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Titel:	Understanding statistics in psychology with SPSS
Von:	Dennis Howitt, Loughborough University, Duncan Cramer, Loughborough University
Person:	Howitt, Dennis 1945- aut Cramer, Duncan 1948-
Hauptverfassende:	Howitt, Dennis 1945- (VerfasserIn), Cramer, Duncan 1948- (VerfasserIn)
Format:	Buch
Sprache:	Englisch
Veröffentlicht:	Harlow, England Pearson [2017]
Ausgabe:	Seventh edition
Notation:	CM 4000 ST 601
Schlagwörter:	Psychometrics Empirische Psychologie Psychometrie Psychologie Datenanalyse SPSS Statistik
Medienzugang:	http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=029675368&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA
Beschreibung:	Revised edition of the authors' Introduction to statistics in psychology, 2013
Umfang:	xxviii, 726 Seiten Illustrationen, Diagramme
ISBN:	9781292134215

Internformat

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adam_text	Brief contents _________________________________J Contents vii Guided tour XX Introduction XXV A ckn o wledgemen ts xxvii 1 Why statistics? 1 Part 1 Descriptive statistics 21 2 Some basics: Variability and measurement 23 3 Describing variables: Tables and diagrams 33 4 Describing variables numerically: Averages, variation and spread 48 5 Shapes of distributions of scores 64 6 Standard deviation and z-scores: Standard unit of measurement in statistics 77 7 Relationships between two or more variables: Diagrams and tables 93 8 Correlation coefficients: Pearson’s correlation and Spearman’s rho 105 9 Regression: Prediction with precision 126 Part 2 Significance testing 141 10 Samples from populations 143 11 Statistical significance for the correlation coefficient: Practical introduction to statistical inference 150 12 Standard error: Standard deviation of the means of samples 164 13 Related t-test: Comparing two samples of related/correlated/paired scores 172 14 Unrelated t-test: Comparing two samples of unrelated/ uncorrelated/independent scores 186 15 What you need to write about your statistical analysis 203 16 Confidence intervals 210 17 Effect size in statistical analysis: Do my findings matter? 221 18 Chi-square: Differences between samples of frequency data 231 19 Probability 251 20 One-tailed versus two-tailed significance testing 257 21 Ranking tests: Nonparametric statistics 263 Part 3 Introduction to analysis of variance 279 22 Variance ratio test: F-ratio to compare two variances 281 23 Analysis of variance (AMOVA): One-way unrelated or uncorrelated ANOVA 290 24 ANOVA for correlated scores or repeated measures 308 25 Two-way or factorial ANOVA for unrelated/uncorrelated scores: Two studies for the price of one? 324 26 Multiple comparisons in ANOVA: A priori and post hoc tests 351 27 Mixed-design ANOVA: Related and unrelated variables together 362 : Wji V :՜ C 1 ·. ; VI BRIEF CONTENTS 28 Analysis of covariance (ANCOVA): Controlling for additional variables 29 Multivariate analysis of variance (MANOVA) 30 Discriminant (function) analysis - especially in MANOVA 31 Statistics and analysis of experiments 379 395 411 425 Part 4 More advanced correlational statistics 437 32 Partial correlation: Spurious correlation, third or confounding variables, suppressor variables 439 33 Factor analysis: Simplifying complex data 451 34 Multiple regression and multiple correlation 474 35 Path analysis 492 36 Analysis of a questionnaire/survey project 508 Part 5 Assorted advanced techniques 519 37 Meta-analysis: Combining and exploring statistical findings from previous research 521 38 Reliability in scales and measurement: Consistency and agreement 540 39 Influence of moderator variables on relationships between two variables 554 40 Statistical power analysis: Getting the sample size right 576 Part 6 Advanced qualitative or nominal techniques 601 41 Log-linear methods: Analysis of complex contingency tables 603 42 Multinomial logistic regression: Distinguishing between several different categories or groups 628 43 Binomial logistic regression 646 Appendices 663 Glossary 699 References 707 Index 713 Contents Guided tour XX Introduction XXV Acknowledgements xxvii 1 Why statistics? 