Psychology Statistics For Dummies

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.

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