{"product_id":"psychology-statistics-for-dummies-isbn-9781119952879","title":"Psychology Statistics For Dummies","description":"\u003cb\u003eThe introduction to statistics that psychology students can't afford to be without\u003c\/b\u003e  \u003cp\u003eUnderstanding 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, \u003ci\u003ePsychology Statistics For Dummies\u003c\/i\u003e 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.\u003c\/p\u003e \u003cp\u003e\u003ci\u003ePsychology Statistics For Dummies:\u003c\/i\u003e\u003c\/p\u003e \u003cul\u003e \u003cli\u003eServes as an easily accessible supplement to doorstop-sized psychology textbooks\u003c\/li\u003e \u003cli\u003eProvides psychology students with psychology-specific statistics instruction\u003c\/li\u003e \u003cli\u003eIncludes clear explanations and instruction on performing statistical analysis\u003c\/li\u003e \u003cli\u003eTeaches students how to analyze their data with SPSS, the most widely used statistical packages among students\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003e\u003cb\u003eIntroduction 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eAbout This Book 2\u003c\/p\u003e \u003cp\u003eWhat You’re Not to Read 2\u003c\/p\u003e \u003cp\u003eFoolish Assumptions 3\u003c\/p\u003e \u003cp\u003eHow this Book is Organised 3\u003c\/p\u003e \u003cp\u003eIcons Used in This Book 4\u003c\/p\u003e \u003cp\u003eWhere to Go from Here 5\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart I: Describing Data 7\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 1: Statistics? I Thought This Was Psychology! 9\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eKnow Your Variables 10\u003c\/p\u003e \u003cp\u003eWhat is SPSS? 11\u003c\/p\u003e \u003cp\u003eDescriptive Statistics 12\u003c\/p\u003e \u003cp\u003eCentral tendency 12\u003c\/p\u003e \u003cp\u003eDispersion 12\u003c\/p\u003e \u003cp\u003eGraphs 13\u003c\/p\u003e \u003cp\u003eStandardised scores 13\u003c\/p\u003e \u003cp\u003eInferential Statistics 13\u003c\/p\u003e \u003cp\u003eHypotheses 14\u003c\/p\u003e \u003cp\u003eParametric and non-parametric variables 14\u003c\/p\u003e \u003cp\u003eResearch Designs 15\u003c\/p\u003e \u003cp\u003eCorrelational design 15\u003c\/p\u003e \u003cp\u003eExperimental design 16\u003c\/p\u003e \u003cp\u003eIndependent groups design 16\u003c\/p\u003e \u003cp\u003eRepeated measures design 17\u003c\/p\u003e \u003cp\u003eGetting Started 18\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 2: What Type of Data Are We Dealing With? 19\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eUnderstanding Discrete and Continuous Variables 20\u003c\/p\u003e \u003cp\u003eLooking at Levels of Measurement 21\u003c\/p\u003e \u003cp\u003eMeasurement properties 21\u003c\/p\u003e \u003cp\u003eTypes of measurement level 23\u003c\/p\u003e \u003cp\u003eDetermining the Role of Variables 24\u003c\/p\u003e \u003cp\u003eIndependent variables 25\u003c\/p\u003e \u003cp\u003eDependent variables 25\u003c\/p\u003e \u003cp\u003eCovariates 26\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 3: Inputting Data, Labelling and Coding in SPSS 27\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eVariable View Window 28\u003c\/p\u003e \u003cp\u003eCreating variable names 29\u003c\/p\u003e \u003cp\u003eDeciding on variable type 30\u003c\/p\u003e \u003cp\u003eDisplaying the data: The width, decimals, columns and align headings 32\u003c\/p\u003e \u003cp\u003eUsing labels 33\u003c\/p\u003e \u003cp\u003eUsing values 34\u003c\/p\u003e \u003cp\u003eDealing with missing data 36\u003c\/p\u003e \u003cp\u003eAssigning the