University of Groningen
Correlation, causation, and dynamics Bhushan, Nitin
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10.33612/diss.126588820
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Bhushan, N. (2020). Correlation, causation, and dynamics: Methodological innovations in sustainable energy behaviour research. University of Groningen. https://doi.org/10.33612/diss.126588820
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References
Abrahamse, W., & Steg, L. (2011). Factors related to household en-ergy use and intention to reduce it: The role of psychological and socio-demographic variables. Human Ecolo Review, 18(1), 30–40.
Abrahamse, W., Steg, L., Vlek, C. A., & Rothengatter, T. (2005). A review of intervention studies aimed at household energy conservation. Journal of environmental psycholo , 25(3), 273–291. doi: 10.1016/j.jenvp.2005.08 .002
Albers, C. J. (2015). Dutch research funding, gender bias, and Simpson’s paradox. Proceedings of the National Academy of Scienc , 112(50). doi: 10.1073/pnas.1518936112
Allcott, H., & Mullainathan, S. (2010). Behavior and energy policy. Science, 327(5970), 1204–1205. doi: 10.1126/science.1180775
Azzalini, A., & Valle, A. D. (1996). The multivariate skew-normal distribu-tion. Biometrika, 83(4), 715-726. doi: 10.1093/biomet/83.4.715
Bamberg, S., Rees, J., & Seebauer, S. (2015). Collective climate ac-tion: Determinants of participation intention in community-based
proenvironmental initiatives. Journal of Environmental Psycholo , 43, 155 -165. doi: 10.1016/j.jenvp.2015.06.006
Behrens, J. T. (1997). Principles and procedures of exploratory data analy-sis. Psychological Methods, 2(2), 131–160. doi: 10.1037/1082-989X.2.2.131
Bhushan, N., Mohnert, F., Sloot, D., Jans, L., Albers, C., & Steg, L. (2019). Using a Gaussian graphical model to explore relationships between items and variables in environmental psychology research. Frontiers in Psycholo , 10, 1050. doi: 10.3389/fpsyg.2019.01050
Bhushan, N., Steg, L., & Albers, C. (2018). Studying the effects of interven-tion programmes on household energy saving behaviours using graphical causal models. Ener Research & Social Science, 45, 75 - 80. (Special Is-sue on the Problems of Methods in Climate and Energy Research) doi: 10.1016/j.erss.2018.07.027
Bollen, K. A. (2002). Latent variables in psychology and the social sciences. Annual review of psycholo , 53(1), 605–634. doi: 10.1146/annurev.psych.53 .100901.135239
Borsboom, D., & Cramer, A. O. (2013). Network analysis: an integrative approach to the structure of psychopathology. Annual review of clinical psycholo , 9, 91–121. doi: 10.1146/annurev-clinpsy-050212-185608
Brewer, M. B. (1991). The social self: On being the same and different at the same time. Personality and Social Psycholo Bulletin, 17(5), 475-482. doi: 10.1177/0146167291175001
Buurkracht. (2018). Tips om direct energiewinst halen. Retrieved from
https://buurkracht.nl/jouw-voordeel (Accessed: 21 August, 2018)
Carey, T. A., & Stiles, W. B. (2016). Some problems with randomized controlled trials and some viable alternatives. Clinical psycholo & psy-chotherapy, 23(1), 87–95. doi: 10.1002/cpp.1942
Chatfield, C. (1985). The initial examination of data. Journal of the Royal Statistical Society. Seri A (General), 148(3), 214–253.
Clayton, S., Devine-Wright, P., Stern, P. C., Whitmarsh, L., Carrico, A., Steg, L., … Bonnes, M. (2015). Psychological research and global climate change. Nature Climate Change, 5(7), 640. doi: 10.1038/NCLIMATE2622
Cole, S. R., Platt, R. W., Schisterman, E. F., Chu, H., Westreich, D., Richardson, D., & Poole, C. (2010). Illustrating bias due to condition-ing on a collider. International journal of epidemiolo , 39(2), 417–420. doi: 10.1093/ije/dyp334
Cook, T. D., Campbell, D. T., & Shadish, W. (2002). Experimental and quasi-experimental designs for generalized causal inference. Houghton Mifflin Boston.
