O quê, para quê e como? Desenvolvendo instrumentos de aferição em epidemiologia
DOI:
https://doi.org/10.11606/s1518-8787.2021055002813Palabras clave:
Medidas em Epidemiologia, Confiabilidade dos Dados, Comparação Transcultural, Estudos de Validação como AssuntoResumen
Embora fundamental para a pesquisa epidemiológica, o desenvolvimento e a adaptação transcultural de instrumentos de aferição têm recebido menos destaque nas discussões metodológicas que permeiam o campo. Em paralelo, a qualidade das mensurações realizadas em muitos estudos epidemiológicos está frequentemente aquém do desejado para a construção de conhecimento sólido sobre o processo saúde-doença. A escassez de sistematizações sobre o que, para que e como aferir na área provavelmente contribui para esse cenário. Nesta revisão, propomos um modelo processual composto por uma sequência de etapas voltadas à mensuração de construtos em níveis aceitáveis de validade, confiabilidade e, por extensão, comparabilidade. Subjaz à proposta a ideia de que não apenas alguns, mas diversos estudos concatenados entre si e sucessivamente mais aprofundados devem ser conduzidos para obter aferições adequadas. A implementação do modelo poderá contribuir para alargar o interesse sobre instrumentos de aferição e, especialmente, para enfrentar os problemas investigados em epidemiologia.
Referencias
Rothman KJ, Greenland S, Lash TL. Modern epidemiology. 3. ed. mid-cycle rev ed. Philadelphia, PA: Lippincott Williams & Wilkins; 2012. 851 p. [ Links ]
Evans AS. Causation and disease: a chronological journey: the Thomas Parran lecture. Am J Epidemiol. 1978;108(4):249-58. https://doi.org/10.1093/oxfordjournals.aje.a112617 [ Links ]
Bastos JL, Reichenheim ME, Moraes CL. Measurement instruments for use in oral epidemiology. In: Peres MA, Antunes JL, Watt RG, editors. Oral epidemiology: a textbook on oral health conditions, research topics and method. New York: Springer; 2021. p. 465-77. (Textbooks in Contemporary Dentistry). [ Links ]
Hennekens CE, Buring JE. Epidemiology in medicine. Boston, MA: Lippincott Williams & Wilkins; 1987. 383 p. [ Links ]
Hernán M, Robins J. Causal Inference: what if. Boca Raton, FL: Chapman & Hall/CRC; 2020. [ Links ]
Reichenheim ME, Moraes CL. Qualidade dos instrumentos epidemiológicos. In: Almeida-Filho N, Barreto M, editores. Epidemiologia & saúde: fundamentos, métodos e aplicações. Rio de Janeiro: Guanabara-Koogan; 2011. p. 150-64. [ Links ]
Streiner DL, Norman GR, Cairney J. Health measurement scales: a practical guide to their development and use. 5. ed. Oxford: Oxford University Press; 2015. 399 p. [ Links ]
Bastos JL, Duquia RP, González-Chica DA, Mesa JM, Bonamigo RR. Field work I: selecting the instrument for data collection. An Bras Dermatol. 2014;89(6):918-23. https://doi.org/10.1590/abd1806-4841.20143884 [ Links ]
Berry JW, Poortinga YH, Segall MH, Dasen PR. Cross-cultural psychology: research and applications. New York: Cambridge University Press; 2002. [ Links ]
Herdman M, Fox-Rushby J, Badia X. “Equivalence” and the translation and adaptation of health-related quality of life questionnaires. Qual Life Res. 1997;6(3):237-47. https://doi.org/10.1023/a:1026410721664 [ Links ]
Reichenheim ME, Moraes CL. Operacionlalização de adaptação transcultural de instrumentos de aferição usados em epidemiologia. Rev Saude Publica. 2007;41(4):665-73. https://doi.org/10.1590/S0034-89102006005000035 [ Links ]
Reichenheim ME, Hökerberg YHM, Moraes CL. Assessing construct structural validity of epidemiological measurement tools: a seven-step roadmap. Cad Saude Publica. 2014;30(5):927-39. https://doi.org/10.1590/0102-311X00143613 [ Links ]
