Details

Psychology Statistics For Dummies

1. Aufl.

von: Donncha Hanna, Martin Dempster
16,99 €
Verlag:	Wiley
Format:	EPUB
Veröffentl.:	10.08.2012
ISBN/EAN:	9781119953944
Sprache:	englisch
Anzahl Seiten:	464

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Beschreibungen

Titelbeschreibung

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: <ul> <li>Serves as an easily accessible supplement to doorstop-sized psychology textbooks</li> <li>Provides psychology students with psychology-specific statistics instruction</li> <li>Includes clear explanations and instruction on performing statistical analysis</li> <li>Teaches students how to analyze their data with SPSS, the most widely used statistical packages among students</li> </ul>

Inhaltsverzeichnis

Introduction 1 About This Book 2 What You’re Not to Read 2 Foolish Assumptions 3 How this Book is Organised 3 Icons Used in This Book 4 Where to Go from Here 5 Part I: Describing Data 7 Chapter 1: Statistics? I Thought This Was Psychology! 9 Know Your Variables 10 What is SPSS? 11 Descriptive Statistics 12 Central tendency 12 Dispersion 12 Graphs 13 Standardised scores 13 Inferential Statistics 13 Hypotheses 14 Parametric and non-parametric variables 14 Research Designs 15 Correlational design 15 Experimental design 16 Independent groups design 16 Repeated measures design 17 Getting Started 18 Chapter 2: What Type of Data Are We Dealing With? 19 Understanding Discrete and Continuous Variables 20 Looking at Levels of Measurement 21 Measurement properties 21 Types of measurement level 23 Determining the Role of Variables 24 Independent variables 25 Dependent variables 25 Covariates 26 Chapter 3: Inputting Data, Labelling and Coding in SPSS 27 Variable View Window 28 Creating variable names 29 Deciding on variable type 30 Displaying the data: The width, decimals, columns and align headings 32 Using labels 33 Using values 34 Dealing with missing data 36 Assigning the level of measurement 37 Data View Window 39 Entering new data 40 Creating new variables 42 Sorting cases 43 Recoding variables 45 Output Window 48 Using the output window 48 Saving your output 51 Chapter 4: Measures of Central Tendency 53 Defining Central Tendency 54 The Mode 55 Determining the mode 55 Knowing the advantages and disadvantages of using the mode 58 Obtaining the mode in SPSS 59 The Median 64 Determining the median 64 Knowing the advantages and disadvantages to using the median 66 Obtaining the median in SPSS 67 The Mean 68 Determining the mean 68 Knowing the advantages and disadvantages to using the mean 69 Obtaining the mean in SPSS 69 Choosing between the Mode, Median and Mean 71 Chapter 5: Measures of Dispersion 73 Defining Dispersion 73 The Range 74 Determining the range 74 Knowing the advantages and disadvantages of using the range 75 Obtaining the range in SPSS 76 The Interquartile Range 78 Determining the interquartile range 78 Knowing the advantages and disadvantages of using the interquartile range 81 Obtaining the interquartile range in SPSS 82 The Standard Deviation 83 Defining the standard deviation 83 Knowing the advantages and disadvantages of using the standard deviation 87 Obtaining the standard deviation in SPSS 87 Choosing between the Range, Interquartile Range and Standard Deviation 89 Chapter 6: Generating Graphs and Charts 91 The Histogram 91 Understanding the histogram 92 Obtaining a histogram in SPSS 96 The Bar Chart 98 Understanding the bar chart 98 Obtaining a bar chart in SPSS 100 The Pie Chart 101 Understanding the pie chart 101 Obtaining a pie chart in SPSS 103 The Box and Whisker Plot 103 Understanding the box and whisker plot 104 Obtaining a box and whisker plot in SPSS 107 Part II: Statistical Significance 111 Chapter 7: Understanding Probability and Inference 113 Examining Statistical Inference 113 Looking at the population and the sample 114 Knowing the limitations of descriptive statistics 115 Aiming to be 95 per cent confident 116 Making Sense of Probability 117 Defining probability 118 Considering mutually exclusive and independent events 118 Understanding conditional probability 121 Knowing about odds 122 Chapter 8: Testing Hypotheses 123 Understanding Null and Alternative Hypotheses 123 Testing the null hypothesis 124 Defining the alternative hypothesis 124 Deciding whether to accept or reject the null hypothesis 125 Taking On Board Statistical Inference Errors 127 Knowing about the Type I error 128 Considering the Type II error 128 Getting it right sometimes 129 Looking at One- and Two-Tailed Hypotheses 130 Using a one-tailed hypothesis 131 Applying a two-tailed hypothesis 131 Confidence Intervals 132 Defining a 95 per cent confidence interval 132 Calculating a 95 per cent confidence interval 133 Obtaining a 95 per cent confidence interval in SPSS 135 Chapter 9: What’s Normal about the Normal Distribution? 