Improved Insight into and Prediction of Network Dynamics by Combining VAR and Dimension Reduction

References

• Abegaz, F., & Wit, E. (2013). Sparse time series chain graphical models for reconstructing genetic networks. Biostatistics, 14(3), 586–599. doi: 10.1093/biostatistics/kxt005.
   PubMed Web of Science ®Google Scholar
• American Psychiatric Association (2013). Diagnostic and statistical manual of mental disorders (5th ed.). Washington, DC: Author.
   Google Scholar
• Babyak, M. A. (2004). What you see may not be what you get: A brief, nontechnical introduction to overfitting in regression-type models. Psychosomatic Medicine, 66(3), 411–421.
   PubMed Web of Science ®Google Scholar
• Bergmeir, C., & Benítez, J. M. (2012). On the use of cross-validation for time series predictor evaluation. Information Sciences, 191, 192–213. doi: 10.1016/j.ins.2011.12.028.
   Web of Science ®Google Scholar
• Boker, S. M., Neale, M. C., & Rausch, J. R. (2004). Latent differential equation modeling with multivariate multi-occasion indicators. In K. van Montfort, H. Oud, & A. Satorra (Eds.), Recent developments on structural equations models: Theory and applications (pp. 151–174). Dordrecht, The Netherlands: Kluwer Academic.
   Google Scholar
• Bolger, N., & Laurenceau, J.-P. (2013). Intensive longitudinal methods: An introduction to diary and experience sampling research. New York, NY: Guilford Press.
   Google Scholar
• Bondell, H. D., & Reich, B. J. (2008). Simultaneous regression shrinkage, variable selection, and supervised clustering of predictors with OSCAR. Biometrics, 64(1), 115–123. doi: 10.1111/j.1541-0420.2007.00843.x.
   PubMed Web of Science ®Google Scholar
• Borsboom, D., & Cramer, A. O. J. (2013). Network analysis: An integrative approach to the structure of psychopathology. Annual Review of Clinical Psychology, 9(1), 91–121. doi: 10.1146/annurev-clinpsy-050212-185608.
   PubMed Web of Science ®Google Scholar
• Brandt, P. T., & Williams, J. T. (2007). Multiple time series models. Thousand Oaks, CA: Sage Publications.
   Google Scholar
• Breiman, L. (2001). Statistical modeling: The two cultures. Statistical Science, 16(3), 199–231. doi: 10.1214/ss/1009213726.
   Web of Science ®Google Scholar
• Bringmann, L. F., Lemmens, L. H. J. M., Huibers, M. J. H., Borsboom, D., & Tuerlinckx, F. (2015). Revealing the dynamic network structure of the Beck Depression Inventory-II. Psychological Medicine, 45(4), 747–757. doi: 10.1017/S0033291714001809
   PubMed Web of Science ®Google Scholar
• Bringmann, L. F., Pe, M. L., Vissers, N., Ceulemans, E., Borsboom, D., Vanpaemel, W., … Kuppens, P. (2016). Assessing temporal emotion dynamics using networks. Assessment, 23(4), 425–435. doi: 10.1177/1073191116645909
   PubMed Web of Science ®Google Scholar
• Bringmann, L. F., Vissers, N., Wichers, M., Geschwind, N., Kuppens, P., Peeters, F., … Tuerlinckx, F. (2013). A network approach to psychopathology: New insights into clinical longitudinal data. PLoS One, 8(4), e60188. 1–13. doi: 10.1371/journal.pone.0060188
   PubMed Web of Science ®Google Scholar
• Bro, R., Kjeldahl, K., Smilde, A. K., & Kiers, H. A. L. (2008). Cross-validation of component models: A critical look at current methods. Analytical and Bioanalytical Chemistry, 390(5), 1241–1251. doi: 10.1007/s00216-007-1790-1.
