likely that you bought it because you’re doing a course on statistics, or you’re doing some research, and you need to know how to analyse data. It’s possible that you didn’t realise when you started your course or research that you’d have to know this much about statistics but now find yourself inexplicably wading, neck high, through the Victorian sewer that is data analysis. The answer to ‘what the hell are you doing here?’ is, therefore, simple: to answer interesting questions you need data. Therefore, one of the reasons why your evil statistics lecturer is forcing you to learn about numbers is because they are a form of data and are vital to the research process. Of course there are forms of data other than numbers that can be used to test and generate theories. When numbers are involved the research is known as glossary term, but you can also generate and test theories by analysing language (such as conversations, magazine articles, media broadcasts and so on). This is known as glossary term and it is a topic for another book not written by me. People can get quite passionate about which of these methods is best, which is a bit silly because they are complimentary, not competing, approaches and there are much more important issues in the worldt2. So, why have you bought it? It’s likely that you bought it because you’re doing a course on statistics, or you’re doing some research, and you need to know how to analyse data. It’s possible that you didn’t realise when you started your course or research that you’d have to know this much about statistics but now find yourself inexplicably wading, neck high, through the Victorian sewer that is data analysis. We will overview the whole research process, from why we conduct glossary term in the first place, through how theories are generated. So, why have you bought it? It’s likely that you bought it because you’re doing a course on statistics, or you’re doing some research, and you need to know how to analyse data. It’s possible that you didn’t realise when you started your course or research that you’d have to know. This is a joke. I thought long and hard about whether to include it because, like many of my jokes, there are people who won’t find it remotely funny. Its inclusion is also making me fear being hunted down and forced to eat my own entrails by a hoard of rabid qualitative researchers. However, it made me laugh, a lot, and despite being vegetarian I’m sure my entrails will taste lovely.

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9MJWJXJFWHMUWTHJXX How do you go about answering an interesting question? The research process is broadly sumHow do I do marised in Figure 1.1. You begin with an obserresearch? vation that you want to understand, and this observation could be anecdotal (you’ve noticed that your cat watches birds when they’re on TV but not when jellyfish are on3), or could be based on some data (you have got several cat owners to keep diaries of their cat’s TV habits and have noticed that lots of them watch birds on TV). From your initial observation you generate explanations, or theories, of those observations, from which you can make predictions (hypotheses). Here’s where the data comes into the process because to test your predictions you need data. First you collect some relevant data (and to do that you need to identify things that can be measured) and then you analyse those data. The glossary term of the data may support your theory or give you cause to modify the theory.

9MJRTIJ I was born on 21st June 1973. Like most people, I don’t remember anything about the first few years of life and like most children I did go through a phase of driving my parents mad by asking ‘Why?’ every glossary term seconds. “Dad, why is the sky blue?”, “Dad, why doesn’t mummy have a willy?” etc. Children are naturally curious about the world. I remember at the age of 3 being at a party of my friend Obe (this was just before he left England to return to Nigeria, much to my distress). X

= 22 + 40 + 53 + 57 + 93 + 98 + 103 + 108 + 116 + 121 + 252 = 1063

It was a hot day, and there was an electric fan blowing cold air around the room. As I said, children are natural scientists and my little scientific brain was working through what seemed like a particularly pressing question: “What happens when you stick your finger into a fan”?

9MJRJINFS To answer these questions we need statistics. Statistics is a bit like sticking your finger in a revolving fan blade: sometimes it’s very painful, but it does give you the power to answer interesting questions. This chapter is going to attempt to explain why statistics are an important part of doing research. We will overview the whole research process, from why we conduct glossary term in the first place, through how theories are generated to why we need data to test these theories. If that doesn’t convince you to read on then maybe the fact that we discover whether coca cola kills sperm will. You’re probably wondering why you have bought this book. To answer these questions we need statistics. Statistics is a bit like sticking your finger in a revolving fan blade: sometimes it’s very painful, but it does give you the power to answer interesting questions. This chapter is going to attempt to explain why statistics are an important part of doing research. We will overview the whole research process, from why we conduct glossary term in the first place, through how theories are generated to why we need data to test these theories. If that doesn’t convince you to read on then maybe the fact that we discover whether coca cola. To answer these questions we need statistics. Statistics is a bit like sticking your finger in a revolving fan blade: sometimes it’s very painful, but it does give you the power to answer interesting questions. To answer these questions we need statistics.

