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In their fifth meeting at the Vrije Universiteit Amsterdam in 2017 (5th WCRI), the World Conference on Research Integrity (WCRI) adopted the Amsterdam Agenda (AA). The AA is a policy statement stipulating six key elements that researchers doing research on aspects of research integrity preferably should use when preregistering their empirical research projects (https://www.wcrif.org/guidance/amsterdam-agenda). The applicants of this submission form performed an ad hoc study in 2019 using data supplied by paper and poster presenters of the 6th WCRI in Hong Kong, 2019, to find out to which degree researchers had pre-registered the study they were going to present at the 6th WCRI, using either the recommendations of the AA or other recommendations. The first applicant (Sijtsma) presented the results in the final plenary session of 6th WCRI. The pilot study showed that 19% of the 308 presenters had preregistered their research. Of the 56 usable cases, less than half of them provided information on one or more of the six key elements of the AA. Others provided information that invalidated their data, such as an uninformative URL. There was no discernable difference between qualitative and quantitative research. We published our results in the journal Research Integrity and Peer Review (Sijtsma et al., 2021).Our study suffered from two limitations. First, we did no plan our research a priori but used the raw data the 6th WCRI organizers made available on an ad hoc basis. These raw data came from the information 6th WCRI participants provided while registering and submitting their paper and poster proposals. This implied that we had to work with data that were not collected with our study questions in mind. Second, given highly restrictive time limits, the WCRI organizers were unable to receive participants’ consent for using their data once we planned our research, and we were unable to receive timely consent of an ethics committee and to preregister our research.The present proposal continues the 2019 research, but improves both its content and fully obeys procedural requirements. That is, we plan the data collection a priori, ask participants for their consent to use their anonymized data, request consent by an ethics committee, and pre-register the study. Sijtsma, K., Emons, W. H. M., Steneck, N. H., & Bouter, L. M. (2021). Steps toward preregistration of research on research integrity. Research Integrity and Peer Review, 6. https://doi.org/10.1186/s41073-021-00108-4

The goodness-of-fit of the unidimensional monotone latent variable model can be assessed using the empirical conditions of nonnegative correlations (Mokken in A theory and procedure of scale-analysis, Mouton, The Hague, 1971), manifest monotonicity (Junker in Ann Stat 21:1359–1378, 1993), multivariate total positivity of order 2 (Bartolucci and Forcina in Ann Stat 28:1206–1218, 2000), and nonnegative partial correlations (Ellis in Psychometrika 79:303–316, 2014). We show that multidimensional monotone factor models with independent factors also imply these empirical conditions; therefore, the conditions are insensitive to multidimensionality. Conditional association (Rosenbaum in Psychometrika 49(3):425–435, 1984) can detect multidimensionality, but tests of it (De Gooijer and Yuan in Comput Stat Data Anal 55:34–44, 2011) are usually not feasible for realistic numbers of items. The only existing feasible test procedures that can reveal multidimensionality are Rosenbaum’s (Psychometrika 49(3):425–435, 1984) Case 2 and Case 5, which test the covariance of two items or two subtests conditionally on the unweighted sum of the other items. We improve this procedure by conditioning on a weighted sum of the other items. The weights are estimated in a training sample from a linear regression analysis. Simulations show that the Type I error rate is under control and that, for large samples, the power is higher if one dimension is more important than the other or if there is a third dimension. In small samples and with two equally important dimensions, using the unweighted sum yields greater power.