   PubMed Web of Science ®Google Scholar
• Brose, A., De Roover, K., Ceulemans, E., & Kuppens, P. (2015). Older adults’ affective experiences across 100 days are less variable and less complex than younger adults’. Psychology and Aging, 30(1), 194–208. doi: 10.1037/a0038690
   PubMed Web of Science ®Google Scholar
• Brose, A., Schmiedek, F., Koval, P., & Kuppens, P. (2015). Emotional inertia contributes to depressive symptoms beyond perseverative thinking. Cognition and Emotion, 29(3), 527–538. doi: 10.1080/02699931.2014.916252
   PubMed Web of Science ®Google Scholar
• Brose, A., Schmiedek, F., Lövdén, M., Molenaar, P. C. M., & Lindenberger, U. (2010). Adult age differences in covariation of motivation and working memory performance: Contrasting between-person and within-person findings. Research in Human Development, 7(1), 61–78. doi: 10.1080/15427600903578177
   Web of Science ®Google Scholar
• Brose, A., Voelkle, M. C., Lövdén, M., Lindenberger, U., & Schmiedek, F. (2015). Differences in the between-person and the within-person structures of affect are a matter of degree. European Journal of Personality, 29(1), 55–71. doi: 10.1002/per.1961
   Web of Science ®Google Scholar
• Browne, M. W. (2001). An overview of analytic rotation in exploratory factor analysis. Multivariate Behavioral Research, 36(1), 111–150. doi: 10.1207/S15327906MBR3601_05.
   Web of Science ®Google Scholar
• Browne, M. W., & Nesselroade, J. R. (2005). Representing psychological processes with dynamic factor models: Some promising uses and extensions of autoregressive moving average time series models. In A. Maydeu-Olivares & J. J. McArdle (Eds.), Contemporary psychometrics: A festschrift for Roderick P. McDonald (pp. 415–452). Mahwah, NJ: Lawrence Erlbaum Associates.
   Google Scholar
• Browne, M. W., & Zhang, G. (2005). DyFA: Dynamic factor analysis of lagged correlation matrices (Version 2.03) [Computer software and manual]. Retrieved from http://faculty.psy.ohio-state.edu/browne/software.php
   Google Scholar
• Bulteel, K., Mestdagh, M., Tuerlinckx, F., & Ceulemans, E. (in press). VAR(1) based models do not outpredict AR(1) models in current psychological applications. Psychological Methods. doi: 10.1037/met0000178.
   Web of Science ®Google Scholar
• Bulteel, K., Tuerlinckx, F., Brose, A., & Ceulemans, E. (2016a). Clustering vector autoregressive models: Capturing qualitative differences in within-person dynamics. Frontiers in Psychology, 7, 1–15, 1540. doi: 10.3389/fpsyg.2016.01540
   PubMed Web of Science ®Google Scholar
• Bulteel, K., Tuerlinckx, F., Brose, A., & Ceulemans, E. (2016b). Using raw VAR regression coefficients to build networks can be misleading. Multivariate Behavioral Research, 51(2–3), 330–344. doi: 10.1080/00273171.2016.1150151
   PubMed Web of Science ®Google Scholar
• Cabrieto, J., Tuerlinckx, F., Kuppens, P., Hunyadi, B., & Ceulemans, E. (2018). Testing for the presence of correlation changes in a multivariate time series: A permutation based approach. Scientific Reports, 8(1), 1–20, 769. doi: 10.1038/s41598-017-19067-2
   PubMed Web of Science ®Google Scholar
• Cabrieto, J., Tuerlinckx, F., Kuppens, P., Wilhelm, F., Liedlgruber, M., & Ceulemans, E. (2018). Capturing correlation changes by applying kernel change point detection on the running correlations. Information Sciences, 447, 117–139. doi: 10.1016/j.ins.2018.03.010
   Web of Science ®Google Scholar
• Cattell, R. B. (1966). The scree test for the number of factors. Multivariate Behavioral Research, 1(2), 245–276. doi: 10.1207/s15327906mbr0102_10
   PubMed Web of Science ®Google Scholar
• Ceulemans, E., & Kiers, H. A. L. (2006). Selecting among three-mode principal component models of different types and complexities: A numerical convex hull based method. British Journal of Mathematical and Statistical Psychology, 59(1), 133–150. doi: 10.1348/000711005X64817
   PubMed Web of Science ®Google Scholar
• Ceulemans, E., & Kiers, H. A. L. (2009). Discriminating between strong and weak structures in three-mode principal component analysis. British Journal of Mathematical and Statistical Psychology, 62(3), 601–620. doi: 10.1348/000711008X369474
   PubMed Web of Science ®Google Scholar
• Ceulemans, E., Wilderjans, T. F., Kiers, H. A. L., & Timmerman, M. E. (2016). Multilevel simultaneous component analysis: A computational shortcut and software package. Behavior Research Methods, 48(3), 1008–1020. doi: 10.3758/s13428-015-0626-8.
