Allison, P. D. (2009). Fixed effects regression models. *Quantitative Applications in the Social Sciences* (160 vol.). SAGE

Allison, P. D., Williams, R., & Moral-Benito, E. (2017). Maximum Likelihood for Cross-lagged Panel Models with Fixed Effects. Socius: Sociological Research for a Dynamic World, 3, 1–17. https://doi.org/10.1177/2378023117710578

Arellano, M., & Bond, S. (1991). Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations. *The Review of Economic Studies*, *58*(2), 277. https://doi.org/10.2307/2297968

Article
Google Scholar

Bailey, D. H., Oh, Y., Farkas, G., Morgan, P., & Hillemeier, M. (2020). Reciprocal effects of reading and mathematics? Beyond the cross-lagged panel model. *Developmental Psychology*, *56*(5), 912–921. https://doi.org/10.1037/dev0000902

Article
Google Scholar

Bell, A., & Jones, K. (2015). Explaining Fixed Effects: Random Effects Modeling of Time-Series Cross-Sectional and Panel Data. *Political Science Research and Methods*, *3*(01), 133–153. https://doi.org/10.1017/psrm.2014.7

Article
Google Scholar

Blossfeld, H. P., & Roßbach, H. G. (Eds.). (2019). *Education as a lifelong process: The German National Educational Panel Study (NEPS). Edition ZfE* (2nd ed.). Springer VS

Boker, S. M., Neale, M. C., Maes, H. H., Wilde, M. J., Spiegel, M., Brick, T. R., Estabrook, R., Bates, T. C., Mehta, P., von Oertzen, T., Gore, R. J., Hunter, M. D., Hackett, D. C., Karch, J., Brandmaier, A. M., Pritikin, J. N., Zahery, M., Kirkpatrick, R. M., Wang, Y., & Niesen, J. (2021). *OpenMx: Extended Structural Equation Modelling* (2.19.8) [Computer software]. https://CRAN.R-project.org/package=OpenMx

Bollen, K. A. (1989). *Structural equations with latent variables. A Wiley-interscience publication*. New York: Wiley

Google Scholar

Bollen, K. A., & Brand, J. E. (2011). A General Panel Model with Random and Fixed Effects: A Structural Equations Approach. *Social Forces*, *89*(1), 1–34. https://doi.org/10.1353/sof.2010.0072

Article
Google Scholar

Bollen, K. A., & Curran, P. J. (2004). Autoregressive latent trajectory (ALT) models: A synthesis of two traditions. *Sociological Methods & Research*, *32*, 336–383. https://doi.org/10.1177/0049124103260222

Cameron, C. E., Kim, H., Duncan, R. J., Becker, D. R., & McClelland, M. M. (2019). Bidirectional and co-developing associations of cognitive, mathematics, and literacy skills during kindergarten. *Journal of Applied Developmental Psychology*, *62*, 135–144. https://doi.org/10.1016/j.appdev.2019.02.004

Article
Google Scholar

Carroll, J. B. (1993). *Human Cognitive Abilities: A Survey of Factor-Analytic Studies*. Cambridge University Press

Cattell, R. B. (1987). *Intelligence: Its Structure, Growth and Action*. Elsevier

Chen, F., & Chalhoub-Deville, M. (2016). Differential and long-term language impact on math. *Language Testing*, *33*(4), 577–605. https://doi.org/10.1177/0265532215594641

Article
Google Scholar

Cirino, P. T., Child, A. E., & Macdonald, K. T. (2018). Longitudinal predictors of the overlap between reading and math skills. *Contemporary Educational Psychology*, *54*, 99–111. https://doi.org/10.1016/j.cedpsych.2018.06.002

Article
Google Scholar

Codding, R. S., Petscher, Y., & Truckenmiller, A. (2015). CBM reading, mathematics, and written expression at the secondary level: Examining latent composite relations among indices and unique predictions with a state achievement test. *Journal of Educational Psychology*, *107*(2), 437–450. https://doi.org/10.1037/a0037520

Article
Google Scholar

Collins, L. M., Schafer, J. L., & Kam, C. M. (2001). A comparison of inclusive and restrictive strategies in modern missing data procedures. *Psychological Methods*, *6*(4), 330–351. https://doi.org/10.1037/1082-989x.6.4.330