Cramer, A. O. J., Sluis, S., Noordhof, A., Wichers, M., Geschwind, N., Aggen, S. H., … Borsboom, D. (2012). Dimensions of normal person-ality as networks in search of equilibrium: You can’t like parties if you don’t like people. European Journal of Personality, 26 (4), 414–431. doi: 10.1002/per.1866
Crutzen, R., & Peters, G.-J. Y. (2017). Scale quality: alpha is an inadequate estimate and factor-analytic evidence is needed first of all. Health Psycholo Review, 11(3), 242-247. doi: 10.1080/17437199.2015.1124240
Dawid, A. P. (1980). Conditional independence for statistical operations. The Annals of Statistics, 8(3), 598–617. doi: 10.1214/aos/1176345011 Dawid, A. P. (2010). Seeing and doing: The Pearlian synthesis. In R. Dechter, H. Geffner, & J. Y. Halpern (Eds.), Heuristics, probability and causality: A tribute to Judea Pearl (pp. 309–325). London: College Publi-cations.
Deaton, A., & Cartwright, N. (2016). Understanding and misunderstand-ing randomized controlled trials (Workmisunderstand-ing Paper No. 22595). National Bureau of Economic Research. doi: http://dx.doi.org/10.3386/w22595
de Groot, J. I. M., & Steg, L. (2008). Value orientations to explain beliefs related to environmental significant behavior: How to measure egoistic, altruistic, and biospheric value orientations. Environment and Behavior, 40(3), 330-354. doi: 10.1177/0013916506297831
de Jongh, M., & Druzdzel, M. J. (2009). A comparison of structural distance measures for causal Bayesian network models. In M. Klopotek, A. Przepiorkowski, S. T. Wierzchon, & K. Trojanowski (Eds.), Recent ad-vanc in intelligent information systems, challenging problems of science, computer science seri (pp. 443 – 456). Warsaw, Poland: Academic Publish-ing House EXIT.
Dempster, A. P. (1972). Covariance selection. Biometrics, 157–175. doi: 10.2307/2528966
Denholm, P., O’Connell, M., Brinkman, G., & Jorgenson, J. (2015). Over-generation from solar ener in California. A field guide to the duck chart (Tech. Rep.). Golden, CO (United States): National Renewable Energy Lab.(NREL).
Dodge, Y., & Rousson, V. (2001). On asymmetric properties of the corre-lation coefficient in the regression setting. The American Statistician, 55(1), 51–54. doi: 10.1198/000313001300339932
Dong, Y., & Peng, C.-Y. J. (2013). Principled missing data methods for researchers. SpringerPl , 2(1), 222. doi: 10.1186/2193-1801-2-222
Eberhardt, F. (2016). Introduction to the foundations of causal discovery. International Journal of Data Science and Analytics, 3(2), 1–11. doi: 10 .1007/s41060-016-0038-6
Eftekharnejad, S., Vittal, V., Heydt, G. T., Keel, B., & Loehr, J. (2013). Impact of increased penetration of photovoltaic generation on power systems. IEEE Transactions on Power Systems, 28(2), 893-901. doi: 10.1109/TPWRS.2012.2216294
Elwert, F. (2013). Handbook of causal analys for social research (S. L. Mor-gan, Ed.). Dordrecht: Springer Netherlands.
Elwert, F., & Winship, C. (2014). Endogenous selection bias: The problem of conditioning on a collider variable. Annual Review of Sociolo , 40, 31– 53. doi: 10.1146/annurev-soc-071913-043455
Enders, C. K. (2001). A primer on maximum likelihood algorithms avail-able for use with missing data. Structural Equation Modeling: A Multidis-ciplinary Journal, 8(1), 128-141. doi: 10.1207/S15328007SEM0801\_7 Enders, C. K., & Bandalos, D. L. (2001). The relative performance of full information maximum likelihood estimation for missing data in structural equation models. Structural Equation Modeling: A Multidisciplinary Journal, 8(3), 430-457. doi: 10.1207/S15328007SEM0803\_5
Epskamp, S., Borsboom, D., & Fried, E. I. (2018). Estimating psychological networks and their accuracy: A tutorial paper. Behavior Research Methods, 50(1), 195–212. doi: 10.3758/s13428-017-0862-1
Epskamp, S., Cramer, A. O. J., Waldorp, L. J., Schmittmann, V. D., & Bors-boom, D. (2012). qgraph: Network visualizations of relationships in psy-chometric data. Journal of Statistical Software, 48(4), 1–18. Retrieved from
http://www.jstatsoft.org/v48/i04/ doi: 10.18637/jss.v048.i04
Fisher, R. A. (1937). The design of experiments. Oliver And Boyd; Edin-burgh; London.