   PubMed Web of Science ®Google Scholar
• Chen, J., & Chen, Z. (2008). Extended Bayesian information criteria for model selection with large model spaces. Biometrika, 95(3), 759–771. doi: 10.1093/biomet/asnO34.
   Web of Science ®Google Scholar
• Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2003). Applied multiple regression/correlation analysis for the behavioral sciences (3rd ed.). New York, NY: Routledge.
   Google Scholar
• Crawford, A. V., Green, S. B., Levy, R., Lo, W.-J., Scott, L., Dubravka, S., & Thompson, M. S. (2010). Evaluation of parallel analysis methods for determining the number of factors. Educational and Psychological Measurement, 70(6), 885–901. doi: 10.1177/0013164410379332.
   Web of Science ®Google Scholar
• de Haan-Rietdijk, S., Voelkle, M. C., Keijsers, L., & Hamaker, E. L. (2017). Discrete- vs. continuous-time modeling of unequally spaced experience sampling method data. Frontiers in Psychology, 8, 1849, 19. doi: 10.3389/fpsyg.2017.01849.
   PubMed Web of Science ®Google Scholar
• De Roover, K., Ceulemans, E., Timmerman, M. E., Nezlek, J. B., & Onghena, P. (2013). Modeling differences in the dimensionality of multiblock data by means of clusterwise simultaneous component analysis. Psychometrika, 78(4), 648–668. doi: 10.1007/S11336-013-9318-4
   PubMed Web of Science ®Google Scholar
• De Roover, K., Ceulemans, E., Timmerman, M. E., Vansteelandt, K., Stouten, J., & Onghena, P. (2012). Clusterwise simultaneous component analysis for analyzing structural differences in multivariate multiblock data. Psychological Methods, 17(1), 100–119. doi: 10.1037/a0025385
   PubMed Web of Science ®Google Scholar
• De Roover, K., Timmerman, M. E., Van Diest, I., Onghena, P., & Ceulemans, E. (2014). Switching principal component analysis for modeling means and covariance changes over time. Psychological Methods, 19(1), 113–132. doi: 10.1037/a0034525
   PubMed Web of Science ®Google Scholar
• Diaconis, P. (1985). Theories of data analysis. In D. C. Hoaglin, F. Mosteller, & J. W. Tukey (Eds.), Exploring data tables, trends, and shapes (pp. 1–36). New York, NY: Wiley.
   Google Scholar
• Driver, C. C., Oud, J. H. L., & Voelkle, M. C. (2017). Continuous time structural equation modeling with R package ctsem. Journal of Statistical Software, 77(5), 1–35. doi: 10.18637/jss.v077.i05.
   Web of Science ®Google Scholar
• Epskamp, S. (2016). graphicalVAR: Graphical VAR for Experience Sampling Data (Version 0.1.4) [Computer software]. Retrieved from https://CRAN.R-project.org/package=graphicalVAR
   Google Scholar
• Epskamp, S., Cramer, A. O. J., Waldorp, L. J., Schmittmann, V. D., & Borsboom, D. (2012). qgraph: Network visualizations of relationships in psychometric data. Journal of Statistical Software, 48(4), 1–18. doi:doi: 10.18637/jss.v048.i04.
   Web of Science ®Google Scholar
• Epskamp, S., Kruis, J., & Marsman, M. (2017). Estimating psychopathological networks: Be careful what you wish for. PLoS One, 12(6), e0179891. 1–13. doi: 10.1371/journal.pone.0179891.
   PubMed Web of Science ®Google Scholar
• Epskamp, S., Waldorp, L. J., Mõttus, R., & Borsboom, D. (2018). The Gaussian graphical model in cross-sectional and time-series data. Multivariate Behavioral Research, 1–28. doi: 10.1080/00273171.2018.1454823.