Article
Google Scholar

Curran, P. J., & Bollen, K. A. (2001). The best of both worlds: Combining autoregressive and latent curve models. In L. M. Collins & A. G. Sayer (Eds.), *New methods for the analysis of change* (pp. 107–135). American Psychological Association. https://doi.org/10.1037/10409-004

Curran, P. J., Howard, A. L., Bainter, S. A., Lane, S. T., & McGinley, J. S. (2014). The separation of between-person and within-person components of individual change over time: A latent curve model with structured residuals. *Journal of Consulting and Clinical Psychology*, *82*, 879–894. https://doi.org/10.1037/a0035297

Article
Google Scholar

Davis, O., Band, G., Pirinen, M., et al. (2014). The correlation between reading and mathematics ability at age twelve has a substantial genetic component. *Nature Communications*, *5*, 4204. https://doi.org/10.1038/ncomms5204

Article
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. https://doi.org/10.18637/jss.v077.i05

Article
Google Scholar

Driver, C., Voelkle, M., & Oud, H. (2021). *ctsemOMX: Continuous Time SEM - „OpenMx“ Based Functions* (1.0.4) [Computer software]. https://CRAN.R-project.org/package=ctsemOMX

Duncan, G. J., Dowsett, C. J., Claessens, A., Magnuson, K., Huston, A. C., Klebanov, P., Pagani, L. S., Feinstein, L., Engel, M., Brooks-Gunn, J., Sexton, H., Duckworth, K., & Japel, C. (2007). School Readiness and Later Achievement. *Developmental Psychology*, *43*(6), 1428–1446. https://doi.org/10.1037/0012-1649.43.6.1428

Article
Google Scholar

Erbeli, F., Shi, Q., Campbell, A. R., Hart, S. A., & Woltering, S. (2021). Developmental dynamics between reading and math in elementary school. *Developmental Science*, *24*(1), e13004. https://doi.org/10.1111/desc.13004

Article
Google Scholar

Fuchs, L. S., Schumacher, R. F., Long, J., Namkung, J., Hamlett, C. L., Jordan, N. C., Siegler, R., Gersten, R., Changas, P., & Cirino, P. T. (2013). Improving at-risk learners’ understanding of fractions. *Journal of Educational Psychology*, *105*(3), 683–700. https://doi.org/10.1037/a0032446

Article
Google Scholar

Gehrer, K., Zimmermann, S., Artelt, C., & Weinert, S. (2013). NEPS framework for assessing reading competence and results from an adult pilot study. *Journal for Educational Research Online*, *5*(2), 50–79

Google Scholar

Gnambs, T., & Lockl, K. (2022). Bidirectional effects between reading and mathematics development across secondary school. *Zeitschrift für Erziehungswissenschaft*. https://doi.org/10.1007

Grimm, K. J. (2008). Longitudinal Associations Between Reading and Mathematics Achievement. *Developmental Neuropsychology*, *33*(3), 410–426. https://doi.org/10.1080/87565640801982486

Article
Google Scholar

Halaby, C. N. (2004). Panel Models in Sociological Research: Theory into Practice. *Annual Review of Sociology*, *30*(1), 507–544. https://doi.org/10.1146/annurev.soc.30.012703.110629

Article
Google Scholar

Hamagami, F., & McArdle, J. J. (2001). Advanced studies of individual differences: Linear dynamic models for longitudinal data analysis. In G. A. Marcoulides, & R. E. Schumacker (Eds.), *New developments and techniques in structural equation modeling* (pp. 203–246). Psychology Press. https://doi.org/10.4324/9781410601858

Hamaker, E. L., Kuiper, R. M., & Grasman, R. P. P. P. (2015). A critique of the cross-lagged panel model. *Psychological Methods*, *20*(1), 102–116. https://doi.org/10.1037/a0038889

Article
Google Scholar

Hecht, M., Hardt, K., Driver, C. C., & Voelkle, M. C. (2019). Bayesian continuous-time Rasch models. *Psychological Methods*, *24*, 516–537. https://doi.org/10.1037/met0000205

Article
Google Scholar

Hecht, M., Horstmann, K. T., Arnold, M., Sherman, R. A., & Voelkle, M. (2022). Modeling dynamic personality theories in a continuous-time framework: An illustration [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/q97pz