level of measurement 37\u003c\/p\u003e \u003cp\u003eData View Window 39\u003c\/p\u003e \u003cp\u003eEntering new data 40\u003c\/p\u003e \u003cp\u003eCreating new variables 42\u003c\/p\u003e \u003cp\u003eSorting cases 43\u003c\/p\u003e \u003cp\u003eRecoding variables 45\u003c\/p\u003e \u003cp\u003eOutput Window 48\u003c\/p\u003e \u003cp\u003eUsing the output window 48\u003c\/p\u003e \u003cp\u003eSaving your output 51\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 4: Measures of Central Tendency 53\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eDefining Central Tendency 54\u003c\/p\u003e \u003cp\u003eThe Mode 55\u003c\/p\u003e \u003cp\u003eDetermining the mode 55\u003c\/p\u003e \u003cp\u003eKnowing the advantages and disadvantages of using the mode 58\u003c\/p\u003e \u003cp\u003eObtaining the mode in SPSS 59\u003c\/p\u003e \u003cp\u003eThe Median 64\u003c\/p\u003e \u003cp\u003eDetermining the median 64\u003c\/p\u003e \u003cp\u003eKnowing the advantages and disadvantages to using the median 66\u003c\/p\u003e \u003cp\u003eObtaining the median in SPSS 67\u003c\/p\u003e \u003cp\u003eThe Mean 68\u003c\/p\u003e \u003cp\u003eDetermining the mean 68\u003c\/p\u003e \u003cp\u003eKnowing the advantages and disadvantages to using the mean 69\u003c\/p\u003e \u003cp\u003eObtaining the mean in SPSS 69\u003c\/p\u003e \u003cp\u003eChoosing between the Mode, Median and Mean 71\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 5: Measures of Dispersion 73\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eDefining Dispersion 73\u003c\/p\u003e \u003cp\u003eThe Range 74\u003c\/p\u003e \u003cp\u003eDetermining the range 74\u003c\/p\u003e \u003cp\u003eKnowing the advantages and disadvantages of using the range 75\u003c\/p\u003e \u003cp\u003eObtaining the range in SPSS 76\u003c\/p\u003e \u003cp\u003eThe Interquartile Range 78\u003c\/p\u003e \u003cp\u003eDetermining the interquartile range 78\u003c\/p\u003e \u003cp\u003eKnowing the advantages and disadvantages of using the interquartile range 81\u003c\/p\u003e \u003cp\u003eObtaining the interquartile range in SPSS 82\u003c\/p\u003e \u003cp\u003eThe Standard Deviation 83\u003c\/p\u003e \u003cp\u003eDefining the standard deviation 83\u003c\/p\u003e \u003cp\u003eKnowing the advantages and disadvantages of using the standard deviation 87\u003c\/p\u003e \u003cp\u003eObtaining the standard deviation in SPSS 87\u003c\/p\u003e \u003cp\u003eChoosing between the Range, Interquartile Range and Standard Deviation 89\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 6: Generating Graphs and Charts 91\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThe Histogram 91\u003c\/p\u003e \u003cp\u003eUnderstanding the histogram 92\u003c\/p\u003e \u003cp\u003eObtaining a histogram in SPSS 96\u003c\/p\u003e \u003cp\u003eThe Bar Chart 98\u003c\/p\u003e \u003cp\u003eUnderstanding the bar chart 98\u003c\/p\u003e \u003cp\u003eObtaining a bar chart in SPSS 100\u003c\/p\u003e \u003cp\u003eThe Pie Chart 101\u003c\/p\u003e \u003cp\u003eUnderstanding the pie chart 101\u003c\/p\u003e \u003cp\u003eObtaining a pie chart in SPSS 103\u003c\/p\u003e \u003cp\u003eThe Box and Whisker Plot 103\u003c\/p\u003e \u003cp\u003eUnderstanding the box and whisker plot 104\u003c\/p\u003e \u003cp\u003eObtaining a box and whisker plot in SPSS 107\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart II: Statistical Significance 111\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 7: Understanding Probability and Inference 113\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eExamining Statistical Inference 113\u003c\/p\u003e \u003cp\u003eLooking