Overview

This book describes methods to prevent avoidable errors and to correct unavoidable ones within the behavioral sciences. A distinguishing feature of this work is that it is accessible to students and researchers of substantive fields of the behavioral sciences and related fields (e.g., health sciences and social sciences). Discussed are methods for errors that come from human and other factors, and methods for errors within each of the aspects of empirical studies. This book focuses on how empirical research is threatened by different types of error, and how the behavioral sciences in particular are vulnerable due to the study of human behavior and human participation in studies. Methods to counteract errors are discussed in depth including how they can be applied in all aspects of empirical studies: sampling of participants, design and implementation of the study, instrumentation and operationalization of theoretical variables, analysis of the data, and reporting of the study results. Students and researchers of methodology, psychology, education, and statistics will find this book to be particularly valuable. Methodologists can use the book to advice clients on methodological issues of substantive research.

Full Product Details

Author:   Gideon J. Mellenbergh
Publisher:   Springer International Publishing AG
Imprint:   Springer International Publishing AG
Edition:   1st ed. 2019
Weight:   0.752kg
ISBN:  

9783319743523

ISBN 10:   331974352
Pages:   376
Publication Date:   27 May 2019
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Hardback
Publisher's Status:   Active
Availability:   Manufactured on demand  
We will order this item for you from a manufactured on demand supplier.