139 Understanding the Normal Distribution 140 Defining the normal distribution 140 Determining whether a distribution is approximately normal 141 Determining Skewness 144 Defining skewness 144 Assessing skewness graphically 145 Obtaining the skewness statistic in SPSS 147 Looking at the Normal Distribution and Inferential Statistics 150 Making inferences about individual scores 151 Considering the sampling distribution 152 Making inferences about group scores 153 Chapter 10: Standardised Scores 155 Knowing the Basics of Standardised Scores 155 Defining standardised scores 156 Calculating standardised scores 156 Using Z Scores in Statistical Analyses 159 Connecting Z scores and the normal distribution 160 Using Z scores in inferential statistics 161 Chapter 11: Effect Sizes and Power 165 Distinguishing between Effect Size and Statistical Significance 165 Exploring Effect Size for Correlations 166 Considering Effect Size When Comparing Differences Between Two Sets of Scores 167 Obtaining an effect size for comparing differences between two sets of scores 167 Interpreting an effect size for differences between two sets of scores 170 Looking at Effect Size When Comparing Differences between More Than Two Sets of Scores 171 Obtaining an effect size for comparing differences between more than two sets of scores 171 Interpreting an effect size for differences between more than two sets of scores 177 Understanding Statistical Power 178 Seeing which factors influence power 179 Considering power and sample size 180 Part III: Relationships between Variables 183 Chapter 12: Correlations 185 Using Scatterplots to Assess Relationships 185 Inspecting a scatterplot 186 Drawing a scatterplot in SPSS 189 Understanding the Correlation Coefficient 190 Examining Shared Variance 191 Using Pearson’s Correlation 192 Knowing when to use Pearson’s correlation 192 Performing Pearson’s correlation in SPSS 193 Interpreting the output 195 Writing up the results 197 Using Spearman’s Correlation 198 Knowing when to use Spearman’s correlation 198 Performing Spearman’s correlation in SPSS 199 Interpreting the output 201 Writing up the results 201 Using Kendall’s Correlation 202 Performing Kendall’s correlation in SPSS 203 Interpreting the output 204 Writing up the results 205 Using Partial Correlation 206 Performing partial correlation in SPSS 206 Interpreting the output 208 Writing up the results 208 Chapter 13: Linear Regression 211 Getting to Grips with the Basics of Regression 212 Adding a regression line 212 Working out residuals 214 Using the regression equation 215 Using Simple Regression 217 Performing simple regression in SPSS 217 Interpreting the output 218 Writing up the results 222 Working with Multiple Variables: Multiple Regression 223 Performing multiple regression in SPSS 224 Interpreting the output 225 Writing up the results 229 Checking Assumptions of Regression 230 Normally distributed residuals 230 Linearity 232 Outliers 234 Multicollinearity 238 Homoscedasticity 240 Type of data 242 Chapter 14: Associations between Discrete Variables 243 Summarising Results in a Contingency Table 244 Observed frequencies in contingency tables 244 Percentaging a contingency table 245 Obtaining contingency tables in SPSS 247 Calculating Chi-Square 249 Expected frequencies 250 Calculating chi-square 251 Obtaining chi-square in SPSS 252 Interpreting the output from chi-square in SPSS 253 Writing up the results of a chi-square analysis 255 Understanding the assumptions of chi-square analysis 256 Measuring the Strength of Association between Two Variables 257 Looking at the odds ratio 257 Phi and Cramer’s V Coefficients 258 Obtaining odds ratio, phi coefficient and Cramer’s V in SPSS 259 Using the McNemar Test 260 Calculating the McNemar test 261 Obtaining a McNemar test in SPSS 262 Part IV: Analysing Independent Groups Research Designs 265 Chapter 15: Independent t-tests and Mann–Whitney Tests 267 Understanding Independent Groups Design 268 The Independent t-test 268 Performing the independent t-test in SPSS 269 Interpreting the output 272 Writing up the results 275 Considering assumptions 275 Mann-Whitney test 277 Performing the Mann–Whitney test in SPSS 278 Interpreting the output 280 Writing up the results 282 Considering assumptions 283 Chapter 16: Between-Groups ANOVA 285 One-Way Between-Groups ANOVA 286 Seeing how ANOVA works 287 Calculating a one-way between-groups ANOVA 288 Obtaining a one-way between-groups