Foygel, R., & Drton, M. (2010). Extended Bayesian information criteria for Gaussian graphical models. In Proceedings of the 23rd International
Conference on Neural Information Processing Systems - Volume 1 (pp. 604– 612). USA: Curran Associates Inc.
Frederiks, E. R., Stenner, K., Hobman, E. V., & Fischle, M. (2016). Evalu-ating energy behavior change programs using randomized controlled trials: Best practice guidelines for policymakers. Ener Research & Social Science, 22, 147–164. doi: 10.1016/j.erss.2016.08.020
Friedman, J., Hastie, T., & Tibshirani, R. (2008). Sparse inverse covariance estimation with the graphical lasso. Biostatistics, 9(3), 432–441. doi: 10 .1093/biostatistics/kxm045
Friedman, J., Hastie, T., & Tibshirani, R. (2014). glasso: Graphical lasso-estimation of Gaussian graphical models [Computer software manual]. Retrieved fromhttps://CRAN.R-project.org/package=glasso (R
package version 1.8)
Garant, D., & Jensen, D. D. (2016). Evaluating causal models by compar-ing interventional distributions. CoRR, abs/1608.04698. Retrieved from
http://arxiv.org/abs/1608.04698
Gautier, A., Hoet, B., Jacqmin, J., & Van Driessche, S. (2019). Self-consumption choice of residential pv owners under net-metering. Ener policy, 128, 648–653. doi: 10.1016/j.enpol.2019.01.055
Greenland, S., Pearl, J., & Robins, J. M. (1999). Causal diagrams for epidemiologic research. Epidemiolo , 10(1), 37–48. doi: 10.1097/ 00001648-199901000-00008
Haavelmo, T. (1943). The statistical implications of a system of simultane-ous equations. Econometrica, 11(1), 1–12. doi: 10.2307/1905714
Hastie, T., & Tibshirani, R. (1986). Generalized additive models. Statistical Science, 1(3), 297–310. doi: 10.1214/ss/1177013604
Heckman, J. J. (2006). Rejoinder: Response to Sobel. Sociological Method-olo , 35(1), 135-150. doi: 10.1111/j.0081-1750.2006.00166.x
Heinze-Deml, C., Maathuis, M. H., & Meinshausen, N. (2018). Causal structure learning. Annual Review of Statistics and Its Application, 5(1), 371-391. doi: 10.1146/annurev-statistics-031017-100630
Hernán, M. A., & Taubman, S. L. (2008). Does obesity shorten life? The importance of well-defined interventions to answer causal questions. Inter-national journal of obesity, 32, 8–14. doi: 10.1038/ijo.2008.82
Hornsey, M. J., & Jetten, J. (2004). The individual within the group: Balancing the need to belong with the need to be different. Personality and Social Psycholo Review, 8(3), 248–264. doi: 10.1207/s15327957pspr0803\ _2
Hyvärinen, A., & Smith, S. M. (2013). Pairwise likelihood ratios for esti-mation of non-Gaussian structural equation models. Journal of Machine Learning Research, 14(1), 111–152.
IPCC. (2014). Climate Change 2014–Impacts, Adaptation and Vulnera-bility: Regional Aspects. Cambridge, United Kingdom and New York, NY, USA: Cambridge University Press.
Jans, L., Postmes, T., & Van der Zee, K. I. (2011). The induction of shared identity: The positive role of individual distinctiveness for groups. Personality and Social Psycholo Bulletin, 37(8), 1130–1141. doi: 10.1177/0146167211407342
Jones, P. J., Mair, P., & McNally, R. J. (2018). Visualizing psychological networks: A tutorial in R. Frontiers in Psycholo , 9, 1742. doi: 10.3389/ fpsyg.2018.01742
Kalisch, M., & Bühlmann, P. (2007). Estimating high-dimensional di-rected acyclic graphs with the PC-algorithm. Journal of Machine Learning Research, 8(1), 613–636.