Hecht, M., & Voelkle, M. C. (2021). Continuous-time modeling in prevention research: An illustration. *International Journal of Behavioral Development*, *45*(1), 19–27. https://doi.org/10.1177/0165025419885026

Article
Google Scholar

Hecht, M., & Zitzmann, S. (2020). A computationally more efficient Bayesian approach for estimating continuous-time models. *Structural Equation Modeling: A Multidisciplinary Journal*, *27*, 829–840. https://doi.org/10.1080/10705511.2020.1719107

Article
Google Scholar

Hecht, M., & Zitzmann, S. (2021). Exploring the unfolding of dynamic effects with continuous-time models: Recommendations concerning statistical power to detect peak cross-lagged effects. *Structural Equation Modeling: A Multidisciplinary Journal*, 1–9. https://doi.org/10.1080/10705511.2021.1914627

Hecht, S. A., Torgesen, J. K., Wagner, R. K., & Rashotte, C. A. (2001). The relations between phonological processing abilities and emerging individual differences in mathematical computation skills: A longitudinal study from second to fifth grades. *Journal of Experimental Child Psychology*, *79*(2), 192–227. https://doi.org/10.1006/jecp.2000.2586

Article
Google Scholar

Heeringa, S., West, B. T., & Berglund, P. A. (2010). Applied survey data analysis. *Chapman & Hall / CRC statistics in the social and behavioral sciences series*. Taylor & Francis. http://www.gbv.eblib.com/patron/FullRecord.aspx?p=555702

Holenstein, M., Bruckmaier, G., & Grob, A. (2020). Transfer effects of mathematical literacy: an integrative longitudinal study. *European Journal of Psychology of Education*, 1–27. https://doi.org/10.1007/s10212-020-00491-4

Horn, J. (1988). Thinking about Human Abilities. In J. R. Nesselroade & R. B. Cattell (Hrsg.), Handbook of Multivariate Experimental Psychology (pp. 645–685). Springer US. https://doi.org/10.1007/978-1-4613-0893-5_19

Hübner, N., Merrell, C., Cramman, H., Little, J., Bolden, D., & Nagengast, B. (2022). Reading to learn? The co-development of mathematics and reading during primary school. *Child Development*, *00*, 1–17. https://doi.org/10.1111/cdev.13817

Article
Google Scholar

Jordan, N. C., Kaplan, D., & Hanich, L. B. (2002). Achievement growth in children with learning difficulties in mathematics: findings of a two-year longitudinal study. *Journal of Educational Psychology*, *94*(3), 586–597. https://doi.org/10.1037//0022-0663.94.3.586

Article
Google Scholar

Kenny, D. A., & Zautra, A. (1995). The trait-state-error model for multiwave data. *Journal of Consulting and Clinical Psychology*, *63*, 52–59. https://doi.org/10.1037/0022-160

Article
Google Scholar

Kenny, D. A., & Zautra, A. (2001). Trait-state models for longitudinal data. In L. M. Collins, & A. G. Sayer (Eds.), *New methods for the analysis of change* (pp. 243–263). American Psychological Association

Koponen, T., Eklund, K., Heikkilä, R., Salminen, J., Fuchs, L., Fuchs, D., & Aro, M. (2020). Cognitive Correlates of the Covariance in Reading and Arithmetic Fluency: Importance of Serial Retrieval Fluency. *Child Development*, *91*(4), 1063–1080. https://doi.org/10.1111/cdev.13287

Article
Google Scholar

Korpipää, H., Koponen, T., Aro, M., Tolvanen, A., Aunola, K., Poikkeus, A. M., Lerkkanen, M. K., & Nurmi, J. E. (2017). Covariation between reading and arithmetic skills from Grade 1 to Grade 7. *Contemporary Educational Psychology*, *51*, 131–140. https://doi.org/10.1016/j.cedpsych.2017.06.005

Article
Google Scholar

Kutscher, T., & Scharl, A. (2020). NEPS Technical Report for Reading: Scaling Results of Starting Cohort 3 for Grade 12. NEPS Survey Papers, volume 67. Leibniz Institute for Educational Trajectories, National Educational Panel Study, Bamberg, Germany