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To answer these questions we need statistics. Statistics is a bit like sticking your finger in a revolving fan blade: sometimes itâ€™s very painful, but it does give you the power to answer interesting questions. 8*1+9*89 Based on what you have read in this section, what qualities do you think a scientific theory should have?

.SNYNFQTGXJW[FYNTSĂ‚SINSLXTRJYMNSL YMFYSJJIXJ]UQFSNSL As I said, children are natural scientists and my little scientific brain was working through what seemed like a particularly pressing question: â€œWhat happens when you stick your finger into a fanâ€?? The answer, as it turned out, was that it fucking hurts1 My point is this, my curiousity to explain the world never went away, and thatâ€™s why Iâ€™m a scientist, and thatâ€™s also why your evil lecturer is forcing you to learn statistics. Itâ€™s because you have a curious mind www.webaddress.com and you want to answer new and exciting questions. To answer these questions we need statistics. Statistics is a bit like sticking your finger in a revolving fan blade: sometimes itâ€™s very painful, but it does give you the power to answer interesting questions. Itâ€™s because you have a curious mind www.webaddress.com and you want to answer new and exciting questions. To answer these questions we need statistics. Statistics is a bit like sticking your finger in a revolving fan blade: sometimes itâ€™s very painful, but it does give you the power to answer interesting questions. To answer these questions we need statistics. Statistics is a bit like sticking your finger in a revolving fan blade. To answer these questions we need statistics.

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Data

The research process

Initial observation (research question)

Generate theory

Identify variables

Generate hypotheses

Measure variables

Collect data to test theory

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Graph data Fit a model

Analyse data

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1 I was born on 21st June 1973. Like most people, I don’t remember anything about the first few years of life and like most children I did go through a phase of driving my parents mad by asking ‘Why?’ every five seconds. a. “Dad, why is the sky blue?” b. “Dad, why doesn’t mummy have a willy?” etc. 2 Children are naturally curious about the world. I remember at the age of 3 being at a party of my friend Obe (this was just before he left England to return to Nigeria, much to my distress). It was a hot day, and there was an electric fan blowing cold air around the room. 3 As I said, children are natural scientists and my little scientific brain was working through what seemed like a particularly pressing question: “What happens when you stick your finger into a fan”? 4 The answer, as it turned out, was that it fucking hurts1 My point is this, my curiousity to explain the world never went away, and that’s why I’m a scientist, and that’s also why your evil lecturer is forcing you to learn. To answer these questions we need statistics. Statistics is a bit like sticking your finger in a revolving fan blade: sometimes it’s very painful, but it does give you the power to answer interesting questions. This chapter is going to attempt to explain why statistics are an important part of doing research. To answer these questions we need statistics. Statistics is a bit like sticking your finger in a revolving fan blade: sometimes it’s very painful, but it does give you the power to answer interesting questions. This chapter is going to attempt to explain why statistics are an important part of doing research. To answer these questions we need statistics. Statistics is a bit like sticking your finger in a revolving fan blade: sometimes it’s very painful, but it does give you the power to answer interesting questions. This chapter is going to attempt to explain why statistics are an important part of doing research. To answer these questions we need statistics. +.,:7*

Click on the apropriate cell in the column labelled Values.

Defining coding variables and their values in SPSS

Then, click on

This activates the Value Labels dialog

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I was born on 21st June 1973. Like most people, I don’t remember anything about the first few years of life and like most children I did go through a phase of driving my parents mad by asking ‘Why?’ every five Second. “Dad, why is the sky blue?”, “Dad, why doesn’t mummy have a willy?” etc.

