Examples of using Observational in English and their translations into Malayalam
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Contrast with observational study.
Observational studies(longitudinal cohort studies).
It's a strictly observational experience.
The observational cohort included 743 patients.
The second main strategy researchers can use with observational data is forecasting.
Use the results from an earlier observational study by Kramer(2012) to decide the number of participants in each condition.
The second main strategy used by researchers with observational data is forecasting.
Further experimental and observational research data suggested a connection between parental pesticide exposure and physical birth defects, low birth weight and fetal death.
A first step to learning from big data is to realize that it is part of a broader category of data that hasbeen used for social research for many years: observational data.
Thus, in addition to business and government records, observational data also includes things like the text of newspaper articles and satellite photos.
A first step to learning from big data is realizing that it is part of a broader category of data that hasbeen used for social research for many years: observational data.
The egg study wasn't a controlled experiment but a so-called observational study, in which scientists pooled several studies on a total 30,000 Americans.
Baseline predictors of response and discontinuation of tumor necrosis factor-α blocking therapy in ankylosing spondylitis:a prospective longitudinal observational cohort study. Arthritis Res Ther. 2011;13:R94.
This study based used a practice-based, observational model to look at the effectiveness of intervention in a selected group of patients with both acute and chronic lower back pain(LBP).
Having a big dataset enables some specific types of research- measuring heterogeneity, studying rareevents, detecting small differences, and making causal estimates from observational data.
In line with these findings, in an observational study with a large population of Italian patients with multiple sclerosis, cognitive/psychiatric disturbances were seen in 3.9% of the cases(Patti et al., 2016).
The magic of true probability sampling is to rule out problems on both measured and unmeasured characteristics(a point that isconsistent with our discussion of matching for causal inference from observational studies in Chapter 2).
It utilizes an arrangement of value administration techniques, mostly observational, factual strategies, and makes an uncommon framework of individuals inside the association who are specialists in these strategies.
Observational research has shown that this can be as high as 10 to 15% of the materials that go into a building, a much higher percentage than the 2.5-5% usually assumed by quantity surveyors and the construction industry.
In addition to the big data used in the two previous examples,researchers can also collect their own observational data, as was wonderfully illustrated by Gary King, Jennifer Pan, and Molly Roberts'(2013) research on censorship by the Chinese government.
First, this is an observational study, and therefore we cannot infer causality for the emergence of the 2 utility groups, and because the individual treatment plans were unknown to us, we cannot comment on any specific type of conservative therapy.
All three of these examples- the working behavior of taxi drivers in New York, friendship formation by students, and social media censorship behavior of the Chinese government-show that relatively simple counting of observational data can enable researchers to test theoretical predictions.
A crude way to think about it is that observational data is everything that does not involve talking with people(e.g., surveys, the topic of chapter 3) or changing people's environments(e.g., experiments, the topic of chapter 4).
Understanding these characteristics enables us to quickly recognize the strengths and weaknesses of existing sources and will help us harness the new sources that will be created in the future. Finally, in Section 2.4, I describe three mainresearch strategies that you can use to learn from observational data: counting things, forecasting things, and approximating an experiment.
A crude way to think about it is that observational data is everything that does not involve talking with people(e.g., surveys, the topic of chapter 3) or changing people's environments(e.g., experiments, the topic of chapter 4).
As with many interventions intended to prevent ill health, the effectiveness of parachutes has not been subjected to rigorous evaluation by using randomised controlled trials. Advocates of evidence based medicine have criticised theadoption of interventions evaluated by using only observational data. We think that everyone might benefit if the most radical protagonists of evidence based medicine organised and participated in a double blind, randomised, placebo controlled, crossover trial of the parachute.”.
The science of modern cosmology, observational and theoretical, clearly indicates that, at one point in time, the whole universe was nothing but a cloud of‘smoke' i.e. an opaque highly dense and hot gaseous….
Although large datasets don't fundamentally change the problems with making causal inference from observational data, matching and natural experiments- two techniques that researchers have developed for making causal claims from observational data- both greatly benefit from large datasets.