at the population and the sample 114\u003c\/p\u003e \u003cp\u003eKnowing the limitations of descriptive statistics 115\u003c\/p\u003e \u003cp\u003eAiming to be 95 per cent confident 116\u003c\/p\u003e \u003cp\u003eMaking Sense of Probability 117\u003c\/p\u003e \u003cp\u003eDefining probability 118\u003c\/p\u003e \u003cp\u003eConsidering mutually exclusive and independent events 118\u003c\/p\u003e \u003cp\u003eUnderstanding conditional probability 121\u003c\/p\u003e \u003cp\u003eKnowing about odds 122\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 8: Testing Hypotheses 123\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eUnderstanding Null and Alternative Hypotheses 123\u003c\/p\u003e \u003cp\u003eTesting the null hypothesis 124\u003c\/p\u003e \u003cp\u003eDefining the alternative hypothesis 124\u003c\/p\u003e \u003cp\u003eDeciding whether to accept or reject the null hypothesis 125\u003c\/p\u003e \u003cp\u003eTaking On Board Statistical Inference Errors 127\u003c\/p\u003e \u003cp\u003eKnowing about the Type I error 128\u003c\/p\u003e \u003cp\u003eConsidering the Type II error 128\u003c\/p\u003e \u003cp\u003eGetting it right sometimes 129\u003c\/p\u003e \u003cp\u003eLooking at One- and Two-Tailed Hypotheses 130\u003c\/p\u003e \u003cp\u003eUsing a one-tailed hypothesis 131\u003c\/p\u003e \u003cp\u003eApplying a two-tailed hypothesis 131\u003c\/p\u003e \u003cp\u003eConfidence Intervals 132\u003c\/p\u003e \u003cp\u003eDefining a 95 per cent confidence interval 132\u003c\/p\u003e \u003cp\u003eCalculating a 95 per cent confidence interval 133\u003c\/p\u003e \u003cp\u003eObtaining a 95 per cent confidence interval in SPSS 135\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 9: What’s Normal about the Normal Distribution? 139\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eUnderstanding the Normal Distribution 140\u003c\/p\u003e \u003cp\u003eDefining the normal distribution 140\u003c\/p\u003e \u003cp\u003eDetermining whether a distribution is approximately normal 141\u003c\/p\u003e \u003cp\u003eDetermining Skewness 144\u003c\/p\u003e \u003cp\u003eDefining skewness 144\u003c\/p\u003e \u003cp\u003eAssessing skewness graphically 145\u003c\/p\u003e \u003cp\u003eObtaining the skewness statistic in SPSS 147\u003c\/p\u003e \u003cp\u003eLooking at the Normal Distribution and Inferential Statistics 150\u003c\/p\u003e \u003cp\u003eMaking inferences about individual scores 151\u003c\/p\u003e \u003cp\u003eConsidering the sampling distribution 152\u003c\/p\u003e \u003cp\u003eMaking inferences about group scores 153\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 10: Standardised Scores 155\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eKnowing the Basics of Standardised Scores 155\u003c\/p\u003e \u003cp\u003eDefining standardised scores 156\u003c\/p\u003e \u003cp\u003eCalculating standardised scores 156\u003c\/p\u003e \u003cp\u003eUsing Z Scores in Statistical Analyses 159\u003c\/p\u003e \u003cp\u003eConnecting Z scores and the normal distribution 160\u003c\/p\u003e \u003cp\u003eUsing Z scores in inferential statistics 161\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 11: Effect Sizes and Power 165\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eDistinguishing between Effect Size and Statistical Significance 165\u003c\/p\u003e \u003cp\u003eExploring Effect Size for Correlations 166\u003c\/p\u003e \u003cp\u003eConsidering Effect Size When Comparing Differences Between Two Sets of Scores 167\u003c\/p\u003e \u003cp\u003eObtaining an effect size for comparing differences between two sets of scores 167\u003c\/p\u003e \u003cp\u003eInterpreting