L Andries Van Der Ark
Biographic Data
| ID | 4168533 |
|---|---|
| NAME | L Andries Van Der Ark |
| GIVEN NAMES | L Andries |
| FAMILY NAME | Van Der Ark |
| SIGNATURE | VAN DER ARK L A |
| AFFILIATIONS | Tilburg University |
| ORCID | 0000-0003-3131-7943 |
| VERIFIED | Yes |
| TOTAL WORKS | 24 |
| TOTAL CITATIONS | 33 |
| AUTHOR COUNT | 22 |
| EDITOR COUNT | 2 |
| FIRST PUBLICATION YEAR | 1999 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 4 |
Bias and precision in true‐score estimation
We discuss two approaches to estimating the true score from classical test theory, each with a corresponding measure of uncertainty due to measurement error: the classical method with the standard error of measurement (SEM) and Kelley's method with the standard error of estimation (SEE). For both approaches, we examined the bias and sampling variability of the true‐score, SEM and SEE estimators, as these properties were largely unknown. For each …
How to Estimate Intraclass Correlation Coefficients for Interrater Reliability from Planned Incomplete Data
The interrater reliability (IRR) of observational data is often estimated by means of intraclass correlation coefficients (ICCs), which are flexible IRR estimators that are based on the variance decomposition of scores obtained by observations. ICCs are typically estimated using mean squares from an ANOVA model, the computation of which is not straightforward for incomplete data. However, many studies in behavioral research use planned missing ob…
Interrater Reliability for Interdependent Social Network Data: A Generalizability Theory Approach
We propose interrater reliability coefficients for observational interdependent social network data, which are dyadic data from a network of interacting subjects that are observed by external raters. Using the social relations model, dyadic scores of subjects' behaviors during these interactions can be decomposed into actor, partner, and relationship effects. These effects constitute different facets of theoretical interest about which researcher…
A novel CAT method for QoL screening: Proof-of-principle study with comparisons to standard methods
These results suggest that LSCAT is a promising method for developing valid and efficient screening tools in HR-QoL research and practice
Is Mistrust in Early Adolescence Referent-specific? Looking at the Validity of Different Mistrust Referents in Sixth Graders
This study examined whether social mistrust in early adolescence was general or referent-specific. We used a multi-trait multi-method approach to examine the validity of mistrust measures across social referents (mistrust toward people in general, toward peers, and toward teachers), using questionnaires and an online task. Sixth graders ( N = 1243, ca. 11–13 years) in southern China reported about mistrust (i.e., general, teacher and peer), their…
Essays on Contemporary Psychometrics
This book 'Essays on Contemporary Psychometrics' provides an overview of contemporary psychometrics, the science devoted to the advancement ...
Essays on Contemporary Psychometrics
A two-step, test-guided Mokken scale analysis, for nonclustered and clustered data
We developed a two-step, test-guided MSA for scale construction that takes into account sample fluctuation of all scalability coefficients and that can be applied to item scores obtained by a nonclustered or clustered sampling design
Item-Score Reliability in Empirical-Data Sets and Its Relationship With Other Item Indices
Reliability is usually estimated for a total score, but it can also be estimated for item scores. Item-score reliability can be useful to assess the repeatability of an individual item score in a group. Three methods to estimate item-score reliability are discussed, known as method MS, method [Formula: see text], and method CA. The item-score reliability methods are compared with four well-known and widely accepted item indices, which are the ite…
Measurement versus prediction in the construction of patient-reported outcome questionnaires: Can we have our cake and eat it
The answers are as follows: (1) Because measurement-based methods tend to maximize inter-item correlations by which predictive validity reduces. (2) Through selecting items that correlate highly with the criterion and lowly with the remaining items. (3) Yes, these methods may lead to different item selections. (4) For a single questionnaire: Yes, but it is problematic because reliability cannot be estimated accurately. For a test battery: Yes, bu…
Analysis of Clinical Data From a Cognitive Diagnosis Modeling Framework
We propose a general cognitive diagnosis model framework to diagnose mental disorders using item scores obtained from clinical measurement instruments. This framework can be used to validate the extent to which the items measure the specific disorders. The method is illustrated using data obtained with the Dutch version of Millon Clinical Multiaxial Inventory-III
Analysis of Clinical Data From Cognitive Diagnosis Modeling Framework
We propose a general cognitive diagnosis model framework to diagnose mental disorders using item scores obtained from clinical measurement instruments. This framework can be used to validate the extent to which the items measure the specific disorders. The method is illustrated using data obtained with the Dutch version of Millon Clinical Multiaxial Inventory-III
Minimum Sample Size Requirements for Mokken Scale Analysis
An automated item selection procedure in Mokken scale analysis partitions a set of items into one or more Mokken scales, if the data allow. Two algorithms are available that pursue the same goal of selecting Mokken scales of maximum length: Mokken’s original automated item selection procedure (AISP) and a genetic algorithm (GA). Minimum sample size requirements for the two algorithms to obtain stable, replicable results have not yet been establis…