;FWNFGQJX This icon implies naturally curious about the world. I remember at the age of 3 being at a party of my friend Obe (this was just before he left England to return to Nigeria, much to my distress).

It was a hot day, and there was an electric fan blowing cold air around the room. As I said, children are natural scientists and my little scientific brain was working through what seemed like a particularly pressing question: “What happens when you stick your finger into a fan”? The answer, as it turned out, was that it fucking hurts1 My point is this, my curiousity to explain the world never went away. It was a hot day, and there was an electric fan blowing cold air around the room. As I said, children are natural scientists and my little scientific brain was working through what seemed like a particularly pressing question: “What happens when you stick your finger into a fan”? It was a hot day, and there was an electric fan blowing cold air around the room. As I said, children are natural scientists and my little scientific brain. “What happens when you stick your finger into a fan”? It was a hot day, and there was an electric fan blowing cold air around the room. “What happens when you stick your finger into a fan”? It was a hot day, and there was an electric fan blowing cold.

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important terms

When doing research there are some important generic terms for variables that you will encounter: Independent variable: a variable thought to be the cause of some effect. This term is usually used in experimental research to denote a variable that the experimenter has manipulated. Dependent variable: a variable thought to be affected by changes in an independent variable. You can think of this variable as an outcome. Predictor variable: a variable thought to predict an outcome variable. This is basically another term for independent variable. Outcome variable: a variable thought to change as a function of changes in a predictor variable. This term could be synonymous with ‘dependent variable’ for the sake of an easy life.

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I was born on 21st June 1973. Like +.,:7* most people, I don’t remember anything Abraham Wald writing, ‘I must about the first few years of life and like not devise test statistics prone to most children I did go through a phase of having inflated standard errors’ driving my parents mad by asking ‘Why?’ on the blackboard 100 times. every five seconds. “Dad, why is the sky blue?”, “Dad, why doesn’t mummy have a willy?” etc. Children are naturally curious about the world. I remember at the age of 3 being at a party of my friend Obe (this was just before he left England to return to Nigeria, remember at the age of 3 being at a party of my friend fore he left England to return to Nigeria, day, and there was an electric fan blowing cold air around the room. As I said, children are glossary term scientists and my little scientific brain was working through what seemed like a particularly pressing question. To answer these questions we need statistics. Statistics is a bit like sticking your finger in a revolving fan blade: sometimes it’s very painful, but it does give you the power to answer interesting questions. This chapter is going to attempt to explain why statistics are an important part of doing research. To answer these questions we need statistics. Statistics is a bit like sticking your finger in a revolving fan blade: sometimes it’s very painful power to answer interesting questions. Statistics is a bit like sticking your finger in a revolving fan blade.

/&3*8:5*7'7&.3 When is a hypothesis not a hypothesis? A good theory should allow us to make statements about the state of the world. Statements about the world are good things: they allow us to make sense of our world, and to make decisions that affect our future. One current example is global warming. Being able to make a definitive statement that global warming is happening, and that it is caused by certain practices in society allows us to change these practices and, hopefully, avert catastrophe. However, not all statements are ones that can be tested using science. Scientific statements are ones that can be verified with reference to empirical evidence, whereas non-scientific statements are ones that cannot be empirically tested. So, statements such as ‘The Led Zeppelin reunion concert in London in 2007 was the best