an effect size for differences between two sets of scores 170\u003c\/p\u003e \u003cp\u003eLooking at Effect Size When Comparing Differences between More Than Two Sets of Scores 171\u003c\/p\u003e \u003cp\u003eObtaining an effect size for comparing differences between more than two sets of scores 171\u003c\/p\u003e \u003cp\u003eInterpreting an effect size for differences between more than two sets of scores 177\u003c\/p\u003e \u003cp\u003eUnderstanding Statistical Power 178\u003c\/p\u003e \u003cp\u003eSeeing which factors influence power 179\u003c\/p\u003e \u003cp\u003eConsidering power and sample size 180\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart III: Relationships between Variables 183\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 12: Correlations 185\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eUsing Scatterplots to Assess Relationships 185\u003c\/p\u003e \u003cp\u003eInspecting a scatterplot 186\u003c\/p\u003e \u003cp\u003eDrawing a scatterplot in SPSS 189\u003c\/p\u003e \u003cp\u003eUnderstanding the Correlation Coefficient 190\u003c\/p\u003e \u003cp\u003eExamining Shared Variance 191\u003c\/p\u003e \u003cp\u003eUsing Pearson’s Correlation 192\u003c\/p\u003e \u003cp\u003eKnowing when to use Pearson’s correlation 192\u003c\/p\u003e \u003cp\u003ePerforming Pearson’s correlation in SPSS 193\u003c\/p\u003e \u003cp\u003eInterpreting the output 195\u003c\/p\u003e \u003cp\u003eWriting up the results 197\u003c\/p\u003e \u003cp\u003eUsing Spearman’s Correlation 198\u003c\/p\u003e \u003cp\u003eKnowing when to use Spearman’s correlation 198\u003c\/p\u003e \u003cp\u003ePerforming Spearman’s correlation in SPSS 199\u003c\/p\u003e \u003cp\u003eInterpreting the output 201\u003c\/p\u003e \u003cp\u003eWriting up the results 201\u003c\/p\u003e \u003cp\u003eUsing Kendall’s Correlation 202\u003c\/p\u003e \u003cp\u003ePerforming Kendall’s correlation in SPSS 203\u003c\/p\u003e \u003cp\u003eInterpreting the output 204\u003c\/p\u003e \u003cp\u003eWriting up the results 205\u003c\/p\u003e \u003cp\u003eUsing Partial Correlation 206\u003c\/p\u003e \u003cp\u003ePerforming partial correlation in SPSS 206\u003c\/p\u003e \u003cp\u003eInterpreting the output 208\u003c\/p\u003e \u003cp\u003eWriting up the results 208\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 13: Linear Regression 211\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eGetting to Grips with the Basics of Regression 212\u003c\/p\u003e \u003cp\u003eAdding a regression line 212\u003c\/p\u003e \u003cp\u003eWorking out residuals 214\u003c\/p\u003e \u003cp\u003eUsing the regression equation 215\u003c\/p\u003e \u003cp\u003eUsing Simple Regression 217\u003c\/p\u003e \u003cp\u003ePerforming simple regression in SPSS 217\u003c\/p\u003e \u003cp\u003eInterpreting the output 218\u003c\/p\u003e \u003cp\u003eWriting up the results 222\u003c\/p\u003e \u003cp\u003eWorking with Multiple Variables: Multiple Regression 223\u003c\/p\u003e \u003cp\u003ePerforming multiple regression in SPSS 224\u003c\/p\u003e \u003cp\u003eInterpreting the output 225\u003c\/p\u003e \u003cp\u003eWriting up the results 229\u003c\/p\u003e \u003cp\u003eChecking Assumptions of Regression 230\u003c\/p\u003e \u003cp\u003eNormally distributed residuals 230\u003c\/p\u003e \u003cp\u003eLinearity 232\u003c\/p\u003e \u003cp\u003eOutliers 234\u003c\/p\u003e \u003cp\u003eMulticollinearity 238\u003c\/p\u003e \u003cp\u003eHomoscedasticity 240\u003c\/p\u003e \u003cp\u003eType of data 242\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 14: Associations between Discrete Variables 