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In psychology and other fields, data often have a cross-classified structure, whereby observations are nested within multiple types of non-hierarchical clusters (e.g., repeated measures cross-classified by persons and stimuli). This paper discusses ways that, in cross-classified multilevel models, slopes of lower-level predictors can implicitly reflect an ambiguous blend of multiple effects (for instance, a purely observation-level effect as well as a unique between-cluster effect for each type of cluster). The possibility of conflating multiple effects of lower-level predictors is well recognized for non-cross-classified multilevel models, but has not been fully discussed or clarified for cross-classified contexts. Consequently, in published cross-classified modeling applications, this possibility is almost always ignored, and researchers routinely specify models that conflate multiple effects. In this paper, we show why this common practice can be problematic, and show how to disaggregate level-specific effects in cross-classified models. We provide a novel suite of options that include fully cluster-mean-centered, partially cluster-mean-centered, and contextual effect models, each of which provides a unique interpretation of model parameters. We further clarify how to avoid both fixed and random conflation, the latter of which is widely misunderstood even in non-cross-classified models. We provide simulation results showing the possible deleterious impact of such conflation in cross-classified models, and walk through pedagogical examples to illustrate the disaggregation of level-specific effects. We conclude by considering additional model complexities that can arise with cross-classification, providing guidance for researchers in choosing among model specifications, and describing newly available software to aid researchers who wish to disaggregate effects in practice. [ABSTRACT FROM AUTHOR]

In a randomized controlled trial, outcomes of different subjects may be independent at baseline, but correlated at a follow‐up measurement due to treatment. This treatment‐related clustering at follow‐up can arise for instance because the treatment is given in a group or because subjects are treated individually but by the same therapist (therapist effect). There is substantial literature on the design and analysis of such trials when estimation of the intervention effect is based on a follow‐up measurement (eg, directly after treatment or at a later time point). However, often the baseline measurement of the outcome is highly correlated with the follow‐up measurement, and this information can be used in the analysis. For a randomized design with a baseline and a follow‐up measurement, we compare sample size requirements for analyses with and without adjustment for this baseline measure. We show that adjusting for baseline reduces required sample size. This reduction depends on the variance of the difference between arms at baseline, the variance of this difference at follow‐up, and the correlation between the two. From this, we derive sample size formulas for partially or fully nested designs, and cluster randomized trials with treatment as a partially or fully cross‐classified factor. Also, we discuss situations where clusters are already present at baseline or where treatment by cluster interaction is present. For the partially nested design, we work out practical design considerations (eg, use of content‐matter input, design factors and optimal allocation ratio) and investigate small sample properties of the sample size formula. [ABSTRACT FROM AUTHOR]

Multiple imputation (MI) is a popular method for handling missing data. In education research, it can be challenging to use MI because the data often have a clustered structure that need to be accommodated during MI. Although much research has considered applications of MI in hierarchical data, little is known about its use in cross-classified data, in which observations are clustered in multiple higher-level units simultaneously (e.g., schools and neighborhoods, transitions from primary to secondary schools). In this article, we consider several approaches to MI for cross-classified data (CC-MI), including a novel fully conditional specification approach, a joint modeling approach, and other approaches that are based on single- and two-level MI. In this context, we clarify the conditions that CC-MI methods need to fulfill to provide a suitable treatment of missing data, and we compare the approaches both from a theoretical perspective and in a simulation study. Finally, we illustrate the use of CC-MI in real data and discuss the implications of our findings for research practice. [ABSTRACT FROM AUTHOR]

A novel Bayesian modeling framework for response accuracy (RA), response times (RTs) and other process data is proposed. In a Bayesian covariance structure modeling approach, nested and crossed dependences within test-taker data (e.g., within a testlet, between RAs and RTs for an item) are explicitly modeled. The local dependences are modeled directly through covariance parameters in an additive covariance matrix. The inclusion of random effects (on person or group level) is not necessary, which allows constructing parsimonious models for responses and multiple types of process data. Bayesian Covariance Structure Models (BCSMs) are presented for various well-known dependence structures. Through truncated shifted inverse-gamma priors, closed-form expressions for the conditional posteriors of the covariance parameters are derived. The priors avoid boundary effects at zero, and ensure the positive definiteness of the additive covariance structure at any layer. Dependences of categorical outcome data are modeled through latent continuous variables. In a simulation study, a BCSM for RAs and RTs is compared to van der Linden's hierarchical model (LHM; van der Linden, 2007). Under the BCSM, the dependence structure is extended to allow variations in test-takers' working speed and ability and is estimated with a satisfying performance. Under the LHM, the assumption of local independence is violated, which results in a biased estimate of the variance of the ability distribution. Moreover, the BCSM provides insight in changes in the speed-accuracy trade-off. With an empirical example, the flexibility and relevance of the BCSM for complex dependence structures in a real-world setting are discussed.