Wilson M. Constructing measures. an item response modeling approach. Mahwah, NJ: Lawrence Erlbaum Associates Publishers; 2005. 284 p. [ Links ]
Duckor BM, Draney K, Wilson M. Measuring measuring: toward a theory of proficiency with the constructing measures framework. J Appl Meas. 2009;10(3):296-319. [ Links ]
Mokkink LB, Terwee CB, Knol DL, Stratford PW, Alonso J, Patrick DL, et al. The COSMIN checklist for evaluating the methodological quality of studies on measurement properties: a clarification of its content. BMC Med Res Methodol. 2010;10:22. https://doi.org/10.1186/1471-2288-10-22 [ Links ]
Mokkink LB, Terwee CB, Patrick DL, Alonso J, Stratford PW, Knol DL, et al. The COSMIN study reached international consensus on taxonomy, terminology, and definitions of measurement properties for health-related patient-reported outcomes. J Clin Epidemiol. 2010;63(7):737-45. https://doi.org/10.1016/j.jclinepi.2010.02.006 [ Links ]
Brown TA. Confirmatory factor analysis for applied research. 2 ed. New York: The Guilford Press; 2015. 462 p. [ Links ]
Asparouhov T, Muthén B. Multiple-group factor analysis alignment. Struct Equ Modeling. 2014;21(4):495-508. https://doi.org/10.1080/10705511.2014.919210 [ Links ]
Kline RB. Principles and practice of structural equation modeling. 4. ed. London: The Guilford Press; 2015. [ Links ]
Van de Schoot R, Schmidt P, De Beuckelaer A. Measurement invariance. Lausanne: Front Media; 2015. 217 p. [ Links ]
Van der Linden WJ. Handbook of item response theory. Boca Raton, FL: Chapman and Hall/CRC; 2018. 1688 p. [ Links ]
Kolen MJ, Brennan RL. Test equating, scaling, and linking: Methods and practices. 3 ed. New York: Springer; 2014. 566 p. [ Links ]
González J, Wiberg M. Applying test equating methods using R. New York: Springer; 2017. 196 p. [ Links ]
Sansivieri V, Wiberg M, Matteucci M. A review of test equating methods with a special focus on IRT-based approaches. Statistica. 2017;77(4):329-52. https://doi.org/10.6092/issn.1973-2201/7066 [ Links ]
Zhao Y, Chan W, Lo BCY. Comparing five depression measures in depressed Chinese patients using item response theory: an examination of item properties, measurement precision and score comparability. Health Qual Life Outcomes. 2017;15(1):60. https://doi.org/10.1186/s12955-017-0631-y [ Links ]
Wahl I, Löwe B, Bjorner JB, Fischer F, Langs G, Voderholzer U, et al. Standardization of depression measurement: a common metric was developed for 11 self-report depression measures. J Clin Epidemiol. 2014;67(1):73-86. https://doi.org/10.1016/j.jclinepi.2013.04.019 [ Links ]
Bond TG, Fox CM. Applying the Rasch model: fundamental measurement in the human sciences. 2. ed. Hove (UK): Psychology Press; 2013. [ Links ]
Muthén BO. Appendix 11 - Estimation of factor scores. In: Mplus - satistical analysis with latent variables technical appendices. Los Angeles, CA: Muthén & Muthén; 1998-2004, p. 47-48. [ Links ]
Masyn KE. Latent class analysis and finite mixture modeling. In: Little TD, editor. The Oxford handbook of quantitative methods. Oxford: Oxford University Press; 2013. p. 551-611. [ Links ]
Davidov E, Schmidt P, Billiet J, Meuleman B, editors. Cross-cultural analysis: methods and applications. 2. ed. London: Routledge; 2018. 648 p. [ Links ]
Reichenheim ME, Interlenghi GS, Moraes CL, Segall-Correa AM, Pérez-Escamilla R, Salles-Costa R. A model-based approach to identify classes and respective cutoffs of the Brazilian Household Food Insecurity Measurement Scale. J Nutr. 2016;146(7):1356-64. https://doi.org/10.3945/jn.116.231845 [ Links ]
