Counteracting Methodological Errors in Behavioral Research

Counteracting Methodological Errors in Behavioral Research

Mellenbergh, Gideon J.

Springer International Publishing AG

05/2019

376

Dura

Inglês

9783319743523

15 a 20 dias

752

Descrição não disponível.
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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Random and Systematic Errors;Sampling Errors;Design Errors;Measurement Errors;Data Analysis Errors;Reporting Errors