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Detecting which variables alter component interpretation across multiple groups: A resampling-based method. Behav Res Methods 2020; 52:236-263. [PMID: 30937846 DOI: 10.3758/s13428-019-01222-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
In psychology, many studies measure the same variables in different groups. In the case of a large number of variables when a strong a priori idea about the underlying latent construct is lacking, researchers often start by reducing the variables to a few principal components in an exploratory way. Herewith, one often wants to evaluate whether the components represent the same construct in the different groups. To this end, it makes sense to remove outlying variables that have significantly different loadings on the extracted components across the groups, hampering equivalent interpretations of the components. Moreover, identifying such outlying variables is important when testing theories about which variables behave similarly or differently across groups. In this article, we first scrutinize the lower bound congruence method (LBCM; De Roover, Timmerman, & Ceulemans in Behavior Research Methods, 49, 216-229, 2017), which was recently proposed for solving the outlying-variable detection problem. LBCM investigates how Tucker's congruence between the loadings of the obtained cluster-loading matrices improves when specific variables are discarded. We show that LBCM has the tendency to output outlying variables that either are false positives or concern very small, and thus practically insignificant, loading differences. To address this issue, we present a new heuristic: the lower and resampled upper bound congruence method (LRUBCM). This method uses a resampling technique to obtain a sampling distribution for the congruence coefficient, under the hypothesis that no outlying variable is present. In a simulation study, we show that LRUBCM outperforms LBCM. Finally, we illustrate the use of the method by means of empirical data.
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Abstract
The personalized approach to psychopathology conceptualizes mental disorder as a complex system of contextualized dynamic processes that is nontrivially specific to each individual, and it seeks to develop formal idiographic statistical models to represent these individual processes. Although the personalized approach draws on long-standing influences in clinical psychology, there has been an explosion of research in recent years following the development of intensive longitudinal data capture and statistical techniques that facilitate modeling of the dynamic processes of each individual's pathology. Advances are also making idiographic analyses scalable and generalizable. We review emerging research using the personalized approach in descriptive psychopathology, precision assessment, and treatment selection and tailoring, and we identify future challenges and areas in need of additional research. The personalized approach to psychopathology holds promise to resolve thorny diagnostic issues, generate novel insights, and improve the timing and efficacy of interventions.
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Affiliation(s)
- Aidan G C Wright
- Department of Psychology, University of Pittsburgh, Pittsburgh, Pennsylvania 15260, USA; ,
| | - William C Woods
- Department of Psychology, University of Pittsburgh, Pittsburgh, Pennsylvania 15260, USA; ,
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Bulteel K, Tuerlinckx F, Brose A, Ceulemans E. Improved Insight into and Prediction of Network Dynamics by Combining VAR and Dimension Reduction. MULTIVARIATE BEHAVIORAL RESEARCH 2018; 53:853-875. [PMID: 30453783 DOI: 10.1080/00273171.2018.1516540] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/10/2017] [Revised: 07/14/2018] [Accepted: 07/19/2018] [Indexed: 06/09/2023]
Abstract
To understand within-person psychological processes, one may fit VAR(1) models (or continuous-time variants thereof) to multivariate time series and display the VAR(1) coefficients as a network. This approach has two major problems. First, the contemporaneous correlations between the variables will frequently be substantial, yielding multicollinearity issues. In addition, the shared effects of the variables are not included in the network. Consequently, VAR(1) networks can be hard to interpret. Second, crossvalidation results show that the highly parametrized VAR(1) model is prone to overfitting. In this article, we compare the pros and cons of two potential solutions to both problems. The first is to impose a lasso penalty on the VAR(1) coefficients, setting some of them to zero. The second, which has not yet been pursued in psychological network analysis, uses principal component VAR(1) (termed PC-VAR(1)). In this approach, the variables are first reduced to a few principal components, which are rotated toward simple structure; then VAR(1) analysis (or a continuous-time analog) is applied to the rotated components. Reanalyzing the data of a single participant of the COGITO study, we show that PC-VAR(1) has the better predictive performance and that networks based on PC-VAR(1) clearly represent both the lagged and the contemporaneous variable relations.
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Affiliation(s)
| | | | - Annette Brose
- a KU Leuven, University of Leuven
- b Humboldt University Berlin
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Stegeman A. Simultaneous Component Analysis by Means of Tucker3. PSYCHOMETRIKA 2018; 83:21-47. [PMID: 28386813 DOI: 10.1007/s11336-017-9568-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/24/2016] [Revised: 01/30/2017] [Indexed: 06/07/2023]
Abstract
A new model for simultaneous component analysis (SCA) is introduced that contains the existing SCA models with common loading matrix as special cases. The new SCA-T3 model is a multi-set generalization of the Tucker3 model for component analysis of three-way data. For each mode (observational units, variables, sets) a different number of components can be chosen and the obtained solution can be rotated without loss of fit to facilitate interpretation. SCA-T3 can be fitted on centered multi-set data and also on the corresponding covariance matrices. For this purpose, alternating least squares algorithms are derived. SCA-T3 is evaluated in a simulation study, and its practical merits are demonstrated for several benchmark datasets.
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Affiliation(s)
- Alwin Stegeman
- Group Science, Engineering and Technology, KU Leuven - Kulak, E. Sabbelaan 53, 8500, Kortrijk, Belgium.
- Department of Electrical Engineering (ESAT), KU Leuven, Kasteelpark Arenberg 10, 3001, Leuven, Belgium.
