Understanding statistics in psychology with SPSS:
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| Main Authors: | , |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Harlow, England
Pearson
[2017]
|
| Edition: | Seventh edition |
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Item Description: | Revised edition of the authors' Introduction to statistics in psychology, 2013 |
| Physical Description: | xxviii, 726 Seiten Illustrationen, Diagramme |
| ISBN: | 9781292134215 |
Staff View
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| 240 | 1 | 0 | |a Introduction to statistics in psychology |
| 245 | 1 | 0 | |a Understanding statistics in psychology with SPSS |c Dennis Howitt, Loughborough University, Duncan Cramer, Loughborough University |
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Record in the Search Index
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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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| dewey-ones | 150 - Psychology |
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| dewey-tens | 150 - Psychology |
| discipline | Informatik Psychologie |
| edition | Seventh edition |
| format | Book |
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| id | DE-604.BV044270750 |
| illustrated | Illustrated |
| indexdate | 2024-07-10T07:48:20Z |
| 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 |
| spelling | Howitt, Dennis 1945- (DE-588)172144787 aut Introduction to statistics in psychology Understanding statistics in psychology with SPSS Dennis Howitt, Loughborough University, Duncan Cramer, Loughborough University Seventh edition Harlow, England Pearson [2017] xxviii, 726 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier Revised edition of the authors' Introduction to statistics in psychology, 2013 Psychometrics Empirische Psychologie (DE-588)4152106-7 gnd rswk-swf Psychometrie (DE-588)4176236-8 gnd rswk-swf Psychologie (DE-588)4047704-6 gnd rswk-swf Datenanalyse (DE-588)4123037-1 gnd rswk-swf SPSS (DE-588)4056588-9 gnd rswk-swf Statistik (DE-588)4056995-0 gnd rswk-swf SPSS (DE-588)4056588-9 s Empirische Psychologie (DE-588)4152106-7 s Statistik (DE-588)4056995-0 s Datenanalyse (DE-588)4123037-1 s Psychometrie (DE-588)4176236-8 s DE-604 Psychologie (DE-588)4047704-6 s 1\p DE-604 Cramer, Duncan 1948- (DE-588)141387270 aut Erscheint auch als Online-Ausgabe, PDF 978-1-292-13424-6 Erscheint auch als Online-Ausgabe, ePub 978-1-292-13425-3 Digitalisierung UB Bamberg - ADAM Catalogue Enrichment application/pdf 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 Inhaltsverzeichnis 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| 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 |
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