1 Overview 1 1.1 Introduction 2 1.2 Research on learning statistics 3 1.3 What makes learning statistics difficult? 4 1.4 Positive about statistics 6 1.5 What statistics doesn’t do 9 1.6 Easing the way 10 1.7 What do 1 need to know to be an effective user of statistics? 12 1.8 A few words about SPSS 14 1.9 Quick guide to the book’s procedures and statistical tests 14 Key points 17 Computer analysis: SPSS Analyze Graphs and Transform drop-down menus 18 Parti Descriptive statistics 21 V______________________________________________________________________________________________________________✓ 2 Some basics: Variability and measurement 23 Overview 23 2.1 Introduction 24 2.2 Variables and measurement 25 2.3 Major types of measurement 26 Key points 30 Computer analysis: Some basics of data entry using SPSS 31 3 Describing variables: Tables and diagrams 33 Overview 33 3.1 Introduction 34 CONTENTS 3.2 Choosing tables and diagrams 35 3.3 Errors to avoid 43 Key points 44 Computer analysis: Tables, diagrams and recoding using SPSS 45 4 Describing variables numerically: Averages, variation and spread 48 Overview 48 4.1 Introduction 49 4.2 Typical scores: mean, median and mode 50 4.3 Comparison of mean, median and mode 53 4.4 Spread of scores: range and interquartile range 53 4.5 Spread of scores: variance 56 Key points 61 Computer analysis: Descriptive statistics using SPSS 62 5 Shapes of distributions of scores 64 Overview 64 5.1 Introduction 65 5.2 Histograms and frequency curves 65 5.3 Normal curve 66 5.4 Distorted curves 68 5.5 Other frequency curves 70 Key points 75 Computer analysis: Frequencies using SPSS 75 6 Standard deviation and ¿’֊scores: Standard unit of measurement in statistics 77 Overview 77 6.1 Introduction 78 6.2 Theoretical background 78 6.3 Measuring the number of standard deviations ֊ the z-score 82 6.4 Use of z-scores 84 6.5 Standard normal distribution 85 6.6 Important feature of z-scores 88 Key points 90 Computer analysis: Standard deviation and z-scores using SPSS 90 7 Relationships between two or more variables: Diagrams and tables 93 Overview 93 7.1 Introduction 94 7.2 Principles of diagrammatic and tabular presentation 95 7.3 Type A: both variables numerical scores 96 7.4 Type B: both variables nominal categories 98 CONTENTS ։x 7.5 Type C: one variable nominal categories, the other numerical scores 100 Key points 102 Computer analysis: Crosstabulation and compound bar charts using SPSS 103 8 Correlation coefficients: Pearson’s correlation and Spearman s rho 105 Overview 105 8.1 Introduction 106 8.2 Principles of the correlation coefficient 107 8.3 Some rules to check out 114 8.4 Coefficient of determination 115 8.5 Significance testing 116 8.6 Spearman s rho - another correlation coefficient 116 8.7 Example from the literature 119 Key points 121 Computer analysis: Correlation coefficients using SPSS 122 Computer analysis: Scattergram using SPSS 124 9 Regression: Prediction with precision 126 Overview 126 9.1 Introduction 127 9.2 Theoretical background and regression equations 129 9.3 Confidence intervals and standard error: how accurate are the predicted score and the regression equations? 