   PubMed Web of Science ®Google Scholar
• Fabrigar, L. R., Wegener, D. T., MacCallum, R. C., & Strahan, E. J. (1999). Evaluating the use of exploratory factor analysis in psychological research. Psychological Methods, 4(3), 272–299. doi: 10.1037/1082-989X.4.3.272.
   Web of Science ®Google Scholar
• Ferrer, E., & Nesselroade, J. R. (2003). Modeling affective processes in dyadic relations via dynamic factor analysis. Emotion, 3(4), 344–360. doi: 10.1037/1528-3542.3.4.344.
   PubMed Web of Science ®Google Scholar
• Golino, H. F., & Epskamp, S. (2017). Exploratory graph analysis: A new approach for estimating the number of dimensions in psychological research. PLoS One, 12(6), e0174035. doi: 10.1371/journal.pone.0174035.
   PubMed Web of Science ®Google Scholar
• Grice, J. W. (2001). Computing and evaluating factor scores. Psychological Methods, 6(4), 430–450. doi: 10.1037/1082-989X.6.4.430.
   PubMed Web of Science ®Google Scholar
• Gross, J. J. (2010). The future’s so bright, I gotta wear shades. Emotion Review, 2(3), 212–216. doi: 10.1177/1754073910361982.
   Web of Science ®Google Scholar
• Hamaker, E. L. (2012). Why researchers should think “within-person”: A paradigmatic rationale. In M. R. Mehl & T. S. Conner (Eds.). Handbook of research methods for studying daily life (pp. 43–61), New York, NY: Guilford.
   Google Scholar
• Hamaker, E. L., Ceulemans, E., Grasman, R. P. P. P., & Tuerlinckx, F. (2015). Modeling affect dynamics: State-of-the-art and future challenges. Emotion Review, 7(4), 316–322. doi: 10.1177/1754073915590619
   Web of Science ®Google Scholar
• Hamilton, J. D. (1994). Time series analysis. Princeton, NJ: Princeton University Press.
   Google Scholar
• Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning: Data mining, inference, and prediction. New York, NY: Springer.
   Google Scholar
• Hautzinger, M., & Bailer, M. (1993). Allgemeine Depressions Skala [General Depression Scale]. Weinheim, Germany: Beltz.
   Google Scholar
• Hendrickson, A. E., & White, P. O. (1964). Promax: A quick method for rotation to oblique simple structure. British Journal of Statistical Psychology, 17(1), 65–70. doi: 10.1111/j.2044-8317.1964.tb00244.x.
   Web of Science ®Google Scholar
• Hoerl, A. E., & Kennard, R. W. (1970). Ridge regression: Biased estimation for nonorthogonal problems. Technometrics, 12(1), 55–67. doi: 10.2307/1267351.
   Web of Science ®Google Scholar
• Horn, J. L. (1965). A rationale and test for the number of factors in factor analysis. Psychometrika, 30(2), 179–185. doi: 10.1007/BF02289447.
   PubMed Web of Science ®Google Scholar
• Hsu, N.-J., Hung, H.-L., & Chang, Y.-M. (2008). Subset selection for vector autoregressive processes using Lasso. Computational Statistics & Data Analysis, 52(7), 3645–3657. doi: 10.1016/j.csda.2007.12.004.
   Web of Science ®Google Scholar
• Jolliffe, I. T. (1982). A note on the use of principal components in regression. Journal of the Royal Statistical Society. Series C (Applied Statistics), 31(3), 300–303. doi: 10.2307/2348005.
   Web of Science ®Google Scholar
• Jolliffe, I. T. (2002). Principal component analysis (2nd ed.). New York, NY: Springer.
   Google Scholar
• Kaiser, H. F. (1958). The Varimax criterion for analytic rotation in factor analysis. Psychometrika, 23(3), 187–200. doi: 10.1007/BF02289233.