Quantitative Psychology Research: The 78th Annual Meeting of the Psychometric Society
The 78th Annual Meeting of the Psychometric Society (IMPS) builds on the Psychometric Society's mission to share quantitative methods relevant to psychology. The chapters of this volume present cutting-edge work in the field. Topics include studies of item response theory, computerized adaptive testing, cognitive diagnostic modeling, and psychological scaling. Additional psychometric topics relate to structural equation modeling, factor analysis,…
Dimensions of cultural consumption among tourists: Multiple correspondence analysis
Standard Errors and Confidence Intervals for Scalability Coefficients in Mokken Scale Analysis Using Marginal Models
Mokken scale analysis is a popular method for scaling dichotomous and polytomous items. Whether or not items form a scale is determined by three types of scalability coefficients: (1) for pairs of items, (2) for items, and (3) for the entire scale. It has become standard practice to interpret the sample values of these scalability coefficients using Mokken’s guidelines, which have been available since the 1970s. For valid assessment of the scalab…
Robust Mokken Scale Analysis by Means of the Forward Search Algorithm for Outlier Detection
Exploratory Mokken scale analysis (MSA) is a popular method for identifying scales from larger sets of items. As with any statistical method, in MSA the presence of outliers in the data may result in biased results and wrong conclusions. The forward search algorithm is a robust diagnostic method for outlier detection, which we adapt here to identify outliers in MSA. This adaptation involves choices with respect to the algorithm's objective functi…
Investigating an Invariant Item Ordering for Polytomously Scored Items
This article discusses the concept of an invariant item ordering (IIO) for polytomously scored items and proposes methods for investigating an IIO in real test data. Method manifest IIO is proposed for assessing whether item response functions intersect. Coefficient H T is defined for polytomously scored items. Given that an IIO holds, coefficient H T expresses the accuracy of the item ordering. Method manifest IIO and coefficient H T are used to…
Multiple Imputation of Incomplete Categorical Data Using Latent Class Analysis
We propose using latent class analysis as an alternative to log-linear analysis for the multiple imputation of incomplete categorical data. Similar to log-linear models, latent class models can be used to describe complex association structures between the variables used in the imputation model. However, unlike log-linear models, latent class models can be used to build large imputation models containing more than a few categorical variables. To …
Outlier Detection in Test and Questionnaire Data
Classical methods for detecting outliers deal with continuous variables. These methods are not readily applicable to categorical data, such as incorrect/correct scores (0/1) and ordered rating scale scores (e.g., 0; : : : ; 4) typical of multi-item tests and questionnaires. This study proposes two definitions of outlier scores suited for categorical data. One definition combines information on outliers from scores on all the items in the test, an…
Multiple Imputation of Item Scores in Test and Questionnaire Data, and Influence on Psychometric Results
The performance of five simple multiple imputation methods for dealing with missing data were compared. In addition, random imputation and multivariate normal imputation were used as lower and upper benchmark, respectively. Test data were simulated and item scores were deleted such that they were either missing completely at random, missing at random, or not missing at random. Cronbach's alpha, Loevinger's scalability coefficient H, and the item …
Attractiveness of cultural activities in European cities: A latent class approach
Investigation and Treatment of Missing Item Scores in Test and Questionnaire Data
This article first discusses a statistical test for investigating whether or not the pattern of missing scores in a respondent-by-item data matrix is random. Since this is an asymptotic test, we investigate whether it is useful in small but realistic sample sizes. Then, we discuss two known simple imputation methods, person mean (PM) and two-way (TW) imputation, and we propose two new imputation methods, response-function (RF) and mean response-f…
An Extended Study into the Relationship between Correspondence Analysis and Latent Class Analysis
Researchers dealing with frequency data today can choose from a vast range of methods, descriptive and inferential. Two such well-known and useful methods are correspondence analysis and latent class analysis. Although these two methods were initially used for different research objectives, they are mathematically related to each other. Relations between these methods, however, have only been reported in the literature regarding the bivariate cas…
Attractiveness of cultural activities in European cities: A latent class approach
Multiple Imputation of Incomplete Categorical Data Using Latent Class Analysis
We propose using latent class analysis as an alternative to log-linear analysis for the multiple imputation of incomplete categorical data. Similar to log-linear models, latent class models can be used to describe complex association structures between the variables used in the imputation model. However, unlike log-linear models, latent class models can be used to build large imputation models containing more than a few categorical variables. To …
An Extended Study into the Relationship between Correspondence Analysis and Latent Class Analysis