Interlenghi GS, Reichenheim ME, Segall-Correa AM, Perez-Escamilla R, Moraes CL, Salles-Costa R. Modeling optimal cutoffs for the Brazilian Household Food Insecurity Measurement Scale in a nationwide representative sample. J Nutr. 2017;147(7):1356-65. https://doi.org/10.3945/jn.117.249581 [ Links ]
De Vet HCW, Terwee CB, Mokkink LB, Knol DL. Measurement in medicine: a practical guide. Cambridge: Cambridge University Press; 2011. 338 p. [ Links ]
Cronbach LJ, Meehl PE. Construct validity in psychological tests. Psychol Bull. 1955;52(4):281-302. https://doi.org/10.1037/h0040957 [ Links ]
VanderWeele T. Explanation in causal inference: methods for mediation and interaction. New York: Oxford University Press USA; 2015. 728 p. [ Links ]
Denzin NK, Lincoln YS, editors. The SAGE handbook of qualitative research. Los Angeles: SAGE Publications; 2011. 766 p. [ Links ]
McDowell I, Newell C. Measuring health: a guide to rating scales and questionnaires. 3. ed. New York: Oxford University Press; 2006. 748 p. [ Links ]
Nunnally JCJ, Bernstein I. Psychometric theory. 2. ed. New York: McGraw-Hill; 1995. [ Links ]
Raykov T, Marcoulides GA. Introduction to psychometric theory. New York: Routledge; 2011. 352 p. [ Links ]
Price LR. Psychometric methods: theory into practice. New York: Guilford Press; 2016. 552 p. [ Links ]
Shavelson RJ, Webb NM. Generalizability theory: a primer. Newbury Park, CA: SAGE Publications; 1991. 137 p. [ Links ]
Beatty PC, Collins D, Kaye L, Padilla JL, Willis GB, Wilmot A, editors. Advances in questionnaire design, development, evaluation and testing. Hoboken, NJ: John Wiley & Sons; 2019. 816 p. [ Links ]
Moser CA, Kalton G. Survey methods in social investigation. 2. ed. London: Heinemann; 1985. [ Links ]
Johnson RL, Morgan GB. Survey scales: a guide to development, analysis, and reporting. New York: Guilford Publications; 2016. 269 p. [ Links ]
DeVellis RF. Scale development: theory and applications. Thousand Oaks, CA: SAGE Publications; 2003. 171 p. [ Links ]
Gorenstein C, Wang YP, Hungerbühler I, compiladores. Instrumentos de avaliação em saúde mental. Porto Alegre, RS: Artmed; 2016. 500 p. [ Links ]
Reise SP, Waller NG. Fitting the two-parameter model to personality data. Appl Psychol Meas. 1990;14:45-58. https://doi.org/10.1177/014662169001400105 [ Links ]
Marsh HW, Muthén B, Asparouhov A, Lüdtke O, Robitzsch A, Morin AJS, et al. Exploratory structural equation modeling, integrating CFA and EFA: application to students’ evaluations of university teaching. Struct Equ Modeling. 2009;16(3):439-76. https://doi.org/10.1080/10705510903008220 [ Links ]
Kim JO, Mueller CW. Factor analysis: statistical methods and practical issues. Beverly Hills, CA: SAGE Publications; 1978. 88 p. (Quantitative Applications in t Quantitative Applications in the Social Sciences; vol. 14). [ Links ]
Wang J, Wang X. Structural equation modeling: applications using Mplus. Chichester (UK): Wiley-Blackwell; 2012. 478 p. [ Links ]
Ford JK, MacCallum RC, Tait M. The application of factor analysis in applied psychology: a critical review and analysis. Pers Psychol. 1986;39(2):291-314. https://doi.org/10.1111/j.1744-6570.1986.tb00583.x [ Links ]
Kamata A, Bauer DJ. A note on the relation between factor analytic and item response theory models. Struct Equ Modeling. 2008;15(1):136-53. https://doi.org/10.1080/10705510701758406 [ Links ]