   Web of Science ®Google Scholar
• Kiers, H. A. L., & Berge, J. M. F. (1994). Hierarchical relations between methods for simultaneous component analysis and a technique for rotation to a simple simultaneous structure. British Journal of Mathematical and Statistical Psychology, 47(1), 109–126. doi: 10.1111/j.2044- 8317.1994.tb01027.x
   Google Scholar
• Kiers, H. A. L., & Smilde, A. K. (2007). A comparison of various methods for multivariate regression with highly collinear variables. Statistical Methods and Applications, 16(2), 193–228. doi: 10.1007/s10260-006-0025-5.
   Web of Science ®Google Scholar
• Liu, Y., & West, S. G. (2016). Weekly cycles in daily report data: An overlooked issue. Journal of Personality, 84(5), 560–579. doi: 10.1111/jopy.12182
   PubMed Web of Science ®Google Scholar
• Lorenzo-Seva, U., Timmerman, M. E., & Kiers, H. A. L. (2011). The Hull method for selecting the number of common factors. Multivariate Behavioral Research, 46(2), 340–364. doi: 10.1080/00273171.2011.564527.
   PubMed Web of Science ®Google Scholar
• Lütkepohl, H. (2005). New introduction to multiple time series analysis. Berlin, Germany: Springer.
   Google Scholar
• McNeish, D. M. (2015). Using lasso for predictor selection and to assuage overfitting: A method long overlooked in behavioral sciences. Multivariate Behavioral Research, 50(5), 471–483. doi: 10.1080/00273171.2015.1036965.
   PubMed Web of Science ®Google Scholar
• Meredith, W., & Millsap, R. E. (1985). On component analyses. Psychometrika, 50(4), 495–507. doi: 10.1007/BF02296266.
   Web of Science ®Google Scholar
• Molenaar, P. C. M. (1985). A dynamic factor model for the analysis of multivariate time series. Psychometrika, 50(2), 181–202. doi: 10.1007/BF02294246.
   Web of Science ®Google Scholar
• Molenaar, P. C. M. (2004). A manifesto on psychology as idiographic science: Bringing the person back into scientific psychology, this time forever. Measurement: Interdisciplinary Research and Perspectives, 2(4), 201–218. doi: 10.1207/s15366359mea0204_1.
   Google Scholar
• Molenaar, P. C. M., & Campbell, C. G. (2009). The new person-specific paradigm in Psychology. Current Directions in Psychological Science, 18(2), 112–117. doi: 10.1111/j.1467-8721.2009.01619.x.
   Web of Science ®Google Scholar
• Molenaar, P. C. M., & Nesselroade, J. R. (2009). The recoverability of P-technique factor analysis. Multivariate Behavioral Research, 44(1), 130–141. doi: 10.1080/00273170802620204.
   PubMed Web of Science ®Google Scholar
• Morana, C. (2012). PC-VAR estimation of vector autoregressive models. Open Journal of Statistics, 2(3), 251–259. doi: 10.4236/ojs.2012.23030.
   Google Scholar
• Newman, M. E. J. (2010). Networks: An introduction. New York, NY: Oxford University Press.
   Google Scholar
• Opsahl, T., Agneessens, F., & Skvoretz, J. (2010). Node centrality in weighted networks: Generalizing degree and shortest paths. Social Networks, 32(3), 245–251. doi: 10.1016/j.socnet.2010.03.006.
   Web of Science ®Google Scholar
• Oud, J. H. L., & Delsing, M. J. M. H. (2010). Continuous time modeling of panel data by means of SEM. In K. van Montfort, J. H. L. Oud, & A. Satorra (Eds.), Longitudinal research with latent variables (pp. 201–244). New York, NY: Springer.
   Google Scholar
• Pe, M. L., Kircanski, K., Thompson, R. J., Bringmann, L. F., Tuerlinckx, F., Mestdagh, M., … Gotlib, I. H. (2015). Emotion-network density in major depressive disorder. Clinical Psychological Science, 3(2), 292–300. doi: 10.1177/2167702614540645.
   PubMed Web of Science ®Google Scholar
• Pearson, K. (1901). On lines and planes of closest fit to systems of points in space. Philosophical Magazine, 2(11), 559–572. doi: 10.1080/14786440109462720.