gig ever5’, ‘Lindt chocolate is the best food’, and ‘This is the worst statistics book in the world’ are all nonscientific; they cannot be proved or disproved. Scientific statements can be confirmed or disconfirmed empirically. ‘Watching ‘Curb your enthusiasm’ makes you happy’, ‘having sex increases levels of the neurotransmitter dopamine’ and ‘Velociraptors ate meat’ are all things that can be tested empirically (provided you can quantify and measure the variables concerned). Non-scientific statements can sometimes be altered to become scientific statements, so, ‘The Beatles were the most influential band ever’ is non-scientific (because it is probably impossible to quantify ‘influence’ in any meaningful way) but by changing the statement to ‘The Beatles were the best selling band ever’ it becomes testable (we can collect data about worldwide record sales and establish whether The Beatles have, in fact, sold more records than any other music artist). Karl Popper, the famous philosopher of science, believed that nonscientific statements. Statements about the world are good things. Non-scientific statements can sometimes be altered to become scientific statements, so, ‘The Beatles were the most influential band ever’ is non-scientific (because it is probably impossible to quantify ‘influence’ in any meaningful way) but by changing the statement.

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9MJWJHNUWTHFQYWFSXKTWRFYNTSTS8588 I was born on 21st June 1973. Like most people, I don’t remember anything about the first few years of life and like most children I did go through a phase of driving my parents mad by asking ‘Why?’ every five seconds. “Dad, why is the sky blue?”, “Dad, why doesn’t mummy have a willy?” etc. Children are naturally curious about the world. I remember at the age of 3 being at a party of my friend Obe (this was just before he left England to return to Nigeria, much to my distress). It was a hot day, and there was an electric fan glossary term air around the room. As I said, children are natural scientists and my little scientific brain was working through what seemed like a particularly pressing question: “What happens when you stick your finger into a fan”? The answer, as it turned out, was that it fucking hurts1 My point is this, my curiousity to explain the world never went away, and that’s why I’m a scientist, and that’s also why your evil lecturer is forcing you to learn statistics. It’s because you have a curious mind and you want to answer new and exciting questions. To answer these questions we need statistics. The answer, as it turned out, was that it fucking hurts1 My point is this, my curiousity to explain the world never went away, and that’s why I’m a scientist, and that’s also why your evil lecturer is forcing you to learn statistics. It’s because you have a curious mind and you want to answer new and exciting questions. To answer these questions we need statistics. The answer, as it turned out, was that it fucking hurts1 My point is this, my curiousity to explain the world. The answer, as it turned out, was that it fucking hurts1 My point is this, my curiousity to explain the world never went away, and that’s why I’m a scientist. The answer, as it turned out, was that it fucking hurts1 My point is this, my curiousity to explain the world never went away.

The editing functions on SPSS are quite a lot better than they used to be and it’s possible to create some very tasteful graphs. However, these Please, Sir, can I facilities are so extensive that I could probably write a whole book on have some more … them. In the interests of saving trees, I have prepared a tutorial that is available in the additional material that can be downloaded from the graphs? facilities are so extensive that I could probably write a whole book on companion website. In particular we look at an example of how to edit an error bar chart to make it conform to some of the guidelines that I talked about at the beginning of this chapter. In doing so we will look at how to edit the axes, add grid lines, change the bar colours, change the background and borders. It’s a very extensive tutorial change the bar colours, change the background and borders!

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<MFYMF[J.INXHT[JWJIFGTZYXYFYNXYNHX$ You’re probably wondering why you have bought this book. Maybe you liked the pictures, maybe you fancied doing some weight training (it is heavy), or perhaps you need to reach something in a high place (it is thick). The chances are though that given the choice of spending your hard earned cash on a statistics book or something more entertaining (a nice novel, a trip to the cinema etc.) you’d chose the later. So, why have you bought it? It’s likely that you bought it because you’re doing a course on statistics, or you’re doing some research, and you need to know how to analyse data. It’s possible that you didn’t realise when you started your course or research that you’d have to know this much about statistics.