134 Key points 137 Computer analysis: Simple regression using SPSS 137 r՜ A Part 2 141 V. Significance testing J 10 Samples from populations 143 Overview 143 10.1 Introduction 144 10.2 Theoretical considerations 144 10.3 Characteristics of random samples 146 10.4 Confidence intervals 147 Key points 148 Computer analysis: Selecting a random sample using SPSS 148 11 Statistical significance for the correlation coefficient: Practical introduction to statistical inference 150 Overview 150 CONTENTS 11.1 Introduction 151 11.2 Theoretical considerations 151 11.3 Back to the real world: null hypothesis 153 11.4 Pearsons correlation coefficient again 155 j 11.5 Spearman’s rho correlation coefficient 159 j Key points 161 Computer analysis: Correlation coefficients using SPSS 162 j 12 Standard error: Standard deviation of the means of samples 164 Overview 164 ! 12.1 Introduction 165 12.2 Theoretical considerations 165 j 12.3 Estimated standard deviation and standard error 167 Key points 169 Computer analysis: Standard error using SPSS 170 13 Related t-test: Comparing two samples of related/correlated/paired scores 172 Overview 172 13.1 Introduction 173 13.2 Dependent and independent variables 175 13.3 Some basic revision 175 13.4 Theoretical considerations underlying the computer analysis 176 13.5 Cautionary note 181 Key points 183 Computer analysis: Related/correlated/paired t-test using SPSS 184 14 Unrelated t-test: Comparing two samples of unrelated/uncorrelated/ independent scores 186 Overview 186 14.1 Introduction 187 14.2 Theoretical considerations 188 14.3 Standard deviation and standard error 193 14.4 Cautionary note 199 Key points 200 Computer analysis: Unrelated/uncorrelated/independent t-test using SPSS 201 15 What you need to write about your statistical analysis 203 Overview 203 15.1 Introduction 204 15.2 Reporting statistical significance 205 15.3 Shortened forms 205 15.4 APA (American Psychological Association) style 206 Key points 209 CONTENTS xi _ ^ 16 Confidence intervals 210 Overview 210 16.1 Introduction 211 16.2 Relationship between significance and confidence intervals 213 16.3 Regression 217 16.4 Writing up a confidence interval using APA style 219 16.5 Other confidence intervals 219 Key points 220 Computer analysis: Examples of SPSS output containing confidence intervals 220 17 Effect size in statistical analysis: Do my findings matter? 221 Overview 221 17.1 Introduction 222 17.2 Statistical significance and effect size 222 17.3 Size of the effect in studies 223 17.4 Approximation for nonparametric tests 225 17.5 Analysis of variance (ANOVA) 225 17.6 Writing up effect sizes using APA style 227 17.7 Have I got a large, medium or small effect size? 227 17.8 Method and statistical efficiency 228 Key points 230 18 Chi-square: Differences between samples of frequency data 231 Overview 231 18.1 Introduction 232 18.2 Theoretical issues 233 18.3 Partitioning chi-square 239 18.4 Important warnings 240 18.5 Alternatives to chi-square 241 18.6 Chi-square and known populations 243 18.7 Chi-square for related samples - the McNemar test 245 18.8 Example from the literature 245 Key points 247 Computer analysis: Chi-square using SPSS 248 Recommended further reading 250 19 Probability 251 Overview 251 19.1 Introduction 252 19.2 Principles of probability 252 19.3 Implications 254 Key points 256 XII CONTENTS 20 One-tailed versus two-tailed significance testing 257 Overview 257 20.1 Introduction 258 20.2 Theoretical considerations 258 20.3 Further requirements 260 Key points 261 Computer analysis: One- and two-tailed statistical significance using SPSS 262 21 Ranking tests: Nonparametric statistics 263 Overview 263 21.1 Introduction 264 21.2 Theoretical considerations 264 21.3 Nonparametric statistical tests 266 21.4 Three or more groups of scores 274 Key points 275 Computer analysis: Two-group ranking tests using SPSS 275 Recommended further reading 277 Part 3 Introduction to analysis of variance 279; 22 Variance ratio test: F-ratio to compare two variances 281 Overview 281 22.1 Introduction 282 22.2 Theoretical issues and application 283 Key points 287 Computer