Detecting Heterogeneity of Intervention Effects in Comparative Judgments

Wiedermann, Wolfgang; Frick, Ulrich; Merkle, Edgar C.

doi:10.1007/s11121-021-01212-z

Cited by 6 publications

(4 citation statements)

References 49 publications

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“…Each terminal node of the tree structure consists of a separate LLBT model with partition-specific model parameters. Wiedermann et al (2021) extended the MOB BT framework to distinguish between focal independent variables (e.g., expertise status) and covariates used for recursive partitioning. The MOB LLBT model for

= 1, …,

subgroups can be written as…”

Section: Methodsmentioning

confidence: 99%

“…where the intercept

and the main effect

cstitute normalizing constants in subgroup

gives the paired comparison decision in group s and partition g (with

= 1 if j

k and

= −1 if k

j ),

and

denote the partition-specific object parameters for the reference group, and

and

are the partition-specific effects capturing potential group differences (cf. Wiedermann et al, 2021 ).…”

Section: Methodsmentioning

confidence: 99%

“…and l k g ( ) denote the partition-specific object parameters for the reference group, and l js g ( ) and l ks g ( ) are the partition-specific effects capturing potential group differences (cf. Wiedermann et al, 2021).…”

Section: Data Analytic Strategymentioning

confidence: 99%

“…This study uses model-based partitioning as a method to analyze what underlies the variability in visual judgments. We use a recently published approach that combines Bradley–Terry (BT) models with model-based recursive partitioning (trees) to detect preference heterogeneity in subgroups ( Wiedermann et al, 2021 ). BT models are well-suited for (art) educational assessment tasks, in which students are instructed to rank images based on given criteria.…”

Section: Introductionmentioning

confidence: 99%

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Assessing Heterogeneity in Students’ Visual Judgment: Model-Based Partitioning of Image Rankings

Tallon

Greenlee²,

Wagner³

et al. 2022

Front. Psychol.

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Differences in the ability of students to judge images can be assessed by analyzing the individual preference order (ranking) of images. To gain insights into potential heterogeneity in judgement of visual abstraction among students, we combine Bradley–Terry preference modeling and model-based recursive partitioning. In an experiment a sample of 1,020 high-school students ranked five sets of images, three of which with respect to their level of visual abstraction. Additionally, 24 art experts and 25 novices were given the same task, while their eye movements were recorded. Results show that time spent on the task, the students’ age, and self-reported interest in visual puzzles had significant influence on rankings. Fixation time of experts and novices revealed that both groups paid more attention to ambiguous images. The presented approach makes the underlying latent scale of visual judgments quantifiable.

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= 1, …,

subgroups can be written as…”

Section: Methodsmentioning

confidence: 99%

“…where the intercept

and the main effect

cstitute normalizing constants in subgroup

gives the paired comparison decision in group s and partition g (with

= 1 if j

k and

= −1 if k

j ),

and

denote the partition-specific object parameters for the reference group, and

and

are the partition-specific effects capturing potential group differences (cf. Wiedermann et al, 2021 ).…”

Section: Methodsmentioning

confidence: 99%

Section: Data Analytic Strategymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Assessing Heterogeneity in Students’ Visual Judgment: Model-Based Partitioning of Image Rankings

Tallon

Greenlee²,

Wagner³

et al. 2022

Front. Psychol.

Self Cite

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Advanced Categorical Data Analysis in Prevention Science

2023

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A variety of health and social problems are routinely measured in the form of categorical outcome data (such as presence/ absence of a problem behavior or stages of disease progression). Therefore, proper quantitative analysis of categorical data lies at the heart of the empirical work conducted in prevention science. Categorical data analysis constitutes a broad dynamic field of methods research and data analysts in prevention science can benefit from incorporating recent advances and developments in the statistical evaluation of categorical outcomes in their methodological repertoire. The present Special Issue, Advanced Categorical Data Analysis in Prevention Science, highlights recent methods developments and illustrates their application in the context of prevention science. Contributions of the Special Issue cover a wide variety of areas ranging from statistical models for binary as well as multi-categorical data, advances in the statistical evaluation of moderation and mediation effects for categorical data, developments in model evaluation and measurement, as well as methods that integrate variable-and person-oriented categorical data analysis. The articles of this Special issue make methodological advances in these areas accessible to the audience of prevention scientists to maintain rigorous statistical practice and decision making. The current paper provides background and rationale for this Special Issue, an overview of the articles, and a brief discussion of some potential future directions for prevention research involving categorical data analysis.

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New Frontiers in Prevention Research Models: Commentary on the Special Issue

Wood

2023

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Detecting Heterogeneity of Intervention Effects in Comparative Judgments

Cited by 6 publications

References 49 publications

Assessing Heterogeneity in Students’ Visual Judgment: Model-Based Partitioning of Image Rankings

Assessing Heterogeneity in Students’ Visual Judgment: Model-Based Partitioning of Image Rankings

Advanced Categorical Data Analysis in Prevention Science

New Frontiers in Prevention Research Models: Commentary on the Special Issue

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