Availability: Methods of meta-analysis | Technische Universität München

Methods of meta-analysis: correcting error and bias in research findings

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Bibliographic Details
Main Authors:	Hunter, John Edward 1939-2002 (Author), Schmidt, Frank L. 1944- (Author)
Format:	Book
Language:	English
Published:	New Park, Calif. Sage 1990
Edition:	1. print.
Subjects:	Ciencias humanas e sociais Meta-analyse Méta-analyse Sciences sociales - Méthodes statistiques Sozialwissenschaften Statistik Meta-Analysis Meta-analysis Research > methods Social sciences > Statistical methods Statistics Methode Psychologie Forschungsmethode Metaanalyse
Links:	http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=003306478&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA
Item Description:	Literaturverz. S. 561 - 576
Physical Description:	592 S.
ISBN:	0803932227 0803932235

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Record in the Search Index

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adam_text	Brief Contents Preface 13 Acknowledgments 20 Part I. Introduction to Meta Analysis 1: Integrating Research Findings Across Studies 23 2: Study Artifacts and their Impact on Study Outcomes 43 Part II. Meta Analysis of Correlations 3: Meta Analysis of Correlations Corrected Individually for Artifacts 93 4: Meta Analysis of Correlations Using Artifact Distributions 158 5: Technical Questions in Meta Analysis of Correlations 199 Part III. Meta Analysis of Experimental Effects and Other Dichotomous Comparisons 6: Treatment Effects: Experimental Artifacts and Their Impact 231 7: Meta Analysis Methods for d Values 267 8: Technical Questions in Meta Analysis of d Values 339 Part IV. General Issues in Meta Analysis 9: Second Order Sampling Error and Related Issues 411 10: Cumulation of Findings Within Studies 451 11: Methods of Integrating Findings Across Studies 468 12: Locating, Selecting, and Evaluating Studies 490 13: General Criticisms of Meta Analysis 506 14: Summary of Psychometric Meta Analysis 535 Appendix 541 References 561 Author Index 577 Subject Index 580 About the Authors 592 Contents Preface 13 Acknowledgments 20 I. Introduction to Meta Analysis Chapter 1: Integrating Research Findings Across Studies 23 General problem and an example 23 A typical interpretation of the example data 24 Conclusion 26 Critique of the sample review 28 Problems with statistical significance tests 29 The confidence interval 31 Meta analysis 33 Role of meta analysis in the behavioral and social sciences 35 Role of meta analysis in theory development 39 Increasing use of meta analysis 40 Meta analysis in industrial organizational psychology 41 Chapter 2: Study Artifacts and Their Impact on Study Outcomes 43 Study artifacts 44 Sampling error 44 Error of measurement 44 Dichotomization 46 Range variation in the independent variable 47 Attrition artifacts: Range variation in the dependent variable 49 Imperfect construct validity in the independent variable 52 Imperfect construct validity in the dependent variable 65 Errors in the data 67 Extraneous factors introduced by study procedure 68 Bias in the sample correlation 69 Sampling error, statistical power, and the interpretation of research literatures 72 An illustration of statistical power 72 A detailed examination of statistical power 75 When and how to cumulate 82 Undercorrection for artifacts in the corrected standard deviation 84 Coding study characteristics and capitalization on chance 85 A look ahead in the book 89 II. Meta Analysis of Correlations Chapter 3: Meta Analysis of Correlations Corrected Individually for Artifacts 93 Introduction and overview 93 Bare bones meta analysis: Correcting for sampling error only 100 Estimation of sampling error 100 Correcting the variance for sampling error and an example 102 Moderator variables analyzed by grouping the data and an example 112 Correcting feature correlations for sampling error and an example 115 Artifacts other than sampling error 117 Error of measurement and correction for attenuation 117 Restriction or enhancement of range 125 Dichotomization of independent and dependent variables 133 Imperfect construct validity in independent and dependent variables 138 Attrition artifacts 139 Extraneous factors 140 Bias in the correlation 141 Multiple simultaneous artifacts 142 Meta analysis of individually corrected correlations 143 Individual study computations 144 Combining across studies 146 Final meta analysis estimation 150 Summary of meta analysis correcting each correlation individually 156 Chapter 4: Meta Analysis of Correlations Using Artifact Distributions 158 Full artifact distribution meta analysis 159 The mean correlation 160 The standard deviation of correlations 163 An example: Error of measurement 173 An example: Unreliability and range restriction 177 An example: Personnel selection with fixed test 179 An example: Personnel selection with varying tests 182 Personnel selection: Findings and formulas in the literature 182 Mixed meta analysis: Partial artifact information in individual studies 187 An example: Dichotomization of both variables 190 Summary of artifact distribution meta analysis of correlations 