analysis: f-ratio test using SPSS 288 23 Analysis of variance (AIMOVA): One-way unrelated or uncorrelated AIMOVA 290 Overview 290 23.1 Introduction 291 23.2 Some revision and some new material 292 23.3 Theoretical considerations 292 23.4 Degrees of freedom 296 23.5 Analysis of variance summary table 302 Key points 305 Computer analysis: Unrelated one-way analysis of variance using SPSS 306 24 AIMOVA for correlated scores or repeated measures 308 Overview 308 24.1 Introduction 309 CONTENTS XIII .................................. - л 24.2 Theoretical considerations underlying the computer analysis 311 24.3 Examples 312 Key points 321 Computer analysis: Related analysis of variance using SPSS 322 25 Two-way or factorial AIMOVA for unrelated/uncorrelated scores: Two studies for the price of one? 324 Overview 324 25.1 Introduction 325 25.2 Theoretical considerations 326 25.3 Steps in the analysis 327 25.4 More on interactions 340 25.5 Three or more independent variables 343 Key points 347 Computer analysis: Unrelated two-way analysis of variance using SPSS 348 26 Multiple comparisons with in ANOVA: A priori and post hoc tests 351 Overview 351 26.1 Introduction 352 26.2 Planned (a priori) versus unplanned (post hoc) comparisons 353 26.3 Methods of multiple comparisons testing 354 26.4 Multiple comparisons for multifactorial ANOVA 354 26.5 Contrasts 355 26.6 Trends 357 Key points 358 Computer analysis: Multiple comparison tests using SPSS 359 Recommended further reading 361 27 Mixed-design ANOVA: Related and unrelated variables together 362 Overview 362 27.1 Introduction 363 27.2 Mixed designs and repeated measures 363 Key points 376 Computer analysis: Mixed design analysis of variance using SPSS 376 Recommended further reading 378 28 Analysis of covariance (ANCOVA): Controlling for additional variables 379 Overview 379 28.1 Introduction 380 28.2 Analysis of covariance 381 Key points 391 Computer analysis: Analysis of covariance using SPSS 392 Recommended further reading 394 xiv CONTENTS 29 Multivariate analysis of variance (MANOVA) 395 Overview 395 29.1 Introduction 396 29.2 MANOVA’s two stages 399 29.3 Doing MANOVA 401 29.4 Reporting your findings 406 Key points 407 Computer analysis: Multivariate analysis of variance using SPSS 408 Recommended further reading 410 30 Discriminant (function) analysis - especially in MANOVA 411 Overview 411 30.1 Introduction 412 30.2 Doing the discriminant function analysis 414 30.3 Reporting your findings 420 Key points 421 Computer analysis: Discriminant function analysis using SPSS 422 Recommended further reading 424 31 Statistics and analysis of experiments 425 Overview 425 31.1 Introduction 426 31.2 The Patent Stats Pack 426 31.3 Checklist 427 31.4 Special cases 431 Key points 431 Computer analysis: Selecting subsamples of your data using SPSS 433 Computer analysis: Recoding groups for multiple comparison tests using SPSS 435 Part 4 More advanced correlational statistics 437 J 32 Partial correlation: Spurious correlation, third or confounding variables, suppressor variables 439 Overview 439 32.1 Introduction 440 32.2 Theoretical considerations 441 32.3 Doing partial correlation 443 32.4 Interpretation 444 CONTENTS 32.5 Multiple control variables 445 32.6 Suppressor variables 445 32.7 Example from the research literature 446 32.8 Example from a student’s work 447 Key points 448 Computer analysis: Partial correlation using SPSS 449 33 Factor analysis: Simplifying complex data 451 Overview 451 33.1 Introduction 452 33.2 A bit of history 453 33.3 Concepts in factor analysis 454 33.4 Decisions, decisions, decisions 456 33.5 Exploratory and confirmatory factor analysis 464 33.6 Example of factor analysis from the literature 466 33.7 Reporting the results 468 Key points 470 Computer analysis: Principal components analysis using