243\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eSummarising Results in a Contingency Table 244\u003c\/p\u003e \u003cp\u003eObserved frequencies in contingency tables 244\u003c\/p\u003e \u003cp\u003ePercentaging a contingency table 245\u003c\/p\u003e \u003cp\u003eObtaining contingency tables in SPSS 247\u003c\/p\u003e \u003cp\u003eCalculating Chi-Square 249\u003c\/p\u003e \u003cp\u003eExpected frequencies 250\u003c\/p\u003e \u003cp\u003eCalculating chi-square 251\u003c\/p\u003e \u003cp\u003eObtaining chi-square in SPSS 252\u003c\/p\u003e \u003cp\u003eInterpreting the output from chi-square in SPSS 253\u003c\/p\u003e \u003cp\u003eWriting up the results of a chi-square analysis 255\u003c\/p\u003e \u003cp\u003eUnderstanding the assumptions of chi-square analysis 256\u003c\/p\u003e \u003cp\u003eMeasuring the Strength of Association between Two Variables 257\u003c\/p\u003e \u003cp\u003eLooking at the odds ratio 257\u003c\/p\u003e \u003cp\u003ePhi and Cramer’s V Coefficients 258\u003c\/p\u003e \u003cp\u003eObtaining odds ratio, phi coefficient and Cramer’s V in SPSS 259\u003c\/p\u003e \u003cp\u003eUsing the McNemar Test 260\u003c\/p\u003e \u003cp\u003eCalculating the McNemar test 261\u003c\/p\u003e \u003cp\u003eObtaining a McNemar test in SPSS 262\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart IV: Analysing Independent Groups Research Designs 265\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 15: Independent t-tests and Mann–Whitney Tests 267\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eUnderstanding Independent Groups Design 268\u003c\/p\u003e \u003cp\u003eThe Independent t-test 268\u003c\/p\u003e \u003cp\u003ePerforming the independent t-test in SPSS 269\u003c\/p\u003e \u003cp\u003eInterpreting the output 272\u003c\/p\u003e \u003cp\u003eWriting up the results 275\u003c\/p\u003e \u003cp\u003eConsidering assumptions 275\u003c\/p\u003e \u003cp\u003eMann-Whitney test 277\u003c\/p\u003e \u003cp\u003ePerforming the Mann–Whitney test in SPSS 278\u003c\/p\u003e \u003cp\u003eInterpreting the output 280\u003c\/p\u003e \u003cp\u003eWriting up the results 282\u003c\/p\u003e \u003cp\u003eConsidering assumptions 283\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 16: Between-Groups ANOVA 285\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eOne-Way Between-Groups ANOVA 286\u003c\/p\u003e \u003cp\u003eSeeing how ANOVA works 287\u003c\/p\u003e \u003cp\u003eCalculating a one-way between-groups ANOVA 288\u003c\/p\u003e \u003cp\u003eObtaining a one-way between-groups ANOVA in SPSS 291\u003c\/p\u003e \u003cp\u003eInterpreting the SPSS output for a one-way\u003c\/p\u003e \u003cp\u003ebetween-groups ANOVA 294\u003c\/p\u003e \u003cp\u003eWriting up the results of a one-way between-groups ANOVA 296\u003c\/p\u003e \u003cp\u003eConsidering assumptions of a one-way\u003c\/p\u003e \u003cp\u003ebetween-groups ANOVA 296\u003c\/p\u003e \u003cp\u003eTwo-Way Between-Groups ANOVA 298\u003c\/p\u003e \u003cp\u003eUnderstanding main effects and interactions 299\u003c\/p\u003e \u003cp\u003eObtaining a two-way between-groups ANOVA in SPSS 300\u003c\/p\u003e \u003cp\u003eInterpreting the SPSS output for a two-way\u003c\/p\u003e \u003cp\u003ebetween-groups ANOVA 301\u003c\/p\u003e \u003cp\u003eWriting up the results of a two-way\u003c\/p\u003e \u003cp\u003ebetween-groups ANOVA 306\u003c\/p\u003e \u003cp\u003eConsidering assumptions of a two-way\u003c\/p\u003e \u003cp\u003ebetween-groups ANOVA 307\u003c\/p\u003e \u003cp\u003eKruskal–Wallis Test 307\u003c\/p\u003e \u003cp\u003eObtaining a Kruskal–Wallis test in SPSS 308\u003c\/p\u003e \u003cp\u003eInterpreting the SPSS output for a Kruskal–Wallis