196 Phase 1 197 Phase 2a: Correcting the mean correlation 197 Phase 2b: Correcting the standard deviation of correlations 197 Chapter 5: Technical Questions in Meta Analysis of Correlations 199 r versus r1: Which should be used? 199 r versus regression slopes and intercepts in meta analysis 202 Range restriction 202 Measurement error 203 Compatability of units across studies 203 Compatability of findings across meta analysis 203 Intrinsic interpretability 205 Technical factors that cause overestimation of SDp 206 Presence of non Pearson rs 206 Presence of outliers 207 Use of r instead of 7 in the sampling error formula 208 Undercorrection for sampling error variance in the presence of range restriction 209 Nonlinearity in range correction 209 Other factors causing overestimation of SDp 211 Criticisms of meta analysis procedures for correlations 212 Does meta analysis assume SDp = 0? 213 Bias in r and use of Fisher s z 213 Erroneous use of confidence intervals 218 Is sampling error variance inaccurately estimated? 220 Errors in the Kemery et al. (1987) study 222 Are estimates of SDp unreliable ? 223 Overestimation of variance due to artifacts 224 Alleged problems with the sampling error variance model 226 III. Meta Analysis of Experimental Effects and Other Dichotomous Comparisons Chapter 6: Treatment Effects: Experimental Artifacts and Their Impact 231 Quantification of the treatment effect: The d statistic and the point biserial correlation 232 Sampling error in d values: Illustrations 236 Case 1: N = 30 236 Case 2:N=68 239 Case 3: #= 400 240 Error of measurement in the dependent variable 241 Error of measurement in the treatment variable 247 Variation across studies in treatment strength 252 Range variation on the dependent variable 253 Dichotomization of the dependent variable 255 Imperfect construct validity in the dependent variable 257 Imperfect construct validity in the treatment variable 260 Bias in the effect size 260 Recording, computational, and transcriptional errors 262 Multiple artifacts and corrections 263 Chapter 7: Meta Analysis Methods for d Values 267 Effect size indices: d and r 268 Maximum value of point biserial r 269 The effect size 271 Correction of the point biserial for unequal sample sizes 274 Examples of the convertibility of r and d 275 Problems of artificial dichotomization 276 An alternative to d: Glass d 276 Meta analysis formulas for Se and 5c 278 Sampling error in the d statistic 281 The standard error for d 281 The confidence interval for d 283 Cumulation and correction of the variance for sampling error 284 Bare bones meta analysis 285 A numerical example 287 Another example: Leadership training by experts 290 Analysis of moderator variables 292 Using study domain subsets 293 Using study characteristic correlations 293 An example: Training by experts versus by managers 295 Another example: Amount of training 299 The correlational moderator analysis 302 Correcting d value statistics for measurement error in the dependent variable 303 Meta analysis oid values corrected individually and an example 306 Artifact distribution meta analysis and an example 311 Measurement error in the dependent variable in experiments 316 Other artifacts and their effects 319 Correcting for multiple artifacts 321 Attenuation effect of multiple artifacts and correction for the same 321 Disattenuation and sampling error: The confidence interval 324 A formula for meta analysis with multiple artifacts 325 Summary of meta analysis of d values 336 Chapter 8: Technical Questions in Meta Analysis of d Values 339 Within subjects experimental designs 339 The potentially perfect power of the pre post design 341 The deficiencies of the between subjects design 343 Error of measurement and the within subjects design 348 The treatment by subject interaction 354 Sampling error 364 Meta analysis and the within subjects design 382 The d statistic 382 The treatment by subjects interaction 383 Statistical power in the two designs 388 Designs matched for number of subjects 389 Designs matched for number of measurements or scores 393 Threats to internal and external validity 397 History 399 Maturation 399 Testing 400 Instrumentation 401 Regression to the mean 401 Reactive arrangements 402 Interaction between testing and treatment 403 Interaction between selection and treatment 403 Bias in observed d values 404 Random versus fixed effects models 405 Use of multiple regression in moderator analysis 408 IV. General Issues in Meta Analysis Chapter 9: Second Order Sampling Error and Related Issues 411 Second order sampling error: General principles 411 Statistical power to detect moderators in meta analysis: General principles 414 Second order meta analyses 421 Second order sampling error: Technical treatment 422 The homogeneous case 426 The heterogeneous case 428 The detection of moderator variables: Technical treatment 436 The theoretically predicted moderator 437 The unsuspected moderator 440 Comparison of statistical power 447 Chapter 10: Cumulation of Findings Within Studies 451 Fully replicated designs 451 Conceptual replication 451 Conceptual replication and confirmatory factor analysis 454 Conceptual replication: An alternative approach 