Research integrity (RI) is crucial for trustworthy research. Rules are important in setting RI standards and improving research practice, but they can lead to increased bureaucracy; without commensurate commitment amongst researchers toward RI, they are unlikely to improve research practices. In this paper, we explore how to combine rules and commitment in fostering RI. Research institutions can govern RI using markets (using incentives), bureaucracies (using rules), and network processes (through commitment and agreements). Based on Habermas’ Theory of Communicative Action, we argue that network processes, as part of the lifeworld, can legitimize systems – that is, market or bureaucratic governance modes. This can regulate and support RI practices in an efficient way. Systems can also become dominant and repress consensus processes. Fostering RI requires a balance between network, market and bureaucratic governance modes. We analyze the institutional response to a serious RI case to illustrate how network processes can be combined with bureaucratic rules. Specifically, we analyze how the Science Committee established at Tilburg University in 2012 has navigated different governance modes, resulting in a normatively grounded and efficient approach to fostering RI. Based on this case, we formulate recommendations to research institutions on how to combine rules and commitment.

In modern test theory, response variables are a function of a common latent variable that represents the measured attribute, and error variables that are unique to the response variables. While considerable thought goes into the interpretation of latent variables in these models (e.g., validity research), the interpretation of error variables is typically left implicit (e.g., describing error variables as residuals). Yet, many psychometric assumptions are essentially assumptions about error and thus being able to reason about psychometric models requires the ability to reason about errors. We propose a causal theory of error as a framework that enables researchers to reason about errors in terms of the data-generating mechanism. In this framework, the error variable reflects myriad causes that are specific to an item and, together with the latent variable, determine the scores on that item. We distinguish two types of item-specific causes: characteristic variables that differ between people (e.g., familiarity with words used in the item), and circumstance variables that vary over occasions in which the item is administered (e.g., a distracting noise). We show that different assumptions about these unique causes (a) imply different psychometric models; (b) have different implications for the chance experiment that makes these models probabilistic models; and (c) have different consequences for item bias, local homogeneity, and reliability coefficient α and the test-retest correlation. The ability to reason about the causes that produce error variance puts researchers in a better position to motivate modeling choices. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

PCA is a popular tool for exploring and summarizing multivariate data, especially those consisting of many variables. PCA, however, is often not simple to interpret, as the components are a linear combination of the variables. To address this issue, numerous methods have been proposed to sparsify the nonzero coefficients in the components, including rotation-thresholding methods and, more recently, PCA methods subject to sparsity inducing penalties or constraints. Here, we offer guidelines on how to choose among the different sparse PCA methods. Current literature misses clear guidance on the properties and performance of the different sparse PCA methods, often relying on the misconception that the equivalence of the formulations for ordinary PCA also holds for sparse PCA. To guide potential users of sparse PCA methods, we first discuss several popular sparse PCA methods in terms of where the sparseness is imposed on the loadings or on the weights, assumed model, and optimization criterion used to impose sparseness. Second, using an extensive simulation study, we assess each of these methods by means of performance measures such as squared relative error, misidentification rate, and percentage of explained variance for several data generating models and conditions for the population model. Finally, two examples using empirical data are considered. Supplementary Information The online version contains supplementary material available at 10.1007/s11336-021-09773-2.

The current practice of reliability analysis is both uniform and troublesome: most reports consider only Cronbach’s α, and almost all reports focus exclusively on a point estimate, disregarding the impact of sampling error. In an attempt to improve the status quo we have implemented Bayesian estimation routines for five popular single-test reliability coefficients in the open-source statistical software program JASP. Using JASP, researchers can easily obtain Bayesian credible intervals to indicate a range of plausible values and thereby quantify the precision of the point estimate. In addition, researchers may use the posterior distribution of the reliability coefficients to address practically relevant questions such as “What is the probability that the reliability of my test is larger than a threshold value of .80?”. In this tutorial article, we outline how to conduct a Bayesian reliability analysis in JASP and correctly interpret the results. By making available a computationally complex procedure in an easy-to-use software package, we hope to motivate researchers to include uncertainty estimates whenever reporting the results of a single-test reliability analysis., publishedVersion

Clinical, medical, and health psychologists use difference scores obtained from pretest–posttest designs employing the same test to assess intraindividual change possibly caused by an intervention addressing, for example, anxiety, depression, eating disorder, or addiction. Reliability of difference scores is important for interpreting observed change. This article compares the well-documented traditional method and the unfamiliar, rarely used item-level method for estimating difference-score reliability. We simulated data under various conditions that are typical of change assessment in pretest–posttest designs. The item-level method had smaller bias and greater precision than the traditional method and may be recommended for practical use.