   Google Scholar
• Pfaff, B. (2008). VAR, SVAR and SVEC models: Implementation within R package vars. Journal of Statistical Software, 27(4), 1–32. doi: 10.18637/jss.v027.i04.
   Web of Science ®Google Scholar
• Rothman, A. J., Levina, E., & Zhu, J. (2010). Sparse multivariate regression with covariance estimation. Journal of Computational and Graphical Statistics, 19(4), 947–962. doi: 10.1198/jcgs.2010.09188.
   PubMed Web of Science ®Google Scholar
• Ryan, O., Kuiper, R. M., & Hamaker, E. L. (in press). A continuous time approach to intensive longitudinal data: What, why and how? In K. van Montfort, J. H. L. Oud, & M. C. Voelkle (Eds.), Continuous time modeling in the behavioral and related sciences. New York, NY: Springer.
   Google Scholar
• Saccenti, E., & Timmerman, M. E. (2017). Considering Horn’s parallel analysis from a random matrix theory point of view. Psychometrika, 82(1), 186–209. doi: 10.1007/s11336-016-9515-z.
   PubMed Web of Science ®Google Scholar
• Schmiedek, F., Lövdén, M., & Lindenberger, U. (2010). Hundred days of cognitive training enhance broad cognitive abilities in adulthood: Findings from the COGITO study. Frontiers in Aging Neuroscience, 2, 27. doi: 10.3389/fnagi.2010.00027.
   PubMed Web of Science ®Google Scholar
• Schuurman, N. K., Houtveen, J. H., & Hamaker, E. L. (2015). Incorporating measurement error in n = 1 psychological autoregressive modeling. Frontiers in Psychology, 6, 1038. doi: 10.3389/fpsyg.2015.01038.
   PubMed Web of Science ®Google Scholar
• Schwarz, G. (1978). Estimating the dimension of a model. The Annals of Statistics, 6(2), 461–464. doi: 10.1214/aos/1176344136.
   Web of Science ®Google Scholar
• Shmueli, G. (2010). To explain or to predict?. Statistical Science, 25(3), 289–310. doi: 10.1214/10-STS330.
   Web of Science ®Google Scholar
• Snijders, T. A. B. (1988). On cross-validation for predictor evaluation in time series. In T. K. Dijkstra (Ed.), On model uncertainty and its statistical implications (pp. 56–69). New York, NY: Springer-Verlag.
   Google Scholar
• Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society. Series B (Methodological), 58, 267–288. doi: 10.1111/j.1467-9868.2011.00771.x.
   Web of Science ®Google Scholar
• Tibshirani, R. (2011). Regression shrinkage and selection via the lasso: A retrospective. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 73(3), 273–282. doi: 10.1111/j.1467-9868.2011.00771.x.
   Web of Science ®Google Scholar
• Timmerman, M. E., & Kiers, H. A. L. (2003). Four simultaneous component models for the analysis of multivariate time series from more than one subject to model intraindividual and interindividual differences. Psychometrika, 68(1), 105–121. doi: 10.1007/BF02296656
   Web of Science ®Google Scholar
• Tsay, R. S. (2005). Analysis of financial time series. Hoboken, NJ: John Wiley & Sons.
   Google Scholar
• van de Leemput, I. A., Wichers, M., Cramer, A. O. J., Borsboom, D., Tuerlinckx, F., Kuppens, P., … Scheffer, M. (2014). Critical slowing down as early warning for the onset and termination of depression. Proceedings of the National Academy of Sciences of the United States of America, 111(1), 87–92. doi: 10.1073/pnas.1312114110.
   PubMed Web of Science ®Google Scholar
• van der Krieke, L., Emerencia, A. C., Bos, E. H., Rosmalen, J. G. M., Riese, H., Aiello, M., … de Jonge, P. (2015). Ecological momentary assessments and automated time series analysis to promote tailored health care: A proof-of-principle study. JMIR Research Protocols, 4(3), e100. doi: 10.2196/resprot.4000.