SPSS 471 Recommended further reading 473 34 Multiple regression and multiple correlation 474 Overview 474 34.1 Introduction 475 34.2 Theoretical considerations 476 34.3 Assumptions of multiple regression 481 34.4 Stepwise multiple regression example 482 34.5 Reporting the results 485 34.6 Example from the published literature 486 Key points 488 Computer analysis: Stepwise multiple regression using SPSS 489 Recommended further reading 491 35 Path analysis 492 Overview 492 35.1 Introduction 493 35.2 Theoretical considerations 493 35.3 Example from published research 500 35.4 Reporting the results 503 Key points 504 Computer analysis: Hierarchical multiple regression using SPSS 505 Recommended further reading 507 XVI CONTENTS 36 Analysis of a questionnaire/survey project 508 Overview 508 36.1 Introduction 509 36.2 Research project 509 36.3 Research hypothesis 511 36.4 Initial variable classification 512 36.5 Further coding of data 513 36.6 Data cleaning 514 36.7 Data analysis 514 Key points 516 Computer analysis: Adding and averaging components of a measure using SPSS 516 Part 5 Assorted advanced techniques 519 y 37 Meta-analysis: Combining and exploring statistical findings from previous research 521 Overview 521 37.1 Introduction 522 37.2 Pearson correlation coefficient as the effect size 524 37.3 Other measures of effect size 524 37.4 Effects of different characteristics of studies 525 37.5 First steps in meta-analysis 526 37.6 Illustrative example 532 37.7 Comparing a study with a previous study 535 37.8 Reporting the results 536 Key points 538 Computer analysis: Some meta-analysis software 538 Recommended further reading 539 38 Reliability in scales and measurement: Consistency and agreement 540 Overview 540 38.1 Introduction 541 38.2 Item-analysis using item-total correlation 541 38.3 Split-half reliability 543 38.4 Alpha reliability 544 38.5 Agreement among raters 547 Key points 551 Computer analysis: Cronbach’s alpha and kappa using SPSS 552 Recommended further reading 553 CONTENTS xvii 39 Influence of moderator variables on relationships between two variables 554 Overview 554 39.1 Introduction 555 39.2 Statistical approaches to finding moderator effects 559 39.3 Hierarchical multiple regression approach to identifying moderator effects (or interactions) 559 39.4 ANOVA approach to identifying moderator effects (Le. interactions) 569 Key points 573 Computer analysis: Regression moderator analysis using SPSS 574 Recommended further reading 575 40 Statistical power analysis: Getting the sample size right 576 Overview 576 401 Introduction 577 40.2 Types of statistical power analysis and their limitations 587 40.3 Doing power analysis 589 40.4 Calculating power 591 40.5 Reporting the results 595 Key points 596 Computer analysis: Power analysis with G*Power 597 r Part 6 V. Advanced qualitative or nominal techniques 601 i 41 Log-linear methods: Analysis of complex contingency tables 603 Overview 603 41.1 Introduction 604 41.2 Two-variable example 606 41.3 Three-variable example 613 41.4 Reporting the results 624 Key points 625 Computer analysis·. Log-linear analysis using SPSS 626 Recommended further reading 627 42 Multinomial logistic regression: Distinguishing between several different categories or groups 628 Overview 628 42.1 Introduction 629 42.2 Dummy variables 631 42.3 What can multinomial logistic regression do? 632 J XVIII CONTENTS 42.4 Worked example 634 42.5 Accuracy of the prediction 635 42.6 How good are the predictors? 