457 Analysis of subgroups 463 Subgroups and loss of power 463 Subgroups and capitalization on chance 464 Subgroups and suppression of data 465 Subgroups and the bias of disaggregation 465 Conclusion: Use total group correlations 466 Summary 466 Chapter 11: Methods of Integrating Findings Across Studies 468 The traditional narrative procedure 468 The traditional voting method 469 Cumulation of p values across studies 471 Statistically correct vote counting methods 472 Vote counting methods yielding only significance levels 473 Vote counting methods yielding estimates of effect sizes 474 Counting positive significant findings 474 Counting positive results 476 Counting positive and negative significant results 477 Meta analysis of research studies 478 Glassian meta analysis methods 479 Study effects meta analysis 482 Homogeneity test based meta analysis 483 Schmidt Hunter meta analysis methods: Validity generalization 484 Psychometric meta analysis 485 Summary 487 Chapter 12: Locating, Selecting, and Evaluating Studies 490 Conducting a thorough literature search 490 Indices 490 Bibliographies and personal queries 494 Complexities of the search for studies 494 What to do about studies with methodological weaknesses 495 What to report in the review 498 Information needed in reports of primary studies 499 Correlational studies 500 Experimental studies 501 Studies using multiple regression 502 Studies using factor analysis 503 Studies using canonical correlation 504 Studies using MANOVA 504 General comments on reporting in primary studies 505 Chapter 13: General Criticisms of Meta Analysis 506 Availability bias 506 Does source bias exist? 507 Effects of methodological quality on mean effect sizes from different sources 509 Statistical methods for handling availability bias 510 Rejection of the entire social science research base 515 Mixing of apples and oranges 516 The logical argument 517 The methodological argument 518 Homogeneity 520 Sampling error 521 Real apples and oranges 521 False defense of the narrative review 527 Checklists of rules for meta analysis 532 Criticisms of corrections for attenuation 533 Chapter 14: Summary of Psychometric Meta Analysis 535 Appendix: Meta analysis computer programs for the PC 541 Meta analysis correcting correlations individually 543 Artifact distribution meta analysis of correlations 548 Meta analysis correcting d values individually 553 Artifact distribution meta analysis of d values 556 Program to create data files 559 References 561 Author Index 577 Subject Index 580 About the Authors 592
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spellingShingle	Hunter, John Edward 1939-2002 Schmidt, Frank L. 1944- Methods of meta-analysis correcting error and bias in research findings Ciencias humanas e sociais larpcal Meta-analyse gtt Méta-analyse Méta-analyse ram Sciences sociales - Méthodes statistiques Sciences sociales - Méthodes statistiques ram Sozialwissenschaften Statistik Meta-Analysis Meta-analysis Research methods Social sciences Statistical methods Statistics Methode (DE-588)4038971-6 gnd Psychologie (DE-588)4047704-6 gnd Forschungsmethode (DE-588)4155046-8 gnd Statistik (DE-588)4056995-0 gnd Metaanalyse (DE-588)4169552-5 gnd
subject_GND	(DE-588)4038971-6 (DE-588)4047704-6 (DE-588)4155046-8 (DE-588)4056995-0 (DE-588)4169552-5
title	Methods of meta-analysis correcting error and bias in research findings
title_auth	Methods of meta-analysis correcting error and bias in research findings
title_exact_search	Methods of meta-analysis correcting error and bias in research findings
title_full	Methods of meta-analysis correcting error and bias in research findings John E. Hunter ; Frank L. Schmidt
title_fullStr	Methods of meta-analysis correcting error and bias in research findings John E. Hunter ; Frank L. Schmidt
title_full_unstemmed	Methods of meta-analysis correcting error and bias in research findings John E. Hunter ; Frank L. Schmidt
title_short	Methods of meta-analysis
title_sort	methods of meta analysis correcting error and bias in research findings
title_sub	correcting error and bias in research findings
topic	Ciencias humanas e sociais larpcal Meta-analyse gtt Méta-analyse Méta-analyse ram Sciences sociales - Méthodes statistiques Sciences sociales - Méthodes statistiques ram Sozialwissenschaften Statistik Meta-Analysis Meta-analysis Research methods Social sciences Statistical methods Statistics Methode (DE-588)4038971-6 gnd Psychologie (DE-588)4047704-6 gnd Forschungsmethode (DE-588)4155046-8 gnd Statistik (DE-588)4056995-0 gnd Metaanalyse (DE-588)4169552-5 gnd
topic_facet	Ciencias humanas e sociais Meta-analyse Méta-analyse Sciences sociales - Méthodes statistiques Sozialwissenschaften Statistik Meta-Analysis Meta-analysis Research methods Social sciences Statistical methods Statistics Methode Psychologie Forschungsmethode Metaanalyse
url	http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=003306478&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA
work_keys_str_mv	AT hunterjohnedward methodsofmetaanalysiscorrectingerrorandbiasinresearchfindings AT schmidtfrankl methodsofmetaanalysiscorrectingerrorandbiasinresearchfindings

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