test 310\u003c\/p\u003e \u003cp\u003eWriting up the results of a Kruskal–Wallis test 311\u003c\/p\u003e \u003cp\u003eConsidering assumptions of a Kruskal–Wallis test 311\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 17: Post Hoc Tests and Planned Comparisons for Independent Groups Designs 313\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003ePost Hoc Tests for Independent Groups Designs 314\u003c\/p\u003e \u003cp\u003eMultiplicity 315\u003c\/p\u003e \u003cp\u003eChoosing a post hoc test 316\u003c\/p\u003e \u003cp\u003eObtaining a Tukey HSD post hoc test in SPSS 317\u003c\/p\u003e \u003cp\u003eInterpreting the SPSS output for a Tukey HSD post hoc test 319\u003c\/p\u003e \u003cp\u003eWriting up the results of a post hoc Tukey HSD test 322\u003c\/p\u003e \u003cp\u003ePlanned Comparisons for Independent Groups Designs 322\u003c\/p\u003e \u003cp\u003eChoosing a planned comparison 323\u003c\/p\u003e \u003cp\u003eObtaining a Dunnett test in SPSS 323\u003c\/p\u003e \u003cp\u003eInterpreting the SPSS output for a Dunnett test 324\u003c\/p\u003e \u003cp\u003eWriting up the results of a Dunnett test 326\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart V: Analysing Repeated Measures Research Designs 327\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 18: Paired t-tests and Wilcoxon Tests 329\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eUnderstanding Repeated Measures Design 329\u003c\/p\u003e \u003cp\u003ePaired t-test 330\u003c\/p\u003e \u003cp\u003ePerforming a paired t-test in SPSS 331\u003c\/p\u003e \u003cp\u003eInterpreting the output 333\u003c\/p\u003e \u003cp\u003eWriting up the results 336\u003c\/p\u003e \u003cp\u003eAssumptions 336\u003c\/p\u003e \u003cp\u003eThe Wilcoxon Test 339\u003c\/p\u003e \u003cp\u003ePerforming the Wilcoxon test in SPSS 339\u003c\/p\u003e \u003cp\u003eInterpreting the output 342\u003c\/p\u003e \u003cp\u003eWriting up the results 343\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 19: Within-Groups ANOVA 347\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eOne-Way Within-Groups ANOVA 347\u003c\/p\u003e \u003cp\u003eKnowing how ANOVA works 348\u003c\/p\u003e \u003cp\u003eThe example 349\u003c\/p\u003e \u003cp\u003eObtaining a one-way within-groups ANOVA in SPSS 353\u003c\/p\u003e \u003cp\u003eInterpreting the SPSS output for a one-way within-groups ANOVA 356\u003c\/p\u003e \u003cp\u003eWriting up the results of a one-way within-groups ANOVA 360\u003c\/p\u003e \u003cp\u003eAssumptions of a one-way within-groups ANOVA 360\u003c\/p\u003e \u003cp\u003eTwo-Way Within-Groups ANOVA 361\u003c\/p\u003e \u003cp\u003eMain effects and interactions 362\u003c\/p\u003e \u003cp\u003eObtaining a two-way within-groups ANOVA in SPSS 363\u003c\/p\u003e \u003cp\u003eInterpreting the SPSS output for a two-way within-groups ANOVA 367\u003c\/p\u003e \u003cp\u003eInterpreting the interaction plot from a two-way within-groups ANOVA 371\u003c\/p\u003e \u003cp\u003eWriting up the results of a two-way within-groups ANOVA 372\u003c\/p\u003e \u003cp\u003eAssumptions of a two-way within-groups ANOVA 373\u003c\/p\u003e \u003cp\u003eThe Friedman Test 374\u003c\/p\u003e \u003cp\u003eObtaining a Friedman test in SPSS 375\u003c\/p\u003e \u003cp\u003eInterpreting the SPSS output for a Friedman test 376\u003c\/p\u003e \u003cp\u003eWriting up the results of a Friedman test 377\u003c\/p\u003e \u003cp\u003eAssumptions of the Friedman test 378\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 20: Post Hoc Tests and Planned Comparisons for Repeated Measures Designs 379\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eWhy do you need to use post hoc tests and planned comparisons? 