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I was born on 21st June 1973. Like most people, I don’t remember anything about the first few years of life and like most children I did go through a phase of driving my parents mad by asking ‘Why?’ every five seconds. “Dad, why is the sky blue?”, “Dad, why doesn’t mummy have a willy?” etc. Children are naturally curious about the world. I remember at the age of 3 being at a party of my friend Obe (this was just before he left England to return to Nigeria, much to my distress). It was a hot day, and there was an electric fan blowing cold air around the room. As I said, children are natural scientists and my little scientific brain was working through what seemed like a particularly pressing question: “What glossary term when you stick your finger into a fan”? The answer, as it turned out, was that it fucking hurts. I was born on 21st June 1973. Like most people, I don’t remember anything about the first few years of life and like most children I did go through a phase of driving my parents mad by asking ‘Why?’ every five seconds. “Dad, why is the sky blue?”, “Dad, why doesn’t mummy have a willy?” etc. Children are naturally curious about the world. I remember at the age of 3 being at a party of my friend Obe (this was just before he left England to return to Nigeria, much to my distress). Children are naturally curious about the world. I remember at the age of 3 being at a party of my friend Obe.

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CHAMORRO-PREMUZIC, T., ET AL. (2008). PERSONALITY AND INDIVIDUAL DIFFERENCES, 44, 965-976.

Why do you like your lecturers? As students you probably have to rate your lecturers at the end of the course. They’ll be some lecturers you like and others that you hate. As a lecturer I find this process horribly depressing (although this has a lot to do with the fact I tend focus on negative feedback and ignore the good stuff). There is some evidence that students tend to pick courses of lecturers who they perceive to be enthusastic and good communicators. In a fascinating study, Tomas Chamorro-Premuzic and his colleagues (Chamorro-Premuzic, Furnham, Christopher, Garwood, & Martin, 2008) tested a slightly different hypothesis, which was that students tend to like lecturers who are like themselves. (This hypothesis will have the students on my course who like my lectures screaming in horror …) First of all they measured students own personalities using a very wellestablished measure (the NEO-FFI) which gives rise to scores on five fundamental personality traits: Neuroticism, Extroversion, Openness to experience, Agreeableness, and Conscientiousness. They also gave students a questionnaire that asked them to rate how much they wanted their lecturer to have each of a list of characteristics. For example, they would be given the description “warm: friendly, warm, sociable, cheerful,

affectionate, outgoing” and asked to rate how much they wanted to see this in a lecturer from -5 (they don’t want this characteristic at all) through 0 (the characteristic is not important) to +5 (I really want this characteristic in my lecturer). The characteristics on the questionnaire all related personality characteristics measured by the NEOFFI. As such, the authors had a measure of how much a student had each of the five core personality characteristics, but also a measure of how much they wanted to see those same characteristics in their lecturer. In doing so, Tomas and his colleagues could test whether, for instance, extroverted students want extrovert lecturers. The data from this study (well, for the variables that I’ve mentioned) are in the file Chamorro- Premuzic. sav. Run some Pearson correlations on these variables to see if students with certain personality characteristics want to see those characteristics in their lecturers. What conclusions can you draw? Answers are in the additional material for this website (or look at Table 3 in the original article, which will also show you how to report. Run some Pearson correlations on these variables to see if students with certain personality characteristics want to see those Run some Pearson correlations on these variables to see if students characteristics in their lecturers. Answers are in the additional material for this website (or look at Table 3 in the original article, which will also show you how to report. Run some Pearson correlations on these variables to see if students with certain personality characteristics want to see those characteristics in their lecturers. What conclusions can you draw?

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Andy field’s discovering Statistics Using SPSS, 3rd edition ‘Field has introduced important new introductory material on statistics... This book is the best blend that I know of a textbook in statistics and a manual on SPSS’ - David c. howell, Professor (Emeritus), University of Vermont The award-winning textbook for students of statistics, now fully up-to-date with SPSS 16: the only textbook your students will need for their entire course! This new edition has been substantially expanded and carefully reorganized by level of study to support students from the beginning of their ﬁrst year right through to advanced and postgraduate level statistics courses.

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Discovering Statistics Using SPSS, sample chapter

Published on Apr 29, 2009

This sample chapter from Andy Field's new book gives you a taster - visit the website to find out more!

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