Kalisch, M., Mächler, M., Colombo, D., Maathuis, M. H., & Bühlmann, P. (2012). Causal inference using graphical models with the R package pcalg. Journal of Statistical Software, 47(11), 1–26. doi: 10.18637/jss.v047.i11 Karlin, B., Zinger, J. F., & Ford, R. (2015). The effects of feedback on energy conservation: A meta-analysis. Psychological Bulletin, 141(6), 1205-1227. doi: 10.1037/a0039650
Keirstead, J. (2007). Behavioural responses to photovoltaic systems in the UK domestic sector. Ener Policy, 35(8), 4128–4141. doi: 10.1016/ j.enpol.2007.02.019
Kievit, R. A., Frankenhuis, W. E., Waldorp, L. J., & Borsboom, D. (2013). Simpson’s paradox in psychological science: a practical guide. Frontiers in Psycholo , 4, 513. doi: 10.3389/fpsyg.2013.00513
Klaassen, E., Frunt, J., & Slootweg, J. (2015). Assessing the impact of distributed energy resources on LV grids using practical measurements. In 23rd Interanational Conference on Electricity Distribution (CIRED), 15-18 June 2015, Lyon, France (pp. 1289–1).
Kobus, C. B., Mugge, R., & Schoormans, J. P. (2013). Washing when the sun is shining! How users interact with a household energy management system. Ergonomics, 56 (3), 451-462. doi: 10.1080/00140139.2012.721522
Koller, D., & Friedman, N. (2009). Probabilistic graphical models: Prin-cipl and techniqu - Adaptive computation and machine learning. Cam-bridge, Massachusetts: The MIT Press.
Lauritzen, S. (2001). Causal inference from graphical models. In O. E. Barndorff-Nielsen, D. R. Cox, & C. Klüppelberg (Eds.), Complex stochastic systems (pp. 63–107). London/Boca Raton: Chapman and Hal-l/CRC Press.
Lauritzen, S. L. (1996). Graphical models (Vol. 17). Oxford: Clarendon Press.
Leach, C. W., Van Zomeren, M., Zebel, S., Vliek, M. L., Pennekamp, S. F., Doosje, B., … Spears, R. (2008). Group-level definition and self-investment: a hierarchical (multicomponent) model of in-group identifi-cation. Journal of Personality and Social Psycholo , 95(1), 144–165. doi: 10.1037/0022-3514.95.1.144
Lilienfeld, S. O., McKay, D., & Hollon, S. D. (2018). Why randomised controlled trials of psychological treatments are still essential. The Lancet Psychiatry, 5(7), 536–538. doi: 10.1016/S2215-0366(18)30045-2
Luthander, R., Widén, J., Nilsson, D., & Palm, J. (2015). Photovoltaic self-consumption in buildings: A review. Applied ener , 142, 80–94. doi: 10.1016/j.apenergy.2014.12.028
Lynn, M., & Snyder, C. R. (2002). Uniqueness seeking. In C. R. Snyder & S. J. Lopez (Eds.), Handbook of positive psycholo (pp. 395–410). New York, NY, US: Oxford University Press.
Masson, T., Jugert, P., & Fritsche, I. (2016). Collective self-fulfilling prophecies: group identification biases perceptions of environmental group norms among high identifiers. Social Influence, 11(3), 185-198. doi: 10.1080/15534510.2016.1216890
Meehl, P. E. (1990). Why summaries of research on psychological theories are often uninterpretable. Psychological reports, 66 (1), 195–244. doi: 10 .2466/PR0.66.1.195-244
Meinshausen, N., & Bühlmann, P. (2010). Stability selection. Journal of the Royal Statistical Society: Seri B (statistical methodolo ), 72(4), 417-473. doi: 10.1111/j.1467-9868.2010.00740.x
Nicholls, L., & Strengers, Y. (2015). Peak demand and the ‘family peak’ period in Australia: Understanding practice (in) flexibility in households
with children. Ener Research & Social Science, 9, 116–124. doi: 10.1016/ j.erss.2015.08.018
Nichols, A. L., & Webster, G. D. (2013). The single-item need to belong scale. Personality and Individual Differenc , 55(2), 189 - 192. doi: 10.1016/ j.paid.2013.02.018
Norwegian Centre for Research Data. (2016a). European social survey round 8 data file edition 2.1. Data Archive and distributor of ESS data for ESS ERIC.