380\u003c\/p\u003e \u003cp\u003eWhy should you not use t-tests? 380\u003c\/p\u003e \u003cp\u003eWhat is the difference between post hoc tests and planned comparisons? 381\u003c\/p\u003e \u003cp\u003ePost Hoc Tests for Repeated Measures Designs 381\u003c\/p\u003e \u003cp\u003eThe example 382\u003c\/p\u003e \u003cp\u003eChoosing a post hoc test 382\u003c\/p\u003e \u003cp\u003eObtaining a post-hoc test for a within-groups ANOVA in SPSS 383\u003c\/p\u003e \u003cp\u003eInterpreting the SPSS output for a post-hoc test 384\u003c\/p\u003e \u003cp\u003eWriting up the results of a post hoc test 386\u003c\/p\u003e \u003cp\u003ePlanned Comparisons for Within Groups Designs 387\u003c\/p\u003e \u003cp\u003eThe example 388\u003c\/p\u003e \u003cp\u003eChoosing a planned comparison 388\u003c\/p\u003e \u003cp\u003eObtaining a simple planned contrast in SPSS 389\u003c\/p\u003e \u003cp\u003eInterpreting the SPSS output for planned comparison tests 391\u003c\/p\u003e \u003cp\u003eWriting up the results of planned contrasts 392\u003c\/p\u003e \u003cp\u003eExamining Differences between Conditions: The Bonferroni Correction 393\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 21: Mixed ANOVA 395\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eGetting to Grips with Mixed ANOVA 395\u003c\/p\u003e \u003cp\u003eThe example 396\u003c\/p\u003e \u003cp\u003eMain Effects and Interactions 397\u003c\/p\u003e \u003cp\u003ePerforming the ANOVA in SPSS 398\u003c\/p\u003e \u003cp\u003eInterpreting the SPSS output for a two-way mixed ANOVA 403\u003c\/p\u003e \u003cp\u003eWriting up the results of a two-way mixed ANOVA 410\u003c\/p\u003e \u003cp\u003eAssumptions 411\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart VI: The Part of Tens 415\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 22: Ten Pieces of Good Advice for Inferential Testing 417\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eStatistical Significance Is Not the Same as Practical Significance 417\u003c\/p\u003e \u003cp\u003eFail to Prepare, Prepare to Fail 418\u003c\/p\u003e \u003cp\u003eDon’t Go Fishing for a Significant Result 418\u003c\/p\u003e \u003cp\u003eCheck Your Assumptions 418\u003c\/p\u003e \u003cp\u003eMy p Is Bigger Than Your p 418\u003c\/p\u003e \u003cp\u003eDifferences and Relationships Are Not Opposing Trends 419\u003c\/p\u003e \u003cp\u003eWhere Did My Post-hoc Tests Go? 419\u003c\/p\u003e \u003cp\u003eCategorising Continuous Data 419\u003c\/p\u003e \u003cp\u003eBe Consistent 420\u003c\/p\u003e \u003cp\u003eGet Help! 420\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 23: Ten Tips for Writing Your Results Section 421\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eReporting the p-value 421\u003c\/p\u003e \u003cp\u003eReporting Other Figures 422\u003c\/p\u003e \u003cp\u003eDon’t Forget About the Descriptive Statistics 422\u003c\/p\u003e \u003cp\u003eDo Not Overuse the Mean 422\u003c\/p\u003e \u003cp\u003eReport Effect Sizes and Direction of Effects 423\u003c\/p\u003e \u003cp\u003eThe Case of the Missing Participants 423\u003c\/p\u003e \u003cp\u003eBe Careful With Your Language 424\u003c\/p\u003e \u003cp\u003eBeware Correlations and Causality 424\u003c\/p\u003e \u003cp\u003eMake Sure to Answer Your Own Question 424\u003c\/p\u003e \u003cp\u003eAdd Some Structure 424\u003c\/p\u003e \u003cp\u003eIndex 425\u003c\/p\u003e   \u003cp\u003e\u003cb\u003eDonncha Hanna, PhD\u003c\/b\u003e is a psychology lecturer at Queen's University Belfast whose primary teaching responsibilities include statistics and research methods. \u003cb\u003eMartin Dempster, PhD\u003c\/b\u003e is a health psychologist and the research coordinator for the Doctorate in Clinical Psychology programme at Queen's University Belfast.     \u003c\/p\u003e\u003cp\u003e\u003cb\u003e\u003ci\u003eLearn to:\u003c\/i\u003e\u003c\/b\u003e \u003c\/p\u003e\u003cul\u003e \u003cli\u003eUse SPSS to analyse data\u003c\/li\u003e \u003cli\u003eMaster statistical methods and procedures using psychology-based explanations and examples\u003c\/li\u003e \u003cli\u003eCreate better reports\u003c\/li\u003e \u003cli\u003eIdentify key concepts and pass your course\u003c\/li\u003e \u003c\/ul\u003e  \u003cp\u003e\u003cb\u003eThe quick, easy way to master all the statistics you'll ever need\u003c\/b\u003e  \u003c\/p\u003e\u003cp\u003eThe bad news first: if you want a psychology degree you'll need to know statistics. Now for the good news: \u003ci\u003ePsychology Statistics For Dummies.\u003c\/i\u003e Featuring jargon-free explanations, step-by-step instructions and dozens of real-life examples, \u003ci\u003ePsychology Statistics For Dummies\u003c\/i\u003e 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. \u003c\/p\u003e\u003cul\u003e \u003cli\u003e\n\u003cb\u003eEase into statistics\u003c\/b\u003e – start out with an introduction to how statistics are used by psychologists, including the types of variables they use and how they measure them\u003c\/li\u003e \u003cli\u003e\n\u003cb\u003eGet your feet wet\u003c\/b\u003e – quickly learn the basics of descriptive statistics, such as central tendency and measures of dispersion, along with common ways of graphically depicting information\u003c\/li\u003e \u003cli\u003e\n\u003cb\u003eMeet your new best friend\u003c\/b\u003e – learn the ins and outs of SPSS, the most popular statistics software package among psychology students, including how to input, manipulate and analyse data\u003c\/li\u003e \u003cli\u003e\n\u003cb\u003eAnalyse this\u003c\/b\u003e – get up to speed on statistical analysis core concepts, such as probability and inference, hypothesis testing, distributions, Z-scores and effect sizes\u003c\/li\u003e \u003cli\u003e\n\u003cb\u003eCorrelate that\u003c\/b\u003e – get the lowdown on common procedures for defining relationships between variables, including linear regressions, associations between categorical data and more\u003c\/li\u003e \u003cli\u003e\n\u003cb\u003eAnalyse by inference\u003c\/b\u003e – master key methods in inferential statistics, including techniques for analysing independent groups designs and repeated-measures research designs\u003c\/li\u003e \u003c\/ul\u003e  \u003cp\u003e\u003cb\u003eOpen the book and find:\u003c\/b\u003e \u003c\/p\u003e\u003cul\u003e \u003cli\u003eWays to describe statistical data\u003c\/li\u003e \u003cli\u003eHow to use SPSS statistical software\u003c\/li\u003e \u003cli\u003eProbability theory and statistical inference\u003c\/li\u003e \u003cli\u003eDescriptive statistics basics\u003c\/li\u003e \u003cli\u003eHow to test hypotheses\u003c\/li\u003e \u003cli\u003eCorrelations and other relationships between variables\u003c\/li\u003e \u003cli\u003eCore concepts in statistical analysis for psychology\u003c\/li\u003e \u003cli\u003eAnalysing research designs\u003c\/li\u003e \u003c\/ul\u003e","brand":"For Dummies","offers":[{"title":"Default Title","offer_id":47989886550245,"sku":"NP9781119952879","price":22.99,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781119952879.jpg?v=1761785796","url":"https:\/\/k12savings.com\/products\/psychology-statistics-for-dummies-isbn-9781119952879","provider":"K12savings","version":"1.0","type":"link"}