636 42.7 Prediction 639 42.8 Interpreting the results 641 42.9 Reporting the results 641 Key points 643 Computer analysis: Multinomial logistic regression using SPSS 644 43 Binomial logistic regression 646 Overview 646 43.1 Introduction 647 43.2 Typical example 651 43.3 Applying the logistic regression procedure 654 43.4 Regression formula 658 43.5 Reporting the results 659 Key points 660 Computer analysis: Binomial logistic regression using SPSS 661 Appendices Appendix A Testing for excessively skewed distributions 663 Appendix B1 Large-sample formulae for the nonparametric tests 666 Appendix B2 Nonparametric tests for three or more groups 668 Computer analysis: Kruskal-Wallis and Friedman nonparametric tests using SPSS 672 Appendix C Extended table of significance for the Pearson correlation coefficient 674 Appendix D Table of significance for the Spearman correlation coefficient 677 Appendix E Extended table of significance for the t-test 680 Appendix F Table of significance for chi-square 683 Appendix G Extended table of significance for the sign test 684 Appendix H Table of significance for the Wilcoxon matched pairs test 687 Appendix I Tables of significance for the Mann-Whitney U-test 690 Appendix J Tables of significance values for the F-distribution 693 Appendix K Table of significance values for t when making multiple t-tests 696 Glossary 699 References 707 Index 713
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author	Howitt, Dennis 1945- Cramer, Duncan 1948-
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id	DE-604.BV044270750
illustrated	Illustrated
indexdate	2024-12-20T17:58:28Z
institution	BVB
isbn	9781292134215
language	English
lccn	016047666
oai_aleph_id	oai:aleph.bib-bvb.de:BVB01-029675368
oclc_num	982191831
open_access_boolean
owner	DE-11 DE-473 DE-BY-UBG DE-739 DE-355 DE-BY-UBR DE-1050
owner_facet	DE-11 DE-473 DE-BY-UBG DE-739 DE-355 DE-BY-UBR DE-1050
physical	xxviii, 726 Seiten Illustrationen, Diagramme
publishDate	2017
publishDateSearch	2017
publishDateSort	2017
publisher	Pearson
record_format	marc
spellingShingle	Howitt, Dennis 1945- Cramer, Duncan 1948- Understanding statistics in psychology with SPSS Psychometrics Empirische Psychologie (DE-588)4152106-7 gnd Psychometrie (DE-588)4176236-8 gnd Psychologie (DE-588)4047704-6 gnd Datenanalyse (DE-588)4123037-1 gnd SPSS (DE-588)4056588-9 gnd Statistik (DE-588)4056995-0 gnd
subject_GND	(DE-588)4152106-7 (DE-588)4176236-8 (DE-588)4047704-6 (DE-588)4123037-1 (DE-588)4056588-9 (DE-588)4056995-0
title	Understanding statistics in psychology with SPSS
title_alt	Introduction to statistics in psychology
title_auth	Understanding statistics in psychology with SPSS
title_exact_search	Understanding statistics in psychology with SPSS
title_full	Understanding statistics in psychology with SPSS Dennis Howitt, Loughborough University, Duncan Cramer, Loughborough University
title_fullStr	Understanding statistics in psychology with SPSS Dennis Howitt, Loughborough University, Duncan Cramer, Loughborough University
title_full_unstemmed	Understanding statistics in psychology with SPSS Dennis Howitt, Loughborough University, Duncan Cramer, Loughborough University
title_short	Understanding statistics in psychology with SPSS
title_sort	understanding statistics in psychology with spss
topic	Psychometrics Empirische Psychologie (DE-588)4152106-7 gnd Psychometrie (DE-588)4176236-8 gnd Psychologie (DE-588)4047704-6 gnd Datenanalyse (DE-588)4123037-1 gnd SPSS (DE-588)4056588-9 gnd Statistik (DE-588)4056995-0 gnd
topic_facet	Psychometrics Empirische Psychologie Psychometrie Psychologie Datenanalyse SPSS Statistik
url	http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=029675368&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA
work_keys_str_mv	AT howittdennis introductiontostatisticsinpsychology AT cramerduncan introductiontostatisticsinpsychology AT howittdennis understandingstatisticsinpsychologywithspss AT cramerduncan understandingstatisticsinpsychologywithspss

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UB Lesesaal Philosophicum 2: Psychologie

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