A novel Bayesian modeling framework for response accuracy (RA), response times (RTs) and other process data is proposed. In a Bayesian covariance structure modeling approach, nested and crossed dependences within test-taker data (e.g., within a testlet, between RAs and RTs for an item) are explicitly modeled. The local dependences are modeled directly through covariance parameters in an additive covariance matrix. The inclusion of random effects (on person or group level) is not necessary, which allows constructing parsimonious models for responses and multiple types of process data. Bayesian Covariance Structure Models (BCSMs) are presented for various well-known dependence structures. Through truncated shifted inverse-gamma priors, closed-form expressions for the conditional posteriors of the covariance parameters are derived. The priors avoid boundary effects at zero, and ensure the positive definiteness of the additive covariance structure at any layer. Dependences of categorical outcome data are modeled through latent continuous variables. In a simulation study, a BCSM for RAs and RTs is compared to van der Linden's hierarchical model (LHM; van der Linden, 2007). Under the BCSM, the dependence structure is extended to allow variations in test-takers' working speed and ability and is estimated with a satisfying performance. Under the LHM, the assumption of local independence is violated, which results in a biased estimate of the variance of the ability distribution. Moreover, the BCSM provides insight in changes in the speed-accuracy trade-off. With an empirical example, the flexibility and relevance of the BCSM for complex dependence structures in a real-world setting are discussed. [ABSTRACT FROM AUTHOR]

• Cross-classification is the ability to categorize multifaceted-entities in many ways. • Age and theory of mind predicts children's cross-classification over and above rule switching. • Advanced level theory of mind is a particularly strong predictor. • The results increase understanding of cross-classification and cognitive development. Cross-classification, the ability to categorize multifaceted entities in many ways, is a remarkable cognitive milestone for children. Past work has focused primarily on documenting the timeline for when children reach cross-classification competence. However, it is not well understood what cognitive factors underpin children's improvements. The current study aimed to examine the contributions of age, theory of mind, and rule switching to children's cross-classification development. We tested 3- to 5-year-old children (N = 75) using a cross-classification task, the Theory of Mind Task Battery, and the Three-Dimensional Change Card Sort test. The results revealed that age and theory of mind predict children's cross-classification over and above the effects of rule switching. The results also revealed that advanced-level theory of mind reasoning is a particularly strong predictor of cross-classification development. These findings increase understanding of cross-classification within children's broader cognitive development. [ABSTRACT FROM AUTHOR]

Abstract: Whether emotions carried by voice and music are processed by the brain using similar mechanisms has long been investigated. Yet neuroimaging studies do not provide a clear picture, mainly due to lack of control over stimuli. Here, we report a functional magnetic resonance imaging (fMRI) study using comparable stimulus material in the voice and music domains—the Montreal Affective Voices and the Musical Emotional Bursts—which include nonverbal short bursts of happiness, fear, sadness, and neutral expressions. We use a multivariate emotion‐classification fMRI analysis involving cross‐timbre classification as a means of comparing the neural mechanisms involved in processing emotional information in the two domains. We find, for affective stimuli in the violin, clarinet, or voice timbres, that local fMRI patterns in the bilateral auditory cortex and upper premotor regions support above‐chance emotion classification when training and testing sets are performed within the same timbre category. More importantly, classifier performance generalized well across timbre in cross‐classifying schemes, albeit with a slight accuracy drop when crossing the voice–music boundary, providing evidence for a shared neural code for processing musical and vocal emotions, with possibly a cost for the voice due to its evolutionary significance. [ABSTRACT FROM AUTHOR]