Researchers dealing with frequency data today can choose from a vast range of methods, descriptive and inferential. Two such well-known and useful methods are correspondence analysis and latent class analysis. Although these two methods were initially used for different research objectives, they are mathematically related to each other. Relations between these methods, however, have only been reported in the literature regarding the bivariate cas…
Dimensions of cultural consumption among tourists: Multiple correspondence analysis
Standard Errors and Confidence Intervals for Scalability Coefficients in Mokken Scale Analysis Using Marginal Models
Mokken scale analysis is a popular method for scaling dichotomous and polytomous items. Whether or not items form a scale is determined by three types of scalability coefficients: (1) for pairs of items, (2) for items, and (3) for the entire scale. It has become standard practice to interpret the sample values of these scalability coefficients using Mokken’s guidelines, which have been available since the 1970s. For valid assessment of the scalab…
An Extended Study into the Relationship between Correspondence Analysis and Latent Class Analysis
Researchers dealing with frequency data today can choose from a vast range of methods, descriptive and inferential. Two such well-known and useful methods are correspondence analysis and latent class analysis. Although these two methods were initially used for different research objectives, they are mathematically related to each other. Relations between these methods, however, have only been reported in the literature regarding the bivariate cas…
Investigation and Treatment of Missing Item Scores in Test and Questionnaire Data
This article first discusses a statistical test for investigating whether or not the pattern of missing scores in a respondent-by-item data matrix is random. Since this is an asymptotic test, we investigate whether it is useful in small but realistic sample sizes. Then, we discuss two known simple imputation methods, person mean (PM) and two-way (TW) imputation, and we propose two new imputation methods, response-function (RF) and mean response-f…
Attractiveness of cultural activities in European cities: A latent class approach
Outlier Detection in Test and Questionnaire Data
Classical methods for detecting outliers deal with continuous variables. These methods are not readily applicable to categorical data, such as incorrect/correct scores (0/1) and ordered rating scale scores (e.g., 0; : : : ; 4) typical of multi-item tests and questionnaires. This study proposes two definitions of outlier scores suited for categorical data. One definition combines information on outliers from scores on all the items in the test, an…
Multiple Imputation of Item Scores in Test and Questionnaire Data, and Influence on Psychometric Results
The performance of five simple multiple imputation methods for dealing with missing data were compared. In addition, random imputation and multivariate normal imputation were used as lower and upper benchmark, respectively. Test data were simulated and item scores were deleted such that they were either missing completely at random, missing at random, or not missing at random. Cronbach's alpha, Loevinger's scalability coefficient H, and the item …
Multiple Imputation of Incomplete Categorical Data Using Latent Class Analysis
We propose using latent class analysis as an alternative to log-linear analysis for the multiple imputation of incomplete categorical data. Similar to log-linear models, latent class models can be used to describe complex association structures between the variables used in the imputation model. However, unlike log-linear models, latent class models can be used to build large imputation models containing more than a few categorical variables. To …
Investigating an Invariant Item Ordering for Polytomously Scored Items
This article discusses the concept of an invariant item ordering (IIO) for polytomously scored items and proposes methods for investigating an IIO in real test data. Method manifest IIO is proposed for assessing whether item response functions intersect. Coefficient H T is defined for polytomously scored items. Given that an IIO holds, coefficient H T expresses the accuracy of the item ordering. Method manifest IIO and coefficient H T are used to…
Robust Mokken Scale Analysis by Means of the Forward Search Algorithm for Outlier Detection
Exploratory Mokken scale analysis (MSA) is a popular method for identifying scales from larger sets of items. As with any statistical method, in MSA the presence of outliers in the data may result in biased results and wrong conclusions. The forward search algorithm is a robust diagnostic method for outlier detection, which we adapt here to identify outliers in MSA. This adaptation involves choices with respect to the algorithm's objective functi…
Dimensions of cultural consumption among tourists: Multiple correspondence analysis
Standard Errors and Confidence Intervals for Scalability Coefficients in Mokken Scale Analysis Using Marginal Models
Mokken scale analysis is a popular method for scaling dichotomous and polytomous items. Whether or not items form a scale is determined by three types of scalability coefficients: (1) for pairs of items, (2) for items, and (3) for the entire scale. It has become standard practice to interpret the sample values of these scalability coefficients using Mokken’s guidelines, which have been available since the 1970s. For valid assessment of the scalab…
Minimum Sample Size Requirements for Mokken Scale Analysis
An automated item selection procedure in Mokken scale analysis partitions a set of items into one or more Mokken scales, if the data allow. Two algorithms are available that pursue the same goal of selecting Mokken scales of maximum length: Mokken’s original automated item selection procedure (AISP) and a genetic algorithm (GA). Minimum sample size requirements for the two algorithms to obtain stable, replicable results have not yet been establis…
