Cap. 20 Referencias

Aguiar Neto, R. R. de. (2010). Estatistica basica aplicada a administracao judiciaria. Colecao Administracao Judiciaria.
Amarasingha, N., & Dissanayake, S. (2014). Gender differences of young drivers on injury severity outcome of highway crashes. Journal of Safety Research, 49, 113.e1–120. https://doi.org/10.1016/j.jsr.2014.03.004
Amatori, S., Zeppa, S. D., Preti, A., Gervasi, M., Gobbi, E., Ferrini, F., Rocchi, M. B. L., Baldari, C., Perroni, F., Piccoli, G., Stocchi, V., Sestili, P., & Sisti, D. (2020). Dietary habits and psychological states during COVID-19 home isolation in italian college students: The role of physical exercise. Nutrients, 12(12), 3660. https://doi.org/10.3390/nu12123660
Babbie, E. R. (1990). Survey research methods / earl babbie. (2nd ed.). Wadsworth Pub. Co.
Baird, D. (1983). The fisher/pearson chi-squared controversy: A turning point for inductive inference. The British Journal for the Philosophy of Science, 34(2), 105–118. https://doi.org/10.1093/bjps/34.2.105
Bandura, A., Ross, D., & Ross, S. A. (1961). Transmission of aggression through imitation of aggressive models. The Journal of Abnormal and Social Psychology, 63(3), 575–582. https://doi.org/10.1037/h0045925
Barker, L. E., & Shaw, K. M. (2015). Best (but oft-forgotten) practices: Checking assumptions concerning regression residuals. The American Journal of Clinical Nutrition, 102(3), 533–539. https://doi.org/10.3945/ajcn.115.113498
Battisti, I. D. E., & Silva Smolski, F. M. da. (2019). Software r: Analise estatistica de dados utilizando um programa livre. Faith.
Bergenholtz, C., MacAulay, S. C., Kolympiris, C., & Seim, I. (2018). Transparency on scientific instruments. EMBO Reports, 19(6). https://doi.org/10.15252/embr.201845853
Borgatta, E. F., & Bohrnstedt, G. W. (1980). Level of measurement. Sociological Methods & Research, 9(2), 147–160. https://doi.org/10.1177/004912418000900202
Bursztyn, L., González, A., & Yanagizawa-Drott, D. (2018). Misperceived social norms: Female labor force participation in saudi arabia. National Bureau of Economic Research. https://doi.org/10.3386/w24736
Campbell, I. (2007). Chi-squared and fisherirwin tests of two-by-two tables with small sample recommendations. Statistics in Medicine, 26(19), 3661–3675. https://doi.org/10.1002/sim.2832
Carvajal-Rodrı́guez, A., Uña-Alvarez, J. de, & Rolán-Alvarez, E. (2009). A new multitest correction (SGoF) that increases its statistical power when increasing the number of tests. BMC Bioinformatics, 10(1). https://doi.org/10.1186/1471-2105-10-209
Cassidy, S. A., Dimova, R., Giguère, B., Spence, J. R., & Stanley, D. J. (2019). Failing grade: 89% of introduction-to-psychology textbooks that define or explain statistical significance do so incorrectly. Advances in Methods and Practices in Psychological Science, 2(3), 233–239. https://doi.org/10.1177/2515245919858072
Chartier, S., & Faulkner, A. (2008). General linear models: An integrated approach to statistics. Tutorials in Quantitative Methods for Psychology, 4(2), 65–78. https://doi.org/10.20982/tqmp.04.2.p065
Cohen, B. (2013). Explaining psychological statistics. John Wiley & Sons.