Teachers differ considerably in their judgment accuracy of motivational student characteristics. Thus far, only few investigations have focused on explaining these differences. In this study, we investigated to what extent groups of characteristics (i.e., student, information, teacher, and class characteristics) derived from the Realistic Accuracy Model ( Funder, 1995 ) are relevant for explaining differences in teachers' judgment accuracy regarding students' school-related self-concept and autonomous motivation. Data from 1239 students and 341 teachers were analyzed using a Bayesian cross-classified multi-level modeling approach. Our analyses showed that variance in teacher judgments is largely due to variation at the level of judgments and less due to variation in the slope (i.e., the accuracy of teacher judgments). Teachers' judgment accuracy varied to a comparable degree across teachers and classes. Significant determinants for these differences were teachers' subject and students' grade point average. [ABSTRACT FROM AUTHOR]

Cross-classified multilevel models deal with data pertaining to two different non-hierarchical classifications. It is unclear how much interpenetration is needed for a cross-classified multilevel model to work well and to estimate the two higher-level effects reliably. The paper investigates this question and the properties of cross-classified multilevel logistic models under various survey conditions. The effects of different membership allocation schemes, total sample sizes, group sizes, number of groups, overall rates of response and the variance partitioning coefficient on the properties of the estimators and the power of the Wald test are considered. The work is motivated by an application to separate area and interviewer effects on survey non-response which are often confounded. The results indicate that limited interviewer dispersion (around three areas per interviewer) provides sufficient interpenetration for good estimator properties. Further dispersion yields only very small or negligible gains in the properties. Interviewer dispersion also acts as a moderating factor on the effect of the other simulation factors (sample size, the ratio of interviewers to areas, the overall probability and the variance values) on the properties of the estimators and test statistics. The results also indicate that a higher number of interviewers for a set number of areas and a set total sample size improves these properties. [ABSTRACT FROM AUTHOR]

The objectives were to develop a Mediterranean oriented semi-quantitative food frequency questionnaire (FFQ) and evaluate its validity in measuring energy and nutrient intakes. For FFQ development, the main challenge was to merge food items and practices reflecting cultural Mediterranean preferences with other food choices ensuing from diet transition to more westernized dietary patterns. FFQ validity was evaluated by comparing nutrient intakes against the average of two 24-h dietary recalls for 179 pregnant women. Although the mean intake values for most nutrients and energy tended to be higher when determined by the FFQ, the Cohen's d was below 0.3. Bland-Altman plots confirmed the agreement between the two methods. Positive significant correlations ranged from 0.35 to 0.77. The proportion of women classified correctly was between 73.2% and 92.2%, whereas gross misclassification was low. Weighted kappa values were between 0.31 and 0.78, while intraclass correlation coefficients were between 0.49 and 0.89. Our methodological approach for the development and validation of this FFQ provides reliable measurements of energy, macro- and micronutrient intakes. Overall, our culture-specific FFQ could serve as a useful assessment tool in studies aiming at monitoring dietary intakes, especially in the Mediterranean region, where countries share common cultural dietary habits. [ABSTRACT FROM AUTHOR]

Configural Frequency Analysis (CFA) is a method for cell-wise testing in contingency tables whether some model is contradicted. The original idea that lead to CFA is that the cell frequencies in contingency tables can not only be used to evaluate statements about the association structure of the variables that span the table, but are worthy of consideration in their own right. CFA is a method that allows researchers to identify patterns (configurations) of variable categories that occur more often or less often than expected based on some chance model. Each cell in this crossclassification is described by a profile of variable categories. These profiles are called configurations. CFA relates individual observed cell frequencies to their expected counterparts, thereby looking for surprising, that is, statistically significant deviations. When a cross-classification cell contains more cases than expected according to some base model, it is said to constitute a CFA "type". When there are fewer cases than expected, a cell is said to constitute a CFA "antitype". Using CFA, researchers inspect either each configuration in this cross-classification or an a priori specified selection of configurations. A large number of tests has been introduced for CFA which differ in power, in their capabilities to detect types and antitypes. Lehmacher's test always has more power than the other tests. The aim of this study is to provide an introductory review for CFA, a method of categorical data analysis originally introduced as a heuristic method the so-called CFA recently has been developed into an inferential method. [ABSTRACT FROM AUTHOR]

Multirater (multimethod, multisource) studies are increasingly applied in psychology. Eid and colleagues (2008) proposed a multilevel confirmatory factor model for multitrait-multimethod (MTMM) data combining structurally different and multiple independent interchangeable methods (raters). In many studies, however, different interchangeable raters (e.g., peers, subordinates) are asked to rate different targets (students, supervisors), leading to violations of the independence assumption and to cross-classified data structures. In the present work, we extend the ML-CFA-MTMM model by Eid and colleagues (2008) to cross-classified multirater designs. The new C4 model (Cross-Classified CTC[M-1] Combination of Methods) accounts for nonindependent interchangeable raters and enables researchers to explicitly model the interaction between targets and raters as a latent variable. Using a real data application, it is shown how credibility intervals of model parameters and different variance components can be obtained using Bayesian estimation techniques. [ABSTRACT FROM AUTHOR]