ANOVA in SPSS 291 Interpreting the SPSS output for a one-way between-groups ANOVA 294 Writing up the results of a one-way between-groups ANOVA 296 Considering assumptions of a one-way between-groups ANOVA 296 Two-Way Between-Groups ANOVA 298 Understanding main effects and interactions 299 Obtaining a two-way between-groups ANOVA in SPSS 300 Interpreting the SPSS output for a two-way between-groups ANOVA 301 Writing up the results of a two-way between-groups ANOVA 306 Considering assumptions of a two-way between-groups ANOVA 307 Kruskal–Wallis Test 307 Obtaining a Kruskal–Wallis test in SPSS 308 Interpreting the SPSS output for a Kruskal–Wallis test 310 Writing up the results of a Kruskal–Wallis test 311 Considering assumptions of a Kruskal–Wallis test 311 Chapter 17: Post Hoc Tests and Planned Comparisons for Independent Groups Designs 313 Post Hoc Tests for Independent Groups Designs 314 Multiplicity 315 Choosing a post hoc test 316 Obtaining a Tukey HSD post hoc test in SPSS 317 Interpreting the SPSS output for a Tukey HSD post hoc test 319 Writing up the results of a post hoc Tukey HSD test 322 Planned Comparisons for Independent Groups Designs 322 Choosing a planned comparison 323 Obtaining a Dunnett test in SPSS 323 Interpreting the SPSS output for a Dunnett test 324 Writing up the results of a Dunnett test 326 Part V: Analysing Repeated Measures Research Designs 327 Chapter 18: Paired t-tests and Wilcoxon Tests 329 Understanding Repeated Measures Design 329 Paired t-test 330 Performing a paired t-test in SPSS 331 Interpreting the output 333 Writing up the results 336 Assumptions 336 The Wilcoxon Test 339 Performing the Wilcoxon test in SPSS 339 Interpreting the output 342 Writing up the results 343 Chapter 19: Within-Groups ANOVA 347 One-Way Within-Groups ANOVA 347 Knowing how ANOVA works 348 The example 349 Obtaining a one-way within-groups ANOVA in SPSS 353 Interpreting the SPSS output for a one-way within-groups ANOVA 356 Writing up the results of a one-way within-groups ANOVA 360 Assumptions of a one-way within-groups ANOVA 360 Two-Way Within-Groups ANOVA 361 Main effects and interactions 362 Obtaining a two-way within-groups ANOVA in SPSS 363 Interpreting the SPSS output for a two-way within-groups ANOVA 367 Interpreting the interaction plot from a two-way within-groups ANOVA 371 Writing up the results of a two-way within-groups ANOVA 372 Assumptions of a two-way within-groups ANOVA 373 The Friedman Test 374 Obtaining a Friedman test in SPSS 375 Interpreting the SPSS output for a Friedman test 376 Writing up the results of a Friedman test 377 Assumptions of the Friedman test 378 Chapter 20: Post Hoc Tests and Planned Comparisons for Repeated Measures Designs 379 Why do you need to use post hoc tests and planned comparisons? 380 Why should you not use t-tests? 380 What is the difference between post hoc tests and planned comparisons? 381 Post Hoc Tests for Repeated Measures Designs 381 The example 382 Choosing a post hoc test 382 Obtaining a post-hoc test for a within-groups ANOVA in SPSS 383 Interpreting the SPSS output for a post-hoc test 384 Writing up the results of a post hoc test 386 Planned Comparisons for Within Groups Designs 387 The example 388 Choosing a planned comparison 388 Obtaining a simple planned contrast in SPSS 389 Interpreting the SPSS output for planned comparison tests 391 Writing up the results of planned contrasts 392 Examining Differences between Conditions: The Bonferroni Correction 393 Chapter 21: Mixed ANOVA 395 Getting to Grips with Mixed ANOVA 395 The example 396 Main Effects and Interactions 397 Performing the ANOVA in SPSS 398 Interpreting the SPSS output for a two-way mixed ANOVA 403 Writing up the results of a two-way mixed ANOVA 410 Assumptions 411 Part VI: The Part of Tens 415 Chapter 22: Ten Pieces of Good Advice for Inferential Testing 417 Statistical Significance Is Not the Same as Practical Significance 417 Fail to Prepare, Prepare to Fail 418 Don’t Go Fishing for a Significant Result 418 Check Your Assumptions 418 My p Is Bigger Than Your p 418 Differences and Relationships Are Not Opposing Trends 419 Where Did My Post-hoc Tests Go? 419 Categorising Continuous Data 419 Be Consistent 420 Get Help! 420 Chapter 23: Ten Tips for Writing Your Results Section 421 Reporting the p-value 421 Reporting Other Figures 422 Don’t Forget About the Descriptive Statistics 422 Do Not Overuse the Mean 422 Report Effect Sizes and Direction of Effects 423 The Case of the Missing Participants 423 Be Careful With Your Language 424 Beware Correlations and Causality 424 Make Sure to Answer Your Own Question 424 Add Some Structure 424 Index 425