   PubMed Web of Science ®Google Scholar
• Van Deun, K., Smilde, A. K., van der Werf, M. J., Kiers, H. A. L., & Van Mechelen, I. (2009). A structured overview of simultaneous component based data integration. BMC Bioinformatics, 10(1), 246. doi: 10.1186/1471-2105-10-246
   PubMed Web of Science ®Google Scholar
• Velicer, W. F., & Jackson, D. N. (1990). Component analysis versus common factor analysis: Some issues in selecting an appropriate procedure. Multivariate Behavioral Research, 25(1), 97–28. doi: 10.1207/s15327906mbr2501_12.
   PubMed Web of Science ®Google Scholar
• Vervloet, M., Van Deun, K., Van den Noortgate, W., & Ceulemans, E. (2016). Model selection in principal covariates regression. Chemometrics and Intelligent Laboratory Systems, 151, 26–33. doi: 10.1016/j.chemolab.2015.12.004
   Web of Science ®Google Scholar
• Vitale, R., Westerhuis, J. A., Naes, T., Smilde, A. K., de Noord, O. E., & Ferrer, A. (2017). Selecting the number of factors in principal component analysis by permutation testing: Numerical and practical aspects. Journal of Chemometrics, 31(12), e2937. 1–15. doi: 10.1002/cem.2937.
   Web of Science ®Google Scholar
• Voelkle, M. C., Oud, J. H. L., Davidov, E., & Schmidt, P. (2012). An SEM approach to continuous time modeling of panel data: Relating authoritarianism and anomia. Psychological Methods, 17(3), 384–192. doi:0.1037/a0027543.
   Google Scholar
• Walls, T. A., & Schafer, J. L. (2006). Models for intensive longitudinal data. New York, NY: Oxford University Press.
   Google Scholar
• Watson, D., Clark, L. A., & Tellegen, A. (1988). Development and validation of brief measures of positive and negative affect: The PANAS scales. Journal of Personality and Social Psychology, 54(6), 1063–1070. doi: 10.1037/0022-3514.54.6.1063.
   PubMed Web of Science ®Google Scholar
• Wichers, M. (2014). The dynamic nature of depression: A new micro-level perspective of mental disorder that meets current challenges. Psychological Medicine, 44(07), 1349–1360. doi: 10.1017/S0033291713001979.
   PubMed Web of Science ®Google Scholar
• Wigman, J. T. W., van Os, J., Borsboom, D., Wardenaar, K. J., Epskamp, S., Klippel, A., … Wichers, M. (2015). Exploring the underlying structure of mental disorders: Cross-diagnostic differences and similarities from a network perspective using a both top-down and a bottom-up approach. Psychological Medicine, 45(11), 2375–2387. doi: 10.1017/S0033291715000331.
   PubMed Web of Science ®Google Scholar
• Wild, B., Eichler, M., Friederich, H.-C., Hartmann, M., Zipfel, S., & Herzog, W. (2010). A graphical vector autoregressive modelling approach to the analysis of electronic diary data. BMC Medical Research Methodology, 10(1), 28. doi: 10.1186/1471-2288-10-28.
   PubMed Web of Science ®Google Scholar
• Wilderjans, T. F., Ceulemans, E., & Meers, K. (2013). CHull: A generic convex hull based model selection method. Behavior Research Methods, 45(1), 1–15. doi: 10.3758/s13428-012-0238-5
   PubMed Web of Science ®Google Scholar
• Wilderjans, T. F., Vande Gaer, E., Kiers, H. A. L., Van Mechelen, I., & Ceulemans, E. (2017). Principal covariates clusterwise regression (PCCR): Accounting for multicollinearity and population heterogeneity in hierarchically organized data. Psychometrika, 82(1), 86–111. doi: 10.1007/s11336-016-9522-0.
   PubMed Web of Science ®Google Scholar
• Wolff, J. K., Brose, A., Lövdén, M., Tesch-Römer, C., Lindenberger, U., & Schmiedek, F. (2012). Health is health is health? Age differences in intraindividual variability and in within-person versus between-person factor structures of self-reported health complaints. Psychology and Aging, 27(4), 881–891. doi: 10.1037/a0029125
   PubMed Web of Science ®Google Scholar
• Zou, H., & Hastie, T. (2005). Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 67(2), 301–320. doi: 10.1111/j.1467-9868.2005.00503.x.
   Web of Science ®Google Scholar