To interpret a person’s change score, one typically transforms the change score into, for example, a percentile, so that one knows a person’s location in a distribution of change scores. Transformed scores are referred to as norms and the construction of norms is referred to as norming. Two often-used norming methods for change scores are the regression-based change approach and the T Scores for Change method. In this article, we discuss the similarities and differences between these norming methods, and use a simulation study to systematically examine the precision of the two methods and to establish the minimum sample size requirements for satisfactory precision.

In this rejoinder, we examine some of the issues Peter Bentler, Eunseong Cho, and Jules Ellis raise. We suggest a methodological solid way to construct a test indicating that the importance of the particular reliability method used is minor, and we discuss future topics in reliability research.

We introduce the special section on nonparametric item response theory (IRT) in Quality of Life Research. Starting from the well-known Rasch model, we provide a brief overview of nonparametric IRT models and discuss the assumptions, the properties, and the investigation of goodness of fit. We provide references to more detailed texts to help readers getting acquainted with nonparametric IRT models. In addition, we show how the rather diverse papers in the special section fit into the nonparametric IRT framework. Finally, we illustrate the application of nonparametric IRT models using data from a questionnaire measuring activity limitations in walking. The real-data example shows the quality of the scale and its constituent items with respect to dimensionality, local independence, monotonicity, and invariant item ordering.

Comparative judgment is a method that allows measurement of a competence by comparison of items with other items. In educational measurement, where comparative judgment is becoming an increasingly popular assessment method, items are mostly students’ responses to an assignment or an examination. For assessments using comparative judgment, the Scale Separation Reliability (SSR) is used to estimate the reliability of the measurement. Previous research has shown that the SSR may overestimate reliability when the pairs to be compared are selected with certain adaptive algorithms, when raters use different underlying models/truths, or when the true variance of the item parameters is below one. This research investigated bias and stability of the components of the SSR in relation to the number of comparisons per item to increase understanding of the SSR. We showed that many comparisons are required to obtain an accurate estimate of the item variance, but that the SSR can be useful even when the variance of the items is overestimated. Lastly, we recommend adjusting the general guideline for the required number of comparisons per item to 41 comparisons per item. This recommendation partly depends on the number of items and the true variance in our simulation study and needs further investigation.

Pairwise comparison is becoming increasingly popular as a holistic measurement method in education. Unfortunately, many comparisons are required for reliable measurement. To reduce the number of required comparisons, we developed an adaptive selection algorithm (ASA) that selects the most informative comparisons while taking the uncertainty of the object parameters into account. The results of the simulation study showed that, given the number of comparisons, the ASA resulted in smaller standard errors of object parameter estimates than a random selection algorithm that served as a benchmark. Rank order accuracy and reliability were similar for the two algorithms. Because the scale separation reliability (SSR) may overestimate the benchmark reliability when the ASA is used, caution is required when interpreting the SSR.

Popular measures of reliability for a single-test administration include coefficient α, coefficient λ2, the greatest lower bound (glb), and coefficient ω. First, we show how these measures can be easily estimated within a Bayesian framework. Specifically, the posterior distribution for these measures can be obtained through Gibbs sampling – for coefficients α, λ2, and the glb one can sample the covariance matrix from an inverse Wishart distribution; for coefficient ω one samples the conditional posterior distributions from a single-factor CFA-model. Simulations show that – under relatively uninformative priors – the 95% Bayesian credible intervals are highly similar to the 95% frequentist bootstrap confidence intervals. In addition, the posterior distribution can be used to address practically relevant questions, such as “what is the probability that the reliability of this test is between .70 and .90?”, or, “how likely is it that the reliability of this test is higher than .80?” In general, the use of a posterior distribution highlights the inherent uncertainty with respect to the estimation of reliability measures.