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A practical guide to understanding reliability in studies of within-person variability. JOURNAL OF RESEARCH IN PERSONALITY 2017. [DOI: 10.1016/j.jrp.2016.06.020] [Citation(s) in RCA: 123] [Impact Index Per Article: 17.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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Heylen J, Van Mechelen I, Verduyn P, Ceulemans E. KSC-N: Clustering of Hierarchical Time Profile Data. PSYCHOMETRIKA 2016; 81:411-433. [PMID: 25491164 DOI: 10.1007/s11336-014-9433-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/21/2014] [Indexed: 06/04/2023]
Abstract
Quite a few studies in the behavioral sciences result in hierarchical time profile data, with a number of time profiles being measured for each person under study. Associated research questions often focus on individual differences in profile repertoire, that is, differences between persons in the number and the nature of profile shapes that show up for each person. In this paper, we introduce a new method, called KSC-N, that parsimoniously captures such differences while neatly disentangling variability in shape and amplitude. KSC-N induces a few person clusters from the data and derives for each person cluster the types of profile shape that occur most for the persons in that cluster. An algorithm for fitting KSC-N is proposed and evaluated in a simulation study. Finally, the new method is applied to emotional intensity profile data.
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Affiliation(s)
- Joke Heylen
- Research Group of Methodology of Educational Sciences, University of Leuven, Tiensestraat 102, 3000 , Leuven, Belgium.
| | - Iven Van Mechelen
- Research Group of Quantitative Psychology and Individual Differences, University of Leuven, Tiensestraat 102, 3000, Leuven, Belgium
| | - Philippe Verduyn
- Research Group of Quantitative Psychology and Individual Differences, University of Leuven, Tiensestraat 102, 3000, Leuven, Belgium
| | - Eva Ceulemans
- Research Group of Methodology of Educational Sciences, University of Leuven, Tiensestraat 102, 3000 , Leuven, Belgium
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How to detect which variables are causing differences in component structure among different groups. Behav Res Methods 2015; 49:216-229. [DOI: 10.3758/s13428-015-0687-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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De Roover K, Timmerman ME, De Leersnyder J, Mesquita B, Ceulemans E. What's hampering measurement invariance: detecting non-invariant items using clusterwise simultaneous component analysis. Front Psychol 2014; 5:604. [PMID: 24999335 PMCID: PMC4064661 DOI: 10.3389/fpsyg.2014.00604] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2014] [Accepted: 05/29/2014] [Indexed: 11/25/2022] Open
Abstract
The issue of measurement invariance is ubiquitous in the behavioral sciences nowadays as more and more studies yield multivariate multigroup data. When measurement invariance cannot be established across groups, this is often due to different loadings on only a few items. Within the multigroup CFA framework, methods have been proposed to trace such non-invariant items, but these methods have some disadvantages in that they require researchers to run a multitude of analyses and in that they imply assumptions that are often questionable. In this paper, we propose an alternative strategy which builds on clusterwise simultaneous component analysis (SCA). Clusterwise SCA, being an exploratory technique, assigns the groups under study to a few clusters based on differences and similarities in the component structure of the items, and thus based on the covariance matrices. Non-invariant items can then be traced by comparing the cluster-specific component loadings via congruence coefficients, which is far more parsimonious than comparing the component structure of all separate groups. In this paper we present a heuristic for this procedure. Afterwards, one can return to the multigroup CFA framework and check whether removing the non-invariant items or removing some of the equality restrictions for these items, yields satisfactory invariance test results. An empirical application concerning cross-cultural emotion data is used to demonstrate that this novel approach is useful and can co-exist with the traditional CFA approaches.
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Affiliation(s)
- Kim De Roover
- Methods, Individual and Cultural Differences, Affect and Social Behavior, KU Leuven Leuven, Belgium
| | - Marieke E Timmerman
- Heymans Institute of Psychology, University of Groningen Groningen, Netherlands
| | - Jozefien De Leersnyder
- Methods, Individual and Cultural Differences, Affect and Social Behavior, KU Leuven Leuven, Belgium
| | - Batja Mesquita
- Methods, Individual and Cultural Differences, Affect and Social Behavior, KU Leuven Leuven, Belgium
| | - Eva Ceulemans
- Methods, Individual and Cultural Differences, Affect and Social Behavior, KU Leuven Leuven, Belgium
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De Roover K, Timmerman ME, Mesquita B, Ceulemans E. Common and cluster-specific simultaneous component analysis. PLoS One 2013; 8:e62280. [PMID: 23667463 PMCID: PMC3648553 DOI: 10.1371/journal.pone.0062280] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2012] [Accepted: 03/19/2013] [Indexed: 11/30/2022] Open
Abstract
In many fields of research, so-called 'multiblock' data are collected, i.e., data containing multivariate observations that are nested within higher-level research units (e.g., inhabitants of different countries). Each higher-level unit (e.g., country) then corresponds to a 'data block'. For such data, it may be interesting to investigate the extent to which the correlation structure of the variables differs between the data blocks. More specifically, when capturing the correlation structure by means of component analysis, one may want to explore which components are common across all data blocks and which components differ across the data blocks. This paper presents a common and cluster-specific simultaneous component method which clusters the data blocks according to their correlation structure and allows for common and cluster-specific components. Model estimation and model selection procedures are described and simulation results validate their performance. Also, the method is applied to data from cross-cultural values research to illustrate its empirical value.
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Affiliation(s)
- Kim De Roover
- Methodology of Educational Sciences Research Unit, KU Leuven, Leuven, Belgium.
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