Cohen, J. (1988). Statistical power analysis for the behavioral sciences. Routledge. https://doi.org/10.4324/9780203771587
Curley, K. (2013). Testing the assumptions of assumptions testing. Casualty Actuarial Society. https://www.casact.org/pubs/forum/13fforum/07-Curley.pdf
David, H. A. (1998). First (?) Occurrence of common terms in probability and statistics-a second list, with corrections. The American Statistician, 52(1), 36. https://doi.org/10.2307/2685564
DeMaris, A. (1995). A tutorial in logistic regression. Journal of Marriage and the Family, 57(4), 956. https://doi.org/10.2307/353415
Draper, S. (2020). In Effect size. https://www.psy.gla.ac.uk/~steve/best/effect.html
Dumsday, T. (2012). Laws of nature dontHaveCeteris paribus clauses, TheyAreCeteris paribus clauses. Ratio, 26(2), 134–147. https://doi.org/10.1111/rati.12000
Ernst, A. F., & Albers, C. J. (2017). Regression assumptions in clinical psychology research practicea systematic review of common misconceptions. PeerJ, 5, e3323. https://doi.org/10.7717/peerj.3323
Everitt, B. (2002). The cambridge dictionary of statistics. Cambridge University Press. http://www.worldcat.org/search?qt=worldcat_org_all&q=052181099X
Feigelson, E. D., & Babu, G. J. (Eds.). (1992). Statistical challenges in modern astronomy. Springer New York. https://doi.org/10.1007/978-1-4613-9290-3
Feil, E. G., Baggett, K., Davis, B., Landry, S., Sheeber, L., Leve, C., & Johnson, U. (2020). Randomized control trial of an internet-based parenting intervention for mothers of infants. Early Childhood Research Quarterly, 50, 36–44. https://doi.org/10.1016/j.ecresq.2018.11.003
Fennell, D. J. (2005). A philosophical analysis of causality in econometrics. [PhD thesis]. London School of Economics; Political Science.
Field, A. P., & Wilcox, R. R. (2017). Robust statistical methods: A primer for clinical psychology and experimental psychopathology researchers. Behaviour Research and Therapy, 98, 19–38. https://doi.org/10.1016/j.brat.2017.05.013
Fox, J. (2016). Applied regression analysis and generalized linear models (3th edition). SAGE.
Frigg, R., & Hartmann, S. (2020). Models in Science. In E. N. Zalta (Ed.), The Stanford encyclopedia of philosophy (Spring 2020). https://plato.stanford.edu/archives/spr2020/entries/models-science/; Metaphysics Research Lab, Stanford University.
Friis, R. H., & Selles, T. A. (2013). Epidemiology for public health practice (5nd ed.). Jones & Barlett Publishers.
Gaudio, A. C., & Zandonade, E. (2001). Proposicao, validacao e analise dos modelos que correlacionam estrutura quimica e atividade biologica. Quimica Nova, 24(5), 658–671. https://doi.org/10.1590/s0100-40422001000500013
Gil, A. C. (2002). Como elaborar de projetos de pesquisa. Editora Atlas S.A.
Glantz, S. A. (2014). Principios de bioestatistica (7 ed.). AMGH.
Goodman, S. N. (1999). Toward evidence-based medical statistics. 1: The p value fallacy. Annals of Internal Medicine, 130(12), 995. https://doi.org/10.7326/0003-4819-130-12-199906150-00008
Greenland, S. (2019). Valid p-values behave exactly as they should: Some misleading criticisms of p-values and their resolution with s-values. The American Statistician, 73(sup1), 106–114. https://doi.org/10.1080/00031305.2018.1529625
Greenwood, D. C., & Freeman, J. V. (2015). How to spot a statistical problem: Advice for a non-statistical reviewer. BMC Medicine, 13(1). https://doi.org/10.1186/s12916-015-0510-5
Gueorguieva, R., & Krystal, J. H. (2004). Move over ANOVA. Archives of General Psychiatry, 61(3), 310. https://doi.org/10.1001/archpsyc.61.3.310
Hayes, A. F. (n.d.). Introduction to mediation, moderation, and conditional process analysis : A regression-based approach. The Guilford Press.
Heidemann, L. A., Araujo, I. S., & Veit, E. A. (2016). Modelagem didático-cientı́fica: Integrando atividades experimentais e o pro-cesso de modelagem cientı́fica no ensino de fı́sica. Caderno Brasileiro de Ensino de Fı́sica, 33(1), 3. https://doi.org/10.5007/2175-7941.2016v33n1p3
Hirschman, D. (2016). Stylized facts in the social sciences. Sociological Science, 3, 604–626. https://doi.org/10.15195/v3.a26
Howell, D. C. (2011). Fundamental Statistics for the Behavioral Sciences. Wadsworth Cengage Learning.