The method of pairwise comparison has been used in a wide range of contexts. In educational measurement, many pairwise comparisons are required for reliable measurement when the comparisons to be performed are selected using the commonly-used semi-random selection algorithm (SSA). We proposed a Bayesian selection algorithm (BSA) to obtain smaller standard errors of parameter estimates and higher reliability compared with the SSA, and we evaluated the performance of these algorithms in a simulation study. We conclude that 1) the BSA should be preferred to the SSA, 2) the number of comparisons required for reliable measurement depends on the object variance, and 3) the Scale Separation Reliability (SSR) may systematically overestimate reliability even when the SSA is used.

Prior to discussing and challenging two criticisms on coefficient \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\alpha $$\end{document}α, the well-known lower bound to test-score reliability, we discuss classical test theory and the theory of coefficient \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\alpha $$\end{document}α. The first criticism expressed in the psychometrics literature is that coefficient \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\alpha $$\end{document}α is only useful when the model of essential \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\tau $$\end{document}τ-equivalence is consistent with the item-score data. Because this model is highly restrictive, coefficient \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\alpha $$\end{document}α is smaller than test-score reliability and one should not use it. We argue that lower bounds are useful when they assess product quality features, such as a test-score’s reliability. The second criticism expressed is that coefficient \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\alpha $$\end{document}α incorrectly ignores correlated errors. If correlated errors would enter the computation of coefficient \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\alpha $$\end{document}α, theoretical values of coefficient \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\alpha $$\end{document}α could be greater than the test-score reliability. Because quality measures that are systematically too high are undesirable, critics dismiss coefficient \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\alpha $$\end{document}α. We argue that introducing correlated errors is inconsistent with the derivation of the lower bound theorem and that the properties of coefficient \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\alpha $$\end{document}α remain intact when data contain correlated errors.

Background A proposal to encourage the preregistration of research on research integrity was developed and adopted as the Amsterdam Agenda at the 5th World Conference on Research Integrity (Amsterdam, 2017). This paper reports on the degree to which abstracts of the 6th World Conference in Research Integrity (Hong Kong, 2019) reported on preregistered research. Methods Conference registration data on participants presenting a paper or a poster at 6th WCRI were made available to the research team. Because the data set was too small for inferential statistics this report is limited to a basic description of results and some recommendations that should be considered when taking further steps to improve preregistration. Results 19% of the 308 presenters preregistered their research. Of the 56 usable cases, less than half provided information on the six key elements of the Amsterdam Agenda. Others provided information that invalidated their data, such as an uninformative URL. There was no discernable difference between qualitative and quantitative research. Conclusions Some presenters at the WCRI have preregistered their research on research integrity, but further steps are needed to increase frequency and completeness of preregistration. One approach to increase preregistration would be to make it a requirement for research presented at the World Conferences on Research Integrity.

Reliability is usually estimated for a test score, but it can also be estimated for item scores. Item-score reliability can be useful to assess the item’s contribution to the test score’s reliability, for identifying unreliable scores in aberrant item-score patterns in person-fit analysis, and for selecting the most reliable item from a test to use as a single-item measure. Four methods were discussed for estimating item-score reliability: the Molenaar–Sijtsma method (method MS), Guttman’s method [Formula: see text], the latent class reliability coefficient (method LCRC), and the correction for attenuation (method CA). A simulation study was used to compare the methods with respect to median bias, variability (interquartile range [IQR]), and percentage of outliers. The simulation study consisted of six conditions: standard, polytomous items, unequal [Formula: see text] parameters, two-dimensional data, long test, and small sample size. Methods MS and CA were the most accurate. Method LCRC showed almost unbiased results, but large variability. Method [Formula: see text] consistently underestimated item-score reliabilty, but showed a smaller IQR than the other methods.

