Cross-Validation and Nested Data Structures

NIR news ◽  
2011 ◽  
Vol 22 (2) ◽  
pp. 17-20 ◽  
Author(s):  
Tom Fearn
Keyword(s):  
Data Structures ◽  
Cross Validation ◽  
Nested Data

Multilevel Models

2018 ◽  
Author(s):  
Peter Miksza ◽  
Kenneth Elpus
Keyword(s):  
Data Structures ◽  
Statistical Approach ◽  
Multilevel Models ◽  
Testing Hypotheses ◽  
Nested Data ◽  
Data Analyses ◽  
Multiple Measurements ◽  
Research Questions ◽  
Multiple Levels ◽  
Common Application

This chapter introduces a statistical approach for analyzing nested data structures that both accounts for the dependence of observations due to hierarchical arrangements and allows for testing hypotheses at multiple levels. The most common application of multilevel models is for analyses of objects (e.g., people) nested within groups or clusters of some sort. Multilevel models can also be applied to longitudinal data analyses such that the “levels” do not refer to objects nested within groups but instead refer to multiple measurements (e.g., measures made at different occasions/time points) nested within individuals. The chapter illustrates some of the major considerations and basic steps for performing multilevel analyses so that the reader can begin to imagine how to apply this technique to the reader’s own research questions.

scholarly journals Nested data structures in array frameworks

2020 ◽  
Vol 1525 ◽  
pp. 012053
Author(s):  
Jim Pivarski ◽  
David Lange ◽  
Peter Elmer
Keyword(s):  
Data Structures ◽  
Nested Data

scholarly journals Foundations of Fine-Grained Explainability

2021 ◽  
pp. 500-523
Author(s):  
Sylvain Hallé ◽  
Hugo Tremblay
Keyword(s):  
Data Structures ◽  
Elementary Functions ◽  
Fine Grained ◽  
Nested Data

AbstractExplainability is the process of linking part of the inputs given to a calculation to its output, in such a way that the selected inputs somehow “cause” the result. We establish the formal foundations of a notion of explainability for arbitrary abstract functions manipulating nested data structures. We then establish explanation relationships for a set of elementary functions, and for compositions thereof. A fully functional implementation of these concepts is finally presented and experimentally evaluated.

Item Bias Analysis of the Young Schema-Questionnaire for Psychopathology, Gender, and Educational Level

2011 ◽  
Vol 27 (1) ◽  
pp. 65-70 ◽  
Author(s):  
Marleen M. Rijkeboer ◽  
Huub van den Bergh ◽  
Jan van den Bout
Keyword(s):  
Educational Level ◽  
Cross Validation ◽  
Item Bias ◽  
Response Patterns ◽  
Dutch Population ◽  
Bias Analysis ◽  
Young Schema Questionnaire ◽  
Item Functioning ◽  
Item Level ◽  
Schema Questionnaire

This study examines the construct validity of the Young Schema-Questionnaire at the item level in a Dutch population. Possible bias of items in relation to the presence or absence of psychopathology, gender, and educational level was analyzed, using a cross-validation design. None of the items of the YSQ exhibited differential item functioning (DIF) for gender, and only one item showed DIF for educational level. Furthermore, item bias analysis did not identify DIF for the presence or absence of psychopathology in as much as 195 of the 205 items comprising the YSQ. Ten items, however, spread over the questionnaire, were found to yield relatively inconsistent response patterns for patients and nonclinical participants.

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