Table of Contents

Preface 1 Random and systematic errors in context 1.1 Research objectives 1.2 Random and systematic errors 1.3 Errors in context 1.3.1 Research questions 1.3.2 Literature review 1.3.3 Sampling 1.3.4 Operationalizations 1.3.5 Designs 1.3.6 Implementation 1.3.7 Data analysis 1.3.8 Reporting 1.4 Recommendations References 2 Probability sampling 2.1 The elements of probability sampling 2.2 Defining the target population 2.3 Constructing the sampling frame 2.4 Probability sampling 2.4.1 Simple random sampling 2.4.2 Sample size 2.4.3 Stratification 2.4.4 Cluster sampling 2.5 Obtaining participation of sampled persons 2.6 Recommendations References 3 Nonprobability sampling 3.1 The main elements of nonprobability sampling 3.2 Strategies to control for bias 3.2.1 Representative sampling 3.2.2 Bias reduction by weighting 3.2.3 Generalization across participant characteristics 3.2.4 Comments 3.3 Recommendations References 4 Random assignment 4.1 Independent and dependent variables 4.2 Association does not mean causation 4.3 Other variable types 4.4 Random assignment to control for selection bias 4.5 Reducing random error variance 4.5.1 Blocking 4.5.2 Covariates 4.6 Cluster randomization 4.7 Missing participants (clusters) 4.8 Random assignment and random selection 4.9 Recommendations References 5 Propensity scores 5.1 The propensity score 5.2 Estimating the propensity score 5.3 Applying the propensity score 5.4 An example 5.5 Comments 5.6 Recommendations References 6 Situational bias 6.1 Standardization 6.2 Calibration 6.3 Blinding 6.4 Random assignment 6.5 Manipulation checks and treatment separation 6.6 Pilot studies 6.7 Replications 6.8 Randomization bias 6.9 Pretest effects 6.10 Response shifts 6.11 Recommendations References 7 Random measurement error 7.1 Tests and test scores 7.2 Measurement precision 7.2.1 Within-person precision 7.2.2 Reliability 7.3 Increasing measurement precision 7.3.1 Item writing 7.3.2 Compiling the test 7.3.3 Classical analysis of test scores 7.3.4 Classical item analysis 7.3.5 Modern item analysis 7.3.6 Test administration 7.3.7 Data processing 7.4 Recommendations References 8 Systematic measurement error 8.1 Cheating 8.2 Person fit 8.3 Satisficing 8.4 Impression management 8.5 Response styles 8.5.1 'Plodding' and 'fumbling' 8.5.2 The extremity and midpoint style 8.5.3 Acquiescence and dissentience 8.6 Item nonresponse 8.7 Coping with systematic errors 8.8 Recommendations References 9 Unobtrusive measurements 9.1 Measurement modes 9.2 Examples of unobtrusive measurements 9.3 Random error of unobtrusive measurements 9.4 Systematic errors of unobtrusive measurements 9.5 Comments 9.6 Recommendations References 10 Test dimensionality 10.1 Types of multidimensionality 10.2 Reliability and test dimensionality 10.3 Detecting test dimensionality 10.3.1 Factor analysis of inter-item product moment correlations 10.3.2 Factor analysis of inter-item tetrachoric and polychoric correlations 10.3.3 Mokken scale analysis 10.3.4 Full-information factor analysis 10.3.5 Comments 10.4 Measurement invariance 10.4.1 Measurement bias with respect to group membership 10.4.2 Measurement invariance and behavioral research 10.5 Recommendations References 11 Coefficients for bivariate relations 11.1 Bivariate relation types 11.2 Variable types 11.3 Classification of coefficients for bivariate relations 11.4 Examples of coefficients 11.4.1 Dichotomous variables and a symmetrical relation 11.4.2 Dichotomous variables and equality of X- and Y-categories 11.4.3 Dichotomous variables and an asymmetrical relation 11.4.4 Nominal-categorical variables and a symmetrical relation 11.4.5 Nominal-categorical variables and equality of X- and Y-categories 11.4.6 Nominal-categorical variables and an asymmetrical relation 11.4.7 Ordinal-categorical variables and a symmetrical relation 11.4.8 Ordinal-categorical variables and equality of X- and Y-categories 11.4.9 Ordinal-categorical variables and an asymmetrical relation 11.4.10 Ranked variables and a symmetrical relation 11.4.11 Continuous variables and a symmetrical relation 11.4.12 Continuous variables and equality of X- and Y-values 11.4.13 Continuous variables and an asymmetrical relation 11.5 Comments 11.6 Recommendations References 12 Null hypothesis testing 12.1 The confidence interval approach to null hypothesis testing 12.1.1 Classical confidence intervals of the means of paired scores 12.1.2 Classical confidence intervals of independent DV score means 12.2 Overlapping CIs 12.3 Conditional null hypothesis testing 12.4 Bootstrap methods 12.4.1 The bootstrap t method for paired DV score means 12.4.2 The bootstrap t method for independent DV score means 12.4.3 The modified percentile bootstrap method for the product moment correlation 12.5 Standardized effect sizes 12.6 Power 12.7 Testing multiple null hypothesis 12.8 Null hypothesis testing and data exploration 12.9 Sequential null hypothesis testing 12.10 Equivalence testing 12.11 Recommendations References 13 Unstandardized effect sizes 13.1 Differences of means 13.2 Probability of superiority 13.3 Linear transformations of observed test scores 13.3.1 The Average Item Score (AIS) transformation 13.3.2 The Proportion of Maximum Possible (POMP) score transformation 13.4 Recommendations References 14 Pretest-posttest change 14.1 The population/single-person fallacy in pretest-posttest studies 14.2 Group change 14.2.1 Within-group pretest-posttest change 14.2.2 Between-groups change 14.3 Single-person change 14.3.1 Single-person observed test score change 14.3.2 Single-person continuous item response change 14.3.3 Single-person dichotomous item response change 14.4 Comments 14.5 Recommendations References 15 Reliability 15.1 The classical model of observed test scores 15.2 Measurement precision 15.2.1 Standard error of measurement 15.2.2 Reliability 15.3 Counter-intuitive properties of the reliability of the observed test score 15.3.1 Reliability of the observed test score and unidimensionality 15.3.2 Reliability and true score estimation precision 15.3.3 Reliability and mean test score estimation precision 15.3.4 Reliability and estimating the difference of two independent test score means 15.3.5 Reliability and testing the null hypothesis of equal independent test score means 15.4 Reliability of the difference score 15.4.1 The classical model of the difference score 15.4.2 Unreliable and reliable difference scores 15.4.3 Reliability of the difference score and estimation precision of the true difference score 15.4.4 Reliability of the difference score and estimation precision of the mean difference score 15.4.5 Reliability of the difference score and testing the null hypothesis of equal means of paired test scores 15.5 Reliability of latent variables 15.5.1 Reliability of latent trait estimates 15.5.2 Reliability and discrete latent variables 15.6 Relevance of the reliability concept 15.7 Recommendations References 16 Missing data 16.1 Missingness types 16.2 Missingness variables 16.3 Data collection methods to reduce missingness 16.4 Sample size maintenance procedures 16.5 Naive missing data methods 16.6 Nonnaive missing variable methods 16.6.1 Statistical methods 16.6.2 Worst-case imputation of missing paired scores 16.6.3 Worst-case imputation of missing independent scores 16.7 Nonnaive missing item methods 16.7.1 Imputing missing maximum performance items 16.7.2 Imputing missing typical response items 16.8 Recommendations References 17 Outliers 17.1 Outlier detection methods 17.2 Outlier detection and correction 17.3 Coping with coincidental outliers 17.4 Coping with noncoincidental outliers 17.5 Content robustness against outliers 17.6 Robust statistics 17.7 Comparing paired scores 17.8 Comparing independent scores 17.9 Association between two variables 17.10 Recommendations References 18 Interactions and specific hypotheses 18.1 Factorial designs 18.2 Main and interaction effects 18.3 Testing main and interaction effects 18.3.1 Continuous and ranked DVs 18.3.3 Dichotomous DVs 18.3.3 Nominal-categorical DVs 18.3.4 Ordinal-categorical DVs 18.4 Nonmanipulable factors 18.5 Dichotomization of nonmanipulable independent variables 18.6 Testing specific substantive hypotheses 18.6.1 Planned comparisons of DV-means 18.6.2 Planned comparisons of DV-logits 18.6.3 Testing multiple null hypotheses of contrasts 18.7 Recommendations References 19 Publishing 19.1 The publication process 19.2 Publication bias 19.3 Replications 19.3.1 Replication hypotheses 19.3.2 Testing a replication hypothesis 19.3.3 Equivalence testing of a linear contrast 19.3.4 A framework for replication research 19.4 Proposals 19.4.1 Attitude towards replication 19.4.2 Editorial policies 19.4.3 Collaboration References 20 Scientific misconduct 20.1 Plagiarism 20.2 Fabrication and falsification 20.3 Questionable scientific practices 20.3.1 Questionable research practices 20.3.2 Questionable editorial practices 20.4 Policies against misconduct 20.4.1 Educational policies 20.4.2 Editorial policies 20.4.3 Formal policies References

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Author Information

Gideon J. Mellenbergh is emeritus professor of Psychological Methods at the University of Amsterdam, former director of the Interuniversity Graduate School of Psychometrics and Sociometrics (IOPS), and emeritus member of the Royal Netherlands Academy of Arts and Sciences (KNAW). His research interests are in the construction of psychological and educational tests, psychometric decision making, measurement invariance, and the analysis of psychometrical concepts. His teaching was on a large number of methodological topics (design, measurement, and data analysis) for audiences that vary from freshmen to dissertation students. He (co-) supervised 89 PhD students who successfully defended their thesis. Recently, he taught courses on methodological consultancy for research master and dissertation students. He published in international methodological journals (e.g., Applied Psychological Measurement, Journal of Educational Measurement, Multivariate Behavioral Research, Psychological Bulletin, Psychological Methods, and Psychometrika), contributed to methodological books, and published the introductory textbook A Conceptual Introduction to Psychometrics.

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