Autorenportrait

Donncha Hanna, PhD is a psychology lecturer at Queen's University Belfast whose primary teaching responsibilities include statistics and research methods. Martin Dempster, PhD is a health psychologist and the research coordinator for the Doctorate in Clinical Psychology programme at Queen's University Belfast.

Back cover copy

Learn to: <ul> <li>Use SPSS to analyse data</li> <li>Master statistical methods and procedures using psychology-based explanations and examples</li> <li>Create better reports</li> <li>Identify key concepts and pass your course</li> </ul> The quick, easy way to master all the statistics you'll ever need The bad news first: if you want a psychology degree you'll need to know statistics. Now for the good news: Psychology Statistics For Dummies. Featuring jargon-free explanations, step-by-step instructions and dozens of real-life examples, Psychology Statistics For Dummies makes the knotty world of statistics a lot less baffling. Rather than padding the text with concepts and procedures irrelevant to the task, the authors focus only on the statistics psychology students need to know. As an alternative to typical, lead-heavy statistics texts or supplements to assigned course reading, this is one book psychology students won't want to be without. <ul> <li>Ease into statistics – start out with an introduction to how statistics are used by psychologists, including the types of variables they use and how they measure them</li> <li>Get your feet wet – quickly learn the basics of descriptive statistics, such as central tendency and measures of dispersion, along with common ways of graphically depicting information</li> <li>Meet your new best friend – learn the ins and outs of SPSS, the most popular statistics software package among psychology students, including how to input, manipulate and analyse data</li> <li>Analyse this – get up to speed on statistical analysis core concepts, such as probability and inference, hypothesis testing, distributions, Z-scores and effect sizes</li> <li>Correlate that – get the lowdown on common procedures for defining relationships between variables, including linear regressions, associations between categorical data and more</li> <li>Analyse by inference – master key methods in inferential statistics, including techniques for analysing independent groups designs and repeated-measures research designs</li> </ul> Open the book and find: <ul> <li>Ways to describe statistical data</li> <li>How to use SPSS statistical software</li> <li>Probability theory and statistical inference</li> <li>Descriptive statistics basics</li> <li>How to test hypotheses</li> <li>Correlations and other relationships between variables</li> <li>Core concepts in statistical analysis for psychology</li> <li>Analysing research designs</li> </ul>