Norwegian Centre for Research Data. (2016b). European social survey round 8 module on public attitud to climate change. Data Archive and distributor of ESS data for ESS ERIC.
Oberst, C. A., Schmitz, H., & Madlener, R. (2019). Are prosumer households that much different? Evidence from stated residential energy consumption in Germany. Ecological Economics, 158, 101–115. doi: 10.1016/j.ecolecon.2018.12.014
Pearl, J. (2009). Causality: Models, reasoning and inference (2nd ed.). New York, NY, USA: Cambridge University Press.
Pearl, J. (2014). Comment: Understanding Simpson’s paradox. The American Statistician, 68(1), 8-13. doi: 10.1080/00031305.2014.876829 Peters, A. M., van der Werff, E., & Steg, L. (2019). Mind the gap: The implications of not acting in line with your planned actions after installing
solar photovoltaics. Frontiers in Psycholo , 10, 1423. doi: 10.3389/fpsyg .2019.01423
Peters, J. (2015). Sid: Structural intervention distance [Computer software manual]. Retrieved fromhttps://CRAN.R-project.org/package=SID
(R package version 1.0)
Peters, J., & Bühlmann, P. (2015). Structural intervention distance for evaluating causal graphs. Neural Computation, 27(3), 771–799. doi: 10 .1162/NECO\_a\_00708
Postmes, T., Baray, G., Haslam, S. A., Morton, T. A., & Swaab, R. I. (2006). The dynamics of personal and social identity formation. In T. Postmes & J. Jetten (Eds.), Individuality and the group : Advanc in social identity (p. 215-236). Thousand Oaks, CA, US: Sage Publications, Inc. doi: 10.4135/9781446211946.n12
Postmes, T., Haslam, S. A., & Jans, L. (2013). A single-item measure of social identification: reliability, validity, and utility. British journal of social psycholo , 52(4), 597–617. doi: 10.1111/bjso.12006
Postmes, T., Haslam, S. A., & Swaab, R. I. (2005). Social influence in small groups: An interactive model of social identity formation. European review of social psycholo , 16 (01), 1–42. doi: 10.1080/10463280440000062 R Core Team. (2017). R: A Language and Environment for Statistical Computing. Vienna, Austria. Retrieved fromhttps://www.R-project .org/
Revelle, W. (2018). psych: Procedures for psychological, psychometric, and personality research [Computer software manual]. Evanston, Illinois. Retrieved fromhttps://CRAN.R-project.org/package=psych (R
package version 1.8.12)
RStudio Team. (2017). RStudio: Integrated Development Environment for R [Computer software manual]. Boston, MA. Retrieved fromhttp:// www.rstudio.com/
Rücker, G., & Schwarzer, G. (2014, 12). Presenting simulation results in a nested loop plot. BMC Medical Research Methodolo , 14(1), 129. doi: 10.1186/1471-2288-14-129
Schafer, J. L. (1997). Analys of incomplete multivariate data. London: Chapman and Hall/CRC.
Schafer, J. L., & Graham, J. W. (2002). Missing data: our view of the state of the art. Psychological methods, 7(2), 147. doi: 10.1037/1082-989X.7.2.147
Schick, L., & Gad, C. (2015). Flexible and inflexible energy engagements— A study of the Danish smart grid strategy. Ener Research & Social Sci-ence, 9, 51–59. doi: 10.1016/j.erss.2015.08.013
Schill, W.-P., Zerrahn, A., & Kunz, F. (2017). Prosumage of solar electric-ity: pros, cons, and the system perspective. DIW Berlin Discussion Paper No. 1637. doi: 10.2139/ssrn.2912814
Schmitt, N. (1996). Uses and abuses of coefficient alpha. Psychological assessment, 8(4), 350. doi: 10.1037/1040-3590.8.4.350
Shadish, W. R., Clark, M. H., & Steiner, P. M. (2008). Can nonrandomized experiments yield accurate answers? A randomized experiment comparing random and nonrandom assignments. Journal of the American Statistical Association, 103(484), 1334-1344. doi: 10.1198/016214508000000733 Shimizu, S., Hoyer, P. O., Hyvarinen, A., & Kerminen, A. (2006). A linear non-Gaussian acyclic model for causal discovery. Journal of Machine Learning Research, 7, 2003–2030.
