Diseño experimental

Evaluación | Biopsicología | Comparativo | Cognitivo | Del desarrollo | Idioma | Diferencias individuales | Personalidad | Filosofía | Social | Métodos | Estadística | Clínico | Educativo | Industrial | Artículos profesionales | Psicología mundial | Estadística: Método científico · Métodos de búsqueda · Diseño experimental · cursos de pregrado de estadistica · Pruebas estadísticas · Teoría de juego · Decision theory This article is in need of attention from a psychologist/academic expert on the subject. Por favor, ayude a reclutar uno, o mejore esta página usted mismo si está calificado. This banner appears on articles that are weak and whose contents should be approached with academic caution. The first statistician to consider a methodology for the design of experiments was Sir Ronald A. Pescador. He described how to test the hypothesis that a certain lady could distinguish by flavor alone whether the milk or the tea was first placed in the cup. While this sounds like a frivolous application, it allowed him to illustrate the most important means of experimental design: Randomization - The process of making something random Replication - repeating the creation of a phenomenon, so that the variability associated with the phenomenon can be estimated Blocking - the arranging of experimental units in groups (blocks) which are similar to one another Orthogonality - Means perpendicular, at right angles or statistically normal. Use of factorial experiments instead of the one-factor-at-a-time method Analysis of the design of experiments was built on the foundation of the analysis of variance, a collection of models in which the observed variance is partitioned into components due to different factors which are estimated and/or tested. Some efficient designs for estimating several main effects simultaneously were found by Raj Chandra Bose and K. Kishen in 1940 at the Indian Statistical Institute, but remained little known until the Plackett-Burman designs were published in Biometrika in 1946. In 1950, Gertrude Mary Cox and William Cochran published the book Experimental Design which became the major reference work on the design of experiments for statisticians for years afterwards. Developments of the theory of linear models have encompassed and surpassed the cases that concerned early writers. Hoy, the theory rests on advanced topics in abstract algebra and combinatorics. As with all other branches of statistics, there is both classical and Bayesian experimental design. Contents 1 Ejemplo 2 Types of design 2.1 Experimental 2.2 Nonexperimental 2.3 Descriptive 3 Ordering of conditions 4 Important considerations 5 Other terms 6 Ver también 7 Referencias 8 External links Example This example is attributed to Harold Hotelling in [1]. Although very simple, it conveys at least some of the flavor of the subject. The weights of eight objects are to be measured using a pan balance that measures the difference between the weight of the objects in the two pans. Each measurement has a random error. The average error is zero; the standard deviations of the probability distribution of the errors is the same number σ on different weighings; and errors on different weighings are independent. Denote the true weights by We consider two different experiments: Weigh each object in one pan, with the other pan empty. Call the measured weight of the ith object Xi for i = 1, ..., 8. Do the eight weighings according to the following schedule and let Yi be the measured difference for i = 1, ..., 8: Then the estimated value of the weight θ1 is The question of design of experiments is: which experiment is betterThe variance of the estimate X1 of θ1 es σ2 if we use the first experiment. But if we use the second experiment, the variance of the estimate given above is σ2/8. Thus the second experiment gives us 8 times as much precision. Many problems of the design of experiments involve combinatorial designs, as in this example. Types of design Some of the most popular designs are sorted below, with the ones at the top being the most powerful at reducing observer-expectancy effect but also most expensive, and in some cases introducing ethical concerns. The ones at the bottom are the most affordable, and are frequently used earlier in the research cycle, to develop strong hypotheses worth testing with the more expensive research approaches. Experimental Randomized controlled trial Double-blind Single-blind Non-blind Nonrandomized controlled trial Randomized database studies Nonexperimental Cohort study Prospective studies Retrospective studies Nested cohort Time-trend study Case-cohort study Case-control study (case series) Nested case-control study Cross-sectional study Descriptive Community survey Ordering of conditions An important aspect of some experiment designs is the ordering of different experimental conditions. A-B-C-D Experimental design Important considerations When choosing a study design, many factors must be taken into account. Different types of studies are subject to different types of bias. Por ejemplo, recall bias is likely to occur in cross-sectional or case-control studies where subjects are asked to recall