Items rarely belong to a single category, but rather can be cross-classified into many categories, each serving a very different basis for induction. Presently, little is known about how children determine which category to use for induction in cross-classification situations. This research examined the role of category coherence in children's and adults’ ( N = 329) inductive reasoning about cross-classified entities. In Study 1 and 2, participants were presented with a person who could be cross-classified into two categories, one coherent and the other incoherent. Category coherence is the extent to which members of a category and/or the properties of a category make sense together, given one's background knowledge. The results showed that by age 5 years, children are systematic in their use of coherence, tending to select the coherent category to inform their inference about the person. There is weaker evidence for 4-year-olds. Study 3 and 4 revealed that 4-year-olds have an appreciation for coherence in tasks of categorization. The results of Study 5 demonstrated 5-year-olds’ and adults’ tendency to use coherence when reasoning about cross-classified entities across different induction tasks. Overall, these results contribute to our emerging understanding of how and why children select some categories versus others during induction with cross-classified items. [ABSTRACT FROM AUTHOR]

This paper compares the performance of trip generation models. Trip generation estimates the number of trips to and from a traffic analysis zone. This process is the first stage of the conventional four-step travel forecasting framework. Although many approaches have been suggested for this step, regression and category analyses have been widely applied. The two methods have generated an acceptable level of performance from the perspective of transport planning. Critical problems, however, have also been observed. In the regression analysis, trip rates are treated as continuous variables that can be negative, which is obviously unrealistic. Furthermore, the method does not incorporate traveler behavior. For the category analysis, its arbitrary way of choosing independent variables and their strata has drawn critiques. The cell-by-cell calculation in this method also increases the concerns about unreliable estimation of trip rates. Censored regression, count data, and discrete choice models have been visited for the alternative of regression approach while the multiple classification method has been conceived for the substitute of the category analysis. A systematic examination of the performance among the models has not been discussed sufficiently yet, which is the motive of this paper. Six representative models – regression, tobit, Poisson, ordered logit, category, and multiple classification analyses – were applied to the home-based work trips in the Seoul metropolitan area. Cross-validation and back-casting were the key for checking the performance among the models. In this process, the measures of correlation, variance, and coincidence were compared. The category-type model was superior in overall performance. [ABSTRACT FROM AUTHOR]

Abstract: Four studies examined the role of generic language in facilitating 4- and 5-year-old children''s ability to cross-classify. Participants were asked to classify an item into a familiar (taxonomic or script) category, then cross-classify it into a novel (script or taxonomic) category with the help of a clue expressed in either generic or specific language. Experiment 1 showed that generics facilitate 5-year-olds’ and adults’ cross-classification when expressed at an appropriate level of generalization (e.g., “foods” and “birthday party things”), whereas Experiment 2 showed that such effects disappeared when labels were at an inappropriate level of generalization (e.g., “pizzas” and “balloons”). Experiments 3 and 4 offered additional controls. Taken together, the findings demonstrate that language can guide and direct children''s multiple categorizations. [Copyright &y& Elsevier]

Two studies examined multiple category reasoning in property induction with cross-classified foods. Pilot tests identified foods that were more typical of a taxonomic category (e.g., 'fruit'; termed 'taxonomic primary') or a script based category (e.g., 'snack foods'; termed 'script primary'). They also confirmed that taxonomic categories were perceived as more coherent than script categories. In Experiment 1 participants completed an induction task in which information from multiple categories could be searched and combined to generate a property prediction about a target food. Multiple categories were more often consulted and used in prediction for script primary than for taxonomic primary foods. Experiment 2 replicated this finding across a range of property types but found that multiple category reasoning was reduced in the presence of a concurrent cognitive load. Property type affected which categories were consulted first and how information from multiple categories was weighted. The results show that multiple categories are more likely to be used for property predictions about cross-classified objects when an object is primarily associated with a category that has low coherence. [ABSTRACT FROM AUTHOR]