Shimizu, S., Inazumi, T., Sogawa, Y., Hyvärinen, A., Kawahara, Y., Washio, T., … Bollen, K. (2011). Directlingam: A direct method for learning a linear non-Gaussian structural equation model. Journal of Machine Learning Research, 12(2), 1225–1248.
Shrier, I., & Platt, R. W. (2008). Reducing bias through directed acyclic graphs. BMC medical research methodolo , 8(1), 70. doi: 10.1186/1471 -2288-8-70
Sloot, D., Jans, L., & Steg, L. (2017). The potential of environmental com-munity initiatives - a social psychological perspective. In A. K. Römpke, G. Reese, I. Fritsche, N. Wiersbinski, & A. W. Mues (Eds.), Outlooks on applying environmental psycholo research (pp. 27–34). Bonn: Federal Agency for Nature Conservation. doi: 10.19217/skr460
Sloot, D., Jans, L., & Steg, L. (2018). Can community energy initiatives motivate sustainable energy behaviours? The role of initiative involvement
and personal pro-environmental motivation. Journal of Environmental Psycholo , 57, 99 - 106. doi: 10.1016/j.jenvp.2018.06.007
Sommerfeld, J., Buys, L., & Vine, D. (2017). Residential consumers’ expe-riences in the adoption and use of solar PV. Ener Policy, 105, 10-16. doi: 10.1016/j.enpol.2017.02.021
Sóskuthy, M. (2017). Generalised additive mixed models for dynamic analysis in linguistics: a practical introduction. arXiv e-prints.
Sovacool, B. K. (2014). What are we doing here? Analyzing fifteen years of energy scholarship and proposing a social science research agenda. Ener Research & Social Science, 1, 1 - 29. doi: 10.1016/j.erss.2014.02.003
Spirtes, P., Glymour, C. N., & Scheines, R. (2000). Causation, prediction, and search. Cambridge, MA, USA: MIT press.
Spirtes, P., Meek, C., & Richardson, T. (1995). Causal inference in the presence of latent variables and selection bias. In P. Besnard & S. Hanks (Eds.), Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence (pp. 499–506). San Francisco, CA, USA: Morgan Kaufmann Publishers Inc.
Spirtes, P., & Zhang, K. (2016). Causal discovery and inference: concepts and recent methodological advances. Applied Informatics, 3(1), 3. doi: 10.1186/s40535-016-0018-x
Steg, L., Dreijerink, L., & Abrahamse, W. (2005). Factors influencing the acceptability of energy policies: A test of VBN theory. Journal of Environ-mental Psycholo , 25(4), 415–425. doi: 10.1016/j.jenvp.2005.08.003 Steg, L., Perlaviciute, G., & van der Werff, E. (2015). Understanding the human dimensions of a sustainable energy transition. Frontiers in Psychol-o , 6 , 805. dPsychol-oi: 10.3389/fpsyg.2015.00805
Steg, L., Perlaviciute, G., van der Werff, E., & Lurvink, J. (2014). The significance of hedonic values for environmentally relevant attitudes, pref-erences, and actions. Environment and Behavior, 46 (2), 163-192. doi: 10.1177/0013916512454730
Steiner, P. M., Kim, Y., Hall, C. E., & Su, D. (2017). Graphical models for quasi-experimental designs. Sociological Methods & Research, 46 (2), 155-188. doi: 10.1177/0049124115582272
Stern, P. C. (2000). New environmental theories: toward a coherent theory of environmentally significant behavior. Journal of Social Issu , 56 (3), 407–424. doi: 10.1111/0022-4537.00175
Tajfel, H., & Turner, J. C. (2001). An integrative theory of intergroup con-flict. In M. A. Hogg & D. Abrams (Eds.), Key readings in social psycholo . intergroup relations: Essential readings (p. 94-109). New York, NY, US: Psychology Press.