Reeve BB, Hays RD, Bjorner JB, Cook KF, Crane PK, Teresi JA, et al. Psychometric evaluation and calibration of health-related quality of life item banks: plans for the Patient-Reported Outcomes Measurement Information System (PROMIS). Med Care. 2007;45(5 Suppl 1):S22-31. https://doi.org/10.1097/01.mlr.0000250483.85507.04 [ Links ]
Chen WH, Thissen D. Local dependence indexes for item pairs using item response theory. J Educ Behav Stat. 1997;22(3):265-89. https://doi.org/10.2307/1165285 [ Links ]
Liu Y, Thissen D. Identifying local dependence with a score test statistic based on the bifactor logistic model. Appl Psychol Meas. 2012;36(8):670-88. https://doi.org/10.1177/0146621612458174 [ Links ]
Yen WM. Effects of local item dependence on the fit and equating performance of the three-parameter logistic model. Appl Psychol Meas. 1984;8(2):125-45. https://doi.org/10.1177/014662168400800201 [ Links ]
Ayala RJ. The theory and practice of item response theory. New York: The Guilford Press; 2009. 448 p. [ Links ]
Paek I, Cole K. Using R for item response theory model applications. London: Routledge; 2019. 271 p. [ Links ]
Podsakoff PM, MacKenzie SB, Lee JY, Podsakoff NP. Common method biases in behavioral research: a critical review of the literature and recommended remedies. J Appl Psychol. 2003;88(5):879-903. https://doi.org/10.1037/0021-9010.88.5.879 [ Links ]
Hair JF, Babin BJ, Anderson RE, Black WC. Multivariate data analysis. 7. ed. London: Cengage Learning EMEA; 2010. 832 p. [ Links ]
Little TD. Longitudinal structural equation modeling. New York: Guilford Press; 2013. 386 p. [ Links ]
Embretson SE, Reise SP. Item response theory for psychologists. Maheah, NJ: Lawrence Erlbaum Associates; 2000. 371 p. (Multivariate Appications Book Series; vol.4). [ Links ]
Hardouin JB, Bonnaud-Antignac A, Sebille V. Nonparametric item response theory using Stata. Stata J. 2011;11(1):30-51. https://doi.org/10.1177/1536867X1101100102 [ Links ]
Sijtsma K, Molenaar IW. Introduction to nonparametric item response theory. Thousand Oaks, CA: SAGE Publications; 2002. 176 p. (Measurement Methods for the Social Science; vol 5). [ Links ]
Sijtsma K, Molenaar IW. Mokken models. In: Van der Linden WJ, editor. Handbook of item response theory; vol 3: Applications. Boca Raton, FL: Chapman and Hall/CRC; 2018; p. 303-321. [ Links ]
Mokken RJ. A theory and procedure of scale analysis. Berlin: De Gruyter Mouton; 1971. 353 p. [ Links ]
Gorsuch RL. Factor analysis. 2. ed. Hillsdale, NJ: Lawrence Erlbaum Associates; 1983. 425 p. [ Links ]
Rummel RJ. Applied factor analysis. 4. ed. Evanston, Ill: Northwestern University Press; 1988. 617 p. [ Links ]
De Boeck P, Wilson M, editors. Explanatory item response models: a generalized linear anad nonlinear approach. New York: Springer; 2004. 382 p. [ Links ]
Lissitz RW, editor. The concept of validity: revisions, new directions and applications. Charlotte, NC: Information Age Pubishing; 2009. 263 p. [ Links ]
Armitage P, Berry G, Matthews JNS. Statistical methods in medical research. 4. ed. London: Blackwell Scientific Publications; 2001. 816 p. [ Links ]
Corder GW, Foreman DI. Nonparametric statistics: a step-by-step approach. 2.ed. Hoboken, NJ: John Wiley & Sons; 2014. 288 p. [ Links ]
Descargas
Publicado
Número
Sección
Licencia
Derechos de autor 2021 Michael Reichenheim, João Luiz Bastos
Esta obra está bajo una licencia internacional Creative Commons Atribución 4.0.
Cómo citar
Datos de los fondos
-
Conselho Nacional de Desenvolvimento Científico e Tecnológico
Números de la subvención 301381/2017-8 -
Conselho Nacional de Desenvolvimento Científico e Tecnológico
Números de la subvención 304503/2018-5
