Donncha Hanna, Martin Dempster

Psychology Statistics for Dummies

• Broschiertes Buch

Produktbeschreibung

Untangle statistics and make correct, dependable conclusions Psychology Statistics For Dummies, 2nd Edition makes statistics accessible to psychology students, covering all the content in a typical undergraduate psychology statistics class. Built on a foundation of jargon-free explanations and real-life examples, this book focuses on information and techniques that psychology students need to know (and nothing more). You'll learn to use the popular SPSS statistics software to calculate statistics and look for patterns in psychological data. And, this helpful guide offers a brief introduction to using the R programming language for statistical analysis--an increasingly important skill for the digital age. You'll also find hands-on practice exercises and examples using recent, real datasets. * Explore the use of statistical analysis in research for the psychology field * Master basic statistics concepts such as means, standard deviations, and correlations * Conduct data analysis using the SPSS software package * Get clear explanations of everything in your psychology statistics course, so you can pass with flying colors This guide is perfect to use as a readable supplement to psychology textbooks and overall coursework. Students in other social and behavioral sciences can also benefit from this stellar primer on statistics.

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Produktdetails

• Verlag: Wiley
• 2nd edition
• Seitenzahl: 400
• Erscheinungstermin: 22. April 2025
• Englisch
• ISBN-13: 9781394295319
• ISBN-10: 1394295316
• Artikelnr.: 71774973

Autorenporträt

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.

Inhaltsangabe

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