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Quantitative Research
 
 
Quantitative Research
Course in Business Comunications
 
Quantitative Research

The following course in Quantitative Research is provided in its entirety by Atlantic International University's "Open Access Initiative" which strives to make knowledge and education readily available to those seeking advancement regardless of their socio-economic situation, location or other previously limiting factors. The University's Open Courses are free and do not require any purchase or registration, they are open to the public.

The course in Quantitative Research contains the following:

  • Lessons in video format with explaination of theoratical content.
  • Complementary activities that will make research more about the topic , as well as put into practice what you studied in the lesson. These activities are not part of their final evaluation.
  • Texts supporting explained in the video.

The Administrative Staff may be part of a degree program paying up to three college credits. The lessons of the course can be taken on line Through distance learning. The content and access are open to the public according to the "Open Access" and " Open Access " Atlantic International University initiative. Participants who wish to receive credit and / or term certificate , must register as students.


Lesson 1: PARAMETRIC TESTS

In statistics, parametric and nonparametric methodologies refer to those in which a set of data has a normal vs. a non-normal distribution, respectively. Parametric tests make certain assumptions about a data set; namely, that the data are drawn from a population with a specific (normal) distribution. Non-parametric tests make fewer assumptions about the data set. The majority of elementary statistical methods are parametric, and parametric tests generally have higher statistical power. If the necessary assumptions cannot be made about a data set, non-parametric tests can be used.

 

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Lesson 2: TYPES - SCIENTIFIC METHODS

The scientific method is a body of techniques for investigating phenomena, acquiring new knowledge, or correcting and integrating previous knowledge.[1] To be termed scientific, a method of inquiry must be based on empirical and measurable evidence subject to specific principles of reasoning.[2] The Oxford English Dictionary defines the scientific method as "a method or procedure that has characterized natural science since the 17th century, consisting in systematic observation, measurement, and experiment, and the formulation, testing, and modification of hypotheses."[3] The chief characteristic which distinguishes the scientific method from other methods of acquiring knowledge is that scientists seek to let realityspeak for itself, supporting a theory when a theory's predictions are confirmed and challenging a theory when its predictions prove false. Although procedures vary from one field of inquiry to another, identifiable features distinguish scientific inquiry from other methods of obtaining knowledge. Scientific researchers propose hypotheses as explanations of phenomena and design experimental studies to test these hypotheses via predictions which can be derived from them.

 

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Lesson 3: HYPOTHETICAL-DEDUCTIVE METHOD

The hypothetico-deductive model or method is a proposed description of scientific method. According to it, scientific inquiry proceeds by formulating a hypothesis in a form that could conceivably be falsified by a test on observable data. A test that could and does run contrary to predictions of the hypothesis is taken as a falsification of the hypothesis. A test that could but does not run contrary to the hypothesis corroborates the theory. It is then proposed to compare the explanatory value of competing hypotheses by testing how stringently they are corroborated by their predictions.

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Lesson 4: SCIENTIFIC THEORY


A scientific theory is a well-substantiated explanation of some aspect of the natural world that is acquired through the scientific method, and repeatedly confirmed through observation and experimentation. As with most (if not all) forms of scientific knowledge, scientific theories are inductive in nature and aim for predictive power and explanatory force. The strength of a scientific theory is related to the diversity of phenomena it can explain, and to its elegance and simplicity (Occam's razor). As additional scientific evidence is gathered, a scientific theory may be rejected or modified if it does not fit the new empirical findings- in such circumstances, a more accurate theory is then desired. In certain cases, the less-accurate unmodified scientific theory can still be treated as a theory if it is useful (due to its sheer simplicity) as an approximation under specific conditions (e.g. Newton's laws of motion as an approximation to special relativity at velocities which are small relative to the speed of light).

 

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Lesson 5: POPULATION PARAMETERS

A statistical parameter is a parameter that indexes a family of probability distributions. It can be regarded as a numerical characteristic of a population or a model.[1] Among parameterized families of distributions are the normal distributions, the Poisson distributions, the binomial distributions, and the exponential distributions. The family of normal distributions has two parameters, the mean and the variance: if these are specified, the distribution is known exactly. The family of chi-squared distributions, on the other hand, has only one parameter, the number of degrees of freedom.

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Lesson 6: THE CHI-SQUARE TEST

A chi-squared test, also referred to as chi-square test or
test, is any statistical hypothesis test in which the sampling distribution of the test statistic is a chi-squared distribution when the null hypothesis is true. Also considered a chi-squared test is a test in which this is asymptotically true, meaning that the sampling distribution (if the null hypothesis is true) can be made to approximate a chi-squared distribution as closely as desired by making the sample size large enough.

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Lesson 7: NON-PARAMETRIC STATISTICS

The Anderson–Darling test is a statistical test of whether a given sample of data is drawn from a given probability distribution. In its basic form, the test assumes that there are no parameters to be estimated in the distribution being tested, in which case the test and its set of critical values is distribution-free. However, the test is most often used in contexts where a family of distributions is being tested, in which case the parameters of that family need to be estimated and account must be taken of this in adjusting either the test-statistic or its critical values. When applied to testing if a normal distribution adequately describes a set of data, it is one of the most powerful statistical tools for detecting most departures from normality. K-sample Anderson–Darling tests are available for testing whether several collections of observations can be modeled as coming from a single population, where the distribution function does not have to be specified.

 

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Lesson 8: SHOWS STATISTICS

Statistics is the study of the collection, organization, analysis, interpretation and presentation of data.[1] It deals with all aspects of data including the planning of data collection in terms of the design of surveys and experiments.[1] When analyzing data, it is possible to use one of two statistics methodologies: descriptive statistics or inferential statistics.

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Lesson 9: THE DETERMINATION OF THE APPROPRIATE SIZE OF A SAMPLE OBJECTIVES

Sample size determination is the act of choosing the number of observations or replicates to include in a statistical sample. The sample size is an important feature of any empirical study in which the goal is to make inferences about a population from a sample. In practice, the sample size used in a study is determined based on the expense of data collection, and the need to have sufficient statistical power. In complicated studies there may be several different sample sizes involved in the study: for example, in a survey sampling involving stratified sampling there would be different sample sizes for each population. In a census, data are collected on the entire population, hence the sample size is equal to the population size. In experimental design, where a study may be divided into different treatment groups, there may be different sample sizes for each group.

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Lesson 10: SAMPLE BIAS, BIAS OF SELECTION AND DOUBLE-BLIND

In statistics, sampling bias is a bias in which a sample is collected in such a way that some members of the intended population are less likely to be included than others. It results in abiased sample, a non-random sample[1] of a population (or non-human factors) in which all individuals, or instances, were not equally likely to have been selected.[2] If this is not accounted for, results can be erroneously attributed to the phenomenon under study rather than to the method of sampling. Medical sources sometimes refer to sampling bias as ascertainment bias.[3][4] Ascertainment bias has basically the same definition,[5][6] but is still sometimes classified as a separate type of bias

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