Jenkins-Smith, H., Ripberger, J., Copeland, G., Nowlin, M., Hughes, T., Fister, A., & Wehde, W. (2017). Quantitative research methods for political science, public policy and public administration. self-published. https://doi.org/10.15763/11244/52244
Jones, C., & Levin, J. (1994). Primary/elementary teachers’ attitudes toward science in four areas related to gender differences in students’ science performance. Journal of Elementary Science Education, 6(1), 46–66. https://doi.org/10.1007/bf03170649
Junior, A. A., Almeida Portugal, A. C. de, Landeira-Fernandez, J., Bullón, F. F., Santos, E. J. R. dos, Vilhena, J. de, & Anunciação, L. (2020). Depression and anxiety symptoms in a representative sample of undergraduate students in spain, portugal, and brazil. Psicologia: Teoria e Pesquisa, 36. https://doi.org/10.1590/0102.3772e36412
Krus, D. J., & Krus, P. H. (1977). Lost: McCalls t scores: why? Educational and Psychological Measurement, 37(1), 257–261. https://doi.org/10.1177/001316447703700134
Lecoutre, B., & Poitevineau, J. (2014). The significance test controversy revisited. Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-662-44046-9
Lix, L. M., Keselman, J. C., & Keselman, H. J. (1996). Consequences of assumption violations revisited: A quantitative review of alternatives to the one-way analysis of variance "f" test. Review of Educational Research, 66(4), 579. https://doi.org/10.2307/1170654
Lumley, T., Diehr, P., Emerson, S., & Chen, L. (2002). The importance of the normality assumption in large public health data sets. Annual Review of Public Health, 23(1), 151–169. https://doi.org/10.1146/annurev.publhealth.23.100901.140546
Madsen, K. B. (1988). Chapter 4 classical experimental psychology. In Advances in psychology (pp. 109–164). Elsevier. https://doi.org/10.1016/s0166-4115(08)60594-4
Matthews, R. (2000). Storks deliver babies ( p = 0.008). Teaching Statistics, 22(2), 36–38. https://doi.org/10.1111/1467-9639.00013
Mayer, R. E. (2007). Old advice for new researchers. Educational Psychology Review, 20(1), 19–28. https://doi.org/10.1007/s10648-007-9061-4
Michell, J. (1993). The origins of the representational theory of measurement: Helmholtz, hölder, and russell. Studies in History and Philosophy of Science Part A, 24(2), 185–206. https://doi.org/10.1016/0039-3681(93)90045-l
Morettin, P. A., & Bussab, W. de O. (2010). Estatistica basica. Saraiva.
Motulsky, H. J. (2014). Common misconceptions about data analysis and statistics. Naunyn-Schmiedebergs Archives of Pharmacology, 387(11), 1017–1023. https://doi.org/10.1007/s00210-014-1037-6
Mujcic, R., & Frijters, P. (2020). The colour of a free ride. The Economic Journal. https://doi.org/10.1093/ej/ueaa090
Neal, J. W., & Neal, Z. (2021). Who are the childfree? https://doi.org/10.31234/osf.io/57bjr
Ocaña-Riola, R. (2016). The use of statistics in health sciences: Situation analysis and perspective. Statistics in Biosciences, 8(2), 204–219. https://doi.org/10.1007/s12561-015-9138-4
Patriota, A. G. (2016). On some assumptions of the null hypothesis statistical testing. Educational and Psychological Measurement, 77(3), 507–528. https://doi.org/10.1177/0013164416667979
Pearson, K. (1900). X. On the criterion that a given system of deviations from the probable in the case of a correlated system of variables is such that it can be reasonably supposed to have arisen from random sampling. The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science, 50(302), 157–175. https://doi.org/10.1080/14786440009463897
Perezgonzalez, J. D. (2015). Fisher, neyman-pearson or NHST? A tutorial for teaching data testing. Frontiers in Psychology, 6. https://doi.org/10.3389/fpsyg.2015.00223
Piff, P. K., Stancato, D. M., Cote, S., Mendoza-Denton, R., & Keltner, D. (2012). Higher social class predicts increased unethical behavior. Proceedings of the National Academy of Sciences, 109(11), 4086–4091. https://doi.org/10.1073/pnas.1118373109
Popper, K. R. (2009). The two fundamental problems of the theory of knowledge. Routledge.
Portugues, E. G. (2020). Lab notes for statistics for social sciences II: Multivariate techniques. In Statistics for Social Sciences II: Multivariate Techniques. https://bookdown.org/egarpor/SSS2-UC3M/
Privitera, G. J. (2016). Statistics for the behavioral sciences. SAGE.