LeFevre, J. A., Fast, L., Skwarchuk, S. L., Smith-Chant, B. L., Bisanz, J., Kamawar, D., & Penner-Wilger, M. (2010). Pathways to Mathematics: Longitudinal Predictors of Performance. *Child Development*, *81*(6), 1753–1767. https://doi.org/10.1111/j.1467-8624.2010.01508.x

Article
Google Scholar

Lohmann, J. F., Zitzmann, S., Voelkle, M. C., & Hecht, M. (2022). A primer on continuous-time modeling in educational research: An exemplary application of a continuous-time latent curve model with structured residuals (CT-LCM-SR) to PISA data. *Large-Scale Assessments in Education*, *10*, 1–32. https://doi.org/10.1186/s40536-022-00126-8

Article
Google Scholar

Lüdtke, O., & Robitzsch, A. (2021). A critique of the random intercept cross-lagged panel model. https://doi.org/10.31234/osf.io/6f85c

Lucas, R. E. (2022, February 14). It’s Time To Abandon The Cross-Lagged Panel Model. https://doi.org/10.31234/osf.io/pkec7

McArdle, J. J., & Hamagami, F. (2001). Latent difference score structural models for linear dynamic analyses with incomplete longitudinal data. In L. M. Collins, & A. G. Sayer (Eds.), *New methods for the analysis of change* (pp. 137–175). American Psychological Association

Moral-Benito, E. (2013). Likelihood-based estimation of dynamic panels with predetermined regressors. *Journal of Business and Economic Statistics*, *31*(4), 451–472. https://doi.org/10.1080/07350015.2013.818003

Article
Google Scholar

Muthén, L. K., & Muthén, B. O. (2017). *Mplus User’s Guide: 8th Edition (Version 8, April 2017)*. Los Angeles, CA: Muthen & Muthen. https://www.statmodel.com/download/MplusUserGuideVer_8.pdf

Google Scholar

Neumann, I., Duchhardt, C., Grüßing, M., Heinze, A., Knopp, E., & Ehmke, T. (2013). Modeling and assessing mathematical competence over the lifespan. *Journal for Educational Research Online*, *5*(2), 80–109

Google Scholar

Orth, U., Clark, D. A., Donnellan, M. B., & Robins, R. W. (2021). Testing prospective effects in longitudinal research: Comparing seven competing cross-lagged models. *Journal of Personality and Social Psychology*, *120*, 1013–1034. https://doi.org/10.1037/pspp0000358

Article
Google Scholar

Oud, J. H. L., & Delsing, M. J. M. H. (2010). Continuous Time Modeling of Panel Data by means of SEM. In K. Montfort, J. H. L. Oud, & A. Satorra (Eds.), *Longitudinal Research with Latent Variables* (pp. 201–244). Springer

Oud, J. H. L., & Voelkle, M. C. (2014). Do missing values exist? Incomplete data handling in cross-national longitudinal studies by means of continuous time modeling. *Quality & Quantity*, *48*, 3271–3288. https://doi.org/10.1007/s11135-013-9955-9

Article
Google Scholar

Petersen, L. A., Litteck, K., & Rohenroth, D. (2020). NEPS Technical Report for Mathematics: Scaling Results of Starting Cohort 3 for Grade 12. NEPS Survey Paper, Volume 75. Leibniz Institute for Educational Trajectories, National Educational Panel Study, Bamberg, Germany

Purpura, D. J., Logan, J. A. R., Hassinger-Das, B., & Napoli, A. R. (2017). Why do early mathematics skills predict later reading? The role of mathematical language. *Developmental Psychology*, *53*(9), 1633–1642. https://psycnet.apa.org/buy/2017-32731-001

Article
Google Scholar

R Core Team. (2021). *R: A language and environment for statistical computing*. R Foundation for Statistical Computing. https://www.R-project.org

Rinne, L. F., Ye, A., & Jordan, N. C. (2020). Development of arithmetic fluency: A direct effect of reading fluency? *Journal of Educational Psychology*, *112*(1), 110–130. https://doi.org/10.1037/edu0000362

Article
Google Scholar

Ryan, O., Kuiper, R. M., & Hamaker, E. L. (2018). 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 (pp. 27–57). Springer International Publishing. https://doi.org/10.1007/978-3-319-77219-6