Abstract: We analyse various Multiple Classification Analysis (MCA) methods to model trip production (generation). We first show that the MCA version most widely used in transport engineering implies a rarely feasible assumption, the transgression of which may drive a significant overestimation of the future number of trips and a systematic bias in its socio-economic composition. To illustrate this effect, we use Monte Carlo simulation and real data from Santiago, Chile to compare the various MCA approaches, concluding that the aforementioned form should be discarded. Our analysis also shows that the MCA method which is more robust to the structure of the underlying model, is the simple calculation of trip rates as averages for each category. Finally, we hint at the need to use more sophisticated formulations than MCA to model trip production. [Copyright &y& Elsevier]

Items commonly belong to many categories. Cross-classification is the classification of a single item into more than one category. This research explored 2- to 6-year-old children's use of 2 different category systems for cross-classification: script (e.g., school-time items, birthday party items) and taxonomic (e.g., animals, clothes). The results of Experiments 1 and 2 show that by a young age, children are able to cross-classify items into both category systems. Experiment 3 found that children mentally represent cross-classified items as simultaneously belonging to both taxonomic and script categories. Experiment 4 found that children often, but do not always, spontaneously activate taxonomic and script cross-classifications. Overall, the results demonstrate that from an early age children form and use both taxonomic and script categories for cross-classification. [ABSTRACT FROM AUTHOR]

The authors discuss two tools to compare tables generated by the cross-classification of data by two characteristics (e.g., fathers' and sons' occupations; home ownership and ethnicity): (1) an algorithm to adjust two tables to have identical marginal frequencies, and (2) a measure of the association between rows and columns in a two-way table and a measure of how the row and column associations differ across two such tables, together with a test of the hypothesis that the associations are identical. The authors compare intergenerational occupational mobility in the United States in the period between 1850 and 1880 with that between 1880 and 1910. [ABSTRACT FROM AUTHOR]

• MVPAlab is a very flexible, powerful and easy-to-use decoding toolbox for multi-dimensional electroencephalography data. • MVPAlab implements exclusive machine learning-based analyses and functionalities, such as parallel computation, significantly reducing the execution time, or frequency contribution analyses, which studies how relevant information is coded across different frequency bands. • MVPAlab also includes an intuitive GUI and data representation utility, which generates ready-to-publish data representations and temporal animations. The study of brain function has recently expanded from classical univariate to multivariate analyses. These multivariate, machine learning-based algorithms afford neuroscientists extracting more detailed and richer information from the data. However, the implementation of these procedures is usually challenging, especially for researchers with no coding experience. To address this problem, we have developed MVPAlab, a MATLAB-based, flexible decoding toolbox for multidimensional electroencephalography and magnetoencephalography data. The MVPAlab Toolbox implements several machine learning algorithms to compute multivariate pattern analyses, cross-classification, temporal generalization matrices and feature and frequency contribution analyses. It also provides access to an extensive set of preprocessing routines for, among others, data normalization, data smoothing, dimensionality reduction and supertrial generation. To draw statistical inferences at the group level, MVPAlab includes a non-parametric cluster-based permutation approach. A sample electroencephalography dataset was compiled to test all the MVPAlab main functionalities. Significant clusters (p<0.01) were found for the proposed decoding analyses and different configurations, proving the software capability for discriminating between different experimental conditions. This toolbox has been designed to include an easy-to-use and intuitive graphic user interface and data representation software, which makes MVPAlab a very convenient tool for users with few or no previous coding experience. In addition, MVPAlab is not for beginners only, as it implements several high and low-level routines allowing more experienced users to design their own projects in a highly flexible manner. [ABSTRACT FROM AUTHOR]

Objective: To compare estimates of effect and variability resulting from standard linear regression analysis and hierarchical multilevel analysis with cross-classified multilevel analysis under various scenarios., Study Design and Setting: We performed a simulation study based on a data structure from an observational study in clinical mental health care. We used a Markov chain Monte Carlo approach to simulate 18 scenarios, varying sample sizes, cluster sizes, effect sizes and between group variances. For each scenario, we performed standard linear regression, multilevel regression with random intercept on patient level, multilevel regression with random intercept on nursing team level and cross-classified multilevel analysis., Results: Applying cross-classified multilevel analyses had negligible influence on the effect estimates. However, ignoring cross-classification led to underestimation of the standard errors of the covariates at the two cross-classified levels and to invalidly narrow confidence intervals. This may lead to incorrect statistical inference. Varying sample size, cluster size, effect size and variance had no meaningful influence on these findings., Conclusion: In case of cross-classified data structures, the use of a cross-classified multilevel model helps estimating valid precision of effects, and thereby, support correct inferences., (Copyright © 2022. Published by Elsevier Inc.)