exposure to risk factors. Subjects with the relevant condition (p. ej.. breast cancer) may be more likely to recall the relevant exposures that they had undergone (p. ej.. hormone replacement therapy) than subjects who don't have the condition. The ecological fallacy may occur when analyses are done on ecological (group-based) data rather than individual data. The nature of this type of analysis tends to overestimate the degree of association between variables. Other terms A "retrospective study" looks at past behavior, while a "prospective study" looks at future behavior. "Superiority trials" are designed to demonstrate that one treatment is more effective than another. "Non-inferiority trials" are designed to demonstrate that a treatment is at least not appreciably worse than another. "Equivalence trials" are designed to demonstrate that one treatment is as effective as another. When using "parallel groups", each patient receives one treatment; en un "crossover study", each patient receives several treatments. A longitudinal study studies a few subjects for a long period of time, while a cross-sectional study involves many subjects measured at once. See also Animal models Between groups design Case study in psychology Conjoint measurement Clinical trial Cohort analysis Debriefing (experimental) Design of experiments Epidemiological methods Experimental control Experimental methods Experiment volunteers Experimentation Followup studies Hypothesis testing Meta-analysis Population Psychometrics Repeated measures Research setting Sampling (experimental) Statistical analysis Statistical variables Test construction ReferencesHerman Chernoff, Sequential Analysis and Optimal Design, SIAM Monograph, 1972. External links Epidemiologic.org Epidemiologic Inquiry online weblog for epidemiology researchers Epidemiology Forum An epidemiology discussion and forum community to foster debates and collaborations in epidemiology Some aspects of study design Tufts University web site [1] Truman State University Political Science Research Design Handbook Description of how to design experiments Articles on Design of Experiments Czitrom (1999) "One-Factor-at-a-Time Versus Designed Experiments", American Statistician, 53, 2. SAS Examples for Experimental Design Please copy and paste this prompt to other appropriate areas. Feel free to edit as necessary Instructions_for_archiving_academic_and_professional_materials Research design: Academic support materials Research design: Académico: Lecture slides Research design: Académico: Lecture notes Research design: Académico: Lecture handouts Research design: Académico: Multimedia materials Research design: Académico: Other academic support materials v·d·e Research methods: Study designs / Design of experiments Overview Clinical trial· Clinical trial protocol· Clinical trial management· Academic clinical trials· Study design Controlled study (EBM I to II-1; A to B) Randomized controlled trial (Blind experiment, Open-label trial) Observational study (EBM II-2 to II-3; B to C) Cross-sectional study vs. Longitudinal study Cohort study (Retrospective cohort study, Prospective cohort study) Case-control study (Nested case-control study) Case series· Case study/Case report Epidemiology/ methods occurrence: Incidencia (Cumulative incidence) · Prevalencia (Point prevalence, Period prevalence) association: absoluto (Absolute risk reduction, Riesgo atribuible, Attributable risk percent) · pariente (Relative risk, Odds ratio, Hazard ratio) otro:Mortality rate· Morbidity· Case fatality· Specificity and sensitivity· Likelihood-ratio test Trial/test types In vitro/In vivo· Animal testing· Animal testing on non-human primates· First-in-man study· Multicenter trial· Seeding trial Analysis of clinical trials Risk-benefit analysis Interpretation of results Selection bias· Correlation does not imply causation· Null result Category• Glosario• List of topics v·d·e Design of experiments Scientific Method Scientific experiment Statistical design Control Internal & external validity Experimental unit Blinding Optimal design: Bayesian Random assignment Randomization Restricted randomization Replication versus subsampling Sample size Treatment & Blocking Treatment Effect size Contrast Interaction Confounding Orthogonality Blocking Covariate Nuisance variable Models & Inference Linear regression Ordinary least squares Bayesian Random effect Mixed model Hierarchical model: Bayesian Analysis of variance (Anova) Cochran's theorem Manova (multivariate) Ancova (covariance) Compare means Multiple comparison Designs: Completely Randomized Factorial Fractional factorial Plackett-Burman Taguchi Response surface methodology Polynomial & rational modeling Box-Behnken Central composite Block Generalized randomized block design (GRBD) Latin square Graeco-Latin square Orthogonal array Latin hypercube Repeated measures design Crossover study Randomized controlled trial Sequential analysis Sequential probability ratio test * Glossary Category Statistics portal Statistical outline Statistical topics This page uses Creative Commons Licensed content from Wikipedia (ver autores).

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