Change scores obtained in pretest-posttest designs are important for evaluating treatment effectiveness and for assessing change of individual test scores in psychological research. However, over the years the use of change scores has raised much controversy. In this article, from a multilevel perspective, we provide a structured treatise on several persistent negative beliefs about change scores and show that these beliefs originated from the confounding of the effects of within-person change on change-score reliability and between-person change differences. We argue that psychometric properties of change scores, such as reliability and measurement precision, should be treated at suitable levels within a multilevel framework. We show that, if examined at the suitable levels with such a framework, the negative beliefs about change scores can be renounced convincingly. Finally, we summarize the conclusions about change scores to dispel the myths and to promote the potential and practical usefulness of change scores.

BACKGROUND Approximately 90% of currently published clinical and public health research is in the form of observational studies. Having a detailed and registered study protocol prior to data collection is important in any empirical study. Without this, there is no reliable way to assess the occurrence of publication bias, outcome reporting bias, and other protocol deviations. However, there is currently no solid guidance available on the information that a protocol for an observational study should contain. OBJECTIVE The aim of this study is to formulate the Standardized Protocol Items Recommendations for Observational Studies (SPIROS) reporting guidelines, which focus on 3 main study designs of analytical epidemiology: cohort, case-control, and cross-sectional studies. METHODS A scoping review of published protocol papers of observational studies in epidemiology will identify candidate items for the SPIROS reporting guidelines. The list of items will be extended with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) checklist items and recommendations from the SPIROS steering committee. This long list serves as the basis for a 2-round Delphi survey among experts to obtain consensus on which items to include. Each candidate item from the long list will be rated on a 5-point Likert scale to assess relevance for inclusion in the SPIROS reporting guidelines. Following the Delphi survey, an expert-driven consensus workshop will be convened to finalize the reporting guidelines. RESULTS A scoping review of published observational study protocols has been completed, with 59 candidate items identified for inclusion into the Delphi survey, itself launched in early 2020. CONCLUSIONS This project aims to improve the timeliness, completeness, and clarity of study protocols of observational studies in analytical epidemiology by producing expert-based recommendations of items to be addressed. These reporting guidelines will facilitate and encourage researchers to prepare and register study protocols of sufficient quality prior to data collection in order to improve the transparency, reproducibility, and quality of observational studies. INTERNATIONAL REGISTERED REPORT PRR1-10.2196/17864

Background Approximately 90% of currently published clinical and public health research is in the form of observational studies. Having a detailed and registered study protocol prior to data collection is important in any empirical study. Without this, there is no reliable way to assess the occurrence of publication bias, outcome reporting bias, and other protocol deviations. However, there is currently no solid guidance available on the information that a protocol for an observational study should contain. Objective The aim of this study is to formulate the Standardized Protocol Items Recommendations for Observational Studies (SPIROS) reporting guidelines, which focus on 3 main study designs of analytical epidemiology: cohort, case-control, and cross-sectional studies. Methods A scoping review of published protocol papers of observational studies in epidemiology will identify candidate items for the SPIROS reporting guidelines. The list of items will be extended with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) checklist items and recommendations from the SPIROS steering committee. This long list serves as the basis for a 2-round Delphi survey among experts to obtain consensus on which items to include. Each candidate item from the long list will be rated on a 5-point Likert scale to assess relevance for inclusion in the SPIROS reporting guidelines. Following the Delphi survey, an expert-driven consensus workshop will be convened to finalize the reporting guidelines. Results A scoping review of published observational study protocols has been completed, with 59 candidate items identified for inclusion into the Delphi survey, itself launched in early 2020. Conclusions This project aims to improve the timeliness, completeness, and clarity of study protocols of observational studies in analytical epidemiology by producing expert-based recommendations of items to be addressed. These reporting guidelines will facilitate and encourage researchers to prepare and register study protocols of sufficient quality prior to data collection in order to improve the transparency, reproducibility, and quality of observational studies. International Registered Report Identifier (IRRID) PRR1-10.2196/17864