Putnam, H. (1980). Models and reality. Journal of Symbolic Logic, 45(3), 464–482. https://doi.org/10.2307/2273415
Quené, H., & Bergh, H. van den. (2004). On multi-level modeling of data from repeated measures designs: A tutorial. Speech Communication, 43(1-2), 103–121. https://doi.org/10.1016/j.specom.2004.02.004
Rhodes, N., & Pivik, K. (2011). Age and gender differences in risky driving: The roles of positive affect and risk perception. Accident Analysis & Prevention, 43(3), 923–931. https://doi.org/10.1016/j.aap.2010.11.015
Rooney, B. J., & Evans, A. N. (2019). Methods in psychological research. SAGE Publications, Inc. https://doi.org/10.4135/9781506384955
Schoenbach, V. J. (2000). Chapter 7: Relating risk factors to health outcomes. In Understanding the fundamentals of epidemiology: An evolving text. Chapel Hill.
Skogli, E. W., Andersen, P. N., & Isaksen, J. (2020). An exploratory study of executive function development in children with autism, after receiving early intensive behavioral training. Developmental Neurorehabilitation, 23(7), 439–447. https://doi.org/10.1080/17518423.2020.1756499
Smart, J. C. (1999). Higher education: Handbook of theory and research. Springer.
Stangor, C. (2010). Introduction to psychology (1nd ed.). Flatworld Knowledge.
Stevens, S. S. (1946). On the theory of scales of measurement. Science, 103(2684), 677–680. https://doi.org/10.1126/science.103.2684.677
Stevens, S. S. (1959). Measurement, psychophysics, and utility. In P. Churchman C. W. & Ratoosh (Ed.), Measurement: Definitions and theories (pp. 18–63). Willey.
Sugianto, D. K. (2017). The moderating effect of age, income, gender, expertise, loyalty program, and critical incident on the influence of customer satisfaction towards customer loyalty in airline industry: A case of PT. x. iBuss Management, 5(1), 70–83.
Thorndike, E. L. (1914). Educational psychology, vol 3: Mental work and fatigue and individual differences and their causes. Teachers College. https://doi.org/10.1037/13796-000
Trafimow, D. (2019). A frequentist alternative to significance testing, p-values, and confidence intervals. Econometrics, 7(2), 26. https://doi.org/10.3390/econometrics7020026
Unger, S. H., & Hansch, C. (1973). Model building in structure-activity relations. Reexamination of adrenergic blocking activity of .beta.-halo-.beta.-arylalkylamines. Journal of Medicinal Chemistry, 16(7), 745–749. https://doi.org/10.1021/jm00265a001
Velleman, P. F., & Wilkinson, L. (1993). Nominal, ordinal, interval, and ratio typologies are misleading. The American Statistician, 47(1), 65–72. https://doi.org/10.1080/00031305.1993.10475938
Wagner, M. B., & Callegari-Jacques, S. M. (1998). Measures of association in epidemiological studies: Relative risk and odds ratio. Journal of Pediatrics, 74(3), 247–251.
Wasserstein, R. L., & Lazar, N. A. (2016). The ASA statement on p-values: Context, process, and purpose. The American Statistician, 70(2), 129–133. https://doi.org/10.1080/00031305.2016.1154108
Weisberg, M. (2013). Three kinds of models. In Simulation and similarity (pp. 7–23). Oxford University Press. https://doi.org/10.1093/acprof:oso/9780199933662.003.0002
Wenke, R. J., Mickan, S., & Bisset, L. (2017). A cross sectional observational study of research activity of allied health teams: Is there a link with self-reported success, motivators and barriers to undertaking research? BMC Health Services Research, 17(1). https://doi.org/10.1186/s12913-017-1996-7
Worley, H. (2006). Road traffic accidents increase dramatically worldwide. https://www.prb.org/roadtrafficaccidentsincreasedramaticallyworldwide/
Wu, H., & Leung, S.-O. (2017). Can likert scales be treated as interval scales?a simulation study. Journal of Social Service Research, 43(4), 527–532. https://doi.org/10.1080/01488376.2017.1329775
Xie, Y. (2015). Dynamic documents with R and knitr (2nd ed.). Chapman; Hall/CRC. http://yihui.name/knitr/
Yap, B. W., & Sim, C. H. (2011). Comparisons of various types of normality tests. Journal of Statistical Computation and Simulation, 81(12), 2141–2155. https://doi.org/10.1080/00949655.2010.520163