Quantitative Psychology Research: The 78th Annual Meeting of the Psychometric Society
The 78th Annual Meeting of the Psychometric Society (IMPS) builds on the Psychometric Society's mission to share quantitative methods relevant to psychology. The chapters of this volume present cutting-edge work in the field. Topics include studies of item response theory, computerized adaptive testing, cognitive diagnostic modeling, and psychological scaling. Additional psychometric topics relate to structural equation modeling, factor analysis,…
Analysis of Clinical Data From Cognitive Diagnosis Modeling Framework
We propose a general cognitive diagnosis model framework to diagnose mental disorders using item scores obtained from clinical measurement instruments. This framework can be used to validate the extent to which the items measure the specific disorders. The method is illustrated using data obtained with the Dutch version of Millon Clinical Multiaxial Inventory-III
Measurement versus prediction in the construction of patient-reported outcome questionnaires: Can we have our cake and eat it
The answers are as follows: (1) Because measurement-based methods tend to maximize inter-item correlations by which predictive validity reduces. (2) Through selecting items that correlate highly with the criterion and lowly with the remaining items. (3) Yes, these methods may lead to different item selections. (4) For a single questionnaire: Yes, but it is problematic because reliability cannot be estimated accurately. For a test battery: Yes, bu…
Analysis of Clinical Data From a Cognitive Diagnosis Modeling Framework
We propose a general cognitive diagnosis model framework to diagnose mental disorders using item scores obtained from clinical measurement instruments. This framework can be used to validate the extent to which the items measure the specific disorders. The method is illustrated using data obtained with the Dutch version of Millon Clinical Multiaxial Inventory-III
Item-Score Reliability in Empirical-Data Sets and Its Relationship With Other Item Indices
Reliability is usually estimated for a total score, but it can also be estimated for item scores. Item-score reliability can be useful to assess the repeatability of an individual item score in a group. Three methods to estimate item-score reliability are discussed, known as method MS, method [Formula: see text], and method CA. The item-score reliability methods are compared with four well-known and widely accepted item indices, which are the ite…
A two-step, test-guided Mokken scale analysis, for nonclustered and clustered data
We developed a two-step, test-guided MSA for scale construction that takes into account sample fluctuation of all scalability coefficients and that can be applied to item scores obtained by a nonclustered or clustered sampling design
Essays on Contemporary Psychometrics
Essays on Contemporary Psychometrics
This book 'Essays on Contemporary Psychometrics' provides an overview of contemporary psychometrics, the science devoted to the advancement ...
Is Mistrust in Early Adolescence Referent-specific? Looking at the Validity of Different Mistrust Referents in Sixth Graders
This study examined whether social mistrust in early adolescence was general or referent-specific. We used a multi-trait multi-method approach to examine the validity of mistrust measures across social referents (mistrust toward people in general, toward peers, and toward teachers), using questionnaires and an online task. Sixth graders ( N = 1243, ca. 11–13 years) in southern China reported about mistrust (i.e., general, teacher and peer), their…
How to Estimate Intraclass Correlation Coefficients for Interrater Reliability from Planned Incomplete Data
The interrater reliability (IRR) of observational data is often estimated by means of intraclass correlation coefficients (ICCs), which are flexible IRR estimators that are based on the variance decomposition of scores obtained by observations. ICCs are typically estimated using mean squares from an ANOVA model, the computation of which is not straightforward for incomplete data. However, many studies in behavioral research use planned missing ob…
Interrater Reliability for Interdependent Social Network Data: A Generalizability Theory Approach
We propose interrater reliability coefficients for observational interdependent social network data, which are dyadic data from a network of interacting subjects that are observed by external raters. Using the social relations model, dyadic scores of subjects' behaviors during these interactions can be decomposed into actor, partner, and relationship effects. These effects constitute different facets of theoretical interest about which researcher…
A novel CAT method for QoL screening: Proof-of-principle study with comparisons to standard methods
These results suggest that LSCAT is a promising method for developing valid and efficient screening tools in HR-QoL research and practice
Bias and precision in true‐score estimation
We discuss two approaches to estimating the true score from classical test theory, each with a corresponding measure of uncertainty due to measurement error: the classical method with the standard error of measurement (SEM) and Kelley's method with the standard error of estimation (SEE). For both approaches, we examined the bias and sampling variability of the true‐score, SEM and SEE estimators, as these properties were largely unknown. For each …
Computer Science (16 works) · Mathematics (15 works) · Statistics (14 works) · Psychology (12 works) · Psychometric Methodologies and Testing (11 works) · Data mining (10 works) · Psychometrics (10 works) · Advanced Statistical Methods and Models (5 works) · Artificial Intelligence (5 works) · Econometrics (5 works)