Get the Knowledge that sets you free...Science and Math for K8 to K12 students

Login / Register

Login to your account

Email
Password
×

Warning

Please Login to Read More...

Collecting Data

2010 Census Results - United States The new apportionment figures were revealed by the Census Bureau, which fulfilled its once-in-a-decade mandate to survey residents in order to gauge population migration across the country.

As of April 1, 2010, the Census Bureau estimated the U.S. population at 308,745,538 million people — an increase of 9.7 percent from the 2000 Census.
Sample survey about smoking Eighty-one per cent of Maltese smokers want to kick the habit, according to a survey by the European Commission's Ex-Smokers Are Unstoppable campaign. The survey, carried out on a sample of 200 people, also showed that almost 52 per cent of Maltese believe that smoking is one of the hardest habits to break. More than four in 10 of those questioned felt that smoking was getting in the way of doing an activity.
Sample Survey & Methods

Introduction: Till now, we learned about exploratory analysis of data whereas in this topic, we discuss various methods for collecting the data. There are four main methods of data collection: Census, Sample survey, Experiment and Observational study.

Census: A census is the process of collecting the data (or) information from all the members in a population. It is also known as complete enumeration of population [i.e., complete count of population]. For example, if we are studying the weights of students of a school, 'all' the students of the school constitute the population.

The information which is collected by census method is accurate and authentic but the authenticity of data is a function of sincerity of enumerators. This method is convenient for small size populations because associated cost and time consumed for collecting the data is less.

If the size of population is large then time consumed for collecting the data is more and we require large number of qualified enumerators for collecting the data. Hence, in case of large populations, data may become outdated by the time it is collected and analyzed.

Sample survey: It is the process of collecting (or) obtaining the information about the whole population by studying a part of it, that is, a sample. The main goal of this is to collect the information without disturbing (or) changing the population.

In this method, data collection is faster and cost is lower because we are considering only a part of a population [i.e., sample]. The information which is obtained from this method is used to make the inferences about the population parameters.

The main problem which occurs in sample survey is bias [which quickly invalidates a sample and makes useful information impossible to obtain]. A sample is biased if in some critical way it does not represent the population. The main technique to avoid bias is to incorporate randomness into the selection process. This randomization protects us from effects and influences.

Nutritional benefits of red wine Alcohol is not bad for our health, only if we choose to drink in moderation. Among the different types of alcoholic drinks, red wine is considered best for health. Red wine is produced by fermenting black grapes. The nutritional benefits of red wine are attributed primarily to the antioxidant constituents of black grape.

According to studies, moderate consumption of red wine can improve the quality of health. It reduces risks of developing coronary artery diseases, reduces cholesterol level and decreases the risk of certain cancers.
How to Use a Niacin Supplement to Lower Cholesterol Niacin is a vitamin in the B-series that is commonly ingested for cholesterol management. Niacin supplements are available over-the-counter or in prescription form. If taken properly, niacin is able to lower our triglyceride and low-density lipoprotein levels while increasing the amount of high-density lipoproteins in our blood.
Experiment and Observational Study

Experiment: An experiment is a controlled study in which the researcher (or) an experimenter should randomly divide an experimental unit [i.e., subjects] into appropriate groups and imposes some sort of treatment on one (or) more groups in order to observe the response.

  • Example: Two studies are run to determine the effect of low levels of wine consumption on cholesterol level. The first study measures the cholesterol levels of 50 volunteers who have not consumed alcohol in the past year and compares these values with their cholesterol levels after 1 year, during which time each volunteer drinks one glass of wine daily.

    The second study measures the cholesterol levels of 50 volunteers who have not consumed alcohol in the past year, randomly picks half the group to drink one glass of wine daily for a year while the others drink no alcohol for the year and finally measures their levels again.

    These both studies apply treatments and measure responses, and so both are experiments.

Experiments often have control group and treatment group. In an experiment, the group in which experimental units receive either no treatment (or) a standard treatment is known as 'control group' and the group in which experimental units receive a treatment to obtain some response is known as 'treatment group'. Based on careful use of control groups, experiments can often indicate cause-and-effect relationships. The critical principles behind the good experimental design include: control, blocking, randomization, replication and generalizability.

Observational study: In an observational study, the researcher simply observes and records the behavior but does not attempt to impose a treatment in order to manipulate the response.

  • Example: In one study on the effect of niacin on cholesterol level, 200 subjects who acknowledged being long time niacin takers had their cholesterol levels compared with those of 200 people who had never taken niacin. This is an observational study because the subjects were not chosen for treatment.

It doesn't show cause and effect relationship because surveyor doesn't attempt to impose a treatment in order to observe (or) manipulate the response.

The importance of breakfast Skipping breakfast doesn't seem to be a problem for most people nowadays. The truth is by missing breakfast, it means that we start the day with less energy. Not just that, there are still a lot of reasons not to skip our breakfast. These are some important reasons:
  • Studies show that breakfast give us lot of advantages, either for kids and adult. By eating breakfast, we'll be able to concentrate more and do better on our tasks, and it will also improve memory too. Those who skip breakfast will have less interest and get irritated easily when confronted with difficult tasks.
Planning and Conducting Surveys: Sampling Methods

Introduction: Most information gathering involves observational studies [where this is a study in which the surveyor simply observes and records the behavior], not controlled experiments [where this is a study in which the surveyor imposes a treatment on experimental units called subjects in order to observe the response].

Moreover, when information gathering has some purpose, many studies come to mind after the data have been gathered and examined. For data collection to be useful, the resulting sample [i.e., a part of a population] must be representative of the population under consideration.

Characteristics of a well-designed and well-conducted survey: A well-designed survey always incorporates chance and probability. However, the use of probability techniques are not enough to ensure a representative sample. Often we don't have a complete listing of the population and so we have to be careful about exactly how we are applying the chance. Even when subjects are picked by chance, they may choose not to respond to the survey (or) they may not be available to respond, thus calling into question how representative the final sample really is ? The wording of the questions must be neutral-subjects give different answers depending on the phrasing.

  • Example: Suppose we are interested in determining the percentage of people in a small town who eat breakfast. How about randomly selecting 200 numbers out of the telephone book, calling each one, and asking whether the respondent is intelligent enough to eat a breakfast every morning ?

    Sol: Random selection is good, but a number of questions should be addressed. For example, are there many people in the town without cell phones (or) with unlisted numbers ? How will the time of day the calls are made affect whether the selected people are reachable ? If people are unreachable, will replacements be randomly chosen in the same way or will this lead to certain class of people being underrepresented ? Finally, even if these issues are satisfactorily addressed, the wording of the question is clearly not neutral unless the phrase intelligent enough is dropped, answers will be almost meaningless.

Sampling Error: No matter how well-designed and how well-conducted a survey is, it still gives a sample statistic as an estimate for a population parameter. Different samples of a population give different sample statistics, all of which are estimates for the same population parameter, and so error, called sampling error. This error can be described using probability.

Sampling error can be reduced by taking the larger sample. For example, consider two studies with different sample sizes [i.e., small size and large size], same sampling methods and same population. The study with a smaller sample size will have more sampling error compared to the study with a larger sample size because as the sample size increases, it approaches the size of the whole population, therefore, it also approaches all the characteristics of the population, thus, decreasing in sampling error.

Flash is Not Installed in Your System. Please Click here to Install. Close
Java is Not Installed in Your System. Please Click here to Install. Close