• General Model of SDT received support on the within-individual level. • A large sample of college students collected across years and courses is used. • Cross-classified path model is used to account for student and course variations. • Unexpected results such as the dominant effect of competence are reported. According to self-determination theory (SDT), the extent to which students' motivation is self-determined is critical for academic performance. SDT also proposes that self-determined academic motivation is facilitated when the learning environment supports the basic psychological needs for autonomy, relatedness, and competence. This model of social support → needs satisfaction → motivation → learning outcomes is termed the general SDT model. Current evidence regarding this general SDT model is limited, in that, to date, no study has examined it in full using within-individual methods, which are critical for understanding inner psychological processes and mechanisms. Using a large and comprehensive college student dataset (total N of responses = 30,765), the current study aims to apply a within-individual analytical approach to the general SDT model. Specifically, we apply a cross-classified path model to account for both the between-student level and between-classroom level nesting structure. This model enables us to explain the relationships between the variables in the general SDT model on three levels: situational (within-student and within-classroom), between-student, and between-classroom. The results generally support the predictions on all three levels. For a student, a classroom, or a student's specific experience within a classroom, the general SDT model received support. Most importantly, when the same student in the same classroom experiences higher levels of autonomy support, she or he is more likely to have her or his psychological needs satisfied and to study for self-determined reasons, which are associated with higher perceived learning performance. Various unexpected results, such as the direct effects of learning climate and the predominance of the competence need, are also reported. Overall, the current research provides a comprehensive and multilevel understanding of the role of self-determination in college classrooms. [ABSTRACT FROM AUTHOR]

The present studies (N = 159) investigated children's and adults' preferences for label and property conjunctions for cross‐classifiable toys. In Study 1, 4‐year‐olds, 5‐year‐olds, and adults participated in a labelling and property attribution task involving experimental toys that belong to two categories and control toys that belong to only one category. In Study 2, the same age groups were tested on a version of the task focusing on the control toys. In Study 3, 4‐ and 5‐year‐olds were tested on a modified version of the labelling and property attribution task involving the experimental and control toys. Finally, Study 4 tested a younger age group, 3‐year‐olds. Overall, the results indicated that by age 3 years, individuals prefer assigning experimental toys (but not controls) with joint labels that are a conjunction of two single category labels. By age 4 years, individuals prefer assigning experimental toys (but not controls) with dual properties that are a conjunction of two single properties. These results document the development of how different types of toys elicit preferences for conjunctions of labels that denote membership in two distinct categories and conjunctions of properties that combine features of these categories. Highlights: This study examines children's preferences in labeling and attributing properties to cross-classifiable toys.Children labeled and attributed properties to toys that belonged to two categories (experimental) and one category (control).The results reveal that by 4 years, children prefer both label and property conjunctions for the experimental toys that can be cross‐classified into more than one category, but not for the control toys. [ABSTRACT FROM AUTHOR]

The accurate assessment of food consumption is crucial in nutritional studies. Since modern nutrition science has become more interested in diet as a whole, studies validating food frequency questionnaires (FFQs) and exploratory dietary patterns are needed. We aimed at examining the relative validity of a 47-item FFQ against three-day food records among three- to six-year-old Finnish children, as well as investigating the consistency of the dietary patterns derived using the principal component analysis (PCA), with food record and FFQ data as inputs. We conducted the PCA without forcing the food record data to match the FFQ items. Altogether, 75% or more of the participants were classified into the same or adjacent quarter of vegetables and fruits as well as sugary food consumption. Furthermore, the intake of folate and vitamin C increased linearly in the quarters of vegetable and fruit consumption, as did the intake of sucrose in quarters of sugary food consumption. Three fairly similar dietary patterns were identified from food records and FFQ data. Concerning the patterns, more than 70% of the participants were classified into the same or adjacent quarter. However, the Spearman correlation coefficients between the respective pattern scores were low (0.25–0.33). The FFQ showed acceptable validity when ranking food group consumption compared to food records. Additionally, the FFQ-derived dietary patterns were consistent with those derived using food record data. [ABSTRACT FROM AUTHOR]