Textor, J., van der Zander, B., Gilthorpe, M. S., Liśkiewicz, M., & Ellison, G. T. (2016). Robust causal inference using directed acyclic graphs: the R
package “dagitty”. International journal of epidemiolo , 45(6), 1887–1894. doi: 10.1093/ije/dyw341
Tiefenbeck, V., Staake, T., Roth, K., & Sachs, O. (2013). For better or for worse? Empirical evidence of moral licensing in a behavioral energy conservation campaign. Ener Policy, 57, 160–171. doi: 10.1016/j.enpol .2013.01.021
Tukey, J. W. (1977). Exploratory data analys (Vol. 2). Reading, Mass.: Addison-Wesley.
Turner, J. C. (1991). Mapping social psycholo seri . social influence. Belmont, CA, US: Thomson Brooks/Cole Publishing Co.
van Rij, J., Wieling, M., Baayen, R. H., & van Rijn, H. (2017). itsadug: Interpreting time seri and autocorrelated data using GAMMs. (R package version 2.3)
van Borkulo, C., Boschloo, L., Kossakowski, J., Tio, P., Schoevers, R., Bors-boom, D., & Boschloo, L. (2017). Comparing network structur on three aspects. Working Paper. doi: 10.13140/RG.2.2.29455.38569
Vandenbroucke, J. P. (2008, 03). Observational research, randomised trials, and two views of medical science. PLOS Medicine, 5(3), 1-5. doi: 10.1371/journal.pmed.0050067
Van der Werff, E., Steg, L., & Keizer, K. (2013). The value of envi-ronmental self-identity: The relationship between biospheric values,
environmental self-identity and environmental preferences, intentions and behaviour. Journal of Environmental Psycholo , 34, 55–63. doi: 10.1016/j.jenvp.2012.12.006
Van Zomeren, M., Spears, R., Fischer, A. H., & Leach, C. W. (2004). Put your money where your mouth is! Explaining collective action tendencies through group-based anger and group efficacy. Journal of Personality and Social Psycholo , 87(5), 649–664. doi: 10.1037/0022-3514.87.5.649
Vine, E., Sullivan, M., Lutzenhiser, L., Blumstein, C., & Miller, B. (2014). Experimentation and the evaluation of energy efficiency programs. Ener Efficiency, 7(4), 627–640. doi: 10.1007/s12053-013-9244-4
West, S. G. (2009). Alternatives to randomized experiments. Current Directions in Psychological Science, 18(5), 299-304. doi: 10.1111/j.1467-8721 .2009.01656.x
Whittaker, J. (2009). Graphical models in applied multivariate statistics. Chichester, England: Wiley Publishing.
Wieling, M. (2018). Analyzing dynamic phonetic data using generalized additive mixed modeling: a tutorial focusing on articulatory differences between L1 and L2 speakers of English. Journal of Phonetics, 70, 86–116. doi: 10.1016/j.wocn.2018.03.002
Wittenberg, I., & Matthies, E. (2016). Solar policy and practice in germany: How do residential households with solar panels use electricity? Ener Research & Social Science, 21, 199–211. doi: 10.1016/j.erss.2016.07.008
Wood, S. (2017). Generalized Additive Models: An Introduction with R, Second Edition. Boca Raton, Florida, USA: Chapman & Hall/CRC Press. doi: 10.1201/9781315370279
Wood, S., Pya, N., & Säfken, B. (2016). Smoothing parameter and model selection for general smooth models. Journal of the American Statistical Association, 111(516), 1548–1563. doi: 10.1080/01621459.2016.1180986 Wright, S. (1934). The method of path coefficients. The Annals of Mathe-matical Statistics, 5(3), 161–215. doi: 10.1214/aoms/1177732676
Zhang, J. (2008, 11). On the completeness of orientation rules for causal discovery in the presence of latent confounders and selection bias. Artificial Intelligence, 172(16-17), 1873–1896. doi: 10.1016/j.artint.2008.08.001