Schmitt, S. A., Geldhof, G. J., Purpura, D. J., Duncan, R., & McClelland, M. M. (2017). Examining the relations between executive function, math, and literacy during the transition to kindergarten: A multi-analytic approach. *Journal of Educational Psychology*, *109*(8), 1120–1140. https://doi.org/10.1037/edu0000193

Article
Google Scholar

Skopek, J. S., Pink, & Bela, D. (2012). *Data Manual. Starting Cohort 3 – From Lower to Upper Secondary School. NEPS SC3 1.0.0. NEPS Research Data Paper*. University of Bamberg

Solon, G., Haider, S. J., & Wooldridge, J. M. (2015). What Are We Weighting For? *The Journal of Human Resources*, *50*(2), 301–316. http://www.jstor.org/stable/24735988

Article
Google Scholar

Sonnega, A., Faul, J. D., Ofstedal, M. B., Langa, K. M., Phillips, J. W. R., & Weir, D. R. (2014). Cohort Profile: the Health and Retirement Study (HRS). *International Journal of Epidemiology*, *43*(2), 576–585. https://doi.org/10.1093/ije/dyu067

Article
Google Scholar

Steptoe, A., Breeze, E., Banks, J., & Nazroo, J. (2013). Cohort Profile: The English Longitudinal Study of Ageing. *International Journal of Epidemiology*, *42*, 1640–1648. https://doi.org/10.1093/ije/dys168

Article
Google Scholar

StataCorp. (2019). *Stata Statistical Software: Release 16. College Station*. TX: StataCorp LLC

Google Scholar

Tourangeau, K., Nord, C., Le, T., Wallner-Allen, K., Vaden-Kiernan, N., Blaker, L., & Najarian, M. (2018). *User’s manual for the ECLS-K: 2011 kindergarten-third grade data file and electronic codebook, public version*. Washington, DC: National Center for Education Statistics

Google Scholar

Usami, S., Murayama, K., & Hamaker, E. L. (2019). A unified framework of longitudinal models to examine reciprocal relations. *Psychological Methods, 24, 637–657*. https://doi.org/10.1037/met0000210

Usami, S. (2021). On the Differences between General Cross-Lagged Panel Model and Random-Intercept Cross-Lagged Panel Model: Interpretation of Cross-Lagged Parameters and Model Choice. *Structural Equation Modeling: A Multidisciplinary Journal*, *28*(3), 331–344. DOI: https://doi.org/10.1080/10705511.2020.1821690

Article
Google Scholar

Vanbinst, K., van Bergen, E., Ghesquière, P., & De Smedt, B. (2020). Cross-domain associations of key cognitive correlates of early reading and early arithmetic in 5-year-olds. *Early Childhood Research Quarterly*, *51*, 144–152. https://doi.org/10.1016/j.ecresq.2019.10.009

Article
Google Scholar

Voelkle, M. C., Gische, C., Driver, C. C., & Lindenberger, U. (2018). The Role of Time in the Quest for Understanding Psychological Mechanisms. *Multivariate Behavioral Research*, *53*(6), 782–805. https://doi.org/10.1080/00273171.2018.1496813

Article
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*(2), 176–192. https://doi.org/10.1037/a0027543

Article
Google Scholar

Voelkle, M. C., & Oud, J. H. L. (2015). Relating latent change score and continuous time models. *Structural Equation Modeling: A Multidisciplinary Journal*, *22*, 366–381. https://doi.org/10.1080/10705511.2014.935918

Article
Google Scholar

Vukovic, R. K., & Lesaux, N. K. (2013). The language of mathematics: Investigating the ways language counts for children’s mathematical development. *Journal of Experimental Child Psychology*, *115*(2), 227–244. https://doi.org/10.1016/j.jecp.2013.02.002

Article
Google Scholar

Wooldridge, J. M. (2010). *Econometric Analysis of Cross Section and Panel Data* (2nd ed.). The MIT Press

Zyphur, M. J., Allison, P. D., Tay, L., Voelkle, M. C., Preacher, K. J., Zhang, Z., Hamaker, E. L., Shamsollahi, A., Pierides, D. C., Koval, P., & Diener, E. (2020). From Data to Causes I: Building A General Cross-Lagged Panel Model (GCLM). *Organizational Research Methods*, *23*(4), 651–687

Article
Google Scholar